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Abstract
This study employs a long time series (1997–2017) of reforecasts based on a version of the ECMWF Integrated Forecast System to evaluate the dependence of medium-range (i.e., 3–15 days) precipitation forecast skill over California on the state of the large-scale atmospheric flow. As a basis for this evaluation, four recurrent large-scale flow regimes over the North Pacific and western North America associated with precipitation in a domain encompassing northern and central California were objectively identified in ECMWF ERA5 reanalysis data for November–March 1981–2017. Two of the regimes are characterized by zonal upper-level flow across the North Pacific, and the other two are characterized by wavy, blocked flow. Forecast verification statistics conditioned on regime occurrence indicate considerably lower medium-range precipitation skill over California in blocking regimes than in zonal regimes. Moreover, forecasts of blocking regimes tend to exhibit larger errors and uncertainty in the synoptic-scale flow over the eastern North Pacific and western North America compared with forecasts of zonal regimes. Composite analyses for blocking forecasts reveal a tendency for errors to develop in conjunction with the amplification of a ridge over the western and central North Pacific. The errors in the ridge tend to be communicated through the large-scale Rossby wave pattern, resulting in misforecasting of downstream trough amplification and, thereby, moisture flux and precipitation over California. The composites additionally indicate that error growth in the blocking ridge can be linked to misrepresentation of baroclinic development as well as upper-level divergent outflow associated with latent heat release.
Significance Statement
This study examines the degree to which the medium-range (out to ∼2-week lead time) precipitation forecast skill over California depends on the large-scale atmospheric flow regime over the North Pacific. An evaluation of retrospective model forecasts from ECMWF for 1997–2017 reveals that the skill tends to be considerably lower in regimes featuring a wavy, “blocked” North Pacific jet stream than in regimes featuring a west–east-oriented jet stream. This difference in skill relates to a tendency for forecasts of blocked regimes to exhibit significantly larger errors than forecasts of zonal regimes. The results could aid forecasters by increasing situational awareness and informing the interpretation and application of model forecasts for precipitation affecting California.
Abstract
This study employs a long time series (1997–2017) of reforecasts based on a version of the ECMWF Integrated Forecast System to evaluate the dependence of medium-range (i.e., 3–15 days) precipitation forecast skill over California on the state of the large-scale atmospheric flow. As a basis for this evaluation, four recurrent large-scale flow regimes over the North Pacific and western North America associated with precipitation in a domain encompassing northern and central California were objectively identified in ECMWF ERA5 reanalysis data for November–March 1981–2017. Two of the regimes are characterized by zonal upper-level flow across the North Pacific, and the other two are characterized by wavy, blocked flow. Forecast verification statistics conditioned on regime occurrence indicate considerably lower medium-range precipitation skill over California in blocking regimes than in zonal regimes. Moreover, forecasts of blocking regimes tend to exhibit larger errors and uncertainty in the synoptic-scale flow over the eastern North Pacific and western North America compared with forecasts of zonal regimes. Composite analyses for blocking forecasts reveal a tendency for errors to develop in conjunction with the amplification of a ridge over the western and central North Pacific. The errors in the ridge tend to be communicated through the large-scale Rossby wave pattern, resulting in misforecasting of downstream trough amplification and, thereby, moisture flux and precipitation over California. The composites additionally indicate that error growth in the blocking ridge can be linked to misrepresentation of baroclinic development as well as upper-level divergent outflow associated with latent heat release.
Significance Statement
This study examines the degree to which the medium-range (out to ∼2-week lead time) precipitation forecast skill over California depends on the large-scale atmospheric flow regime over the North Pacific. An evaluation of retrospective model forecasts from ECMWF for 1997–2017 reveals that the skill tends to be considerably lower in regimes featuring a wavy, “blocked” North Pacific jet stream than in regimes featuring a west–east-oriented jet stream. This difference in skill relates to a tendency for forecasts of blocked regimes to exhibit significantly larger errors than forecasts of zonal regimes. The results could aid forecasters by increasing situational awareness and informing the interpretation and application of model forecasts for precipitation affecting California.
Abstract
The impact of assimilating dropsonde data from the 2020 Atmospheric River (AR) Reconnaissance (ARR) field campaign on operational numerical weather forecasts was assessed. Two experiments were executed for the period from 24 January to 18 March 2020 using the National Centers for Environmental Prediction (NCEP) Global Forecast System version 15 (GFSv15) with a four-dimensional hybrid ensemble–variational (4DEnVar) data assimilation system. The control run (CTRL) used all of the routinely assimilated data and included data from 628 ARR dropsondes, whereas the denial run (DENY) excluded the dropsonde data. Results from 17 intensive observing periods (IOPs) indicate a mixed impact for mean sea level pressure and geopotential height over the Pacific–North American (PNA) region in CTRL compared to DENY. The overall local impact over the U.S. West Coast and Gulf of Alaska for the 17 IOPs is neutral (−0.45%) for integrated vapor transport (IVT), but positive for wind and moisture profiles (0.5%–1.0%), with a spectrum of statistically significant positive and negative impacts for various IOPs. The positive dropsonde data impact on precipitation forecasts over U.S. West Coast domains appears driven, in part, by improved low-level moisture and wind fields at short-forecast lead times. Indeed, data gaps, especially for accurate and unbiased moisture profiles and wind fields, can be at least partially mitigated to improve U.S. West Coast precipitation forecasts.
Abstract
The impact of assimilating dropsonde data from the 2020 Atmospheric River (AR) Reconnaissance (ARR) field campaign on operational numerical weather forecasts was assessed. Two experiments were executed for the period from 24 January to 18 March 2020 using the National Centers for Environmental Prediction (NCEP) Global Forecast System version 15 (GFSv15) with a four-dimensional hybrid ensemble–variational (4DEnVar) data assimilation system. The control run (CTRL) used all of the routinely assimilated data and included data from 628 ARR dropsondes, whereas the denial run (DENY) excluded the dropsonde data. Results from 17 intensive observing periods (IOPs) indicate a mixed impact for mean sea level pressure and geopotential height over the Pacific–North American (PNA) region in CTRL compared to DENY. The overall local impact over the U.S. West Coast and Gulf of Alaska for the 17 IOPs is neutral (−0.45%) for integrated vapor transport (IVT), but positive for wind and moisture profiles (0.5%–1.0%), with a spectrum of statistically significant positive and negative impacts for various IOPs. The positive dropsonde data impact on precipitation forecasts over U.S. West Coast domains appears driven, in part, by improved low-level moisture and wind fields at short-forecast lead times. Indeed, data gaps, especially for accurate and unbiased moisture profiles and wind fields, can be at least partially mitigated to improve U.S. West Coast precipitation forecasts.
Abstract
Inland flooding from landfalling tropical cyclones (TCs) is a major cause of death and damage to property and infrastructure worldwide. The mid-Atlantic region of the United States was devastated by Hurricane Irene and Tropical Storm Lee during late August–early September 2011, when the two storms produced sequential heavy rainfall and record flooding. Many rivers and streams reached their all-time record discharge to date. This study aims at 1) better understanding and predicting TC rainfall using various observed rainfall products and a high-resolution coupled atmosphere–wave–ocean model, namely, the Unified Wave Interface-Coupled Model (UWIN-CM), 2) characterizing inland flooding using streamflow data, and 3) improving prediction of TC-induced inland flooding using UWIN-CM and a machine learning K-nearest-neighbor (KNN) model. The results show that there is a wide range of uncertainty in satellite and radar–gauge-observed rainfall products in terms of rain-rate distribution and cumulative rainfall over the mid-Atlantic region. UWIN-CM rainfall is closer to the radar–gauge data than satellite data over land. Streamflow in most large rivers (>500 cfs) peaked after Lee, which reflects the sequential rainfall contributions of the two storms. The rainfall–streamflow–discharge response times were dependent on the size of the stream and the peak rain rates. To better predict rainfall and flooding, UWIN-CM and observed rainfall are used with the machine learning KNN regression model for prediction of severity of TC-induced inland flooding hazard. These results demonstrate the value of a stepped approach for rainfall and flood prediction toward a fully coupled atmosphere–ocean–land/hydrology model in the future.
Abstract
Inland flooding from landfalling tropical cyclones (TCs) is a major cause of death and damage to property and infrastructure worldwide. The mid-Atlantic region of the United States was devastated by Hurricane Irene and Tropical Storm Lee during late August–early September 2011, when the two storms produced sequential heavy rainfall and record flooding. Many rivers and streams reached their all-time record discharge to date. This study aims at 1) better understanding and predicting TC rainfall using various observed rainfall products and a high-resolution coupled atmosphere–wave–ocean model, namely, the Unified Wave Interface-Coupled Model (UWIN-CM), 2) characterizing inland flooding using streamflow data, and 3) improving prediction of TC-induced inland flooding using UWIN-CM and a machine learning K-nearest-neighbor (KNN) model. The results show that there is a wide range of uncertainty in satellite and radar–gauge-observed rainfall products in terms of rain-rate distribution and cumulative rainfall over the mid-Atlantic region. UWIN-CM rainfall is closer to the radar–gauge data than satellite data over land. Streamflow in most large rivers (>500 cfs) peaked after Lee, which reflects the sequential rainfall contributions of the two storms. The rainfall–streamflow–discharge response times were dependent on the size of the stream and the peak rain rates. To better predict rainfall and flooding, UWIN-CM and observed rainfall are used with the machine learning KNN regression model for prediction of severity of TC-induced inland flooding hazard. These results demonstrate the value of a stepped approach for rainfall and flood prediction toward a fully coupled atmosphere–ocean–land/hydrology model in the future.
Abstract
Forecast skill from dynamical forecast models decreases quickly with projection time due to various errors. Therefore, postprocessing methods, from simple bias correction methods to more complicated multiple linear regression–based model output statistics, are used to improve raw model forecasts. Usually, these methods show clear forecast improvement over the raw model forecasts, especially for short-range weather forecasts. However, linear approaches have limitations because the relationship between predictands and predictors may be nonlinear. This is even truer for extended range forecasts, such as week-3–4 forecasts. In this study, neural network techniques are used to seek or model the relationships between a set of predictors and predictands, and eventually to improve week-3–4 precipitation and 2-m temperature forecasts made by the NOAA/NCEP Climate Forecast System. Benefitting from advances in machine learning techniques in recent years, more flexible and capable machine learning algorithms and availability of big datasets enable us not only to explore nonlinear features or relationships within a given large dataset, but also to extract more sophisticated pattern relationships and covariabilities hidden within the multidimensional predictors and predictands. Then these more sophisticated relationships and high-level statistical information are used to correct the model week-3–4 precipitation and 2-m temperature forecasts. The results show that to some extent neural network techniques can significantly improve the week-3–4 forecast accuracy and greatly increase the efficiency over the traditional multiple linear regression methods.
Abstract
Forecast skill from dynamical forecast models decreases quickly with projection time due to various errors. Therefore, postprocessing methods, from simple bias correction methods to more complicated multiple linear regression–based model output statistics, are used to improve raw model forecasts. Usually, these methods show clear forecast improvement over the raw model forecasts, especially for short-range weather forecasts. However, linear approaches have limitations because the relationship between predictands and predictors may be nonlinear. This is even truer for extended range forecasts, such as week-3–4 forecasts. In this study, neural network techniques are used to seek or model the relationships between a set of predictors and predictands, and eventually to improve week-3–4 precipitation and 2-m temperature forecasts made by the NOAA/NCEP Climate Forecast System. Benefitting from advances in machine learning techniques in recent years, more flexible and capable machine learning algorithms and availability of big datasets enable us not only to explore nonlinear features or relationships within a given large dataset, but also to extract more sophisticated pattern relationships and covariabilities hidden within the multidimensional predictors and predictands. Then these more sophisticated relationships and high-level statistical information are used to correct the model week-3–4 precipitation and 2-m temperature forecasts. The results show that to some extent neural network techniques can significantly improve the week-3–4 forecast accuracy and greatly increase the efficiency over the traditional multiple linear regression methods.
Abstract
Tropical cyclone tornadoes (TCTORs) are a hazard to life and property during landfalling tropical cyclones (TCs). The threat is often spread over a wide area within the TC envelope and must be continually evaluated as the TC moves inland and dissipates. To anticipate the risk of TCTORs, forecasters may use high-resolution, rapidly updating model analyses and short-range forecasts such as the Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR), and an ingredients-based approach similar to that used for forecasting continental midlatitude tornadoes. Though RAP and HRRR errors have been identified in typical midlatitude convective environments, this study evaluates the performance of the RAP and the HRRR within the TC envelope, with particular attention given to sounding-derived parameters previously identified as useful for TCTOR forecasting. A sample of 1730 observed upper-air soundings is sourced from 13 TCs that made landfall along the U.S. coastline between 2017 and 2019. The observed soundings are paired with their corresponding model gridpoint soundings from the RAP analysis, RAP 12-h forecast, and HRRR 12-h forecast. Model errors are calculated for both the raw sounding variables of temperature, dewpoint, and wind speed, as well as for the selected sounding-derived parameters. Results show a moist bias that worsens with height across all model runs. There are also statistically significant underpredictions in stability-related parameters such as convective available potential energy (CAPE) and kinematic parameters such as vertical wind shear.
Abstract
Tropical cyclone tornadoes (TCTORs) are a hazard to life and property during landfalling tropical cyclones (TCs). The threat is often spread over a wide area within the TC envelope and must be continually evaluated as the TC moves inland and dissipates. To anticipate the risk of TCTORs, forecasters may use high-resolution, rapidly updating model analyses and short-range forecasts such as the Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR), and an ingredients-based approach similar to that used for forecasting continental midlatitude tornadoes. Though RAP and HRRR errors have been identified in typical midlatitude convective environments, this study evaluates the performance of the RAP and the HRRR within the TC envelope, with particular attention given to sounding-derived parameters previously identified as useful for TCTOR forecasting. A sample of 1730 observed upper-air soundings is sourced from 13 TCs that made landfall along the U.S. coastline between 2017 and 2019. The observed soundings are paired with their corresponding model gridpoint soundings from the RAP analysis, RAP 12-h forecast, and HRRR 12-h forecast. Model errors are calculated for both the raw sounding variables of temperature, dewpoint, and wind speed, as well as for the selected sounding-derived parameters. Results show a moist bias that worsens with height across all model runs. There are also statistically significant underpredictions in stability-related parameters such as convective available potential energy (CAPE) and kinematic parameters such as vertical wind shear.
Abstract
A sensitivity analysis for the horizontal localization scale is performed for a numerical weather prediction (NWP) system that uses a 30-s update to refresh a 500-m mesh with observations from a new-generation multiparameter phased array weather radar (MP-PAWR). Testing is performed using three case studies of convective weather events that occurred during August–September 2019, with the aim to determine the most suitable scale for short-range forecasting of precipitating convective systems and to better understand model behavior to a rapid update cycle. Results showed that while the model could provide useful skill at lead times up to 30 min, forecasts would consistently overestimate rainfall and were unable to outperform nowcasts performed with a simple advection model. Using a larger localization scale, e.g., 4 km, generated stronger convective and dynamical instability in the analyses that made conditions more favorable for spurious and intense convection to develop in forecasts. It was demonstrated that lowering the localization scale reduced the size of analysis increments during early cycling, limiting the buildup of these conditions. Improved representation of the localized convection in the initial conditions was suggested as an important step to mitigating this issue in the model.
Abstract
A sensitivity analysis for the horizontal localization scale is performed for a numerical weather prediction (NWP) system that uses a 30-s update to refresh a 500-m mesh with observations from a new-generation multiparameter phased array weather radar (MP-PAWR). Testing is performed using three case studies of convective weather events that occurred during August–September 2019, with the aim to determine the most suitable scale for short-range forecasting of precipitating convective systems and to better understand model behavior to a rapid update cycle. Results showed that while the model could provide useful skill at lead times up to 30 min, forecasts would consistently overestimate rainfall and were unable to outperform nowcasts performed with a simple advection model. Using a larger localization scale, e.g., 4 km, generated stronger convective and dynamical instability in the analyses that made conditions more favorable for spurious and intense convection to develop in forecasts. It was demonstrated that lowering the localization scale reduced the size of analysis increments during early cycling, limiting the buildup of these conditions. Improved representation of the localized convection in the initial conditions was suggested as an important step to mitigating this issue in the model.
Abstract
The Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) aims to improve our understanding of extreme rainfall processes in the East Asian summer monsoon. A convection-permitting ensemble-based data assimilation and forecast system (the PSU WRF-EnKF system) was run in real time in the summers of 2020–21 in advance of the 2022 field campaign, assimilating all-sky infrared (IR) radiances from the geostationary Himawari-8 and GOES-16 satellites, and providing 48-h ensemble forecasts every day for weather briefings and discussions. This is the first time that all-sky IR data assimilation has been performed in a real-time forecast system at a convection-permitting resolution for several seasons. Compared with retrospective forecasts that exclude all-sky IR radiances, rainfall predictions are statistically significantly improved out to at least 4–6 h for the real-time forecasts, which is comparable to the time scale of improvements gained from assimilating observations from the dense ground-based Doppler weather radars. The assimilation of all-sky IR radiances also reduced the forecast errors of large-scale environments and helped to maintain a more reasonable ensemble spread compared with the counterpart experiments that did not assimilate all-sky IR radiances. The results indicate strong potential for improving routine short-term quantitative precipitation forecasts using these high-spatiotemporal-resolution satellite observations in the future.
Significance Statement
During the summers of 2020/21, the PSU WRF-EnKF data assimilation and forecast system was run in real time in advance of the 2022 Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP), assimilating all-sky (clear-sky and cloudy) infrared radiances from geostationary satellites into a numerical weather prediction model and providing ensemble forecasts. This study presents the first-of-its-kind systematic evaluation of the impacts of assimilating all-sky infrared radiances on short-term qualitative precipitation forecasts using multiyear, multiregion, real-time ensemble forecasts. Results suggest that rainfall forecasts are improved out to at least 4–6 h with the assimilation of all-sky infrared radiances, comparable to the influence of assimilating radar observations, with benefits in forecasting large-scale environments and representing atmospheric uncertainties as well.
Abstract
The Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) aims to improve our understanding of extreme rainfall processes in the East Asian summer monsoon. A convection-permitting ensemble-based data assimilation and forecast system (the PSU WRF-EnKF system) was run in real time in the summers of 2020–21 in advance of the 2022 field campaign, assimilating all-sky infrared (IR) radiances from the geostationary Himawari-8 and GOES-16 satellites, and providing 48-h ensemble forecasts every day for weather briefings and discussions. This is the first time that all-sky IR data assimilation has been performed in a real-time forecast system at a convection-permitting resolution for several seasons. Compared with retrospective forecasts that exclude all-sky IR radiances, rainfall predictions are statistically significantly improved out to at least 4–6 h for the real-time forecasts, which is comparable to the time scale of improvements gained from assimilating observations from the dense ground-based Doppler weather radars. The assimilation of all-sky IR radiances also reduced the forecast errors of large-scale environments and helped to maintain a more reasonable ensemble spread compared with the counterpart experiments that did not assimilate all-sky IR radiances. The results indicate strong potential for improving routine short-term quantitative precipitation forecasts using these high-spatiotemporal-resolution satellite observations in the future.
Significance Statement
During the summers of 2020/21, the PSU WRF-EnKF data assimilation and forecast system was run in real time in advance of the 2022 Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP), assimilating all-sky (clear-sky and cloudy) infrared radiances from geostationary satellites into a numerical weather prediction model and providing ensemble forecasts. This study presents the first-of-its-kind systematic evaluation of the impacts of assimilating all-sky infrared radiances on short-term qualitative precipitation forecasts using multiyear, multiregion, real-time ensemble forecasts. Results suggest that rainfall forecasts are improved out to at least 4–6 h with the assimilation of all-sky infrared radiances, comparable to the influence of assimilating radar observations, with benefits in forecasting large-scale environments and representing atmospheric uncertainties as well.
Abstract
Multiple linear regression models were developed for 1–3-day lead forecasts of maximum and minimum temperature for two locations in the city of Lima, on the central coast of Peru (12°S), and contrasted with the operational forecasts issued by the National Meteorological and Hydrological Service—SENAMHI and the output of a regional numerical atmospheric model. We developed empirical models, fitted to data from the 2000–13 period, and verified their skill for the 2014–19 period. Since El Niño produces a strong low-frequency signal, the models focus on the high-frequency weather and subseasonal variability (60-day cutoff). The empirical models outperformed the operational forecasts and the numerical model. For instance, the high-frequency annual correlation coefficient and root-mean-square error (RMSE) for the 1-day lead forecasts were 0.37°–0.53° and 0.74°–1.76°C for the empirical model, respectively, but from around −0.05° to 0.24° and 0.88°–4.21°C in the operational case. Only three predictors were considered for the models, including persistence and large-scale atmospheric indices. Contrary to our belief, the model skill was lowest for the austral winter (June–August), when the extratropical influence is largest, suggesting an enhanced role of local effects. Including local specific humidity as a predictor for minimum temperature at the inland location substantially increased the skill and reduced its seasonality. There were cases in which both the operational and empirical forecast had similar strong errors and we suggest mesoscale circulations, such as the low-level cyclonic vortex over the ocean, as the culprit. Incorporating such information could be valuable for improving the forecasts.
Significance Statement
We wanted to compare the temperature of the operational forecast of the Meteorological and Hydrological Service, an atmospheric model, and persistence with the observed temperatures on the Peruvian central coast. In addition, we generated an empirical forecast model considering both atmospheric and local predictors. We got better results with this empirical model, considering the highest Pearson correlations and the lowest RMSE values. These results will allow us to use this empirical model as the main tool to automate the forecast on the central coast of Peru. Future work should be aimed at testing the skill of this model for forecasting in other cities of Peru.
Abstract
Multiple linear regression models were developed for 1–3-day lead forecasts of maximum and minimum temperature for two locations in the city of Lima, on the central coast of Peru (12°S), and contrasted with the operational forecasts issued by the National Meteorological and Hydrological Service—SENAMHI and the output of a regional numerical atmospheric model. We developed empirical models, fitted to data from the 2000–13 period, and verified their skill for the 2014–19 period. Since El Niño produces a strong low-frequency signal, the models focus on the high-frequency weather and subseasonal variability (60-day cutoff). The empirical models outperformed the operational forecasts and the numerical model. For instance, the high-frequency annual correlation coefficient and root-mean-square error (RMSE) for the 1-day lead forecasts were 0.37°–0.53° and 0.74°–1.76°C for the empirical model, respectively, but from around −0.05° to 0.24° and 0.88°–4.21°C in the operational case. Only three predictors were considered for the models, including persistence and large-scale atmospheric indices. Contrary to our belief, the model skill was lowest for the austral winter (June–August), when the extratropical influence is largest, suggesting an enhanced role of local effects. Including local specific humidity as a predictor for minimum temperature at the inland location substantially increased the skill and reduced its seasonality. There were cases in which both the operational and empirical forecast had similar strong errors and we suggest mesoscale circulations, such as the low-level cyclonic vortex over the ocean, as the culprit. Incorporating such information could be valuable for improving the forecasts.
Significance Statement
We wanted to compare the temperature of the operational forecast of the Meteorological and Hydrological Service, an atmospheric model, and persistence with the observed temperatures on the Peruvian central coast. In addition, we generated an empirical forecast model considering both atmospheric and local predictors. We got better results with this empirical model, considering the highest Pearson correlations and the lowest RMSE values. These results will allow us to use this empirical model as the main tool to automate the forecast on the central coast of Peru. Future work should be aimed at testing the skill of this model for forecasting in other cities of Peru.
Abstract
In a midlatitude coastal region such as the New York Bight (NYB), the general thermodynamic structure and dynamics of the sea-breeze circulation is poorly understood. The NYB sea-breeze circulation is often amplified by and coterminous with other regional characteristics and phenomena such as complex coastal topology, a low-level jet (LLJ), and coastal upwelling. While typically considered a summertime phenomenon, the NYB sea-breeze circulation occurs year-round. This study creates a methodology to objectively identify sea-breeze days and their associated LLJs from 2010 to 2020. Filtering parameters include surface-based observations of sea level pressure (SLP) gradient and diurnal tendencies, afternoon wind speed and direction tendencies, air temperature gradient, and the dewpoint depression. LLJs associated with the sea-breeze circulation typically occur within 150–300 m MSL and are identified using a coastal New York State Mesonet (NYSM) profiler site. Along coastal Long Island, there are on average 32 sea-breeze days annually, featuring winds consistently backing to the south and strengthening at or around 1800 UTC (1400 EDT). The NYB LLJ is most frequent in the summer months. Sea-breeze days are classified into two categories: classic and hybrid. A classic sea breeze is driven primarily by both cross-shore pressure and temperature gradients, with light background winds; while a hybrid sea breeze occurs in combination with other larger-scale features, such as frontal systems. Both types of sea breeze are similarly distributed with a maximum frequency during July.
Abstract
In a midlatitude coastal region such as the New York Bight (NYB), the general thermodynamic structure and dynamics of the sea-breeze circulation is poorly understood. The NYB sea-breeze circulation is often amplified by and coterminous with other regional characteristics and phenomena such as complex coastal topology, a low-level jet (LLJ), and coastal upwelling. While typically considered a summertime phenomenon, the NYB sea-breeze circulation occurs year-round. This study creates a methodology to objectively identify sea-breeze days and their associated LLJs from 2010 to 2020. Filtering parameters include surface-based observations of sea level pressure (SLP) gradient and diurnal tendencies, afternoon wind speed and direction tendencies, air temperature gradient, and the dewpoint depression. LLJs associated with the sea-breeze circulation typically occur within 150–300 m MSL and are identified using a coastal New York State Mesonet (NYSM) profiler site. Along coastal Long Island, there are on average 32 sea-breeze days annually, featuring winds consistently backing to the south and strengthening at or around 1800 UTC (1400 EDT). The NYB LLJ is most frequent in the summer months. Sea-breeze days are classified into two categories: classic and hybrid. A classic sea breeze is driven primarily by both cross-shore pressure and temperature gradients, with light background winds; while a hybrid sea breeze occurs in combination with other larger-scale features, such as frontal systems. Both types of sea breeze are similarly distributed with a maximum frequency during July.
Abstract
A 16-member convective-scale ensemble prediction system (CEPS) developed at the Central Weather Bureau (CWB) of Taiwan is evaluated for probability forecasts of convective precipitation. To address the issues of limited predictability of convective systems, the CEPS provides short-range forecasts using initial conditions from a rapid-updated ensemble data assimilation system. This study aims to identify the behavior of the CEPS forecasts, especially the impact of different ensemble configurations and forecast lead times. Warm-season afternoon thunderstorms (ATs) from 30 June to 4 July 2017 are selected. Since ATs usually occur between 1300 and 2000 LST, this study compares deterministic and probabilistic quantitative precipitation forecasts (QPFs) launched at 0500, 0800, and 1100 LST. This study demonstrates that initial and boundary perturbations (IBP) are crucial to ensure good spread–skill consistency over the 18-h forecasts. On top of IBP, additional model perturbations have insignificant impacts on upper-air and precipitation forecasts. The deterministic QPFs launched at 1100 LST outperform those launched at 0500 and 0800 LST, likely because the most-recent data assimilation analyses enhance the practical predictability. However, it cannot improve the probabilistic QPFs launched at 1100 LST due to inadequate ensemble spreads resulting from limited error growth time. This study points out the importance of sufficient initial condition uncertainty on short-range probabilistic forecasts to exploit the benefits of rapid-update data assimilation analyses.
Significance Statement
This study aims to understand the behavior of convective-scale short-range probabilistic forecasts in Taiwan and the surrounding area. Taiwan is influenced by diverse weather systems, including typhoons, mei-yu fronts, and local thunderstorms. During the past decade, there has been promising improvement in predicting mesoscale weather systems (e.g., typhoons and mei-yu fronts). However, it is still challenging to provide timely and accurate forecasts for rapid-evolving high-impact convection. This study provides a reference for the designation of convective-scale ensemble prediction systems; in particular, those with a goal to provide short-range probabilistic forecasts. While the findings cannot be extrapolated to all ensemble prediction systems, this study demonstrates that initial and boundary perturbations are the most important factors, while the model perturbation has an insignificant effect. This study suggests that in-depth studies are required to improve the convective-scale initial condition accuracy and uncertainty to provide reliable probabilistic forecasts within short lead times.
Abstract
A 16-member convective-scale ensemble prediction system (CEPS) developed at the Central Weather Bureau (CWB) of Taiwan is evaluated for probability forecasts of convective precipitation. To address the issues of limited predictability of convective systems, the CEPS provides short-range forecasts using initial conditions from a rapid-updated ensemble data assimilation system. This study aims to identify the behavior of the CEPS forecasts, especially the impact of different ensemble configurations and forecast lead times. Warm-season afternoon thunderstorms (ATs) from 30 June to 4 July 2017 are selected. Since ATs usually occur between 1300 and 2000 LST, this study compares deterministic and probabilistic quantitative precipitation forecasts (QPFs) launched at 0500, 0800, and 1100 LST. This study demonstrates that initial and boundary perturbations (IBP) are crucial to ensure good spread–skill consistency over the 18-h forecasts. On top of IBP, additional model perturbations have insignificant impacts on upper-air and precipitation forecasts. The deterministic QPFs launched at 1100 LST outperform those launched at 0500 and 0800 LST, likely because the most-recent data assimilation analyses enhance the practical predictability. However, it cannot improve the probabilistic QPFs launched at 1100 LST due to inadequate ensemble spreads resulting from limited error growth time. This study points out the importance of sufficient initial condition uncertainty on short-range probabilistic forecasts to exploit the benefits of rapid-update data assimilation analyses.
Significance Statement
This study aims to understand the behavior of convective-scale short-range probabilistic forecasts in Taiwan and the surrounding area. Taiwan is influenced by diverse weather systems, including typhoons, mei-yu fronts, and local thunderstorms. During the past decade, there has been promising improvement in predicting mesoscale weather systems (e.g., typhoons and mei-yu fronts). However, it is still challenging to provide timely and accurate forecasts for rapid-evolving high-impact convection. This study provides a reference for the designation of convective-scale ensemble prediction systems; in particular, those with a goal to provide short-range probabilistic forecasts. While the findings cannot be extrapolated to all ensemble prediction systems, this study demonstrates that initial and boundary perturbations are the most important factors, while the model perturbation has an insignificant effect. This study suggests that in-depth studies are required to improve the convective-scale initial condition accuracy and uncertainty to provide reliable probabilistic forecasts within short lead times.
