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Building the foundations for a physi...

Ringerud, Sarah.

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Building the foundations for a physically based passive microwave precipitation retrieval algorithm over the US Southern Great Plains.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Building the foundations for a physically based passive microwave precipitation retrieval algorithm over the US Southern Great Plains./
Author:	Ringerud, Sarah.
Description:	114 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-10(E), Section: B.
Contained By:	Dissertation Abstracts International76-10B(E).
Subject:	Atmospheric sciences. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3706396
ISBN:	9781321801934

Building the foundations for a physically based passive microwave precipitation retrieval algorithm over the US Southern Great Plains.
Ringerud, Sarah.

Building the foundations for a physically based passive microwave precipitation retrieval algorithm over the US Southern Great Plains. - 114 p.

Source: Dissertation Abstracts International, Volume: 76-10(E), Section: B.

Thesis (Ph.D.)--Colorado State University, 2015.

The recently launched NASA Global Precipitation Measurement Mission (GPM) offers the opportunity for a greatly increased understanding of global rainfall and the hydrologic cycle. The GPM algorithm team has made improvements in passive microwave remote sensing of precipitation over land a priority for this mission, and implemented a framework allowing for algorithm advancement for individual land surface types as new techniques are developed. In contrast to the radiometrically cold ocean surface, land emissivity in the microwave is large with highly dynamic variability. An accurate understanding of the instantaneous, dynamic emissivity in terms of the associated surface properties is necessary for a physically based retrieval scheme over land, along with realistic profiles of frozen and liquid hydrometeors. In an effort to better simulate land surface microwave emissivity, a combined modeling technique is developed and tested over the US Southern Great Plains (SGP) area. The National Centers for Environmental Prediction (NCEP) Noah land surface model is utilized for surface information, with inputs optimized for SGP. A physical emissivity model, using land surface model data as input, is used to calculate emissivity at the 10 GHz frequency, combining contributions from the underlying soil and vegetation layers, including the dielectric and roughness effects of each medium. An empirical technique is then applied, based upon a robust set of observed channel covariances, extending the emissivity calculations to all channels. The resulting emissivities can then be implemented in calculation of upwelling microwave radiance, and combined with ancillary datasets to compute brightness temperatures (Tbs) at the top of the atmosphere (TOA). For calculation of the hydrometeor contribution, reflectivity profiles from the Tropical Rainfall Measurement Mission Precipitation Radar (TRMM-PR) are utilized along with coincident Tbs from the TRMM radiometer (TMI), and cloud resolving model data from NASA-Goddard's MMF model. Ice profiles are modified to be consistent with the higher frequency microwave Tbs. Resulting modeled TOA Tbs show correlations to observations of 0.9 along with biases 1K or less and small RMS error and show improved agreement over the use of climatological emissivity values. The synthesis of the emissivity and cloud resolving model input with satellite and ancillary datasets leads to creation of a unique Tb database for SGP that includes both dynamic surface and atmospheric information physically consistent with the LSM, emissivity model, and atmospheric information, for use in a Bayesian-type precipitation retrieval scheme utilizing a technique that can easily be applied to GPM as data becomes available.

ISBN: 9781321801934Subjects--Topical Terms:

3168354
Atmospheric sciences.

Building the foundations for a physically based passive microwave precipitation retrieval algorithm over the US Southern Great Plains.
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520 $a The recently launched NASA Global Precipitation Measurement Mission (GPM) offers the opportunity for a greatly increased understanding of global rainfall and the hydrologic cycle. The GPM algorithm team has made improvements in passive microwave remote sensing of precipitation over land a priority for this mission, and implemented a framework allowing for algorithm advancement for individual land surface types as new techniques are developed. In contrast to the radiometrically cold ocean surface, land emissivity in the microwave is large with highly dynamic variability. An accurate understanding of the instantaneous, dynamic emissivity in terms of the associated surface properties is necessary for a physically based retrieval scheme over land, along with realistic profiles of frozen and liquid hydrometeors. In an effort to better simulate land surface microwave emissivity, a combined modeling technique is developed and tested over the US Southern Great Plains (SGP) area. The National Centers for Environmental Prediction (NCEP) Noah land surface model is utilized for surface information, with inputs optimized for SGP. A physical emissivity model, using land surface model data as input, is used to calculate emissivity at the 10 GHz frequency, combining contributions from the underlying soil and vegetation layers, including the dielectric and roughness effects of each medium. An empirical technique is then applied, based upon a robust set of observed channel covariances, extending the emissivity calculations to all channels. The resulting emissivities can then be implemented in calculation of upwelling microwave radiance, and combined with ancillary datasets to compute brightness temperatures (Tbs) at the top of the atmosphere (TOA). For calculation of the hydrometeor contribution, reflectivity profiles from the Tropical Rainfall Measurement Mission Precipitation Radar (TRMM-PR) are utilized along with coincident Tbs from the TRMM radiometer (TMI), and cloud resolving model data from NASA-Goddard's MMF model. Ice profiles are modified to be consistent with the higher frequency microwave Tbs. Resulting modeled TOA Tbs show correlations to observations of 0.9 along with biases 1K or less and small RMS error and show improved agreement over the use of climatological emissivity values. The synthesis of the emissivity and cloud resolving model input with satellite and ancillary datasets leads to creation of a unique Tb database for SGP that includes both dynamic surface and atmospheric information physically consistent with the LSM, emissivity model, and atmospheric information, for use in a Bayesian-type precipitation retrieval scheme utilizing a technique that can easily be applied to GPM as data becomes available.
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