Conditioning Tropical Rainfall Measuring Mission (TRMM) data using ground based rainfall data.

Abstract

Rainfall helps to structure society in a geographical sense, thus correct capturing of rainfall data and recording is very importantant in ensuring that water resources planners have information that can be used to make informed decisions concerning agriculture and water provision to people, the environment and other industries.With a loss in a number of old reliable rainfall gauges, implementation of new gauges and also missing data, there is a need to evaluate other options like Tropical Rainfall Measuring Mission (TRMM) data. The tropics play an important role in the global hydrological cycle, and tropical rainfall is the critical component of this role. TRMM provides systematic, multi-year, visible, infrared, and microwave estimates of rainfall in the tropics and subtropics as key inputs to weather and climate research. The TRMM satellite orbited around the Earth and it was not sun synchronous. The TRMM science team developed a range of gridded rainfall products; the product used for this research was 3B42RT which is a similar rainfall product to 3B42. Furthermore, TRMM data was selected at the same locations with intent to have the ground based gauge stations measurements compared with TRMM satellite derived precipitation pixel value at the same site. The data considered was from March 1st, 2000 to February 28th, 2010. In the 10 year period, the analysis was for the daily, pentads, monthly and annual data comparisons. The different methods applied for analysing and comparing TRMM and Block Averaged Gauge Data (BAGD) datasets were linear regression, standardization, cross validation and the introduction of quantilequantile (Q-Q) transform methods. Considering the high variability in time and space of rainfall and that the gauges used to measure BADG are at times sparse, TRMM had a high potential to estimate precipitation relatively accurately over large areas. TRMM pixel values can be used to get information on an area that does not have gauges or is poorly gauged. The research findings indicate that it is likely that TRMM data will be useful for large-scale hydrology and agriculture, particularly at the monthly scale, in contrast with daily.