1. IPWG, M. Desbois, AMMA, Melbourne, October 20061 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

2. IPWG, M. Desbois, AMMA, Melbourne, October 20062 Special observations and developments for AMMA AMMA 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.

3. IPWG, M. Desbois, AMMA, Melbourne, October 20063 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

4. IPWG, M. Desbois, AMMA, Melbourne, October 20064 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

5. IPWG, M. Desbois, AMMA, Melbourne, October 20065 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.

6. IPWG, M. Desbois, AMMA, Melbourne, October 20066 Equation 2 EPSAT-SG scheme (Estimation des Pluies par SATellite – Seconde Génération) Equation 1 Neural Network MSG IR Channels SRTM Digital Elevation Model Rainfall probability images (P r ) GPCP1dd rainfall images (I gpcp ) Potential rainfall intensity images (I p ) EPSAT-SG rainfall estimation (I e ) A is a disc of about 125kms radius c A is the centre of A d is the considered day T is the period [d-15days,d+15days] dt 1 corresponds to 1 day dt 2 corresponds to 15 minute da corresponds to 1 MSG pixel Equation 1 Equation 2 Estimated Rainfall Intensity at time t during day d and position a: Final product resolutions: Space resolution : 3 kms Time resolution : 15 minutes 2A25 TRMM precipitation Radar data

7. IPWG, M. Desbois, AMMA, Melbourne, October 20067 Collocation between Pr image (2A25 TRMM data - red- ) and EPSAT-SG probability of rainfall -gray levels-

8. IPWG, M. Desbois, AMMA, Melbourne, October 20068 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

9. IPWG, M. Desbois, AMMA, Melbourne, October 20069 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

10. IPWG, M. Desbois, AMMA, Melbourne, October 200610 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

11. IPWG, M. Desbois, AMMA, Melbourne, October 200611 August 2004 Third Decade GPCP/krieged surface data EPSAT-SG/krieged surface data

12. IPWG, M. Desbois, AMMA, Melbourne, October 200612 1x1BIASRMSDWRMSDNRMSDR2R2 SKILL GPCP 1dd 100% (280) 7,7424,321,931,770,590,12 EPSAT 2-0 100% (280) 3,2319,021,361,340,640,51 GPCP 1dd 50% (140) 6,8519,182,142,080,620,08 EPSAT 2-0 50% (140) 3,0913,251,401,320,700,60 Ground 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.

13. IPWG, M. Desbois, AMMA, Melbourne, October 200613 Smaller time scales EPSAT-SG and validation data for the square degrees of Niger and Benin, for different time resolutions Time resolution NigerBenin 15’ 0.49 0.35 1h 0.54 0.47 6h 0.710.72 24h 0.790.77 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 R 2 of the time series, for different accumulation times

14. IPWG, M. Desbois, AMMA, Melbourne, October 200614 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

15. IPWG, M. Desbois, AMMA, Melbourne, October 200615 Application to 2006 : Examples of decadal estimates of rainfall, preliminary operational version of EPSAT-SG >250 mm >150 mm 80 mm 60 mm

16. IPWG, M. Desbois, AMMA, Melbourne, October 200616 Application to 2006 : Examples of decadal estimates of rainfall, CPC product 200 mm >250 mm 80 mm <100 mm

17. IPWG, M. Desbois, AMMA, Melbourne, October 200617 Application to 2006 : Examples of decadal estimates of rainfall, EPSAT-SG provisional product 1-10 July 11-20 July 21-31 July 1-10 August 11-20 August 21-31 August

18. IPWG, M. Desbois, AMMA, Melbourne, October 200618 Latitude-time Hovmöller of the 2006 African Monsoon, at two different longitudes (2°E Niamey, 7.5°W Bamako)

19. IPWG, M. Desbois, AMMA, Melbourne, October 200619 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 scales It allows users to integrate at the space-time scales of interest for them Further validations and intercomparisons will take place with the full AMMA surface validation network operating in 2006 The 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…

20. IPWG, M. Desbois, AMMA, Melbourne, October 200620 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