Intercomparison of Satellite Precipitation with Gauge Data Using Point Frequency Analysis

Özcan O., Musaoğlu N.


  • Publication Type: Article / Article
  • Volume: 13 Issue: 2
  • Publication Date: 2017
  • Journal Indexes: Emerging Sources Citation Index (ESCI)
  • Istanbul Technical University Affiliated: Yes


Climate has a dynamic structure denoting perpetual variability in temporal and spatial scales. Depending on space and time, rainfall amount has the most variation of the components of the climate system. In this study, the remote sensing dataset Tropical Rainfall Measurement Mission (TRMM) product at the 3hour time scale has been validated with daily rain gauge measurements in order to characterize rainfall variability and to evaluate satellite rain estimates for agricultural and hydrological applications in the Southeastern Anatolia region. The precipitation retrievals from the TRMM satellite were compared with data from seven surface rain gauges within the period of 1998-2012. Spatiotemporal patterns through statistical analyses and regional frequency relationship were identified by fitting Generalized Extreme Value (GEV) rainfall distribution to the rainfall time series, and the fitting results were analyzed focusing on the behaviour of the shape parameter. In addition, spatial patterns and correlations of rainfall events across the study area were also analyzed by the calculation of the 90th, 95th and 99th percentiles. Furthermore, the recurrence intervals of large rainstorms have been identified for the rain gauge stations with the associated TRMM grid time series and spatial patterns in the study area have been evaluated. Thematic maps of the appropriate distribution function parameters were produced by performing the spatial evaluations of the designated regions with pixel-based point frequency analysis. Results indicate that there exist large discrepancies between rain gauge and TRMM data at mean rainfall values; however, least squares fits indicate reliable and quite linear correlation for the 90th, 95th and 99th percentiles (r(2)= 0.70, 0.77 and 0.75 respectively) and the annual maximum daily amount of precipitation (r(2)= 0.69). Recurrence intervals derived from rain gauge measurements for 10 to 40-year periods and a moving-window of 14-year intervals yielded similar results. Ultimately, the spatiotemporal pattern analysis of the computed extreme statistics is conducted using geographic information systems. Although rainfalls from each TRMM 3B42 grid cell are generally overestimated compared against rain gauge data, data compare well for stations that were located at approximately the mean elevation of the related TRMM 3B42 grids. The validated products can also be used as a framework for predicting the impact of hydrologic events in this area.