Browse
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 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 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 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.
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 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 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 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.
Abstract
Probabilistic forecasting is a common activity in many fields of the Earth sciences. Assessing the quality of probabilistic forecasts—probabilistic forecast verification—is therefore an essential task in these activities. Numerous methods and metrics have been proposed for this purpose; however, the probabilistic verification of vector variables of ensemble forecasts has received less attention than others. Here we introduce a new approach that is applicable for verifying ensemble forecasts of continuous, scalar and two-dimensional vector data. The proposed method uses a fixed radius near-neighbors search to compute two information-based scores, the ignorance score (the logarithmic score) and the information gain, which quantifies the skill gain from the reference forecast. Basic characteristics of the proposed scores were examined using idealized Monte Carlo simulations. The results indicated that both the Continuous Ranked Probability Score (CRPS) and the proposed score with a relatively small ensemble size (< 25) are not proper in terms of the forecast dispersion. The proposed verification method was successfully used to verify the Madden-Julian Oscillation index, which is a two-dimensional quantity. The proposed method is expected to advance probabilistic ensemble forecasts in various fields.
Abstract
Probabilistic forecasting is a common activity in many fields of the Earth sciences. Assessing the quality of probabilistic forecasts—probabilistic forecast verification—is therefore an essential task in these activities. Numerous methods and metrics have been proposed for this purpose; however, the probabilistic verification of vector variables of ensemble forecasts has received less attention than others. Here we introduce a new approach that is applicable for verifying ensemble forecasts of continuous, scalar and two-dimensional vector data. The proposed method uses a fixed radius near-neighbors search to compute two information-based scores, the ignorance score (the logarithmic score) and the information gain, which quantifies the skill gain from the reference forecast. Basic characteristics of the proposed scores were examined using idealized Monte Carlo simulations. The results indicated that both the Continuous Ranked Probability Score (CRPS) and the proposed score with a relatively small ensemble size (< 25) are not proper in terms of the forecast dispersion. The proposed verification method was successfully used to verify the Madden-Julian Oscillation index, which is a two-dimensional quantity. The proposed method is expected to advance probabilistic ensemble forecasts in various fields.
Abstract
The development and intensification of low-level mesocyclones in supercell thunderstorms has often been attributed, at least in part, to augmented streamwise vorticity generated baroclinically in the forward flank of supercells. However, the ambient streamwise vorticity of the environment (often quantified via storm-relative helicity), especially near the ground, is particularly skillful at discriminating between nontornadic and tornadic supercells. This study investigates whether the origins of the inflow air into supercell low-level mesocyclones, both horizontally and vertically, can help explain the dynamical role of environmental versus storm-generated vorticity in the development of low-level mesocyclone rotation. Simulations of supercells, initialized with wind profiles common to supercell environments observed in nature, show that the air bound for the low-level mesocyclone primarily originates from the ambient environment (rather than from along the forward flank) and from very close to the ground, often in the lowest 200 - 400 m of the atmosphere. Given that the near-ground environmental air comprises the bulk of the inflow into low-level mesocyclones, this likely explains the forecast skill of environmental streamwise vorticity in the lowest few hundred meters of the atmosphere. The low-level mesocyclone does not appear to require much augmentation from the development of additional horizontal vorticity in the forward flank. Instead, the dominant contributor to vertical vorticity within the low-level mesocyclone is from the environmental horizontal vorticity. This study provides further context to the on-going discussion regarding the development of rotation within supercell low-level mesocyclones.
Abstract
The development and intensification of low-level mesocyclones in supercell thunderstorms has often been attributed, at least in part, to augmented streamwise vorticity generated baroclinically in the forward flank of supercells. However, the ambient streamwise vorticity of the environment (often quantified via storm-relative helicity), especially near the ground, is particularly skillful at discriminating between nontornadic and tornadic supercells. This study investigates whether the origins of the inflow air into supercell low-level mesocyclones, both horizontally and vertically, can help explain the dynamical role of environmental versus storm-generated vorticity in the development of low-level mesocyclone rotation. Simulations of supercells, initialized with wind profiles common to supercell environments observed in nature, show that the air bound for the low-level mesocyclone primarily originates from the ambient environment (rather than from along the forward flank) and from very close to the ground, often in the lowest 200 - 400 m of the atmosphere. Given that the near-ground environmental air comprises the bulk of the inflow into low-level mesocyclones, this likely explains the forecast skill of environmental streamwise vorticity in the lowest few hundred meters of the atmosphere. The low-level mesocyclone does not appear to require much augmentation from the development of additional horizontal vorticity in the forward flank. Instead, the dominant contributor to vertical vorticity within the low-level mesocyclone is from the environmental horizontal vorticity. This study provides further context to the on-going discussion regarding the development of rotation within supercell low-level mesocyclones.
Abstract
The national upgrade of the operational weather radar network to include polarimetric capabilities has lead to numerous studies focusing on polarimetric radar signatures commonly observed in supercells. One such signature is the horizontal separation of regions of enhanced differential reflectivity (ZDR) and specific differential phase (KDP) values due to hydrometeor size sorting. Recent observational studies have shown that the orientation of this separation tends to be more perpendicular to storm motion in supercells that produce tornadoes. Although this finding has potential operational utility, the physical relationship between this observed radar signature and tornadic potential is not known. This study uses an ensemble of supercell simulations initialized with tornadic and nontornadic environments to investigate this connection. The tendency for tornadic supercells to have a more perpendicular separation orientation was reproduced, although to a lesser degree. This difference in orientation angles was caused by stronger rearward storm-relative flow in the nontornadic supercells, leading to a rearward shift of precipitation and, therefore, the enhanced KDP region within the supercell. Further, this resulted in an unfavorable rearward shift of the negative buoyancy region, which led to an order of magnitude less baroclinic generation of circulation in the nontornadic simulations compared to tornadic simulations.
Abstract
The national upgrade of the operational weather radar network to include polarimetric capabilities has lead to numerous studies focusing on polarimetric radar signatures commonly observed in supercells. One such signature is the horizontal separation of regions of enhanced differential reflectivity (ZDR) and specific differential phase (KDP) values due to hydrometeor size sorting. Recent observational studies have shown that the orientation of this separation tends to be more perpendicular to storm motion in supercells that produce tornadoes. Although this finding has potential operational utility, the physical relationship between this observed radar signature and tornadic potential is not known. This study uses an ensemble of supercell simulations initialized with tornadic and nontornadic environments to investigate this connection. The tendency for tornadic supercells to have a more perpendicular separation orientation was reproduced, although to a lesser degree. This difference in orientation angles was caused by stronger rearward storm-relative flow in the nontornadic supercells, leading to a rearward shift of precipitation and, therefore, the enhanced KDP region within the supercell. Further, this resulted in an unfavorable rearward shift of the negative buoyancy region, which led to an order of magnitude less baroclinic generation of circulation in the nontornadic simulations compared to tornadic simulations.
Abstract
Forecasting mesoscale convective systems (MCSs) and precipitation over complex terrain is an ongoing challenge even for convective permitting numerical models. Here, we show the value of combining mesoscale constraints to improve short-term MCS forecasts for two events during the North American monsoon season in 2013, including: 1) the initial specification of moisture, via GPS-precipitable water vapor (PWV) data assimilation (DA), 2) kinematics via modification of cumulus parameterization, and 3) microphysics via modification of cloud microphysics parameterization. A total of five convective-permitting Weather Research Forecasting (WRF) model experiments is conducted for each event to elucidate the impact of these constraints. Results show that combining GPS-PWV DA with a modified Kain-Fritsch scheme and double moment microphysics provides relatively the best forecast of both North American monsoon MCSs and convective precipitation in terms of timing, location, and intensity relative to available precipitation and cloud-top temperature observations. Additional examination on the associated reflectivity, vertical wind field, equivalent potential temperature, and hydrometeor distribution of MCS events show the added value of each individual constraint to forecast performance.
Abstract
Forecasting mesoscale convective systems (MCSs) and precipitation over complex terrain is an ongoing challenge even for convective permitting numerical models. Here, we show the value of combining mesoscale constraints to improve short-term MCS forecasts for two events during the North American monsoon season in 2013, including: 1) the initial specification of moisture, via GPS-precipitable water vapor (PWV) data assimilation (DA), 2) kinematics via modification of cumulus parameterization, and 3) microphysics via modification of cloud microphysics parameterization. A total of five convective-permitting Weather Research Forecasting (WRF) model experiments is conducted for each event to elucidate the impact of these constraints. Results show that combining GPS-PWV DA with a modified Kain-Fritsch scheme and double moment microphysics provides relatively the best forecast of both North American monsoon MCSs and convective precipitation in terms of timing, location, and intensity relative to available precipitation and cloud-top temperature observations. Additional examination on the associated reflectivity, vertical wind field, equivalent potential temperature, and hydrometeor distribution of MCS events show the added value of each individual constraint to forecast performance.
Abstract
Ensemble sensitivity analysis (ESA) is a numerical method by which the potential value of a single additional observation can be estimated using an ensemble numerical weather forecast. By performing ESA observation targeting on runs of the TTU WRF Ensemble from the Spring of 2016, a dataset of predicted variance reductions (hereafter referred to as target values) was obtained over approximately 6 weeks’ worth of convective forecasts for the central US. It was then ascertained from these cases that the geographic variation in target values is large for any one case, with local maxima often several standard deviations higher than the mean and surrounded by sharp gradients. Radiosondes launched from the surface, then, would need to take this variation into account to properly sample a specific target by launching upstream of where the target is located rather than directly underneath. In many cases, the difference between the maximum target value in the vertical and the actual target value observed along the balloon path was multiple standard deviations. This may help explain the lower-than-expected forecast improvements observed in previous ESA targeting experiments, especially the Mesoscale Predictability Experiment (MPEX). If target values are a good predictor of observation value, then it is possible that taking the balloon path into account in targeting systems for radiosonde deployment may substantially improve on the value added to the forecast by individual observations.
Abstract
Ensemble sensitivity analysis (ESA) is a numerical method by which the potential value of a single additional observation can be estimated using an ensemble numerical weather forecast. By performing ESA observation targeting on runs of the TTU WRF Ensemble from the Spring of 2016, a dataset of predicted variance reductions (hereafter referred to as target values) was obtained over approximately 6 weeks’ worth of convective forecasts for the central US. It was then ascertained from these cases that the geographic variation in target values is large for any one case, with local maxima often several standard deviations higher than the mean and surrounded by sharp gradients. Radiosondes launched from the surface, then, would need to take this variation into account to properly sample a specific target by launching upstream of where the target is located rather than directly underneath. In many cases, the difference between the maximum target value in the vertical and the actual target value observed along the balloon path was multiple standard deviations. This may help explain the lower-than-expected forecast improvements observed in previous ESA targeting experiments, especially the Mesoscale Predictability Experiment (MPEX). If target values are a good predictor of observation value, then it is possible that taking the balloon path into account in targeting systems for radiosonde deployment may substantially improve on the value added to the forecast by individual observations.
Abstract
In this study, we analyze various sources of CAPE in the environment and their contributions to its time tendency that will complement forecast models and operational analyses that are relatively temporally (∼1 h) coarse. As a case study, the relative roles of direct insolation and near-surface moisture advection in the recovery CAPE on 31 March 2016 in northern Alabama are examined using VORTEX-Southeast (VORTEX-SE) observations and numerical simulations. In between rounds of nontornadic morning storms and tornadic evening storms, CAPE over the VORTEX-SE domain increased from near zero to at least 500 J kg−1. A timeline of the day’s events is provided with a focus on the evolution of the lower levels of the atmosphere. We focus on its responses to solar insolation and moisture advection, which we hypothesize as the main mechanisms behind the recovery of CAPE. Data from the University of Massachusetts S-Band frequency-modulated, continuous-wave (FMCW) radar and NOAA National Severe Storms Laboratory (NSSL) Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS), and high-resolution EnKF analyses from the Advanced Regional Prediction System (ARPS) are used to characterize the boundary layer evolution in the pre-tornadic storm environment. It is found that insolation-driven surface diabatic heating was the primary driver of rapid CAPE recovery on this day. The methodology developed in this case can be applied in other scenarios to diagnose the primary drivers of CAPE development.
Significance Statement
The mechanisms by which atmospheric instability recovers can vary widely and are often a source of uncertainty in forecasting. We want to understand how and why the environment destabilized enough to produce an evening tornado following morning storms on 31 March 2016. To do this, we used model data and observations from a collocated radar and profiler. It was found that heating from the sun at the surface was the primary cause of destabilization in the environment.
Abstract
In this study, we analyze various sources of CAPE in the environment and their contributions to its time tendency that will complement forecast models and operational analyses that are relatively temporally (∼1 h) coarse. As a case study, the relative roles of direct insolation and near-surface moisture advection in the recovery CAPE on 31 March 2016 in northern Alabama are examined using VORTEX-Southeast (VORTEX-SE) observations and numerical simulations. In between rounds of nontornadic morning storms and tornadic evening storms, CAPE over the VORTEX-SE domain increased from near zero to at least 500 J kg−1. A timeline of the day’s events is provided with a focus on the evolution of the lower levels of the atmosphere. We focus on its responses to solar insolation and moisture advection, which we hypothesize as the main mechanisms behind the recovery of CAPE. Data from the University of Massachusetts S-Band frequency-modulated, continuous-wave (FMCW) radar and NOAA National Severe Storms Laboratory (NSSL) Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS), and high-resolution EnKF analyses from the Advanced Regional Prediction System (ARPS) are used to characterize the boundary layer evolution in the pre-tornadic storm environment. It is found that insolation-driven surface diabatic heating was the primary driver of rapid CAPE recovery on this day. The methodology developed in this case can be applied in other scenarios to diagnose the primary drivers of CAPE development.
Significance Statement
The mechanisms by which atmospheric instability recovers can vary widely and are often a source of uncertainty in forecasting. We want to understand how and why the environment destabilized enough to produce an evening tornado following morning storms on 31 March 2016. To do this, we used model data and observations from a collocated radar and profiler. It was found that heating from the sun at the surface was the primary cause of destabilization in the environment.
Abstract
Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in situ observations from the MOSAiC campaign (respectively, 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions.
Significance Statement
This study illustrates a novel method based on machine learning for reducing the systematic surface temperature errors that characterize multiple atmospheric reanalyses in sea ice–covered regions of the Arctic under clear-sky conditions. The correction applied to the temperature field is consistent with the local weather and the sea ice and snow conditions, meaning that it responds to seasonal changes in sea ice cover as well as to its long-term decline due to global warming. The corrected reanalysis temperature can be employed to support polar research activities, and in particular to better simulate the evolution of the interacting sea ice and ocean system within numerical models.
Abstract
Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in situ observations from the MOSAiC campaign (respectively, 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions.
Significance Statement
This study illustrates a novel method based on machine learning for reducing the systematic surface temperature errors that characterize multiple atmospheric reanalyses in sea ice–covered regions of the Arctic under clear-sky conditions. The correction applied to the temperature field is consistent with the local weather and the sea ice and snow conditions, meaning that it responds to seasonal changes in sea ice cover as well as to its long-term decline due to global warming. The corrected reanalysis temperature can be employed to support polar research activities, and in particular to better simulate the evolution of the interacting sea ice and ocean system within numerical models.
Abstract
Midlatitude cyclones approaching coastal mountain ranges experience flow modifications on a variety of scales including orographic lift, blocking, mountain waves, and valley flows. During the 2015/16 Olympic Mountain Experiment (OLYMPEX), a pair of scanning ground radars observed precipitating clouds as they were modified by these orographically induced flows. The DOW radar, positioned to scan up the windward Quinault Valley, conducted RHI scans during 285 h of precipitation, 80% of which contained reversed, down-valley flow at lower levels. The existence of down-valley flow in the Quinault Valley was found to be well correlated with upstream flow blocking and the large-scale sea level pressure gradient orientated down the valley. Deep down-valley flow occurred in environments with high moist static stability and southerly winds, conditions that are common in prefrontal sectors of midlatitude cyclones in the coastal Pacific Northwest. Finally, a case study of prolonged down-valley flow in a prefrontal storm sector was simulated to investigate whether latent heat absorption (cooling) contributed to the event. Three experiments were conducted: a Control simulation and two simulations where the temperature tendencies from melting and evaporation were separately turned off. Results indicated that evaporative cooling had a stronger impact on the event’s down-valley flow than melting, likely because evaporation occurred within the low-level down-valley flow layer. Through these experiments, we show that evaporation helped prolong down-valley flow for several hours past the time of the event’s warm frontal passage.
Significance Statement
This paper analyzes the characteristics of down-valley flow over the windward Quinault Valley on the Olympic Peninsula of Washington State using data from OLYMPEX, with an emphasis on regional pressure differences and blocking metrics. Results demonstrate that the location of precipitation over the Olympic Peninsula is shifted upstream during events with deep down-valley flow, consistent with blocked upstream airflow. A case study of down-valley flow highlights the role of evaporative cooling to prolong the flow reversal.
Abstract
Midlatitude cyclones approaching coastal mountain ranges experience flow modifications on a variety of scales including orographic lift, blocking, mountain waves, and valley flows. During the 2015/16 Olympic Mountain Experiment (OLYMPEX), a pair of scanning ground radars observed precipitating clouds as they were modified by these orographically induced flows. The DOW radar, positioned to scan up the windward Quinault Valley, conducted RHI scans during 285 h of precipitation, 80% of which contained reversed, down-valley flow at lower levels. The existence of down-valley flow in the Quinault Valley was found to be well correlated with upstream flow blocking and the large-scale sea level pressure gradient orientated down the valley. Deep down-valley flow occurred in environments with high moist static stability and southerly winds, conditions that are common in prefrontal sectors of midlatitude cyclones in the coastal Pacific Northwest. Finally, a case study of prolonged down-valley flow in a prefrontal storm sector was simulated to investigate whether latent heat absorption (cooling) contributed to the event. Three experiments were conducted: a Control simulation and two simulations where the temperature tendencies from melting and evaporation were separately turned off. Results indicated that evaporative cooling had a stronger impact on the event’s down-valley flow than melting, likely because evaporation occurred within the low-level down-valley flow layer. Through these experiments, we show that evaporation helped prolong down-valley flow for several hours past the time of the event’s warm frontal passage.
Significance Statement
This paper analyzes the characteristics of down-valley flow over the windward Quinault Valley on the Olympic Peninsula of Washington State using data from OLYMPEX, with an emphasis on regional pressure differences and blocking metrics. Results demonstrate that the location of precipitation over the Olympic Peninsula is shifted upstream during events with deep down-valley flow, consistent with blocked upstream airflow. A case study of down-valley flow highlights the role of evaporative cooling to prolong the flow reversal.
Abstract
It has been long recognized that in the retrieved thermodynamic fields using multiple-Doppler radar synthesized winds, an unknown constant exists on each horizontal level, leading to an ambiguity in the retrieved vertical structure. In this study, the traditional thermodynamic retrieval scheme is significantly improved by the implementation of the Equation of State (EoS) as an additional constraint. With this new formulation, the ambiguity of the vertical structure can be explicitly identified and removed from the retrieved three-dimensional thermodynamic fields. The only in situ independent observations needed to perform the correction are the pressure and temperature measurements taken at a single surface station. If data from multiple surface stations are available, a strategy is proposed to obtain a better estimate of the unknown constant. Experiments in this research were conducted under the observation system simulation experiment (OSSE) framework to demonstrate the validity of the new approach. Problems and possible solutions associated with using real datasets and potential future extended applications of this new method are discussed.
Abstract
It has been long recognized that in the retrieved thermodynamic fields using multiple-Doppler radar synthesized winds, an unknown constant exists on each horizontal level, leading to an ambiguity in the retrieved vertical structure. In this study, the traditional thermodynamic retrieval scheme is significantly improved by the implementation of the Equation of State (EoS) as an additional constraint. With this new formulation, the ambiguity of the vertical structure can be explicitly identified and removed from the retrieved three-dimensional thermodynamic fields. The only in situ independent observations needed to perform the correction are the pressure and temperature measurements taken at a single surface station. If data from multiple surface stations are available, a strategy is proposed to obtain a better estimate of the unknown constant. Experiments in this research were conducted under the observation system simulation experiment (OSSE) framework to demonstrate the validity of the new approach. Problems and possible solutions associated with using real datasets and potential future extended applications of this new method are discussed.
