LMD/IPSL 1 Ahmedabad Megha-Tropique Meeting 17-20 October 2005 Combination of MSG and TRMM for precipitation estimation over Africa (AMMA project experience)

LMD/IPSL 1 Ahmedabad Megha-Tropique Meeting 17-20 October 2005

Combination of MSG and TRMM for precipitation estimation over Africa (AMMA

project experience)

Franck Chopin & Jean Claude Bergès


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


Research Products

• Rainfall Probability images

• Potential Intensity images

• Rainfall Estimations images


Figure 1 : Feed Forward network Representation

Neural Network Design and Rainfall Probability


The 15 MSG neural networks inputs

• Temperature indicator:• IR_10.8• IR multichannel indicators:• IR_10.8 - WV_06.2• IR_10.8 - WV_07.3• IR_10.8 - IR_8.7• IR_10.8 - IR_9.7• IR_10.8 - IR_12.0• IR_10.8 - IR_13.4• Temporal difference

indicator:• IR_10.8 - 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


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.


Validation of the Rainfall Probability Results

Mean of absolute value biases on four partition classes for four procedures

0

0,005

0,01

0,015

0,02

0,025

0,03

Day Slot 1° Longitude 1° Latitude

partition classes

mea

n o

f ab

solu

te v

alu

e b

iase

s

Infrared Threshold

Probability Matching

Neural Network (10)

Neural Network (1000)

Figure 2 : Mean of absolute value biases on four partition classes for four procedures


Collocation between Pr image and 2A25 TRMM data


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


From Rainfall Probability to Estimated Rainfall Intensity

To produce estimated rainfall intensities, a probability matching formula (1) is here applied. The estimated rainfall intensity Ie is the product between the corresponding rainfall probability image Pr , assessed by the feed forward neural network described above, and a potential rainfall intensity Ip.

(1)rpe PII

We still have to compute the potential rainfall intensity images





Research products


Reference Dataset and Downscaling Formula :

To compute a potential rainfall intensity Ip image, a reference rainfall intensity Ir 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.


Downscaling Formula :

AT

rp

AT

r dadttaPTAIdadttaI ,,, (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)rpr PII


Examples of Potential Intensity Image

Potential rainfall intensity 16th May 2004 mm/h



Potential rainfall intensity 15th June 2004 mm/h



Potential rainfall intensity 15th July 2004 mm/h



Potential rainfall intensity 15th August 2004 mm/h



Potential rainfall intensity 15th September 2004 mm/h



Potential Rainfall Intensity 15th October 2004 mm/h


Link Between Elevation and Potential Intensity (1)


Link Between Relief and Potential Intensity (2)

mm/hCo-localisation between elevation and Potential Rainfall Intensity image

15th of August 2004


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.





Research products


Estimated Rainfall Intensity

Once the potential rainfall intensity Ip images are computed, the estimated rainfall intensity at time t during day d and position a can be calculated with the formula (3) :

taPdaItaI rpe ,,, (3)

Let Pr(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) :

Td

rpe daPdaITaI ,,, (4)


Example of Rainfall Estimation Image (1)

mm

Rainfall Accumulation during the month of July 2004


Example of Rainfall Estimation Image (2)

mm

Rainfall Accumulation from 1st to 10th of July 2004And co-localisation with West African Elevation


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)


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


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


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


1x1 BIAS RMSD WRMSD NRMSD R² SKILL

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

SRA 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

SRA 2-0 50% (140) 3,09 13,25 1,40 1,32 0,70 0,60

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 :



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

SRA 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

SRA 2-0 50% (140) 3,09 13,25 1,40 1,32 0,70 0,60



Two studies have been done :• First one considers all the grid validation cells



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

SRA 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

SRA 2-0 50% (140) 3,09 13,25 1,40 1,32 0,70 0,60



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


Full decades validation with the 0.5 space resolution

0,5x0,5 BIAS RMSD WRMSD NRMSD R² SKILL

SRA 2-0 100% (14400) 0,54 20,49 1,17 1,20 0,67 0,66SRA 2-0 50% (7200) 0,30 12,47 1,20 1,23 0,70 0,67SRA 2-0 10% (1440) 0,31 5,51 1,05 1,12 0,74 0,70

Here, all the ten days periods of the 2004 rainy season are considered as one unique dataset. This represents a grid of 14400 cells.The SRA underestimates the most important rainfall accumulations

We can notice a good symmetry in term of bias on this dataset


Class Validation

TOTAL 44,20 26,76 24,23 3,54 1,27 100,00150 + 0,00 0,00 0,01 0,00 0,11 0,12

100-150 0,00 0,11 2,85 0,90 0,67 4,5350-100 0,87 7,16 16,77 2,47 0,48 27,7525-50 5,86 14,53 4,17 0,17 0,01 24,740-25 37,47 4,96 0,43 0,00 0,00 42,86

SRA/SOL(mm) 0-25 25-50 50-100 100-150 150 + TOTAL

Contingency table of the 14400 0.5 degree grid cells (%)


Class Validation

TOTAL 44,20 26,76 24,23 3,54 1,27 100,00150 + 0,00 0,00 0,01 0,00 0,11 0,12

100-150 0,00 0,11 2,85 0,90 0,67 4,5350-100 0,87 7,16 16,77 2,47 0,48 27,7525-50 5,86 14,53 4,17 0,17 0,01 24,740-25 37,47 4,96 0,43 0,00 0,00 42,86

SRA/SOL(mm) 0-25 25-50 50-100 100-150 150 + TOTAL

• The intra-classes proportion are quite similar between reference dataset and SRA estimates



Class Validation

TOTAL 44,20 26,76 24,23 3,54 1,27 100,00150 + 0,00 0,00 0,01 0,00 0,11 0,12

100-150 0,00 0,11 2,85 0,90 0,67 4,5350-100 0,87 7,16 16,77 2,47 0,48 27,7525-50 5,86 14,53 4,17 0,17 0,01 24,740-25 37,47 4,96 0,43 0,00 0,00 42,86

SRA/SOL(mm) 0-25 25-50 50-100 100-150 150 + TOTAL


• Around 70% of data are on the diagonal.



Class Validation

TOTAL 44,20 26,76 24,23 3,54 1,27 100,00150 + 0,00 0,00 0,01 0,00 0,11 0,12

100-150 0,00 0,11 2,85 0,90 0,67 4,5350-100 0,87 7,16 16,77 2,47 0,48 27,7525-50 5,86 14,53 4,17 0,17 0,01 24,740-25 37,47 4,96 0,43 0,00 0,00 42,86

SRA/SOL(mm) 0-25 25-50 50-100 100-150 150 + TOTAL



• Around 98% of data are on the three-diagonal.



Class Validation



• Around 98% of data are on the three-diagonal.

• That is another demonstration of the good coherence between validation and estimated data.


TOTAL 44,20 26,76 24,23 3,54 1,27 100,00150 + 0,00 0,00 0,01 0,00 0,11 0,12

100-150 0,00 0,11 2,85 0,90 0,67 4,5350-100 0,87 7,16 16,77 2,47 0,48 27,7525-50 5,86 14,53 4,17 0,17 0,01 24,740-25 37,47 4,96 0,43 0,00 0,00 42,86

SRA/SOL(mm) 0-25 25-50 50-100 100-150 150 + TOTAL


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.


THANK YOU

Documents

LMD/IPSL 1 Ahmedabad Megha-Tropique Meeting 17-20 October 2005 Combination of MSG and TRMM for precipitation estimation over Africa (AMMA project experience)