Ciao,

have a look to this paper:

http://journals.ametsoc.org/doi/pdf/10.1175/BAMS-D-13-00164.1

... and the answers to this question:

https://www.researchgate.net/post/What_is_the_best_satellite_rainfall_product

Luca

I suggest you review Huffman et al (Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., ... & Stocker, E. F. (2007). The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of Hydrometeorology, 8(1), 38-55.).  

Thanks Brian is this reference available on RG.

Ciao,

have a look to this paper:

http://journals.ametsoc.org/doi/pdf/10.1175/BAMS-D-13-00164.1

... and the answers to this question:

https://www.researchgate.net/post/What_is_the_best_satellite_rainfall_product

Luca

TRMM's 0.25-deg resolution is too coarse to capture localised convective storms (

Alternatively, you can use CFSR: http://globalweather.tamu.edu/

A new satellite-derived rainfall dataset, obtained from soil moisture data, is also available on my website and on RG (see attached publication). If you need higher spatial resolution for a given area, we are available to process the data.

Ciao,

Luca

Article Soil as a natural rain gauge: Estimating global rainfall fro...

Any precipitation data set will have problems, I suggest you study each of the offerings.

- TMPA is based on polar orbiting satellites, so is episodic.  Better for statistical climate studies than runoff models.

- CFSR is model based, and prediction of rainfall by models is not very good, missing the higher amplitude swings of extreme events while often misplacing lighter events.

- Satellite Soil moisture measurements are impacted by vegetation, changing not just with vegetation amount but vegetation hydration

- Rainfall estimates by geosychronous infrared satellite do not capture individual events well, but are good for monthly averages (and the best ones such as SCaMPR are good for daily average estimates).

- Microwave satellite estimates are episodic like TRMM, but many satellites can be joined together.  Unfortunately over land they rely on ice rain processes, so are not as reliable over land, especially in the Tropics or for orographically induced rain where warm rain processes may obtain.

- Blending algorithms such as Cmorph exist that can fill in the gaps between microwave satellites using infrared data to advect it, but you still are left with the weaknesses of the original microwave data.  SCaMPR recalibrates Infrared data based on Microwaves, but has the same problem.

- Rain gauges are often not available where you need them, and lose calibration in high wind

- radar, if available, may be the best, but is based on assumed calibration curves that may be off (especially for low rain) and liquid water detected at cloud level does not always correspond to liquid water hitting the ground.

So you see, you have a large platter of choices.  I suggest calibrating against the actual river flow to see which works best.  You may need a combination.  And carefully read about each rain retrieval algorithm, you can't just blindly trust any of them!!!

A bit late, but you might still be interested

an example of study in West Africa

I hope it helps

Conference Paper Prise en compte des variabilités spatio-temporelles de la pl...