Nine satellite rainfall estimations (SREs) were evaluated for the first time over the South American Andean plateau watershed by comparison with rain gauge data acquired between 2005 and 2007. The comparisons were carried out at the annual, monthly and daily time steps. All SREs reproduce the salient pattern of the annual rain field, with a marked north–south gradient and a lighter east–west gradient. However, the intensity of the gradient differs among SREs: it is well marked in the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 (TMPA-3B42), Precipitation Estimation from remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Global Satellite Mapping of Precipitation (GSMaP) products, and it is smoothed out in the Climate prediction center MORPHing (CMORPH) products. Another interesting difference among products is the contrast in rainfall amounts between the water surfaces (Lake Titicaca) and the surrounding land. Some products (TMPA-3B42, PERSIANN and GSMaP) show a contradictory rainfall deficit over Lake Titicaca, which may be due to the emissivity contrast between the lake and the surrounding lands and warm rain cloud processes. An analysis differentiating coastal Lake Titicaca from inland pixels confirmed this trend. The raw or Real Time (RT) products have strong biases over the study region. These biases are strongly positive for PERSIANN (above 90%), moderately positive for TMPA-3B42 (28%), strongly negative for CMORPH (− 42%) and moderately negative for GSMaP (− 18%). The biases are associated with a deformation of the rain rate frequency distribution: GSMaP underestimates the proportion of rainfall events for all rain rates; CMORPH overestimates the proportion of rain rates below 2 mm day − 1 ; and the other products tend to overestimate the proportion of moderate to high rain rates. These biases are greatly reduced by the gauge adjustment in the TMPA-3B42, PERSIANN and CMORPH products, whereas a negative bias becomes positive for GSMaP. TMPA-3B42 Adjusted (Adj) version 7 demonstrates the best overall agreement with gauges in terms of correlation, rain rate distribution and bias. However, PERSIANN-Adj's bias in the southern part of the domain is very low.
Bibliographical noteFunding Information:
This work was supported by the Centre National d'Etudes Spatiales (CNES) in the framework of the HASM project (Hydrology of Altiplano: from Spatial to Modeling). The first author is grateful to the SENAMHI and IHH for the release of their rainfall information and to the IRD (Institut de Recherche pour le Développement) and CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) Brazil for their financial support. The authors are also thankful for the two anonymous reviewers for their constructive reviews that enhanced this work.
The Servicios Nacional de Meteorología e Hidrología from Bolivia and Peru (SENAMHI) are in charge of the meteorological network. Apart from SENAMHI, the Instituto de Hidrología y Hidráulica (IHH) of the University of San Andres in La Paz, supported by the Swedish International Development Cooperation (SIDA) operates some meteorological stations around Lake Poopó and the Institut de Recherche pour le développement (IRD) around Lakes Poopó and Titicaca. SENAMHI from both Bolivia and Peru, IHH and IRD information is available on a daily basis. A total of 176 stations are available from the 1960 to 2012 periods, but the number has considerably decreased between the 1960s and the present. For the 2005–2007 period and after quality control, 59 stations were available for this study.
The GSMaP project is sponsored by the Japan Science and Technology (JST) agency under the Core Research for Evolutional Science and Technology (CREST) framework. GSMaP activities are promoted by the Japan Aerospace Exploration Agency (JAXA) Precipitation Measuring Mission (PMM) science team. GSMaP uses a combination of PMW and IR sensors ( Table 1 ) ( GSMaP, 2012 ). The PMW sensors used include TMI, AMSR-E, SSM/I and SSMIS on board the DMSP satellite; AMSU-A/-B on board the NOAA satellite; and MHS on board MetOp satellites. The algorithms used to retrieve the rainfall rate from PMWs utilize brightness temperature and are based on Aonashi and Liu (2000) . Over the ocean, the algorithm developed by Shige et al. (2009) is used. For more information, please refer to GSMaP (2012) . IR data from GEO satellites (MTSAT; meteosat-7/-8; GOES-11/-12) merged with a 4 km spatial resolution are used to increase the temporal and spatial resolution. To do so, a Kalman filter refines PMW rainfall estimation propagation by using the atmospheric moving vector derived from two successive IR images ( Ushio et al., 2009 ).
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- Andean plateau
- Satellite rainfall estimation