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The primary signal used in all current passive microwave precipitation retrieval algorithms over land is the depression of the instantaneous brightness temperature (TB) caused by ice scattering. This study presents a new methodology to retrieve instantaneous precipitation rate over land by using TB temporal variation (TB) at 19GHz, which primarily reflects the surface emissivity variation due to the precipitation impact. As a proof-of-concept, we exploit observations from five polar-orbiting satellites over the Southern Great Plains of the United States. Results show that TB at 19GHz correlate well with the instantaneous precipitation rate. Further analysis shows that TB at 19GHz is better correlated with the precipitation rate when multiple satellite observations are used due to the much shorter revisit time for a certain location. The retrieved instantaneous precipitation rate over Southern Great Plains from TB at 19GHz reasonably agrees with the surface radar observations, with the correlation, the root-mean-square error and the bias being 0.49, 2.39mm/hr, and 6.54%, respectively. Future work seeks to combine the ice scattering signal at high frequencies and this surface emissivity variation signal at low frequencies to achieve an optimal retrieval performance.

Plain Language Summary Current precipitation estimation technique via satellite passive microwave observations links the hydrometers in the air to the surface precipitation intensity. That is, the cold brightness temperature (TB) at high-frequency channels (e.g., 85GHz) indicates heavy precipitation. The TB observations from low-frequency channels such as 19GHz are largely discounted. This study presents a new idea to link the surface condition variation to the precipitation intensity, by using TB temporal variation (TB) at 19GHz from five polar-orbiting satellites. Results show that TB at 19GHz correlate well with the precipitation rate. The estimated instantaneous precipitation rate over the Southern Great Plains of United States from TB at 19GHz reasonably agrees with the ground radar observations, with the correlation, the root-mean-square error, and the bias at 0.49, 2.39mm/hr, and 6.54%, respectively.