Stochastic Filtering of Rain Pro les using Radar, Surface-Referenced Radar, or Combined Radar/Radiometer Measurements
نویسندگان
چکیده
This paper describes a computationally e cient nearly-optimal Bayesian algorithm to estimate rain (and drop-size-distribution) pro les, given a radar re ectivity pro le at a single attenuating wavelength. In addition to estimating the averages of all the mutually ambiguous combinations of rain parameters that can produce the data observed, the approach also calculates the r.m.s. uncertainty in its estimates (this uncertainty thus quanti es the \amount of ambiguity" in the \solution"). The paper also describes a more general approach that can make estimates based on a radar re ectivity pro le together with an approximate measurement of the path-integrated attenuation, or a radar re ectivity pro le and a set of passive microwave brightness temperatures. This more general \combined" algorithm is currently being adapted for the Tropical Rainfall Measuring Mission.
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