Browse
Abstract
The New York State Mesonet (NYSM) has provided continuous in situ and remote sensing observations near the surface and within the lower troposphere since 2017. The dense observing network can capture the evolution of mesoscale motions with high temporal and spatial resolution. The objective of this study was to investigate whether the assimilation of NYSM observations into numerical weather prediction models could be beneficial for improving model analysis and short-term weather prediction. The study was conducted using a convective event that occurred in New York on 21 June 2021. A line of severe thunderstorms developed, decayed, and then reintensified as it propagated eastward across the state. Several data assimilation (DA) experiments were conducted to investigate the impact of NYSM data using the operational DA system Gridpoint Statistical Interpolation with rapid update cycles. The assimilated datasets included National Centers for Environmental Prediction Automated Data Processing global upper-air and surface observations, NYSM surface observations, Doppler lidar wind retrievals, and microwave radiometer (MWR) thermodynamic retrievals at NYSM profiler sites. In comparison with the control experiment that assimilated only conventional data, the timing and location of the convection reintensification was significantly improved by assimilating NYSM data, especially the Doppler lidar wind data. Our analysis indicated that the improvement could be attributed to improved simulation of the Mohawk–Hudson Convergence. We also found that the MWR DA resulted in degraded forecasts, likely due to large errors in the MWR temperature retrievals. Overall, this case study suggested the positive impact of assimilating NYSM surface and profiler data on forecasting summertime severe weather.
Abstract
The New York State Mesonet (NYSM) has provided continuous in situ and remote sensing observations near the surface and within the lower troposphere since 2017. The dense observing network can capture the evolution of mesoscale motions with high temporal and spatial resolution. The objective of this study was to investigate whether the assimilation of NYSM observations into numerical weather prediction models could be beneficial for improving model analysis and short-term weather prediction. The study was conducted using a convective event that occurred in New York on 21 June 2021. A line of severe thunderstorms developed, decayed, and then reintensified as it propagated eastward across the state. Several data assimilation (DA) experiments were conducted to investigate the impact of NYSM data using the operational DA system Gridpoint Statistical Interpolation with rapid update cycles. The assimilated datasets included National Centers for Environmental Prediction Automated Data Processing global upper-air and surface observations, NYSM surface observations, Doppler lidar wind retrievals, and microwave radiometer (MWR) thermodynamic retrievals at NYSM profiler sites. In comparison with the control experiment that assimilated only conventional data, the timing and location of the convection reintensification was significantly improved by assimilating NYSM data, especially the Doppler lidar wind data. Our analysis indicated that the improvement could be attributed to improved simulation of the Mohawk–Hudson Convergence. We also found that the MWR DA resulted in degraded forecasts, likely due to large errors in the MWR temperature retrievals. Overall, this case study suggested the positive impact of assimilating NYSM surface and profiler data on forecasting summertime severe weather.
Abstract
We analyze the evolution of convective available potential energy (CAPE) and convective inhibition (CIN) in the days leading up to episodes of high CAPE in North America. The widely accepted theory for CAPE buildup, known as the advection hypothesis, states that high moist static energy (MSE) parcels of air moving north from the Gulf of Mexico become trapped under warm but dry parcels moving east from over elevated dry terrain. If and when the resulting CIN erodes, severe convection can occur due to the large energy difference between the boundary layer parcels and cool air aloft. However, our results, obtained via backward Lagrangian tracking of parcels at locations of peak CAPE, show that large values of CAPE are generated mainly via boundary layer moistening in the days leading up to the time of peak CAPE, and that a large portion of this moisture buildup happens on the day of peak CAPE. On the other hand, the free-tropospheric temperature above these tracked parcels rarely changes significantly over the days leading up to such occurrences. In addition, the CIN that allows for this buildup of CAPE arises mostly from unusually strong boundary layer cooling the night before peak CAPE, and has a contribution from differential advection of unusually warm air above the boundary layer to form a capping inversion. These results have important implications for the climatology of severe convective events, as it emphasizes the role of surface properties and their gradients in the frequency and intensity of high CAPE occurrences.
Significance Statement
Severe convective events, such as thunderstorms, tornadoes, and hail storms, are among the most deadly and destructive weather systems. Although forecasters are quite good at predicting the probability of these events a few days in advance, there is currently no reliable seasonal prediction method of severe convection. We show that the buildup of energy for severe convection relies on both strong surface evaporation during the day of peak energy and anomalous cooling the night before. This progress represents a step toward understanding what controls the frequency of severe convective events on seasonal and longer time scales, including the effect of greenhouse gas–induced climate change.
Abstract
We analyze the evolution of convective available potential energy (CAPE) and convective inhibition (CIN) in the days leading up to episodes of high CAPE in North America. The widely accepted theory for CAPE buildup, known as the advection hypothesis, states that high moist static energy (MSE) parcels of air moving north from the Gulf of Mexico become trapped under warm but dry parcels moving east from over elevated dry terrain. If and when the resulting CIN erodes, severe convection can occur due to the large energy difference between the boundary layer parcels and cool air aloft. However, our results, obtained via backward Lagrangian tracking of parcels at locations of peak CAPE, show that large values of CAPE are generated mainly via boundary layer moistening in the days leading up to the time of peak CAPE, and that a large portion of this moisture buildup happens on the day of peak CAPE. On the other hand, the free-tropospheric temperature above these tracked parcels rarely changes significantly over the days leading up to such occurrences. In addition, the CIN that allows for this buildup of CAPE arises mostly from unusually strong boundary layer cooling the night before peak CAPE, and has a contribution from differential advection of unusually warm air above the boundary layer to form a capping inversion. These results have important implications for the climatology of severe convective events, as it emphasizes the role of surface properties and their gradients in the frequency and intensity of high CAPE occurrences.
Significance Statement
Severe convective events, such as thunderstorms, tornadoes, and hail storms, are among the most deadly and destructive weather systems. Although forecasters are quite good at predicting the probability of these events a few days in advance, there is currently no reliable seasonal prediction method of severe convection. We show that the buildup of energy for severe convection relies on both strong surface evaporation during the day of peak energy and anomalous cooling the night before. This progress represents a step toward understanding what controls the frequency of severe convective events on seasonal and longer time scales, including the effect of greenhouse gas–induced climate change.
Abstract
Owing to the increasing share of variable renewable energies in the electricity mix, the European energy sector is becoming more weather sensitive. In this regard, skillful subseasonal predictions of essential climate variables can provide considerable socioeconomic benefits to the energy sector. The aim of this study is therefore to improve the European subseasonal predictions of 100-m wind speed and 2-m temperature, which we achieve through statistical downscaling. We employ redundancy analysis (RDA) to estimate spatial patterns of variability from large-scale fields that allow for the best prediction of surface fields. We compare explanatory powers between the patterns obtained using RDA against those derived using principal component analysis (PCA), when used as predictors in multilinear regression models to predict surface fields, and show that the explanatory power of the former is superior to that of the latter. Subsequently, we employ the estimated relationship between RDA patterns and surface fields to produce statistical probabilistic predictions of gridded surface fields using dynamical ensemble predictions of RDA patterns. We finally demonstrate how a simple combination of dynamical and statistical predictions of surface fields significantly improves the accuracy of subseasonal predictions of both variables over a large part of Europe. We attribute the improved accuracy of these combined predictions to improvements in reliability and resolution.
Abstract
Owing to the increasing share of variable renewable energies in the electricity mix, the European energy sector is becoming more weather sensitive. In this regard, skillful subseasonal predictions of essential climate variables can provide considerable socioeconomic benefits to the energy sector. The aim of this study is therefore to improve the European subseasonal predictions of 100-m wind speed and 2-m temperature, which we achieve through statistical downscaling. We employ redundancy analysis (RDA) to estimate spatial patterns of variability from large-scale fields that allow for the best prediction of surface fields. We compare explanatory powers between the patterns obtained using RDA against those derived using principal component analysis (PCA), when used as predictors in multilinear regression models to predict surface fields, and show that the explanatory power of the former is superior to that of the latter. Subsequently, we employ the estimated relationship between RDA patterns and surface fields to produce statistical probabilistic predictions of gridded surface fields using dynamical ensemble predictions of RDA patterns. We finally demonstrate how a simple combination of dynamical and statistical predictions of surface fields significantly improves the accuracy of subseasonal predictions of both variables over a large part of Europe. We attribute the improved accuracy of these combined predictions to improvements in reliability and resolution.
Abstract
Dynamics of low-level flows in the eye of Typhoon Haishen (2020) in its late phase of intensification are investigated with a special rapid-scan observation of the Himawari-8 geosynchronous satellite conducted every 30 s. This is accomplished by deriving storm-relative atmospheric motion vectors at an unprecedentedly high spatiotemporal resolution by tracking clouds across five consecutive visible-light reflectivity. The overall low-level circulation center was situated several kilometers away from the storm center defined in terms of the inner edge of the lower part of eyewall clouds. The shift direction is rearward of the storm translation, consistently with a numerical study of the tropical cyclone (TC) boundary layer. Over the analysis period of 10 h, azimuthal-mean tangential wind around this center was increased at each radius within the eye, and the rotational angular velocity was nearly homogenized. The instantaneous low-level circulation center is found to orbit around the overall circulation center at distances around 5 km. Its orbital angular speed was close to the maximum angular speed of azimuthal-mean tangential winds. This rotating transient disturbance is found to transport angular momentum inward, which explains the tangential wind increase and the angular velocity homogenization in the eye. These features are consistent with an algebraically growing wavenumber-1 barotropic instability, whose impact on TC structures has not been explored. This instability enhances wavenumber-1 asymmetry in ring-shaped vorticity, which can be induced by various processes such as translation, environmental shear, and exponential barotropic instability. Therefore, it may appear broadly in TCs to affect wind distribution in their eyes.
Significance Statement
Axially asymmetric transient features in the inner cores of tropical storms have been suggested to profoundly affect the structures and the time evolutions of tropical storms. However, the scarcity of observations has hindered studying such processes observationally. By using a specially conducted high-frequency satellite imaging of Typhoon Haishen (2020), we derived atmospheric motion vectors nearly homogeneously at an unprecedentedly high spatiotemporal resolution. Various kinds of asymmetric motions in low-level flows in the eye were found. Of particular interest is a special type of wavenumber-1 instability whose role has not drawn much attention; the instability was found to provide angular momentum transport consistent with the measured homogenization of the rotation.
Abstract
Dynamics of low-level flows in the eye of Typhoon Haishen (2020) in its late phase of intensification are investigated with a special rapid-scan observation of the Himawari-8 geosynchronous satellite conducted every 30 s. This is accomplished by deriving storm-relative atmospheric motion vectors at an unprecedentedly high spatiotemporal resolution by tracking clouds across five consecutive visible-light reflectivity. The overall low-level circulation center was situated several kilometers away from the storm center defined in terms of the inner edge of the lower part of eyewall clouds. The shift direction is rearward of the storm translation, consistently with a numerical study of the tropical cyclone (TC) boundary layer. Over the analysis period of 10 h, azimuthal-mean tangential wind around this center was increased at each radius within the eye, and the rotational angular velocity was nearly homogenized. The instantaneous low-level circulation center is found to orbit around the overall circulation center at distances around 5 km. Its orbital angular speed was close to the maximum angular speed of azimuthal-mean tangential winds. This rotating transient disturbance is found to transport angular momentum inward, which explains the tangential wind increase and the angular velocity homogenization in the eye. These features are consistent with an algebraically growing wavenumber-1 barotropic instability, whose impact on TC structures has not been explored. This instability enhances wavenumber-1 asymmetry in ring-shaped vorticity, which can be induced by various processes such as translation, environmental shear, and exponential barotropic instability. Therefore, it may appear broadly in TCs to affect wind distribution in their eyes.
Significance Statement
Axially asymmetric transient features in the inner cores of tropical storms have been suggested to profoundly affect the structures and the time evolutions of tropical storms. However, the scarcity of observations has hindered studying such processes observationally. By using a specially conducted high-frequency satellite imaging of Typhoon Haishen (2020), we derived atmospheric motion vectors nearly homogeneously at an unprecedentedly high spatiotemporal resolution. Various kinds of asymmetric motions in low-level flows in the eye were found. Of particular interest is a special type of wavenumber-1 instability whose role has not drawn much attention; the instability was found to provide angular momentum transport consistent with the measured homogenization of the rotation.
Abstract
An essential component of four-dimensional variational data assimilation is the tangent linear model (TLM), which is a linearized version of the full nonlinear forecast model. A relatively new approach to calculating the TLM is a regression model called the ensemble tangent linear model (ETLM). Here we validate the ETLM for linearizing a nonorographic gravity wave drag (NGWD) subgrid-scale model. The regression is applied to an ensemble created by perturbing the atmospheric state and calculating one time step of the NGWD model. The ETLM is validated using independent perturbations based on archived analysis increments. We examine how the skill of the NGWD ETLM depends on the choice of ensemble perturbation, ensemble size, amount of ensemble inflation/deflation, and the size of the localization stencil. After examining the nearly perfect results using a large ETLM ensemble (100 000 members), optimal tuning is then performed for 150–500 members. For smaller ETLM ensembles, spurious noise due to sampling error could be reduced either by downscaling the perturbations or by localizing the ETLM. The impact of localization decreases as the ETLM ensemble size increases. We then validate the ETLM using one year of archived DA analysis increments. The skill varies over time with percentage errors relative to persistence forecasts (where 100% is no skill, 0% is a perfect forecast) generally ranging from ∼50% to 90% (∼40% to 80%) for ETLMs with 150 (500) members. The ETLM is also shown to propagate small increments (1% of the size of analysis increments) with fractional errors of ∼10%.
Abstract
An essential component of four-dimensional variational data assimilation is the tangent linear model (TLM), which is a linearized version of the full nonlinear forecast model. A relatively new approach to calculating the TLM is a regression model called the ensemble tangent linear model (ETLM). Here we validate the ETLM for linearizing a nonorographic gravity wave drag (NGWD) subgrid-scale model. The regression is applied to an ensemble created by perturbing the atmospheric state and calculating one time step of the NGWD model. The ETLM is validated using independent perturbations based on archived analysis increments. We examine how the skill of the NGWD ETLM depends on the choice of ensemble perturbation, ensemble size, amount of ensemble inflation/deflation, and the size of the localization stencil. After examining the nearly perfect results using a large ETLM ensemble (100 000 members), optimal tuning is then performed for 150–500 members. For smaller ETLM ensembles, spurious noise due to sampling error could be reduced either by downscaling the perturbations or by localizing the ETLM. The impact of localization decreases as the ETLM ensemble size increases. We then validate the ETLM using one year of archived DA analysis increments. The skill varies over time with percentage errors relative to persistence forecasts (where 100% is no skill, 0% is a perfect forecast) generally ranging from ∼50% to 90% (∼40% to 80%) for ETLMs with 150 (500) members. The ETLM is also shown to propagate small increments (1% of the size of analysis increments) with fractional errors of ∼10%.
Abstract
All-sky assimilation of brightness temperatures (BTs) from GOES-16 infrared water vapor channels is challenging, primarily because these channels are sensitive to cloud ice that causes large nonlinear errors in the forecast and forward models. Thus, bias correction (BC) for all-sky assimilation of GOES-16 BTs is vital. This study examines the impacts of different BC schemes, especially for a scheme with a quartic polynomial of cloud predictors (the ASRBC4 scheme), on the analysis and WRF Model forecasts of tropical cyclones when assimilating the all-sky GOES-16 channel-8 BTs using the NCEP GSI-based 3D ensemble–variational hybrid data assimilation (DA) system with variational BC (VarBC). Long-term statistics are performed during the NASA Convective Processes Experiment field campaign (2017). Results demonstrate that the ASRBC4 scheme effectively reduces the average of all-sky scaled observation-minus-backgrounds (OmBs) in a cloudy sky and alleviates their nonlinear conditional biases with respect to the symmetric cloud proxy variable, in contrast to the BC schemes without the cloud predictor or with a first-order cloud predictor. In addition, adopting the ASRBC4 scheme in DA decreases the positive temperature increments at 200 hPa and the accompanying midlevel cyclonic wind increments in the analysis of Tropical Storm (TS) Cindy (2017). Applying the ASRBC4 scheme also leads to better storm-track predictions for TS Cindy (2017) and Hurricane Laura (2022), compared to experiments with other BC schemes. Overall, this study highlights the importance of reducing nonlinear biases of OmBs in a cloudy sky for successful all-sky assimilation of BTs from GOES-16 infrared water vapor channels.
Abstract
All-sky assimilation of brightness temperatures (BTs) from GOES-16 infrared water vapor channels is challenging, primarily because these channels are sensitive to cloud ice that causes large nonlinear errors in the forecast and forward models. Thus, bias correction (BC) for all-sky assimilation of GOES-16 BTs is vital. This study examines the impacts of different BC schemes, especially for a scheme with a quartic polynomial of cloud predictors (the ASRBC4 scheme), on the analysis and WRF Model forecasts of tropical cyclones when assimilating the all-sky GOES-16 channel-8 BTs using the NCEP GSI-based 3D ensemble–variational hybrid data assimilation (DA) system with variational BC (VarBC). Long-term statistics are performed during the NASA Convective Processes Experiment field campaign (2017). Results demonstrate that the ASRBC4 scheme effectively reduces the average of all-sky scaled observation-minus-backgrounds (OmBs) in a cloudy sky and alleviates their nonlinear conditional biases with respect to the symmetric cloud proxy variable, in contrast to the BC schemes without the cloud predictor or with a first-order cloud predictor. In addition, adopting the ASRBC4 scheme in DA decreases the positive temperature increments at 200 hPa and the accompanying midlevel cyclonic wind increments in the analysis of Tropical Storm (TS) Cindy (2017). Applying the ASRBC4 scheme also leads to better storm-track predictions for TS Cindy (2017) and Hurricane Laura (2022), compared to experiments with other BC schemes. Overall, this study highlights the importance of reducing nonlinear biases of OmBs in a cloudy sky for successful all-sky assimilation of BTs from GOES-16 infrared water vapor channels.
Abstract
Using coastal radar and surface observations of Taiwan, an investigation of intensity and structure variations in the inner core of Typhoon Haitang (0505) is conducted. Within a 3-h period (1933–2233 UTC 17 July 2005), Haitang experienced intensity vacillation with merging of the eyewall with a rainband induced by the coastal barrier jet (CBJ); concentric eyewall breakdown and weakening; and eyewall recovery, contraction, and re-intensification. The northerly flow of the CBJ converged with the southerly flow of a leeside meso-low to form a west–east line of convection south of the storm when the storm was still 100 km offshore. The rainband propagated radially inward and triggered eyewall–rainband interaction. The interaction resulted in approximately 30% amplification of precipitation and 20% decrease in the axisymmetric tangential wind. Barotropic instability is speculated to be the underlying dynamic process. The recovery of the eyewall, following nearshore eyewall axisymmetrization and contraction, resulted in a 40% intensity increase before landfall.
Significance Statement
The behaviors and underlying physical processes of a landfalling tropical cyclone (TC) under the influence of complex terrain are studied by using coastal radars. The TC inner core structure and intensity change, including concentric eyewall breakdown, weakening, eyewall recovery, eyewall contraction, and re-intensification that occurred 3 h before TC made landfall are documented. The terrain-induced coastal barrier jet and leeside meso-low helped to form an intense line convection near the southern fringe of the eyewall and triggered the rainband–eyewall interaction.
Abstract
Using coastal radar and surface observations of Taiwan, an investigation of intensity and structure variations in the inner core of Typhoon Haitang (0505) is conducted. Within a 3-h period (1933–2233 UTC 17 July 2005), Haitang experienced intensity vacillation with merging of the eyewall with a rainband induced by the coastal barrier jet (CBJ); concentric eyewall breakdown and weakening; and eyewall recovery, contraction, and re-intensification. The northerly flow of the CBJ converged with the southerly flow of a leeside meso-low to form a west–east line of convection south of the storm when the storm was still 100 km offshore. The rainband propagated radially inward and triggered eyewall–rainband interaction. The interaction resulted in approximately 30% amplification of precipitation and 20% decrease in the axisymmetric tangential wind. Barotropic instability is speculated to be the underlying dynamic process. The recovery of the eyewall, following nearshore eyewall axisymmetrization and contraction, resulted in a 40% intensity increase before landfall.
Significance Statement
The behaviors and underlying physical processes of a landfalling tropical cyclone (TC) under the influence of complex terrain are studied by using coastal radars. The TC inner core structure and intensity change, including concentric eyewall breakdown, weakening, eyewall recovery, eyewall contraction, and re-intensification that occurred 3 h before TC made landfall are documented. The terrain-induced coastal barrier jet and leeside meso-low helped to form an intense line convection near the southern fringe of the eyewall and triggered the rainband–eyewall interaction.
Abstract
Tropical cyclones (TCs) generated over the Arabian Sea can cause significant damage to infrastructure, human lives, landfall, and property near inshore and maritime trade route areas. A key to successful prediction of TCs is a skillful prediction of potential cyclogenesis locations. This study focuses on evaluating three genesis potential indices (GPIs) derived from a global reanalysis (ERA5) and dynamically downscaling using a regional model (WRF) for two TC cases: Gonu in 2007 and Kyarr in 2019, selected by analyzing the accumulated cyclone energy trend from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset over the period of 1981–2019. The two TCs belong to category 4 and above on the Saffir–Simpson scale. To test the sensitivity of downscaling to cumulus parameterizations, two WRF experiments were conducted using the Kain–Fritsch and New Tiedke cumulus schemes, respectively. The calculated genesis locations with help of the three GPIs were compared with IBTrACS. The results show that 1) all indices have reasonable skills in reproducing genesis locations, although their performances differ somewhat; 2) the dynamic downscaling with two WRF experiments added value to the study by comparing two numerical schemes for estimating genesis locations; and 3) WRF with the New Tiedke and Kain–Fritsch schemes showed good skill in reproducing the spatial distribution of the most relevant dynamical parameters. The pattern correlations are well correlated with environmental parameters of untransformed GPI and higher correlations with binary logarithmic transformed GPI. The applicability to other cyclones is also tested (e.g., TC Nilofar in 2014) with encouraging results. This study demonstrates the usefulness of GPIs for forecasting TC genesis in the region.
Significance Statement
The trend analysis of accumulated cyclone energy (ACE) over the Arabian Sea (AS) shows an increase over the period of 1981–2019 with the highest ACE values for 2019. The genesis potential indices (GPI) show strong ability for use as a forecasting tool for tropical cyclone genesis, and hence, are helpful for providing a reference for future studies. WRF experiments were able to reproduce the GPI with slight differences from the observations and ERA5. WRF schemes show good performance in reproducing key meteorological fields. The analysis of the GPI and WRF schemes shows the potential to be implemented for maritime forecasts of the tropical cyclones in the region. This study will be helpful scientifically and strategically with a significant impact on socioeconomic activities in the region.
Abstract
Tropical cyclones (TCs) generated over the Arabian Sea can cause significant damage to infrastructure, human lives, landfall, and property near inshore and maritime trade route areas. A key to successful prediction of TCs is a skillful prediction of potential cyclogenesis locations. This study focuses on evaluating three genesis potential indices (GPIs) derived from a global reanalysis (ERA5) and dynamically downscaling using a regional model (WRF) for two TC cases: Gonu in 2007 and Kyarr in 2019, selected by analyzing the accumulated cyclone energy trend from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset over the period of 1981–2019. The two TCs belong to category 4 and above on the Saffir–Simpson scale. To test the sensitivity of downscaling to cumulus parameterizations, two WRF experiments were conducted using the Kain–Fritsch and New Tiedke cumulus schemes, respectively. The calculated genesis locations with help of the three GPIs were compared with IBTrACS. The results show that 1) all indices have reasonable skills in reproducing genesis locations, although their performances differ somewhat; 2) the dynamic downscaling with two WRF experiments added value to the study by comparing two numerical schemes for estimating genesis locations; and 3) WRF with the New Tiedke and Kain–Fritsch schemes showed good skill in reproducing the spatial distribution of the most relevant dynamical parameters. The pattern correlations are well correlated with environmental parameters of untransformed GPI and higher correlations with binary logarithmic transformed GPI. The applicability to other cyclones is also tested (e.g., TC Nilofar in 2014) with encouraging results. This study demonstrates the usefulness of GPIs for forecasting TC genesis in the region.
Significance Statement
The trend analysis of accumulated cyclone energy (ACE) over the Arabian Sea (AS) shows an increase over the period of 1981–2019 with the highest ACE values for 2019. The genesis potential indices (GPI) show strong ability for use as a forecasting tool for tropical cyclone genesis, and hence, are helpful for providing a reference for future studies. WRF experiments were able to reproduce the GPI with slight differences from the observations and ERA5. WRF schemes show good performance in reproducing key meteorological fields. The analysis of the GPI and WRF schemes shows the potential to be implemented for maritime forecasts of the tropical cyclones in the region. This study will be helpful scientifically and strategically with a significant impact on socioeconomic activities in the region.
Abstract
This study examined the impact of assimilating 3D temperature and water vapor information in addition to radar observations in a multiscale weather system. A frontal system with extremely heavy rainfall over northern Taiwan was selected. Using the WRF–LETKF Radar Assimilation System, we performed three sets of observing system simulation experiments to assimilate radar observations with or without thermodynamic variables obtained using different methods. First, assimilating the radar data for 2 h showed better structure and short-term forecast than 1 h. Second, we assimilated radar data and thermodynamic variables from a perfect model simulation. The results of the analysis revealed that when a precipitation position error occurred in the background field, assimilating thermodynamic information with the radar data could correct the dynamic structure and shorten the spinup assimilation period, resulting in substantial improvements to the quantitative precipitation forecast. Third, we applied a thermodynamics retrieval algorithm for a feasibility study. With a warm and wet bias of the retrieved fields, assimilating the temperature data had significant impact on the midlevel of stratiform areas and the forecast of the heavy rainfall was consequently improved. Assimilating the water vapor information helped reconstruct the range and intensity of the cold pool, but the improvement of rainfall forecast was limited. The optimal results of analysis and short-term forecast were achieved when both retrieved temperature and water vapor fields were assimilated. In conclusion, assimilating thermodynamic variables in the precipitation system is feasible for shortening the spinup period of data assimilation and improving the analysis and short-term forecast.
Abstract
This study examined the impact of assimilating 3D temperature and water vapor information in addition to radar observations in a multiscale weather system. A frontal system with extremely heavy rainfall over northern Taiwan was selected. Using the WRF–LETKF Radar Assimilation System, we performed three sets of observing system simulation experiments to assimilate radar observations with or without thermodynamic variables obtained using different methods. First, assimilating the radar data for 2 h showed better structure and short-term forecast than 1 h. Second, we assimilated radar data and thermodynamic variables from a perfect model simulation. The results of the analysis revealed that when a precipitation position error occurred in the background field, assimilating thermodynamic information with the radar data could correct the dynamic structure and shorten the spinup assimilation period, resulting in substantial improvements to the quantitative precipitation forecast. Third, we applied a thermodynamics retrieval algorithm for a feasibility study. With a warm and wet bias of the retrieved fields, assimilating the temperature data had significant impact on the midlevel of stratiform areas and the forecast of the heavy rainfall was consequently improved. Assimilating the water vapor information helped reconstruct the range and intensity of the cold pool, but the improvement of rainfall forecast was limited. The optimal results of analysis and short-term forecast were achieved when both retrieved temperature and water vapor fields were assimilated. In conclusion, assimilating thermodynamic variables in the precipitation system is feasible for shortening the spinup period of data assimilation and improving the analysis and short-term forecast.
Abstract
This paper evaluates the representation of turbulence and its effect on transport and dispersion within multiscale and microscale-only simulations in an urban environment. These simulations, run using the Weather Research and Forecasting Model with the addition of an immersed boundary method, predict transport and mixing during a controlled tracer release from the Joint Urban 2003 field campaign in Oklahoma City, Oklahoma. This work extends the results of a recent study through analysis of turbulence kinetic energy and turbulence spectra and their role in accurately simulating wind speed, direction, and tracer concentration. The significance and role of surface heat fluxes and use of the cell perturbation method in the numerical simulation setup are also examined. Our previous study detailed the model development necessary for our multiscale simulations, examined model skill at predicting wind speeds and tracer concentrations, and demonstrated that dynamic downscaling from mesoscale to microscale through a sequence of nested simulations can improve predictions of transport and dispersion relative to a microscale-only simulation forced by idealized meteorology. Here, predictions are compared with observations to assess qualitative agreement and statistical model skill at predicting wind speed, wind direction, tracer concentration, and turbulent kinetic energy at locations throughout the city. We also investigate the scale distribution of turbulence and the associated impact on model skill, particularly for predictions of transport and dispersion. Our results show that downscaled large-scale turbulence, which is unique to the multiscale simulations, significantly improves predictions of tracer concentrations in this complex urban environment.
Significance Statement
Simulations of atmospheric transport and mixing in urban environments have many applications, including pollution modeling for urban planning or informing emergency response following a hazardous release. These applications include phenomena with spatial scales spanning from millimeters to kilometers. Most simulations resolve flow only within the urban area of interest, omitting larger scales of turbulence and regional influences. This study examines a method that resolves both the small and large-scale flow features. We evaluate simulation accuracy by comparing predictions with observations from an experiment involving the release of a tracer gas in Oklahoma City, Oklahoma, with emphasis on correctly modeling turbulent fluctuations. Our results demonstrate the importance of resolving large-scale flow features when predicting transport and dispersion in urban environments.
Abstract
This paper evaluates the representation of turbulence and its effect on transport and dispersion within multiscale and microscale-only simulations in an urban environment. These simulations, run using the Weather Research and Forecasting Model with the addition of an immersed boundary method, predict transport and mixing during a controlled tracer release from the Joint Urban 2003 field campaign in Oklahoma City, Oklahoma. This work extends the results of a recent study through analysis of turbulence kinetic energy and turbulence spectra and their role in accurately simulating wind speed, direction, and tracer concentration. The significance and role of surface heat fluxes and use of the cell perturbation method in the numerical simulation setup are also examined. Our previous study detailed the model development necessary for our multiscale simulations, examined model skill at predicting wind speeds and tracer concentrations, and demonstrated that dynamic downscaling from mesoscale to microscale through a sequence of nested simulations can improve predictions of transport and dispersion relative to a microscale-only simulation forced by idealized meteorology. Here, predictions are compared with observations to assess qualitative agreement and statistical model skill at predicting wind speed, wind direction, tracer concentration, and turbulent kinetic energy at locations throughout the city. We also investigate the scale distribution of turbulence and the associated impact on model skill, particularly for predictions of transport and dispersion. Our results show that downscaled large-scale turbulence, which is unique to the multiscale simulations, significantly improves predictions of tracer concentrations in this complex urban environment.
Significance Statement
Simulations of atmospheric transport and mixing in urban environments have many applications, including pollution modeling for urban planning or informing emergency response following a hazardous release. These applications include phenomena with spatial scales spanning from millimeters to kilometers. Most simulations resolve flow only within the urban area of interest, omitting larger scales of turbulence and regional influences. This study examines a method that resolves both the small and large-scale flow features. We evaluate simulation accuracy by comparing predictions with observations from an experiment involving the release of a tracer gas in Oklahoma City, Oklahoma, with emphasis on correctly modeling turbulent fluctuations. Our results demonstrate the importance of resolving large-scale flow features when predicting transport and dispersion in urban environments.
