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Abstract
The Warn-on-Forecast (WoF) program, driven by advanced data assimilation and ensemble design of numerical weather prediction (NWP) systems, seeks to advance 0–3-h NWP to aid National Weather Service warnings for thunderstorm-induced hazards. An early prototype of the WoF prediction system is the National Severe Storms Laboratory (NSSL) Experimental WoF System for ensembles (NEWSe), which comprises 36 ensemble members with varied initial conditions and parameterization suites. In the present study, real-time 3-h quantitative precipitation forecasts (QPFs) during spring 2016 from NEWSe members are compared against those from two real-time deterministic systems: the operational High Resolution Rapid Refresh (HRRR, version 1) and an upgraded, experimental configuration of the HRRR. All three model systems were run at 3-km horizontal grid spacing and differ in initialization, particularly in the radar data assimilation methods. It is the impact of this difference that is evaluated herein using both traditional and scale-aware verification schemes. NEWSe, evaluated deterministically for each member, shows marked improvement over the two HRRR versions for 0–3-h QPFs, especially at higher thresholds and smaller spatial scales. This improvement diminishes with forecast lead time. The experimental HRRR model, which became operational as HRRR version 2 in August 2016, also provides added skill over HRRR version 1.
Abstract
The Warn-on-Forecast (WoF) program, driven by advanced data assimilation and ensemble design of numerical weather prediction (NWP) systems, seeks to advance 0–3-h NWP to aid National Weather Service warnings for thunderstorm-induced hazards. An early prototype of the WoF prediction system is the National Severe Storms Laboratory (NSSL) Experimental WoF System for ensembles (NEWSe), which comprises 36 ensemble members with varied initial conditions and parameterization suites. In the present study, real-time 3-h quantitative precipitation forecasts (QPFs) during spring 2016 from NEWSe members are compared against those from two real-time deterministic systems: the operational High Resolution Rapid Refresh (HRRR, version 1) and an upgraded, experimental configuration of the HRRR. All three model systems were run at 3-km horizontal grid spacing and differ in initialization, particularly in the radar data assimilation methods. It is the impact of this difference that is evaluated herein using both traditional and scale-aware verification schemes. NEWSe, evaluated deterministically for each member, shows marked improvement over the two HRRR versions for 0–3-h QPFs, especially at higher thresholds and smaller spatial scales. This improvement diminishes with forecast lead time. The experimental HRRR model, which became operational as HRRR version 2 in August 2016, also provides added skill over HRRR version 1.
Abstract
A new analysis of sea surface temperature (SST) observations indicates notable uncertainty in observed decadal climate variability in the second half of the twentieth century, particularly during the decades following World War II. The uncertainties are revealed by exploring SST data binned separately for the two predominant measurement types: “engine-room intake” (ERI) and “bucket” measurements. ERI measurements indicate large decreases in global-mean SSTs from 1950 to 1975, whereas bucket measurements indicate increases in SST over this period before bias adjustments are applied but decreases after they are applied. The trends in the bias adjustments applied to the bucket data are larger than the global-mean trends during the period 1950–75, and thus the global-mean trends during this period derive largely from the adjustments themselves. This is critical, since the adjustments are based on incomplete information about the underlying measurement methods and are thus subject to considerable uncertainty. The uncertainty in decadal-scale variability is particularly pronounced over the North Pacific, where the sign of low-frequency variability through the 1950s to 1970s is different for each measurement type. The uncertainty highlighted here has important—but in our view widely overlooked—implications for the interpretation of observed decadal climate variability over both the Pacific and Atlantic basins during the mid-to-late twentieth century.
Abstract
A new analysis of sea surface temperature (SST) observations indicates notable uncertainty in observed decadal climate variability in the second half of the twentieth century, particularly during the decades following World War II. The uncertainties are revealed by exploring SST data binned separately for the two predominant measurement types: “engine-room intake” (ERI) and “bucket” measurements. ERI measurements indicate large decreases in global-mean SSTs from 1950 to 1975, whereas bucket measurements indicate increases in SST over this period before bias adjustments are applied but decreases after they are applied. The trends in the bias adjustments applied to the bucket data are larger than the global-mean trends during the period 1950–75, and thus the global-mean trends during this period derive largely from the adjustments themselves. This is critical, since the adjustments are based on incomplete information about the underlying measurement methods and are thus subject to considerable uncertainty. The uncertainty in decadal-scale variability is particularly pronounced over the North Pacific, where the sign of low-frequency variability through the 1950s to 1970s is different for each measurement type. The uncertainty highlighted here has important—but in our view widely overlooked—implications for the interpretation of observed decadal climate variability over both the Pacific and Atlantic basins during the mid-to-late twentieth century.
Abstract
For the Voluntary Observing Ships Special Observing Project for the North Atlantic (VSOP-NA), the layout, meteorological instrumentation, and observing practices of 45 voluntary observing ships (VOS) operating in the North Atlantic were cataloged. Over a two-year period these ships provided extra information with each observation, and the effect of different observing practices has been quantified by using analysis fields from an atmospheric forecast model as a comparison standard. Biases of order several tenths of a degree Celsius were detected in sea surface temperature data from engine intake thermometers, in dewpoint temperatures from screens (and to a lesser extent, psychrometers), and in air temperatures due to solar heating. Wind speeds from anemometers were high compared to visual winds by about 2 kt for winds up to about 25 kt. The VSOP-NA data do not, however, indicate which is the more accurate. Correction for anemometer height and use of the WMO Commission for Marine Meteorology version of the Beaufort scale reduced this difference significantly. The result of these corrections on mean heat flux estimates was only a few watts per square meter but much greater changes resulted for particular areas and seasons. The project identified observing methods that are to be preferred for future use on the VOS, and demonstrated that the combined use of VOS data and a forecasting model allowed the detection of biases both in the observations and in the model analyses.
Abstract
For the Voluntary Observing Ships Special Observing Project for the North Atlantic (VSOP-NA), the layout, meteorological instrumentation, and observing practices of 45 voluntary observing ships (VOS) operating in the North Atlantic were cataloged. Over a two-year period these ships provided extra information with each observation, and the effect of different observing practices has been quantified by using analysis fields from an atmospheric forecast model as a comparison standard. Biases of order several tenths of a degree Celsius were detected in sea surface temperature data from engine intake thermometers, in dewpoint temperatures from screens (and to a lesser extent, psychrometers), and in air temperatures due to solar heating. Wind speeds from anemometers were high compared to visual winds by about 2 kt for winds up to about 25 kt. The VSOP-NA data do not, however, indicate which is the more accurate. Correction for anemometer height and use of the WMO Commission for Marine Meteorology version of the Beaufort scale reduced this difference significantly. The result of these corrections on mean heat flux estimates was only a few watts per square meter but much greater changes resulted for particular areas and seasons. The project identified observing methods that are to be preferred for future use on the VOS, and demonstrated that the combined use of VOS data and a forecasting model allowed the detection of biases both in the observations and in the model analyses.
Tropical cyclone (TC) monitoring requires the use of multiple satellites and sensors to accurately assess TC location and intensity. Visible and infrared (vis/IR) data provide the bulk of TC information, but upper-level cloud obscurations inherently limit this important dataset during a storm's life cycle. Passive microwave digital data and imagery can provide key storm structural details and offset many of the vis/IR spectral problems. The ability to view storm rainbands, eyewalls, impacts of shear, and exposed low-level circulations, whether it is day or night, makes passive microwave data a significant tool for the satellite analyst. Passive microwave capabilities for TC reconnaissance are demonstrated via a near-real-time Web page created by the Naval Research Laboratory in Monterey, California. Examples are used to illustrate tropical cyclone monitoring. Collocated datasets are incorporated to enable the user to see many aspects of a storm's organization and development by quickly accessing one location.
Tropical cyclone (TC) monitoring requires the use of multiple satellites and sensors to accurately assess TC location and intensity. Visible and infrared (vis/IR) data provide the bulk of TC information, but upper-level cloud obscurations inherently limit this important dataset during a storm's life cycle. Passive microwave digital data and imagery can provide key storm structural details and offset many of the vis/IR spectral problems. The ability to view storm rainbands, eyewalls, impacts of shear, and exposed low-level circulations, whether it is day or night, makes passive microwave data a significant tool for the satellite analyst. Passive microwave capabilities for TC reconnaissance are demonstrated via a near-real-time Web page created by the Naval Research Laboratory in Monterey, California. Examples are used to illustrate tropical cyclone monitoring. Collocated datasets are incorporated to enable the user to see many aspects of a storm's organization and development by quickly accessing one location.
Under the auspices of the National Polar-orbiting Operational Environmental Satellite System's (NPOESS) Integrated Program Office (IPO), the Naval Research Laboratory (NRL) has developed “NexSat” (www.nrlmry.navy.mil/nexsat_pages/nexsat_home.html)—a public-access online demonstration over the continental United States (CONUS) of near-real-time environmental products highlighting future applications from the Visible/Infrared Imager/Radiometer Suite (VIIRS). Based on a collection of operational and research-grade satellite observing systems, NexSat products include the detection, enhancement, and where applicable, physical retrieval of deep convection, low clouds, light sources at night, rainfall, snow cover, aircraft contrails, thin cirrus layers, dust storms, and cloud/aerosol properties, all presented in the context of value-added imagery. The purpose of NexSat is threefold: 1) to communicate the advanced capabilities anticipated from VIIRS, 2) to present this information in near–real time for use by forecasters, resource managers, emergency response teams, civic planners, the aviation community, and various government agencies, and 3) to augment the NRL algorithm development multisensor/model-fusion test bed for accelerated transitions to operations during the NPOESS era. This paper presents an overview of NexSat, highlighting selected products from the diverse meteorological phenomenology over the CONUS.
Under the auspices of the National Polar-orbiting Operational Environmental Satellite System's (NPOESS) Integrated Program Office (IPO), the Naval Research Laboratory (NRL) has developed “NexSat” (www.nrlmry.navy.mil/nexsat_pages/nexsat_home.html)—a public-access online demonstration over the continental United States (CONUS) of near-real-time environmental products highlighting future applications from the Visible/Infrared Imager/Radiometer Suite (VIIRS). Based on a collection of operational and research-grade satellite observing systems, NexSat products include the detection, enhancement, and where applicable, physical retrieval of deep convection, low clouds, light sources at night, rainfall, snow cover, aircraft contrails, thin cirrus layers, dust storms, and cloud/aerosol properties, all presented in the context of value-added imagery. The purpose of NexSat is threefold: 1) to communicate the advanced capabilities anticipated from VIIRS, 2) to present this information in near–real time for use by forecasters, resource managers, emergency response teams, civic planners, the aviation community, and various government agencies, and 3) to augment the NRL algorithm development multisensor/model-fusion test bed for accelerated transitions to operations during the NPOESS era. This paper presents an overview of NexSat, highlighting selected products from the diverse meteorological phenomenology over the CONUS.
Abstract
In recent years, a growing partnership has emerged between the Met Office and the designated U.S. national centers for expertise in severe weather research and forecasting, that is, the National Oceanic and Atmospheric Administration (NOAA) National Severe Storms Laboratory (NSSL) and the NOAA Storm Prediction Center (SPC). The driving force behind this partnership is a compelling set of mutual interests related to predicting and understanding high-impact weather and using high-resolution numerical weather prediction models as foundational tools to explore these interests.
The forum for this collaborative activity is the NOAA Hazardous Weather Testbed, where annual Spring Forecasting Experiments (SFEs) are conducted by NSSL and SPC. For the last decade, NSSL and SPC have used these experiments to find ways that high-resolution models can help achieve greater success in the prediction of tornadoes, large hail, and damaging winds. Beginning in 2012, the Met Office became a contributing partner in annual SFEs, bringing complementary expertise in the use of convection-allowing models, derived in their case from a parallel decadelong effort to use these models to advance prediction of flash floods associated with heavy thunderstorms.
The collaboration between NSSL, SPC, and the Met Office has been enthusiastic and productive, driven by strong mutual interests at a grassroots level and generous institutional support from the parent government agencies. In this article, a historical background is provided, motivations for collaborative activities are emphasized, and preliminary results are highlighted.
Abstract
In recent years, a growing partnership has emerged between the Met Office and the designated U.S. national centers for expertise in severe weather research and forecasting, that is, the National Oceanic and Atmospheric Administration (NOAA) National Severe Storms Laboratory (NSSL) and the NOAA Storm Prediction Center (SPC). The driving force behind this partnership is a compelling set of mutual interests related to predicting and understanding high-impact weather and using high-resolution numerical weather prediction models as foundational tools to explore these interests.
The forum for this collaborative activity is the NOAA Hazardous Weather Testbed, where annual Spring Forecasting Experiments (SFEs) are conducted by NSSL and SPC. For the last decade, NSSL and SPC have used these experiments to find ways that high-resolution models can help achieve greater success in the prediction of tornadoes, large hail, and damaging winds. Beginning in 2012, the Met Office became a contributing partner in annual SFEs, bringing complementary expertise in the use of convection-allowing models, derived in their case from a parallel decadelong effort to use these models to advance prediction of flash floods associated with heavy thunderstorms.
The collaboration between NSSL, SPC, and the Met Office has been enthusiastic and productive, driven by strong mutual interests at a grassroots level and generous institutional support from the parent government agencies. In this article, a historical background is provided, motivations for collaborative activities are emphasized, and preliminary results are highlighted.
The Storm Studies in the Arctic (STAR) network (2007–2010) conducted a major meteorological field project from 10 October–30 November 2007 and in February 2008, focused on southern Baffin Island, Nunavut, Canada—a region that experiences intense autumn and winter storms. The STAR research program is concerned with the documentation, better understanding, and improved prediction of meteorological and related hazards in the Arctic, including their modification by local topography and land–sea ice–ocean transitions, and their effect on local communities. To optimize the applicability of STAR network science, we are also communicating with the user community (northern communities and government sectors). STAR has obtained a variety of surface-based and unique research aircraft field measurements, high-resolution modeling products, and remote sensing measurements (including Cloudsat) as part of its science strategy and has the first arctic Cloudsat validation dataset. In total, 14 research flights were flown between 5 and 30 November 2007, with eight coinciding with Cloudsat passes. The aircraft was outfitted with many instruments that measure cloud microphysical parameters and three unique Doppler-polarized airborne radars operating in Ka, X and W bands. The project area, instrumentation platforms, real-time forecasts, storm cases, and results thus far are discussed in this article. A number of synoptic and mesoscale features were sampled—such as fronts, upslope/terrain-enhanced precipitation, convective precipitation, and boundary layer clouds/precipitation—as well as targeted Cloudsat missions. One significant and unique event included a research flight into an intense high-latitude storm leftover from Hurricane Noel—an intense tropical and extratropical disturbance that caused many fatalities in the tropics and extensive damage on the eastern North American seaboard. These synoptic and mesoscale features and high-latitude storms will be studied in detail over the next several years. It is anticipated that scientific progress in better understanding the nature of these arctic storms and their hazards will lead to improved conceptual models and improved prediction of such events.
The Storm Studies in the Arctic (STAR) network (2007–2010) conducted a major meteorological field project from 10 October–30 November 2007 and in February 2008, focused on southern Baffin Island, Nunavut, Canada—a region that experiences intense autumn and winter storms. The STAR research program is concerned with the documentation, better understanding, and improved prediction of meteorological and related hazards in the Arctic, including their modification by local topography and land–sea ice–ocean transitions, and their effect on local communities. To optimize the applicability of STAR network science, we are also communicating with the user community (northern communities and government sectors). STAR has obtained a variety of surface-based and unique research aircraft field measurements, high-resolution modeling products, and remote sensing measurements (including Cloudsat) as part of its science strategy and has the first arctic Cloudsat validation dataset. In total, 14 research flights were flown between 5 and 30 November 2007, with eight coinciding with Cloudsat passes. The aircraft was outfitted with many instruments that measure cloud microphysical parameters and three unique Doppler-polarized airborne radars operating in Ka, X and W bands. The project area, instrumentation platforms, real-time forecasts, storm cases, and results thus far are discussed in this article. A number of synoptic and mesoscale features were sampled—such as fronts, upslope/terrain-enhanced precipitation, convective precipitation, and boundary layer clouds/precipitation—as well as targeted Cloudsat missions. One significant and unique event included a research flight into an intense high-latitude storm leftover from Hurricane Noel—an intense tropical and extratropical disturbance that caused many fatalities in the tropics and extensive damage on the eastern North American seaboard. These synoptic and mesoscale features and high-latitude storms will be studied in detail over the next several years. It is anticipated that scientific progress in better understanding the nature of these arctic storms and their hazards will lead to improved conceptual models and improved prediction of such events.
No abstract available.
No abstract available.
Abstract
Led by NOAA’s Storm Prediction Center and National Severe Storms Laboratory, annual spring forecasting experiments (SFEs) in the Hazardous Weather Testbed test and evaluate cutting-edge technologies and concepts for improving severe weather prediction through intensive real-time forecasting and evaluation activities. Experimental forecast guidance is provided through collaborations with several U.S. government and academic institutions, as well as the Met Office. The purpose of this article is to summarize activities, insights, and preliminary findings from recent SFEs, emphasizing SFE 2015. Several innovative aspects of recent experiments are discussed, including the 1) use of convection-allowing model (CAM) ensembles with advanced ensemble data assimilation, 2) generation of severe weather outlooks valid at time periods shorter than those issued operationally (e.g., 1–4 h), 3) use of CAMs to issue outlooks beyond the day 1 period, 4) increased interaction through software allowing participants to create individual severe weather outlooks, and 5) tests of newly developed storm-attribute-based diagnostics for predicting tornadoes and hail size. Additionally, plans for future experiments will be discussed, including the creation of a Community Leveraged Unified Ensemble (CLUE) system, which will test various strategies for CAM ensemble design using carefully designed sets of ensemble members contributed by different agencies to drive evidence-based decision-making for near-future operational systems.
Abstract
Led by NOAA’s Storm Prediction Center and National Severe Storms Laboratory, annual spring forecasting experiments (SFEs) in the Hazardous Weather Testbed test and evaluate cutting-edge technologies and concepts for improving severe weather prediction through intensive real-time forecasting and evaluation activities. Experimental forecast guidance is provided through collaborations with several U.S. government and academic institutions, as well as the Met Office. The purpose of this article is to summarize activities, insights, and preliminary findings from recent SFEs, emphasizing SFE 2015. Several innovative aspects of recent experiments are discussed, including the 1) use of convection-allowing model (CAM) ensembles with advanced ensemble data assimilation, 2) generation of severe weather outlooks valid at time periods shorter than those issued operationally (e.g., 1–4 h), 3) use of CAMs to issue outlooks beyond the day 1 period, 4) increased interaction through software allowing participants to create individual severe weather outlooks, and 5) tests of newly developed storm-attribute-based diagnostics for predicting tornadoes and hail size. Additionally, plans for future experiments will be discussed, including the creation of a Community Leveraged Unified Ensemble (CLUE) system, which will test various strategies for CAM ensemble design using carefully designed sets of ensemble members contributed by different agencies to drive evidence-based decision-making for near-future operational systems.
