An evaluation and regional error modeling methodology for near-real-time satellite rainfall data over Australia

Abstract

In providing uniform spatial coverage, satellite‐based rainfall estimates can potentially benefit hydrological modeling, particularly for flood prediction. Maximizing the value of information from such data requires knowledge of its error. The most recent Tropical Rainfall Measuring Mission (TRMM) 3B42RT (TRMM‐RT) satellite product version 7 (v7) was used for examining evaluation procedures against in situ gauge data across mainland Australia at a daily time step, over a 9 year period. This provides insights into estimating uncertainty and informing quantitative error model development, with methodologies relevant to the recently operational Global Precipitation Measurement mission that builds upon the TRMM legacy. Important error characteristics highlighted for daily aggregated TRMM‐RT v7 include increasing (negative) bias and error variance with increasing daily gauge totals and more reliability at detecting larger gauge totals with a probability of detection of <0.5 for rainfall < ~3 mm/d. Additionally, pixel location within clusters of spatially contiguous TRMM‐RT v7 rainfall pixels (representing individual rain cloud masses) has predictive ability for false alarms. Differences between TRMM‐RT v7 and gauge data have increasing (positive) bias and error variance with increasing TRMM‐RT estimates. Difference errors binned within 10 mm/d increments of TRMM‐RT v7 estimates highlighted negatively skewed error distributions for all bins, suitably approximated by the generalized extreme value distribution. An error model based on this distribution enables bias correction and definition of quantitative uncertainty bounds, which are expected to be valuable for hydrological modeling and/or merging with other rainfall products. These error characteristics are also an important benchmark for assessing if/how future satellite rainfall products have improved.

The Assessment and Comparison of TMPA and IMERG Products Over the Major Basins of Mainland China

“…From Figure 4, it is observed a high proportion (approximately 60% of SPE's detections) of sever overestimation for all five SPEs, but the components of sever overestimation are different. In other words, the SPE usually overestimate the light rain but underestimate the medium-to-heavy rain (>20 mm/day) (Pipunic et al, 2015;Yong et al, 2010). With regard to severe underestimation, for all five SPEs, the fraction of it is relatively low (approximately 17% of SPE's detections).…”

Evaluation of TRMM Product for Monitoring Drought in the Kelantan River Basin, Malaysia

“…Water 2017, 9, 57 6 of 15 contrast, the poor performance of the TMPA-3B43 in the dry season could be attributed to poor detection capability in light precipitation (less than 3 mm/day) [46]. Larger RMSE values were found during the wet season (e.g., SON and DJF) because of the scaling up effect of the precipitation rate from an hourly to monthly scale.…”

Improving the Regional Applicability of Satellite Precipitation Products by Ensemble Algorithm

“…The most widely used products include the Climate Prediction Center morphing technique (CMORPH) [1], the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT)'s Multi-sensor Precipitation Estimate (MPE) [2], the European Centre for Medium-Range Weather Forecasts (ECMWF)'s Era-Interim product [3], the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), the PERSIANN Cloud Classification System estimation (PERSIANN-CCS) [4], the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis products (TMPA) versions 6 (3B42-V6) and 7 (3B42-V7) [5,6] and the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) product [7]. Numerous studies have been made to evaluate the performance of these precipitation satellite products on the regional and global scale (e.g., Asia [8][9][10][11], North and Central America [12][13][14][15][16], South America [17][18][19][20], Europe [21][22][23][24], Australia [25][26][27][28], Oceans [29,30], other [31][32][33][34][35][36] and in Pakistan [37][38][39][40][41]...…”