An intercomparison of 10-day satellite precipitation products during West African monsoon

Abstract

International audienceIn the frame of the African Monsoon Multidisciplinary Analyses (AMMA) programme, a specific rainfall algorithm (EPSAT-SG; Estimation of Precipitation by SATellites ? Second Generation) was developed for the requirements of the scientific community and an intercomparison exercise was undertaken to assess the performance of various rainfall analyses to help users of satellite precipitation estimates to take into consideration the limitations of these products. The intercomparison exercise presented in this article includes three regional precipitation products as well as seven operational global products that are publicly available and easily accessible on websites. This study has been performed using validation data from rain gauge observations analyses on the Sahelian region provided by the AGRHYMET centre, for three rainy seasons from 2004 to 2006. The 10 different satellite-based precipitation products are verified against the same reference ground-based dataset of 10-day rainfall accumulations at the 0.5° × 0.5° latitude-longitude resolution. The performance of the different precipitation algorithms is assessed according to various indicators such as the behaviour of the precipitation distributions, several statistical parameters and spatial distribution of the errors. All the statistical results indicate that the three 'near-real-time' products (3B42-RT, CPC MORPHing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)) have a poorer performance than the other products considered for intercomparison. In fact these algorithms cannot make use of useful inputs such as rain gauge observations that are not available at near real time. It is noted that the simple basic Geostationary Operational Environmental Satellite (GOES) Precipitation Index (GPI) product performs better, with a higher skill score index. The three products Global Precipitation Climatology Project (GPCP)-1dd, Global Satellite Mapping of Precipitation (GSMaP)-MVK and Tropical RainfallMeasuring Mission (TRMM)- 3B42 obtain better statistical results but the best results are obtained by the precipitation products created specifically for thisAfrican region.TheEPSAT-SGproduct has the best performance according to several statistical criteria including skill score, coefficient of determination and rootmean square (RMS) errorwhereas theRainFall Estimation (RFE)-2.0 estimates offer the bestmatchwith validation estimates in term of distribution and bias. The Tropical Applications ofMeteorology using SATellite and other data (TAMSAT) estimates have also similar statistically good results

Comparison and evaluation of satellite derived precipitation products for hydrological modeling of the Zambezi River Basin

“…All the satellite products reach similar values, except for the volume ratio, for which CMORPH is overestimating the rainfall by about 40 %, TRMM is overestimating the rainfall by about 20 % and FEWS is close to 1. CMORPH's strong positive bias has already been documented for West Africa (Jobard et al, 2011). However, it seems to be more reliable over Ethiopia where it performs better than TRMM 3B42 and FEWS RFE2.0 at the 10-daily time step (Dinku et al, 2007) and underestimates the rainfall by 11 % at the daily time step (Romilly and Gebremichael, 2011).…”

“…CMORPH shows superior performance when compared to TRMM 3B42. The performance of seven operational global products, including TRMM 3B42, CMORPH, and FEWS RFE 2.0 was also evaluated during the West African monsoon at a 10-daily time step (Jobard et al, 2011). CMORPH exhibited the worst skills (strong positive bias), TRMM 3B42 displayed a moderate aptitude and FEWS RFE 2.0 the best performance in terms of distribution and bias.…”

The Use of Remotely Sensed Rainfall for Managing Drought Risk: A Case Study of Weather Index Insurance in Zambia

“…Given the heterogeneous nature of the African rainfall climate, CCD fields are regionally calibrated assuming a linear relationship between CCD and rainfall for each calendar month using historic gauge measurements, ensuring the resulting estimates reflect the expected local conditions [17][18][19]. The TAMSAT method has shown high levels of skill across Africa [8,10,35,[39][40][41][42].…”

Woody plant cover estimation in drylands from Earth Observation based seasonal metrics

“…The delineation (Fig. 2) is derived from African Rainfall Climatology Version 2 (1982-2013) satellite based rainfall data (Jobard, Chopin, Berges, & Roca, 2011). The rainy season is directly linked to the West African Monsoon with a length of 1-4 months, an annual peak in precipitation in August (Barbé & Lebel, 1997), and an increasing rate of annual rainfall along the north-south gradient with approximately 1-2 mm per km (Le Houérou, 1980).…”