AbstractProminent achievements made in addressing global precipitation using satellite passive microwave retrievals are often overshadowed by their performance at finer spatial and temporal scales, where large variability in cloud morphology poses an obstacle for accurate precipitation measurements. This is especially true over land, with precipitation estimates being based on an observed mean relationship between high frequency (e.g. 89 GHz) brightness temperature depression (i.e. the ice-scattering signature) and surface precipitation rate. This indirect relationship between the observed (brightness temperatures) and state (precipitation) vectors often leads to inaccurate estimates, with more pronounced biases (e.g., -30% over the US) observed during the extreme events. This study seeks to mitigate these errors by employing previously established relationships between cloud structures and large-scale environments such as CAPE, wind shear, humidity distribution and aerosol concentrations to form a stronger relationship between the precipitation and scattering signal. The GPM passive microwave operational precipitation retrieval (GPROF) for the GMI sensor is modified to offer additional information on atmospheric conditions to its Bayesian-based algorithm. The modified algorithm is allowed to use large-scale environment to filter out a priori states that do not match the general synoptic condition relevant to the observation and thus reduce the difference between the assumed and observed variability in ice-to-rain ratio. Using the ground Multi-Radar Multi-Sensor (MRMS) network over the US, the results demonstrate outstanding potentials in improving the accuracy of heavy precipitation over land. It is found that individual synoptic parameters can remove 20-30% of existing bias and up to 50% when combined, while preserving the overall performance of the algorithm.
Journal of Hydrometeorology – American Meteorological Society
Published: Oct 4, 2017
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