International audienceThe India-France SARAL/AltiKa mission is the first Ka-band altimetric mission dedi-cated to oceanography. The mission objectives are primarily the observation of the oceanic mesoscales but also include coastal oceanography, global and regional sea level monitoring, data assimilation, and operational oceanography. Secondary objectives include ice sheet and inland waters monitoring. One year after launch, the results widely confirm the nominal expectations in terms of accuracy, data quality and data availability in general.Today's performances are compliant with specifications with an overall observed performance for the Sea Surface Height RMS of 3.4 cm to be compared to a 4 cm requirement. Some scientific examples are provided that illustrate some salient features of today's SARAL/AltiKa data with regard to standard altimetry: data availability, data accuracy at the mesoscales, data usefulness in costal area, over ice sheet, and for inland waters

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This paper presents the validation of monthly precipitation using Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA)-3B43 product with conventional rain gauge observations for the period 1998-2007 over Kyrgyzstan. This study is carried out to quantify the accuracy of TMPA-3B43 product over the high latitude and complex orographic region. The present work is quite important because it is highly desirable to compare the TMPA precipitation product with the ground truth data at a regional scale, so that the satellite product can be fine-tuned at that scale. For the validation, four different types of spatial collocation have been used: station wise, climatic zone wise, topographically and seasonal. The analysis has been done at the same spatial and temporal scales in order to eliminate the sampling biases in the comparisons. The results show that TMPA-3B43 product has statistically significant correlation (r=0.36-0.88) with rain gauge data over the most parts of the country. The minimum linear correlation is observed around the large continental water bodies (e.g., Issyk-Kul lake; r=0.17-0.19). The overall result suggests that the precipitation estimated using TMPA-3B43 product performs reasonably well over the plain regions and even over the orographic regions except near the big lake regions. Also, the negative bias suggests the systematic underestimation of high precipitation by TMPA-3B43 product. The analyses suggest the need of a better algorithm for precipitation estimation over this region separately to capture the different types of rain events more reliably.

[1] In the present study, an attempt was made to estimate rainfall by synergistically analyzing collocated thermal infrared (TIR) brightness temperatures from Meteosat along with rainfall estimates from active microwave precipitation radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) over Indian land and oceanic regions. In this study, we used broad and frequent TIR measurements from a geostationary satellite for rainfall estimation, calibrating them with sparse but more accurate PR rain rates. To make the algorithm robust, we used a two-step procedure. First, a cloud classification scheme was applied to TIR measurements using the 6.7 mm water vapor channel and TIR radiances to delineate the rain-bearing clouds. Next, the concurrent TIR and PR observations were used to establish a regression relation between them. The relationship thus established was used to estimate rainfall from TIR measurements by applying it to rain-producing systems during southwest and northeast monsoons and tropical cyclones. Comparisons were made with TRMM-merged (3B42 V6) data, Global Precipitation Climatology Project (GPCP) monthly rain rate data, ground-based rain gauge observations from automatic weather stations, and Doppler weather radar over India. The results from combined infrared and microwave sensors were in very good agreement with the ground-based measurements, TRMM-3B42 V6, as well as GPCP.Citation: Mishra, A., R. M. Gairola, A. K. Varma, and V. K. Agarwal (2010), Remote sensing of precipitation over Indian land and oceanic regions by synergistic use of multisatellite sensors,