Multi-senseor satellite precipitation estimates on the African continent using combined morphing and histogram-matching techniquesMalte Diederich1, Aynur Bozoglu2, Clemens Simmer1, Alessandro Bataglia1

1 University Bonn2 EUMETSAT

Collaboration between IMPETUS, AMPE, and Precip-AMMA projects

Multisensor Satellite Precipitation EstimatesGoals of presented work:Provide high resolution precipitation estimates (Meteosat-7/MSG resolution) in West Africa (IMPETUS Project)Create a product that can be merged with and used to dis/reagdregate ground observationsTest and recommend possibilities for upgrades to the EUMETSAT MPE => AMPEAssess product useability for climatology, hydrology, agriculture (as far as possible)

Procedure:Merge information from geostationary IR images and passive microwave platformsProbability Matching MorphingIdentify non-raining clouds with SEVIRI Channels 7 to 10

Examine possibilities for regionalized calibrationGround validation with interpolated kriged products as well as point measurements

SensorsGeostationary SatellitesMETEOSAT 7: 30 minutes sampling interval5 km resolution at sub-satellite point11 m IR channel

Low-earth orbit Satellites

Passive Microwave Sensors:

TMI (TRMM Tropical Rainfall Measuring Mission) 2A12 ProductAMSR-E (AQUA Satellite)3 SSM/I (DMSP F13, F14, F15) (3 AMSU-B) (NOAA 15, 16, 17) (Nesdis or simple scattering Algorithm)

MSG-1: 15 minutes sampling interval3 km resolution at sub-satellite point10 m IR channelCloud Analysis with some potential for discriminating non-raining clouds

Probability matching of IR and RR:Accumulate co-located passive micro-wave rain estimates and brightness temperature measurements: 200 km, +-4 day accumulation windows, spaced at 10 km and 1 dayMatch the cumulative distribution function of rain estimates and IR 11 m brightness temperatures to obtain a look-up table, associating the coldest cloud temperature with the highest rain rate. (histogram-matching)The resulting look-up table translates IR radiances into rain rates

HistogramsIR RadianceRain RateLook-up tableIR RadianceRain Rate

Multisensor Satellite Precipitation EstimateMorphing of PMW rain estimatesCalculation of advection vectors by cross-correlating subsequent IR images (10 m):Size of windows for cross-correlation determines if tops of small clouds or storm systems are tracked.Scan duringOverpass2 h beforepropagatedbackward2 h afterpropagatedforward

Combining Morphing and Histogram MatchingForward and backward propagated PMW scans are merged with propability matching estimates using a weighted averaging system based on time to the last/next PMW scan:Propagated PMW weights decrease linearly with time from overpass, weight reaches 0 at 2 hours from original scan

Temporal sampling of microwave-overpasses changes with latitude. At 50 Latitiude, histogram-matching component gives negligable contribution

Example of weighting on one day at 10 degree latitude

PMW scanForward propagated PMWBackward propagated PMWHistogram-Matching

Ground ValidationEvaluation of Morphing and Probability matching performance.Regional and temporal distribution of systematic errors

Data Sets available for validation:Benin:DMN / CATCH / AMMA / IMPETUSmonthly accumulations kriged at 0.1 resolution from a dense gauge network, Daily point measurements from 2002 to 2005Sahel region: AGRHYMET procucts for June-September 2004, AMMA intercomparison excercise10-day accumulations from filtered synop stations10-day accumulations from 800 stations kriged at 0.5 resolution:Furthermore: Daily synop data from GPCC for AMPE validation: High density and quality in Europe, scarce and with gaps in AfricaDaily Nigerian and South African gauge observations

Ground ValidationBeninMonthly sums for the Benin, June-September 2002Comparisons with a 0.1x0.1 degree Kriging product (IMPETUS)Histogram Matchingwith morphing

Regionaly dependant biases

Gradients in air humidity and moisture advection from north to south may lead to altered relation between ice in cloud and surface rain

01 mm 11 mm 21 mm 31 mm 41 mm 51 mm 61 mmFalse alarm ratioProbablity of detectionSkill scoreShape of probability density function of daily estimates agreed well with grouund observations, but skill for correct daily prediction diminishes with intensity

Data quality of rainfall measurements in BeninQuality of ground measurements can be estimated from time series of satellite estimates. Some stations display other accumulation time than 6 UTC

Reliability of satellite and gauge cross-validationSatellite/gauge skill at gauge pointGauge/gauge skill as function of distanceIn addition: Flag single days if ground observation is extremely unrealisticFollowing validation of gauges in Benin with satellite data:7% of rain days given by gauges can not have ocured on the associated dateAt least 6% of non-raining days (between 6% and 20%) given by gauges should have been rainy days

Ground Validation histogram matching onlywith morphing

Lower performance in Europe, but morphing still improves estimatesInfra-red screaning: Post-Processing of rain product where semitransparent clouds (SEVIRI Channels 8 and 10) and clear sky (Channel 10) are declared to be no rain areasLower performance in european test area due to:underestimation of costal precipitation in microwave productsOrographic rainfall partially recognized by PMW displaced by merging schemeVery small convective cells not detected by coarse resolution PMW

9.2004Corr.BiasrmsdMorphed + IRS0.44-3855morphed0.42-3651Histo.0.28-4460

8.2004Corr.BiasrmsdMorphed + IRS0.57-2652morphed0.56-2351Histo.0.44-3663

7.2004Corr.BiasrmsdMorphed + IRS0.51-1843morphed0.49-1443Histo.0.35-2450

Future plansValidate other regions of Africa with filtered synop stations (GPCC-input, Nigeria, South Africa)homogeneization of the Passive Micro-Wave estimates with respect to pdf and biasTest other AMSU-B productsOptimize weighting system beteween morphing and histogram matching with TRMM radar ground measurementsImprove morphing estinates using EUMETSAT cloud type productsConclusions

Morphing technique superior to probablility matching

It is recommended to recalibrate PMW estimates regionaly, especially coastal areas:Positive bias in Sahel Negative bias in Europestrong bias gradient from moist to dry, especially near coasts

Satellite estiamtes are relatively good at detecting rainfall even at point scale, but quantitative skill not so good

Even in a relatively dense network like Benin, there are some regions where satellite is better for detecting rain than interpolated gauge productsAdding IR screening or classification may inprove morphing estimates

ReminderVertical reflectivity profile measured by MRR in Benin