Search Results
paradigm. In the other case, data are available to constrain only some of the variables, and the remaining ones are constrained using the model’s ability to simulate them to be compatible with the time evolution of available observations. Methods such as 4D-Var (e.g., Talagrand 1997 ) are designed to handle such situations. For mesoscale forecasting, the second scenario generally applies: balloon soundings and other measurements of all model fields throughout the atmosphere are sparse in space and
paradigm. In the other case, data are available to constrain only some of the variables, and the remaining ones are constrained using the model’s ability to simulate them to be compatible with the time evolution of available observations. Methods such as 4D-Var (e.g., Talagrand 1997 ) are designed to handle such situations. For mesoscale forecasting, the second scenario generally applies: balloon soundings and other measurements of all model fields throughout the atmosphere are sparse in space and
paper is the first in a series of papers that provides a unified and comprehensive description of the modeling technology, the challenges associated with the operational use of the forecasting system, and the scientific insights gained by its use. The U.S. Army test ranges are typically located where there is strong local forcing from complex orography or coastlines, resulting in myriad mesoscale processes ( Rife et al. 2002 ). These processes include coastal breezes; orographic effects such as
paper is the first in a series of papers that provides a unified and comprehensive description of the modeling technology, the challenges associated with the operational use of the forecasting system, and the scientific insights gained by its use. The U.S. Army test ranges are typically located where there is strong local forcing from complex orography or coastlines, resulting in myriad mesoscale processes ( Rife et al. 2002 ). These processes include coastal breezes; orographic effects such as
with the expected LWC already calculated as part of the CIP and FIP icing severity algorithms ( Bernstein et al. 2006b ; Wolff et al. 2009 ). Diagnoses and short-term forecasts of icing, SLD, and mesoscale variability thereof, can be further enhanced through expanding the use of the satellite-, radar-, surface-, and model-based features described above. Acknowledgments The authors would like to express our sincere thanks to the crews of the NASA-Glenn Twin Otter, the NRC Convair-580, and the
with the expected LWC already calculated as part of the CIP and FIP icing severity algorithms ( Bernstein et al. 2006b ; Wolff et al. 2009 ). Diagnoses and short-term forecasts of icing, SLD, and mesoscale variability thereof, can be further enhanced through expanding the use of the satellite-, radar-, surface-, and model-based features described above. Acknowledgments The authors would like to express our sincere thanks to the crews of the NASA-Glenn Twin Otter, the NRC Convair-580, and the
; Xue et al. 2010 ; Johnson et al. 2011 ; Johnson and Wang 2012 , 2013 ). CAPS typically extracted perturbations from the operational Short Range Ensemble Forecast (SREF; Du et al. 2009 ) system and added them to a high-resolution control [such as the North American Mesoscale Forecast System (NAM; Rogers et al. 2009 ) analysis interpolated onto the 4-km domain] to produce their initial high-resolution ensembles ( Kong et al. 2008 , 2009 ). Elsewhere, approaches for producing high
; Xue et al. 2010 ; Johnson et al. 2011 ; Johnson and Wang 2012 , 2013 ). CAPS typically extracted perturbations from the operational Short Range Ensemble Forecast (SREF; Du et al. 2009 ) system and added them to a high-resolution control [such as the North American Mesoscale Forecast System (NAM; Rogers et al. 2009 ) analysis interpolated onto the 4-km domain] to produce their initial high-resolution ensembles ( Kong et al. 2008 , 2009 ). Elsewhere, approaches for producing high
1. Introduction Part I of this series of papers ( Liu et al. 2008a ) provides an overview of an operational mesogamma-scale forecast model, called the Real-Time Four-Dimensional Data Assimilation (RTFDDA) system, that is in use at the U.S. Army Test and Evaluation Command (ATEC) test ranges. The forecast component of RTFDDA is based on the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5; Grell et al. 1995 ). The forecast model
1. Introduction Part I of this series of papers ( Liu et al. 2008a ) provides an overview of an operational mesogamma-scale forecast model, called the Real-Time Four-Dimensional Data Assimilation (RTFDDA) system, that is in use at the U.S. Army Test and Evaluation Command (ATEC) test ranges. The forecast component of RTFDDA is based on the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5; Grell et al. 1995 ). The forecast model
convective processes may not be analyzed properly by ensemble-based assimilation systems, nor predicted adequately by high-resolution ensembles, if this model uncertainty is not represented in the assimilation and forecast system. However, diversity in physical parameterizations can also complicate the interpretation of the forecasts, and can complicate quantitative methods such as ensemble-based synoptic and mesoscale analysis (e.g., Hakim and Torn 2008 ), as those methods assume that all ensemble
convective processes may not be analyzed properly by ensemble-based assimilation systems, nor predicted adequately by high-resolution ensembles, if this model uncertainty is not represented in the assimilation and forecast system. However, diversity in physical parameterizations can also complicate the interpretation of the forecasts, and can complicate quantitative methods such as ensemble-based synoptic and mesoscale analysis (e.g., Hakim and Torn 2008 ), as those methods assume that all ensemble
reconfigured to support daily weather forecasts in China for the event as part of the Research Demonstration Project (hereafter referred as SREF-B08RDP) under the auspices of the World Weather Research Program (WWRP) of the World Meteorological Organization (WMO). Taking advantage of this SREF-B08RDP project, a fog prediction scheme was quantitatively and objectively verified using this mesoscale ensemble data over eastern China to fulfill three goals. The first goal is to examine the effectiveness of a
reconfigured to support daily weather forecasts in China for the event as part of the Research Demonstration Project (hereafter referred as SREF-B08RDP) under the auspices of the World Weather Research Program (WWRP) of the World Meteorological Organization (WMO). Taking advantage of this SREF-B08RDP project, a fog prediction scheme was quantitatively and objectively verified using this mesoscale ensemble data over eastern China to fulfill three goals. The first goal is to examine the effectiveness of a
improving ensemble analyses and forecasts for rapid environmental assessment for the U.S. Navy. The system has been extensively tested with real observations from conventional meteorological networks and meteorological satellites. In particular, assimilation experiments have been designed and performed to investigate how the TES technique performs with conventional meteorological observations and satellite data on the synoptic scale, mesoscale, and storm-allowing scale. This paper reports the results
improving ensemble analyses and forecasts for rapid environmental assessment for the U.S. Navy. The system has been extensively tested with real observations from conventional meteorological networks and meteorological satellites. In particular, assimilation experiments have been designed and performed to investigate how the TES technique performs with conventional meteorological observations and satellite data on the synoptic scale, mesoscale, and storm-allowing scale. This paper reports the results
temporal features in gridded fields. To demonstrate it, we apply our approach to wind at 10 m (AGL), as simulated by a mesoscale NWP model. Variations in 10-m winds over minutes to hours are of great concern to many who rely on weather forecasts. For example, at the test ranges operated by the U.S. Army Test and Evaluation Command (ATEC), who sponsored this work, accurate forecasts of near-surface wind are critical for planning and conducting transport-and-dispersion experiments, precision airdrop
temporal features in gridded fields. To demonstrate it, we apply our approach to wind at 10 m (AGL), as simulated by a mesoscale NWP model. Variations in 10-m winds over minutes to hours are of great concern to many who rely on weather forecasts. For example, at the test ranges operated by the U.S. Army Test and Evaluation Command (ATEC), who sponsored this work, accurate forecasts of near-surface wind are critical for planning and conducting transport-and-dispersion experiments, precision airdrop
206 WEATHER AND FORECASTING VOLUME IGrants ATM-8407142 and 8600543.An Experiment in Mesoscale Weather Forecas~ng in the Michigan Area DENNIS G. BAKERDepartment of Atmospheric & Oceanic Science, University of Michigan, Ann Arbor, MI 48109(Manuscript received 23 July 1986, in final form 25 September 1986)ABSTRACT During an experiment in mesoscale weather forecasting in the Michigan area, consensus
206 WEATHER AND FORECASTING VOLUME IGrants ATM-8407142 and 8600543.An Experiment in Mesoscale Weather Forecas~ng in the Michigan Area DENNIS G. BAKERDepartment of Atmospheric & Oceanic Science, University of Michigan, Ann Arbor, MI 48109(Manuscript received 23 July 1986, in final form 25 September 1986)ABSTRACT During an experiment in mesoscale weather forecasting in the Michigan area, consensus
