{'fraction_non_alphanumeric': 0.023069207622868605, 'fraction_numerical': 0.007522567703109328, 'mean_word_length': 6.0, 'pattern_counts': {'":': 0, '<': 2, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An evaluation and regional error modeling methodology for near‐real‐time satellite rainfall data over Australia

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.