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Re-Analysis ESMF Earth System Modeling Framework FAR False-alarm ratio GFS Global Forecast System GoC Gulf of California GOES Geostationary Operational Environmental Satellite GPM Global Precipitation Measurement GPS Global positioning system GSMaP Global Satellite Mapping of Precipitation HAS Department of Hydrology and Atmospheric Sciences IV Inverted trough JAXA Japan Aerospace Exploration Agency LT Local time MCS Mesoscale convective system NAM North American Mesoscale Forecast System NAME
Re-Analysis ESMF Earth System Modeling Framework FAR False-alarm ratio GFS Global Forecast System GoC Gulf of California GOES Geostationary Operational Environmental Satellite GPM Global Precipitation Measurement GPS Global positioning system GSMaP Global Satellite Mapping of Precipitation HAS Department of Hydrology and Atmospheric Sciences IV Inverted trough JAXA Japan Aerospace Exploration Agency LT Local time MCS Mesoscale convective system NAM North American Mesoscale Forecast System NAME
AUGUST 1992 BURK AND THOMPSON 925Airmass Modification over the Gulf of Mexico: Mesoscale Model and Airmass Transformation Model Forecasts STEPHEN D. BURK AND WILLIAM T. THOMPSONNaval Oceanographic and Atmospheric Research Laboratory, Atmospheric Directorate, Monterey, California(Manuscript received 8 April 1991, in final form 2 December 1991)ABSTRACT Several numerical models are
AUGUST 1992 BURK AND THOMPSON 925Airmass Modification over the Gulf of Mexico: Mesoscale Model and Airmass Transformation Model Forecasts STEPHEN D. BURK AND WILLIAM T. THOMPSONNaval Oceanographic and Atmospheric Research Laboratory, Atmospheric Directorate, Monterey, California(Manuscript received 8 April 1991, in final form 2 December 1991)ABSTRACT Several numerical models are
floats aliases mesoscale variability. It is generally accepted that such data have to be assimilated into dynamically based models in order to map ocean states, and also to physically interpret the observed variability. Many practical schemes for sequentially assimilating ocean data are variants of the Kalman filter. For example the ensemble Kalman filter (e.g., Evensen 2006 ) is being used to make 10-day forecasts of the North Atlantic and Arctic Ocean on a weekly schedule (more information is
floats aliases mesoscale variability. It is generally accepted that such data have to be assimilated into dynamically based models in order to map ocean states, and also to physically interpret the observed variability. Many practical schemes for sequentially assimilating ocean data are variants of the Kalman filter. For example the ensemble Kalman filter (e.g., Evensen 2006 ) is being used to make 10-day forecasts of the North Atlantic and Arctic Ocean on a weekly schedule (more information is
–1-h forecasts and SPC mesoscale analyses using VORTEX2 soundings . Wea. Forecasting , 27 , 667 – 683 , https://doi.org/10.1175/WAF-D-11-00096.1 . 10.1175/WAF-D-11-00096.1 Coniglio , M. C. , and M. D. Parker , 2020 : Insights into supercells and their environments from three decades of targeted radiosonde observations . Mon. Wea. Rev. , 148 , 4893 – 4915 , https://doi.org/10.1175/MWR-D-20-0105.1 . 10.1175/MWR-D-20-0105.1 Coniglio , M. C. , J. Correia Jr ., P. T. Marsh , and
–1-h forecasts and SPC mesoscale analyses using VORTEX2 soundings . Wea. Forecasting , 27 , 667 – 683 , https://doi.org/10.1175/WAF-D-11-00096.1 . 10.1175/WAF-D-11-00096.1 Coniglio , M. C. , and M. D. Parker , 2020 : Insights into supercells and their environments from three decades of targeted radiosonde observations . Mon. Wea. Rev. , 148 , 4893 – 4915 , https://doi.org/10.1175/MWR-D-20-0105.1 . 10.1175/MWR-D-20-0105.1 Coniglio , M. C. , J. Correia Jr ., P. T. Marsh , and
). In 2014, E-4DWX was extended to three more government test sites in the Great Basin of the United States: White Sands Missile Range (WSMR), New Mexico; Yuma Proving Ground (YPG), Arizona; and Electronic Proving Ground (EPG), Arizona. For brevity, this paper focuses on just DPG’s experience with E-4DWX. b. Testing and forecasting at DPG One of DPG’s primary missions is to test equipment that detects chemical and biological hazards. Such tests are very sensitive to mesoscale and microscale weather
). In 2014, E-4DWX was extended to three more government test sites in the Great Basin of the United States: White Sands Missile Range (WSMR), New Mexico; Yuma Proving Ground (YPG), Arizona; and Electronic Proving Ground (EPG), Arizona. For brevity, this paper focuses on just DPG’s experience with E-4DWX. b. Testing and forecasting at DPG One of DPG’s primary missions is to test equipment that detects chemical and biological hazards. Such tests are very sensitive to mesoscale and microscale weather
prediction (NWP) models, dedicated aerosol forecast models, or a combination of both to produce solar exposure forecasts on daily time scales. Breitkreuz et al. (2009) examined the capability of mesoscale forecasts to predict clear-sky conditions. Their studies focused on aerosol chemical transport, which is a significant atmospheric parameter that determines solar exposure on clear-sky days. Breitkreuz et al. (2009) developed Aerosol-Based Forecasts of Solar Irradiance for Energy Applications (AFSOL
prediction (NWP) models, dedicated aerosol forecast models, or a combination of both to produce solar exposure forecasts on daily time scales. Breitkreuz et al. (2009) examined the capability of mesoscale forecasts to predict clear-sky conditions. Their studies focused on aerosol chemical transport, which is a significant atmospheric parameter that determines solar exposure on clear-sky days. Breitkreuz et al. (2009) developed Aerosol-Based Forecasts of Solar Irradiance for Energy Applications (AFSOL
1. Introduction Mesoscale convective systems (MCSs) are the primary source of precipitation across the Great Plains and Midwest during the summer months ( Fritsch et al. 1986 ; Stensrud 1996 ; Ashley et al. 2003 ; Jirak and Cotton 2007 ; Coniglio et al. 2010 ) and provide the rainfall needed for agricultural purposes; thus, better forecasts of nocturnal convection benefit farmers ( Jirak et al. 2003 ). Nocturnal MCS development and sustenance is often tied to the occurrence of the Great
1. Introduction Mesoscale convective systems (MCSs) are the primary source of precipitation across the Great Plains and Midwest during the summer months ( Fritsch et al. 1986 ; Stensrud 1996 ; Ashley et al. 2003 ; Jirak and Cotton 2007 ; Coniglio et al. 2010 ) and provide the rainfall needed for agricultural purposes; thus, better forecasts of nocturnal convection benefit farmers ( Jirak et al. 2003 ). Nocturnal MCS development and sustenance is often tied to the occurrence of the Great
and coastal regions compared to raw mesoscale ensemble forecasts, with modest skill increases after sunrise. The challenge in adding skill to the ensemble by statistically adjusting zero or near-zero q c predictions from the members is in knowing whether fog is likely. The strategy is intentionally conservative such that the fog prediction is taken directly from the NWP model when fog is predicted. The probability of light fog can only be increased from zero, reducing complexity while adding
and coastal regions compared to raw mesoscale ensemble forecasts, with modest skill increases after sunrise. The challenge in adding skill to the ensemble by statistically adjusting zero or near-zero q c predictions from the members is in knowing whether fog is likely. The strategy is intentionally conservative such that the fog prediction is taken directly from the NWP model when fog is predicted. The probability of light fog can only be increased from zero, reducing complexity while adding
-decreasing grid spacings, there will always be a scale below which wavelengths are truncated, and chaotic, nonlinear processes are implicitly resolved, or parameterized. Parameterization is used in operational NWP models, such as the North American Mesoscale (NAM) model and the Global Forecast System (GFS), to capture the planetary boundary layer (PBL), cloud microphysics, and other subgrid-scale processes. The “spread” of parameterization schemes, each with their own set of biases and random errors
-decreasing grid spacings, there will always be a scale below which wavelengths are truncated, and chaotic, nonlinear processes are implicitly resolved, or parameterized. Parameterization is used in operational NWP models, such as the North American Mesoscale (NAM) model and the Global Forecast System (GFS), to capture the planetary boundary layer (PBL), cloud microphysics, and other subgrid-scale processes. The “spread” of parameterization schemes, each with their own set of biases and random errors
temporally limited in coverage due to their source from 1- or 3-hourly model output with spatial grid limitations that are too large to represent a mesoscale undular bore event. Thus, it is only these data taken together with all recommended forecast parameters that provide a cumulative indication that such an event is likely to occur.
temporally limited in coverage due to their source from 1- or 3-hourly model output with spatial grid limitations that are too large to represent a mesoscale undular bore event. Thus, it is only these data taken together with all recommended forecast parameters that provide a cumulative indication that such an event is likely to occur.
