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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.
Abstract
In the last few years, central Europe faced a number of severe, record-breaking heatwaves. Previous studies focused on predictability of heatwaves on medium-range to subseasonal time scales (5–30 days). However, also short-range (3-day) forecasts of maximum temperature (Tmax) can exhibit substantial errors even on larger spatial scales. This study investigates the causes of short-range forecast errors in Tmax over central Europe for the summers of 2015–20 using the 50-member ensemble of the operational ECMWF-IFS (ECMWF-ENS). The 3-day forecast errors, individually calculated for each ensemble member with respect to a 0–18-h control forecast, are fed into a multivariate linear regression model to study the relative importance of different error sources. Outside of heatwaves, errors in Tmax forecasts are predominantly caused by incorrectly predicted downwelling shortwave radiation, mainly due to errors in low cloud cover. During heatwaves, ECMWF-ENS exhibits a systematic underestimation of Tmax (−0.4 K), which is exacerbated under clear-sky and low wind conditions, and other error sources gain importance: the second most important error source is over- or underestimation of nocturnal temperatures in the residual layer. Additional Lagrangian trajectory analysis for the years 2018–20 (due to limited data availability) suggests a link to accumulating errors in near-surface diabatic heating of air masses associated with forecast errors in residence time over land and cloud cover. Regionally, other physical processes can be of dominant importance during heatwaves. Coastal regions are influenced by errors in near-surface wind whereas errors in soil moisture are more important in southeastern parts of central Europe.
Abstract
In the last few years, central Europe faced a number of severe, record-breaking heatwaves. Previous studies focused on predictability of heatwaves on medium-range to subseasonal time scales (5–30 days). However, also short-range (3-day) forecasts of maximum temperature (Tmax) can exhibit substantial errors even on larger spatial scales. This study investigates the causes of short-range forecast errors in Tmax over central Europe for the summers of 2015–20 using the 50-member ensemble of the operational ECMWF-IFS (ECMWF-ENS). The 3-day forecast errors, individually calculated for each ensemble member with respect to a 0–18-h control forecast, are fed into a multivariate linear regression model to study the relative importance of different error sources. Outside of heatwaves, errors in Tmax forecasts are predominantly caused by incorrectly predicted downwelling shortwave radiation, mainly due to errors in low cloud cover. During heatwaves, ECMWF-ENS exhibits a systematic underestimation of Tmax (−0.4 K), which is exacerbated under clear-sky and low wind conditions, and other error sources gain importance: the second most important error source is over- or underestimation of nocturnal temperatures in the residual layer. Additional Lagrangian trajectory analysis for the years 2018–20 (due to limited data availability) suggests a link to accumulating errors in near-surface diabatic heating of air masses associated with forecast errors in residence time over land and cloud cover. Regionally, other physical processes can be of dominant importance during heatwaves. Coastal regions are influenced by errors in near-surface wind whereas errors in soil moisture are more important in southeastern parts of central Europe.
Abstract
The Dynamical–Statistical–Analog Ensemble Forecast model for Landfalling Typhoon Precipitation (DSAEF_LTP) was developed as a supplementary method to numerical weather prediction (NWP). A successful strategy for improving the forecasting skill of the DSAEF_LTP model is to include as many relevant variables as possible in the generalized initial value (GIV) of this model. In this study, a new variable, TC translation speed, is incorporated into the DSAEF_LTP model, producing a new version of this model named DSAEF_LTP-4. Then, the best scheme of the model for South China is obtained by applying this model to the forecast of the accumulated rainfall of 13 landfalling tropical cyclones (LTCs) that occurred over South China during 2012–14. In addition, the forecast performance of the best scheme is estimated by forecast experiments with eight LTCs in 2015–16 over South China, and then compared to that of the other versions of the DSAEF_LTP model and three NWP models (i.e., ECMWF, GFS, and T639). Results show further the improved performance of the DSAEF_LTP-4 model in simulating precipitation of ≥250 and ≥100 mm. However, the forecast performance of DSAEF_LTP-4 is less satisfactory than DSAEF_LTP-2. This is mainly because of a large proportion of TCs with anomalous tracks and more sensitivity to the characteristics of experiment samples of DSAEF_LTP-4. Of significance is that the DSAEF_LTP model performs better than three NWP models for LTCs with typical tracks.
Significance Statement
The purpose of this study is to improve the performance of the Dynamical–Statistical–Analog Ensemble Forecast model for Landfalling Typhoon Precipitation (DSAEF_LTP) model by incorporating typhoon translation speed similarity. Compared with the dynamical models, which are more prone to misses, the DSAEF_LTP model is more prone to false alarms. The superiority of the DSAEF_LTP model shows especially in predicting the precipitation of TCs with typical tracks.
Abstract
The Dynamical–Statistical–Analog Ensemble Forecast model for Landfalling Typhoon Precipitation (DSAEF_LTP) was developed as a supplementary method to numerical weather prediction (NWP). A successful strategy for improving the forecasting skill of the DSAEF_LTP model is to include as many relevant variables as possible in the generalized initial value (GIV) of this model. In this study, a new variable, TC translation speed, is incorporated into the DSAEF_LTP model, producing a new version of this model named DSAEF_LTP-4. Then, the best scheme of the model for South China is obtained by applying this model to the forecast of the accumulated rainfall of 13 landfalling tropical cyclones (LTCs) that occurred over South China during 2012–14. In addition, the forecast performance of the best scheme is estimated by forecast experiments with eight LTCs in 2015–16 over South China, and then compared to that of the other versions of the DSAEF_LTP model and three NWP models (i.e., ECMWF, GFS, and T639). Results show further the improved performance of the DSAEF_LTP-4 model in simulating precipitation of ≥250 and ≥100 mm. However, the forecast performance of DSAEF_LTP-4 is less satisfactory than DSAEF_LTP-2. This is mainly because of a large proportion of TCs with anomalous tracks and more sensitivity to the characteristics of experiment samples of DSAEF_LTP-4. Of significance is that the DSAEF_LTP model performs better than three NWP models for LTCs with typical tracks.
Significance Statement
The purpose of this study is to improve the performance of the Dynamical–Statistical–Analog Ensemble Forecast model for Landfalling Typhoon Precipitation (DSAEF_LTP) model by incorporating typhoon translation speed similarity. Compared with the dynamical models, which are more prone to misses, the DSAEF_LTP model is more prone to false alarms. The superiority of the DSAEF_LTP model shows especially in predicting the precipitation of TCs with typical tracks.
Abstract
Model improvement efforts involve an evaluation of changes in model skill in response to changes in model physics and parameterization. When using wind measurements from various remote sensors to determine model forecast accuracy, it is important to understand the effects of measurement-uncertainty differences among the sensors resulting from differences in the methods of measurement, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. Here we quantify instrument measurement variability in 80-m wind speed during WFIP2 and its impact on the calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m. Model errors were found to be 2–3 m s−1. Differences in errors as determined by various instruments at each site amounted to about 10% of this value, or 0.2–0.3 m s−1. Changes in model skill due to physics or grid-resolution updates also differed depending on the instrument used to determine the errors; most of the instrument-to-instrument differences were ∼0.1 m s−1, but some reached 0.3 m s−1. All instruments at a given site mostly showed consistency in the sign of the change in error. In two examples, though, the sign changed, illustrating a consequence of differences in measurements: errors determined using one instrument may show improvement in model skill, whereas errors determined using another instrument may indicate degradation. This possibility underscores the importance of having accurate measurements to determine the model error.
Significance Statement
To evaluate model forecast accuracy using remote sensing instruments, it is important to understand the effects of measurement uncertainties due to differences in the methods of measurement and data processing techniques, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. In this study, three types of collocated remote sensing systems are used to quantify the impact of measurement variability on the magnitude of calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m.
Abstract
Model improvement efforts involve an evaluation of changes in model skill in response to changes in model physics and parameterization. When using wind measurements from various remote sensors to determine model forecast accuracy, it is important to understand the effects of measurement-uncertainty differences among the sensors resulting from differences in the methods of measurement, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. Here we quantify instrument measurement variability in 80-m wind speed during WFIP2 and its impact on the calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m. Model errors were found to be 2–3 m s−1. Differences in errors as determined by various instruments at each site amounted to about 10% of this value, or 0.2–0.3 m s−1. Changes in model skill due to physics or grid-resolution updates also differed depending on the instrument used to determine the errors; most of the instrument-to-instrument differences were ∼0.1 m s−1, but some reached 0.3 m s−1. All instruments at a given site mostly showed consistency in the sign of the change in error. In two examples, though, the sign changed, illustrating a consequence of differences in measurements: errors determined using one instrument may show improvement in model skill, whereas errors determined using another instrument may indicate degradation. This possibility underscores the importance of having accurate measurements to determine the model error.
Significance Statement
To evaluate model forecast accuracy using remote sensing instruments, it is important to understand the effects of measurement uncertainties due to differences in the methods of measurement and data processing techniques, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. In this study, three types of collocated remote sensing systems are used to quantify the impact of measurement variability on the magnitude of calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m.
