MULTI-SOURCES PRECIPITATION ESTIMATIONK. Tesfagiorgis, S. E. Mahani, R. Khanbilvardi(NOAA-CREST, CCNY, CUNY, NY-10031)

David Kitzmiller (NOAA-NWS Collaborator) (NOAA NWS/HL, Silver Spring, MD-20910)

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NOAA-NESDIS CoRP 7th Annual SymposiumFort Collins, CO., August, 2010

Outline 2

Objectives Data sets Comparison of the different precipitation estimation algorithms

The different bias correction techniques Spatial corrections Results for study cases

Conclusion Future work

Objectives 3

To improve Satellite Precipitation Estimation (SPE) by selecting appropriate bias correction technique.

To develop a Multi-Sources Rainfall Estimation algorithm to help optimal rainfall estimations. Be capable of extending radar like outputs inside radar gap regions using satellite and the surrounding radar rainfall estimations.

Data Sets4

Hourly 4kmx4km resolution for the Oklahoma region bounded by

94.50-100o W Longitude 34.50-37.0o N Latitude

Satellite Rainfall Estimations selected from the following NESDIS models AE (Auto-Estimator) GMSRA (GOES Multispectral Rainfall Algorithm)

HE (Hydro-Estimator) SCaMPR-(Self Calibrating Multivariate Precipitation Retrieval)

Blend-(IR/Microwave Blended Algorithm) Radar Rainfall Estimation

Radar Stage IV (ST-IV) Rain-gauge Measurements

8677 cases, 8677x62x137=73,702,438 pixels considered

Comparison of the Different Rainfall Estimations

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Satellite Rainfall

Estimations

Radar Rainfall Estimation

Yes No

Yes Hits False Alarms

No Misses Correct negatives

Observed yes Observed no

Satellite Rainfall Estimation

Bias Score

False Alarm Ratio

GMSRA 2.71 0.63

HE 1.73 0.46

SCaMPR 2.41 0.68

Auto-Estimator

2.08 0.53

MissesHits

msFalse AlarHits

Score BiasmsFalse AlarHits

msFalse Alar

Ratio Alarm False

Bias Corrections6

• Field Bias CorrectionGenerally it helps for:– Intensity correction– Frequency correction• Methods of bias corrections:

– Ratios of Mean, Median, Maximum– Mean of Ratio of the corresponding rainy pixels in both Satellite and Radar Rainfall Estimations

– Bias ratio field using Inverse Distance method

Bias Corrections: continued…

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€

F =RR

SPE

€

F =RRmedian

SPEmedian

€

F =RRmax

SPEmax

We need to calculate the Multiplicative factors (F) for Bias corrections

RR-Radar Rainfalls and SPE-Satellite Rainfall Estimates

Method 1 of Bias correction

The ratio of Max and Mean gave a better output. However, ratio of max is not stable and reliable.

Bias Corrections: continued…

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• How about Spatial Errors that might have already existed?

• Before working on the Bias Corrections, it is important to make spatial corrections between the satellite and the radar rainfall estimations.

• Spatial Correction using the Method of Least Squares (Brogan 1985):– Apply the method of Hill Climbing to cluster rainy pixels; because the clustered corresponding rainy pixels are easier to pick up

– Pick corresponding points (Rainy Pixels)– Write Least Square equations and apply the method of least squares on these points as shown in equations shown below.

Spatial Correction

CoefficientsCoefficients InterpretationsInterpretations

Shift in longitude

Scale in longitude

Shear in the longitude

CoefficientsCoefficients InterpretationsInterpretations

Shift in latitude

Scale in latitude

Shear in the latitude

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N

i

iN

ji

Slati

Slonij

Rlat

N

i

iN

ji

Slati

Slonij

Rlon

0 0)()()(

0 0)()()(

01

10

0022

11

2

1

)( )( 1

......

)( )( 1

)( )( 1

)(

......

)(

)(

zzz SlatSlon

SlatSlon

SlatSlon

Rlon

Rlon

Rlon

00

10

01

01

10

0022

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2

1

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......

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......

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zzz SlonSlat

SlonSlat

SlonSlat

Rlat

Rlat

Rlat

00

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01

Linear form of the equations with

N=3

R-Radar

S-Satellite

Spatial Correction 10

Corresponding P

ixel Locations

Method 2 of Bias Correction

Bias Corrections: continued…

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- Calculate the bias ratio between ST IV and HE- Calulate the bias field using Inverse distance weight technique- Multiply HE by the mean field bias

Method 2 provides a more radar like output both spatially and intesity wise.

Bias Correction: continued…

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The performance of bias field method for a winter case

Bias Correction: continued…13

Ensembles of rainfall for a pixel around the center of the study area

•Ensemble generation of bias fields•Instead of 1, 100 realizations

Bias Correction continued….

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Uncorrected Corrected

Pixels used in the development of the algorithm are not part of the CORR analysis

Case-2006071022 (YYYYMMDDHH)

Case-2006122917 (YYYYMMDDHH)

Conclusion15

Hydro-Estimator has a better detection capability than the others, so that it is chosen for further studies that will include radar estimations and rain-gauge measurements.

There are cases where the alignment algorithm faces difficulties. When rainfall is very cluttered in radar and continuous in satellite estimations.

In these cases it is difficult to pick up corresponding rainy pixels.

However we can still apply the Bias field generation Algorithm without doing the alignment.

Ensemble generation helps to account other errors (Eg. physical, paralax).

Generation of bias fields can potentially be used to correct satellite estimations in radar gap regions.

Ongoing Works

Check the performance of the model in other geographical locations.

Implement a technique that will give a multi-sources rainfall algorithm by merging the radar and the satellite estimations.

Produce gridded rain-gauge measuremts using Bayesian Kriging and/or inverse distance method.

Merge the gridded rain-gauge with the combined radar-satellite rainfall estimation.

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Acknowledgements

This study was partially supported and monitored by the National Oceanic and Atmospheric Administration (NOAA) under grant number NA06OAR4810162. The statements contained within this presentation are not the opinions of the funding agency or the U.S. government, but reflect the author’s opinions.

I would like to thank Robert Kuligowski (Ph.D.) for providing all the necessary data.

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