Browse
Abstract
The prediction of supercooled large drops (SLD) from the Thompson-Eidhammer (TE) microphysics scheme—run as part of the High-Resolution Rapid Refresh (HRRR) model— is evaluated with observations from the In-Cloud Icing and Large drop Experiment (ICICLE) field campaign. These observations are also used to train a random forest machine-learning (ML) model, which is then used to predict SLD from several variables derived fromHRRRmodel output. Results provide insight on the limitations and benefits of each model. Generally, the ML model results in an increase in the probability of detection (POD) and false alarm rate (FAR) of SLD compared to prediction from TE microphysics. Additionally, the POD of SLD increases with increasing forecast lead time for both models, likely since clouds and precipitation have more time to develop as forecast length increases. Since SLD take time to develop in TE microphysics and may be poorly represented in short-term (< 3 h) forecasts, the ML model can provide improved short-term guidance on supercooled large-drop icing conditions. Results also show that TE microphysics predicts a frequency of SLD in cold (< −10°C) or high ice water content (IWC) environments that is too low compared to observations, whereas the ML model better captures the relative frequency of SLD in these environments.
Abstract
The prediction of supercooled large drops (SLD) from the Thompson-Eidhammer (TE) microphysics scheme—run as part of the High-Resolution Rapid Refresh (HRRR) model— is evaluated with observations from the In-Cloud Icing and Large drop Experiment (ICICLE) field campaign. These observations are also used to train a random forest machine-learning (ML) model, which is then used to predict SLD from several variables derived fromHRRRmodel output. Results provide insight on the limitations and benefits of each model. Generally, the ML model results in an increase in the probability of detection (POD) and false alarm rate (FAR) of SLD compared to prediction from TE microphysics. Additionally, the POD of SLD increases with increasing forecast lead time for both models, likely since clouds and precipitation have more time to develop as forecast length increases. Since SLD take time to develop in TE microphysics and may be poorly represented in short-term (< 3 h) forecasts, the ML model can provide improved short-term guidance on supercooled large-drop icing conditions. Results also show that TE microphysics predicts a frequency of SLD in cold (< −10°C) or high ice water content (IWC) environments that is too low compared to observations, whereas the ML model better captures the relative frequency of SLD in these environments.
Abstract
One of the most prominent mesoscale phenomenon in the coastal zone is the sea breeze/land breeze circulation. The pattern and its implications for the weather in coastal areas is well described and with mesoscale resolving operational NWP models the circulation can be captured. In this study, a straightforward method to identify sea and land breezes based on the change in wind direction in the column above a grid point on the coastline is presented. The method was tested for southern Sweden using archived output from the HARMONIE-AROME model with promising results, describing both the seasonal and diurnal cycles well. In areas with a complex coastline, such as narrow straits, the concept of land-sea breeze becomes less clear, and several ways to address this problem for the suggested method are discussed. With an operational index of the sea and land breezes, the forecaster can better understand and express the weather situation and add value for people in the coastal zone. Further, the indices can be used to study systematic biases in the model and to create climatologies of the sea and land breezes.
Abstract
One of the most prominent mesoscale phenomenon in the coastal zone is the sea breeze/land breeze circulation. The pattern and its implications for the weather in coastal areas is well described and with mesoscale resolving operational NWP models the circulation can be captured. In this study, a straightforward method to identify sea and land breezes based on the change in wind direction in the column above a grid point on the coastline is presented. The method was tested for southern Sweden using archived output from the HARMONIE-AROME model with promising results, describing both the seasonal and diurnal cycles well. In areas with a complex coastline, such as narrow straits, the concept of land-sea breeze becomes less clear, and several ways to address this problem for the suggested method are discussed. With an operational index of the sea and land breezes, the forecaster can better understand and express the weather situation and add value for people in the coastal zone. Further, the indices can be used to study systematic biases in the model and to create climatologies of the sea and land breezes.
Abstract
An incremental analysis update (IAU) scheme is successfully implemented into a WRF/WRFDA-based hourly cycling data assimilation system with the goal to reduce the imbalance introduced by the high-frequency intermittent data assimilation, especially when radar data is included. With the application of IAU, the analysis increment is smoothly introduced into the model integration over a time window centered at the analysis time. As in digital filter initialization (DFI), the IAU scheme is able to limit large shocks in the early part of a model forecast. Compared to DFI, IAU does better in hydrometeor spin-up and produces more continuous precipitation forecasts from cycle to cycle. The run with IAU is shown to improve the precipitation forecast skills (10+% for CSI scores) compared to the regular cycling forecasts without IAU. The data assimilation system with IAU is also able to accept more observations due to balanced first-guess fields. Comparable results are obtained in IAU tests when the time-varying weights are used versus constant weights. Because of its better property, the IAU with the time-varying weights is implemented in the operational system.
Abstract
An incremental analysis update (IAU) scheme is successfully implemented into a WRF/WRFDA-based hourly cycling data assimilation system with the goal to reduce the imbalance introduced by the high-frequency intermittent data assimilation, especially when radar data is included. With the application of IAU, the analysis increment is smoothly introduced into the model integration over a time window centered at the analysis time. As in digital filter initialization (DFI), the IAU scheme is able to limit large shocks in the early part of a model forecast. Compared to DFI, IAU does better in hydrometeor spin-up and produces more continuous precipitation forecasts from cycle to cycle. The run with IAU is shown to improve the precipitation forecast skills (10+% for CSI scores) compared to the regular cycling forecasts without IAU. The data assimilation system with IAU is also able to accept more observations due to balanced first-guess fields. Comparable results are obtained in IAU tests when the time-varying weights are used versus constant weights. Because of its better property, the IAU with the time-varying weights is implemented in the operational system.
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
Simulation of atmosphere-ocean-ice interactions in coupled Earth modelling systems with kilometer-scale resolution is a newchallenge in operational numericalweather prediction. This study presents an assessment of sensitivity experiments performed with different sea-ice products in a convective-scale weather forecasting system for the European Arctic. On kilometer-scale resolution sea-ice products are challenged by the large footprint of passive microwave satellite observations and issues with spurious sea-ice detection of the higher resolution retrievals based on Synthetic-Aperture Radar instruments. We perform sensitivity experiments with sea-ice concentration fields of (1) the global ECMWF-IFS forecast system, (2) a newly developed multi-sensor product processed through a coupled sea ice-ocean forecasting system, and (3) the AMSR2 product based on passive microwave observations. There are significant differences between the products on O(100km)-scales in the Northern Barents Sea and along the Marginal Ice Zone north of the Svalbard archipelago and towards the Fram Strait. These differences have a direct impact on the modelled surface skin temperature over ocean and sea ice, the turbulent heat flux, and 2 meter air temperature (T2M). An assessment of Arctic weather stations shows a significant improvement of forecasted T2M in the north and east of Svalbard when using the new multi-sensor product, however, south of Svalbard this product has a negative impact. The different sea-ice products are resulting in changes of the surface turbulent heat flux of up to 400W/m 2 which in turn results in T2M variations of up to 5°C. Over a two-day forecast lead time this can lead to uncertainties in weather forecasts of about 1°C even hundreds of kilometers away from the sea-ice.
Abstract
Simulation of atmosphere-ocean-ice interactions in coupled Earth modelling systems with kilometer-scale resolution is a newchallenge in operational numericalweather prediction. This study presents an assessment of sensitivity experiments performed with different sea-ice products in a convective-scale weather forecasting system for the European Arctic. On kilometer-scale resolution sea-ice products are challenged by the large footprint of passive microwave satellite observations and issues with spurious sea-ice detection of the higher resolution retrievals based on Synthetic-Aperture Radar instruments. We perform sensitivity experiments with sea-ice concentration fields of (1) the global ECMWF-IFS forecast system, (2) a newly developed multi-sensor product processed through a coupled sea ice-ocean forecasting system, and (3) the AMSR2 product based on passive microwave observations. There are significant differences between the products on O(100km)-scales in the Northern Barents Sea and along the Marginal Ice Zone north of the Svalbard archipelago and towards the Fram Strait. These differences have a direct impact on the modelled surface skin temperature over ocean and sea ice, the turbulent heat flux, and 2 meter air temperature (T2M). An assessment of Arctic weather stations shows a significant improvement of forecasted T2M in the north and east of Svalbard when using the new multi-sensor product, however, south of Svalbard this product has a negative impact. The different sea-ice products are resulting in changes of the surface turbulent heat flux of up to 400W/m 2 which in turn results in T2M variations of up to 5°C. Over a two-day forecast lead time this can lead to uncertainties in weather forecasts of about 1°C even hundreds of kilometers away from the sea-ice.
Abstract
With several seasons of Geostationary Lightning Mapper (GLM), this work revisits incorporating lightning observations into operational tropical cyclone rapid intensification guidance. GLM provides freely available, real-time lightning data over the central and eastern North Pacific and North Atlantic oceans. A long-term lightning data set is needed to use GLM in a statistical–dynamical operational application to capture the relationship between lightning and the rare occurrence of rapid intensification. This work uses the World Wide Lightning Location Network (WWLLN) data set from 2005 to 2017 to develop lightning-based predictors for rapid intensification guidance models. The models mimic the operational Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index and Rapid Intensification Prediction Aid frameworks. The frameworks are averaged to form a consensus as a means to isolate the impact of the lightning predictors. Two configurations for lightning predictors are assessed: a spatial configuration with 0- to 100-km inner-core and 200- to 300-km rainband area for the preceding 6 h predictors and a temporal configuration with an inner-core only for the preceding 0 to 1 h, 0 to 6 h, and 6 to 12 h. When tested on the 2018 to 2021 seasons, the temporal configuration adds skill primarily to the 12–48 h forecasts when compared to the no-lightning version and Rapid Intensification Operational Consensus. When WWLLN is replaced with GLM, minor changes to the prediction are observed suggesting that this approach is suitable for operational applications and provides a new baseline for tropical cyclone lightning-based rapid intensification aids.
Abstract
With several seasons of Geostationary Lightning Mapper (GLM), this work revisits incorporating lightning observations into operational tropical cyclone rapid intensification guidance. GLM provides freely available, real-time lightning data over the central and eastern North Pacific and North Atlantic oceans. A long-term lightning data set is needed to use GLM in a statistical–dynamical operational application to capture the relationship between lightning and the rare occurrence of rapid intensification. This work uses the World Wide Lightning Location Network (WWLLN) data set from 2005 to 2017 to develop lightning-based predictors for rapid intensification guidance models. The models mimic the operational Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index and Rapid Intensification Prediction Aid frameworks. The frameworks are averaged to form a consensus as a means to isolate the impact of the lightning predictors. Two configurations for lightning predictors are assessed: a spatial configuration with 0- to 100-km inner-core and 200- to 300-km rainband area for the preceding 6 h predictors and a temporal configuration with an inner-core only for the preceding 0 to 1 h, 0 to 6 h, and 6 to 12 h. When tested on the 2018 to 2021 seasons, the temporal configuration adds skill primarily to the 12–48 h forecasts when compared to the no-lightning version and Rapid Intensification Operational Consensus. When WWLLN is replaced with GLM, minor changes to the prediction are observed suggesting that this approach is suitable for operational applications and provides a new baseline for tropical cyclone lightning-based rapid intensification aids.
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
Urban heat islands (UHIs) may increase the likelihood that frost sensitive plants will escape freezing nighttime temperatures in late spring and early fall. Using data from 151 temperature sensors in the Madison, Wisconsin, region during March 2012 through October 2016, we found that during time periods when National Weather Service (NWS) issued freeze warnings (threshold of 0.0°C) or frost advisories (threshold of 2.22°C) were valid, temperatures in Madison’s most densely populated, built-up areas often did not fall below the respective temperature thresholds. Urban locations had a mean minimum temperature of 0.72°C and 1.39°C for spring and fall freeze warnings, respectively, compared to −0.52°C and −0.53°C for rural locations. On average, 31% of the region’s land area experienced minimum temperatures above the respective temperature thresholds during freeze warnings and frost advisories, and the likelihood of temperatures falling below critical temperature thresholds increased as the distance away from core urban centers increased. The urban-rural temperature differences were greatest in fall compared to spring, and when sensor temperatures did drop below thresholds, the maximum time spent at or below thresholds was highest for rural locations during fall freeze warnings (6.2 hours) compared to urban locations (4.8 hours). These findings potentially have widely varying implications for the general public and industry. UHIs create localized, positive perturbations to nighttime temperatures that are difficult to account for in forecasts; therefore, freeze warnings and frost advisories may have varying degrees of verification in medium-sized cities like Madison, Wisconsin that are surrounded by cropland and natural vegetation.
Abstract
Urban heat islands (UHIs) may increase the likelihood that frost sensitive plants will escape freezing nighttime temperatures in late spring and early fall. Using data from 151 temperature sensors in the Madison, Wisconsin, region during March 2012 through October 2016, we found that during time periods when National Weather Service (NWS) issued freeze warnings (threshold of 0.0°C) or frost advisories (threshold of 2.22°C) were valid, temperatures in Madison’s most densely populated, built-up areas often did not fall below the respective temperature thresholds. Urban locations had a mean minimum temperature of 0.72°C and 1.39°C for spring and fall freeze warnings, respectively, compared to −0.52°C and −0.53°C for rural locations. On average, 31% of the region’s land area experienced minimum temperatures above the respective temperature thresholds during freeze warnings and frost advisories, and the likelihood of temperatures falling below critical temperature thresholds increased as the distance away from core urban centers increased. The urban-rural temperature differences were greatest in fall compared to spring, and when sensor temperatures did drop below thresholds, the maximum time spent at or below thresholds was highest for rural locations during fall freeze warnings (6.2 hours) compared to urban locations (4.8 hours). These findings potentially have widely varying implications for the general public and industry. UHIs create localized, positive perturbations to nighttime temperatures that are difficult to account for in forecasts; therefore, freeze warnings and frost advisories may have varying degrees of verification in medium-sized cities like Madison, Wisconsin that are surrounded by cropland and natural vegetation.
Abstract
Identifying modes of convection can be useful in both forecasting and research. For example, it allows for potentially different impacts to be determined in forecasting contexts and stratification of model behavior in research contexts. One area where identification could be particularly beneficial is elevated convection. Elevated convection is not routinely examined (outside of an operational environment) within a physical-process perspective in operational numerical weather prediction model evaluation or verification. Using convection-allowing model (CAM) output the characteristics of four elevated convection diagnostics (based on boundary layer, Convective Available Potential Energy (CAPE) ratios, downdraft, and inflow layer properties) are examined in operational forecasts during the UK Testbed Summer 2021 run at the Met Office. A survey of the practical use of these diagnostics in a simulated operational environment revealed that diagnostics based on CAPE ratios and inflow layer properties were preferred. These diagnostics were the smoothest varying in both space and time. Treating the CAPE ratio and downdraft properties diagnostics as proxies for updrafts and downdrafts, respectively, showed that updrafts were slightly more likely to be resolved than downdrafts. However, a substantial proportion of both are unresolved in current CAMs. Filtering the CAPE ratios by the inflow layer properties led to improved spatial and temporal characteristics, and thus indicates a potentially useful diagnostic for both research and forecasting.
Abstract
Identifying modes of convection can be useful in both forecasting and research. For example, it allows for potentially different impacts to be determined in forecasting contexts and stratification of model behavior in research contexts. One area where identification could be particularly beneficial is elevated convection. Elevated convection is not routinely examined (outside of an operational environment) within a physical-process perspective in operational numerical weather prediction model evaluation or verification. Using convection-allowing model (CAM) output the characteristics of four elevated convection diagnostics (based on boundary layer, Convective Available Potential Energy (CAPE) ratios, downdraft, and inflow layer properties) are examined in operational forecasts during the UK Testbed Summer 2021 run at the Met Office. A survey of the practical use of these diagnostics in a simulated operational environment revealed that diagnostics based on CAPE ratios and inflow layer properties were preferred. These diagnostics were the smoothest varying in both space and time. Treating the CAPE ratio and downdraft properties diagnostics as proxies for updrafts and downdrafts, respectively, showed that updrafts were slightly more likely to be resolved than downdrafts. However, a substantial proportion of both are unresolved in current CAMs. Filtering the CAPE ratios by the inflow layer properties led to improved spatial and temporal characteristics, and thus indicates a potentially useful diagnostic for both research and forecasting.
Abstract
This work investigates the impact of tropical sea surface temperature (SST) biases on the subseasonal to seasonal (S2S) precipitation forecast skill over the Contiguous United States (CONUS) in the Unified Forecast System (UFS) coupled model Prototype 6. Boreal summer (June - September) and winter (December - March) for 2011-2018 were analyzed. The impact of tropical West Pacific (WP) and tropical North Atlantic (TNA) warm SST biases is evaluated using multivariate linear regression analysis. A warm SST bias over the WP influences the CONUS precipitation remotely through a Rossby wave-train in both seasons. During boreal winter, a warm SST bias over the TNA partly affects the magnitude of the North Atlantic Subtropical High (NASH)’s center, which in the reforecasts is weaker than in reanalysis. The weaker NASH favors an enhanced moisture transport from the Gulf of Mexico, leading to increased precipitation over the southeast U. S. Compared to reanalysis, during boreal summer, the NASH’s center is also weaker and in addition, its position is displaced to the northeast. The displacement further affects the CONUS summer precipitation. The SST biases over the two tropical regions and their impact become stronger as the forecast lead increases from week 1 to 4. These tropical biases explain up to 10% of the CONUS precipitation biases on the S2S time scale.
Abstract
This work investigates the impact of tropical sea surface temperature (SST) biases on the subseasonal to seasonal (S2S) precipitation forecast skill over the Contiguous United States (CONUS) in the Unified Forecast System (UFS) coupled model Prototype 6. Boreal summer (June - September) and winter (December - March) for 2011-2018 were analyzed. The impact of tropical West Pacific (WP) and tropical North Atlantic (TNA) warm SST biases is evaluated using multivariate linear regression analysis. A warm SST bias over the WP influences the CONUS precipitation remotely through a Rossby wave-train in both seasons. During boreal winter, a warm SST bias over the TNA partly affects the magnitude of the North Atlantic Subtropical High (NASH)’s center, which in the reforecasts is weaker than in reanalysis. The weaker NASH favors an enhanced moisture transport from the Gulf of Mexico, leading to increased precipitation over the southeast U. S. Compared to reanalysis, during boreal summer, the NASH’s center is also weaker and in addition, its position is displaced to the northeast. The displacement further affects the CONUS summer precipitation. The SST biases over the two tropical regions and their impact become stronger as the forecast lead increases from week 1 to 4. These tropical biases explain up to 10% of the CONUS precipitation biases on the S2S time scale.
