Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds
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http://dataverse.jpl.nasa.gov/citation?persistentId=doi:10.48577/jpl.TBWKGP
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The profile of the liquid water content (LWC) in clouds provides fundamental information for understanding the internal structure of clouds, their radiative effects, propensity to precipitate, and degree of entrainment and mixing with the surrounding environment. In principle, differential absorption techniques based on coincident dual-frequency radar reflectivity (DFR) observations have the potential to provide the LWC profile. Previous DFR efforts were challenged by the fact that the measurable differential attenuation for small quantities of LWC is usually comparable to the system measurement error. This typically renders the retrieval impractical, as the uncertainty can become many times greater than the retrieved value itself. Theoretically, this drawback can be mitigated following two interconnected approaches: (1) increasing the frequency separation between the dual-frequency radar system to measure greater differential attenuation and (2) increasing the radar operating frequency to reduce the instrument measurement random error. Our recently developed 239 GHz radar was deployed at the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) along with a variety of collocated remote-sensing and in-situ instruments. We have combined Ka-band (35 GHz) and G-band (239 GHz) observations to retrieve the LWC from more than 100 vertical profiles of shallow clouds with typical amounts of LWC smaller than 1 gm-3 20 . We theoretically and experimentally demonstrate that the Ka-band and G-band pair of frequencies offers at least a 65% relative improvement in the LWC retrieval sensitivity compared to previous works reported in the literature using lower-frequency radars. This new technique provides a missing capability to determine the LWC in the challenging low liquid water path (LWP) range (< 200 gm-2 ) and suggests a way forward to characterize microphysical and dynamical processes more precisely in shallow clouds.
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创建时间:
2024-11-18



