Browse
Abstract
We evaluate the short-term weather forecast performance of three flavors of artificial neural networks (NNs): feed forward back propagation, radial basis function, and generalized regression. To prepare the application of the NNs to an operational setting, we tune NN hyperparameters using over two years of historical data. Five objective guidance products serve as predictors to the NNs: North American Mesoscale and Global Forecast System model output statistics (MOS) forecasts, the High-Resolution Rapid Refresh (HRRR) model, National Weather Service forecasts, and the National Blend of Models product. We independently test NN performance using 96 real-time forecasts of temperature, wind, and precipitation across 11 U.S. cities made during the WxChallenge, a weather forecasting competition. We demonstrate that all NNs significantly improve short-range weather forecasts relative to the traditional objective guidance aids used to train the networks. For example, 1-day maximum and minimum temperature forecast error is 20%–30% lower than MOS. However, NN improvement over multiple linear regression for short-term forecasts is not significant. We suggest this may be attributed to the small number of training samples, the operational nature of the experiment, and the short forecast lead times. Regardless, our results are consistent with previous work suggesting that applying NNs to model forecasts can have a positive impact on operational forecast skill and will become valuable tools when integrated into the forecast enterprise.
Significance Statement
We used approximately two years of historical weather data and objective forecasts for a number of cities to tune a series of artificial neural networks (NNs) to forecast 1-day values of maximum and minimum temperature, maximum sustained wind speed, and quantitative precipitation. We compare forecast error against common objective guidance and multiple linear regression. We found that the NNs exhibit about 25% lower error than common objective guidance for temperature forecasting and 50% lower error for wind speed. Our results suggest that NNs will be a valuable contributor to improving weather forecast skill when adopted into the existing forecast enterprise.
Abstract
We evaluate the short-term weather forecast performance of three flavors of artificial neural networks (NNs): feed forward back propagation, radial basis function, and generalized regression. To prepare the application of the NNs to an operational setting, we tune NN hyperparameters using over two years of historical data. Five objective guidance products serve as predictors to the NNs: North American Mesoscale and Global Forecast System model output statistics (MOS) forecasts, the High-Resolution Rapid Refresh (HRRR) model, National Weather Service forecasts, and the National Blend of Models product. We independently test NN performance using 96 real-time forecasts of temperature, wind, and precipitation across 11 U.S. cities made during the WxChallenge, a weather forecasting competition. We demonstrate that all NNs significantly improve short-range weather forecasts relative to the traditional objective guidance aids used to train the networks. For example, 1-day maximum and minimum temperature forecast error is 20%–30% lower than MOS. However, NN improvement over multiple linear regression for short-term forecasts is not significant. We suggest this may be attributed to the small number of training samples, the operational nature of the experiment, and the short forecast lead times. Regardless, our results are consistent with previous work suggesting that applying NNs to model forecasts can have a positive impact on operational forecast skill and will become valuable tools when integrated into the forecast enterprise.
Significance Statement
We used approximately two years of historical weather data and objective forecasts for a number of cities to tune a series of artificial neural networks (NNs) to forecast 1-day values of maximum and minimum temperature, maximum sustained wind speed, and quantitative precipitation. We compare forecast error against common objective guidance and multiple linear regression. We found that the NNs exhibit about 25% lower error than common objective guidance for temperature forecasting and 50% lower error for wind speed. Our results suggest that NNs will be a valuable contributor to improving weather forecast skill when adopted into the existing forecast enterprise.
Abstract
Atmospheric rivers (ARs) are long and narrow regions in the atmosphere of enhanced integrated water vapor transport (IVT) and can produce extreme precipitation and high societal impacts. Reliable and skillful forecasts of landfalling ARs in the western United States are critical to hazard preparation and aid in decision support activities, such as Forecast-Informed Reservoir Operations (FIRO). The purpose of this study is to compare the cool-season water year skill of the NCEP Global Ensemble Forecast System (GEFS) and ECMWF Ensemble Prediction System (EPS) forecasts of IVT along the U.S. West Coast for 2017–20. The skill is analyzed using probability-over-threshold forecasts of IVT magnitudes ≥ 250 kg m−1 s−1 (P 250) using contingency table skill metrics in coastal Northern California and along the west coast of North America. Analysis of P 250 with lead time (dProg/dt) found that the EPS provided ∼1 day of additional lead time for situational awareness over the GEFS at lead times of 6–10 days. Forecast skill analysis highlights that the EPS leads over the GEFS with success ratios 0.10–0.15 higher at lead times > 6 days for P 250 thresholds of ≥25% and ≥50%, while event-based skill analysis using the probability of detection (POD) found that both models were largely similar with minor latitudinal variations favoring higher POD for each model in different locations along the coast. The relative skill of the EPS over the GEFS is largely attributed to overforecasting by the GEFS at longer lead times and an increase in the false alarm ratio.
Significance Statement
The purpose of this study is to evaluate the efficacy of the NCEP Global Ensemble Forecast System (GEFS) and the ECMWF Ensemble Prediction System (EPS) in forecasting enhanced water vapor transport along the U.S. West Coast commonly associated with landfalling atmospheric rivers and heavy precipitation. The ensemble models allow us to calculate the probability that enhanced water vapor transport will occur, thereby providing situational awareness for decision-making, such as in hazard mitigation and water resource management. The results of this study indicate that the EPS model is on average more skillful than the GEFS model at lead times of ∼6–10 days with a higher success ratio and lower false alarm ratio.
Abstract
Atmospheric rivers (ARs) are long and narrow regions in the atmosphere of enhanced integrated water vapor transport (IVT) and can produce extreme precipitation and high societal impacts. Reliable and skillful forecasts of landfalling ARs in the western United States are critical to hazard preparation and aid in decision support activities, such as Forecast-Informed Reservoir Operations (FIRO). The purpose of this study is to compare the cool-season water year skill of the NCEP Global Ensemble Forecast System (GEFS) and ECMWF Ensemble Prediction System (EPS) forecasts of IVT along the U.S. West Coast for 2017–20. The skill is analyzed using probability-over-threshold forecasts of IVT magnitudes ≥ 250 kg m−1 s−1 (P 250) using contingency table skill metrics in coastal Northern California and along the west coast of North America. Analysis of P 250 with lead time (dProg/dt) found that the EPS provided ∼1 day of additional lead time for situational awareness over the GEFS at lead times of 6–10 days. Forecast skill analysis highlights that the EPS leads over the GEFS with success ratios 0.10–0.15 higher at lead times > 6 days for P 250 thresholds of ≥25% and ≥50%, while event-based skill analysis using the probability of detection (POD) found that both models were largely similar with minor latitudinal variations favoring higher POD for each model in different locations along the coast. The relative skill of the EPS over the GEFS is largely attributed to overforecasting by the GEFS at longer lead times and an increase in the false alarm ratio.
Significance Statement
The purpose of this study is to evaluate the efficacy of the NCEP Global Ensemble Forecast System (GEFS) and the ECMWF Ensemble Prediction System (EPS) in forecasting enhanced water vapor transport along the U.S. West Coast commonly associated with landfalling atmospheric rivers and heavy precipitation. The ensemble models allow us to calculate the probability that enhanced water vapor transport will occur, thereby providing situational awareness for decision-making, such as in hazard mitigation and water resource management. The results of this study indicate that the EPS model is on average more skillful than the GEFS model at lead times of ∼6–10 days with a higher success ratio and lower false alarm ratio.
Abstract
The forecast skill for week-2 wintertime surface air temperature (SAT) over the Northern Hemisphere by the Model for Prediction Across Scales–Atmosphere (MPAS-A) is evaluated and compared with operational forecast systems that participate in the Subseasonal to Seasonal Prediction project (S2S). An intercomparison of the MPAS against the China Meteorological Administration (CMA) model and the European Centre for Medium-Range Weather Forecasts (ECMWF) model was performed using 10-yr reforecasts. Comparing the forecast skill for SAT and atmospheric circulation anomalies at a lead of 2 weeks among the three models, the MPAS shows skill lower than the ECMWF model but higher than the CMA model. The gap in skills between the MPAS model and CMA model is not as large as that between the ECMWF model and MPAS model. Additionally, an intercomparison of the MPAS model against 10 S2S models is presented by using real-time forecasts since 2016 stored in the S2S database. The results show that the MPAS model has forecast skill for week-2 to week-4 wintertime SAT comparable to that in most S2S models. The MPAS model tends to be at an intermediate level compared to current operational forecast models.
Abstract
The forecast skill for week-2 wintertime surface air temperature (SAT) over the Northern Hemisphere by the Model for Prediction Across Scales–Atmosphere (MPAS-A) is evaluated and compared with operational forecast systems that participate in the Subseasonal to Seasonal Prediction project (S2S). An intercomparison of the MPAS against the China Meteorological Administration (CMA) model and the European Centre for Medium-Range Weather Forecasts (ECMWF) model was performed using 10-yr reforecasts. Comparing the forecast skill for SAT and atmospheric circulation anomalies at a lead of 2 weeks among the three models, the MPAS shows skill lower than the ECMWF model but higher than the CMA model. The gap in skills between the MPAS model and CMA model is not as large as that between the ECMWF model and MPAS model. Additionally, an intercomparison of the MPAS model against 10 S2S models is presented by using real-time forecasts since 2016 stored in the S2S database. The results show that the MPAS model has forecast skill for week-2 to week-4 wintertime SAT comparable to that in most S2S models. The MPAS model tends to be at an intermediate level compared to current operational forecast models.
Abstract
This study demonstrates an approach to expand and improve the current prediction capability of the National Water Model (NWM). The primary objective is to examine the potential benefit of real-time local stage measurements in streamflow prediction, particularly for local communities that do not benefit from the improved streamflow forecasts due to the current data assimilation (DA) scheme. The proposed approach incorporates real-time local stage measurements into the NWM streamflow DA procedure by using synthetic rating curves (SRC) developed based on an established open-channel flow model. For streamflow DA and its evaluation, we used 6-yr (2016–21) data collected from 140 U.S. Geological Survey (USGS) stations, where quality-assured rating curves are consistently maintained (verification stations), and 310 stage-only stations operated by the Iowa Flood Center and the USGS in Iowa. The evaluation result from NWM’s current DA configuration based on the USGS verification stations indicated that DA improves streamflow prediction skills significantly downstream from the station locations. This improvement tends to increase as the drainage scale becomes larger. The result from the new DA configuration including all stage-only sensors showed an expanded domain of improved predictions, compared to those from the open-loop simulation. This reveals that the real-time low-cost stage sensors are beneficial for streamflow prediction, particularly at small basins, while their utility appears to be limited at large drainage areas because of the inherent limitations of lidar-based channel geometry used for the SRC development. The framework presented in this study can be readily applied to include numerous stage-only stream gauges nationwide in the NWM modeling and forecasting procedures.
Abstract
This study demonstrates an approach to expand and improve the current prediction capability of the National Water Model (NWM). The primary objective is to examine the potential benefit of real-time local stage measurements in streamflow prediction, particularly for local communities that do not benefit from the improved streamflow forecasts due to the current data assimilation (DA) scheme. The proposed approach incorporates real-time local stage measurements into the NWM streamflow DA procedure by using synthetic rating curves (SRC) developed based on an established open-channel flow model. For streamflow DA and its evaluation, we used 6-yr (2016–21) data collected from 140 U.S. Geological Survey (USGS) stations, where quality-assured rating curves are consistently maintained (verification stations), and 310 stage-only stations operated by the Iowa Flood Center and the USGS in Iowa. The evaluation result from NWM’s current DA configuration based on the USGS verification stations indicated that DA improves streamflow prediction skills significantly downstream from the station locations. This improvement tends to increase as the drainage scale becomes larger. The result from the new DA configuration including all stage-only sensors showed an expanded domain of improved predictions, compared to those from the open-loop simulation. This reveals that the real-time low-cost stage sensors are beneficial for streamflow prediction, particularly at small basins, while their utility appears to be limited at large drainage areas because of the inherent limitations of lidar-based channel geometry used for the SRC development. The framework presented in this study can be readily applied to include numerous stage-only stream gauges nationwide in the NWM modeling and forecasting procedures.
Abstract
The Dynamical–Statistical–Analog Ensemble Forecast for Landfalling Typhoon Daily Precipitation (DSAEF_LTP_D) model is introduced in this paper. To improve the DSAEF_LTP_D model’s forecasting ability, tropical cyclone (TC) translation speed was introduced. Taking Supertyphoon Lekima (2019), which produced widespread heavy rainfall from 9 to 11 August 2019 as the target TC, two simulation experiments associated with the prediction of daily precipitation were conducted: the first involving the DSAEF_LTP_D model containing only the TC track (the actual trajectory of the TC center), named DSAEF_LTP_D-1; and the second containing both TC track and translation speed, named DSAEF_LTP_D-2. The results show the following: 1) With TC translation speed added into the model, the forecasting performance for heavy rainfall (24-h accumulated precipitation exceeding 50 and 100 mm) on 9 and 10 August improves, being able to successfully capture the center of heavy rainfall, but the forecasting performance is the same as DSAEF_LTP_D-1 on 11 August. 2) Compared with four numerical weather prediction (NWP) models (i.e., ECMWF, GFS, GRAPES, and SMS-WARMS), the TS100 + TS50 (the sum of TS values for predicting 24-h accumulated precipitation of ≥100 and ≥50 mm) of DSAEF_LTP_D-2 is comparable to the best performer of the NWP models (ECMWF) on 9 and 10 August, while the performance of DSAEF_LTP_D model for predicting heavy rainfall on 11 August is poor. 3) The newly added similarity regions make up for the deficiency that the similarity regions are narrower when the TC track is northward, which leads to DSAEF_LTP_D-2 having a better forecasting performance for heavy rainfall on 11 August, with the TS100 + TS50 increasing from 0.3021 to 0.4286, an increase of 41.87%.
Abstract
The Dynamical–Statistical–Analog Ensemble Forecast for Landfalling Typhoon Daily Precipitation (DSAEF_LTP_D) model is introduced in this paper. To improve the DSAEF_LTP_D model’s forecasting ability, tropical cyclone (TC) translation speed was introduced. Taking Supertyphoon Lekima (2019), which produced widespread heavy rainfall from 9 to 11 August 2019 as the target TC, two simulation experiments associated with the prediction of daily precipitation were conducted: the first involving the DSAEF_LTP_D model containing only the TC track (the actual trajectory of the TC center), named DSAEF_LTP_D-1; and the second containing both TC track and translation speed, named DSAEF_LTP_D-2. The results show the following: 1) With TC translation speed added into the model, the forecasting performance for heavy rainfall (24-h accumulated precipitation exceeding 50 and 100 mm) on 9 and 10 August improves, being able to successfully capture the center of heavy rainfall, but the forecasting performance is the same as DSAEF_LTP_D-1 on 11 August. 2) Compared with four numerical weather prediction (NWP) models (i.e., ECMWF, GFS, GRAPES, and SMS-WARMS), the TS100 + TS50 (the sum of TS values for predicting 24-h accumulated precipitation of ≥100 and ≥50 mm) of DSAEF_LTP_D-2 is comparable to the best performer of the NWP models (ECMWF) on 9 and 10 August, while the performance of DSAEF_LTP_D model for predicting heavy rainfall on 11 August is poor. 3) The newly added similarity regions make up for the deficiency that the similarity regions are narrower when the TC track is northward, which leads to DSAEF_LTP_D-2 having a better forecasting performance for heavy rainfall on 11 August, with the TS100 + TS50 increasing from 0.3021 to 0.4286, an increase of 41.87%.
Abstract
The National Severe Storm Laboratory’s Warn-on-Forecast System (WoFS) is a convection-allowing ensemble with rapidly cycled data assimilation (DA) of various satellite and radar datasets designed for prediction at 0–6-h lead time of hazardous weather. With the focus on short lead times, WoFS predictive accuracy is strongly dependent on its ability to accurately initialize and depict the evolution of ongoing storms. Since it takes multiple DA cycles to fully “spin up” ongoing storms, predictive skill is likely a function of storm age at the time of model initialization, meaning that older storms that have been through several DA cycles will be forecast with greater accuracy than newer storms that initiate just before model initialization or at any point after. To quantify this relationship, we apply an object-based spatial tracking and verification approach to map differences in the probability of detection (POD), in space–time, of predicted storm objects from WoFS with respect to Multi-Radar Multi-Sensor (MRMS) reflectivity objects. Object-tracking/matching statistics are computed for all suitable and available WoFS cases from 2017 to 2021. Our results indicate sharply increasing POD with increasing storm age for lead times within 3 h. PODs were about 0.3 for storm objects that emerge 2–3 h after model initialization, while for storm objects that were at least an hour old at the time of model initialization by DA, PODs ranged from around 0.7 to 0.9 depending on the lead time. These results should aid in forecaster interpretation of WoFS, as well as guide WoFS developers on improving the model and DA system.
Significance Statement
The Warn-on-Forecast System (WoFS) is a collection of weather models designed to predict individual thunderstorms. Before the models can predict storms, they must ingest radar and satellite observations to put existing storms into the models. Because storms develop at different times, more observations will exist for some storms in the model domain than others, which results in WoFS forecasts with different accuracy for different storms. This paper estimates the differences in accuracy for storms that have existed for a long time and those that have not by tracking observed and predicted storms. We find that the likelihood of WoFS accurately predicting a thunderstorm nearly doubles if the storm has existed for over an hour prior to the forecast. Understanding this relationship between storm age and forecast accuracy will help forecasters better use WoFS predictions and guide future research to improve WoFS forecasts.
Abstract
The National Severe Storm Laboratory’s Warn-on-Forecast System (WoFS) is a convection-allowing ensemble with rapidly cycled data assimilation (DA) of various satellite and radar datasets designed for prediction at 0–6-h lead time of hazardous weather. With the focus on short lead times, WoFS predictive accuracy is strongly dependent on its ability to accurately initialize and depict the evolution of ongoing storms. Since it takes multiple DA cycles to fully “spin up” ongoing storms, predictive skill is likely a function of storm age at the time of model initialization, meaning that older storms that have been through several DA cycles will be forecast with greater accuracy than newer storms that initiate just before model initialization or at any point after. To quantify this relationship, we apply an object-based spatial tracking and verification approach to map differences in the probability of detection (POD), in space–time, of predicted storm objects from WoFS with respect to Multi-Radar Multi-Sensor (MRMS) reflectivity objects. Object-tracking/matching statistics are computed for all suitable and available WoFS cases from 2017 to 2021. Our results indicate sharply increasing POD with increasing storm age for lead times within 3 h. PODs were about 0.3 for storm objects that emerge 2–3 h after model initialization, while for storm objects that were at least an hour old at the time of model initialization by DA, PODs ranged from around 0.7 to 0.9 depending on the lead time. These results should aid in forecaster interpretation of WoFS, as well as guide WoFS developers on improving the model and DA system.
Significance Statement
The Warn-on-Forecast System (WoFS) is a collection of weather models designed to predict individual thunderstorms. Before the models can predict storms, they must ingest radar and satellite observations to put existing storms into the models. Because storms develop at different times, more observations will exist for some storms in the model domain than others, which results in WoFS forecasts with different accuracy for different storms. This paper estimates the differences in accuracy for storms that have existed for a long time and those that have not by tracking observed and predicted storms. We find that the likelihood of WoFS accurately predicting a thunderstorm nearly doubles if the storm has existed for over an hour prior to the forecast. Understanding this relationship between storm age and forecast accuracy will help forecasters better use WoFS predictions and guide future research to improve WoFS forecasts.
Abstract
To better forecast tropical cyclone (TC) intensity change and understand forecast uncertainty, it is critical to recognize the inherent limitations of forecast models. The distributions of intensity change for statistical–dynamical models are too narrow, and some intensity change forecasts are shown to have larger errors and biases than others. The Intensity Bias and Uncertainty Scheme (IBUS) is developed in an intensity change framework, which estimates the bias and the standard deviation of intensity forecast errors. The IBUS is developed and applied to the Decay Statistical Hurricane Intensity Prediction Scheme (DSHP), the Logistic Growth Equation Model (LGEM), and official National Hurricane Center (NHC) forecasts (OFCL) separately. The analysis uses DSHP, LGEM, and OFCL forecasts from 2010 to 2019 in both the Atlantic and east Pacific basins. Each IBUS contains both a bias correction and forecast uncertainty estimate that is tested on the training dataset and evaluated on the 2020 season. The IBUS is able to reduce intensity biases and improve forecast errors beyond 120 h in each model and basin relative to the original forecasts. The IBUS is also able to communicate forecast uncertainty that explains ∼7%–11% of forecast variance at 48 h for DSHP and LGEM in the Atlantic. Better performance is found in the east Pacific at 96 h where the IBUS explains up to 30% of the errors in DSHP and 14% of the errors for LGEM. The IBUS for OFCL explains 9%–13% of the 48-h forecast uncertainty in the Atlantic and east Pacific with up to 30% variance explained for east Pacific forecasts at 96 h. IBUS for OFCL has the capability to provide intensity forecast uncertainty similar to the “cone of uncertainty” for track forecasts.
Abstract
To better forecast tropical cyclone (TC) intensity change and understand forecast uncertainty, it is critical to recognize the inherent limitations of forecast models. The distributions of intensity change for statistical–dynamical models are too narrow, and some intensity change forecasts are shown to have larger errors and biases than others. The Intensity Bias and Uncertainty Scheme (IBUS) is developed in an intensity change framework, which estimates the bias and the standard deviation of intensity forecast errors. The IBUS is developed and applied to the Decay Statistical Hurricane Intensity Prediction Scheme (DSHP), the Logistic Growth Equation Model (LGEM), and official National Hurricane Center (NHC) forecasts (OFCL) separately. The analysis uses DSHP, LGEM, and OFCL forecasts from 2010 to 2019 in both the Atlantic and east Pacific basins. Each IBUS contains both a bias correction and forecast uncertainty estimate that is tested on the training dataset and evaluated on the 2020 season. The IBUS is able to reduce intensity biases and improve forecast errors beyond 120 h in each model and basin relative to the original forecasts. The IBUS is also able to communicate forecast uncertainty that explains ∼7%–11% of forecast variance at 48 h for DSHP and LGEM in the Atlantic. Better performance is found in the east Pacific at 96 h where the IBUS explains up to 30% of the errors in DSHP and 14% of the errors for LGEM. The IBUS for OFCL explains 9%–13% of the 48-h forecast uncertainty in the Atlantic and east Pacific with up to 30% variance explained for east Pacific forecasts at 96 h. IBUS for OFCL has the capability to provide intensity forecast uncertainty similar to the “cone of uncertainty” for track forecasts.
Abstract
The degree of improvement in convective representation in NWP with horizontal grid spacings finer than 3 km remains debatable. While some research suggests subkilometer horizontal grid spacing is needed to resolve details of convective structures, other studies have shown that decreasing grid spacing from 3–4 to 1–2 km offers little additional value for forecasts of deep convection. In addition, few studies exist to show how changes in vertical grid spacing impact thunderstorm forecasts, especially when horizontal grid spacing is simultaneously decreased. The present research investigates how warm-season central U.S. simulated MCS cold pools for 11 observed cases are impacted by decreasing horizontal grid spacing from 3 to 1 km, while increasing the vertical levels from 50 to 100 in WRF runs. The 3-km runs with 100 levels produced the deepest and most negatively buoyant cold pools compared to all other grid spacings since updrafts were more poorly resolved, resulting in a higher flux of rearward-advected frozen hydrometeors, whose melting processes were augmented by the finer vertical grid spacing, which better resolved the melting layer. However, the more predominant signal among all 11 cases was for more expansive cold pools in 1-km runs, where the stronger and more abundant updrafts focused along the MCS leading line supported a larger volume of concentrated rearward hydrometeor advection and resultant latent cooling at lower levels.
Abstract
The degree of improvement in convective representation in NWP with horizontal grid spacings finer than 3 km remains debatable. While some research suggests subkilometer horizontal grid spacing is needed to resolve details of convective structures, other studies have shown that decreasing grid spacing from 3–4 to 1–2 km offers little additional value for forecasts of deep convection. In addition, few studies exist to show how changes in vertical grid spacing impact thunderstorm forecasts, especially when horizontal grid spacing is simultaneously decreased. The present research investigates how warm-season central U.S. simulated MCS cold pools for 11 observed cases are impacted by decreasing horizontal grid spacing from 3 to 1 km, while increasing the vertical levels from 50 to 100 in WRF runs. The 3-km runs with 100 levels produced the deepest and most negatively buoyant cold pools compared to all other grid spacings since updrafts were more poorly resolved, resulting in a higher flux of rearward-advected frozen hydrometeors, whose melting processes were augmented by the finer vertical grid spacing, which better resolved the melting layer. However, the more predominant signal among all 11 cases was for more expansive cold pools in 1-km runs, where the stronger and more abundant updrafts focused along the MCS leading line supported a larger volume of concentrated rearward hydrometeor advection and resultant latent cooling at lower levels.
Abstract
Several past studies have demonstrated improvement in forecasting convective precipitation by decreasing model grid spacing to the point of explicitly resolving deep convection. Real-case convective modeling studies have attempted to identify what model grid spacing feasibly provides the most optimal forecast given computational constraints. While Part I of this manuscript investigated changes in MCS cold pool characteristics with varied vertical and horizontal grid spacing, Part II explores changes in skill for MCS spatial placement, forward speed, and QPFs among runs with decreased horizontal and vertical grid spacing by employing the same WRF-ARW runs as in Part I. QPF forecast skill significantly improved for later portions of the MCS life cycle when decreasing horizontal grid spacing from 3 to 1 km with the part double-moment Thompson microphysics scheme. Some improvements were present in QPFs with higher precipitation amounts in the early stages of MCSs simulated with the single-moment WSM6 microphysics scheme. However, significant improvements were not common with MCS placement or QPF of the entire precipitation swath with either the Thompson or WSM6 schemes, suggesting that the benefit to MCS QPFs with decreased horizontal grid spacings is limited. Furthermore, increasing vertical resolution from 50 to 100 levels worsened WSM6 scheme QPF skill in some cases, suggesting that choices of or improvement in model physics may be equally or more positively impactful to NWP forecasts than grid spacing changes.
Abstract
Several past studies have demonstrated improvement in forecasting convective precipitation by decreasing model grid spacing to the point of explicitly resolving deep convection. Real-case convective modeling studies have attempted to identify what model grid spacing feasibly provides the most optimal forecast given computational constraints. While Part I of this manuscript investigated changes in MCS cold pool characteristics with varied vertical and horizontal grid spacing, Part II explores changes in skill for MCS spatial placement, forward speed, and QPFs among runs with decreased horizontal and vertical grid spacing by employing the same WRF-ARW runs as in Part I. QPF forecast skill significantly improved for later portions of the MCS life cycle when decreasing horizontal grid spacing from 3 to 1 km with the part double-moment Thompson microphysics scheme. Some improvements were present in QPFs with higher precipitation amounts in the early stages of MCSs simulated with the single-moment WSM6 microphysics scheme. However, significant improvements were not common with MCS placement or QPF of the entire precipitation swath with either the Thompson or WSM6 schemes, suggesting that the benefit to MCS QPFs with decreased horizontal grid spacings is limited. Furthermore, increasing vertical resolution from 50 to 100 levels worsened WSM6 scheme QPF skill in some cases, suggesting that choices of or improvement in model physics may be equally or more positively impactful to NWP forecasts than grid spacing changes.
Abstract
Florida annually leads the United States in lightning-caused fatalities. While many studies have examined the lightning frequency maximum near Cape Canaveral, relatively little attention has been paid to the western Florida peninsula, which features a similar warm-season lightning event density. Of particular concern are first cloud-to-ground (FCG) lightning events in developing thunderstorms, which are difficult to predict with sufficient lead time and can catch people off guard. This study performs an environmental analysis of warm-season (May–September) FCG events (2014–21) across the western Florida peninsula using high-resolution model analysis data, including a comparison to null (No CG) days. FCG events and No CG days are first identified from ground-based lightning data and partitioned into nine synoptic-scale flow regimes. Next, spatiotemporal distributions of FCG events are elucidated for the western Florida peninsula. An ingredients-based analysis shows that the convective environment one hour before FCG events during strong south-southeast flow features the largest amounts of moisture, but the smallest instability values and weak midtropospheric lapse rates, primarily due to warm advection and moisture transport from the Atlantic Ocean. Environments one hour before FCG events in all nine flow regimes feature markedly greater instability values, larger relative humidity values, and steeper midtropospheric lapse rates than do No CG days. Results emphasize that instability and moisture are the key ingredients for warm-season FCG events in the region. Convective parameter statistical distributions and composite soundings populate an online dashboard that can be used by regional forecasters to better predict FCG events and increase alert lead times.
Significance Statement
Florida annually leads the United States in lightning fatalities. Of particular concern are first cloud-to-ground (FCG) lightning events, which are difficult to forecast and can catch people off guard especially during outdoor recreational activities and labor. We investigate the environmental characteristics of warm-season FCG events across the western Florida peninsula. Among nine regional flow patterns, some are associated with a less moist and more unstable atmosphere one hour before an FCG event, while other regimes exhibit a more moist and less unstable atmosphere. However, regardless of flow pattern, FCG events consistently feature substantially greater instability and moisture than do null events. Key findings are displayed on an online dashboard, to better inform regional forecasters.
Abstract
Florida annually leads the United States in lightning-caused fatalities. While many studies have examined the lightning frequency maximum near Cape Canaveral, relatively little attention has been paid to the western Florida peninsula, which features a similar warm-season lightning event density. Of particular concern are first cloud-to-ground (FCG) lightning events in developing thunderstorms, which are difficult to predict with sufficient lead time and can catch people off guard. This study performs an environmental analysis of warm-season (May–September) FCG events (2014–21) across the western Florida peninsula using high-resolution model analysis data, including a comparison to null (No CG) days. FCG events and No CG days are first identified from ground-based lightning data and partitioned into nine synoptic-scale flow regimes. Next, spatiotemporal distributions of FCG events are elucidated for the western Florida peninsula. An ingredients-based analysis shows that the convective environment one hour before FCG events during strong south-southeast flow features the largest amounts of moisture, but the smallest instability values and weak midtropospheric lapse rates, primarily due to warm advection and moisture transport from the Atlantic Ocean. Environments one hour before FCG events in all nine flow regimes feature markedly greater instability values, larger relative humidity values, and steeper midtropospheric lapse rates than do No CG days. Results emphasize that instability and moisture are the key ingredients for warm-season FCG events in the region. Convective parameter statistical distributions and composite soundings populate an online dashboard that can be used by regional forecasters to better predict FCG events and increase alert lead times.
Significance Statement
Florida annually leads the United States in lightning fatalities. Of particular concern are first cloud-to-ground (FCG) lightning events, which are difficult to forecast and can catch people off guard especially during outdoor recreational activities and labor. We investigate the environmental characteristics of warm-season FCG events across the western Florida peninsula. Among nine regional flow patterns, some are associated with a less moist and more unstable atmosphere one hour before an FCG event, while other regimes exhibit a more moist and less unstable atmosphere. However, regardless of flow pattern, FCG events consistently feature substantially greater instability and moisture than do null events. Key findings are displayed on an online dashboard, to better inform regional forecasters.
