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Abstract
The Marshall Fire on 30 December 2021 became the most destructive wildfire costwise in Colorado history as it evolved into a suburban firestorm in southeastern Boulder County, driven by strong winds and a snow-free and drought-influenced fuel state. The fire was driven by a strong downslope windstorm that maintained its intensity for nearly 11 hours. The southward movement of a large-scale jet axis across Boulder County brought a quick transition that day into a zone of upper-level descent, enhancing the midlevel inversion providing a favorable environment for an amplifying downstream mountain wave. In several aspects, this windstorm did not follow typical downslope windstorm behavior. NOAA rapidly updating numerical weather prediction guidance (including the High-Resolution Rapid Refresh) provided operationally useful forecasts of the windstorm, leading to the issuance of a High-Wind Warning (HWW) for eastern Boulder County. No Red Flag Warning was issued due to a too restrictive relative humidity criterion (already published alternatives are recommended); however, owing to the HWW, a countywide burn ban was issued for that day. Consideration of spatial (vertical and horizontal) and temporal (both valid time and initialization time) neighborhoods allows some quantification of forecast uncertainty from deterministic forecasts—important in real-time use for forecasting and public warnings of extreme events. Essentially, dimensions of the deterministic model were used to roughly estimate an ensemble forecast. These dimensions including run-to-run consistency are also important for subsequent evaluation of forecasts for small-scale features such as downslope windstorms and the tropospheric features responsible for them, similar to forecasts of deep, moist convection and related severe weather.
Significance Statement
The Front Range windstorm of 30 December 2021 combined extreme surface winds (>45 m s−1) with fire ignition resulting in an extraordinary and quickly evolving, extremely destructive wildfire–urban interface fire event. This windstorm differed from typical downslope windstorms in several aspects. We describe the observations, model guidance, and decision-making of operational forecasters for this event. In effect, an ensemble forecast was approximated by use of a frequently updated deterministic model by operational forecasters, and this combined use of temporal, spatial (horizontal and vertical), and other forecast dimensions is suggested to better estimate the possibility of such extreme events.
Abstract
The Marshall Fire on 30 December 2021 became the most destructive wildfire costwise in Colorado history as it evolved into a suburban firestorm in southeastern Boulder County, driven by strong winds and a snow-free and drought-influenced fuel state. The fire was driven by a strong downslope windstorm that maintained its intensity for nearly 11 hours. The southward movement of a large-scale jet axis across Boulder County brought a quick transition that day into a zone of upper-level descent, enhancing the midlevel inversion providing a favorable environment for an amplifying downstream mountain wave. In several aspects, this windstorm did not follow typical downslope windstorm behavior. NOAA rapidly updating numerical weather prediction guidance (including the High-Resolution Rapid Refresh) provided operationally useful forecasts of the windstorm, leading to the issuance of a High-Wind Warning (HWW) for eastern Boulder County. No Red Flag Warning was issued due to a too restrictive relative humidity criterion (already published alternatives are recommended); however, owing to the HWW, a countywide burn ban was issued for that day. Consideration of spatial (vertical and horizontal) and temporal (both valid time and initialization time) neighborhoods allows some quantification of forecast uncertainty from deterministic forecasts—important in real-time use for forecasting and public warnings of extreme events. Essentially, dimensions of the deterministic model were used to roughly estimate an ensemble forecast. These dimensions including run-to-run consistency are also important for subsequent evaluation of forecasts for small-scale features such as downslope windstorms and the tropospheric features responsible for them, similar to forecasts of deep, moist convection and related severe weather.
Significance Statement
The Front Range windstorm of 30 December 2021 combined extreme surface winds (>45 m s−1) with fire ignition resulting in an extraordinary and quickly evolving, extremely destructive wildfire–urban interface fire event. This windstorm differed from typical downslope windstorms in several aspects. We describe the observations, model guidance, and decision-making of operational forecasters for this event. In effect, an ensemble forecast was approximated by use of a frequently updated deterministic model by operational forecasters, and this combined use of temporal, spatial (horizontal and vertical), and other forecast dimensions is suggested to better estimate the possibility of such extreme events.
Abstract
Several new precipitation-type algorithms have been developed to improve NWP predictions of surface precipitation type during winter storms. In this study, we evaluate whether it is possible to objectively declare one algorithm as superior to another through comparison of three precipitation-type algorithms when validated using different techniques. The apparent skill of the algorithms is dependent on the choice of performance metric—algorithms can have high scores for some metrics and poor scores for others. It is also possible for an algorithm to have high skill at diagnosing some precipitation types and poor skill with others. Algorithm skill is also highly dependent on the choice of verification data/methodology. Just by changing what data are considered “truth,” we were able to substantially change the apparent skill of all algorithms evaluated herein. These findings suggest an objective declaration of algorithm “goodness” is not possible. Moreover, they indicate that the unambiguous declaration of superiority is difficult, if not impossible. A contributing factor to algorithm performance is uncertainty of the microphysical processes that lead to phase changes of falling hydrometeors, which are treated differently by each algorithm, thus resulting in different biases in near −0°C environments. These biases are evident even when algorithms are applied to ensemble forecasts. Hence, a multi-algorithm approach is advocated to account for this source of uncertainty. Although the apparent performance of this approach is still dependent on the choice of performance metric and precipitation type, a case-study analysis shows it has the potential to provide better decision support than the single-algorithm approach.
Significance Statement
Many investigators are developing new-and-improved algorithms to diagnose the surface precipitation type in winter storms. Whether these algorithms can be declared as objectively superior to existing strategies is unknown. Herein, we evaluate different methods to measure algorithm performance to assess whether it is possible to state one algorithm is superior to another. The results of this study suggest such claims are difficult, if not impossible, to make, at least not for the algorithms considered herein. Because algorithms can have certain biases, we advocate a multi-algorithm approach wherein multiple algorithms are applied to forecasts and a probabilistic prediction of precipitation type is generated. The potential value of this is demonstrated through a case-study analysis that shows promise for enhanced decision support.
Abstract
Several new precipitation-type algorithms have been developed to improve NWP predictions of surface precipitation type during winter storms. In this study, we evaluate whether it is possible to objectively declare one algorithm as superior to another through comparison of three precipitation-type algorithms when validated using different techniques. The apparent skill of the algorithms is dependent on the choice of performance metric—algorithms can have high scores for some metrics and poor scores for others. It is also possible for an algorithm to have high skill at diagnosing some precipitation types and poor skill with others. Algorithm skill is also highly dependent on the choice of verification data/methodology. Just by changing what data are considered “truth,” we were able to substantially change the apparent skill of all algorithms evaluated herein. These findings suggest an objective declaration of algorithm “goodness” is not possible. Moreover, they indicate that the unambiguous declaration of superiority is difficult, if not impossible. A contributing factor to algorithm performance is uncertainty of the microphysical processes that lead to phase changes of falling hydrometeors, which are treated differently by each algorithm, thus resulting in different biases in near −0°C environments. These biases are evident even when algorithms are applied to ensemble forecasts. Hence, a multi-algorithm approach is advocated to account for this source of uncertainty. Although the apparent performance of this approach is still dependent on the choice of performance metric and precipitation type, a case-study analysis shows it has the potential to provide better decision support than the single-algorithm approach.
Significance Statement
Many investigators are developing new-and-improved algorithms to diagnose the surface precipitation type in winter storms. Whether these algorithms can be declared as objectively superior to existing strategies is unknown. Herein, we evaluate different methods to measure algorithm performance to assess whether it is possible to state one algorithm is superior to another. The results of this study suggest such claims are difficult, if not impossible, to make, at least not for the algorithms considered herein. Because algorithms can have certain biases, we advocate a multi-algorithm approach wherein multiple algorithms are applied to forecasts and a probabilistic prediction of precipitation type is generated. The potential value of this is demonstrated through a case-study analysis that shows promise for enhanced decision support.
Abstract
Visible satellite imagery is widely used by operational weather forecast centers for tropical and extratropical cyclone analysis and marine forecasting. The absence of visible imagery at night can significantly degrade forecast capabilities, such as determining tropical cyclone center locations or tracking warm-topped convective clusters. This paper documents ProxyVis imagery, an infrared-based proxy for daytime visible imagery developed to address the lack of visible satellite imagery at night and the limitations of existing nighttime visible options. ProxyVis was trained on the VIIRS day/night band imagery at times close to the full moon using VIIRS IR channels with closely matching GOES-16/17/18, Himawari-8/9, and Meteosat-9/10/11 channels. The final operational product applies the ProxyVis algorithms to geostationary satellite data and combines daytime visible and nighttime ProxyVis data to create full-disk animated GeoProxyVis imagery. The simple versions of the ProxyVis algorithm enable its generation from earlier GOES and Meteosat satellite imagery. ProxyVis offers significant improvement over existing operational products for tracking nighttime oceanic low-level clouds. Further, it is qualitatively similar to visible imagery for a wide range of backgrounds and synoptic conditions and phenomena, enabling forecasters to use it without special training. ProxyVis was first introduced to National Hurricane Center (NHC) operations in 2018 and was found to be extremely useful by forecasters becoming part of their standard operational satellite product suite in 2019. Currently, ProxyVis implemented for GOES-16/18, Himawari-9, and Meteosat-9/10/11 is being used in operational settings and evaluated for transition to operations at multiple NWS offices and the Joint Typhoon Warning Center.
Significance Statement
This paper describes ProxyVis imagery, a new method for combining infrared channels to qualitatively mimic daytime visible imagery at nighttime. ProxyVis demonstrates that a simple linear regression can combine just a few commonly available infrared channels to develop a nighttime proxy for visible imagery that significantly improves a forecaster’s ability to track low-level oceanic clouds and circulation features at night, works for all current geostationary satellites, and is useful across a wide range of backgrounds and meteorological scenarios. Animated ProxyVis geostationary imagery has been operational at the National Hurricane Center since 2019 and is also currently being transitioned to operations at other NWS offices and the Joint Typhoon Warning Center.
Abstract
Visible satellite imagery is widely used by operational weather forecast centers for tropical and extratropical cyclone analysis and marine forecasting. The absence of visible imagery at night can significantly degrade forecast capabilities, such as determining tropical cyclone center locations or tracking warm-topped convective clusters. This paper documents ProxyVis imagery, an infrared-based proxy for daytime visible imagery developed to address the lack of visible satellite imagery at night and the limitations of existing nighttime visible options. ProxyVis was trained on the VIIRS day/night band imagery at times close to the full moon using VIIRS IR channels with closely matching GOES-16/17/18, Himawari-8/9, and Meteosat-9/10/11 channels. The final operational product applies the ProxyVis algorithms to geostationary satellite data and combines daytime visible and nighttime ProxyVis data to create full-disk animated GeoProxyVis imagery. The simple versions of the ProxyVis algorithm enable its generation from earlier GOES and Meteosat satellite imagery. ProxyVis offers significant improvement over existing operational products for tracking nighttime oceanic low-level clouds. Further, it is qualitatively similar to visible imagery for a wide range of backgrounds and synoptic conditions and phenomena, enabling forecasters to use it without special training. ProxyVis was first introduced to National Hurricane Center (NHC) operations in 2018 and was found to be extremely useful by forecasters becoming part of their standard operational satellite product suite in 2019. Currently, ProxyVis implemented for GOES-16/18, Himawari-9, and Meteosat-9/10/11 is being used in operational settings and evaluated for transition to operations at multiple NWS offices and the Joint Typhoon Warning Center.
Significance Statement
This paper describes ProxyVis imagery, a new method for combining infrared channels to qualitatively mimic daytime visible imagery at nighttime. ProxyVis demonstrates that a simple linear regression can combine just a few commonly available infrared channels to develop a nighttime proxy for visible imagery that significantly improves a forecaster’s ability to track low-level oceanic clouds and circulation features at night, works for all current geostationary satellites, and is useful across a wide range of backgrounds and meteorological scenarios. Animated ProxyVis geostationary imagery has been operational at the National Hurricane Center since 2019 and is also currently being transitioned to operations at other NWS offices and the Joint Typhoon Warning Center.
Abstract
East African countries benefit economically from the largest freshwater lake in Africa: Lake Victoria (LV). Around 30 million people live along its coastline, and 5.4 million people subsist on its fishing industry. However, more than 1000 fishermen die annually by high-wave conditions often produced by severe convective wind phenomena, which marks this lake one of the deadliest places in the world for hazardous weather impacts. The World Meteorological Organization launched the 3-yr High Impact Weather Lake System (HIGHWAY) project, with the main objective to reduce loss of lives and economic goods in the lake basin and improve the resilience of the local communities. The project conducted a field campaign in 2019 aiming to provide forecasters with high-resolution observations and to study the storm life cycle over the lake basin. The research discussed here used the S-band polarimetric Tanzania radar from the field campaign to investigate the diurnal cycle of the convective mode over the lake. We classified the lake storms occurring during the two wet seasons into six different convective modes and present their diurnal evolution, organization, and main radar-based attributes, thereby extending the knowledge of convection on the lake. The result is the creation of a “convection catalog for Lake Victoria,” using the operational forecast lake sectors, and defining the exact times for the different timeslots resulting from the HIGHWAY project for the marine forecast. This will inform methods to improve the marine operational forecasts for Lake Victoria, and to provide the basis for new standard operation procedures (SOP) for severe weather surveillance and warning.
Significance Statement
In this work we use new radar data over Lake Victoria, Africa, to study convective mode organization and its diurnal cycle over the lake. This work is of particular importance due to the numerous hazardous weather events and related accidents on the lake, including capsized boats, plane crashes, floods, and hailstorms on the shore settlements, that are responsible for a high annual fatality toll. Results of our analyses provide updated information for operational marine forecasts using relevant time segments and sectors of the lake to improve nowcasting operations in Lake Victoria.
Abstract
East African countries benefit economically from the largest freshwater lake in Africa: Lake Victoria (LV). Around 30 million people live along its coastline, and 5.4 million people subsist on its fishing industry. However, more than 1000 fishermen die annually by high-wave conditions often produced by severe convective wind phenomena, which marks this lake one of the deadliest places in the world for hazardous weather impacts. The World Meteorological Organization launched the 3-yr High Impact Weather Lake System (HIGHWAY) project, with the main objective to reduce loss of lives and economic goods in the lake basin and improve the resilience of the local communities. The project conducted a field campaign in 2019 aiming to provide forecasters with high-resolution observations and to study the storm life cycle over the lake basin. The research discussed here used the S-band polarimetric Tanzania radar from the field campaign to investigate the diurnal cycle of the convective mode over the lake. We classified the lake storms occurring during the two wet seasons into six different convective modes and present their diurnal evolution, organization, and main radar-based attributes, thereby extending the knowledge of convection on the lake. The result is the creation of a “convection catalog for Lake Victoria,” using the operational forecast lake sectors, and defining the exact times for the different timeslots resulting from the HIGHWAY project for the marine forecast. This will inform methods to improve the marine operational forecasts for Lake Victoria, and to provide the basis for new standard operation procedures (SOP) for severe weather surveillance and warning.
Significance Statement
In this work we use new radar data over Lake Victoria, Africa, to study convective mode organization and its diurnal cycle over the lake. This work is of particular importance due to the numerous hazardous weather events and related accidents on the lake, including capsized boats, plane crashes, floods, and hailstorms on the shore settlements, that are responsible for a high annual fatality toll. Results of our analyses provide updated information for operational marine forecasts using relevant time segments and sectors of the lake to improve nowcasting operations in Lake Victoria.
Abstract
Spatial patterns of tropical cyclone tornadoes (TCTs), and their relationship to patterns of mesoscale predictors within U.S. landfalling tropical cyclones (LTCs) are investigated using multicase composites from 27 years of reanalysis data (1995–2021). For 72 cases of LTCs with wide-ranging TC intensities at landfall, daytime TCT frequency maxima are found in the northeast, right-front, and downshear-right quadrants when their composites are constructed in ground-relative, TC-heading relative, and environmental shear relative coordinates, respectively. TCT maxima are located near maxima of 10-m–700-hPa bulk wind difference (BWD), which are enhanced by the TC circulation. This proxy for bulk vertical shear in roughly the lowest 3 km is among the best predictors of maximum TCT frequency. Relative to other times, the position of maximum TCT frequency during the afternoon shifts ∼100 km outward from the LTC center toward larger MLCAPE values. Composites containing the strongest LTCs have the strongest maximum 10-m–700-hPa and 10-m–500-hPa BWDs (∼20 m s−1) with nearby maximum frequencies of TCTs. Corresponding composites containing weaker LTCs but still many TCTs, had bulk vertical shear values that were ∼20% smaller (∼16 m s−1). Additional composites of cases having similarly weak average LTC strength at landfall, but few or no TCTs, had both maximum bulk vertical shears that were an additional ∼20% lower (∼12 m s−1) and smaller MLCAPE. TCT environments occurring well inland are distinguished from others by having stronger westerly shear and a west–east-oriented baroclinic zone (i.e., north–south temperature gradient) that enhances mesoscale ascent and deep convection on the LTC’s east side.
Abstract
Spatial patterns of tropical cyclone tornadoes (TCTs), and their relationship to patterns of mesoscale predictors within U.S. landfalling tropical cyclones (LTCs) are investigated using multicase composites from 27 years of reanalysis data (1995–2021). For 72 cases of LTCs with wide-ranging TC intensities at landfall, daytime TCT frequency maxima are found in the northeast, right-front, and downshear-right quadrants when their composites are constructed in ground-relative, TC-heading relative, and environmental shear relative coordinates, respectively. TCT maxima are located near maxima of 10-m–700-hPa bulk wind difference (BWD), which are enhanced by the TC circulation. This proxy for bulk vertical shear in roughly the lowest 3 km is among the best predictors of maximum TCT frequency. Relative to other times, the position of maximum TCT frequency during the afternoon shifts ∼100 km outward from the LTC center toward larger MLCAPE values. Composites containing the strongest LTCs have the strongest maximum 10-m–700-hPa and 10-m–500-hPa BWDs (∼20 m s−1) with nearby maximum frequencies of TCTs. Corresponding composites containing weaker LTCs but still many TCTs, had bulk vertical shear values that were ∼20% smaller (∼16 m s−1). Additional composites of cases having similarly weak average LTC strength at landfall, but few or no TCTs, had both maximum bulk vertical shears that were an additional ∼20% lower (∼12 m s−1) and smaller MLCAPE. TCT environments occurring well inland are distinguished from others by having stronger westerly shear and a west–east-oriented baroclinic zone (i.e., north–south temperature gradient) that enhances mesoscale ascent and deep convection on the LTC’s east side.
Abstract
The primary source of guidance used by the Storm Surge Unit (SSU) at the National Hurricane Center (NHC) for issuing storm surge watches and warnings is the Probabilistic Tropical Storm Surge model (P-Surge). P-Surge is an ensemble of Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model forecasts that is generated based on historical error distributions from NHC official forecasts. A probabilistic framework is used for operational storm surge forecasting to account for uncertainty related to the tropical cyclone track and wind forcing. Previous studies have shown that the size of a storm’s wind field is an important factor that can affect storm surge. A simple radius of maximum wind (RMW) prediction scheme was developed to forecast RMW based on NHC forecast parameters. Verification results indicate this scheme is an improvement over the RMW forecasts used by previous versions of P-Surge. To test the impact of the updated RMW forecasts in P-Surge, retrospective cases were selected from 25 storms from 2008 to 2020 that had an adequate number of observations. Evaluation of P-Surge forecasts using these improved RMW forecasts shows that the probability of detection is higher for most probability of exceedance thresholds. In addition, the forecast reliability is improved, and there is an increase in the number of high probability forecasts for extreme events at longer lead times. The improved RMW forecasts were recently incorporated into the operational version of P-Surge (v2.9), and serve as an important step toward extending the lead time of skillful and reliable storm surge forecasts.
Abstract
The primary source of guidance used by the Storm Surge Unit (SSU) at the National Hurricane Center (NHC) for issuing storm surge watches and warnings is the Probabilistic Tropical Storm Surge model (P-Surge). P-Surge is an ensemble of Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model forecasts that is generated based on historical error distributions from NHC official forecasts. A probabilistic framework is used for operational storm surge forecasting to account for uncertainty related to the tropical cyclone track and wind forcing. Previous studies have shown that the size of a storm’s wind field is an important factor that can affect storm surge. A simple radius of maximum wind (RMW) prediction scheme was developed to forecast RMW based on NHC forecast parameters. Verification results indicate this scheme is an improvement over the RMW forecasts used by previous versions of P-Surge. To test the impact of the updated RMW forecasts in P-Surge, retrospective cases were selected from 25 storms from 2008 to 2020 that had an adequate number of observations. Evaluation of P-Surge forecasts using these improved RMW forecasts shows that the probability of detection is higher for most probability of exceedance thresholds. In addition, the forecast reliability is improved, and there is an increase in the number of high probability forecasts for extreme events at longer lead times. The improved RMW forecasts were recently incorporated into the operational version of P-Surge (v2.9), and serve as an important step toward extending the lead time of skillful and reliable storm surge forecasts.
Abstract
A technique used widely to forecast the potential for QLCS mesovortices is known as the “Three Ingredients Method” (3IM). The 3IM states that mesovortices are favored where 1) the QLCS cold pool and ambient low-level shear are said to be nearly balanced or slightly shear dominant, 2) where the component of the 0–3-km wind shear normal to the convective line is ≥30 kt (1 kt ≈ 0.51 m s−1), and 3) where a rear-inflow jet or enhanced outflow causes a surge or bow along the convective line. Despite its widespread use in operational settings, this method has received little evaluation in formal literature. To evaluate the 3IM, radiosonde observations are compared to radar-observed QLCS properties. The distance between the gust front and high reflectivity in the leading convective line (the “U-to-R distance”), the presence of rear-inflow surges, and mesovortices (MVs) were each assessed across 1820 line segments within 50 observed QLCSs. Although 0–3-km line-normal wind shear is statistically different between MV-genesis and null segments, values are ≤30 kt for 44% of MV-genesis segments. The 0–6-km line-normal wind shear also shows strong discrimination between MV-genesis and null segments and displays the best linear relationship of the U-to-R distance (a measure of system balance) among layers tested, although the scatter and overlap in distributions suggest that many factors can impact MV genesis (as expected). Overall, most MVs occur where the U-to-R distance lies between −5 and 5 km in the presence of a rear-inflow surge, along with positive 0–1-km wind shear, 0–3-km wind shear > 10 kt, and 0–6-km wind shear > 20 kt (all line-normal).
Significance Statement
Near the leading edge of thunderstorm lines, areas of rotation that can produce tornadoes and strong winds (“mesovortices”) often develop rapidly. Despite advances in understanding mesovortices, few operational guidelines exist to anticipate their genesis. One popular method used to forecast mesovortices—the “Three Ingredients Method”—is evaluated in this study. Our work confirms the importance of two of the ingredients—a surge of outflow winds and thunderstorms that stay nearly atop the leading edge of the outflow. However, we find that many mesovortices occur below the threshold of low-level wind shear ascribed by the forecast method. Refinements to the method are suggested, including the favorable distance between the leading edge of the outflow and thunderstorm updrafts and lower bounds of wind shear over multiple layers, below which mesovortices may be unlikely.
Abstract
A technique used widely to forecast the potential for QLCS mesovortices is known as the “Three Ingredients Method” (3IM). The 3IM states that mesovortices are favored where 1) the QLCS cold pool and ambient low-level shear are said to be nearly balanced or slightly shear dominant, 2) where the component of the 0–3-km wind shear normal to the convective line is ≥30 kt (1 kt ≈ 0.51 m s−1), and 3) where a rear-inflow jet or enhanced outflow causes a surge or bow along the convective line. Despite its widespread use in operational settings, this method has received little evaluation in formal literature. To evaluate the 3IM, radiosonde observations are compared to radar-observed QLCS properties. The distance between the gust front and high reflectivity in the leading convective line (the “U-to-R distance”), the presence of rear-inflow surges, and mesovortices (MVs) were each assessed across 1820 line segments within 50 observed QLCSs. Although 0–3-km line-normal wind shear is statistically different between MV-genesis and null segments, values are ≤30 kt for 44% of MV-genesis segments. The 0–6-km line-normal wind shear also shows strong discrimination between MV-genesis and null segments and displays the best linear relationship of the U-to-R distance (a measure of system balance) among layers tested, although the scatter and overlap in distributions suggest that many factors can impact MV genesis (as expected). Overall, most MVs occur where the U-to-R distance lies between −5 and 5 km in the presence of a rear-inflow surge, along with positive 0–1-km wind shear, 0–3-km wind shear > 10 kt, and 0–6-km wind shear > 20 kt (all line-normal).
Significance Statement
Near the leading edge of thunderstorm lines, areas of rotation that can produce tornadoes and strong winds (“mesovortices”) often develop rapidly. Despite advances in understanding mesovortices, few operational guidelines exist to anticipate their genesis. One popular method used to forecast mesovortices—the “Three Ingredients Method”—is evaluated in this study. Our work confirms the importance of two of the ingredients—a surge of outflow winds and thunderstorms that stay nearly atop the leading edge of the outflow. However, we find that many mesovortices occur below the threshold of low-level wind shear ascribed by the forecast method. Refinements to the method are suggested, including the favorable distance between the leading edge of the outflow and thunderstorm updrafts and lower bounds of wind shear over multiple layers, below which mesovortices may be unlikely.
Abstract
Road surface temperatures are a critical factor in determining driving conditions, especially during winter storms. Road temperature observations across the United States are sparse and located mainly along major highways. A machine learning–based system for nowcasting the probability of subfreezing road surface temperatures was developed at NSSL to allow for widespread monitoring of road conditions in real time. In this article, these products were evaluated over two winter seasons. Strengths and weaknesses in the nowcast system were identified by stratifying the evaluation metrics into various subsets. These results show that the current system performed well in general, but significantly underpredicted the probability of subfreezing roads during frozen precipitation events. Machine learning experiments were performed to attempt to address these issues. Evaluations of these experiments indicate reduction in errors when precipitation phase was included as a predictor and precipitating cases were more substantially represented in the training data for the machine learning system.
Significance Statement
The purpose of this study is to better understand the strengths and weaknesses of a system that predicts the probability of subfreezing road surface temperatures. We found that the system performed well in general, but underpredicted the probabilities when frozen precipitation was predicted to reach the surface. These biases were substantially improved by modifying the system to increase its focus on situations with falling precipitation. The updated system should allow for improved monitoring and forecasting of potentially hazardous conditions during winter storms.
Abstract
Road surface temperatures are a critical factor in determining driving conditions, especially during winter storms. Road temperature observations across the United States are sparse and located mainly along major highways. A machine learning–based system for nowcasting the probability of subfreezing road surface temperatures was developed at NSSL to allow for widespread monitoring of road conditions in real time. In this article, these products were evaluated over two winter seasons. Strengths and weaknesses in the nowcast system were identified by stratifying the evaluation metrics into various subsets. These results show that the current system performed well in general, but significantly underpredicted the probability of subfreezing roads during frozen precipitation events. Machine learning experiments were performed to attempt to address these issues. Evaluations of these experiments indicate reduction in errors when precipitation phase was included as a predictor and precipitating cases were more substantially represented in the training data for the machine learning system.
Significance Statement
The purpose of this study is to better understand the strengths and weaknesses of a system that predicts the probability of subfreezing road surface temperatures. We found that the system performed well in general, but underpredicted the probabilities when frozen precipitation was predicted to reach the surface. These biases were substantially improved by modifying the system to increase its focus on situations with falling precipitation. The updated system should allow for improved monitoring and forecasting of potentially hazardous conditions during winter storms.
Abstract
Atmospheric River Reconnaissance has held field campaigns during cool seasons since 2016. These campaigns have provided thousands of dropsonde data profiles, which are assimilated into multiple global operational numerical weather prediction models. Data denial experiments, conducted by running a parallel set of forecasts that exclude the dropsonde information, allow testing of the impact of the dropsonde data on model analyses and the subsequent forecasts. Here, we investigate the differences in skill between the control forecasts (with dropsonde data assimilated) and denial forecasts (without dropsonde data assimilated) in terms of both precipitation and integrated vapor transport (IVT) at multiple thresholds. The differences are considered in the times and locations where there is a reasonable expectation of influence of an intensive observation period (IOP). Results for 2019 and 2020 from both the European Centre for Medium-Range Weather Forecasts (ECMWF) model and the National Centers for Environmental Prediction (NCEP) global model show improvements with the added information from the dropsondes. In particular, significant improvements in the control forecast IVT generally occur in both models, especially at higher values. Significant improvements in the control forecast precipitation also generally occur in both models, but the improvements vary depending on the lead time and metrics used.
Significance Statement
Atmospheric River Reconnaissance is a program that uses targeted aircraft flights over the northeast Pacific to take measurements of meteorological fields. These data are then ingested into global weather models with the intent of improving the initial conditions and resulting forecasts along the U.S. West Coast. The impacts of these observations on two global numerical weather models were investigated to determine their influence on the forecasts. The integrated vapor transport, a measure of both wind and humidity, saw significant improvements in both models with the additional observations. Precipitation forecasts were also improved, but with differing results between the two models.
Abstract
Atmospheric River Reconnaissance has held field campaigns during cool seasons since 2016. These campaigns have provided thousands of dropsonde data profiles, which are assimilated into multiple global operational numerical weather prediction models. Data denial experiments, conducted by running a parallel set of forecasts that exclude the dropsonde information, allow testing of the impact of the dropsonde data on model analyses and the subsequent forecasts. Here, we investigate the differences in skill between the control forecasts (with dropsonde data assimilated) and denial forecasts (without dropsonde data assimilated) in terms of both precipitation and integrated vapor transport (IVT) at multiple thresholds. The differences are considered in the times and locations where there is a reasonable expectation of influence of an intensive observation period (IOP). Results for 2019 and 2020 from both the European Centre for Medium-Range Weather Forecasts (ECMWF) model and the National Centers for Environmental Prediction (NCEP) global model show improvements with the added information from the dropsondes. In particular, significant improvements in the control forecast IVT generally occur in both models, especially at higher values. Significant improvements in the control forecast precipitation also generally occur in both models, but the improvements vary depending on the lead time and metrics used.
Significance Statement
Atmospheric River Reconnaissance is a program that uses targeted aircraft flights over the northeast Pacific to take measurements of meteorological fields. These data are then ingested into global weather models with the intent of improving the initial conditions and resulting forecasts along the U.S. West Coast. The impacts of these observations on two global numerical weather models were investigated to determine their influence on the forecasts. The integrated vapor transport, a measure of both wind and humidity, saw significant improvements in both models with the additional observations. Precipitation forecasts were also improved, but with differing results between the two models.
Abstract
This study investigates regional, seasonal biases in convection-allowing model forecasts of near-surface temperature and dewpoint in areas of particular importance to forecasts of severe local storms. One method compares model forecasts with objective analyses of observed conditions in the inflow sectors of reported tornadoes. A second method captures a broader sample of environments, comparing model forecasts with surface observations under certain warm-sector criteria. Both methods reveal a cold bias across all models tested in Southeast U.S. cool-season warm sectors. This is an operationally important bias given the thermodynamic sensitivity of instability-limited severe weather that is common in the Southeast cool season. There is not a clear bias across models in the Great Plains warm season, but instead more varied behavior with differing model physics.
Significance Statement
The severity of thunderstorms and the types of hazards they produce depend in part on the low-level temperature and moisture in the near-storm environment. It is important for numerical forecast models to accurately represent these fields in forecasts of severe weather events. We show that the most widely used short-term, high-resolution forecast models have a consistent cold bias of about 1 K (up to 2 K in certain cases) in storm environments in the southeastern U.S. cool season. Human forecasters must recognize and adjust for this bias, and future model development should aim to improve it.
Abstract
This study investigates regional, seasonal biases in convection-allowing model forecasts of near-surface temperature and dewpoint in areas of particular importance to forecasts of severe local storms. One method compares model forecasts with objective analyses of observed conditions in the inflow sectors of reported tornadoes. A second method captures a broader sample of environments, comparing model forecasts with surface observations under certain warm-sector criteria. Both methods reveal a cold bias across all models tested in Southeast U.S. cool-season warm sectors. This is an operationally important bias given the thermodynamic sensitivity of instability-limited severe weather that is common in the Southeast cool season. There is not a clear bias across models in the Great Plains warm season, but instead more varied behavior with differing model physics.
Significance Statement
The severity of thunderstorms and the types of hazards they produce depend in part on the low-level temperature and moisture in the near-storm environment. It is important for numerical forecast models to accurately represent these fields in forecasts of severe weather events. We show that the most widely used short-term, high-resolution forecast models have a consistent cold bias of about 1 K (up to 2 K in certain cases) in storm environments in the southeastern U.S. cool season. Human forecasters must recognize and adjust for this bias, and future model development should aim to improve it.
