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Ismail Yucel

Abstract

This study implements a new land-cover classification and surface albedo from the Moderate Resolution Imaging Spectroradiometer (MODIS) in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and investigates its effects on regional near-surface atmospheric state variables as well as the planetary boundary layer evolution for two dissimilar U.S. regions. Surface parameter datasets are determined by translating the 17-category MODIS classes into the U.S. Geological Survey (USGS) and Simple Biosphere (SiB) categories available for use in MM5. Changes in land-cover specification or associated parameters affected surface wind, temperature, and humidity fields, which, in turn, resulted in perceivable alterations in the evolving structure of the planetary boundary layer. Inclusion of the MODIS albedo into the simulations enhanced these impacts further. Area-averaged comparisons with ground measurements showed remarkable improvements in near-surface temperature and humidity at both study areas when MM5 is initialized with MODIS land-cover and albedo data. Influence of both MODIS surface datasets is more significant at a semiarid location in the southwest of the United States than it is in a humid location in the mid-Atlantic region. Intense summertime surface heating at the semiarid location creates favorable conditions for strong land surface forcing. For example, when the simulations include MODIS land cover and MODIS albedo, respective error reduction rates were 6% and 11% in temperature and 2% and 2.5% in humidity in the southwest of the United States. Error reduction rates in near-surface atmospheric fields are considered important in the design of mesoscale weather simulations.

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Ismail Yucel, W. James Shuttleworth, James Washburne, and Fei Chen

Abstract

Data derived at the National Centers for Environmental Prediction via four-dimensional data assimilation using the Eta Model were evaluated against surface observations from two observational arrays, one located in the semihumid, continental climate of Oklahoma and Kansas and the second in the semiarid climate of southern Arizona. Comparison was made for the period of the Global Energy Water-cycle Experiment Continental-scale International Project’s “GIST” dataset in 1994 and their “ESOP-95” dataset in 1995, and for the months of March and May in 1996. Coding errors in the Eta Model’s postprocessor used to diagnose near-surface temperature and humidity are shown to have compromised the GIST and ESOP-95 near-surface data. A procedure was devised to correct the GIST and ESOP-95 near-surface fields by mimicking the corrected code used in the Eta Model since January 1996. Comparison with observations revealed that modeled surface solar radiation is significantly overestimated except in clear-sky conditions. This discrepancy in cloudy-sky solar radiation was altered little by the substantial January 1996 revisions to Eta Model physics, but the revisions are shown to have greatly improved the model’s ability to capture daily and seasonal variations in near-surface air temperature, specific humidity, and wind speed. The poorly modeled surface radiation complicates evaluation of modeled surface energy fluxes, but comparison with observations suggests that the modeled daytime Bowen ratio may be systematically high. This study clearly demonstrates the strong sensitivity of model-calculated, near-surface variables to the physics used to describe surface interactions in the data assimilation model. To mitigate against this and to aid intercomparisons between other data, it is recommended that model-derived data always include sufficient information to allow potential users to recalculate the extrapolation to the surface using a user-defined model of surface–atmosphere exchanges.

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Ismail Yucel, W. James Shuttleworth, X. Gao, and S. Sorooshian

Abstract

This study investigates the extent to which assimilating high-resolution remotely sensed cloud cover into the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) provides an improved regional diagnosis of downward shortwave surface radiation fluxes and precipitation and enhances the model's ability to make short-range prediction. The high-resolution (4 km × 4 km) clear- and cloudy-sky radiances derived using a cloud-screening algorithm from visible band Geostationary Operational Environmental Satellite (GOES) data were used in the University of Maryland Global Energy and Water Cycle Experiment's Surface Radiation Budget (UMD GEWEX/SRB) model to infer the vertically integrated cloud mass via cloud optical thickness. Three-dimensional cloud fields were created that took their horizontal distribution from the satellite image but derived their vertical distribution, in part, from the fields simulated by MM5 during the time step immediately prior to assimilation and, in part, from the observed cloud-top height derived from the infrared band of GOES. Linear interpolation was used to derive 1-min cloud images between 15-min GOES samples, and the resulting images were ingested every minute. Comparisons were made between modeled and observed data taken from the Arizona Meteorological Network (AZMET) in southern Arizona for model runs with and without cloud ingestion. Cloud ingestion substantially improved the ability of the MM5 model to capture temporal and spatial variations in surface fields associated with cloud cover. Experiments in which the model was operated in forecast mode suggest that cloud ingestion gave some limited enhancement in MM5 short-term prediction ability for up to 3 h. However, an analysis suggests that, in order to get additional forecasting capability, it will be necessary to modify the atmospheric dynamics and thermodynamics in the model to be consistent with the ingested cloud fields.

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Ismail Yucel, W. James Shuttleworth, R. T. Pinker, L. Lu, and S. Sorooshian

Abstract

This study investigates the extent to which assimilating high-resolution remotely sensed cloud cover into the Regional Atmospheric Modeling System (RAMS) provides an improved regional diagnosis of downward short- and longwave surface radiation fluxes and precipitation. An automatic procedure was developed to derive high-resolution (4 km × 4 km) fields of fractional cloud cover from visible band Geostationary Operational Environmental Satellite (GOES) data using a tracking procedure to determine the clear-sky composite image. Initial studies, in which RAMS surface shortwave radiation fluxes were replaced by estimates obtained by applying satellite-derived cloud cover in the University of Maryland Global Energy and Water Cycle Experiment's Surface Radiation Budget (UMD GEWEX/SRB) model, revealed problems associated with inconsistencies between the revised solar radiation fields and the RAMS-calculated incoming longwave radiation and precipitation fields. Consequently, in this study, the relationship between cloud albedo, optical depth, and water/ice content used in the UMD GEWEX/SRB model was applied instead to provide estimates of whole-column cloud water/ice that were ingested into RAMS. This potentially enhances the realism of the modeled short- and longwave radiation and precipitation. The ingested cloud image took the horizontal distribution of clouds from the satellite image but derives its vertical distribution from the fields simulated by RAMS in the time step immediately prior to assimilation. The resulting image was ingested every minute, with linear interpolation used to derive the 1-min cloud images between 15-min GOES samples. Comparisons were made between modeled and observed data taken from the Arizona Meteorological Network (AZMET) weather station network in southern Arizona for model runs with and without cloud ingestion. Cloud ingestion was found to substantially improve the ability of the RAMS model to capture temporal and spatial variations in surface fields associated with cloud cover. An initial test suggests that cloud ingestion enhanced RAMS short-term forecast ability.

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