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Interpreting millimeter‐wave radiances over tropical convective clouds
Author(s) -
Haddad Z. S.,
Sawaya R. C.,
Kacimi S.,
Sy O. O.,
Turk F. J.,
Steward J.
Publication year - 2017
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2016jd025923
Subject(s) - radiometer , remote sensing , environmental science , brightness temperature , millimeter , radar , wavelength , meteorology , brightness , physics , geology , computer science , optics , telecommunications
Attempts to interpret the measurements of millimeter‐wave radiometers over tropical storms must overcome the difficulty of modeling the scattering signatures of hydrometeors at these frequencies. Most approaches to date try to retrieve surface precipitation, to which the observations are not directly sensitive. In fact, millimeter wavelengths are most sensitive to the scattering from solid hydrometeors within the upper levels of the cloud. Millimeter‐wavelength radiometers have a definite advantage over the lower frequency radiometers in that they have finer spatial resolution to resolve deep convection. Preliminary analyses summarized here indicate that the measurements are indeed sensitive to the depth and intensity of convection. The challenge is to derive a robust approach to make quantitative estimates of the characteristics of the convection directly from the observations, and conversely to derive a robust forward representation of the dependence of the radiances on the underlying moisture fields, to enable effective data assimilation. This is accomplished using a two‐step semiempirical approach: first, nearly simultaneous coincident observations by millimeter‐wave radiometers and orbiting atmospheric profiling radars are used to enforce unbiased consistency between modeled brightness temperatures and radar and radiometer observations; second, the departure from the first‐step mean empirical relations are explained in terms of the moisture variables, using cloud‐resolving simulations with different microphysical schemes, including an original microphysical representation that proves to be more consistent with remote sensing observations than existing schemes. The results are a retrieval approach and a forward representation that are unbiased by construction, with uncertainties quantified by the corresponding conditional variances.