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Carla Cardinali1 & Tom Hamill2 (co‐chairs) 1ECMWF Data Division2NOAA Earth System Research Lab, Physical Sciences Division
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Systems
Bin Wang (Institute Atm. Phys)Chris Velden (U. of Wisconsin)Daryl Kleist (U. of Maryland)Nadia Fourrie (Meteo‐France)Mark Buehner (Canadian Met. C.)Mikhail Tsyrulnikov (Roshydromet)Saroja Polavarapu (Environ. Canada)Sharan Majumdar (U. of Miami)Stefan Klink (DWD)
WWRP‐DAOS Terms of ReferenceThe Data Assimilation and Observing Systems (DAOS) working group (WG) will provide guidance to the WWRP on international efforts to optimise the use of the current WMO Global Observing System (GOS). It will also provide guidance on which data assimilation methods may provide the highest‐quality analysis products possible from the GOS. Through these activities, the DAOS‐WG will facilitate the development of advanced numerical weather prediction (NWP) capabilities, especially to improve high‐impact weather forecasts. DAOS will be primarily concerned with data assimilation and observing system issues from the convective scale to planetary scales and for forecasts with time ranges of hours to weeks.
To achieve its mission, the DAOS WG will:
• Provide community consensus guidance on data assimilation issues, including the development of advanced methods for data assimilation.
• Promote research activities that will lead to a better use of existing observations and that will objectively quantify the impact of current and future observation for NWP.
• Assist WWRP projects and other WMO working groups in achieving their scientific objectives by providing expert advice on the use of observations and data assimilation techniques (e.g. PPP, WGNE).
• To organize and provide the scientific steering committee for the WMO Data Assimilation Symposium, which is to be held approximately every 4 years. 2
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OUTLINE
• Assimilation methods• Observing System Satellite & Ground‐Based
• Green House Gas Observations, Estimation and Forecast
• Observation‐Based Forecast verification Forecast departure FSOI‐Jo
• Interaction with WWRP WGs and WGNE
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Assimilation methods
• Most operational centers have moved toward some variant of hybrid (ensemble‐variational) assimilation scheme Met Office, EC considering ensemble 4D‐En‐Var with perturbed observations NCEP is in final testing of hybrid 4D‐En‐Var for the GFS, which is on the schedule to be implemented in
early 2016 EC is also testing a new scale dependent localization scheme that allows for cross‐waveband correlations
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Scale‐dependent covariance localizationForecast impact – Comparison against ERA‐Interim
T+24hZonal mean
Control is better
Scale-Dependent is better
Std Dev difference for U
NorthPole
South Pole
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EnvironmentCanada
Assimilation methods
• Most operational centers have moved toward some variant of hybrid (ensemble‐variational) assimilation scheme Met Office, EC considering ensemble 4D‐En‐Var with perturbed observations NCEP is in final testing of hybrid 4D‐En‐Var for the GFS, which is on the schedule to be implemented in early 2016 EC is also testing a new scale dependent localization scheme that allows for cross‐waveband correlations
• Various efforts on the assimilation of observations in cloudy and precipitating areas continue to progress. At NCEP, an initial implementation of all‐sky microwave radiances is to be included with the 4D‐En‐Var
implementation in early 2016. Significant efforts have been put into adding new analysis variables, modifying background error estimates, performing quality control, and adjusting observation errors
JCSDA ‘dynamic’ approach combining cloud fraction and advection, particularly useful application for nowcasting ECMWF progress on the assimilation of cloudy and precipitation MW and IR
o consolidation of techniques to allow for the assimilation of MW water vapor and imager channelso activating GMI and AMSR‐2o switching some MHS channels to all‐sky modeo other progress on maritime stratocumulus and issues with clouds in high‐latitude cold air outbreaks o eventually including cloud parameters in the analysis control vector
Assimilation of observations in cloudy and precipitating regions
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Forecast Sensitivity to Observation Impact (FSOI)as monitored at NWP centres
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ECMWF
Satellite systems• The constellation of operational geostationary and polar‐orbiting satellites remains stable, though a few polar
orbiting satellites are no longer providing data (MADRAS, OSCAT, TRMM). • Japan’s Himawari‐8 geostationary satellite is becoming operational, with ‐9 to be launched in 2016. The next
generation in Europe‐Meteosat and United States‐GOES‐R set to be launched within 2020• Atmospheric Motion Vectors AMVs are now available from a large number of geostationary and polar‐orbiting
satellites, and rapid‐scan mode will be activated aboard Himawari‐8 focused on Japan and typhoons in 2016 Global re‐analyses GOES AMVs will be reprocessed back to 1995 delivery in 2016 CIMSS U. of Wisconsin
• A variety of polar and other Low Earth Orbit satellites are planned for launch in the near future. NASA and JAXA have launched the Global Precipitation Mission GPM Core Observatory that is designed to work with, and anchor, the constellation of satellites and ground systems from the partner agencies of Japan, Europe, India, as well as U.S. agencies. It will seamlessly combine all the measurements into a single global precipitation data set every 3 hours.
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GPM Constellation Concept
GPM Core Observatory(NASA/JAXA )
- DPR (Ku & Ka band)- GMI (10-183 GHz)
Suomi NPP(NASA/NOAA)
MetOp B/C(EUMETSAT)
JPSS-1 (NOAA)
DMSP F17/F18/F19/F20(USA0DOD)
GCOM-W1(JAXA)
NOAA 18/19(NOAA)
Megha-Tropiques(CNES/ISRO)
Next-Generation Unified Global Precipitation Products Using GPM Core Observatory as ReferencePrecipitation rates everywhere in the world every three hours 10
TRMM(NASA/JAXA)
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Ground‐Based and In‐Situ Observations• Global aircraft observations increasing; ~ 750,000 obs / day in 2015. Since December 2014, all European
AMDAR observations (approx. 65,000 obs per day) are reported on GTS according to the WIGOS AMDAR BUFR template
• Ship‐borne measurements. Globally, there are two regular ASAP fleets. The European fleet comprises of 18ships in the North Atlantic; Japanese fleet comprises of 2 governmental research ships performing soundings mainly in the North Pacific
• Ground‐based GNSS Zenith total delay ZTD data provide humidity information to NWP. Time resolution is high, spatial resolution varies with region. During the last year the density of sites has increased, particularly in parts of Europe
• Globally, growing wind profiler networks are responding to the need for more wind observations as documented e.g. in the WMO Statements of Guidance concerning global NWP. In terms of number of observing sites the European networks is stable whilst the Australian network is growing and the Asiannetwork is even growing strongly
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Ground‐Based Observations (continued)
• Barometer buoys are the most efficient way to measure sea level pressure 850 in operation 2015, 700 in June 2014, 400 in June 2013. Good coverage in the North Atlantic and
Indian Ocean, Arctic has improved significantly. Few tropical areas, Pacific Ocean
• Visual observations (waves, visibility, clouds, past and present weather) taken on Voluntary Observing Ships VOS has continued to decrease in number The growth of number of VOS equipped with Automatic Weather Stations AWS marks a pause but should
restart within a couple of years again. There are 220 AWS onboard VOS at present. The migration to BUFR for VOS data is far to be complete. Some data providing centres are waiting for a
new BUFR template
• S‐band reservation for weather radars is under threat: World Radio‐communication Conference in November 2015
• On global scale, the illegal use of the C‐band for telecommunication is a major problem. Concerning this issue there’s an accepted article in BAMS: ‘The Threat to Weather Radars by Wireless Technology’ S‐band radars 40% are most commonly deployed in tropical and temperate climate zones with heavy rain
or large hail C‐band radars 53% oppositely areas deployed: climates with no heavy rain or large hail
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Greenhouse gas observation, state estimation and forecasting• Satellite
After the launch of the first dedicated GHG satellite mission (GOSAT in 2009) it is now joined by JPL’s OCO‐2 (Orbiting Carbon Observatory) launched in July 2014 CarbonSat is one of 2 candidates for ESA’s Earth Explorer mission 8, for which a decision will be made this November
• Ground‐based EC’s network is now complete with 23 sites In Europe, 95 stations across 12 countries will be coordinated as part of the Integrated Carbon Observing System
Research Infrastructure (ICOS‐RI) network ( https://www.icos‐ri.eu/greenhouse‐gases) DWD network with 8 tall tower completed in 2017 for NRT through Copernicus Atm. Mon. Ser. CAMS
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Critical issues for the progress in coupled modeling and data assimilation
CO2 and CH4 in NRT. CAMS gets GOSAT retrievals 5 days behind real time ‐> 2 days In situ‐obs (except for a few ICOS stations) are only available with a few months to one year lag
Complementarity of the different observing systems: OCO‐2 mission has given a lot of impetus for the NRT availability ‐> better spatial coverage but larger
systematic and random errors ‐> only total/partial column info In situ (e.g. TCCON, NOAA, ICOS, Environment Canada’s networks) better quality ‐> funding problem Aircrafts (e.g. IAGOS) provide vertical info
Greenhouse gas observation, state estimation and forecasting
• Coupled modeling and data assimilation is continuing to be explored by various groups 2 research groups in China have built EnKFs for coupled state and flux estimation CCDAS Carbon Cycle DAS approach (Max Planck) to simultaneously retrieve ecosystem model parameters
+ GHG flux and concentration with the focus being on the biospheric model Source estimates from flux inversions rely on the fidelity of a transport model, so the flux inversion
community continues to assess issues of transport errors through inter‐comparisons addressing boundary layer mixing, age‐of‐air and flux estimates using GOSAT (Houweling et al, 2015, JGR) ‐> good estimate of biospheric fluxes critical to getting good forecast products
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• Forecast quality assessment is of crucial importance In general, standard verification tools are applied that compare forecasts with verifying analyses.
Verifying against own analysis or against operational analysis can produce rather different results
• A very powerful verification field would be the one constituted by all the available and quality controlled observations (conventional and satellite based). Model forecast can be compared with respect to the observations at the observation locations. This comparison is performed in observation space and the model interpolation is achieved by using the same operators (e.g. temporal integration, interpolation scheme and radiative transfer model) embedded in the data assimilation system.
Model trajectory run is performed to compute forecast departures Forecast departures are then opportunely archived Forecast verification with respect to AMSU‐A, IASI, ALL‐SKY, GPS‐RO ‐> geographical maps and
vertical profiles of the main statistical indices as STD, Mean and RMS FSOI‐Jo: new objective function which verify 24h forecast against observations
Observation‐Based Forecast Verification
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Day 4 Mean Obs‐Fc
Day 4 Mean(Obs‐OsuiteFc) ‐ (Obs‐EsuiteFc)
ECMWF
Observation‐Based Forecast Verification AMSUA‐A ch 14
North Pole Stratospheric warming
20‐30 Dec 2014
ECMWF
Observation‐Based Forecast Verification: GPS‐RO
24 H Observation Impact FSOIForecast verified against analysis
Forecast verified against observations
+ FcE increase‐ FcE decrease
In the analysis0 1
Averge DFS
18ECMWF
Observation‐Based Forecast Verification: AMSU‐A ch 14
Working Groupon
NumericalExperimentation
1) Coupled data assimilation and its impacts on S2S forecasts‐ jointly sponsored workshop2) Quantifying impacts of improvements in the observing system on sub‐seasonal forecasts3) Identifying cases where coupled DA and forecasts would have a strong role at S2S timescales‐MJO
PDEF1) Development of ensembles and model uncertainty in ensembles‐ attending each other group meetings2) Prioritization of coupling state components: coupling of ocean and land with the atmosphere
WGNE a) YOPP and PPP: observation strategies for model development, data denial observing system experiments
in polar regions, quantifying analysis uncertainties in polar regions, observation‐based forecast verification
b) WGNE‐DAOS mutual interests: coordination of activities on reanalyses, common observational databases, and coupled data assimilation
c) DAOS, WGNE, and PDEF. A possible jointly supported workshop on stochastic parameterization, possibly supporting the upcoming 11‐14 April 2016 ECMWF workshop on representation of model uncertainties.
d) DAOS and WGNE/Transpose‐AMIP: WGNE notes that much could be learned from testing of coupled systems in data assimilation mode.
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Data assimilation& ObservingSystems
PPP1) OSEs (e.g. Surf. Pressure as Drifter)2) Observation‐Based Forecast Verification3) Advising in the conduct of selected OSSEs, e.g. YOPP optimal deployment of observations4) Promoting research into polar DA
HiWeather1) Facilitating demonstrations of the impact of novel HRES/4D observing capabilities, e.g. surface data and all phases of precipitation2) Facilitating the development of new nowcasting techniques, blending in forecast information from rapid update data assimilation and NWP systems3) Facilitating assessments of model error in DA and EPS (in collaboration with PDEF)4) Facilitating inter‐comparison studies of multi‐scale, coupled DA for selected cases such as FDPs.5) Promoting the development of tools to assess the sensitivity of hazard forecasts to observational inputs.
Supplementary slides
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• USA: Suomi-NPP ATMS, CrIS• Europe: METOP-A/B IASI, AMSU-A/MHS, GRAS• Japan: GCOM-W1 AMSR-2 • China: FY-3B & -3C: New MW channels at 118GHz on -3C• India/France: Megha-Tropiques SAPHIR, SCARAB, MADRAS• India/Italy: Oceansat-2 OCM, ROSA, OSCAT • Russia: Meteor-M N2 CRIS-like, MHS-like• ASCAT and RapidScat: scatterometer-derived ocean surface winds• Cosmic constellation: GPS-RO bending angles (4 of 6 operating)• GPM constellation -- TRMM replacement
Near Future
OperationalGeostationary Polar
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Operational
Polar AMVs Product Suite
Operational
Operational
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• Most current mesoscale operational systems use continuously cycled data assimilation at kilometric horizontal scale (commonly 3D‐Var) with explicit grids
• Many mesoscale DA research groups are working toward generating estimates of the 4‐dimensional, state‐dependent background‐error statistics at mesoscale with 4D‐Var or hybrid data assimilation within the context of a rapid update cycle, a preliminary step before the En‐Var implementation. Scale‐selective localization of ensemble covariances is an area of active research.
• Observation must be quality controlled and bias corrected and available in NRT very quickly to be used in rapid update cycle assimilation schemes. Despite the need for high temporal availability and high spatial resolution observations, the data such as satellite radiances are still typically thinned, and/or explicit observation correlations are taken into account in data assimilation scheme. Currently, the aircraft Mode‐S data are assimilated in test mode at Météo‐France and the UK Met Office. The assimilation of cloud information is continuing to be developed.
• Data assimilation schemes will become increasingly more detailed in space 1km‐100m and time finer than existing observation network with nowcasting range (which required very frequent updating). The future assimilation systems will focus on the lower part of the atmosphere; on clouds, turbulent eddies, and boundary layers. The observation representativeness of observations, particularly near the surface, where most of the observations that could measure mesoscale features, are important to quantify better in the future. There is also a general trend to move towards a more complete earth‐system modeling and data assimilation, even at the mesoscale, with couplings between ocean, surface, aerosols and atmosphere. Tools for the forecast sensitivity to observation impact are being developed for the convective scale.
Mesoscale Data Assimilation
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• In preparation for a possible 2016 DAOS workshop on coupled data assimilation, we discussed anticipated issues that should become a focus of the meeting. Most oceanatmosphere data assimilation to date has been “weakly coupled,” i.e., the background forecasts are integrated with a coupled model but the assimilations are performed separately. A primary challenge with both landatmosphere and oceanatmosphere coupled data assimilation is whether the cross covariancesbetween the state components will be realistic. A recent article on landsurface model ( L S M ) co m p a r i s o n s with flux o b s e r v a t i o n s (B est et al . 2015) h i g h l i g h t e d t h e c h a l l e n g e s a n d substandard performance of current‐generation LSMs, indicating that there is the potential for biased covariance information between state components. That said, by developing coupled data assimilation methods, we are likely to learn at a faster rate about the underlying model deficiencies, so promoting the development of coupled DA methods is a priority for DAOS.
Coupled Data Assimilation
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