ISDA 2018

Oral Presentations

Monday

2.1 KEYNOTE: Development and research ofGSI-based EnVar for convective scale radar dataassimilation: challenges and recent progressMain Author: Xuguang WangInstitution: School of Meteorology, University ofOklahoma, USA

Convective-scale data assimilation (DA) posesgreat challenges due to the intermittent nature,smaller spatial and temporal scales of the phenom-ena themselves; involvement of multiple scales andtheir interactions; and nonlinearity of the obser-vations and priors. This presentation discussesrecent development and research of the GSI-basedEnVar for convective scale radar DA. Issue asso-ciated with the nonlinear reflectivity operator isfirst discussed. When hydrometeor mixing ratiosare used as state variables, large differences ofthe cost function gradients with respect to thesmall hydrometeor mixing ratios and wind preventefficient convergence. A new method is proposedand implemented in GSI-EnVar. The new methoddirectly adds the reflectivity as a state variable,avoiding the tangent linear and adjoint of the nonlin-ear operator and therefore resolves the issue. Thismethod is implemented for three US operationalconvection-allowing regional prediction systemsincluding HRRR, NAM-CONUS and WoF. The effectof the new method is demonstrated and system-atically evaluated with a variety of warm seasonconvective scale phenomena. Challenges associatedwith nonlinearity are also manifested in ensemblebased convective scale targeted observation. Theability of the technique to provide meaningfullyinsight into scanning strategies is investigated withthe multi-function phased array radar (MPAR) andtornadic supercells. The results show advantagesto utilizing the targeting algorithm for sub-hourconvective scale forecasts. Accurate prediction oftornado-like-vortices (TLV) requires depicting theinternal structure of thunderstorms in the analysis.Therefore radar DA at the sub-kilometer resolutionis needed. Due to the expense of running sub-kilometer background ensembles, a dual resolutionEnVar system is developed to produce the analysisat a sub-kilometer grid where a high resolution (HR)analysis is produced (e.g., sub-kilometer) ingestinga low resolution background ensemble, avoiding theneed of a costly HR ensemble. Impact of sub-kmanalysis on TLV simulations and maintenance isdiscussed.

2.2 Direct assimilation of 3D radar reflectivi-ties with an ensemble-based data assimilationsystemMain Author: Alberto de LozarInstitution: Deutscher Wetterdienst, Offenbach,GermanyCo-Authors: Axel Seifert, Ulrich Blahak

The assimilation of radar reflectivities at theDeutscher Wetterdienst is currently done by per-turbations of the temperature field, via latent heat

nudging (LHN). The new ensemble-based dataassimilation system COSMO-KENDA allows fordirectly assimilating radar reflectivities in a morenatural way, because it modifies all simulationfields based on the correlations of the ensemble.However, our first experiments did not show a clearimprovement over LHN, which could be related tothe complexities of COSMO-KENDA. We will discussthe challenges of assimilating radar reflectivitieswith COSMO-KENDA, and the different methodsthat we are testing to overcome these challenges.The experiments are evaluated by comparing 6-hours forecast results with radar, surface-stationand radiosonde measurements. We focus on theperiod May-June 2016 over Germany, during whichseveral severe storms were observed. Our newCOSMO-KENDA setup clearly outperforms the orig-inal one and even improves LHN in some scores.

2.3 Direct Variational Assimilation of RadarReflectivity and Radial Velocity Data: Issueswith Nonlinear Reflectivity Operator and Solu-tionsMain Author: Chengsi LiuInstitution: Center for Analysis and Prediction ofStorms, University of Oklahoma, Norman, U.S.Co-Authors: Rong Kong, Ming Xue

Studies have shown that the assimilation of radardata is helpful for convective-scale numericalweather prediction. So far, the assimilation ofreflectivity data has been achieved using eitherensemble Kalman filter or indirect methods suchas complex cloud analysis or via pre-retrieval ofhydrometeors. To directly assimilate radar datawithin variational framework, some issues associ-ated with the nonlinearity of the reflectivity operatorarise When using hydrometeor mixing ratios as thecontrol variables (CVq), the gradient of the costfunction can become dominantly large, making theassimilation of reflectivity in storm regions and ofradial velocity ineffective. To address this issue, alower limit on hydrometeor mixing ratios or equiv-alent reflectivity (qLim or ZeLim) is imposed onthe reflectivity observation operator. In addition, aseparate analysis pass (VrPass) is used to assimilateradial velocity. When logarithmic hydrometeor mix-ing ratios are used as the control variables (CVlogq)instead, the issue of the extremely large gradientis avoided. However, the analysis increments areinappropriately spread. As a solution, a lower limit isadded to the background when converting the log(q)increment to q increment(XbLim). Through perfect-model observing system simulation experimentsfor a simulated supercell storm, the capability ofdirectly assimilating radial velocity and reflectivitywithin 3DVar or En3DVar frameworks using CVq andCVlogq are examined. The results indicate that theanalysis and forecast from En3DVar using CVq withZeLim treatment are closest to the truth. The 3DVarusing CVlogq with XbLim produces better analysesof the storm intensity and has faster minimizationconvergence speed than using CVq. However,with the VrPass treatment, the storm analyses of3DVar using CVq are greatly improved, and its pre-

diction of storm location is better than using CVlogq.

2.4 Assimilation of GPM/DPR in Km-scaleHybrid-4DVar systemMain Author: Yasutaka IkutaInstitution: Japan Meteorological Agency

A new km-scale hybrid-4DVar data assimilationsystem is being developed to improve short-rangeprecipitation forecasts at the Japan MeteorologicalAgency. One of the purposes of developing thissystem is to enable the assimilation of high spatialand temporal resolution observations related tohydrometeors. For assimilation of such observa-tion, a new simplified 6-class 3-ice 1-moment bulkcloud microphysics scheme suitable for the tangentlinearization has been developed. In addition, thebackground error covariance of hydrometeors isconstructed using ensemble perturbations becausethe vertical error correlation depends stronglyon meteorological situations. Using this km-scalehybrid-4DVar data assimilation system, the impact ofDual-frequency Precipitation Radar (DPR) on boardthe Global Precipitation Measurement (GPM) coresatellite has been investigated. The DPR instrumentwas developed by the Japan Aerospace ExplorationAgency (JAXA) in cooperation with the NationalInstitute of Information and Communications Tech-nology (NICT). This space-borne precipitation radarcan observe three dimensional distribution of re-flectivity all over the earth. The result of GPM/DPRassimilation experiment will be presented.

2.5 Towards the Assimilation of Radial Winds inan Ensemble Kalman Filter on the ConvectiveScaleMain Author: Elisabeth BauernschubertInstitution: Deutscher Wetterdienst, Offenbach,GermanyCo-Authors: Klaus Stephan, Christoph Schraff,Hendrik Reich, Ulrich Blahak, Roland Potthast

With the Kilometer Scale Ensemble Data Assim-ilation System KENDA the German Weather Service(DWD) and the Consortium for Small Scale Model-ing (COSMO) have developed an Ensemble KalmanFilter for Data Assimilation on the convective scale(c.f. Schraff et al, QJRMS 2016). It is operational atDWD since March 2017. The setup of the KENDAsystem consists of the ensemble data assimilationsystem for conventional observations over centralEurope in combination with the assimilation ofRADAR composits based on latent heat nudging(LHN, Stephan et al 2011).The new project SINFONY at DWD aims to inte-

grate Nowcasting and NWP in order to develop aseamless integrated forecasting system for the con-vective scale. A major aspect of this is the incorpora-tion of further high resolution observational sourcesinto the KENDA system like RADAR data fromDWD’sradar network.The new weather radar network of DWD includes

17 dual-polarimetric C-Band Doppler radars evenlydistributed throughout Germany for complete cover-age. The radars offer unique 3-dimensional informa-

tion about dynamical and microphysical characteris-tics of precipitating clouds in high spatial and tem-poral resolutions. First tests on the assimilation ofradar reflectivities within the KENDA system havebeen done by Bick et al, QJRMS 2016. The assimila-tion is based on the EMVORADO 3D-RADAR forwardoperator developed by Zheng and Blahak (Zheng etal 2016).

We will present ongoing work on the assimilationof 3D-RADAR radial winds by the KENDA system,based on the EMVORADO forward operator. Thefocus will be on the time period May-June 2016 withsevere convective storms over Germany. Previousexperiments and statistical evaluations suggest acombination with LHN, superobbing and temporalthinning of the radial winds and concentration onthe lower elevations. We will discuss the impact ofthe assimilation of radial winds on the forecasts ofthe COSMO model (precipitation and upper air).

2.6 State-dependent Additive Covariance Infla-tion for Radar Reflectivity AssimilationMain Author: Sho YokotaInstitution: Meteorological Research Institute,Japan Meteorological Agency, Tsukuba, JapanCo-Authors: Hiromu Seko, Masaru Kunii, HiroshiYamauchi, Eiichi Sato

Direct assimilation of radar reflectivity can correctnot only the hydrometeor but also the atmosphericstate (e.g., wind, temperature, water vapor) basedon the forecast error covariance including inter-variable correlation. This correlation can be givenclimatologically in three-dimensional variationalmethod (3D-Var) or calculated by the linear modelin four-dimensional variational method (4D-Var).However, reasonable estimation of the correlationhas not been sufficiently accomplished. In en-semble Kalman filter (EnKF), the correlation canbe calculated by ensemble forecasts without thelarge cost of development. However, the accuratecalculation is limited to only the case that thehydrometeor exists at each analysis point in thefirst-guess fields of multiple ensemble members.To assimilate radar reflectivity based on the morereasonable inter-variable correlation, we proposea new method for adding ensemble perturbationsof radar reflectivity produced from the atmosphericstate in points where the hydrometeor does notexist in the first-guess fields before assimilation withEnKF. This may be regarded as a kind of the additiveinflation (Mitchell and Houtekamer 2000). However,the added perturbations are not given by randomsampling but are created based on perturbations ofwind, temperature, and water vapor at each analysispoint multiplied by their sensitivities to radar reflec-tivity calculated in the whole computational domainincluding the rainfall region. Such perturbations areexpected to have reasonable correlation with theatmospheric state. To confirm the advantage of thisstate-dependent additive inflation, we conductedassimilation experiments of the reflectivity of theMeteorological Research Institute advanced C-bandsolid-state polarimetric radar with the 50-memberlocal ensemble transform Kalman filter (LETKF)

for the case of the tornadic supercell on 6 May2012. As a consequence of the comparison betweenexperiments with and without the state-dependentadditive inflation, the assimilation with this inflationimproved short-term rainfall forecasts through mod-ifying wind and water vapor.

2.7 Construction of an ensemble nowcastingwith use of overlapping assimilation windowsMain Author: Xiaohua YangInstitution: Danish Meteorological Institute

Recent decades have seen increasingly morefrequent occurrence of extreme summer convec-tions in Europe, often characterised by rather smallhorizontal scales and short life cycles, hence withrather limited predictability. In order to providetimely warning for such high impact weather, de-velopment of an NWP based nowcasting system,which assimilates rapidly delivered observationdata with frequent update, is necessary. AtDanish Meteorological Institute (DMI), a 3D-VarHARMONIE-AROME nowcasting system has beenunder development, configured on a 750 m griddomain covering Denmark. At present the suite con-sists of two parallel 2-hour cycled suites, one withbasetime at odd hour and another at even hour. It isplanned that the system will be expanded in the nearfuture, resulted in a nowcasting configuration withsub-hourly launch interval, e.g. down to about every10 min. Main observations data assimilated are,among others, rapidly delivered radar reflectivityand aircraft/MODE-S data. The nowcasting systemapplies similar cycling strategy as featured in DMI’soperational 24-member, 2.5 km grid COntinuousMeso-scale Ensemble Prediction System (COMEPS)which is updated each hour around clock. As inCOMEPS, the novice feature of such cycling strat-egy is the use of overlapping assimilation window,which, through a separation of background and firstguess in 3DVAR cycling, enables a frequent analysisupdate while keeping a relatively longer assimilationwindow for individual members. From compara-tive studies, it is shown that use of overlappingwindow offers a good balance to take care of theneed of frequent launch for assimilating observationand the need for a sufficiently long window, latterto ensure observation amount and a suppressedspin-up. Moreover, through time lagging, shortrange forecasts from consecutive base-time can becombined to form nowcasting ensemble, providingprobabilistic forecasts in similar fashion as in oper-ational short range EPS.

6.1 KEYNOTE: Parameter estimation beyondthe augmented state approach: Expectation-Maximization algorithmMain Author: Manuel PulidoInstitution: Department of Meteorology, Universityof Reading, UK

One standard methodology to estimate physicalmodel parameters from observations in data assim-ilation techniques is to augment the state spacewith the parameters. This methodology presents

an overall success when estimating deterministicparameters. On the other hand, the collapse ofthe parameter posterior distribution found in bothensemble Kalman filters and particle filters is amajor contention point when one is interested inestimating model error covariances or stochasticparameters. To overcome this intrinsic limitation, Iwill give an overview on a statistical learning methodthat combines the Expectation-Maximization (EM)algorithmwith an ensemble Kalman filter to estimatestatistical parameters that give the maximum of theobservation likelihood given a set of observations.Numerical experiments with toy models will beshown, in which the method is applied to infer modelerror covariances and deterministic and stochasticphysical parameters from noisy observations incoarse-grained dynamical models. The algorithmsare able to identify an optimal stochastic param-eterization with a good accuracy under moderateobservational noise. The proposed EnKF-EM is apromising statistical learning method for estimatingmodel error and for constraining stochastic param-eterizations in high-dimensional geophysical models.

6.2 The use of stochastic physics in operationalglobal data assimilation with NGGPS-FV3GFSMain Author: Daryl KleistInstitution: NOAA/NWS/NCEP/EMC, College Park,MD, USACo-Authors: Phil Pegion, Jeff Whitaker, Rahul Maha-jan, Catherine Thomas

The operational global data assimilation system(GDAS) at NCEP has utilized a hybrid ensemble-variational (EnVar) scheme since May 2012. Sincethe original implementation, the scheme has hadan 80-member ensemble at reduced resolutionthat is cycled along with the full resolution controland updated each cycle with a serial, square rootfilter (EnSRF, Whitaker and Hamill 2002). Therepresentation of various components of systemerrors is critically important in order for the en-semble to properly represent the background errorcovariances. The original implementation utilizeda combination of relaxation to prior spread (RTPS,multiplicative) and lagged-forecast pair perturba-tions added to the ensemble posterior (Whitakerand Hamill 2012). The system has since evolved tosupplement and eventually replace the additive per-turbations with various so-called stochastic physicsparameterizations. The three schemes that arecurrently used operationally are stochastically per-turbed physics tendencies (SPPT), stochastic energybackscatter (SKEB), and stochastically perturbedboundary layer humidity (SHUM).

Here, we will present an update on the use ofstochastic physics within the context of the NextGeneration Global Prediction System using theFV3-GFS model. Preliminary results as part of a pre-implementation “beta” version will be presented.We will also discuss future directions includingmodifications to SPPT for perturbing with moregranularity rather than total physics tendencies.Finally, we will explore the implications of variousstochastic physics choices on the utilization of

scale-dependent localization and all-sky radianceassimilation.

6.3 Joint parameter and state estimationwith ensemble Kalman filter based algorithmsfor convective scale applicationsMain Author: Yvonne RuckstuhlInstitution: Meteorology, Ludwig-Maximilians-Universität, München, GermanyCo-Authors: Tijana Janjic

Representation of clouds in convection permit-ting models is sensitive to NWP model parametersthat are often very crudely known (for exampleroughness length). Our goal is to allow for un-certainty in these parameters and estimate themfrom data using the ensemble Kalman filter (EnKF)approach. However, to deal with difficulties as-sociated with convective scale applications, suchas non-Gaussianity and constraints on state andparameter values, modifications to the classicalEnKF are necessary. In this study, we evaluateseveral recently developed EnKF based algorithmsthat either explicitly incorporate constraints such asmass conservation and positivity of precipitation, orintroduce higher order moments on the joint stateand parameter estimation problem. We comparetheir results to the localized EnKF on a commonidealized test case. The test case uses perfect modelexperiments with the one dimensional modifiedshallow water model that was designed to mimicimportant properties of convection.

6.4 A Bayesian approach to non-Gaussianmodel error modelingMain Author: Michael TsyrulnikovInstitution: Meteorological Data Assimilation De-partment, Hydrometcentre of Russia, Moscow,RussiaCo-Authors: Dmitry Gayfulin

A new Bayesian stochastic model Error ModelingScheme (BEMS) is proposed. The BEMS generalizesthe SPPT and, like the SPPT, works at the point-wiselevel, imposing the spatio-temporal structure by anexternal stochastic pattern generator.At each model grid point, the perturbed next-step

model variables should be pseudo-random drawsfrom the “target distribution”, the conditional distri-bution of the truth at the next model time step giventhe data available from the model at the current timestep. The latter are postulated to be (i) the modelstate, (ii) the physical tendency, and (iii) the total ten-dency. The model state is regarded as the referencepoint and part of a prior distribution. The two ten-dencies are treated as data and thus enter a two-termlikelihood. The density of the target distribution (theposterior) is, therefore, the product of the three func-tions. It appears that two of these functions can beobjectively estimated from the model data whilst thethird function needs to be modeled.The advantages of the new scheme are the follow-

ing. First, as compared to the SPPT, the objectivelyspecified components of the BEMS allow to incorpo-rate additional information in the model error sim-

ulation scheme and thus reduce the degree of arbi-trariness of the scheme. Second, due to the prior,the BEMS guarantees that the perturbed model vari-ables (i.e. the simulated truth) are within their al-lowable ranges (no negative humidity etc.) and havezero probability density at the edges of the allowableranges. Third, in contrast to the SPPT, in the BEMSthe physical tendency is perturbed even if the modelphysical tendency is zero. This acts like an additiveperturbation component, which can be useful e.g. fortriggering convection.A fast numerical posterior sampling technique is

developed. First results for ensemble forecasts withthe COSMO model are presented.

6.5 Diagnosing weak-constraint model er-ror forcing and variational bias correctioninteraction in the ECMWF assimilation systemMain Author: Jacky FarnanInstitution: Research Department, ECMWF, Read-ing, UKCo-Authors: Elias Valur Hólm, Heather Lawrence,Niels Bormann, Patrick Laloyaux, Cristina Lupu

ECMWF has recently re-introduced weak-constraint4D-Var with model error forcing active above 40hPa.During the development it was noted that the firstguess departures and bias correction for several in-struments were reduced when the weak-constraintformulation was used, particularly for channelspeaking in the upper atmosphere. We describethe interaction between model error forcing andvariational bias correction (VarBC) and the effecton the mean analysis state. Both the model errorforcing and VarBC have spin-up periods of severalmonths but there has been very little research intohow they interact and if either dominates over theother. To investigate this we vary three parametersat initialisation; the initial atmospheric state, themodel error forcing and VarBC. The initial condi-tions were taken from a spun-up strong-constraintexperiment and/or a spun-up weak-constraint exper-iment. The model error forcing and VarBC can becold-started or warm-started at initialisation from aspun-up experiment. As expected the initial atmo-spheric state was found to have very little influencebeyond 10-12 days. Neither the model error forcingnor VarBC spin-up to the same state after severalmonths of experimentation but instead stabiliseat a different state. Both the model error forcingand VarBC initialisation were found to influencethe mean state in the upper atmosphere 6 monthsafter initialisation. Their influence on the meanatmospheric state is distinct, different in magnitudeand in character. The significance and uncertaintiesin the upper atmospheric state is discussed.

Tuesday

1.1 KEYNOTE: Data assimilation for convective-scale Numerical Weather PredictionMain Author: Nils GustafssonInstitution: SMHI, Norrköping Sweden

Fundamental problems and applied methods indata assimilation for convective-scale numericalweather prediction will be surveyed. The methodsinclude variational methods (3D-Var and 4D-Var),ensemble methods (LETKF) and hybrids betweenvariational and ensemble methods (3DEnVar and4DEnVar). Limitations in the formulation staticbackground error statistics, generally applied in3D-Var and 4D-Var, is contrasted with the potentialof ensemble-based background error constraints.Other assimilation algorithms, like latent heatnudging, are additionally applied to improve themodel initial state, with emphasis on convectivescales. It will be demonstrated that the quality offorecasts based on initial data from convective-scaledata assimilation is significantly better than thequality of forecasts from simple downscaling oflarger-scale initial data. The duration of impactdepends however on the weather situation, thesize of the computational domain and the datathat are assimilated. It will furthermore shownthat more-advanced methods applied at convectivescales provide improvements compared to simplermethods. Challenges in research and developmentfor improvements of convective-scale data assim-ilation will also be reviewed and discussed. Thedifficulty of handling the wide range of spatial andtemporal scales makes development of multi-scaleassimilation methods and space-time covariancelocalization techniques important. Improved utiliza-tion of observations at convective-scale is important.In order to extract more information from existingobserving systems of convective-scale phenomena,for example weather radar data and satellite imagedata, it is necessary to provide improved statisticaldescriptions of the observation errors associatedwith these observations.

1.2 On the operational use of the Kilometre-scale Ensemble Data Assimilation (KENDA) atDWDMain Author: Christoph SchraffInstitution: DWD, Offenbach, GermanyCo-Authors: Hendrik Reich, Andreas Rhodin, RolandPotthast, Klaus Stephan

A Local Ensemble Transform Kalman Filter (LETKF)data assimilation for convection-permitting NWPhas been developed in the framework of a COSMOPriority Project called KENDA (Km-scale ENsemble-based Data Assimilation). In March 2017, this wasintroduced operationally at the German WeatherService (DWD) to provide the initial conditionsboth for the deterministic and ensemble forecastswith the convection-permitting model configura-tion COSMO-DE. As with the former observationnudging scheme, the current operational setup forthe LETKF uses only conventional observations

(radiosonde, aircraft, wind profiler, surface stationdata), but is combined with latent heat nudging forthe use of radar-derived precipitation rates. Theuse of KENDA initial conditions was found to have avery clear positive impact on the ensemble forecastsand to also improve the deterministic forecasts ofconvective precipitation in summer, as shown at theISDA 2016. However, later tests for winter 2016 /2017 revealed some problems with low stratus.

Here, we will present major (pre-)operationalchanges in 2017. The introduction of additivecovariance inflation in the LETKF analysis stepresulted in clear improvements for the simulation oflow stratus. Further, a soft limiter has been imposedon the explicit soil moisture perturbations in orderto decrease the excessive ensemble spread in thepredicted 2-m temperature in clear-sky conditionsduring the summer season. Adding Mode-S aircraftdata had further positive impact. In addition tothese operational changes, we will also give a verybrief overview of ongoing projects that deal withadditional, high-resolution observation data in viewof their future operational use in the KENDA system.

1.3 Assimilation of cloud-affected radiancesin idealized simulations of deep convectionMain Author: Josef SchröttleInstitution: Department of Physics, HErZ DataAssimilation, University of MunichCo-Authors: Martin Weissmann, Leonhard Scheck,Axel Hutt

We assimilate brightness temperature measure-ments in the infrared water vapor bands withobservation system simulation experiments. Theseexperiments offer the opportunity for assimilatingcloud-affected radiances in parameter studies inan idealized setup with deep convection. The con-vection evolves over the course of one day. Thefirst convective clouds form as liquid water cloudsand appear in the visible satellite spectral range(0.6 μm). As the affected radiances in the infraredis assimilated directly for water vapor bands. Asthe convection evolves ice clouds form at higheraltitudes. The signature of the ice clouds on cloud-affected radiances in the infrared is assimilateddirectly for water vapor bands 6.2 μm and 7.3 μm.We apply different observation error models, studysensitivities to filtering observations, and calculatethe sensitivity to scale dependent error growth ofradar reflectivity Z, wind, temperature, relativehumidity, water vapor and cloud ice. We achievean improvement of radar-reflectivity fields in thefirst-guess by 30 % when assimilating the watervapor infrared satellite radiances. Short range fore-casts of 1 h of the brightness temperature improvesignificantly up to 50 %. We provide an outlook onassimilating: brightness temperature differences,clouds as binary objects, and cloud-affected radi-ances in infrared window channels.

4.1 Non-linear, non-Gaussian effects in theECMWF 4D-VarMain Author: Massimo BonavitaInstitution: Earth System Assimilation, ECMWF,

Reading, United KingdomCo-Authors: Elias Holm, Peter Lean

Nonlinearities in the model evolution and in theobservation operators, and insufficiently dense oraccurate observations may cause the error statis-tics of a data assimilation system to deviate fromthe standard Gaussian assumptions. These effectsare becoming ever more important as both theresolution of the model and the data assimilationsystems increase, and the use of observations withcomplex, non-linear observation operators becomespervasive. Incremental 4D-Var, used operationallyat ECMWF, deals with non-linear, non-Gaussianeffects through successive re-linearizations of theanalysis problem and modelling of non-Gaussianpriors. We will discuss strengths and limitations ofthis approach with reference to recent experience inthe ECMWF analysis system and the implications ofthese findings in terms of data assimilation strategyat ECMWF.

4.2 Improving the MOGREPS global ensembleusing 4D-ensemble variational data assimila-tionMain Author: Gordon InverarityInstitution: Met Office, Exeter, UKCo-Authors: Neill Bowler, Adam Clayton, MohamedJardak, MarekWlasak, Lucian Anton, Andrew Lorenc

The Met Office’s global ensemble prediction systemMOGREPS-G uses an Ensemble Transform KalmanFilter (ETKF) to assimilate observational informa-tion into the ensemble. We are working towardsreplacing this with an ensemble of 4D-ensemblevariational data assimilations (En-4DEnVar) in orderto retire the ETKF software and instead share com-mon code with our deterministic hybrid 4DVar dataassimilation system. This will reduce maintenanceoverheads and enable MOGREPS-G to more easilyincorporate deterministic data assimilation develop-ments to produce better ensemble forecasts. Modeluncertainties are simulated through a combinationof stochastic kinetic energy backscatter, randomparameters and additive inflation using an archiveof analysis increments, which also contributesan element of bias correction. Sampling errorsare countered using: waveband scale-dependentlocalisation; a combination of relaxation to priorperturbations and relaxation to prior spread; andby increasing the number of ensemble membersfrom 44 to 100. The ensemble is recentred on aweighted combination of the deterministic analysisand ensemble mean. The mean-perturbation methodis used to improve the computational efficiency ofthe En-4DEnVar step, with perturbation minimisa-tions run in parallel using MPI communicators toimprove computational scalability and reduce therun-time to below the target of 15 minutes. Thisupgrade improves the performance of MOGREPS-Gfor ensemble forecasting and at least matches theperformance of our deterministic data assimilationwhen its forecast perturbations from the ensem-ble mean are used in hybrid 4DVar. Indeed, theforecast perturbations have already been used as

training data for the global static covariances soonto be implemented in our operational suite aheadof the En-4DEnVar implementation planned for 2018.

4.3 Advances in cloud and water vapor analyseswithin NGGPS-FV3GFSMain Author: Catherine ThomasInstitution: NCEP/EMC/IMSG, College Park, MD,USACo-Authors: Rahul Mahajan, Daryl Kleist, FanglinYang

As part of the Next Generation Global Prediction Sys-tem (NGGPS), NCEP is working towards replacingthe spectral dynamical core of the Global ForecastSystem (GFS) with the Finite-Volume Cubed-SphereDynamical Core (FV3). Many changes have beenmade to the Gridpoint Statistical Interpolation (GSI)analysis system to accommodate the new dynam-ical core. In this talk, we will detail some of thechallenges faced with regards to clouds and watervapor. One of the advances of the FV3GFS is movingthe cloud microphysics parameterization from thatof Zhao and Carr (1997) to the GFDL microphysicsof Chen and Lin (2011, 2013). Unlike the previousscheme, the new microphysics parameterizationpredicts individual hydrometeor species. We willdetail the modifications that needed to be madefor the cloud analysis, which is currently computedas a total cloud condensate variable. We will alsodiscuss possible future directions for the cloudanalysis within the GSI, including sending individualhydrometeors to the radiative transfer model andproducing individual hydrometeor analyses. In ad-dition to the cloud analysis, modifications are beingmade to the water vapor analysis for the upcomingFV3GFS implementation. The current operationalGFS contains excessive stratospheric water vapor.We will present short term mitigation strategies,such as tapering the water vapor increments withheight and relaxing to climatology, as well as pre-liminary results.

4.4 The accuracy of efficient particle filtersMain Author: Peter Jan van LeeuwenInstitution: Department of Meteorology and NCEO,University of Reading, Reading, UK

Particle filters that are not degenerate in high-dimensional systems by construction have beenderived. Although they have been successfullyapplied to high-dimensional geophysical problems,we know that they are biased for infinite ensemblesizes. That limit has no practical value, but thereis a desperate need to understand how accuratethese filters really are for small ensemble sizes. Thereason that the filters work well must be becausethe bias is smaller than the Monte-Carlo noise, butit is very difficult to make progress through tryingto quantify these two from theory.

Instead, we will explore numerical evidence. Ahigh-dimensional highly nonlinear system consistingof a very large number of uncoupled Lorenz 63models is used to test the performance of a newequal-weight particle filter. The advantage of this set

up is that although the system is high-dimensional,with no hidden low-dimensional attractor, and highlynonlinear, we can easily generate the true poste-rior pdf. We will show that this new equal-weightparticle filter can estimate the posterior mean wellon this system, with only 20 global particles, whereEnsemble Kalman Filters fail. Furthermore, we willinvestigate how well this particle filter can generatecorrect postrior pdf shapes, focusing on observingstrategies that lead to multi-modal and/or highlyskewed cases. In most cases the filter does remark-ably well, but sometimes it fails in this extremelydifficult problem, and we will discuss initial thoughtson why this happens. If time permits we will discussextensions to high-dimensional parameter estima-tion as well.

4.5 On Localized Particle Filters for the GlobalWeather Prediction Model ICONMain Author: Anne WalterInstitution: German Meteorological Service (DWD),Offenbach, GermanyCo-Authors: Roland Potthast, Andreas Rhodin

Most of the state-of-the-art data assimilation meth-ods are based on an implicit assumption on theGaussianity of the underlying distributions. If theensemble distribution is strongly non-Gaussian theanalysis is known to be strongly sub-optimal. In thiscase, various other forms of explicit filters are underdevelopment. Here we study a particle filter (PF) inthe framework of large-scale data assimilation.The idea is to sample some probability distribu-

tion, where then number of samples reflects the localstrength of the probability density. It is well-knownthat in a high-dimensional framework, PFs sufferfrom so-called filter divergence under the curse of di-mensionality. This means that usually only very fewof the ensemble members carry all the weight in theassimilation step. This immediately destroys the di-versity of the ensemble and leads to useless behaviorwhen applied in an iterative way. To our knowledge,PFs have not yet been used in an operational envi-ronment or been applied to an operational weathermodel.We have implemented a localized adaptive particle

filter (LAPF) within the global operational frameworkof German Weather Service. Our implementation in-cludes a set of tools to ensure a proper spread controlfor the ensemble, such that filter divergence can beavoided. Our approach ensures the stability of thePF. We demonstrate the feasibility of the method byrunning our implementation for a test period of onemonth with a global, with state space dimension ofapproximately n=1.5·10^8. A comparison with oper-ational scores shows that the LAPF is able to providea reasonable atmospheric analysis in a large-scaleenvironment.The LAPF is an approximation to Bayes formula.

We discuss the implicit assumptions and approxima-tion steps made by the scheme and suggest furtherdevelopment steps which can be understood as therealization of a Metropolis-Hastings sampler, i.e. aLocalized Markov Chain Particle Filter (LMCPF).

4.6 Nonlinear data assimilation using syn-chronisation in a particle filterMain Author: Flavia Rodrigues PinheiroInstitution: Department of Meteorology, Universityof Reading, Reading, UKCo-Authors: Peter Jan van Leeuwen, Gernot Geppert

Current data assimilation methods still face prob-lems in strongly nonlinear cases. A promisingsolution is a particle filter, which provides a repre-sentation of the model probability density function(pdf) by a discrete set of particles. As the basic par-ticle filter does not work in high-dimensional cases,the performance can be improved by consideringthe proposal density freedom. A potential choiceof proposal density might come from the synchro-nisation theory, in which one tries to synchronisethe model with the true evolution of a system usingone-way coupling via the observations. In practice,an extra term is added to the model equations,damping the growth of instabilities transversal tothe synchronisation manifold. When only part of thesystem is observed, synchronisation can be achievedvia a time embedding, similar to smoothers in dataassimilation.

In this work we propose a fully nonlinear data as-similation framework which consists of two stages:1) use the new ensemble synchronisation, a time em-bedding scheme similar to an ensemble smoother or4DEnsVar, to relax the particles towards the truth.Promising results for this scheme are obtained, asglobal synchronisation errors reach values smallerthan observation errors, even in partly observedsystems; 2) combine these efficient particles withan extension of the Implicit Equal-Weights ParticleFilter, a particle filter that ensures equal weights forall particles, avoiding filter degeneracy by construc-tion. The performance of the framework is testedin a 16,384-dimensional barotropic vorticity model.Promising results will be shown and the pros andcons of this new idea will be discussed.

4.7 A Hybrid Kalman-Nonlinear EnsembleTransform FilterMain Author: Lars NergerInstitution: Alfred Wegener Institute, Bremerhaven,Germany

A hybrid ensemble transform filter is developedwhich combines a Kalman-filter update provided bythe analysis scheme of the local ensemble transformKalman filter (LETKF) and a nonlinear analysisfollowing the nonlinear ensemble transform filter(NETF). The NETF computes an analysis ensemblefrom particle weights and has been shown that it canyield smaller errors than the LETKF for sufficientlylarge ensembles. The new hybrid filter, referredto as Kalman-nonlinear ensemble transform filter(KNETF) is motived from combining the stabilityof the LETKF with the nonlinear proporties of theNETF to obtain improved assimilation results forsmaller ensembles. The KNETF is domain-localizedas the LETKF and can hence be applied to high-dimensional nonlinear models. Its performancedepends on the choice of the hybridization weight

which shifts the analysis solution between theLETKF and NETF analyses. Strategies to adaptivelycontrol this weight are discussed and the perfor-mance of the hybrid KNETF algorithm is assessedfor experiments with the Lorenz-96 model.

5.1 KEYNOTE: Insight and evidence moti-vating the simplification of dual-analysis hybridsystems into single-analysis hybrid systemsMain Author: Ricardo TodlingInstitution: NASA/Global Modeling and AssimilationOfficeCo-Authors: Amal El Akkraoui, Fabio Diniz

Many hybrid data assimilation systems currentlyused for NWP employ some form of dual-analysissystem approach. Typically a hybrid variationalanalysis is responsible for creating initial conditionsfor high-resolution forecasts, and an ensembleanalysis system is responsible for creating sampleperturbations used to form the flow-dependent partof the background error covariance required in thehybrid analysis component. In many of these, thetwo analysis components employ different method-ologies, e.g., variational and ensemble Kalmanfilter. In such cases, it is not uncommon to haveobservations treated rather differently betweenthe two analyses components; recentering of theensemble analysis around the hybrid analysis is usedto compensated for such differences. Furthermore,in many cases, the hybrid variational high-resolutionsystem implements some type of four-dimensionalapproach, whereas the underlying ensemble systemrelies on a three-dimensional approach, which againintroduces discrepancies in the overall system.Connected to these is the expectation that one canreliably estimate observation impact on forecastsissued from hybrid analyses by using an ensembleapproach based on the underlying ensemble strat-egy of dual-analysis systems. Just the realizationthat the ensemble analysis makes substantiallydifferent use of observations as compared to theirhybrid counterpart should serve as enough evidenceof the implausibility of such expectation.

This presentation assembles numerous anec-dotal evidence to illustrate the fact that hybriddual-analysis systems must, at the very minimum,strive for consistent use of the observations in bothanalysis sub-components. Simpler than that, thiswork suggests that hybrid systems can reliably beconstructed without the need to employ a dual-analysis approach. In practice, the idea of relyingon a single analysis system is appealing from acost-maintenance perspective. More generally,single-analysis systems avoid contradictions such ashaving to choose one sub-component to generateperformance diagnostics to another, possibly notfully consistent, component.

5.2 Observations in convective-scale ensembledata assimilation: Actual and potential impactMain Author: Tobias NeckerInstitution: HErZ, LMU Munich, GermanyCo-Authors: Martin Weissmann, Stefan Geiss, Take-masa Miyoshi

Current regional forecasting systems particularlyaim at the forecast of convective events and relatedhazards. Most weather centers use high-resolutionensemble forecasts that resolve convection explic-itly. Nevertheless, fast error growth on these scalesleads to a low predictability. Improving forecaststherefore requires a frequent cycling with obser-vations that are dense in space and time. Modernregional observing system provide various obser-vations that are not exploited yet but applicablefor convective-scale data assimilation. Addition-ally, new mesoscale observation networks couldbe installed. However, little knowledge exists onwhat types of observations are most beneficial onwhich spatial and temporal scales. This informationis crucial for deciding on the deployment of newobservations, the refinement of existing observingsystems and where to put efforts in the assimilationof currently unexploited observations. For this rea-son, the HErZ research group at LMU investigatesthe actual and potential impact of observations withfocus on the operational convective-scale ensembledata assimilation system of Deutscher Wetterdienst.An ensemble forecast sensitivity to observations(EFSOI) method has been used to evaluate thecontribution of various observations to the reductionin forecast error. This provided information aboutthe actual observation impact of already assimilatedobservations in a six-week high impact weatherperiod in summer 2016. Furthermore, ensemblesensitivity analysis (ESA) has been applied to 12-hforecasts to investigated the potential impact ofpossible observations. Both approaches considerseveral different forecast error metrics to calculatethe sensitivities and impacts. Ideally, a metricshould reflect quantities in which the forecast usersare most interested in (e.g. precipitation or surfacetemperature/wind). Our studies revealed a particu-lar sensitivity of the forecast to surface pressure aswell as upper level wind observations. ESA studieswith a 1000-member ensemble simulation providedadditional information on the temporal and spatialscale of potential impacts.

5.3 Impact of Assimilating Multi-Scale PECANField Campaign Observations on the NumericalPrediction of Bores and Bore-Initiated Convec-tionMain Author: Hristo ChipilskiInstitution: School of Meteorology, University ofOklahoma, Norman, OK, United StatesCo-Authors: Xuguang Wang, David Parsons

The Plains Elevated Convection at Night FieldCampaign (PECAN) provided a unique set of ob-servations within the lower troposphere. Theirimportance for numerical weather prediction (NWP)was studied within a GSI-based convection-allowingensemble data assimilation system. In particular,this study examined the impact of assimilatingPECAN observations from 5-6 July 2015 on theforecast skill of bore-initiated convection, whichis deemed to be an important mechanism for themaintenance of nocturnal mesoscale convective

systems (MCSs). This impact was investigatedseparately with respect to the bore, its environmentand the subsequent bore-initiated convection inorder to address the multi-sale nature of the studiedphenomenon.

Several data addition experiments were performedto understand the effect of assimilating differentPECAN observations on the forecast quality of thebore and the bore-initiated convection. The latterincluded observations from the Atmospheric EmittedRadiance Interferometer (AERI), aircraft, surface,lidar, wind profiler and sounding instruments. Themajority of these observation types exerted positiveimpact on the bore forecast, especially with respectto the environmental conditions in which the boredeveloped. On the other hand, improvements to theconvective part of the forecast were only evident inthe numerical simulations utilizing AERI data. Theimpact of PECAN observations in this case studywas shown to be relatively short-term, which hassome important implications for the predictabilityof the studied phenomenon. The aim of this pre-sentation is to not only overview the subjective andobjective aspects of the aforementioned results, butalso to further explain how the PECAN observa-tions were able to improve the forecast results fromboth dynamical and thermodynamical points of view.

5.4 The downstream impact of dropsondeand extra radiosonde observations conductedduring the NAWDEX field campaign in 2016Main Author: Matthias SchindlerInstitution: Meteorological Institute (MIM), LMU,Munich, GermanyCo-Authors: Martin Weissmann, Andreas Schäfler,Gabor Radnoti

Dropsonde observations from three researchaircrafts in the north-Atlantic region as well asseveral hundred additionally launched radiosondesover Canada and Europe were collected during thetransatlantic field campaign NAWDEX in autumn2016. In addition, over 500 dropsondes were de-ployed during NOAA’s SHOUT and Reconnaissancemissions in the west-Atlantic basin, complementingthe conventional observing network for a total of 13intensive observation periods. This unique datasetwas assimilated within the framework of cycleddata denial experiments performed with the globalmodel of the ECMWF. Routinely computed dataassimilation diagnostics such as forecast sensitivityto observation impact and first guess departureare evaluated in addition to results obtained fromthe deterministic data denial OSE. The presentedapproach enables an investigation of the observa-tional impact on downstream weather evolutionas well as an evaluation of model errors and theirpotential sources as related to specific weatherfeatures such as warm conveyor belts. Results sofar suggest a superior forecast performance whenadditional observations are taken into account, withboth dropsonde and additional radiosonde observa-tions exhibiting an overall beneficial impact on theevolution of the short-range forecast error.

5.5 Observation impact diagnostics in anensemble data assimilation systemMain Author: Olaf StillerInstitution: Data Assimilation, DWD, Germany

Observation impact diagnostics have been de-veloped to assess the impact of subgroups ofobservations in the data assimilation (DA) processwithout the need of performing data denial ex-periments. These diagnostics are based on a costfunction which measures the observation impactwith respect to some verifying ”truth”.Here (as in Kalnay et al. 2012) such diagnostics

are used for an ensemble system, the LETKF/ENVARsystem of the DWD’s global Icon model. As in Som-mer&Weissmann (2016), observations are used forthe verifying ”truth”. In a first step, the method is ap-plied to well established observations like radio son-des, radio occultations and AMSU A radiances and,to avoid ambiguities between assimilation and modelissues, the investigation focuses on impacts on theanalysis (not on the forecast).It is shown that the cost function can be split into

two components one of which should have expecta-tion value zero if the analysis was optimal (as mea-sured by the cost function). The other component isstrictly negative if the observations are pulling themodel state closer to the verifying ”truth”. While thefirst component yields an optimality condition for theDA process, the second component shows the consis-tency between analysed observations and the verify-ing truth (here other observations).For the well established observations considered

here, the overall consistency is found (second cri-terion) to be generally quite good. Exceptions aresome AMSU A channels for which bias problemscould be identified in some regions. From the opti-mality condition (first criterion) radio sondes and ra-dio occultations seem to be well employed but AMSUA seems to be assimilated with a far to strong weightby the DA system.The aim of this work is to establish a well un-

derstood diagnostic tool which helps identifyingobservation subgroups whose usage is sub optimalin our DA system.

Wednesday

1.4 Mesoscale dynamical regimes and balancein convective-scale DAMain Author: George CraigInstitution: Meteorological Instutute, LMU MunichCo-Authors: Tobias Selz

Different dynamical regimes in the atmosphereare identified by comparing the magnitude ofterms in the governing equations in a seven day,convection permitting simulation of weather overEurope and the eastern Atlantic Ocean. As ex-pected, geostrophic balance holds approximately onlarge scales and hydrostatic balance is valid overa wide range of the mesoscale. Interestingly, fora wide range of scales from about 10 to 500 km,the atmosphere approximately satisfies a form aweak temperature gradient balance, where verti-cal motion is determined by the requirement thatadiabatic cooling approximately equals diabaticwarming. The accuracy of this balance is less thanthat of geostrophic balance on synoptic scale, butto the extent that it holds, it allows a distinctionbetween divergent motions driven by convectiveclouds and those associated with transient gravitywaves, and may represent a useful constraint forconvective-scale data assimilation.

1.5 The Met Office hourly 4D-Var system,status and plansMain Author: Marco MilanInstitution: Met Office, FitzRoy road, EX1 3PB,Exeter, UKCo-Authors: Bruce Macpherson, Gareth Dow, Gor-don Inverarity, Robert Tubbs, Marek Wlasak

In July 2017, the operational Met Office UKVmodel moved from 3-hourly 3D-Var to hourly cycling4D-Var. The hourly cycle allows the model to assim-ilate observations and generate new short-periodforecasts more frequently, while 4D-Var generates aflow dependent assimilation method. In introducingthe hourly cycling, we were obliged to shorten theobservation cut-off time from 75 to 45 minutes,which led to the loss of some key observations, par-ticularly lower tropospheric radiosonde data. Theloss of such data does have a detrimental impacton the longer forecasts. A test version will allow alonger cut-off on cycles with more radiosonde data.Improvement of the observational input to UKV 4D-

Var is always a priority. The latest version now as-similates roadside visibility observations to supple-ment the already valuable impact of roadside tem-perature and humidity data. Better short-period vis-ibility forecasts are of interest, for example, to avia-tion customers. Mode-S aircraft data are also undertest and available from an increasing number of UKairports. Wind reports are of similar quality to AM-DARs while the temperature data available in the UKare of lower quality.Developments in the background error covariance

matrix (B) are also under review. To date the UKVhas used covariances computed using the laggedNMC method. Those currently operational were de-

rived from (t+6-t+3) forecast differences in 3-hourly3D-Var trials. Since moving to an hourly cycle, testsof shorter forecast lead times and smaller lead timedifferences within the NMC method have been done.However they are not yet competitive with the oper-ational B. New methods are under study, including ahybrid 4D-Var technique using the convective-scaleensemble.

1.6 Development of a storm-scale particlefilter for investigating predictability of convec-tion initiation and developmentMain Author: Takuya KawabataInstitution: Meteorological Research Institute,Japan Meteorological Agency, Tsukuba, JapanCo-Authors: Genta Ueno

A particle filter (PF) with the JMA meso-scale non-hydrostatic model (JMANHM; NHM-PF) has beendeveloped in 2017. The aim is to study predictabilityof convection initiations and developments withweak forcings. In general, convections withoutstrong forcings (e.g., cold fronts, tropical cyclones,mountains) seem to be initiated randomly. Then, itis difficult to detect exact factors for the initiations.Moreover, PDFs of these predictability are thoughtto be non-Gaussian, which has made it difficult topredict and even investigate such phenomena, sofar. While, it is able to deal with the non-Gaussianitywhen PF is applied for these researches. The NHM-PF employs a sampling importance resampling (SIR)filter with advanced observations such as GNSSintegrated water vapor, dual polarimetric radarsand conventional observations provided by NHM-4DVAR. These rich observations are important toconstrain the initiations, but these may be causeof filter collapse. A short assimilation period andintroduction of model error should mitigate thiscollapse. The idea of this study is to investigatenon-Gaussianities in environmental fields (winds,temperature, water vapor) before the initiations aswell as interior cumulonimbus (cloud microphysics)after the initiations. Detailed descriptions on thisstudy and the NHM-PF will be presented.

1.7 Data assimilation of GNSS Zenith TotalDelays in KMA convective scale modelMain Author: Eun-Hee KimInstitution: Numerical Modeling Center/Korea Me-teorological AdministrationCo-Authors: Eunhee Lee, Seungwoo Lee, Yong HeeLee

The convective scale NWP model Local Data Assimi-lation and Prediction System, LDAPS) is operationalat Korea Meteorological Administration (KMA). TheLDAPS model has a resolution of 1.5 km and usesthe three dimensional variational (3D-Var) dataassimilation scheme with a cycle of 3 hours. Forshort-range forecasting at a kilometer scale it is im-portant to utilize observations with high spatial andtemporal resolution. Atmospheric moisture-relatedinformation obtained from Global Navigation Satel-lite System (GNSS) observations have been firstlyused in LDAPS. It is because using Zenith Total

Delay (ZTD) data obtained from a ground-based net-work of GNSS receivers can fill gap of lacking rapidhumidity information to improve the predictability ofshort-term forecast. Among total about 100 stationsover the Korean Peninsula, the 40 ground-basedGNSS stations were preferentially utilized and as-sessed over a one-month summer period. In orderto confirm the quality of GNSS data, GNSS-derivedprecipitable water vapor (PWV) was compared toradiosonde observation. The observation-processingsystem includes quality control and bias correctionfor each point. Assimilation of GNSS ZTD resultedin a positive impact on lower tropospheric humidityand rainfall forecasts against radiosonde observa-tions and hourly precipitation observations. Around100 ground-based GNSS stations can be recentlyused in near real time by NMSC (National Meteoro-logical Satellite Center)/KMA and are being testedin LDAPS and its impact was evaluated throughheavy rainfall case. In addition, in order to removethe correlation between the observation points, itapplied to take priority through the estimation ofobservation error.

1.8 Is 30-second update fast enough forconvection-resolving data assimilation?Main Author: Takemasa MiyoshiInstitution: Data Assimilation Research Team,RIKEN, Kobe, JapanCo-Authors: Juan Ruiz, Guo-Yuan Lien, Toshiki Tera-mura, Yasumitsu Maejima, Keiichi Kondo, HideyukiSakamoto

For local severe weather forecasting at 100-mresolution with 30-minute lead time, we have beenworking on the “Big Data Assimilation” (BDA) effortfor super-rapid 30-second cycle of an ensembleKalman filter. We have presented two papers withthe concept and case studies (Miyoshi et al. 2016,BAMS; Proceedings of the IEEE). Since then, wehave performed more experiments in multipletorrential rain cases, and all showed promisingresults. We were hoping that we could assumethe Gaussian error distribution in 30-second fore-casts before strong nonlinear dynamics distort theerror distribution for rapidly-changing convectivestorms. However, using 1000 ensemble members,the reduced-resolution version of the BDA system at1-km grid spacing with 30-second updates showedubiquity of highly non-Gaussian PDF associated withconvective activities. Although our results so farwith multiple case studies were quite successful,this gives us a doubt about our Gaussian assumptioneven if the data assimilation interval is short enoughcompared with the system’s chaotic time scale. Wetherefore pose a question if the 30-second updateis fast enough for convection-resolving data assim-ilation under the Gaussian assumption. To answerthis question, we aim to gain combined knowledgefrom BDA case studies with different cycle intervals,1000-member experiments with 1-km grid spac-ing, 4D-EnKF experiments with different windowlengths, and toy-model experiments with dense andfrequent observations. In this presentation, wewill show the most up-to-date results of the BDA

research, and will discuss about the question if the30-second update is fast enough for convective-scaledata assimilation.

1.9 Representation of model error in con-vective scale data assimilationMain Author: Yuefei ZengInstitution: MIM, University of Munich, GermanyCo-Authors: Tijana Janjic, Alberto de Lozar , UlrichBlahak, Matthias Sommer, Hendrik Reich

The Kilometre-scale ENsemble Data Assimila-tion (KENDA) system has been run operationallyat the Deutscher Wetterdienst (DWD) since March2017. KENDA uses the Local Ensemble TransformKalman Filter (LETKF) algorithm to assimilate datafor convective scale data assimilation application.To account for large scale model error as well assampling errors, the multiplicative inflation, additiveinflation and the relaxation to prior perturbations(RTTP) method have been implemented in theKENDA.

In this work, we introduce another type of additiveinflation based on the model resolution errors, whichare samples of differences between two COSMO-DEforecast runs at 1.4 and 2.8 km. The experiments,that assimilate conventional observations and radarreflectivity data, are conducted to investigate thosedifferent ways of representing model errors. Theexperiment with the newly implemented additiveinflation results in better analyses during assimila-tion cycles compared to independent radar velocitydata and improved ensemble forecasts up to severalhours.

1.10 Assimilation of temperature and hu-midity profiles from a Raman LidarMain Author: Daniel LeuenbergerInstitution: Numerical Weather Prediction, Me-teoSwiss, Zürich, SwitzerlandCo-Authors: Alexander Haefele

MeteoSwiss operates a Raman lidar to measuretropospheric humidity and temperature profileswith high temporal and vertical resolution. The lidaris fully automatic, running unattended and yields adata availability of more than 50% on average. Themaximum vertical range is 5 km during day and 10km asl during nighttime in cloud free conditions.In order to investigate the value of the lidar profileobservations in Numerical Weather Prediction, twotwo-day assimilation experiments with the opera-tional, convective-scale COSMO-LETKF-based dataassimilation system of MeteoSwiss (2.2km grid sizeand 40 members) have been conducted: a caseof fog formation in the Swiss Plateau and a caseof summer convection. In this contribution, wecompare ensemble analyses and forecasts using theoperational set of observations from radiosondes,wind profiler, surface stations, aircrafts and radar-derived surface precipitation profile with those, inwhich the additional, 30min-frequency observationsof the Raman Lidar have been taken into account.

8.1 KEYNOTE: Coupled Earth System As-

similation in NWP at ECMWFMain Author: Phil BrowneInstitution: European Centre for Medium-RangeWeather ForecastsCo-Authors: Patricia De Rosnay, Hao Zuo

As part of the long-term strategy, ECMWF willsoon use produce all its forecasts using a coupledocean-atmosphere-land forecast model. Thereforeinitial conditions for all components need to be sup-plied. In this talk I will describe how different levelsof coupled assimilation are used for the variouscomponents of the system.In the first part of the talk I will focus on the land

surface-atmosphere interactions that use a weaklycoupled assimilation approach. I will discuss the im-pacts and future plans for snow and soil moistureanalyses at ECMWF.As a first step towards having a consistent analysis

across the atmosphere and the ocean components,weakly coupled data assimilation (WCDA) has beenimplemented. In this talk I will describe the impactof WCDA through the transfer of fields such as sea-ice concentration and ocean currents from the oceananalysis to the atmosphere. Further I will discussWCDA through sea surface temperature (SST) andwhy we choose to limit this to the tropics and not theextra-tropics.In parallel a stronger form of coupled analysis has

been developed - Quasi-strongly coupled assimila-tion (or outer loop coupling). I will show results thatsuggest improvements from this stronger form ofcoupling extend further into the atmosphere than byusing WCDA, but discuss the challenges that needto be overcome to make this an operationally viablemethod.

8.2 COUPLED DATA ASSIMILATION WITHTERRSYSMPMain Author: Harrie-Jan Hendricks-FranssenInstitution: Agrosphere (IBG-3), Forschungszen-trum Julich GmbH, Julich, GermanyCo-Authors: Prabhakar Shrestha, Wolfgang Kurtz,Bibi Naz, Hongjuan Zhang, Stefan Kollet, ClemensSimmer, Harry Vereecken

The Terrestrial Systems Modeling Platform(TerrSysMP) consists of component models forthe atmosphere (Consortium for Small-Scale Model-ing; COSMO), the land surface (NCAR CommunityLand Model; CLM) and subsurface (ParFlow) toimprove our understanding of time-space patternsof energy, water and carbon fluxes and feedbacks inthe terrestrial system. TerrSysMP uses an externalcoupler (OASIS3 coupler interfaced with MCT;OASIS3-MCT). Predictions with TerrSysMP areaffected by random and systematic errors, whichin part are related to the many uncertain inputparameters and forcings. The data assimilationframeworks PDAF and DART have been coupledto TerrSysMP, which allow a correction of modelstates and parameters by real-time measurements.With TerrSysMP-PDAF up to 10^8 unknown statesand parameters can be estimated, for each of the10^2 ensemble members. The TerrSysMP-PDAF

and TerrSysMP-DART frameworks have been testedin a series of synthetic and real-world experimentsranging from the hillslope scale to the continentalscale, and for the assimilation of different data typeslike soil moisture, soil temperature, groundwaterlevels, river water levels and boundary layer tem-perature profile. In the presentation three exampleswill demonstrate the potential of data assimilationin combination with TerrSysMP, with a focus oncoupled data assimilation.

8.3 Assimilation of land surface tempera-ture in the coupled land atmosphere systemMain Author: Christine SgoffInstitution: Goethe University, Frankfurt, GermanyCo-Authors: Annika Schomburg, Juerg Schmidli

The near-surface weather and climate variabil-ity is strongly influenced by complex interactionsbetween the land surface and the atmosphericboundary layer. To obtain realistic boundary layersimulations an accurate simulation of the couplingbetween land surface and boundary layer atmo-sphere is essential. To improve the simulation ofthis tightly-coupled system, an observing systemsimulation experiment (OSSE) is designed to assim-ilate land surface temperature (LST) derived fromgeostationary satellites. LST is an important compo-nent of the surface energy balance and crucial forthe realistic simulation of soil temperature, latentand sensible heat flux. As “Nature Run” a high-resolution COSMO simulation has been performedand from this “true state” the synthetic observationsfor the OSSE have been derived. The assimilationprocess is based on the local ensemble transformKalman filter (LETKF). Technically this is realizedby the LETKF framework used at DWD (KENDA,Schraff et al. 2015). Within this idealized setting,the impact of hourly LST assimilation on soil andlower atmosphere variables and processes will beinvestigated. To gain improved knowledge aboutboundary layer processes and evolution, idealizedexperiments with different surface characteristicsand boundary layer regimes will be conducted. Firstresults will be presented.

8.4 Use of satellite soil moisture informationfor Nowcasting-Short Range NWP forecastsMain Author: Paride FerranteInstitution: COMET / EUMETSATCo-Authors: Francesca Marcucci, Lucio Torrisi,Valerio Cardinali

Many physical, chemical and biological processestaking place at the land surface are strongly in-fluenced by the amount of water stored withinthe upper soil layers. Therefore, many scientificdisciplines require soil moisture observations fordeveloping, evaluating and improving their simula-tion models. One of these disciplines is meteorologywhere soil moisture is important due to its controlon the exchange of heat and water between thesoil and the lower atmosphere. The assimilation ofsoil moisture observations and the development ofan algorithm to initialize soil moisture is crucial to

improve the forecasts of low level air temperatureand humidity: if not suitably constrained, the soilmoisture in a numerical model will drift from thetrue climate, resulting in erroneous boundary layerforecasts. An operational soil moisture product isgenerated by HSAF from the Advanced Scatterom-eter (ASCAT), that is a C-band active microwaveremote sensing instrument flown on board of theMETOP satellite series. The aim of the presentwork is to assimilate the HSAF soil moisture datawith the COMET ensemble data assimilation sys-tem, based on the LETKF algorithm, in order toinfluence not only the soil parameters, but alsothe near surface atmospheric fields. The HSAFsoil moisture observations have been operationallymonitored since January 2015, in order to performdata quality control, bias correction and to inves-tigate different approaches to define a suitableobservation operator. A preliminary version of soilmoisture LETKF assimilation has been developedin the COSMO-KENDA (Kilometre-scale ENsembleData Assimilation) scheme. An experimental suitehas been also implemented, in order to evaluatethe impact of HSAF soil moisture data assimilationand to assess how the analysis scheme could beimproved.

8.5 Treating sample covariances for use instrongly coupled atmosphere-ocean data as-similationMain Author: Amos S. LawlessInstitution: School of Mathematical, Physical andComputational Sciences, University of Reading,Reading, U.K.Co-Authors: Polly J. Smith, Nancy K. Nichols

Covariance information derived from an ensemblecan be used to define the a priori atmosphere-oceanforecast error cross covariances required for vari-ational strongly coupled atmosphere-ocean dataassimilation. Due to restrictions on sample size,ensemble covariances are routinely rank deficientand/ or ill-conditioned and marred by samplingnoise; thus they require some level of modificationbefore they can be used in a standard variationalassimilation framework. Here, we compare meth-ods for improving the rank and conditioning ofmultivariate sample error covariance matrices inthe context of strongly coupled atmosphere-oceandata assimilation. The first method, reconditioning,alters the matrix eigenvalues directly; this preservesthe correlation structures but does not removesampling noise. We show it is better to reconditionthe correlation matrix rather than the covariancematrix, as this prevents small but dynamically im-portant modes from being lost. The second method,model state-space localisation via the Schur product,effectively removes sample noise, but can dampensmall cross-correlation signals. A combination thatexploits the merits of each is found to offer aneffective alternative.

Thursday

7.1 How does covariance inflation impactEnKF-initialized convection-allowing ensembleforecasts?Main Author: Craig SchwartzInstitution: The National Center for AtmosphericResearch (NCAR)Co-Authors: Glen Romine, Kathryn Fossell

Covariance inflation is an important componentof ensemble Kalman filters (EnKFs) to maintainproper ensemble spread. While different inflationmethods have been compared and contrasted inglobal models, little work has examined how infla-tion impacts EnKF-initialized convection-allowingensemble forecasts, which are becoming morecommon.To fill this void, four experiments were performed

that were identically configured except with regardto their inflation schemes. One experiment employedprior adaptive state-space inflation, while a secondused both prior and posterior adaptive state-spaceinflation. A third experiment employed a combi-nation of prior adaptive state-space inflation and a“spread restoration”method that provides additionalinflation during observation assimilation, and thefourth experiment used a state-space “relaxation toprior spread” (RTPS) approach that considerably dif-fers from the adaptive inflation method used in theother three experiments. All experiments used con-tinuously cycling EnKF data assimilation for a 35-day period with 50 ensemble members at 15-km hor-izontal grid spacing over a limited-area domain, and15-km analysis ensembles were downscaled to 3-kmto initialize 48-hour, 10-member convection-allowingensemble forecasts over the conterminous UnitedStates.Including spread restoration improved both the

EnKF (as measured by prior observation-spacestatistics) and 3-km precipitation forecast quality.However, the RTPS experiment initialized 3-km pre-cipitation forecasts that were comparable to or bet-ter than those initialized by the other three exper-iments. As the RTPS approach is simpler than theadaptive inflation scheme, these results suggest us-ing RTPS is an attractive method for implementationin EnKFs that initialize convection-allowing ensem-ble forecasts.7.2 Predictability of Deep Convection in

Idealized Experiments under Radar Data As-similationMain Author: Kevin BachmannInstitution: Hans-Ertel-Centre for Weather Re-search, LMU, Munich, GermanyCo-Authors: Christian Keil

Increasing computational resources now allow theuse of storm-scale ensembles in numerical weatherprediction with potentially profound beneficial im-pact on the prediction of convective precipitation.Therefore, several weather services are developingand evaluating high-resolution, limited-area ensem-ble prediction systems like COSMO-KENDA at DWD.In this context, the use of spatially and temporally

high resolved observations, such as radar data,to provide perturbed initial conditions, becomesincreasingly important. The practical predictabilitylimits of convective precipitation in such a state-of-the-art system and their sensitivities to observationsare investigated in a perfect model experiment, withonly initial condition errors.For horizontal-homogeneous, convectively un-

stable conditions favoring long-lived convection,we found skillful predictions for scales as small as50 km after 8 h lead time. For experiments withfewer observations or larger observation errors,the predictability decreases, but scales above 100km remain predictable. Additionally, we foundthat the presence of orography in the experimentsincreases the predictability, as it acts as a triggerfor deep convection. This positive effect decreaseswith improving quality of the initial conditions forup to 6 forecast hours. Thereafter, the orographyexhibits increasing impact on the forecast skillagain. Similar experiments with only large-scaleinitial condition errors or with artificial model errorwill be performed soon and should provide furtherinsight into realistic initial condition errors and theirgrowth.

7.3 Ensemble initial conditions targeted atthe convective scaleMain Author: Jan KellerInstitution: Hans-Ertel-Centre for Weather Re-search, Climate Monitoring and Diagnostics, Bonn,GermanyCo-Authors: Clarissa Figura, Andreas Hense

The estimation of fast growing error modes of asystem is a key interest of ensemble data assimila-tion when assessing uncertainty in initial conditions.As initially small-scale error growth structurescongregate to large scale structures over time, it iscrucial to reintroduce new meaningful perturbationsin order to estimate reasonable initial conditions forsmall-scale forecasting, e.g., forecasting events onthe convective scale.With respect to such uncertainty structures asso-

ciated with convective scale events and the corre-sponding forecast error growth, we employ the so-called self-breeding approach. It allows for the es-timation of initial ensemble perturbations targetedat maximizing the error growth on convective timescales while taking into account the challenges aris-ing from using a limited area model.Using this approach, we present results from case

study experiments for convective events using theNWP model COSMO in its convection-permittingsetup. We also investigate the potential of theobtained uncertainty structures for enhancing thedata assimilation process with respect to convectionin the model’s LETKF data assimilation scheme(KENDA).

7.4 Assimilation of horizontal line-of-sightwinds with a mesoscale EnKF data assimilationsystem over the northern Atlantic and EuropeMain Author: Matic ŠavliInstitution: University of Ljubljana, Ljubljana, Slove-

niaCo-Authors: Nedjeljka Žagar, Jeffrey L. Anderson

The forthcoming ADM-Aeolus mission of the Eu-ropean Space Agency will provide global coverageof vertical profiles of horizontal line-of-sight (HLOS)wind twice per day. In previous studies, the potentialimpact of HLOS winds has been extensively evalu-ated using the ECMWF variational data assimilationsystem and a significant impact was reported overthe tropics. Over Europe, a minor impact was ob-tained for short forecast ranges but a positive impactwas sustained in the medium range. As part of thepreparation for the use of HLOS winds in mesoscaleNWP systems in Europe, we shall examine theimpact of HLOS winds in an ensemble Kalman filter(EnKF) limited area data assimilation system overEurope and the North Atlantic. The study uses theWeather Research and Forecast model (WRF) andthe ensemble adjustment Kalman filter (EAKF) fromthe Data Assimilation Research Testbed (DART). TheObserving System Simulation Experiment (OSSE)framework was developed with a 50-member EAKFnested in the operational 50-member ensembleprediction system of ECMWF using model-leveldata available twice per day. The importance ofthe flow-dependent background-error covarianceswas explored using experiments without covarianceinflation. The value of HLOS winds is compared witha single wind component and full wind information.In areas of strong covariances such as along frontsin the Atlantic, multivariate information providessignificant analysis increments, especially if theHLOS observation is aligned along the front. It isfurthermore shown that the assimilation of windinformation in terms of the HLOS component mayproduce better analyses than the assimilation ofeither the zonal or meridional winds.

4.8 Exploiting Nonlinear Relations betweenObservations and State Variables in EnsembleKalman FiltersMain Author: Jeffrey AndersonInstitution: CISL, NCAR, Boulder, USA

Serial implementations of ensemble Kalman filterscan partially separate the challenges of non-gaussianobservation likelihoods and prior probability dis-tributions from those associated with nonlinearrelations between state variables and observedquantities. A number of serial algorithms havebeen developed that deal with non-gaussianity inobservation space. There has been less work ondealing with the challenges of nonlinearity and mostproposed algorithms have not provided significantimprovements in large geophysical applications.

Three methods for computing the ensemble incre-ments for a model state variable given incrementsfor an observation are described and compared. Thefirst is the standard linear regression which is im-plicitly used in ensemble filters like the EnKF andin the classical Kalman filter. The second methodis a rank regression that is appropriate for nonlin-ear relations that are either monotonically increas-ing or decreasing as a function of the observed vari-

able. This method extends aspects of the rank his-togram filter method that allow arbitrary likelihoodsand priors in observation space. The thirdmethod in-crementally regresses observation space incrementsonto state variables. The algorithm also incorpo-rates local linear regression. The regression coef-ficient (analogous to the Kalman gain) for each en-semble member is computed using a subset of theprior ensemble members. Combining local regres-sion with an incremental update allows ensemble fil-ters to compute significantly improved posteriors fora wide range of nonlinear prior distributions with lit-tle degradation to solutions for linear cases.

Examples are shown for specific classes of non-linear bivariate priors and for low-order dynamicalsystems. A discussion of the relative costs andcapabilities of three algorithms is presented. Thechallenges of recognizing and exploiting nonlinearrelations in the presence of noise is highlighted.

4.9 Mixed Gaussian-Lognormal Based Vari-ational Data AssimilationMain Author: Steven FletcherInstitution: Cooperative Institute for Research inthe Atmosphere, Colorado State University, FortCollins, Colorado, USACo-Authors: Anton Kliewer, Andrew Jones, JohnForsythe

An underlying assumption made in most formsof variational and ensemble based data assimilationsystems is that the associated errors are Gaussiandistributed random variables. However, whendealing with variables that cannot go negative,then it is not always optimal to apply a Gaussianmodel for the errors. At the Cooperative Institutefor Research in the Atmosphere (CIRA) at ColoradoState University we have developed a theory thatallows for the minimization of the errors that area combination of Gaussian and lognormal randomvariables simultaneously. This approach is possiblethrough defining a mixed Gaussian-lognormal prob-ability density function. In this presentation, weshall present details about the mixed distributionand show examples with toy problems, as well aswith a 1-dimensional microwave retrieval system formixing ratio and temperature. We shall also presenta new observational quality control measure thatwould enable lognormally distributed observationserrors to be screened to allow for a more consistentmethod for screening these sets of non-Gaussianobservation errors.

4.10 Preservation of physical properties withEnsemble-type Kalman Filter AlgorithmsMain Author: Tijana Janjic PfanderInstitution: Hans Ertel Centre for Weather Research,DWDCo-Authors: Yuefei Zeng, Yvonne Ruckstuhl

In recent work we illustrated benefits of conser-vative data assimilation procedures in very simpleidealized setups designed for convective scale dataassimilation as well as in setups more appropriatefor global data assimilation. Following the con-

servation principle, first conservation of mass andpreservation of positivity have been shown to be im-portant constraints for data assimilation algorithms.These two constraints have been incorporated into anew algorithm, based on the ensemble Kalman filter(EnKF) and the quadratic programming (QPEns:Quadratic Programming Ensemble filter). For stateestimation, the inclusion of the constrained estima-tion can improve the ensemble Kalman filter resultsin case of strongly non-Gaussian error distributions.In particular, it was shown that it is important topreserve both positivity and mass in order to obtaingood estimates of the fields when the error in loca-tion dominates and that the mass conservation- andpositivity-constrained rain significantly suppressesnoise seen in localized EnKF results.

In the setup more appropriate for the global dataassimilation, we extended QPEns algorithm to repli-cate properties of nonlinear dynamical systems suchas conservation of energy and enstrophy. Idealizedexperiments were performed using a 2D shallowwater model, with selected constraints derived fromthe nature run. Although all experiments exhibitedcomparable RMSE, the kinetic energy and enstro-phy spectra in experiments with the enstrophyconstraint (on the globally integrated enstrophy)were considerably closer to the true spectra, inparticular at the smallest resolvable scales. There-fore, imposing conservation of enstrophy withinthe data assimilation algorithm effectively avoidsthe spurious energy cascade of rotational part andthereby successfully suppresses the noise. The14-day deterministic and ensemble free forecast,starting from the initial condition enforced by bothtotal energy and enstrophy constraints, producedthe best prediction.

9.1 KEYNOTE: Chemical data assimilationMain Author: Richard MénardInstitution: Environment and Climate ChangeCanadaCo-Authors: Martin Deshaies-Jacques, SergeySkachko

Why are we doing chemical data assimilation?There is of course purpose for inverse modeling,monitoring, and forecasting, but another reasonperhaps less known, is its application to assessthe impact of air pollution on human health – agrowing problem as more people lives in urbanareas and megacities. Although there are many timescales of atmospheric chemistry from which derivedifferent applications and formulations, common toall lies fundamentally a mass estimation problem.We discuss the properties of mass conservation, onthe first and second moments and on the pdf itself.We also discuss its implication to data assimila-tion and numerical schemes, and discuss chemicalassimilation schemes that have been developedspecifically to chemical tracers. For atmosphericchemistry as in other applications, the analysis isactually a product by itself. We present a generaltheory of evaluating analyses by cross-validation,and not through forecasting. The theory generalizesthe Desroziers et al. diagnostics where the use of

independent observations renders the estimationproblem well-posed.

9.2 Recent developments on the ECMWF’sCAMS data assimilation suite.Main Author: Jérôme BarréInstitution: Copernicus Atmospheric MonitoringService, ECMWF, UKCo-Authors: Melanie Ades, Antje Inness, AnnaAgusti-Panareda, Richard Engelen, Johannes Flem-ming, Zak Kipling, Sebastien Massart, Mark Par-rington, Vincent-Henri Peuch

The European Union’s Copernicus AtmosphereMonitoring Service (CAMS) operationally providesdaily forecasts of global atmospheric compositionand regional air quality. It uses the ECMWF’sIntegrated Forecasting System (IFS), that includesmeteorological and atmospheric composition vari-ables, such as reactive gases, greenhouse gasesand aerosols. In this presentation, we will focuson new developments of the system such as: Theintegration of the new Sentinel-5p satellite thathas been successfully launched in October 2017.The sensor provides global-scale high horizontalresolution observations of key components (ozone,nitrogen dioxide, sulfur dioxide, carbon monoxide,aerosols and methane). We will show first results ofthe monitoring of S5p data with the CAMS system.The efforts of updating the system to an Ensemble ofData Assimilation (EDA), i.e. running an ensembleof variational analysis updates and subsequent fore-casts. Although the CAMS background error is stillformulated using the NMC method, first results arenow being produced with the NWP flow dependentbackground error covariance matrix with seasonaland time dependent variances. Lastly, we will coverthe plans and updates on emissions inversions andthe challenges to retrieve emission informationfrom satellite data within the IFS. We will showthe benefits of using the ensemble information thatcould be retrieved from the EDA in order to infersources and potentially separate the inversion sec-tors e.g. anthropogenic, biogenic, biomass-burningemissions.

9.3 Source identification from image-typemeasurement data for atmospheric chemistrymodelsMain Author: Alexey PenenkoInstitution: ICMMG SB RAS, Novosibirsk, Russian FederationCo-Authors: Zhadyra Mukatova, Ann Blem

The inverse source problems for atmosphericchemistry models with image-type measurementdata like concentrations time series or verticalconcentrations profiles are considered. The sensi-tivity operators composed of the sets of the adjointproblem solutions allow to transform the inverseproblem stated as the system of ODE or PDE tothe family of operator equations depending on thegiven set of orthogonal functions in the space ofthe measurement results [1]. The proper choiceof the orthogonal functions allows optimizing the

dimensionality of the problem thus allowing for theefficient solution of the resulting operator equationwith the relevant iterative methods for nonlinearill-posed operator equations (e.g. the methodsbased on the truncated SVD). This reduction of thedimensionality can be done by choosing the set ofleft singular vectors corresponding to the largestsingular values of a sensitivity operator, constructedfor the largest feasible (for the computer used)number of the orthogonal functions from somegeneral basis (e.g. trigonometric) in the space ofthe measurement results.The work has been supported by the RSF project

17-71-10184.References [1] Penenko A.V., 2018. Consistent

numerical schemes for solving nonlinear inversesource problems with the gradient-type algorithmsand the Newton-Kantorovich methods. NumericalAnalysis and Applications, 2018 (in press).

9.4 Quantitative estimation of volcanic ashemissions and its uncertainty by a combinedminimization-particle smootherMain Author: Philipp FrankeInstitution: Forschungszentrum Jülich IEK-8, Jülich,GermanyCo-Authors: Anne Caroline Lange, Hendrik Elbern

Volcanic eruptions may pose a threat to humans, cli-mate, economy, and aviation. For decision makers,the estimation of volcanic ash in the atmosphereand its uncertainty is essential to perform accu-rate safety precautions. Currently, there exists nomethod for estimating the emissions of volcanic ashand its uncertainty for longer lasting eruptions, inwhich the eruption strength varies temporally independence on the emission height. This issue isadressed by the chemical part of the Ensemble forStochastic Intergration of Atmospheric Simulations(ESIAS-chem) that comprises a particle smootherin combination with an ensemble-based discreteextension of the Nelder-Mead minimization method.The extended Nelder-Mead method is efficient inresolving the temporal and vertical distribution ofthe emissions in an ensemble environment. The par-ticle smoother reduces the variance of the analysisensemble in order to provide an accurate estimateof the emission profile. The system validation ad-dresses the special challenge of ash cloud heightanalyses in case of observations restricted to bulkcolumn mass loading information as is typical inthe case of geostationary satellite data like thusobtained by SEVIRI. The ensemble version of theEURopean Air polution Dispersion - Inverse Model(EURAD-IM) is integrated into ESIAS-chem. Identi-cal twin experiments identified the dependence ofthe system analysis on the wind conditions. Understrong wind conditions at the volcano, the temporaland vertical varying volcanic emissions are analyzedup to an error of only 10% by assimilating columnmass loadings. For weak wind conditions, theanalysis accuracy of the emission profile is limited,because the volcanic ash emitted at different timesand heights is not sufficiently separated. In thiscase, only an enlongated assimilation window im-

proves the analysis. The ability of ESIAS-chem toestimate real volcanic emissions is tested in a casestudy of the 2010 eruption of the Eyjafjallajökullvolcano, Iceland.

3.1 KEYNOTE: All-sky assimilation of satel-lite radiances for global weather forecastingMain Author: Alan GeerInstitution: ECMWF, Reading, UK

After much progress in forecast modelling, dataassimilation, and in radiative transfer modelling,it is possible to assimilate satellite observationsaffected by cloud and precipitation. This brings newinformation in the most dynamically active regions ofthe atmosphere, and it helps correct errors in cloudand precipitation forecasts. At ECMWF around 20%of the constraint on the short-range operationalforecast now comes from all-sky microwave radi-ances sensitive to humidity, cloud and precipitation.Important components in all-sky assimilation are (i)a radiative transfer model capable of simulating thescattering from cloud and precipitation particles;(ii) a data assimilation system capable of updatingthe initial conditions to correct errors in cloud andprecipitation forecasts; and (iii) an observation errormodel that accounts for large representation errorsin the presence of cloud and precipitation. Such anapproach also relies on high-quality cloud and pre-cipitation forecasts, meaning that systematic errorsin the forecast model need special attention (seeabstract by K. Lonitz). The operational use of all-skymicrowave radiances is being improved in variousareas, including better representation of scatteringfrom frozen hydrometeors. Progress towards theassimilation of all-sky infrared radiances is contin-uing, but it is still hard to demonstrate benefits inoperational forecasting. The framework that hasworked so well for microwave observations needsfurther development to support infrared radiances.For example, the observation error model needs torepresent inter-channel error correlations alongsidethe inflation of error variances in cloudy conditions.Also, a better treatment of cloud overlap is requiredin the observation operator. However, more funda-mental issues are being revealed: all-sky infraredradiances do not just see cloud and water vapour,but also mesoscale features such as inertia-gravitywaves and equatorial waves that are not yet properlyhandled in data assimilation systems.

3.2 Using solar satellite channels forconvective-scale data assimilationMain Author: Leonhard ScheckInstitution: LMU Munich / Hans-Ertel-Centre forWeather ResearchCo-Authors: Bernhard Mayer, Martin Weissmann

High-resolution observations from instrumentson geostationary satellites provide a wealth of infor-mation about convective activity and are thereforeseen as a important type of observation for convec-tive scale data assimilation (DA). In particular thesolar channels provide information on the cloud dis-tribution, cloud microphysical properties and cloud

structure with high temporal and spatial resolution.However, in operational DA systems currently onlyclear sky thermal infrared and microwave radianceobservations are used, which mainly provide tem-perature and humidity information. Sufficiently fastand accurate forward operators for visible and near-infrared radiances are not yet available, becausemultiple scattering makes radiative transfer (RT) atsolar wavelengths complicated and computationallyexpensive.

Recently, we have developed a look-up table based1D RT method that is orders of magnitude fasterthan conventional radiative transfer solvers forthe visible spectrum and similarly accurate. Theaccuracy of the method has been increased furtherby means of fast approximations for 3D RT effectsthat are related to the inclination of cloud tops. Aforward operator based on this RT method has beenimplemented to simulate synthetic MSG-SEVIRIimages from COSMO-DE model output and is nowintegrated in the Km-scale Ensemble Data Assim-ilation (KENDA) system of DWD. We demonstratethat assimilating 0.6mu SEVIRI observations duringstrongly convective days in summer 2016 improvesthe cloud distribution in forecasts for several hourswithout degrading the beneficial impact of conven-tional observations significantly. Furthermore, weinvestigate the impact of varying important assimi-lation parameters like the superobbing length scaleand the onservation error.

3.3 Towards improving the accuracy of lowclouds and convective initiation in an LETKFMain Author: Lilo BachInstitution: Deutscher Wetterdienst, Offenbach,GermanyCo-Authors: Christoph Schraff, Christina Köpken-Watts, Ulrich Blahak, Leonhard Scheck, RolandPotthast

In the framework of the SINFONY (Seamless Inte-grated Forecasting System) project of DeutscherWetterdienst, nowcasting and NWP methods areintegrated to develop a seamless probabilisticprediction system for the convective scale. Thetargeted forecast horizon ranges from minutes to 12hours. In addition to the integration of approachesand algorithms of the two usually separated areas,individual components of the future system areenhanced to develop an improved representationof heavy convective events. One key goal of SIN-FONY is the incorporation of further observationalsources into the data assimilation system KENDA(Kilometre-scale ensemble data assimilation; Schraffet al., 2016) of DWD. So far, work on assimilatingradar reflectivities and radial winds is carried outto improve the analysis in the presence of precip-itation. However, to allow for accurate warningsahead of convective precipitation and to approachthe goal of a seamless transition from nowcasting toNWP, our objective is to improve the representationof convection already at the stage of its initiation.For that purpose, possibilities for the assimilationof cloud information derived from visible channels(SEVIRI-VIS) of geostationary satellites using the

new fast and accurate observation operator MFASIS(Scheck et al. 2016) are explored. Visible channelsare considered to have potential in the context ofe.g. convective initiation and wintertime low stratussince they provide a more pronounced contrastof low clouds to the earth’s surface than infraredchannels. We present the methodology and resultsof different case studies using the convective-scalemodel COSMO-DE of DWD. Thereby we place spe-cial emphasis on the question of how to verticallyposition and localize the SEVIRI-VIS data represent-ing a vertically integrated physical quantity.

3.4 Addressing biases in cloudy situationsusing the all-sky assimilation of microwaveradiancesMain Author: Katrin LonitzInstitution: Research Department, ECMWF, Read-ing, UKCo-Authors: Alan Geer, Richard Forbes, Peter Bech-told

In an assimilation system, for most radiancesbias correction is done only in clear-sky conditions.With the all-sky assimilation of microwave radiancesit has become necessary to address biases in cloudyand precipitating conditions. At ECMWF, the firstguess departures are normalised by the observationerror in order to identify the most significant biasesin the assimilation system. Different approachescan be used to account for biases in cloudy regions,for example in regions of stratocumulus clouds andcold-air outbreaks. The normal approach to correctfor biases would be to use a linear bias correctionmodel, but so far this has not often proved tobe a viable way to correct for complex situationdependent cloud related biases. Instead, resultssuggest that it is better to exclude observationsfrom the assimilation in some situations markedby cloud related biases, as for example in cold airoutbreak regions. In these regions, the clouds arenot well captured by most NWP and climate models.This leads to a shortwave radiation bias of about0.2-0.4Wm-2 and a bias in the microwave of about5-10K. The most optimal way to address this wouldbe to improve the model to reduce or eliminatethe bias. In fact, studying biases in the cold-airoutbreak regions through the all-sky assimilationof microwave radiances has helped to identify thecause of this long-standing model error. In the latestcycle of the IFS important first steps have beentaken to improve the representation of clouds incold-air outbreak regions. In this study, we show thedifferent approaches taken depending on the natureand impact of the cloud related bias. Furthermore,we demonstrate the potential to improve the rep-resentation of clouds inside the forecast model andthe forward model by studying cloud related biases.

3.5 Assimilation of Himawari-8 All-Sky Ra-diances Every 10 Minutes: A Case of theSeptember 2015 Kanto-Tohoku rainfallMain Author: Takumi HondaInstitution: RIKEN AICS, Kobe, JapanCo-Authors: Shunji Kotsuki, Guo-Yuan Lien, Yasum-

itsu Maejima, Kozo Okamoto, Takemasa Miyoshi

Severe rainfalls and their flood risk have attractedconsiderable attention from society because of theirprofound impacts including loss of life. To mitigatedisasters associated with severe rainfalls, it is es-sential to obtain accurate precipitation forecasts interms of intensity, location, and timing. To do so,data assimilation plays an essential role to providebetter initial conditions. In particular, geostationarysatellites are among themost important data sourcesbecause they can frequently observe a cloud systemwith broad coverage. In 2015, Japan Meteorolog-ical Agency (JMA) started full operations of a newgeneration satellite Himawari-8, which is capable ofevery-10-minute full disk observation and enablesto refresh precipitation and flood predictions asfrequently as every 10 minutes. This study aims todemonstrate the advantage of frequent updates ofprecipitation and flood risk predictions by assimi-lating all-sky Himawari-8 infrared (IR) radiances.We use an advanced regional data assimilationsystem known as the SCALE-LETKF, composed ofa regional numerical weather prediction (NWP)model (SCALE-RM) and the Local Ensemble Trans-form Kalman Filter (LETKF). We focus on a majordisaster case in Japan known as September 2015Kanto-Tohoku heavy rainfall in which a meridionalprecipitation band associated with a tropical cycloneinduced a record-breaking rainfall and eventuallycaused a collapse of a Kinu River levee. By assimi-lating a moisture sensitive IR band (band 9, 6.9 µm)of Himawari-8 every 10 minutes into a 6-km meshSCALE-LETKF, the heavy precipitation forecasts aregreatly improved. We run a rainfall-runoff modelusing the improved precipitation forecasts andpredict flood risk with longer lead times.

Friday

7.5 KEYNOTE: On the scale- and case-dependence of the predictability of precipi-tationMain Author: Madalina SurcelInstitution: Department of Atmospheric and OceanicSciences, McGill University, CanadaCo-Authors: Isztar Zawadzki, M. K. Yau

Given the high-dimensional phase space of at-mospheric phenomena, atmospheric predictabilityis usually quantified in terms of the ability to predicta particular weather feature or weather variable.Even when only one particular weather variable isconsidered, its predictability is still found to be bothscale- and case-dependent.This talk presents lessons drawn from a study of

the predictability of precipitation by storm-scale en-sembles. Using a large data set of high-resolution en-semble precipitation forecasts over the ContinentalUS, with different types of perturbation methodolo-gies and mesoscale data assimilation, several ques-tions are addressed. First, a methodology is pre-sented that is used to characterize the scale depen-dence of the predictability of precipitation by an en-semble that accounts for both initial condition andmodel errors. The predictability by the ensemble isfound to be very short-lived on average - 2 hours forscales smaller than 20 km and 10 hours for scalessmaller than 200 km. Then, the case-dependenceof precipitation predictability is also explored andthe impact of different sources of forecast errorsis assessed. While events characterized by large-scale forcing seem more predictable at large scalesthan diurnally forced events, small-scale predictabil-ity is always short-lived. Furthermore, statisticalrelationships between various event classifiers andpredictability indices are found to be very weak. Theresults also show that all types of error sources con-sidered in this study fail to explain the entire forecasterror at small scales and for short forecast times, butthat large-scale initial condition uncertainties are amajor error source.Finally, the gain in quantitative precipitation skill

achieved through radar-data-assimilation, the ad-vantage of sophisticated NWP models over simplerstatistical forecasting methods for very-short termforecasting and the impacts of these findings forproducing probability forecasts are also discussed.

7.6 Estimating the intrinsic limit of predictabil-ity using a stochastic convection schemeMain Author: Tobias SelzInstitution: Meteorologisches Institut, LMU,München

Global model simulations together with a stochasticconvection scheme are used to asses the intrin-sic limit of predictability that originates from theconvection up to planetary scales. The stochasticconvection scheme has been shown to introduce anappropriate amount of variability onto the modelgrid without the need to resolve the convection ex-plicitly. This greatly saves computational costs and

enables a setup of twelve cases distributed over oneyear with 5 members generated by the stochasticconvection scheme for each case. ECMWF forecastsare used to reflect current capabilities of forecastingand a direct comparison is performed to assess howmuch current weather prediction systems can bepossibly improved. As a metric, difference kineticenergy at 300hPa over the midlatitudes, both northand south, is used. The resulting limit is estimatedto be about 17 days when a threshold of 90% of thedifference kinetic energy saturation level is applied.If the same threshold is applied to the ECMWFforecasts, the forecast limit is about 11 days, whichresults in a difference of six forecast days. Thismean that e.g. a todays 8 day forecast can become a14 day forecast in the future at best. These 6 days ofpotential improvement can be roughly divided into3.5 days through perfecting the initial conditionsand 2.5 days through perfecting the model.

7.7 Spread-Skill properties of the global ICON-EPS in comparison to the ECMWF-EPS andconsequences for data assimilationMain Author: Michael DenhardInstitution: Deutscher Wetterdiesnt, FrankfurterStraße 135, 63067 Offenbach

Since January 2018 DWD runs a global ICONEnsemble suite with 40 members and approx. 40kmhorizontal resolution with a grid refinement forEurope down to 20km. The forecasts start fromanalysis states generated by the Local EnsembleTransform Kalman Filter (LETKF) data assimila-tion (DA) system at DWD. Different sets of modelparameter distinguish the ensemble members butno stochastic perturbations are added during theforecast. Therefore, the spread properties of theICON-EPS are mainly determined by the variationsin the analysis ensemble. The relation betweenspread and skill is perfect, if small spreads relate tosituations of high predictability while large spreadsare not useless. We show that the ICON-EPS andthe ECMWF-EPS do not provide forecasts with aproper spread-skill relation in the short range whenverifying against analysis fields. This confirmsthe results of Leutbecher and Palmer (2008) forthe ECMWF-EPS, who found an improving spreadskill relation during the forecast. In these systemsthe spread is not growing in the same way as theerror. Hamill and Whitaker (2011) showed thatbalancing the perturbed states by using evolvedperturbations helps in generating faster growingperturbations. We further argue that it is not onlythe balancing of model states or the amplitude ofa perturbation what matters; it is the direction instate space which essentially determines the growthof perturbations. The assimilation steps of DA cyclesmay shift the background states to different placesin the state space of the model with a possibly dif-ferent tangent linear space and thus different errorgrowth properties. Consequently, the backgroundperturbations must be adapted to the new condi-tions to optimally explore the error growth potential.

3.6 Towards real-time assimilation of cloud

radar and lidar observations in the ECMWF4D-Var SystemMain Author: Mark FieldingInstitution: European Centre for Medium–RangeWeather Forecasts (ECMWF), Shinfield Park, Read-ing, UKCo-Authors: Marta Janisková

Active observations from profiling instrumentssuch as cloud radar or lidar contain a wealth ofinformation on the vertical structure of clouds andprecipitation. However, most NWP centres onlyassimilate observations of cloud with limited verti-cal information, such as radiances or near-surfacerain rates. The immense detail provided by suchactive sensors, of great benefit to enhancing ourunderstanding of clouds, paradoxically increasesthe difficulty in their use to initialize a forecastor generate a re-analysis, which we explore here.For example, any mis-match in the positioning ofclouds between observation and model is difficult toreconcile. For radar, the forward model is extremelysensitive to assumptions on hydrometeor size. Forlidar, any errors in the forward model can compoundquickly where the signal is attenuated.Characterising the observation errors correctly is

particularly problematic, which tends to be highlysituation-dependent. Diagnostic methods, which of-ten rely on static observations errors, are likely tostruggle. We therefore take an ‘error inventory’ ap-proach, where we quantify the contribution fromdifferent sources of error. In particular, the nar-row field-of-view of the instruments relative to themodel scale means that the representativity error of-ten dominates. We will demonstrate that, by treat-ing this as a sampling error, we can use the lo-cal variance of the measurement to provide a flow-dependent estimate of the representativity error.The method also predicts the correlations in repre-sentativity error, which can be large in the vertical.The error from uncertainties in the forward mod-els are also accounted for, using a Monte Carlo ap-proach where parameters are perturbed within phys-ical bounds. Crucial practical elements, such as biascorrection and screening of observations will also bediscussed.The upcoming EarthCARE mission, due for launch

in 2019, offers a unique opportunity to exploit thesenew observations and assess their benefit for NWP.

3.7 Ensemble-Based Data Assimilation ofGPM/DPR Reflectivity into the NonhydrostaticIcosahedral Atmospheric Model NICAMMain Author: Shunji KotsukiInstitution: RIKEN Advanced Institute for Computa-tional Science, Kobe, JapanCo-Authors: Koji Terasaki, Takemasa Miyoshi

This study aims to improve the precipitation fore-casts from numerical weather prediction modelsthrough effective assimilation of satellite-observedprecipitation data. The assimilation of precipitationdata is known to be difficult mainly due to highlynon-Gaussian statistics of precipitation-related vari-ables involved with complicated cloud processes

such as water phase changes. We have been devel-oping a global atmospheric data assimilation systemNICAM-LETKF, which comprises the NonhydrostaticICosahedral Atmospheric Model (NICAM) and LocalEnsemble Transform Kalman Filter (LETKF). Usingthe NICAM-LETKF system, Kotsuki et al. (2017,JGR) successfully improved the weather forecastsby assimilating the Japan Aerospace eXplorationAgency (JAXA)’s Global Satellite Mapping of Pre-cipitation (GSMaP) data into the NICAM at 112-kmhorizontal resolution. However, assimilating space-borne precipitation radar data remains to be achallenging issue. This study pioneers to assimilateradar reflectivity measured by the Dual-frequencyPrecipitation Radar (DPR) onboard the Global Pre-cipitation Measurement (GPM) core satellite into theNICAM. We conduct the NICAM-LETKF experimentsat 28-km horizontal resolution with explicit cloudmicrophysics of a single-moment 6-class bulk micro-physics scheme. To simulate GPM/DPR reflectivityfrom NICAM model outputs, the joint-simulator(Hashino et al. 2013; JGR) is used. Our initialtests were promising, showing a better match withthe observed reflectivity by assimilating GPM/DPRreflectivity into NICAM forecasts. This presentationwill include the most recent progress up to the timeof the symposium.

3.8 Nonlinear Bias Correction for SatelliteData Assimilation using A Taylor Series Polyno-mial Expansion of the Observation DeparturesMain Author: Jason OtkinInstitution: Department of Mathematics, Universityof Reading, Reading, UKCo-Authors: Roland Potthast, Amos Lawless

Output from a high-resolution ensemble dataassimilation system is used to assess the abilityof a nonlinear bias correction (NBC) method thatuses a Taylor series polynomial expansion of theobservation-minus-background departures to re-move linear and nonlinear conditional biases fromall-sky satellite infrared brightness temperatures.Univariate and multivariate NBC experiments wereperformed in which the satellite zenith angle andvariables sensitive to clouds and water vapor wereused as the bias correction predictors. The resultsshowed that even though the bias of the entire errordistribution is equal to zero regardless of the orderof the Taylor series expansion, that there are oftenlarge conditional biases that vary as a nonlinearfunction of the predictor value. The linear 1st orderTaylor series term had the largest impact on theentire distribution as measured by reductions inthe variance; however, large conditional biasesoften remained across the distribution when plottedas a function of the predictor. These conditionalbiases were typically reduced to near zero when thenonlinear 2nd and 3rd order terms were used. Theunivariate results showed that variables sensitive tothe cloud top height are effective NBC predictorsespecially when higher order Taylor series termsare used. Comparison of statistics for clear-skyand cloudy-sky matched observations revealed thatnonlinear bias corrections are more important for

cloudy-sky observations as signified by the muchlarger impact of the 2nd (quadratic) and 3rd (cubic)order terms on the conditional biases. Together,these results indicate that the NBC method is aneffective method to remove the bias from all-skysatellite infrared brightness temperatures.

4.11 Episodic, non-linear and non-Gaussian:ensemble data assimilation for bounded semi-positive definite variables like cloud.Main Author: Craig BishopInstitution: Naval Research Laboratory

The uncertainty distributions of forecasts of episodicvariables such as clouds, precipitation, fire and iceoften feature a finite probability of non-existenceand/or skewness. For such variables, existing dataassimilation techniques such as 4DVAR, the Ensem-ble Kalman Filter (EnKF) and the Particle Filter (PF)fail when a finite amount of the variable is observedbut the prior forecast uncertainty distribution as-signs a probability density of zero to this observation.Here we extend the previously developed Gamma,Inverse-Gamma and Gaussian (GIGG) variation onthe EnKF to accommodate finite probabilities of non-existence. The resulting GIGG-Delta filter has theremarkable property that when rain (for example)is observed, the GIGG-Delta filter always producesa posterior ensemble of raining ensemble memberseven if not one of the prior forecast ensemble mem-bers contain rain. In addition, the GIGG-Delta filteraccurately solves Bayes’ theorem when the priorand observational uncertainties are given by gammaand inverse-gamma pdfs, respectively. To improvethe multi-variate dynamical balance of the posteriordistributions, a new iterative balancing procedureis proposed that improves the ability of the EnKF toproduce balanced posterior states. The approachis tested, illustrated and compared with existingtechniques using a hierarchy of idealized models.

4.12 Toward improved LETKF assimilationof non-local and dense observation by directcovariance localization in model spaceMain Author: Daisuke HottaInstitution: Meteorological Research Institute,Japan Meteorological Agency, Tsukuba, Japan

Covariance localization is an indispensable com-ponent of ensemble-based data assimilation systemswith a limited member size. The benefit of lo-calization is two-fold: (1) it suppresses spuriouscorrelation due to sampling error, and (2) mitigatesthe rank-deficiency issue of the sample covariancematrix.

In Local Ensemble Transform Kalman Filter(LETKF), localization is typically implemented by do-main localization (i.e., by performing analysis inde-pendently at each model grid) and by applying theso-called R-localization, in which the impact of an ob-servation to the analyzed state variable is artificiallydamped by inflating the observation error varianceby a factor that is a decreasing function of the phys-ical distance between the observation and the ana-lyzed grid.

Recent study at Japan Meteorological Agency us-ing its operational global LETKF revealed that, whileR-localization effectively suppresses spurious impactfrom an observation onto remote grids, it does nothelp to alleviate the rank deficiency issue withinthe local analysis, hindering the LETKF from ex-tracting information from dense observations. R-localization also poses difficulty when assimilatingnon-local observations (e.g., ground-based GNSS ob-servations, satellite radiances, or even in-situ sur-face pressure observations) whose physical locationsare not clearly defined.To resolve the above issues, we explore possibility

of applying model-space localization within theframework of LETKF. The advantage of model-spacelocalization over the R-localization will be discussedusing an idealized one-dimensional toy system.The toy system is designed with assimilation ofground-based GNSS observations mind, but themethodology should also work for other non-localand dense observation types such as satellite radi-ances.

4.13 Developments in the ECMWF humiditybackground errorsMain Author: Elias HolmInstitution: European Centre for Medium-RangeWeather Forecasts

The background error used in 4D-Var and theEDA has unbalanced relative humidity as controlvariable, with analytic balance operator betweenhumidity and temperature in cloudy conditions.Recent developments have added humidity back-ground error variances taken directly from theEnsemble of Data Assimilations (EDA), like for allother variables. This replaces earlier backgroundand level dependent statistical estimates of thehumidity variances. This change improved forecastscores and background fit to observations, includingwinds, and went operational 11 July 2017. We willshow some selected results from this change anddiscuss some further developments with a newanalytic balance operator coupling humidity anddynamic background errors.