Multi-senseor satellite precipitation estimates on Multi-senseor satellite precipitation estimates on the African continent using combined morphing the African continent using combined morphing

and histogram-matching techniquesand histogram-matching techniques

Malte DiederichMalte Diederich11, Aynur Bozoglu, Aynur Bozoglu22, Clemens , Clemens SimmerSimmer11, Alessandro Bataglia, Alessandro Bataglia11

11 University Bonn University Bonn22 EUMETSAT EUMETSAT

Collaboration between IMPETUS, AMPE, and Precip-AMMA Collaboration between IMPETUS, AMPE, and Precip-AMMA projectsprojects

Multisensor Satellite Precipitation EstimatesMultisensor Satellite Precipitation Estimates

Goals of presented work:Goals of presented work: Provide high resolution precipitation estimates (Meteosat-7/MSG resolution) in West Provide high resolution precipitation estimates (Meteosat-7/MSG resolution) in West

Africa (IMPETUS Project)Africa (IMPETUS Project) Create a product that can be merged with and used to dis/reagdregate ground Create a product that can be merged with and used to dis/reagdregate ground

observationsobservations Test and recommend possibilities for upgrades to the EUMETSAT MPE => AMPETest and recommend possibilities for upgrades to the EUMETSAT MPE => AMPE Assess product useability for climatology, hydrology, agriculture (as far as possible)Assess product useability for climatology, hydrology, agriculture (as far as possible)

Procedure:Procedure:Merge information from geostationary IR images and passive microwave platformsMerge information from geostationary IR images and passive microwave platforms Probability Matching Probability Matching MorphingMorphing Identify non-raining clouds with SEVIRI – Channels 7 to 10Identify non-raining clouds with SEVIRI – Channels 7 to 10

Examine possibilities for regionalized calibrationExamine possibilities for regionalized calibration Ground validation with interpolated kriged products as well as point measurementsGround validation with interpolated kriged products as well as point measurements

SensorsSensors

Geostationary SatellitesMETEOSAT 7:METEOSAT 7: •30 minutes sampling interval30 minutes sampling interval•5 km resolution at sub-satellite point5 km resolution at sub-satellite point•11 11 µm IR channelµm IR channel

Low-earth orbit Satellites

Passive Microwave Sensors:

•TMITMI (TRMM Tropical Rainfall (TRMM Tropical Rainfall Measuring Mission) 2A12 ProductMeasuring Mission) 2A12 Product

•AMSR-EAMSR-E (AQUA Satellite)(AQUA Satellite)

•3 SSM/I3 SSM/I (DMSP F13, F14, F15) (DMSP F13, F14, F15)

•(3 AMSU-B)(3 AMSU-B) (NOAA 15, 16, 17) (NOAA 15, 16, 17) (Nesdis or simple scattering (Nesdis or simple scattering Algorithm)Algorithm)

MSG-1:MSG-1: •15 minutes sampling interval15 minutes sampling interval•3 km resolution at sub-satellite point3 km resolution at sub-satellite point•10 10 µm IR channelµm IR channel•Cloud Analysis with some potential for discriminating Cloud Analysis with some potential for discriminating non-raining cloudsnon-raining clouds

Probability matching of IR and RR:Probability matching of IR and RR:

1.1. Accumulate co-located passive micro-wave rain estimates and Accumulate co-located passive micro-wave rain estimates and

brightness temperature measurements:brightness temperature measurements: 200 km, +-4 200 km, +-4 day accumulation windows, day accumulation windows, spaced at 10 km and 1 dayspaced at 10 km and 1 day

2.2. Match the Match the cumulative distribution functioncumulative distribution function of rain estimates and IR of rain estimates and IR 11 11 μμm m brightness temperatures to obtain a look-up table, brightness temperatures to obtain a look-up table, associating the associating the coldest cloud temperature with the highest rain coldest cloud temperature with the highest rain raterate. (histogram-matching). (histogram-matching)

3.3. The resulting The resulting look-up tablelook-up table translates translates IR radiances into rain ratesIR radiances into rain rates

Histograms

IR Radiance

Rain Rate

Look-up table

IR Radiance

Ra

in R

ate

Multisensor Satellite Precipitation EstimateMultisensor Satellite Precipitation EstimateMorphing of PMW rain estimatesMorphing of PMW rain estimates

Calculation of advection vectors by cross-correlating subsequent IR images (10 μm):

Size of windows for cross-correlation determines if tops of small clouds or storm systems are tracked.

Scan duringOverpass

2 h beforepropagatedbackward

2 h afterpropagatedforward

Combining Morphing and Histogram MatchingCombining Morphing and Histogram Matching

Forward and backward propagated Forward and backward propagated PMW scansPMW scans are are mergedmerged with with propability matching estimatespropability matching estimates using a weighted using a weighted averaging averaging systemsystem based on time to the last/next PMW scan: based on time to the last/next PMW scan:

Propagated PMW weights decrease linearly with time from Propagated PMW weights decrease linearly with time from overpass, weight reaches 0 at 2 hours from original scanoverpass, weight reaches 0 at 2 hours from original scan

Temporal sampling of microwave-overpasses changes with latitude. Temporal sampling of microwave-overpasses changes with latitude.

At 50 Latitiude, histogram-matching component gives negligable At 50 Latitiude, histogram-matching component gives negligable contributioncontribution

Example of weighting on one day at 10 degree latitude

PMW scanForward propagated PMWBackward propagated PMWHistogram-Matching

Ground ValidationGround Validation1. Evaluation of Morphing and Probability matching performance.2. Regional and temporal distribution of systematic errors

Data Sets available for validation:Benin:DMN / CATCH / AMMA / IMPETUS• monthly 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

excercise• 10-day accumulations from filtered synop stations• 10-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 Africa• Daily Nigerian and South African gauge observations

Ground ValidationGround ValidationBeninBenin

Monthly sums for the Benin, June-September 2002Comparisons with a 0.1x0.1 degree Kriging product (IMPETUS)

Histogram Matching with 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 mm01 mm 11 mm 21 mm 31 mm 41 mm 51 mm 61 mm

False alarm ratio

Probablity of detection

Skill score

Shape 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 Data quality of rainfall measurements in Beninmeasurements in Benin

Quality 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 Reliability of satellite and gauge cross-validationcross-validation

Satellite/gauge skill at gauge pointGauge/gauge skill as function of distance

In 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 date•At least 6% of non-raining days (between 6% and 20%) given by gauges should have been rainy days

Ground ValidationGround Validation

histogram matching only with morphing

9.20049.2004

Corr.Corr. BiasBias rmsdrmsd

Morphed + IRSMorphed + IRS 0.440.44 -38-38 5555

morphedmorphed 0.420.42 -36-36 5151

Histo.Histo. 0.280.28 -44-44 6060

8.20048.2004

Corr.Corr. BiasBias rmsdrmsd

Morphed + IRSMorphed + IRS 0.570.57 -26-26 5252

morphedmorphed 0.560.56 -23-23 5151

Histo.Histo. 0.440.44 -36-36 6363

7.20047.2004

Corr.Corr. BiasBias rmsdrmsd

Morphed + IRSMorphed + IRS 0.510.51 -18-18 4343

morphedmorphed 0.490.49 -14-14 4343

Histo.Histo. 0.350.35 -24-24 5050

Lower performance in Europe, but morphing still improves estimates

Infra-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 areas

Lower performance in european test area due to:•underestimation of costal precipitation in microwave products•Orographic rainfall partially recognized by PMW displaced by merging scheme•Very small convective cells not detected by coarse resolution PMW

Future plansFuture plans•Validate 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 bias•Test other AMSU-B products•Optimize weighting system beteween morphing and histogram matching with TRMM radar ground measurements•Improve morphing estinates using EUMETSAT cloud type products

Conclusions

Morphing technique superior to probablility matching

It is recommended to recalibrate PMW estimates regionaly, especially coastal areas:•Positive bias in Sahel •Negative bias in Europe•strong 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

ReminderReminder

Vertical reflectivity profile measured by MRR in Benin