IV WMO Impact Workshop 2008

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


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 ?)

IV WMO Impact Workshop 2008

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