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Abstract
On 7 February 2020 a relatively deep cyclone (∼980 hPa) with midlevel frontogenesis produced heavy snow (20–30 mm liquid equivalent) over western and central New York State. Despite these characteristics, the precipitation was not organized into a narrow band of intensive snowfall. This event occurred during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. Using coordinated flight legs across New York State, a remote sensing aircraft (ER-2) sampled above the cloud, while a P-3 aircraft collected in-cloud data. These data are used to validate several Weather Research and Forecasting (WRF) Model simulations at 2- and 0.67-km grid spacing using different initial and boundary conditions (RAP, GFS, and ERA5 analyses) and microphysics schemes (Thompson and P3). The differences between the WRF runs are used to explore sensitivity to initial conditions and microphysics schemes. All 18–24-h runs realistically produced a broad sloping region of frontogenesis at midlevels typically; however, there were relatively large (20%–30%) uncertainties in the magnitude of this forcing using different analyses and initialization times. The differences in surface precipitation distribution are small (<10%) among the microphysics schemes, likely because there was little riming in the region of heaviest precipitation. Those runs with frontogenesis closest to the RAP analysis and a surface precipitation underprediction of 20%–30% have too little ice aloft and at low levels, suggesting deficiencies in ice generation and snow growth aloft in those runs. The 0.67-km grid produced more realistic convective cells aloft, but only 5%–10% more precipitation than the 2-km grid.
Significance Statement
Heavy snowfall from U.S. East Coast winter storms can cause major societal problems, yet few studies have investigated these storms using field observations and model data. This study focuses on the 7 February 2020 event, where 20–40 cm of snow fell over west-central New York. Our analysis shows a broad region of ascent, rather than a concentrated region favoring a well-defined snowband was the primary process contributing to snowfall. Last, model microphysics were validated within this storm using the in situ aircraft data. Errors in the snow generation aloft and snow growth at low levels likely contributed to the simulated surface precipitation underprediction, but most of the forecast uncertainty is from initial conditions for this short-term (∼24-h lead time) forecast.
Abstract
On 7 February 2020 a relatively deep cyclone (∼980 hPa) with midlevel frontogenesis produced heavy snow (20–30 mm liquid equivalent) over western and central New York State. Despite these characteristics, the precipitation was not organized into a narrow band of intensive snowfall. This event occurred during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. Using coordinated flight legs across New York State, a remote sensing aircraft (ER-2) sampled above the cloud, while a P-3 aircraft collected in-cloud data. These data are used to validate several Weather Research and Forecasting (WRF) Model simulations at 2- and 0.67-km grid spacing using different initial and boundary conditions (RAP, GFS, and ERA5 analyses) and microphysics schemes (Thompson and P3). The differences between the WRF runs are used to explore sensitivity to initial conditions and microphysics schemes. All 18–24-h runs realistically produced a broad sloping region of frontogenesis at midlevels typically; however, there were relatively large (20%–30%) uncertainties in the magnitude of this forcing using different analyses and initialization times. The differences in surface precipitation distribution are small (<10%) among the microphysics schemes, likely because there was little riming in the region of heaviest precipitation. Those runs with frontogenesis closest to the RAP analysis and a surface precipitation underprediction of 20%–30% have too little ice aloft and at low levels, suggesting deficiencies in ice generation and snow growth aloft in those runs. The 0.67-km grid produced more realistic convective cells aloft, but only 5%–10% more precipitation than the 2-km grid.
Significance Statement
Heavy snowfall from U.S. East Coast winter storms can cause major societal problems, yet few studies have investigated these storms using field observations and model data. This study focuses on the 7 February 2020 event, where 20–40 cm of snow fell over west-central New York. Our analysis shows a broad region of ascent, rather than a concentrated region favoring a well-defined snowband was the primary process contributing to snowfall. Last, model microphysics were validated within this storm using the in situ aircraft data. Errors in the snow generation aloft and snow growth at low levels likely contributed to the simulated surface precipitation underprediction, but most of the forecast uncertainty is from initial conditions for this short-term (∼24-h lead time) forecast.
Abstract
We present environmental and polarimetric radar observations of a long-lived December supercell that tracked approximately 750 km from Arkansas to northern Kentucky. The storm was associated with two long-track EF4 tornadoes, one of which was among the longest-tracked tornadoes recorded in the United States. The supercell’s life cycle is documented from 2000 UTC 10 to 0700 UTC 11 December 2021, using data from five operational polarimetric weather radars. After convection initiation in central Arkansas, it took nearly 4 h for a supercell to develop. Afterward, the storm’s Z DR column and arc became anomalously large leading up to genesis of the first EF4 tornado. During this time, the storm’s environment had moderate convective available potential energy (CAPE) and strong deep-layer shear. A cell interaction at about 0200 UTC disrupted the supercell updraft, weakening the Z DR arc and column, and initiating the largest radar-implied hailfall event observed with this storm. The remnant circulation associated with the first EF4 tornado did not fully dissipate, and it appeared to merge with the low-level mesocyclone on the nose of a rear-flank downdraft surge likely initiated by the hailfall. It is hypothesized that this merger was important to the intensification of the storm’s second EF4 tornado, which lasted nearly 3 h and traveled approximately 267 km. During the second EF4 tornado the storm experienced decreasing CAPE and increasing storm relative helicity. Increasing interactions with other cells eventually weakened the storm, and its original updraft was obscured before the storm’s remnants dissipated in northern Kentucky.
Significance Statement
In December 2021, a long-lived supercell thunderstorm produced two long-track, violent tornadoes, including one that produced historic damage across western Kentucky. Radar observations indicate that, once the storm became a supercell, its updraft became anomalously large relative to similar storms studied prior. Simultaneously its storm-relative inflow strengthened markedly, supporting the first long-lived tornado. Interactions with a developing thunderstorm disrupted the supercell’s updraft, leading to hail fallout and updraft weakening. The remnant circulation associated with the first strong tornado merged with the supercell’s updraft and became a rotation source for the second long-track tornado, which persisted for nearly 3 h. Eventually interactions with other thunderstorms weakened the supercell.
Abstract
We present environmental and polarimetric radar observations of a long-lived December supercell that tracked approximately 750 km from Arkansas to northern Kentucky. The storm was associated with two long-track EF4 tornadoes, one of which was among the longest-tracked tornadoes recorded in the United States. The supercell’s life cycle is documented from 2000 UTC 10 to 0700 UTC 11 December 2021, using data from five operational polarimetric weather radars. After convection initiation in central Arkansas, it took nearly 4 h for a supercell to develop. Afterward, the storm’s Z DR column and arc became anomalously large leading up to genesis of the first EF4 tornado. During this time, the storm’s environment had moderate convective available potential energy (CAPE) and strong deep-layer shear. A cell interaction at about 0200 UTC disrupted the supercell updraft, weakening the Z DR arc and column, and initiating the largest radar-implied hailfall event observed with this storm. The remnant circulation associated with the first EF4 tornado did not fully dissipate, and it appeared to merge with the low-level mesocyclone on the nose of a rear-flank downdraft surge likely initiated by the hailfall. It is hypothesized that this merger was important to the intensification of the storm’s second EF4 tornado, which lasted nearly 3 h and traveled approximately 267 km. During the second EF4 tornado the storm experienced decreasing CAPE and increasing storm relative helicity. Increasing interactions with other cells eventually weakened the storm, and its original updraft was obscured before the storm’s remnants dissipated in northern Kentucky.
Significance Statement
In December 2021, a long-lived supercell thunderstorm produced two long-track, violent tornadoes, including one that produced historic damage across western Kentucky. Radar observations indicate that, once the storm became a supercell, its updraft became anomalously large relative to similar storms studied prior. Simultaneously its storm-relative inflow strengthened markedly, supporting the first long-lived tornado. Interactions with a developing thunderstorm disrupted the supercell’s updraft, leading to hail fallout and updraft weakening. The remnant circulation associated with the first strong tornado merged with the supercell’s updraft and became a rotation source for the second long-track tornado, which persisted for nearly 3 h. Eventually interactions with other thunderstorms weakened the supercell.
Abstract
This study presents observational findings of air–sea turbulent heat flux anomalies during the onset of the South China Sea summer monsoon (SCSSM) in 2021 and explains the mechanism for high-resolution heat flux variations. Turbulent heat flux discrepancies are not uniform throughout the basin but indicate a significant regional disparity in the South China Sea (SCS), which also experiences evident year-to-year variability. Based on buoy- and cruise-based air–sea measurements, high-temporal-resolution (less than hourly) anomalies in the latent heat flux during the SCSSM burst are unexpectedly determined by sea–air humidity differences instead of wind effects under near-neutral and mixed marine atmospheric boundary layer (MABL) stability conditions. However, latent heat anomalies are mainly induced by wind speed under changing MABL conditions. The sensible heat flux is much weaker, with its anomalies dominated by sea–air temperature differences regardless of the boundary layer condition. The observational results are used to examine the discrepancies in turbulent heat fluxes and associated air–sea variables in reanalysis products. The comparisons indicate that latent and sensible heat fluxes in the reanalysis are overestimated by approximately 55 and 3 W m−2, respectively. These overestimations are mainly induced by higher estimates of sea–air humidity/temperature differences. The relative humidity is underestimated by approximately 4.2% in the two high-resolution reanalysis products. The higher SST (near-surface specific humidity) and lower air temperature (specific air humidity) eventually lead to higher estimates of sea–air humidity/temperature differences (1.75 g kg−1/0.25°C), which are the dominant factors controlling the variations in the air–sea turbulent heat fluxes.
Significance Statement
Air–sea interactions are significant in predicting the onset of East Asian monsoon systems, including the SCSSM. During the SCSSM in 2021, four buoys and cruise observations are used to investigate anomalies in the latent and sensible heat fluxes. The physical mechanism of the variations in turbulent heat fluxes under different MABL stability conditions is explored in this study. The humidity and wind speed anomalies play roles under mixed boundary conditions in determining the high-resolution variations in latent heat fluxes. Based on these observational results, the heat fluxes and associated air–sea variables from reanalysis products are compared to identify the differences in the operational systems. These comparison results can help improve the reanalysis to obtain better monsoon predictions.
Abstract
This study presents observational findings of air–sea turbulent heat flux anomalies during the onset of the South China Sea summer monsoon (SCSSM) in 2021 and explains the mechanism for high-resolution heat flux variations. Turbulent heat flux discrepancies are not uniform throughout the basin but indicate a significant regional disparity in the South China Sea (SCS), which also experiences evident year-to-year variability. Based on buoy- and cruise-based air–sea measurements, high-temporal-resolution (less than hourly) anomalies in the latent heat flux during the SCSSM burst are unexpectedly determined by sea–air humidity differences instead of wind effects under near-neutral and mixed marine atmospheric boundary layer (MABL) stability conditions. However, latent heat anomalies are mainly induced by wind speed under changing MABL conditions. The sensible heat flux is much weaker, with its anomalies dominated by sea–air temperature differences regardless of the boundary layer condition. The observational results are used to examine the discrepancies in turbulent heat fluxes and associated air–sea variables in reanalysis products. The comparisons indicate that latent and sensible heat fluxes in the reanalysis are overestimated by approximately 55 and 3 W m−2, respectively. These overestimations are mainly induced by higher estimates of sea–air humidity/temperature differences. The relative humidity is underestimated by approximately 4.2% in the two high-resolution reanalysis products. The higher SST (near-surface specific humidity) and lower air temperature (specific air humidity) eventually lead to higher estimates of sea–air humidity/temperature differences (1.75 g kg−1/0.25°C), which are the dominant factors controlling the variations in the air–sea turbulent heat fluxes.
Significance Statement
Air–sea interactions are significant in predicting the onset of East Asian monsoon systems, including the SCSSM. During the SCSSM in 2021, four buoys and cruise observations are used to investigate anomalies in the latent and sensible heat fluxes. The physical mechanism of the variations in turbulent heat fluxes under different MABL stability conditions is explored in this study. The humidity and wind speed anomalies play roles under mixed boundary conditions in determining the high-resolution variations in latent heat fluxes. Based on these observational results, the heat fluxes and associated air–sea variables from reanalysis products are compared to identify the differences in the operational systems. These comparison results can help improve the reanalysis to obtain better monsoon predictions.
Abstract
Localization is the key component to the successful application of ensemble data assimilation (DA) to high-dimensional problems in the geosciences. We study the impact of sampling error and its amelioration through localization using both analytical development and numerical experiments. Specifically, we show how sampling error in covariance estimates accumulates and spreads throughout the entire domain during the computation of the Kalman gain. This results in a bias, which is the dominant issue in unlocalized ensemble DA, and, surprisingly, we find that it depends directly on the number of independent observations but only indirectly on the state dimension. Our derivations and experiments further make it clear that an important aspect of localization is a significant reduction of bias in the Kalman gain, which in turn leads to an increased accuracy of ensemble DA. We illustrate our findings on a variety of simplified linear and nonlinear test problems, including a cycling ensemble Kalman filter applied to the Lorenz-96 model.
Significance Statement
The dampening of long-range correlations has been the key to the success of ensemble data assimilation in global numerical weather prediction. In this paper, we show how noise in covariance estimates propagates through the state estimation process and corrupts state estimates. We show that this noise results in a bias and that this bias depends on the number of observations and not, as might be expected, on the state dimension. We go on to show how dampening long-range covariances through a process referred to as “localization” helps to mitigate the detrimental effects of this sampling noise.
Abstract
Localization is the key component to the successful application of ensemble data assimilation (DA) to high-dimensional problems in the geosciences. We study the impact of sampling error and its amelioration through localization using both analytical development and numerical experiments. Specifically, we show how sampling error in covariance estimates accumulates and spreads throughout the entire domain during the computation of the Kalman gain. This results in a bias, which is the dominant issue in unlocalized ensemble DA, and, surprisingly, we find that it depends directly on the number of independent observations but only indirectly on the state dimension. Our derivations and experiments further make it clear that an important aspect of localization is a significant reduction of bias in the Kalman gain, which in turn leads to an increased accuracy of ensemble DA. We illustrate our findings on a variety of simplified linear and nonlinear test problems, including a cycling ensemble Kalman filter applied to the Lorenz-96 model.
Significance Statement
The dampening of long-range correlations has been the key to the success of ensemble data assimilation in global numerical weather prediction. In this paper, we show how noise in covariance estimates propagates through the state estimation process and corrupts state estimates. We show that this noise results in a bias and that this bias depends on the number of observations and not, as might be expected, on the state dimension. We go on to show how dampening long-range covariances through a process referred to as “localization” helps to mitigate the detrimental effects of this sampling noise.
Abstract
In this study, a marine fog event that occurred from 0000 to 1800 UTC 7 September 2018 near Canada’s Grand Banks is used to investigate the sensitivity of simulated fog properties to six model parameters found primarily in the microphysics scheme. To do so, we ran a large suite of regional simulations that spanned the life cycle of the fog event using the Regional Atmospheric Modeling System (RAMS). We randomly selected parameter combinations for the simulation suite and used Gaussian process regression to emulate the response of a variety of simulated fog properties to the parameters. We find that the microphysics shape parameter, which controls the relative width of the droplet size distribution, and the aerosol number concentration have the greatest impact on fog in terms of spatial extent, duration, and surface visibility. In general, parameters that reduce mean fall speed of droplets and/or suppress drizzle formation lead to reduced visibility in fog but also delayed onset, shorter lifetimes, and reduced spatial extent. The importance of the distribution width suggests a need for better characterization of this property for fog droplet distributions and better treatment of this property in microphysics schemes.
Abstract
In this study, a marine fog event that occurred from 0000 to 1800 UTC 7 September 2018 near Canada’s Grand Banks is used to investigate the sensitivity of simulated fog properties to six model parameters found primarily in the microphysics scheme. To do so, we ran a large suite of regional simulations that spanned the life cycle of the fog event using the Regional Atmospheric Modeling System (RAMS). We randomly selected parameter combinations for the simulation suite and used Gaussian process regression to emulate the response of a variety of simulated fog properties to the parameters. We find that the microphysics shape parameter, which controls the relative width of the droplet size distribution, and the aerosol number concentration have the greatest impact on fog in terms of spatial extent, duration, and surface visibility. In general, parameters that reduce mean fall speed of droplets and/or suppress drizzle formation lead to reduced visibility in fog but also delayed onset, shorter lifetimes, and reduced spatial extent. The importance of the distribution width suggests a need for better characterization of this property for fog droplet distributions and better treatment of this property in microphysics schemes.
Abstract
In July 2021, Typhoon In-Fa (TIF) triggered a significant indirect heavy precipitation event (HPE) in central China and a direct HPE in eastern China. Both these events led to severe disasters. However, the synoptic-scale conditions and the impacts of these HPEs on future estimations of return periods remain poorly understood. Here, we find that the remote HPE that occurred ∼2200 km ahead of TIF over central China was a predecessor rain event (PRE). The PRE unfolded under the equatorward entrance of the upper-level westerly jet. This event, which encouraged divergent and adiabatic outflow in the upper level, subsequently intensified the strength of the upper-level westerly jet. In contrast, the direct HPE in eastern China was due primarily to the long duration and slow movement of TIF. The direct HPE occurred in areas situated less than 200 km from TIF’s center and to the left of TIF’s propagation trajectory. Anomaly analyses reveal favorable thermodynamic and dynamic conditions and abundant atmospheric moisture that sustained TIF’s intensity. A saddle-shaped pressure field in the north of eastern China and peripheral weak steering flow impeded TIF’s movement northward. Hydrologically, the inclusion of these two HPEs in the historical record leads to a decrease in the estimated return periods of similar HPEs. Our findings highlight the potential difficulties that HPEs could introduce for the design of hydraulic engineering infrastructure as well as for the disaster mitigation measures required to alleviate future risk, particularly in central China.
Abstract
In July 2021, Typhoon In-Fa (TIF) triggered a significant indirect heavy precipitation event (HPE) in central China and a direct HPE in eastern China. Both these events led to severe disasters. However, the synoptic-scale conditions and the impacts of these HPEs on future estimations of return periods remain poorly understood. Here, we find that the remote HPE that occurred ∼2200 km ahead of TIF over central China was a predecessor rain event (PRE). The PRE unfolded under the equatorward entrance of the upper-level westerly jet. This event, which encouraged divergent and adiabatic outflow in the upper level, subsequently intensified the strength of the upper-level westerly jet. In contrast, the direct HPE in eastern China was due primarily to the long duration and slow movement of TIF. The direct HPE occurred in areas situated less than 200 km from TIF’s center and to the left of TIF’s propagation trajectory. Anomaly analyses reveal favorable thermodynamic and dynamic conditions and abundant atmospheric moisture that sustained TIF’s intensity. A saddle-shaped pressure field in the north of eastern China and peripheral weak steering flow impeded TIF’s movement northward. Hydrologically, the inclusion of these two HPEs in the historical record leads to a decrease in the estimated return periods of similar HPEs. Our findings highlight the potential difficulties that HPEs could introduce for the design of hydraulic engineering infrastructure as well as for the disaster mitigation measures required to alleviate future risk, particularly in central China.
Abstract
Balloon-borne radiosondes are launched twice daily at coordinated times worldwide to assist with weather forecasting. Data collection from each flight is usually terminated when the balloon bursts at an altitude above 20 km. This paper highlights cases where the balloon’s turnaround occurs at lower altitudes and is associated with ice formation on the balloon, a weather condition of interest to aviation safety. Four examples of such cases are shown, where the balloon oscillates between 3- and 6-km altitude before rising to high altitudes and bursting. This oscillation is due to the accumulation and melting of ice on the balloon, causing the pattern to repeat multiple times. An analysis of National Weather Service radiosonde data over a 5-yr period and a global dataset from the National Centers for Environmental Information from 1980 to 2020 identified that 0.18% of soundings worldwide satisfied these criteria. This indicates that weather conditions important to aviation safety are not rare in the worldwide database. We recommend that soundings that show descent at altitudes lower than typically expected continue to be tracked, particularly given that these up–down-oscillating soundings can provide valuable information for weather forecasting on days with significant precipitation and icing conditions that might lead to aviation safety concerns.
Abstract
Balloon-borne radiosondes are launched twice daily at coordinated times worldwide to assist with weather forecasting. Data collection from each flight is usually terminated when the balloon bursts at an altitude above 20 km. This paper highlights cases where the balloon’s turnaround occurs at lower altitudes and is associated with ice formation on the balloon, a weather condition of interest to aviation safety. Four examples of such cases are shown, where the balloon oscillates between 3- and 6-km altitude before rising to high altitudes and bursting. This oscillation is due to the accumulation and melting of ice on the balloon, causing the pattern to repeat multiple times. An analysis of National Weather Service radiosonde data over a 5-yr period and a global dataset from the National Centers for Environmental Information from 1980 to 2020 identified that 0.18% of soundings worldwide satisfied these criteria. This indicates that weather conditions important to aviation safety are not rare in the worldwide database. We recommend that soundings that show descent at altitudes lower than typically expected continue to be tracked, particularly given that these up–down-oscillating soundings can provide valuable information for weather forecasting on days with significant precipitation and icing conditions that might lead to aviation safety concerns.
Abstract
Recent advances in hail trajectory modeling regularly produce datasets containing millions of hail trajectories. Because hail growth within a storm cannot be entirely separated from the structure of the trajectories producing it, a method to condense the multidimensionality of the trajectory information into a discrete number of features analyzable by humans is necessary. This article presents a three-dimensional trajectory clustering technique that is designed to group trajectories that have similar updraft-relative structures and orientations. The new technique is an application of a two-dimensional method common in the data mining field. Hail trajectories (or “parent” trajectories) are partitioned into segments before they are clustered using a modified version of the density-based spatial applications with noise (DBSCAN) method. Parent trajectories with segments that are members of at least two common clusters are then grouped into parent trajectory clusters before output. This multistep method has several advantages. Hail trajectories with structural similarities along only portions of their length, e.g., sourced from different locations around the updraft before converging to a common pathway, can still be grouped. However, the physical information inherent in the full length of the trajectory is retained, unlike methods that cluster trajectory segments alone. The conversion of trajectories to an updraft-relative space also allows trajectories separated in time to be clustered. Once the final output trajectory clusters are identified, a method for calculating a representative trajectory for each cluster is proposed. Cluster distributions of hailstone and environmental characteristics at each time step in the representative trajectory can also be calculated.
Significance Statement
To understand how a storm produces large hail, we need to understand the paths that hailstones take in a storm when growing. We can simulate these paths using computer models. However, the millions of hailstones in a simulated storm create millions of paths, which is hard to analyze. This article describes a machine learning method that groups together hailstone paths based on how similar their three-dimensional structures look. It will let hail scientists analyze hailstone pathways in storms more easily, and therefore better understand how hail growth happens.
Abstract
Recent advances in hail trajectory modeling regularly produce datasets containing millions of hail trajectories. Because hail growth within a storm cannot be entirely separated from the structure of the trajectories producing it, a method to condense the multidimensionality of the trajectory information into a discrete number of features analyzable by humans is necessary. This article presents a three-dimensional trajectory clustering technique that is designed to group trajectories that have similar updraft-relative structures and orientations. The new technique is an application of a two-dimensional method common in the data mining field. Hail trajectories (or “parent” trajectories) are partitioned into segments before they are clustered using a modified version of the density-based spatial applications with noise (DBSCAN) method. Parent trajectories with segments that are members of at least two common clusters are then grouped into parent trajectory clusters before output. This multistep method has several advantages. Hail trajectories with structural similarities along only portions of their length, e.g., sourced from different locations around the updraft before converging to a common pathway, can still be grouped. However, the physical information inherent in the full length of the trajectory is retained, unlike methods that cluster trajectory segments alone. The conversion of trajectories to an updraft-relative space also allows trajectories separated in time to be clustered. Once the final output trajectory clusters are identified, a method for calculating a representative trajectory for each cluster is proposed. Cluster distributions of hailstone and environmental characteristics at each time step in the representative trajectory can also be calculated.
Significance Statement
To understand how a storm produces large hail, we need to understand the paths that hailstones take in a storm when growing. We can simulate these paths using computer models. However, the millions of hailstones in a simulated storm create millions of paths, which is hard to analyze. This article describes a machine learning method that groups together hailstone paths based on how similar their three-dimensional structures look. It will let hail scientists analyze hailstone pathways in storms more easily, and therefore better understand how hail growth happens.
Abstract
To simulate the large-scale impacts of wind farms, wind turbines are parameterized within mesoscale models in which grid sizes are typically much larger than turbine scales. Five wind-farm parameterizations were implemented in the Weather Research and Forecasting (WRF) Model v4.3.3 to simulate multiple operational wind farms in the North Sea, which were verified against a satellite image, airborne measurements, and the FINO-1 meteorological mast data on 14 October 2017. The parameterization by Volker et al. underestimated the turbulence and wind speed deficit compared to measurements and to the parameterization of Fitch et al., which is the default in WRF. The Abkar and Porté-Agel parameterization gave close predictions of wind speed to that of Fitch et al. with a lower magnitude of predicted turbulence, although the parameterization was sensitive to a tunable constant. The parameterization by Pan and Archer resulted in turbine-induced thrust and turbulence that were slightly less than that of Fitch et al., but resulted in a substantial drop in power generation due to the magnification of wind speed differences in the power calculation. The parameterization by Redfern et al. was not substantially different from Fitch et al. in the absence of conditions such as strong wind veer. The simulations indicated the need for a turbine-induced turbulence source within a wind-farm parameterization for improved prediction of near-surface wind speed, near-surface temperature, and turbulence. The induced turbulence was responsible for enhancing turbulent momentum flux near the surface, causing a local speed-up of near-surface wind speed inside a wind farm. Our findings highlighted that wakes from large offshore wind farms could extend 100 km downwind, reducing downwind power production as in the case of the 400-MW Bard Offshore 1 wind farm whose power output was reduced by the wakes of the 402-MW Veja Mate wind farm for this case study.
Significance Statement
Because wind farms are smaller than the common grid spacing of numerical weather prediction models, the impacts of wind farms on the weather have to be indirectly incorporated through parameterizations. Several approaches to parameterization are available and the most appropriate scheme is not always clear. The absence of a turbulence source in a parameterization leads to substantial inaccuracies in predicting near-surface wind speed and turbulence over a wind farm. The impact of large clusters of offshore wind turbines in the wind field can exceed 100 km downwind, resulting in a substantial loss of power for downwind turbines. The prediction of this power loss can be sensitive to the chosen parameterization, contributing to uncertainty in wind-farm economic planning.
Abstract
To simulate the large-scale impacts of wind farms, wind turbines are parameterized within mesoscale models in which grid sizes are typically much larger than turbine scales. Five wind-farm parameterizations were implemented in the Weather Research and Forecasting (WRF) Model v4.3.3 to simulate multiple operational wind farms in the North Sea, which were verified against a satellite image, airborne measurements, and the FINO-1 meteorological mast data on 14 October 2017. The parameterization by Volker et al. underestimated the turbulence and wind speed deficit compared to measurements and to the parameterization of Fitch et al., which is the default in WRF. The Abkar and Porté-Agel parameterization gave close predictions of wind speed to that of Fitch et al. with a lower magnitude of predicted turbulence, although the parameterization was sensitive to a tunable constant. The parameterization by Pan and Archer resulted in turbine-induced thrust and turbulence that were slightly less than that of Fitch et al., but resulted in a substantial drop in power generation due to the magnification of wind speed differences in the power calculation. The parameterization by Redfern et al. was not substantially different from Fitch et al. in the absence of conditions such as strong wind veer. The simulations indicated the need for a turbine-induced turbulence source within a wind-farm parameterization for improved prediction of near-surface wind speed, near-surface temperature, and turbulence. The induced turbulence was responsible for enhancing turbulent momentum flux near the surface, causing a local speed-up of near-surface wind speed inside a wind farm. Our findings highlighted that wakes from large offshore wind farms could extend 100 km downwind, reducing downwind power production as in the case of the 400-MW Bard Offshore 1 wind farm whose power output was reduced by the wakes of the 402-MW Veja Mate wind farm for this case study.
Significance Statement
Because wind farms are smaller than the common grid spacing of numerical weather prediction models, the impacts of wind farms on the weather have to be indirectly incorporated through parameterizations. Several approaches to parameterization are available and the most appropriate scheme is not always clear. The absence of a turbulence source in a parameterization leads to substantial inaccuracies in predicting near-surface wind speed and turbulence over a wind farm. The impact of large clusters of offshore wind turbines in the wind field can exceed 100 km downwind, resulting in a substantial loss of power for downwind turbines. The prediction of this power loss can be sensitive to the chosen parameterization, contributing to uncertainty in wind-farm economic planning.
Abstract
Methods to improve the representation of hail in the Thompson–Eidhammer microphysics scheme are explored. A new two-moment and predicted density graupel category is implemented into the Thompson–Eidhammer scheme. Additionally, the one-moment graupel category’s intercept parameter is modified, based on hail observations, to shift the properties of the graupel category to become more hail-like since the category is designed to represent both graupel and hail. Finally, methods to diagnose maximum expected hail size at the surface and aloft are implemented. The original Thompson–Eidhammer version, the newly implemented two-moment and predicted density graupel version, and the modified (to be more hail-like) one-moment version are evaluated using a case that occurred during the Plains Elevated Convection at Night (PECAN) field campaign, during which hail-producing storms merged into a strong mesoscale convective system. The three versions of the scheme are evaluated for their ability to predict hail sizes compared to observed hail sizes from storm reports and estimated from radar, their ability to predict radar reflectivity signatures at various altitudes, and their ability to predict cold-pool features like temperature and wind speed. One key benefit of using the two-moment and predicted density graupel category is that the simulated reflectivity values in the upper levels of discrete storms are clearly improved. This improvement coincides with a significant reduction in the areal extent of graupel aloft, also seen when using the updated one-moment scheme. The two-moment and predicted density graupel scheme is also better able to predict a wide variety of hail sizes at the surface, including large (>2-in. diameter) hail that was observed during this case.
Abstract
Methods to improve the representation of hail in the Thompson–Eidhammer microphysics scheme are explored. A new two-moment and predicted density graupel category is implemented into the Thompson–Eidhammer scheme. Additionally, the one-moment graupel category’s intercept parameter is modified, based on hail observations, to shift the properties of the graupel category to become more hail-like since the category is designed to represent both graupel and hail. Finally, methods to diagnose maximum expected hail size at the surface and aloft are implemented. The original Thompson–Eidhammer version, the newly implemented two-moment and predicted density graupel version, and the modified (to be more hail-like) one-moment version are evaluated using a case that occurred during the Plains Elevated Convection at Night (PECAN) field campaign, during which hail-producing storms merged into a strong mesoscale convective system. The three versions of the scheme are evaluated for their ability to predict hail sizes compared to observed hail sizes from storm reports and estimated from radar, their ability to predict radar reflectivity signatures at various altitudes, and their ability to predict cold-pool features like temperature and wind speed. One key benefit of using the two-moment and predicted density graupel category is that the simulated reflectivity values in the upper levels of discrete storms are clearly improved. This improvement coincides with a significant reduction in the areal extent of graupel aloft, also seen when using the updated one-moment scheme. The two-moment and predicted density graupel scheme is also better able to predict a wide variety of hail sizes at the surface, including large (>2-in. diameter) hail that was observed during this case.
