5.-9. märz 2007, bad herrenalb thorsten reinhardt, institut für geophysik und meteorologie,...

5.-9. März 2007, Bad Herrenalb

Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

QUEST

Quantitative Evaluation of Regional Precipitation Forecasts Using Multi-

Dimensional Remote Sensing Observations



Quantitative evaluation of regional precipitation forecasts using

multi-dimensional remote sensing observations

Partnership Susanne Crewell, Thorsten Reinhardt, University of Cologne (IGM) Jürgen Fischer, Anja Hünerbein, FU Berlin (FUB) George Craig, Martin Hagen, Monika Pfeifer, (DLR) Michael Baldauf, Deutscher Wetterdienst (DWD) Nicole van Lipzig, Ingo Meirold-Mautner, Katholieke Universiteit Leuven

(KUL), Belgium (QUEST-B)

Contributes to PQP Goals Identification of physical and chemical processes responsible for the

deficiencies in quantitative precipitation forecast Determination and use of the potentials of existing and new data and

process descriptions to improve quantitative precipitation forecast



satellite

MSG ~ 5km; 15min Cloud Mask Cloud top pressureMODIS ~ 1km; 1day Cloud Mask Optical thickness

IPT / Micro-wave

1D vertical; Lindenberg (and Cabauw) temperature profile humidity profile LWC

GPS

~ 147 stations; Germany; 15min IWV

Ceilometer

17 stations;Germany; 1min; ranges up to 4km Cloud base height Cloud cover (<4km)

Radar

DWD radar composite; 1km; 5min Rain rate RANIE combined radar and gauge analysisPolarimetric radar (DLR)

Quantitative evaluation of regional precipitation forecasts using multi-dimensional remote sensing observations (QUEST)

Observations

Surface: rain gauges



QUEST: StrategyObservations- multi-frequency radiances- polarimetric radar quantities- ground-based and space-borne observations

Forward Operator Retrieval

Weather Forecasts- three-dimensional description of the forecasted atmospheric state - focus on Lokal-Modell Kürzestfrist (LMK)

- SynPolRad (polari. radar)- SynSat (MSG, MODIS)- SynSatMic (AMSU, SSM/I)

- water vapour- cloud properties- precipitation

MODIS LM MESO-NH MM5MODIS LM MESO-NH MM5

Schröder et al. [2006]



QUEST: ApproachCase Studies (ongoing)

Model Sensitivity Runs

Tool development SynPolRad SynSat (-Mic) MSG µ-phys. retrievals verification measures ..

Hypothesis formulation"What are the crucial variables/processes

to observe and to improve?"

Model Improvement (new) cloud microphysics land surface turbulence

comparison tools test of hypotheses

Identification of systematic model deficits

Long-Term EvaluationLokal-Modell Kürzestfrist test suites GOP duration 2007 benefits of high

resolution modelling

Conditional verification regionalization diurnal cycle weather situation dep.

Cross correlation of different variables

"How important is physical consistency?"

case study selection for

process studies



Case studies versus long-term evaluationC

ase

Stud

ies

Long

-Ter

m E

valu

atio

n

+ Detailed analysis

+ Formulation of hypothesis

+ Tool development

- Low significance

- Subjectively chosen cases

+ Sensitivity runs feasible /physical explanation

+ High significance

- Difficult to identify physical mechanism

- Automated analysis

+ Objective selection of cases



• Mesh size: x = 2.8 km • direct simulation of deep convection• convection parameterization for shallow part

only• assimilation of radar data by latent heat

nudging method

• timestep T=25 s• 421 x 461 x 50 gridpoints,

lowest model level in 10 m above surface

• Centre of domain: 10 °E, 50 °N• Forecast time: 21 h, started every 3 h• Boundary conditions from

Lokal-Modell Europa (“COSMO-LME”) with x = 7 km

Deutscher Wetterdienst‘sLokal-Modell Kürzestfrist (“COSMO-LMK”)



LMKRadar LM

Case study 26-08-2004 (M. Baldauf DWD)

3.9 mm/day 5.2 mm/day 3.4 mm/daymm/day

Accumulated precipitation over 24 hr



General Observation Period (GOP)Year 2007

http://gop.meteo.uni-koeln.de

Central activity of QUEST in second phase of PP.



gather as many data about the atmospheric state as possible within an area covering Germany and it neighboring states.

to provide information of all kinds of precipitation types

to identify systematic model deficits

to select case studies for specific problems

to relate the COPS results to a broader perspective (longer time series and larger spatial domain)

GOP Organization and PerformanceThe General Observation Period ─ January to December 2007 ─ encompasses COPS in time and space

http://gop.meteo.uni-koeln.de



GOP

Partnership Karl Bumke (IFM-Geomar) Disdrometer observations (WP 3) Susanne Crewell (IGM) Overall GOP organisation Galina Dick (GFZ) GPS observations (WP 5) Jürgen Fischer (FUB) Satellite observations (WP 7) Martin Hagen (DLR) GOP weather radar data (WP 2) Thomas Hauf (UHan) Lightning networks (WP 6) Christian Koziar (Thomas Hanisch) (DWD) Access to DWD observations (all WPs) Armin Mathes (Univ. Bonn) Coordination/QC rain gauges (WP 1) Mario Mech (IGM) GOP data management Gerhard Peters (UHH) Micro Rain Radar (WP 3) Matthias Wiegner (LMU) EARLINET Observations (WP 4) and many more

+ DKRZ (Claudia Wunram, Hannes Thiermann)+ COPS

General Observation Period 2007



Rain gauge

GOP Ingredients: Precipitation

Weather Radar

Drop Size Distribution

WP-GOP-1 Rain gauges; DWD precip analyses (RANIE, REGNIE)

WP-GOP-2 Weather Radar

WP-GOP-3 Drop Size Distribution DSD

several hundred independent observations by DWD, water authorities, environmental agencies etcDWD analyses: RANIE, REGNIE

DWD radar network and research radars, 3D volume scans, PI, RY, QY, RADOLAN

vertical structure at about 15 locations with Micro Rain Radar (MRR)

Continuous precipitation observation with high temporal resolution



Lindenberg

Zingst

Helgoland

Lichtenau

WienIMKMPI_4/IG4DWD_2

UKO

DLR

MPI_1/IG2DWD_1

MPI_2MPI_3/IG1UHH_1/IG3

MPI

UHH_2

UBO_1UBO_2

LAMP

LAMP

Micro Rain Radar MRR-2

Optical Distrometer ODM470_1

Optical Distrometer FD12P

Optical Distrometer PARSIVEL

Distrometer JOSS/WALDVOGEL

Scanning X-Band Radar (LAWR)

DLR Inst. Phys. Atmos., OberpfaffenhofenDWD_1 R. Assmann Obs., LindenbergDWD_2 Met.. Obs. HohenpeissenbergIG_1-4 IfM Geomar, KielIMK Inst. Met. Klim., KarlsruheLAMP Laboratoire de Météorologie PhysiqueMPI_1-4 MPI Hamburg UBO_1-2 Uni BonnUHH_1-2 Uni HamburgUKÖ Uni KölnWien Uni Wien

GOP-3

Gerhard Peters



WP-GOP-4 Lidar (aerosol, cloud base, mixing layer height)

WP-GOP-5 GPS water vapour column

WP-GOP-6 Lightning networks

WP-GOP-7 Satellite observations (cloud properties, water vapor, aerosol)

WP-GOP-8 Meteorological stations

Lidar

GOP Ingredients: Auxillary Information

GPS

AMF

EARLINET stations (4), about 100 lidar ceilometer stations in Germany

DWD: ca 147 stations in LMK area, ca 200 in LME area + GPS COPS + Switzerland

European and national networks VLF and VHF

MSG, MODIS, MERIS, AMSU, CLOUDSAT, CALIPSO

ARM Mobile Facility (AMF), Lindenberg, diverse universities and research institutes



GOP-7: SatellitesMSG:- cloud mask- cloud top pressure (+temperature?), - optical depth - IR brightness temperature

MODIS:- cloud mask- cloud optical thickness τ - liquid water path LWP- effective radius reff

- geometric cloud thickness H - IWV - aerosol?

MERIS:- cloud mask- cloud optical thickness τ - cloud top pressure (+temperature?)



Evaluation Areas Northsea Baltic Sea Alps North western German lowland North eastern German lowland Low mountain ranges COPS area Countries (D, B, A, CH, NL, F) River catchments (in Germany)

LMK domain



Diurnal comparisons / plots, processed near real-time (“Quicklooks”) Radiosoundings: Plots for each sounding in Germany and neigbouring countries

- Stüve diagramm together with corresponding +12h LMK forecast- differences of temperature, specific humidity and wind speed forecasts (+0,+3,+6,+9,+12,+15,+18,+21 h) at each model level

GPS, Ceilometer: Daily colour coded maps of BIAS/RMSE of cloud base height (ceilometer) and IWV (GPS); LMK vs. observation

GOP - First order model evaluation

Monthly comparisons / plots.

Radiosoundings: Bias and RMSE profiles for temperature, humidity and wind for all stations

Ceilometer / GPS: Monthly time series of Bias/RMSE for each station or region (depending on number of stations within regions)

Ceilometer / GPS: Monthly analysis of mean diurnal cycle and comparison to differnet model runs (lagged ensemble)



Example for GPS Quicklook



Example for Radiosonding Quicklooks



Task: Archiving model outputTotal LMK output too large for permanent storage!

=> Therefore: Extracting model output relevant for model evaluation :

3 types of data extraction:

1.) statistics over (sub-) areas, timeseries at stations (1-d in time)

2.) column output at individual gridpoints (2-d in height and time)

3.) field output (3-d in x,y,t)



1d output (time series)

-- statistics of individual quantities (precip, wind, …) in (sub-)domains for direct evaluation & classification für weather-type dependent evaluation

-- time series of near-surface variables Werte at Synop stations

-- time series of integrated water vapour (IWV) at GPS stations

-- time series of cloud base height at ceilometer stations

-- time series of precipitation at precipitation stations



2d output (column output)All available variables at certain gridpoints with vertically sounding instruments:

-- Radiosonding stations

-- Micro rain radars (ca 15)

-- ARM Mobile Facility

-- Earlinet stations

-- Cloudnet stations

-- COPS Supersites

-- Meteorological Observatories



Field Output

-- brightness temperature of synthetic MSG channels-- radar reflectivity in 850 hPa, max. radar reflectivity in column-- Integrated condensate (TQC,TQR,TQS,TQG)-- height of cloud top and cloud base-- cloud cover (CLCT, CLCL, CLCM, CLCH)-- optical thickness-- precipitation (R, S, G), rates and sums-- radiation balances-- CAPE-- HZERO; 850-hPA temperature, wind; 500-hPa geopotential-- albedo, ground temperature

For AMSU: all prognostic variables at overpass times

for comparison with area covering instruments (radar, satellite)



Cloud top MSGCloud base Ceilometer

Cloud cover MSG

Examples of LTE (I): cloud parameters



Example of LTE (II): cloud cover Cloud cover (%) LMK00 / LMK12 BIAS (%) STD (%) Korrelation

LMK total 8 / 5 9 / 9 0.80 / 0.80

North Sea 9 / 8 17 / 17 0.72 / 0.70

Alpes 6 / 2 14 / 15 0.78 / 0.81

Lowlands 9 / 7 17 / 17 0.68 / 0.70

Low mountains 7 / 5 15 / 16 0.68 / 0.67

Poldirad area 5 / 2 17 / 17 0.72 / 0.75

COPS area 4 / 0 22 / 20 0.49 / 0.61

Meteosat Second Generation comparison: July 2004

CabauwLindenberg

AMF

run 00UTC run 12UTC



MSG data – Cloud top pressure

• Overestimation of cloud top height by model• Model simulates realistically no great variation

throughout a day.

OBSmodel

Daily cycle Daily cycle of RSME

(run started at 00UTC)

Example of LTE (III): cloud cover



Summary LMK LTE

LE

Boundary layer too thin and too wet IWV generally well

predicted IWV Bias of

-0.85 kg/m2 for runs started at 12 UTC

Clouds too thick Cloud cover in good

agreement with MSG

Precipitation underestimated by 20%

(problem addressed in the maintime by several model changes)

Daily cycle not well forecast

Case studies to look into more detail in the problems



Observation

LMK,

Thompson,

Rain, snow, graupel

LMK, 2 comp.

Rain, snow

LMK, 3 comp.

Rain, snow, graupel

Reflectivity

Case study example: pol.radar

By Monika Pfeifer,DLR



Observation

LMK :3 comp.

Rain, snow, graupel

Hydrometeor Classification

LMK :

2 comp.

Rain, snow

LMK:

Thompson

Rain, snow,

graupel

Case study example: pol.radar

By Monika Pfeifer,DLR



Summary QUEST Goals

• Optimization and refinement of existing evaluation tools• Identification of systematic errors in precipitation and cloud fields

forecasts• Exploitation of the complementary information of the different remote

sensing observations; model consistency; cross-correlation of model performance for different variables

• Evaluate model using long-term observations collected during the GOP

• Provide an independent test bed for model improvements• Improve LMK performance by changes in the treatment of cloud

microphysics, turbulence, land surface,… (motivated by results of model evaluation)

5.-9. märz 2007, bad herrenalb thorsten reinhardt, institut für geophysik und meteorologie,...

Documents

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quantitative precipitation

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