IV WMO Impact Workshop 2008Alexander Cress Regional impact studies performed in the COSMO community Alexander Cress, Reinhold Hess Christoph Schraff German

IV WMO Impact Workshop 2008 Alexander Cress

Regional impact studies performed in the COSMO community

Alexander Cress, Reinhold Hess Christoph Schraff German Weather Service , Offenbach am Main, Germany

Email: [email protected]

• Introduction

• Use of radar reflectivity measurements

• Assimilation of satellite radiances with 1D-VAR and Nudging

• Use of VAD profiles, GPS tomography data and scatterometer winds

• E-AMDAR humidity data

• Experiment testing a reduced radiosonde network over Europe (EUCOS proposal)

• Summary

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Lokal-Model COSMO-EU (LME) und COSMO-DE (LMK)

COSMO-EU (regional model): non-hydrostatic, rotated lat-lon grid, mesh-size: 7kmterrain-following hyprid coordinate with 40 layers up to 20 hPaforecast range: 78 h every 6 hoursprognostic cloud ice, prognostic rain schemesboundary values from GME

Analysis: continuous nudging schemeobservations: radiosonde, pilots, wind profiler,

aircraft, synops, buoys, shipscut-off: 2h30minvariational soil moisture analysis

COSMO-DE (lokal model): similar to COSMO-EUforecast range 18 h every 3 hmesh-size: 2.8 km, explicit convectionlatent heat nudging of radar reflectivitiesboundary values of COSMO-EU

Data base

• Reflectivity from „Precipitation scan“

(DX-Produkt):

– Elevation angle: 0.5°-1.8°

– Spatial resolution: 1km x 1°,

max. Range 128 km

– Temporal resolution: 5 Minuten

– Quality control mechanism:

Clutter, spikes, rings, AnaProp, …

• Data processing:

– Correction of orographic shading

– variable Z-R-relations

– Composit of 16 German locations

– Bright band correction


Basic principles of latent heat nudging

•LHN: specific form of Nudging to assimilate radar reflectivities respectivly the derived precipitation

•Problem: precipitation is a „derived“ model parameter with limited feedback on the thermodynamic structure of the model

•Aim: Adjust the dynamic of the model, that it produces the observed precipitation at

the right place and time ⇨ relation to a thermodynamic relevant model variable (temperature, humidity) is required

•Solution: Adjustment of the latent heat in the model yields to a temperature (humidity) change

Results of an operational application

Animation of hourly precipitation 26. August 2006

Assimilation: 09 UTC

Radar Without LHN With LHN


Animation of hourly precipitation sum 26. August 2006













Free forecast 12 UTC + 1 H























24H precipitation sum: 26.08.2006 (6 UTC – 6 UTC)

Assimilation



Skill scores for 32 runs in AUGUST 2006

Threshold 1.0 mm/h

ASS Free forecast ASS Free forecast

ETS FBI LHNNoLHN

LHNNoLHN


Skill scores for 32 runs in AUGUST 2006

Threshold 5.0 mm/h

ASS Free forecast ASS Free forecast

ETS FBI LHNNoLHN

LHNNoLHN

Summary and outlook

• Radar reflectivities are assimilated within the COSMO-DE to improve the

forecasts of convective systems

• Latent Heat Nudging (LHN) of radar reflectivities prove to be a proper method

• Assumption: Precipitation rate at the surface is proportional to the vertical

integral of latent heat release within a model column

• A positive impact of the LHN during the assimilation could be demonstrated

• The positive benefit of LHN can be verified up to nine hours in the free

forecast

• Additional investigations are needed to extent the positive benefit beyond

nine hours (additional use of radar winds to adjust the dynamic structure in

the model)

• Inclusion of European radar sites

COSMO-Project:Assimilation of satellite radiances with 1D-Var and

NudgingGermany, Italy, Poland

1DVAR + Nudging

i.e. RETRIEVE temperature and humidity profiles and then nudge them as “pseudo”-observations

Goals of Project:• Assimilate radiances (SEVIRI, ATOVS, AIRS/IASI) in COSMO-EU

• Explore the use of nonlinear observation operators with Nudging

no thinning of 298 ATOVS 30 ATOVS by old thinning (3) 30 ATOVS, correl. scale 70%

40 ATOVS by thinning (3) 82 ATOVS by thinning (2) 82 ATOVS, correl. scale 70%

T-‘analysis increments’ from ATOVS, after 1 timestep (sat only), k = 20

Reinhold Hess, 20 Athens, 2007

no thinning of 298 ATOVS 30 ATOVS by old thinning (3) 30 ATOVS, correl. scale 70%

40 ATOVS by thinning (3) 82 ATOVS by thinning (2) 82 ATOVS, correl. scale 70%

T-‘analysis increments’ from ATOVS, after 30 minutes (sat only), k = 20

Reinhold Hess, 22

GME forecast for 48 hours

Athens, 2007

mean sea level pressure & max. 10-m wind gusts

analysis+ 48 h, REF (no 1DVAR)

valid for 20 March 2007 , 0 UTC

+ 48 h, 1DVAR-THIN3 + 48 h, 1DVAR-THIN2

m/s

Reinhold Hess, 24

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COSMO Priority Project: Assimilation of Satellite Radiances with 1DVAR and Nudging

Status of Developments September 2007 technical implementation ready (ATOVS/SEVIRI, including debugging) basic monitoring of radiances (day by day basis) basic set up, first case studies available tuning required

nudging coefficients/thinning of observations bias correction/stratosphere background errors/humidity correlations

long term evaluation already studies on spatially localised background error covariance matrices

(SREPS for SEVIRI) implementation for AIRS/IASI has just started

Use of 1D-Var developments already for other activities:• GPS tomography• Radar reflectivities

To be done: tuning, testing, tuning

Athens, 2007


Use of VAD Wind profiles in the Lokal Modell of DWD

• Vertical Wind Profiles provided by Doppler weather radars using the Velocity-Azimuth Display (VAD) technique

• Before using VAD wind profiles extensive monitoring has to be done

• Bad measurement sides are blacklisted

• Use of VAD wind profiles in LM in the same way as pilot wind profiles


VAD statistics of background(LME) - observations

Mean over used VAD stations (020207 – 120207)

Left: BIAS Right: RMS

• BIAS and RMS comparable to radiosonde statistics

• Almost no bias in wind velocity and direction


02933 10204


Station: 10338

blacklisted


Verification of LME forecastsagainst radiosondes

Results

Mainly neutral

Slightly positive for + 48 h RMS geopotential height

GPS tomography: – comprehensive monitoring (14 months) of quasi-operational tomography profiles

(at CSCS) against Payerne radiosonde and COSMO fields done

results: tomographic refractivity profiles have rather large errors unless COSMO forecasts are included as background info

– start working on assimilating humidity profiles derived from tomography retrievals

– new PhD (Perler) at ETH started working on tomography method itself

1.2 Multi-Sensor Humidity Analysis (incl. GPS-obs)

Daniel Leuenberger (MCH)

• BIAS – wet bias below 1500 m, large dry bias around 2000 m• Summer: 10 – 15 ppm (~1.5-2.5 g/kg) or ~35% • Winter: 5 ppm (~ 0.75 g/kg) or 20%• (much) larger than NWP model (+12h / +24h fc)

• STD – Summer: up to 12 ppm (~1.8 g/kg) or 10% – 30% in PBL• Winter: up to 7 ppm (~1 g/kg) or 20% – 40% in PBL• slightly smaller than NWP model (+12h / +24h fc)

1.4 Assimilation of Screen-level Observations PBL Analysis

Jean-Marie Bettems, Oliver Marchand, Andre Walser (MCH),

Andrea Rossa + collaborators (ARPA-Veneto), Antonella Sanna (ARPA-Piemonte)

• main objects: data selection, extrapolation to 10 m, vertical + horizontal structure functions

• Diploma work at MCH (Lilian Blaser): 9 case studies, standard assimilation parameters

– 10-m wind ass.: analysis impact: positive at surface, also for upper-air wind speed forecast impact: neutral, except 1 positive case (+8 h)

– 2-m temperature & humidity additionally (1 convective case): clear positive impact on analysis of surface parameters, negative for upper-air wind speed

– surface pressure (1 winter case): slight negative impact on 10-m wind, not due to geostrophic correction

– need to select representative stations, need appropriate vertical structure functions (impact of screen-level obs reaching high)

» up to now: only case studies done

1.4 Assimilation of Screen-level Observations PBL Analysis

Antonella Sanna, M. Milelli, D. Cane, D. Rabuffetti (ARPA-Piemonte)

• Sensitivity study (1 case with floodings, 2.8 km resolution) on assimilation of non-GTS data and soil moisture initialisation (PREVIEW framework):

– clear positive impact from ass of high-res 10-m wind and 2-m temperature data and with nudging parameters adjusted to fit denser obs network

– no benefit from replacing IC soil moisture by FEST-WB (hydrological model for floods)

5 June 2002, 12 UTCdiurnal cycle T2m

5 June 2002, 12 UTC - 18-h precipitation sum

analysis 12-h forecast

T -profiles

CTRL: interpol. anaSET2: standard nudgingSET3: + nudge T2m, v10m

adjusted parametersSET4: + init. soil moisture

1.5 Assimilation of Scatterometer Wind

Heinz-Werner Bitzer (MetBw), Alexander Cress, Christoph Schraff (DWD) • nudging of scatterometer wind data as buoy observations technically implemented,

taking into account all quality control / bias correction steps developed for use in GME

• idealised case studies: model rejects largest part of 10-m wind info unless mass field is explicitly balanced

derive surface pressure analysis correction in geostrophic balance with 10-m wind analysis increments (implies need to solve Poisson equation):

implemented, model now accepts data

• first real case study computed

QSCAT 19 June 2007, 6 – 9 UTC

48N

50N

15 W

Opr (no QSCAT) – Exp (QSCAT)PMSL 19 June 2007, 9 UTC hPa

1.5 Assimilation of Scatterometer Wind

Heinz-Werner Bitzer (MetBw), Alexander Cress, Christoph Schraff (DWD)

m/s 10-m wind gusts

analysis (21 June 2007, 12 UTC) + 48-h , no QSCAT + 48-h , with QSCAT

minor impact, central pressure error reduced from – 5 hPa to – 3 hPa

IV WMO Impact Workshop Alexander Cress

E-AMDAR humidity measurements

Three Lufthansa airplanes are implemented with humidity sensor

First humidity data in bufr format became available at the beginning of 2007

DWD started a data monitoring (obs – bg) using its global (GME) and regional model (LME)

What additional humidity data from airplanes will be of use to regional NWP ?

What potential savings to the radiosonde network (complemt or reduction) willsuch a programme bring?

IV WMO Impact Workshop Alexander Cress

00 UTC 12 UTC

All aircrafts


EU4593

00 UTC 12 UTC


EU5331

00 UTC 12 UTC


Summary of the preliminary results

• Monitoring and blacklisting is very important for a successful use of VAD wind profiles. Quality differs substantially from station to station

• VAD wind measurements produce no additional noise in the analyses The wind and geopotential height fields are in balance for different pressure levels => wind observations are successfully used in the Lokal Modell

• Overall impact of VAD wind profiles on forecast quality is neutral

• GPS tomography refractivity profiles have rather large errors unless COSMO forecasts are included as background


Summary of the preliminary results

• Assimilation of screen-level observations show positive impact on surface analysis but neutral to slightly negative for upper-air analysis

• Small positive impact of using QuikScat scatterometer winds in nudging assimilation of COSMO-EU

• Monitoring of AMDAR humidity profiles showed a strong negative humidity bias (higher than radiosondes) decreasing with height and also a warm temperature bias

Experiment testing a reduced radiosonde network over Europe

• Experimentdesign

• Following a EUCOS Redesign proposal

• Only 32 Radiosondenstations in Europe (EUCOS Area)

• With emphasis on the western borders of Europe and offshore islands v No changes over eastern Europe

• All other observing systems unchanged

Time range: 09.02.2006 to 13.03.2006

• Assimilation und forecasts (00 und 12 UTC) with COSMO-EU


00 UTC

12 UTC

Verification area: 40oN – 60oN and -5oW – 25oE

Sea surface pressure bias09.02.06 – 13.03.06

00 UTC

EUCOS ExperimentRoutine

12 UTC


Frequency Bias of low level clouds09.02.06 – 13.03.06

00 UTC



12 UTC


Bias of wind direction09.02.2006 – 13.03.2006


00 UTC


12 UTC


12 UT

12 UT

Summary

• Reducing the radiosonde network has a negative impact on the quality of COSMO-EU analysis

• Negative impact was largest on humidity fields

• Negative impact diminished after 12 hours

• Slightly negative impact on sea surface pressure

• On average scattered (broken) clouds are overestimated and strong clouds (overcasting situations) are underestimated

• Increasing wind direction bias during forecast

• Negative impact is higher at 00 UTV compared to 12 UTC (Compensation by aircraft data ?)

Documents

IV WMO Impact Workshop 2008Alexander Cress Regional impact studies performed in the COSMO community Alexander Cress, Reinhold Hess Christoph Schraff German