Assimilation of satellite-derived cloud cover into the Regional Atmospheric Model System (RAMS) and its impacts on modeled surface fields

Persistent Link:
http://hdl.handle.net/10150/279909
Title:
Assimilation of satellite-derived cloud cover into the Regional Atmospheric Model System (RAMS) and its impacts on modeled surface fields
Author:
Yucel, Ismail
Issue Date:
2001
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:
The goal of this study is to provide an improved, high resolution, regional diagnosis of three important surface variables on the land surface energy and water balance, namely the downward short-wave and downward long-wave surface radiation fluxes, and precipitation. Cloud cover is a key parameter linking and controlling these three terms. An automatic procedure was developed to derive high-resolution (4 km x 4 km) fields of fractional cloud cover from visible band, (GOES series) geostationary satellite data using a novel tracking procedure to determine the clear-sky composite image. In our initial data assimilation studies, the surface short-wave radiation fluxes calculated by RAMS were simply replaced by the equivalent estimated values obtained by applying this high-resolution satellite-derived cloud cover in the UMD GEWEX/SRB model. However, this initial study revealed problems associated with inconsistencies between the revised solar radiation fields and the RAMS-calculated incoming long-wave radiation and precipitation fields, because modeled cloud cover remained unchanged and, consequently, these other surface fields retained their low, clear-sky values. It was recognized that the UMD GEWEX/SRB model provides an important relationship between cloud albedo, cloud optical depth and cloud water/ice. Thus, exploration was made of feasibility of directly assimilating vertically integrated cloud water/ice fields to update modeled cloud cover. This approach will not only enhance the realism of radiation scheme in RAMS, but it may also dramatically increase the model's capability to predict the location of precipitation, thus enhancing the ability of such mesoscale modeling systems to make accurate short-term forecasts of precipitation. This, in turn, would benefit flood forecasting as an associate hydrologic response. In the method adopted, the assimilated image takes the horizontal distribution of cloud from the satellite image but it retains a vertical distribution which is the area-average simulated by RAMS across the modeled domain in the time step immediately prior to cloud assimilation. Cloud assimilation is made every minute, with linear interpolation applied to derive cloud images for each minute between two GOES samples. Comparisons were made between modeled and observed data taken from the AZMET weather station network for model runs with and without cloud assimilation to demonstrate the improvement in RAMS' ability to describe surface radiation and precipitation fields. Cloud assimilation was found to substantially improve the RAMS model's ability to capture both the temporal and spatial variations in surface fields associated with observed cloud cover. The sensitivity of these comparisons to model initiation was explored by making five ensemble runs starting from different initiation. In general, RAMS with cloud assimilation technique is not sensitive to realistic perturbation of initial conditions.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Hydrology.; Physics, Atmospheric Science.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Hydrology and Water Resources
Degree Grantor:
University of Arizona
Advisor:
Shuttleworth, W. James

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleAssimilation of satellite-derived cloud cover into the Regional Atmospheric Model System (RAMS) and its impacts on modeled surface fieldsen_US
dc.creatorYucel, Ismailen_US
dc.contributor.authorYucel, Ismailen_US
dc.date.issued2001en_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.abstractThe goal of this study is to provide an improved, high resolution, regional diagnosis of three important surface variables on the land surface energy and water balance, namely the downward short-wave and downward long-wave surface radiation fluxes, and precipitation. Cloud cover is a key parameter linking and controlling these three terms. An automatic procedure was developed to derive high-resolution (4 km x 4 km) fields of fractional cloud cover from visible band, (GOES series) geostationary satellite data using a novel tracking procedure to determine the clear-sky composite image. In our initial data assimilation studies, the surface short-wave radiation fluxes calculated by RAMS were simply replaced by the equivalent estimated values obtained by applying this high-resolution satellite-derived cloud cover in the UMD GEWEX/SRB model. However, this initial study revealed problems associated with inconsistencies between the revised solar radiation fields and the RAMS-calculated incoming long-wave radiation and precipitation fields, because modeled cloud cover remained unchanged and, consequently, these other surface fields retained their low, clear-sky values. It was recognized that the UMD GEWEX/SRB model provides an important relationship between cloud albedo, cloud optical depth and cloud water/ice. Thus, exploration was made of feasibility of directly assimilating vertically integrated cloud water/ice fields to update modeled cloud cover. This approach will not only enhance the realism of radiation scheme in RAMS, but it may also dramatically increase the model's capability to predict the location of precipitation, thus enhancing the ability of such mesoscale modeling systems to make accurate short-term forecasts of precipitation. This, in turn, would benefit flood forecasting as an associate hydrologic response. In the method adopted, the assimilated image takes the horizontal distribution of cloud from the satellite image but it retains a vertical distribution which is the area-average simulated by RAMS across the modeled domain in the time step immediately prior to cloud assimilation. Cloud assimilation is made every minute, with linear interpolation applied to derive cloud images for each minute between two GOES samples. Comparisons were made between modeled and observed data taken from the AZMET weather station network for model runs with and without cloud assimilation to demonstrate the improvement in RAMS' ability to describe surface radiation and precipitation fields. Cloud assimilation was found to substantially improve the RAMS model's ability to capture both the temporal and spatial variations in surface fields associated with observed cloud cover. The sensitivity of these comparisons to model initiation was explored by making five ensemble runs starting from different initiation. In general, RAMS with cloud assimilation technique is not sensitive to realistic perturbation of initial conditions.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectHydrology.en_US
dc.subjectPhysics, Atmospheric Science.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.advisorShuttleworth, W. Jamesen_US
dc.identifier.proquest3002537en_US
dc.identifier.bibrecord.b41431972en_US
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