nadoes Experiment-II (VORTEX2) field project was conducted in the spring of 2009 and 2010 (Wur-ma... more nadoes Experiment-II (VORTEX2) field project was conducted in the spring of 2009 and 2010 (Wur-man et al., 2010).1 The size and scope of the Verification of the Ori-gins of Rotation in Tornadoes Experiment-II (VOR-TEX2) field project (Wurman et al., 2010) required accurate forecasts to be made in order to plan the mission of the day and set up for future missions on following days. In 2009, the steering commit-tee was responsible for making the forecasts with input from the VOC. Each member of the steering committee would take turns producing the brief-ing for the daily morning PI mission planning-meeting. VORTEX2 utilized an armada of 35-40 vehicles with a variety of mobile observ-ing equipment. More than 100 scientists, students and media traveled over much of the Great Plains during the project. Although successful forecasts were made, the amount of time required of the steering commit-tee to create forecasts distracted from other mis-sion planning duties. To remove this distract...
The VORTEX field project was conducted during the spring of 2009 and 2010. The authors had to pro... more The VORTEX field project was conducted during the spring of 2009 and 2010. The authors had to provide logistical support in forecasting severe weather to project investigators. This paper summarizes various forecast methods that were employed during the project.
The Advanced Regional Prediction System (ARPS) prediction model is employed to perform high resol... more The Advanced Regional Prediction System (ARPS) prediction model is employed to perform high resolution numerical simulations of a mesoscale convective system and associated cyclonic line-end vortex (LEV) that spawned several tornadoes in central Oklahoma on 8-9 May 2007. The simulation uses a 1000 km x 1000 km domain with 2 km horizontal grid spacing. The ARPS three-dimensional variational data assimilation (3DVAR) is used to assimilate a variety of different data types. All experiments assimilate routine surface and upper air observations as well as wind profiler and Oklahoma Mesonet data over a 1 h assimilation window. A subset of experiments assimilates radar data. Cloud and hydrometeor fields as well as in-cloud temperature are adjusted based on radar reflectivity data through the ARPS complex cloud analysis procedure. Radar data are assimilated from the WSR-88D network as well as from the Engineering Research Center for Collaborative and Adaptive Sensing of the Atmosphere's (CASA) network of four X-band Doppler radars. Three hour forecasts are launched at the end of the assimilation window. The structure and evolution of the MCS and LEV are markedly better forecast throughout the forecast period in experiments in which radar data are assimilated. The assimilation of CASA radar data in addition to WSR-88D data improves the analyzed location of the convective gust front through improved low-level wind analysis, leading to a slightly better forecast track of the LEV on the 2 km grid.
... Ming Xue1,2, Keith Brewster1, Dan Weber1, Kevin W. Thomas1, Fanyou Kong1, and Eric Kemp1 1Cen... more ... Ming Xue1,2, Keith Brewster1, Dan Weber1, Kevin W. Thomas1, Fanyou Kong1, and Eric Kemp1 1Center for Analysis and Prediction of ... Mike Pflugmacher, and Wayne Louis Hoyenga of NCSA and Ralph Roskies, David O'Neal, Sergiu Sanielevici, Katherine Vargo, Chad Viz ...
Bulletin of the American Meteorological Society, 2015
The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of wh... more The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of which is to improve the accuracy of short-term (0–6 h) wind power forecasts for the wind energy industry. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that included the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models and, second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the United States (the upper Great Plains and Texas) and included 12 wind profiling radars, 12 sodars, several lidars and surface flux stations, 184 instrumented tall towers, and over 400 ...
The deployment of small unmanned aircraft systems (UAS) to collect routine in situ vertical profi... more The deployment of small unmanned aircraft systems (UAS) to collect routine in situ vertical profiles of the thermodynamic and kinematic state of the atmosphere in conjunction with other weather observations could significantly improve weather forecasting skill and resolution. High-resolution vertical measurements of pressure, temperature, humidity, wind speed and wind direction are critical to the understanding of atmospheric boundary layer processes integral to air–surface (land, ocean and sea ice) exchanges of energy, momentum, and moisture; how these are affected by climate variability; and how they impact weather forecasts and air quality simulations. We explore the potential value of collecting coordinated atmospheric profiles at fixed surface observing sites at designated times using instrumented UAS. We refer to such a network of autonomous weather UAS designed for atmospheric profiling and capable of operating in most weather conditions as a 3D Mesonet. We outline some of th...
One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s... more One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among ma...
The Nationwide Network of Networks (NNoN) concept was introduced by the National Research Council... more The Nationwide Network of Networks (NNoN) concept was introduced by the National Research Council to address the growing need for a national mesoscale observing system and the continued advancement toward accurate high-resolution numerical weather prediction. The research test bed known as the Dallas–Fort Worth (DFW) Urban Demonstration Network was created to experiment with many kinds of mesoscale observations that could be used in a data assimilation system. Many nonconventional observations, including Earth Networks and Citizen Weather Observer Program surface stations, are combined with conventional operational data to form the test bed network. A principal component of the NNoN effort is the quantification of observation impact from several different sources of information. In this study, the GSI-based EnKF system was used together with the WRF-ARW Model to examine impacts of observations assimilated for forecasting convection initiation (CI) in the 3 April 2014 hail storm case...
On 24 May 2011, Oklahoma experienced an outbreak of tornadoes, including one rated EF5 on the enh... more On 24 May 2011, Oklahoma experienced an outbreak of tornadoes, including one rated EF5 on the enhanced Fujita (EF) scale and two rated EF4. The extensive observation network in this area makes this an ideal case to examine the impact of using five different microphysics parameterization schemes, including single-, double-, and triple-moment microphysics, in an efficient high-resolution data assimilation system suitable for nowcasting and short-term forecasting with low latencies. Additionally, the real-time configuration of the 1-km ARPS, which assimilated increments produced by 3DVAR with cloud analysis using incremental analysis updating (IAU), had success providing a good baseline forecast. ARPS forecasts of 0–2 h are verified using observation-point, neighborhood, and object-based verification techniques. The object-based verification technique uses updraft helicity fields to represent mesocyclone centers, which are verified against tornado locations from three supercells of int...
In this two-part paper, the impact of level-II Weather Surveillance Radar-1988 Doppler (WSR-88D) ... more In this two-part paper, the impact of level-II Weather Surveillance Radar-1988 Doppler (WSR-88D) radar reflectivity and radial velocity data on the prediction of a cluster of tornadic thunderstorms in the Advanced Regional Prediction System (ARPS) model is studied. Radar reflectivity data are used primarily in a cloud analysis procedure that retrieves the amount of hydrometeors and adjusts in-cloud temperature, moisture, and cloud fields, while radial velocity data are analyzed through a three-dimensional variational (3DVAR) data assimilation scheme that contains a 3D mass divergence constraint in the cost function. In Part I, the impact of the cloud analysis and modifications to the scheme are discussed. In this part, the impact of radial velocity data and the mass divergence constraint in the 3DVAR cost function are studied. The case studied is that of the 28 March 2000 Fort Worth tornadoes. The addition of the radial velocity improves the forecasts beyond that experienced with the cloud analysis alone. The prediction is able to forecast the morphology of individual storm cells on the 3-km grid up to 2 h; the rotating supercell characteristics of the storm that spawned two tornadoes are well captured; timing errors in the forecast are less than 15 min and location errors are less than 10 km at the time of the tornadoes. When forecasts were made with radial velocity assimilation but not reflectivity, they failed to predict nearly all storm cells. Using the current 3DVAR and cloud analysis procedure with 10-min intermittent assimilation cycles, reflectivity data are found to have a greater positive impact than radial velocity. The use of radial velocity does improve the storm forecast when combined with reflectivity assimilation, by, for example, improving the forecasting of the strong low-level vorticity centers associated with the tornadoes. Positive effects of including a mass divergence constraint in the 3DVAR cost function are also documented.
nadoes Experiment-II (VORTEX2) field project was conducted in the spring of 2009 and 2010 (Wur-ma... more nadoes Experiment-II (VORTEX2) field project was conducted in the spring of 2009 and 2010 (Wur-man et al., 2010).1 The size and scope of the Verification of the Ori-gins of Rotation in Tornadoes Experiment-II (VOR-TEX2) field project (Wurman et al., 2010) required accurate forecasts to be made in order to plan the mission of the day and set up for future missions on following days. In 2009, the steering commit-tee was responsible for making the forecasts with input from the VOC. Each member of the steering committee would take turns producing the brief-ing for the daily morning PI mission planning-meeting. VORTEX2 utilized an armada of 35-40 vehicles with a variety of mobile observ-ing equipment. More than 100 scientists, students and media traveled over much of the Great Plains during the project. Although successful forecasts were made, the amount of time required of the steering commit-tee to create forecasts distracted from other mis-sion planning duties. To remove this distract...
The VORTEX field project was conducted during the spring of 2009 and 2010. The authors had to pro... more The VORTEX field project was conducted during the spring of 2009 and 2010. The authors had to provide logistical support in forecasting severe weather to project investigators. This paper summarizes various forecast methods that were employed during the project.
The Advanced Regional Prediction System (ARPS) prediction model is employed to perform high resol... more The Advanced Regional Prediction System (ARPS) prediction model is employed to perform high resolution numerical simulations of a mesoscale convective system and associated cyclonic line-end vortex (LEV) that spawned several tornadoes in central Oklahoma on 8-9 May 2007. The simulation uses a 1000 km x 1000 km domain with 2 km horizontal grid spacing. The ARPS three-dimensional variational data assimilation (3DVAR) is used to assimilate a variety of different data types. All experiments assimilate routine surface and upper air observations as well as wind profiler and Oklahoma Mesonet data over a 1 h assimilation window. A subset of experiments assimilates radar data. Cloud and hydrometeor fields as well as in-cloud temperature are adjusted based on radar reflectivity data through the ARPS complex cloud analysis procedure. Radar data are assimilated from the WSR-88D network as well as from the Engineering Research Center for Collaborative and Adaptive Sensing of the Atmosphere's (CASA) network of four X-band Doppler radars. Three hour forecasts are launched at the end of the assimilation window. The structure and evolution of the MCS and LEV are markedly better forecast throughout the forecast period in experiments in which radar data are assimilated. The assimilation of CASA radar data in addition to WSR-88D data improves the analyzed location of the convective gust front through improved low-level wind analysis, leading to a slightly better forecast track of the LEV on the 2 km grid.
... Ming Xue1,2, Keith Brewster1, Dan Weber1, Kevin W. Thomas1, Fanyou Kong1, and Eric Kemp1 1Cen... more ... Ming Xue1,2, Keith Brewster1, Dan Weber1, Kevin W. Thomas1, Fanyou Kong1, and Eric Kemp1 1Center for Analysis and Prediction of ... Mike Pflugmacher, and Wayne Louis Hoyenga of NCSA and Ralph Roskies, David O'Neal, Sergiu Sanielevici, Katherine Vargo, Chad Viz ...
Bulletin of the American Meteorological Society, 2015
The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of wh... more The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of which is to improve the accuracy of short-term (0–6 h) wind power forecasts for the wind energy industry. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that included the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models and, second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the United States (the upper Great Plains and Texas) and included 12 wind profiling radars, 12 sodars, several lidars and surface flux stations, 184 instrumented tall towers, and over 400 ...
The deployment of small unmanned aircraft systems (UAS) to collect routine in situ vertical profi... more The deployment of small unmanned aircraft systems (UAS) to collect routine in situ vertical profiles of the thermodynamic and kinematic state of the atmosphere in conjunction with other weather observations could significantly improve weather forecasting skill and resolution. High-resolution vertical measurements of pressure, temperature, humidity, wind speed and wind direction are critical to the understanding of atmospheric boundary layer processes integral to air–surface (land, ocean and sea ice) exchanges of energy, momentum, and moisture; how these are affected by climate variability; and how they impact weather forecasts and air quality simulations. We explore the potential value of collecting coordinated atmospheric profiles at fixed surface observing sites at designated times using instrumented UAS. We refer to such a network of autonomous weather UAS designed for atmospheric profiling and capable of operating in most weather conditions as a 3D Mesonet. We outline some of th...
One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s... more One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among ma...
The Nationwide Network of Networks (NNoN) concept was introduced by the National Research Council... more The Nationwide Network of Networks (NNoN) concept was introduced by the National Research Council to address the growing need for a national mesoscale observing system and the continued advancement toward accurate high-resolution numerical weather prediction. The research test bed known as the Dallas–Fort Worth (DFW) Urban Demonstration Network was created to experiment with many kinds of mesoscale observations that could be used in a data assimilation system. Many nonconventional observations, including Earth Networks and Citizen Weather Observer Program surface stations, are combined with conventional operational data to form the test bed network. A principal component of the NNoN effort is the quantification of observation impact from several different sources of information. In this study, the GSI-based EnKF system was used together with the WRF-ARW Model to examine impacts of observations assimilated for forecasting convection initiation (CI) in the 3 April 2014 hail storm case...
On 24 May 2011, Oklahoma experienced an outbreak of tornadoes, including one rated EF5 on the enh... more On 24 May 2011, Oklahoma experienced an outbreak of tornadoes, including one rated EF5 on the enhanced Fujita (EF) scale and two rated EF4. The extensive observation network in this area makes this an ideal case to examine the impact of using five different microphysics parameterization schemes, including single-, double-, and triple-moment microphysics, in an efficient high-resolution data assimilation system suitable for nowcasting and short-term forecasting with low latencies. Additionally, the real-time configuration of the 1-km ARPS, which assimilated increments produced by 3DVAR with cloud analysis using incremental analysis updating (IAU), had success providing a good baseline forecast. ARPS forecasts of 0–2 h are verified using observation-point, neighborhood, and object-based verification techniques. The object-based verification technique uses updraft helicity fields to represent mesocyclone centers, which are verified against tornado locations from three supercells of int...
In this two-part paper, the impact of level-II Weather Surveillance Radar-1988 Doppler (WSR-88D) ... more In this two-part paper, the impact of level-II Weather Surveillance Radar-1988 Doppler (WSR-88D) radar reflectivity and radial velocity data on the prediction of a cluster of tornadic thunderstorms in the Advanced Regional Prediction System (ARPS) model is studied. Radar reflectivity data are used primarily in a cloud analysis procedure that retrieves the amount of hydrometeors and adjusts in-cloud temperature, moisture, and cloud fields, while radial velocity data are analyzed through a three-dimensional variational (3DVAR) data assimilation scheme that contains a 3D mass divergence constraint in the cost function. In Part I, the impact of the cloud analysis and modifications to the scheme are discussed. In this part, the impact of radial velocity data and the mass divergence constraint in the 3DVAR cost function are studied. The case studied is that of the 28 March 2000 Fort Worth tornadoes. The addition of the radial velocity improves the forecasts beyond that experienced with the cloud analysis alone. The prediction is able to forecast the morphology of individual storm cells on the 3-km grid up to 2 h; the rotating supercell characteristics of the storm that spawned two tornadoes are well captured; timing errors in the forecast are less than 15 min and location errors are less than 10 km at the time of the tornadoes. When forecasts were made with radial velocity assimilation but not reflectivity, they failed to predict nearly all storm cells. Using the current 3DVAR and cloud analysis procedure with 10-min intermittent assimilation cycles, reflectivity data are found to have a greater positive impact than radial velocity. The use of radial velocity does improve the storm forecast when combined with reflectivity assimilation, by, for example, improving the forecasting of the strong low-level vorticity centers associated with the tornadoes. Positive effects of including a mass divergence constraint in the 3DVAR cost function are also documented.
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