Use of radar data in ALADIN Marián Jurašek [email protected] Slovak Hydrometeorological Institute

Use of radar data in ALADINUse of radar data in ALADIN

MariMarián Jurašekán Jurašek

[email protected]@shmu.sk

Slovak Hydrometeorological InstituteSlovak Hydrometeorological Institute

Use of radar data in ALADIN EWGLAM/SRNWP/COST717, Lisbon 2003

– current work with radar and ALADIN data in current work with radar and ALADIN data in

ALADIN’s countriesALADIN’s countries

– future: “Research plan for radar data future: “Research plan for radar data

assimilation in ALADIN”assimilation in ALADIN”

Contents of presentationContents of presentation

– assimilation of radar data not yet developed for assimilation of radar data not yet developed for

ALADINALADIN

– usage of radar data:usage of radar data:– for ALADIN verificationfor ALADIN verification– together with ALADIN in hydrological modelstogether with ALADIN in hydrological models– together with ALADIN in nowcasting application together with ALADIN in nowcasting application

– all work is done only on national level all work is done only on national level

Current statusCurrent status


AUSTRIAAUSTRIA

– archiving 1h cumulated precipitation from 10 archiving 1h cumulated precipitation from 10

minutes radar data in lat-lon gridminutes radar data in lat-lon grid

– archiving precipitation fields based on surface archiving precipitation fields based on surface

observations in the same gridobservations in the same grid

– mainly used for ALADIN convective rainfall mainly used for ALADIN convective rainfall

forecast visual verificationforecast visual verification


AUSTRIA (2)AUSTRIA (2)

– first study made for eastern alpine domainfirst study made for eastern alpine domain

– period: summer 2003period: summer 2003

– first results:first results:– ALADIN precipitation forecast is not selective enough in ALADIN precipitation forecast is not selective enough in

spacespace

– early bias with regard to the onset of precipitationearly bias with regard to the onset of precipitation– in some areas model generates convective precipitation in some areas model generates convective precipitation

on almost every day during the summer seasonon almost every day during the summer season



Hourly precipitation rate estimated by radar measurement

Hourly precipitation rate interpolated from the local obs. network



Convective cloudiness and

precipitation prognosed by ALADIN Convective precipitation (hourly rate)

prognosed by ALADIN

17th August 2003, 17 UTC



– results leaded to the experiments withresults leaded to the experiments with modified trigger functions in the ALADIN deep convection scheme.

– larger project connected with larger project connected with integrated flood forecasting system will probably start next year

– combination surface precipitation observations, combination surface precipitation observations,

radar and ALADINradar and ALADIN


CZECH REPUBLICCZECH REPUBLIC

– ALADIN data used for generating radar image ALADIN data used for generating radar image

forecastforecast

– wind field data - crucial point of the radar echo wind field data - crucial point of the radar echo

predictionprediction

– tested 3 different methods for wind field calculationtested 3 different methods for wind field calculation– COTRECCOTREC– Wavelet Transform DecompositionWavelet Transform Decomposition– ALADIN geopotential field at 700 hPaALADIN geopotential field at 700 hPa


CZECH REPUBLIC (2)CZECH REPUBLIC (2)

COTRECCOTREC– comparison of two consecutive radar images using comparison of two consecutive radar images using

some similarity criteriasome similarity criteria– smoothing of final fieldsmoothing of final field

WAVELETWAVELET– similar to COTREC, but radar image is decomposed to similar to COTREC, but radar image is decomposed to

subspaces using the wavelet transformationsubspaces using the wavelet transformation– calculation of decomposition similarity criteria at several calculation of decomposition similarity criteria at several

different detail levelsdifferent detail levels– smoothing of final field like in COTRECsmoothing of final field like in COTREC



ALADINALADIN– cloud motion is controlled by air mass flow at approx. 3-cloud motion is controlled by air mass flow at approx. 3-

5 km above sea level5 km above sea level

– it corresponds with geopotential at 700 hPA (cca 3 km)it corresponds with geopotential at 700 hPA (cca 3 km)

– ALADIN data interpolated to the image size and to the ALADIN data interpolated to the image size and to the resolution of radar dataresolution of radar data

– motion field calculated from geostrophical approximationmotion field calculated from geostrophical approximation


CZECH REPUBLIC (4)CZECH REPUBLIC (4)COTREC

Wind field calculated by COTREC method

Forecasted radar image by COTREC method



Forecasted radar image by WAVELET method

Wind field calculated by WAVELET method

WAVELET



Forecasted radar image by ALADIN method

Wind field calculated by ALADIN method

ALADIN



Results of comparison:Results of comparison:– all methods improve radar informationall methods improve radar information– for all methods, similar decrease in forecast for all methods, similar decrease in forecast

quality with forecast timequality with forecast time– in most cases, the ALADIN method is slightly in most cases, the ALADIN method is slightly

worse worse – ALADIN method needs only one radar imageALADIN method needs only one radar image– ALADIN method has the smallest hardware ALADIN method has the smallest hardware

requirementsrequirements



Conclusion:Conclusion:

– in operational use only COTREC and ALADIN in operational use only COTREC and ALADIN

methodsmethods

– forecasted radar image generated every 10 forecasted radar image generated every 10

minutesminutes

– forecasted for +10 min +20 min ... +90 minforecasted for +10 min +20 min ... +90 min


FRANCEFRANCE

– study of flood event in southern France from 8study of flood event in southern France from 8 thth

to 10to 10thth September 2002 September 2002

– visual verification (comparison) of cumulative visual verification (comparison) of cumulative

radar rain with cumulative ALADIN rain forecastradar rain with cumulative ALADIN rain forecast

– main goal: to see the general evolution of the main goal: to see the general evolution of the

precipitationprecipitation


FRANCE (2)FRANCE (2)

48 h cumulated precipitationmeasured by radar

48 h cumulated precipitationforecasted by ALADIN



– space-time interpolation of precipitation fieldsspace-time interpolation of precipitation fields

– pattern matching applied on radar datapattern matching applied on radar data

– attempt to prepare radar data for non-visual attempt to prepare radar data for non-visual

verificationverification

– radar data space filtered to ALADIN resolution radar data space filtered to ALADIN resolution

(cca 10 km)(cca 10 km)

– spectral study of radar and ALADIN spectral study of radar and ALADIN

precipitationprecipitation



– result of spectral study:result of spectral study: forecast error is like forecast error is like white noise, no difference between frontal and white noise, no difference between frontal and convective precipitationconvective precipitation

– spectral filtering used for separating large- from spectral filtering used for separating large- from small scale precipitationsmall scale precipitation

– discrepancy between radar and model data discrepancy between radar and model data separated to:separated to:– large scale scaling errorlarge scale scaling error– large scale geometrical deformation errorlarge scale geometrical deformation error– small scale residualsmall scale residual



– Computation of optimal deformation operator:Computation of optimal deformation operator:– correlation method - numerically too expensive, correlation method - numerically too expensive,

already for 200 x 200 points fieldalready for 200 x 200 points field– incremental variational techniqueincremental variational technique

– highly non-quadratic pattern matching cost highly non-quadratic pattern matching cost function function problem with optimisation if good first guess not problem with optimisation if good first guess not

availableavailable


HUNGARYHUNGARY

– radar and ALADIN data as input for hydrological radar and ALADIN data as input for hydrological model DIWA (DIstributed WAtershed) running model DIWA (DIstributed WAtershed) running outside of Hungarian Meteorological Serviceoutside of Hungarian Meteorological Service

– input for model:input for model:– ALADIN precipitation forecastALADIN precipitation forecast– ALADIN min/max temperatureALADIN min/max temperature– ECMWF forecasts ( as ALADIN + ensemble)ECMWF forecasts ( as ALADIN + ensemble)– calibrated 12 h cumulated precipitation from radar calibrated 12 h cumulated precipitation from radar

measurementsmeasurements


HUNGARY (2)HUNGARY (2)

Example of calibrated 12h radar precipitationUse of radar data in ALADIN EWGLAM/SRNWP/COST717, Lisbon 2003

Example of hydrological model simulation

HUNGARY (3)HUNGARY (3)

Verification of 72 h hydrological model forecastover 110 days period

hour forecast

day

Obs. Forecast


Conclusion of first partConclusion of first part

– none radar data assimilation to ALADINnone radar data assimilation to ALADIN

– usage of radar data together with ALADIN data is not usage of radar data together with ALADIN data is not

coordinatedcoordinated

– only some applications on national levelonly some applications on national level


Research PlanResearch Plan

– in June 2003 prepared proposal of workplan for in June 2003 prepared proposal of workplan for research of radar data assimilation for ALADINresearch of radar data assimilation for ALADIN

– radar data - essential for mesoscale assimilationradar data - essential for mesoscale assimilation– available radar data:available radar data:

– reflectivityreflectivity– instantaneous rainrateinstantaneous rainrate– cumulated rainfallcumulated rainfall– doppler wind , wind shear, turbulencedoppler wind , wind shear, turbulence– vertical wind profilevertical wind profile– quantities from multiple polarisation measurementsquantities from multiple polarisation measurements


Which radar data?Which radar data?

– each has advantages and disadvantageseach has advantages and disadvantages

– not all available on all radar sitesnot all available on all radar sites

– reflectivity seems to be available on most reflectivity seems to be available on most European sitesEuropean sites

– not common form of data:not common form of data:–““PPI” imagesPPI” images–volume datavolume data


The philosophyThe philosophy

– learning from satellites learning from satellites – remote sensing process is complex and nonlinearremote sensing process is complex and nonlinear

we should assimilate quantity close to measured we should assimilate quantity close to measured (reflectivity instead of rainrate)(reflectivity instead of rainrate)

derived quantities contain hardly correctable errorsderived quantities contain hardly correctable errors

– observation operator for simulation of reflectivity observation operator for simulation of reflectivity for each radarfor each radar development of system for radar data against model development of system for radar data against model

data monitoringdata monitoring


The philosophy (2)The philosophy (2)

– biases and big errors cannot be handled by biases and big errors cannot be handled by 3D/4D-Var3D/4D-Var

software for detection and removal of corrupted datasoftware for detection and removal of corrupted data

study of space- and time structure of biases between study of space- and time structure of biases between

simulated and observed data for bias correctionsimulated and observed data for bias correction

each radar processed independentlyeach radar processed independently

thinning of too dense data consistently with the thinning of too dense data consistently with the

resolution of the analysesresolution of the analyses


The philosophy (3)The philosophy (3)

– very accurate modelling of physical process of very accurate modelling of physical process of observationobservation precisely interpolating / averaging model variables along precisely interpolating / averaging model variables along

the radar beam paththe radar beam path

– physical part of observation operator should be physical part of observation operator should be

prepared by radar specialistsprepared by radar specialists

– observation operator should relatively independent observation operator should relatively independent

from modelfrom model


Things To DoThings To Do

– get samples of very good quality radar reflectivity get samples of very good quality radar reflectivity datadata

– get idea of fields needed to simulate reflectivityget idea of fields needed to simulate reflectivity

– get simple (to start with) reflectivity simulation get simple (to start with) reflectivity simulation

formulaeformulae

– specify obs. operator by list of necessary model specify obs. operator by list of necessary model

fields, information about observationfields, information about observation


Things To Do (2)Things To Do (2)

– carefully specify the technical implementation of carefully specify the technical implementation of previous thingsprevious things

– implement radar data into the ODB processingimplement radar data into the ODB processing

– implement direct interpolation of model fieldsimplement direct interpolation of model fields

– convert model field to the reflectivity, compute and convert model field to the reflectivity, compute and store difference with observationstore difference with observation

– study monitoring statisticsstudy monitoring statistics


Things To Do (3)Things To Do (3)

– code tangent linear and adjoint of obs. operatorcode tangent linear and adjoint of obs. operator

– simulate one radar pixelsimulate one radar pixel

– study the impact of reflectivity assimilation to study the impact of reflectivity assimilation to forecastforecast

– run several cycles of data assimilation to see the run several cycles of data assimilation to see the cumulative effectcumulative effect

– retune preprocessing and analysis parameters retune preprocessing and analysis parameters


Thank You for your attention!Thank You for your attention!

[email protected]@shmu.sk


Documents

Use of radar data in ALADIN Marián Jurašek [email protected] Slovak Hydrometeorological Institute