LMD/IPSL 1 Ahmedabad Megha-Tropique Meeting October 2005 Combination of MSG and TRMM for precipitation estimation over Africa (AMMA project experience) Franck Chopin & Jean Claude Bergès

LMD/IPSL 2 Ahmedabad Megha-Tropique Meeting October 2005 Data and Experimentation area Input dataset:  Full flow of MSG/HRI (from 6.2 to 13.4)  TRMM/PR from 3G68Land  GPCP 1 degree daily rainfall Validation dataset :  Dense raingauges network (IRD & AGRHYMET supplied) Experimentation Area :  A window on West-Africa from May to October 2004 (25W to 25E and 5S to 20N) This is the AMMA West Africa Region

LMD/IPSL 3 Ahmedabad Megha-Tropique Meeting October 2005 Research Products Rainfall Probability images Potential Intensity images Rainfall Estimations images

LMD/IPSL 4 Ahmedabad Megha-Tropique Meeting October 2005 Figure 1 : Feed Forward network Representation Neural Network Design and Rainfall Probability

LMD/IPSL 5 Ahmedabad Megha-Tropique Meeting October 2005 The 15 MSG neural networks inputs Temperature indicator: IR_10.8 IR multichannel indicators: IR_ WV_06.2 IR_ WV_07.3 IR_ IR_8.7 IR_ IR_9.7 IR_ IR_12.0 IR_ IR_13.4 Temporal difference indicator: IR_ IR_10.8-prev. Local variance indicators: Var. 5x5 WV_06.2 Max. 5x5 WV_06.2 Var. 5x5 IR_10.8 Max. 5x5 IR_10.8 Geographic indicators: Solar zenithal angle Solar azimuthal angle Altitude derived from SRTM data

LMD/IPSL 6 Ahmedabad Megha-Tropique Meeting October 2005 Validation of the Rainfall Probability Results Different methods have been considered : Infrared Temperature Threshold : The proportion of rainy cases according to TRMM 2A25 product has been estimated. To minimize the bias, the threshold value used has been selected in order to contain the same proportion of rainy cases. Probability Matching : This method estimates the rainfall probability for each infrared temperature threshold. Feed Forward Neural Network : This is the proposed method with two different iteration number (10 and 1000) during the learning step. The mean of absolute value biases on different partition classes during the 2000 rainy season from June to September has been evaluated for the Meteosat-7 algorithm version. Four partitions are considered : day, half an hour (slot), 1 degree longitude and 1 degree latitude.

LMD/IPSL 7 Ahmedabad Megha-Tropique Meeting October 2005 Validation of the Rainfall Probability Results Figure 2 : Mean of absolute value biases on four partition classes for four procedures

LMD/IPSL 8 Ahmedabad Megha-Tropique Meeting October 2005 Collocation between Pr image and 2A25 TRMM data

LMD/IPSL 9 Ahmedabad Megha-Tropique Meeting October 2005 Rainfall Probability Product description Space Resolution : 3km Time Resolution : 15 minutes Methodological error during the learning phase : 17% Will be soon validated against surface products at different space and time scales

LMD/IPSL 10 Ahmedabad Megha-Tropique Meeting October 2005 From Rainfall Probability to Estimated Rainfall Intensity To produce estimated rainfall intensities, a probability matching formula (1) is here applied. The estimated rainfall intensity I e is the product between the corresponding rainfall probability image P r, assessed by the feed forward neural network described above, and a potential rainfall intensity I p. (1) We still have to compute the potential rainfall intensity images

LMD/IPSL 11 Ahmedabad Megha-Tropique Meeting October 2005 Rainfall Probability images Potential Intensity images Rainfall Estimations images Research products

LMD/IPSL 12 Ahmedabad Megha-Tropique Meeting October 2005 Reference Dataset and Downscaling Formula : To compute a potential rainfall intensity I p image, a reference rainfall intensity I r dataset is necessary. It has to be quoted that this estimator is more directly related to rainfall but does not allow a follow up of phenomena as fine as geostationary satellite images. It has been decided to use the 1°x 1° grid synthesis daily GPCP data.

LMD/IPSL 13 Ahmedabad Megha-Tropique Meeting October 2005 Downscaling Formula : (2) This step allows to evaluate the potential rainfall intensity in a cell grid area A for a given period T. From the relation (1), we define a downscaling step in time and space equation (2). (1)

LMD/IPSL 14 Ahmedabad Megha-Tropique Meeting October 2005 Examples of Potential Intensity Image Potential rainfall intensity 16 th May 2004 mm/h

LMD/IPSL 15 Ahmedabad Megha-Tropique Meeting October 2005 Examples of Potential Intensity Image Potential rainfall intensity 15 th June 2004 mm/h

LMD/IPSL 16 Ahmedabad Megha-Tropique Meeting October 2005 Examples of Potential Intensity Image Potential rainfall intensity 15 th July 2004 mm/h

LMD/IPSL 17 Ahmedabad Megha-Tropique Meeting October 2005 Examples of Potential Intensity Image Potential rainfall intensity 15 th August 2004 mm/h

LMD/IPSL 18 Ahmedabad Megha-Tropique Meeting October 2005 Examples of Potential Intensity Image Potential rainfall intensity 15 th September 2004 mm/h

LMD/IPSL 19 Ahmedabad Megha-Tropique Meeting October 2005 Examples of Potential Intensity Image Potential Rainfall Intensity 15 th October 2004 mm/h

LMD/IPSL 20 Ahmedabad Megha-Tropique Meeting October 2005 Link Between Elevation and Potential Intensity (1)

LMD/IPSL 21 Ahmedabad Megha-Tropique Meeting October 2005 Link Between Relief and Potential Intensity (2) mm/h Co-localisation between elevation and Potential Rainfall Intensity image 15 th of August 2004

LMD/IPSL 22 Ahmedabad Megha-Tropique Meeting October 2005 Potential Intensity Product Description Space Resolution : 3km Time Resolution : 1 Day Remark : The short time high rainfalls can’t be retrieved thanks to this product.

LMD/IPSL 23 Ahmedabad Megha-Tropique Meeting October 2005 Rainfall Probability images Potential Intensity images Rainfall Estimations images Research products

LMD/IPSL 24 Ahmedabad Megha-Tropique Meeting October 2005 Estimated Rainfall Intensity Once the potential rainfall intensity I p images are computed, the estimated rainfall intensity at time t during day d and position a can be calculated with the formula (3) : (3) Let P r (a,d) be the cumulative rainfall probability during day d and position a. The estimated rainfall accumulation during a period T can be easily computed with the equation (4) : (4)

LMD/IPSL 25 Ahmedabad Megha-Tropique Meeting October 2005 Example of Rainfall Estimation Image (1) mm Rainfall Accumulation during the month of July 2004

LMD/IPSL 26 Ahmedabad Megha-Tropique Meeting October 2005 Example of Rainfall Estimation Image (2) mm Rainfall Accumulation from 1 st to 10 th of July 2004 And co-localisation with West African Elevation

LMD/IPSL 27 Ahmedabad Megha-Tropique Meeting October 2005 Rainfall Estimation Product Description Space Resolution : 3km Time Resolution : 15 minutes Remarks : –This product has to be integrated in time and space in order to reduce the bias of the estimations. –The time and space resolutions provided allows to integrate this product very easily (in a watershed or from 6am to 6am the day after for example) All this processing chain has been called Sliding Rescaling Algorithm (SRA)

LMD/IPSL 28 Ahmedabad Megha-Tropique Meeting October 2005 Validation (1) Validation krigged data : Provided by IRD and AGRHYMET Space resolutions : 0.5, 1 and 2.5 degrees Time resolution : ten days periods The validation datasets have been provided with an estimate of its uncertainty ε for each grid cell

LMD/IPSL 29 Ahmedabad Megha-Tropique Meeting October 2005 Validation (2) 1° x 1° grid rainfall accumulation during the third decade of August 2004 from the IRD raingauge dataset Estimated rainfall accumulation during the third decade of August 2004

LMD/IPSL 30 Ahmedabad Megha-Tropique Meeting October 2005 Validation (2) 1° x 1° grid rainfall accumulation during the third decade of August 2004 from the IRD raingauge dataset 1° x 1° Grid estimated rainfall accumulation during the third decade of August 2004

LMD/IPSL 31 Ahmedabad Megha-Tropique Meeting October x1BIASRMSDWRMSDNRMSDR²SKILL GPCP 1dd 100% (280) 7,7424,321,931,770,590,12 SRA % (280) 3,2319,021,361,340,640,51 GPCP 1dd 50% (140) 6,8519,182,142,080,620,08 SRA % (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 SRA Two studies have been done :

LMD/IPSL 32 Ahmedabad Megha-Tropique Meeting October x1BIASRMSDWRMSDNRMSDR²SKILL GPCP 1dd 100% (280) 7,7424,321,931,770,590,12 SRA % (280) 3,2319,021,361,340,640,51 GPCP 1dd 50% (140) 6,8519,182,142,080,620,08 SRA % (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 SRA Two studies have been done : First one considers all the grid validation cells

LMD/IPSL 33 Ahmedabad Megha-Tropique Meeting October x1BIASRMSDWRMSDNRMSDR²SKILL GPCP 1dd 100% (280) 7,7424,321,931,770,590,12 SRA % (280) 3,2319,021,361,340,640,51 GPCP 1dd 50% (140) 6,8519,182,142,080,620,08 SRA % (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 SRA Two studies have been done : First one considers all the grid validation cells Second one takes into account the 50% grid validation cells with the lowest validation uncertainty

LMD/IPSL 34 Ahmedabad Megha-Tropique Meeting October 2005 Full decades validation with the 0.5 space resolution Here, all the ten days periods of the 2004 rainy season are considered as one unique dataset. This represents a grid of cells. The SRA underestimates the most important rainfall accumulations We can notice a good symmetry in term of bias on this dataset

LMD/IPSL 35 Ahmedabad Megha-Tropique Meeting October 2005 Class Validation Contingency table of the degree grid cells (%)

LMD/IPSL 36 Ahmedabad Megha-Tropique Meeting October 2005 Class Validation The intra-classes proportion are quite similar between reference dataset and SRA estimates Contingency table of the degree grid cells (%)

LMD/IPSL 37 Ahmedabad Megha-Tropique Meeting October 2005 Class Validation The intra-classes proportion are quite similar between reference dataset and SRA estimates Around 70% of data are on the diagonal. Contingency table of the degree grid cells (%)

LMD/IPSL 38 Ahmedabad Megha-Tropique Meeting October 2005 Class Validation The intra-classes proportion are quite similar between reference dataset and SRA estimates Around 70% of data are on the diagonal. Around 98% of data are on the three-diagonal. Contingency table of the degree grid cells (%)

LMD/IPSL 39 Ahmedabad Megha-Tropique Meeting October 2005 Class Validation The intra-classes proportion are quite similar between reference dataset and SRA estimates Around 70% of data are on the diagonal. Around 98% of data are on the three-diagonal. That is another demonstration of the good coherence between validation and estimated data. Contingency table of the degree grid cells (%)

LMD/IPSL 40 Ahmedabad Megha-Tropique Meeting October 2005 Conclusion Splitting estimation algorithm in two steps avoids introduction of local correction parameters to match with high rainfall intensity patterns. Downscaling rainfall on geostationary resolution allows to match with any final product grid size or any watershed for hydrological models. This method has been validated for the decades rainfall accumulation during the 2004 rainy season and seems to be better than GPCP on our dataset in particular in term of bias. This algorithm has to be considered as a way to improve the reference dataset (here GPCP) tacking into account the rainfall probability for each pixel and for every slot of the studied period.

LMD/IPSL 41 Ahmedabad Megha-Tropique Meeting October 2005 THANK YOU