IPWG, M. Desbois, AMMA, Melbourne, October 20061 satellite rainfall estimation and validation in the frame of AMMA (the African Monsoon Multidisciplinary

IPWG, M. Desbois, AMMA, Melbourne, October 2006

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satellite rainfall estimation and validation in the frame of AMMA (the African Monsoon Multidisciplinary Analysis)

Michel Desbois*, Franck Chopin*, Isabelle Jobard*, Abdou Ali**, Abou Amani**, Thierry Lebel***,

and the EU Precipamma Group

*LMD-IPSL-CNRS**Agrhymet Center, Niamey, Niger

***LTHE-IRD, Grenoble


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Special observations and developments for AMMAAMMA observations are covering different time periods : Long term (10 years and more), Extended (3 years), Special Observing Periods (in 2006).

Observations include :

-operational networks (meteorology, hydrology, aerosols),

-enhanced networks (radiosoundings)

-Specific measurements in supersites

-Aircrafts and ships observations during SOP’s

-Specific collection and processing of satellite observations (AMMASAT)

Specific databases are constructed to collect and distribute the data to the project partners, without operational objectives.


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Specific needs of AMMA : multidisciplinary

Requirements of precipitation products from different communities :

Hydrologists,Surface water budget analysts and modelists (SVATs),Agronomists,Climate modelists,Mesoscale modelists, Intraseasonal variability analysts,Various impacts people, for example health impacts specialists

Lead to requirements for time and space scales ranging from 10 days, 1 X 1°, to hours, 10 x 10 km (continuously). Not accounting for instantaneous estimates needed for assimilation in forecast models


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Requirements for satellite precipitation estimations during AMMA

Use the best time resolution available in that area : 15 minutes with Meteosat Second Generation,

Ensure the capablity of this satellite to detect properly rainfall areas,

Ensure the consistency of rainfall measured at different time-space scales

Ensure the consistency with a reference product at large space-time scales.

Provide estimations of errors at the different scales retrieved


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Development and validation of specific algorithms

(precipamma group)

European group (LMD/CNRS Paris, CNR Bologna + Ibimet, Univ. Of Bonn, Tamsat Univ. Reading) Tests on year 2004 (validation data provided by AGRHYMET and IRD) First results show satisfactory results of the LMD method based on MSG (EPSAT-SG) real time application of EPSAT-SG trained on previous years on rainy season 2006 (AMMA SOP) Methods will be re-runned with complete data sets and validated against AMMA-SOP data (including radar data). New intercomparison exercises planned.


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Equation 2

EPSAT-SG scheme(Estimation des Pluies par SATellite – Seconde Génération)

( )( )

( )∫∫

∫∫=

AT

r

AT

gpcp

ApdadttaP

dadttaI

dcI22

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,

,

,

Equation 1

Neural NetworkMSG IR Channels

SRTM Digital Elevation Model

Rainfall probability images (Pr) GPCP1dd rainfall images (Igpcp)

Potential rainfall intensity images (Ip)

EPSAT-SG rainfall estimation (Ie)

A is a disc of about 125kms radiuscA is the centre of Ad is the considered dayT is the period [d-15days,d+15days]dt1 corresponds to 1 daydt2 corresponds to 15 minuteda corresponds to 1 MSG pixel

Equation 1

( ) ( ) ( )daItaPtaI pre ,,, ×=

Equation 2Estimated Rainfall Intensity at time t during day d and position a:

Final product resolutions:Space resolution : 3 kms

Time resolution : 15 minutes

2A25 TRMM precipitationRadar data


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Collocation between Pr image (2A25 TRMM data - red- ) and EPSAT-SG probability of rainfall -gray levels-


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Validation from krieged data :

• Provided by IRD and AGRHYMET

• Space resolutions : 0.5, 1 and 2.5 degrees

• Time resolution : 10-day periods

The validation datasets have been provided with an estimate of its uncertainty for each grid cell.

More detailed data sets inside boxes Niger, Benin.

Example of validation on 10 days periods, 1 degree x 1 degree


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Validation (contd)

Full space resolution Estimated Rainfall accumulation during the third 10-day period of

August 2004

1°x1° rainfall accumulation from the IRD & AGRHYMET

Raingauge Dataset during the third decade of August 2004


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Validation (contd)

1°x1° Estimated Rainfall accumulation over validation area during the third 10-day period of

August 2004

1°x1° rainfall accumulation from the IRD & AGRHYMET

Raingauge Dataset during the third decade of August 2004


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August 2004 Third Decade

GPCP/krieged surface dataEPSAT-SG/krieged surface data


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1x1 BIAS RMSD WRMSD NRMSD R2 SKILL

GPCP 1dd 100% (280) 7,74 24,32 1,93 1,77 0,59 0,12

EPSAT 2-0 100% (280) 3,23 19,02 1,36 1,34 0,64 0,51

GPCP 1dd 50% (140) 6,85 19,18 2,14 2,08 0,62 0,08

EPSAT 2-0 50% (140) 3,09 13,25 1,40 1,32 0,70 0,60Ground Data MIN RAIN 2,26 MAX RAIN 145,72 MEAN RAIN 39,19

Comparison between GPCP 1dd and EPSAT-SG

Two validation studies have been done :• First one considers all the validation grid cells.• Second one takes into account only the 50% validation grid cells with the lowest krigging uncertainty.


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Smaller time scales

EPSAT-SG and validation data for the square degrees of Niger and Benin, for different time resolutions

Time resolution Niger Benin

15’ 0.49 0.35

1h 0.54 0.47

6h 0.71 0.72

24h 0.79 0.77

23 mm

17 mm

39 mm

5 mm

rain estimation per event over the degree square of Niamey for 2004

Probability of rainfall, first decade August 2004, with surface measured rainfall of the full event

R2 of the time series, for different accumulation times


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Application to 2006 : Example of near real time estimation of rainfall from EPSAT-SG for the needs of the AMMA campaigns

Accumulated over a 3 hours period


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Application to 2006 : Examples of decadal estimates of rainfall, preliminary operational version of EPSAT-SG

>250 mm>150 mm

80 mm

60 mm


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Application to 2006 : Examples of decadal estimates of rainfall, CPC product

200 mm>250 mm

80 mm

<100 mm


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Application to 2006 : Examples of decadal estimates of rainfall, EPSAT-SG provisional product1-10 July

11-20 July

21-31 July

1-10 August

11-20 August

21-31 August


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Latitude-time Hovmöller of the 2006 African Monsoon, at two different longitudes (2°E Niamey, 7.5°W Bamako)


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Conclusion and future developments

Epsat-SG gives results compatible with operational precip algorithms (CPC, GPCP)Quality of the results has been estimated at different space-time scalesIt allows users to integrate at the space-time scales of interest for themFurther validations and intercomparisons will take place with the full AMMA surface

validation network operating in 2006The method remains basically an MSG IR algorithm. Although using a set of channels,

it cannot detect very high rainfall rates on short time periods. The duration and extension

of events is overestimated, while the max intensities are underestimated.The method is presently educated by the TRMM radar, and tuned to GPCP monthly

accumulations. Other data sets may replace these entries (passive microwave, surface

networks)The method is transferable for practical applications. This is of particular interest for

African institutions. Tests have to be performed over other regions covered by MSG or

equivalent satellites.The education through a space radar is efficient. A new precip radar in tropical orbit

would be useful after TRMM…


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The 13 MSG neural networks inputs

• IR Temperature indicator:• 10.8 µm• IR multichannel indicators:• 10.8 µm - 6.2 µm• 10.8 µm - 7.3 µm• 10.8 µm - 8.7 µm• 10.8 µm - 9.7 µm• 10.8 µm - 12.0 µm• 10.8 µm - 13.4 µm• Temporal difference indicator:• 10.8 µm - previous 10.8 µm

• Local variance indicators:

• Variance 5x5 6.2 µm• Maximum 5x5 6.2 µm• Variance 5x5 10.8 µm• Maximum 5x5 10.8 µm• Geographic indicators:• Altitude derived from SRTM

data

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IPWG, M. Desbois, AMMA, Melbourne, October 20061 satellite rainfall estimation and validation in the frame of AMMA (the African Monsoon Multidisciplinary