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- Author or Editor: David Richardson x
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Abstract
The relative operating characteristic (ROC) curve is a popular diagnostic tool in forecast verification, with the area under the ROC curve (AUC) used as a verification metric measuring the discrimination ability of a forecast. Along with calibration, discrimination is deemed as a fundamental probabilistic forecast attribute. In particular, in ensemble forecast verification, AUC provides a basis for the comparison of potential predictive skill of competing forecasts. While this approach is straightforward when dealing with forecasts of common events (e.g., probability of precipitation), the AUC interpretation can turn out to be oversimplistic or misleading when focusing on rare events (e.g., precipitation exceeding some warning criterion). How should we interpret AUC of ensemble forecasts when focusing on rare events? How can changes in the way probability forecasts are derived from the ensemble forecast affect AUC results? How can we detect a genuine improvement in terms of predictive skill? Based on verification experiments, a critical eye is cast on the AUC interpretation to answer these questions. As well as the traditional trapezoidal approximation and the well-known binormal fitting model, we discuss a new approach that embraces the concept of imprecise probabilities and relies on the subdivision of the lowest ensemble probability category.
Abstract
The relative operating characteristic (ROC) curve is a popular diagnostic tool in forecast verification, with the area under the ROC curve (AUC) used as a verification metric measuring the discrimination ability of a forecast. Along with calibration, discrimination is deemed as a fundamental probabilistic forecast attribute. In particular, in ensemble forecast verification, AUC provides a basis for the comparison of potential predictive skill of competing forecasts. While this approach is straightforward when dealing with forecasts of common events (e.g., probability of precipitation), the AUC interpretation can turn out to be oversimplistic or misleading when focusing on rare events (e.g., precipitation exceeding some warning criterion). How should we interpret AUC of ensemble forecasts when focusing on rare events? How can changes in the way probability forecasts are derived from the ensemble forecast affect AUC results? How can we detect a genuine improvement in terms of predictive skill? Based on verification experiments, a critical eye is cast on the AUC interpretation to answer these questions. As well as the traditional trapezoidal approximation and the well-known binormal fitting model, we discuss a new approach that embraces the concept of imprecise probabilities and relies on the subdivision of the lowest ensemble probability category.
Abstract
Early awareness of extreme precipitation can provide the time necessary to make adequate event preparations. At the European Centre for Medium-Range Weather Forecasts (ECMWF), one tool that condenses the forecast information from the Integrated Forecasting System ensemble (ENS) is the extreme forecast index (EFI), an index that highlights regions that are forecast to have potentially anomalous weather conditions compared to the local climate. This paper builds on previous findings by undertaking a global verification throughout the medium-range forecast horizon (out to 15 days) on the ability of the EFI for water vapor transport [integrated vapor transport (IVT)] and precipitation to capture extreme observed precipitation. Using the ECMWF ENS for winters 2015/16 and 2016/17 and daily surface precipitation observations, the relative operating characteristic is used to show that the IVT EFI is more skillful than the precipitation EFI in forecast week 2 over Europe and western North America. It is the large-scale nature of the IVT, its higher predictability, and its relationship with extreme precipitation that result in its potential usefulness in these regions, which, in turn, could provide earlier awareness of extreme precipitation. Conversely, at shorter lead times the precipitation EFI is more useful, although the IVT EFI can provide synoptic-scale understanding. For the whole globe, the extratropical Northern Hemisphere, the tropics, and North America, the precipitation EFI is more useful throughout the medium range, suggesting that precipitation processes not captured in the IVT are important (e.g., tropical convection). Following these results, the operational implementation of the IVT EFI is currently being planned.
Abstract
Early awareness of extreme precipitation can provide the time necessary to make adequate event preparations. At the European Centre for Medium-Range Weather Forecasts (ECMWF), one tool that condenses the forecast information from the Integrated Forecasting System ensemble (ENS) is the extreme forecast index (EFI), an index that highlights regions that are forecast to have potentially anomalous weather conditions compared to the local climate. This paper builds on previous findings by undertaking a global verification throughout the medium-range forecast horizon (out to 15 days) on the ability of the EFI for water vapor transport [integrated vapor transport (IVT)] and precipitation to capture extreme observed precipitation. Using the ECMWF ENS for winters 2015/16 and 2016/17 and daily surface precipitation observations, the relative operating characteristic is used to show that the IVT EFI is more skillful than the precipitation EFI in forecast week 2 over Europe and western North America. It is the large-scale nature of the IVT, its higher predictability, and its relationship with extreme precipitation that result in its potential usefulness in these regions, which, in turn, could provide earlier awareness of extreme precipitation. Conversely, at shorter lead times the precipitation EFI is more useful, although the IVT EFI can provide synoptic-scale understanding. For the whole globe, the extratropical Northern Hemisphere, the tropics, and North America, the precipitation EFI is more useful throughout the medium range, suggesting that precipitation processes not captured in the IVT are important (e.g., tropical convection). Following these results, the operational implementation of the IVT EFI is currently being planned.
Abstract
The 3 May 1999 Oklahoma City storm is unique from a weather radar perspective because a long-track violent tornado passed within close range of several Doppler radars, because a detailed damage survey was conducted immediately after the event, and because high-quality visual observations of the tornado were available. The tornado passed relatively close (15–60 km) to two fixed-location Doppler radars: the National Weather Service Weather Surveillance Radar-1988 Doppler (WSR-88D) at Twin Lakes (KTLX) and the Radar Operations Center WSR-88D test radar at Norman (KCRI). Two mobile Doppler on Wheels (DOW) radars also observed the tornado at very close range (3–8 km). The data from DOW2 are nearly continuous for a substantial portion of the tornado's life and provide detailed information on the high-resolution velocity field in and around the tornado. These data permit good estimates of tornado rotational velocity and diameter. The data also allow comparison with tornado damage survey estimates of damage/intensity and with visual observations of tornado size. Further, one can compare the high-resolution DOW data to the lower-resolution WSR-88D observations to determine how much tornado information is lost because of the broader beamwidth (longer range) and longer gate length of the WSR-88Ds. Results of the study will be useful in determining how better to interpret future WSR-88D observations of near-range tornadoes, including any possible real-time estimation of tornado intensity and evolution.
Abstract
The 3 May 1999 Oklahoma City storm is unique from a weather radar perspective because a long-track violent tornado passed within close range of several Doppler radars, because a detailed damage survey was conducted immediately after the event, and because high-quality visual observations of the tornado were available. The tornado passed relatively close (15–60 km) to two fixed-location Doppler radars: the National Weather Service Weather Surveillance Radar-1988 Doppler (WSR-88D) at Twin Lakes (KTLX) and the Radar Operations Center WSR-88D test radar at Norman (KCRI). Two mobile Doppler on Wheels (DOW) radars also observed the tornado at very close range (3–8 km). The data from DOW2 are nearly continuous for a substantial portion of the tornado's life and provide detailed information on the high-resolution velocity field in and around the tornado. These data permit good estimates of tornado rotational velocity and diameter. The data also allow comparison with tornado damage survey estimates of damage/intensity and with visual observations of tornado size. Further, one can compare the high-resolution DOW data to the lower-resolution WSR-88D observations to determine how much tornado information is lost because of the broader beamwidth (longer range) and longer gate length of the WSR-88Ds. Results of the study will be useful in determining how better to interpret future WSR-88D observations of near-range tornadoes, including any possible real-time estimation of tornado intensity and evolution.
Abstract
In support of the Next Generation Global Prediction System (NGGPS) project, processes leading to convection initiation in the North American Mesoscale Forecast System, version 3 (NAMv3) are explored. Two severe weather outbreaks—occurring over the southeastern United States on 28 April 2014 and the central Great Plains on 6 May 2015—are forecast retrospectively using the NAMv3 CONUS (4 km) and Fire Weather (1.33 km) nests, each with 5-min output. Points of convection initiation are identified, and patterns leading to convection initiation in the model forecasts are determined. Results indicate that in the 30 min preceding convection initiation at a grid point, upward motion at low levels of the atmosphere enables a parcel to rise to its level of free convection, above which it is accelerated by the buoyancy force. A moist absolutely unstable layer (MAUL) typically is produced at the top of the updraft. However, when strong updrafts are collocated with large vertical gradients of potential temperature and moisture, noisy vertical profiles of temperature, moisture, and hydrometeor concentration develop beneath the rising MAUL. The noisy profiles found in this study are qualitatively similar to those that resulted in NAMv3 failures during simulations of Hurricane Joaquin in 2015. The CM1 cloud model is used to reproduce these noisy profiles, and results indicate that the noise can be mitigated by including explicit vertical diffusion in the model. Left unchecked, the noisy profiles are shown to impact convective storm features such as cold pools, precipitation, updraft helicity intensity and tracks, and the initiation of spurious convection.
Abstract
In support of the Next Generation Global Prediction System (NGGPS) project, processes leading to convection initiation in the North American Mesoscale Forecast System, version 3 (NAMv3) are explored. Two severe weather outbreaks—occurring over the southeastern United States on 28 April 2014 and the central Great Plains on 6 May 2015—are forecast retrospectively using the NAMv3 CONUS (4 km) and Fire Weather (1.33 km) nests, each with 5-min output. Points of convection initiation are identified, and patterns leading to convection initiation in the model forecasts are determined. Results indicate that in the 30 min preceding convection initiation at a grid point, upward motion at low levels of the atmosphere enables a parcel to rise to its level of free convection, above which it is accelerated by the buoyancy force. A moist absolutely unstable layer (MAUL) typically is produced at the top of the updraft. However, when strong updrafts are collocated with large vertical gradients of potential temperature and moisture, noisy vertical profiles of temperature, moisture, and hydrometeor concentration develop beneath the rising MAUL. The noisy profiles found in this study are qualitatively similar to those that resulted in NAMv3 failures during simulations of Hurricane Joaquin in 2015. The CM1 cloud model is used to reproduce these noisy profiles, and results indicate that the noise can be mitigated by including explicit vertical diffusion in the model. Left unchecked, the noisy profiles are shown to impact convective storm features such as cold pools, precipitation, updraft helicity intensity and tracks, and the initiation of spurious convection.
Abstract
A key aim of observational campaigns is to sample atmosphere–ocean phenomena to improve understanding of these phenomena, and in turn, numerical weather prediction. In early 2018 and 2019, the Atmospheric River Reconnaissance (AR Recon) campaign released dropsondes and radiosondes into atmospheric rivers (ARs) over the northeast Pacific Ocean to collect unique observations of temperature, winds, and moisture in ARs. These narrow regions of water vapor transport in the atmosphere—like rivers in the sky—can be associated with extreme precipitation and flooding events in the midlatitudes. This study uses the dropsonde observations collected during the AR Recon campaign and the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) to evaluate forecasts of ARs. Results show that ECMWF IFS forecasts 1) were colder than observations by up to 0.6 K throughout the troposphere; 2) have a dry bias in the lower troposphere, which along with weaker winds below 950 hPa, resulted in weaker horizontal water vapor fluxes in the 950–1000-hPa layer; and 3) exhibit an underdispersiveness in the water vapor flux that largely arises from model representativeness errors associated with dropsondes. Four U.S. West Coast radiosonde sites confirm the IFS cold bias throughout winter. These issues are likely to affect the model’s hydrological cycle and hence precipitation forecasts.
Abstract
A key aim of observational campaigns is to sample atmosphere–ocean phenomena to improve understanding of these phenomena, and in turn, numerical weather prediction. In early 2018 and 2019, the Atmospheric River Reconnaissance (AR Recon) campaign released dropsondes and radiosondes into atmospheric rivers (ARs) over the northeast Pacific Ocean to collect unique observations of temperature, winds, and moisture in ARs. These narrow regions of water vapor transport in the atmosphere—like rivers in the sky—can be associated with extreme precipitation and flooding events in the midlatitudes. This study uses the dropsonde observations collected during the AR Recon campaign and the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) to evaluate forecasts of ARs. Results show that ECMWF IFS forecasts 1) were colder than observations by up to 0.6 K throughout the troposphere; 2) have a dry bias in the lower troposphere, which along with weaker winds below 950 hPa, resulted in weaker horizontal water vapor fluxes in the 950–1000-hPa layer; and 3) exhibit an underdispersiveness in the water vapor flux that largely arises from model representativeness errors associated with dropsondes. Four U.S. West Coast radiosonde sites confirm the IFS cold bias throughout winter. These issues are likely to affect the model’s hydrological cycle and hence precipitation forecasts.
