Assimilation of satellite-derived precipitation into the regional atmospheric model system (RAMS) and its impacts on the weather and hydrology in the southwest United States

Persistent Link:
http://hdl.handle.net/10150/280227
Title:
Assimilation of satellite-derived precipitation into the regional atmospheric model system (RAMS) and its impacts on the weather and hydrology in the southwest United States
Author:
Yi, Han
Issue Date:
2002
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
This dissertation examines the improvement in predicting weather and hydrology in the southwestern United States by assimilating satellite-derived precipitation estimates into a numerical mesoscale model. For this investigation the Regional Atmospheric Model System (RAMS) was used and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) were assimilated into the RAMS' own land surface scheme; Land Ecosystem Atmosphere Feedback model version 2 (LEAF-2). The simulations were conducted for periods of 36 hours--12 hours of initialization and 24 hours of prediction (from July 8th 0000 UTC to 9th 1200 UTC 1999). The control run underpredicted precipitation over southwestern Arizona and showed an excessive precipitation pattern over northeastern Arizona. This precipitation bias was also responsible for biases in surface fluxes such as soil moisture and evapotranspiration. With a realistic surface shortwave radiation adjustment and the improvement of atmospheric state variables within the central model domains during the assimilation period, there was a slight enhancement for near surface temperature and moisture. However, RAMS still performed poorly and improved only marginally for precipitation prediction. The impact of the assimilation of PERSIANN precipitation estimates on soil moisture was significant however, and this accordingly improved the 2m-high temperature and relative humidity. The general pattern of precipitation showed improvement but was still inaccurate the location and intensity of precipitation. To investigate the soil moisture-precipitation feedback mechanism, RAMS simulations were performed with varying initial soil moisture saturation rates starting from a completely dry condition of 0% to a fully saturated condition of 100%. Analysis showed that with less than 20% of initial soil moisture saturation, more than 70% of the water that precipitated into the analysis domain was due to the indirect effect of soil moisture. This explains in part why initial soil moisture improvements for the southwestern United States during the summer had a limited impact on the prediction of precipitation. Finally, model simulations were performed and analyzed to demonstrate the sensitivity of vegetation parameters in RAMS on land surface and near-surface atmospheric variables in the southwestern United States.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Geophysics.; Hydrology.; Physics, Atmospheric Science.; Remote Sensing.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Hydrology and Water Resources
Degree Grantor:
University of Arizona
Advisor:
Sorooshian, Soroosh

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleAssimilation of satellite-derived precipitation into the regional atmospheric model system (RAMS) and its impacts on the weather and hydrology in the southwest United Statesen_US
dc.creatorYi, Hanen_US
dc.contributor.authorYi, Hanen_US
dc.date.issued2002en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThis dissertation examines the improvement in predicting weather and hydrology in the southwestern United States by assimilating satellite-derived precipitation estimates into a numerical mesoscale model. For this investigation the Regional Atmospheric Model System (RAMS) was used and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) were assimilated into the RAMS' own land surface scheme; Land Ecosystem Atmosphere Feedback model version 2 (LEAF-2). The simulations were conducted for periods of 36 hours--12 hours of initialization and 24 hours of prediction (from July 8th 0000 UTC to 9th 1200 UTC 1999). The control run underpredicted precipitation over southwestern Arizona and showed an excessive precipitation pattern over northeastern Arizona. This precipitation bias was also responsible for biases in surface fluxes such as soil moisture and evapotranspiration. With a realistic surface shortwave radiation adjustment and the improvement of atmospheric state variables within the central model domains during the assimilation period, there was a slight enhancement for near surface temperature and moisture. However, RAMS still performed poorly and improved only marginally for precipitation prediction. The impact of the assimilation of PERSIANN precipitation estimates on soil moisture was significant however, and this accordingly improved the 2m-high temperature and relative humidity. The general pattern of precipitation showed improvement but was still inaccurate the location and intensity of precipitation. To investigate the soil moisture-precipitation feedback mechanism, RAMS simulations were performed with varying initial soil moisture saturation rates starting from a completely dry condition of 0% to a fully saturated condition of 100%. Analysis showed that with less than 20% of initial soil moisture saturation, more than 70% of the water that precipitated into the analysis domain was due to the indirect effect of soil moisture. This explains in part why initial soil moisture improvements for the southwestern United States during the summer had a limited impact on the prediction of precipitation. Finally, model simulations were performed and analyzed to demonstrate the sensitivity of vegetation parameters in RAMS on land surface and near-surface atmospheric variables in the southwestern United States.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectGeophysics.en_US
dc.subjectHydrology.en_US
dc.subjectPhysics, Atmospheric Science.en_US
dc.subjectRemote Sensing.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineHydrology and Water Resourcesen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorSorooshian, Sorooshen_US
dc.identifier.proquest3073278en_US
dc.identifier.bibrecord.b43479273en_US
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