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- Author or Editor: Frédéric Fabry x
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Abstract
An attempt was made to statistically gauge the importance of moisture variability on convection initiation by analyzing data collected by radar, surface stations, soundings, and airborne in situ sensors over the 7 weeks of the International H2O Project (IHOP_2002). Based on radar refractivity data, the spatial structure of humidity near the surface proved to be very anisotropic, crosswind variability being typically twice as large as along-wind variability, in part as a result of the west-to-east climatological gradient in moisture across the Oklahoma panhandle. Variability in humidity was largest from the afternoon to sunset and smallest a few hours before and after sunrise. At the surface, variograms of refractivity increase almost linearly with scale in the crosswind direction, suggesting that the field of moisture shows little in terms of local maxima and minima. Higher in the boundary layer, moisture variability increases at small scales because of the entrainment of dry capping stable layer air as the daytime boundary layer grows, and the rate of that dry-air entrainment could be used to calculate surface moisture variability.
The effect of the observed variability in moisture and temperature in the upper boundary layer on convective inhibition was quantified and contrasted with the effect expected from boundary layer updrafts. At synoptic scales and at the upper end of the mesoscale, the location of convection initiation is most sensitive to the variability in temperature. At smaller scales, storm development becomes extremely sensitive to the strength of updrafts; however, those same updrafts also magnify the effect of moisture and temperature variability, as a result of which the effect of small-scale moisture variability cannot be ignored. Some of the consequences of these findings on the representativeness of radiosonde measurements in the boundary layer and instrumentation needs for convection initiation forecasting are surveyed.
Abstract
An attempt was made to statistically gauge the importance of moisture variability on convection initiation by analyzing data collected by radar, surface stations, soundings, and airborne in situ sensors over the 7 weeks of the International H2O Project (IHOP_2002). Based on radar refractivity data, the spatial structure of humidity near the surface proved to be very anisotropic, crosswind variability being typically twice as large as along-wind variability, in part as a result of the west-to-east climatological gradient in moisture across the Oklahoma panhandle. Variability in humidity was largest from the afternoon to sunset and smallest a few hours before and after sunrise. At the surface, variograms of refractivity increase almost linearly with scale in the crosswind direction, suggesting that the field of moisture shows little in terms of local maxima and minima. Higher in the boundary layer, moisture variability increases at small scales because of the entrainment of dry capping stable layer air as the daytime boundary layer grows, and the rate of that dry-air entrainment could be used to calculate surface moisture variability.
The effect of the observed variability in moisture and temperature in the upper boundary layer on convective inhibition was quantified and contrasted with the effect expected from boundary layer updrafts. At synoptic scales and at the upper end of the mesoscale, the location of convection initiation is most sensitive to the variability in temperature. At smaller scales, storm development becomes extremely sensitive to the strength of updrafts; however, those same updrafts also magnify the effect of moisture and temperature variability, as a result of which the effect of small-scale moisture variability cannot be ignored. Some of the consequences of these findings on the representativeness of radiosonde measurements in the boundary layer and instrumentation needs for convection initiation forecasting are surveyed.
Abstract
The ability of data assimilation to correct for initial conditions depends on the presence of a usable signal in the variables observed as well as on the capability of instruments to detect that signal. In , the nature, properties, and limits in the usability of signals in model variables were investigated. Here, the focus is on studying the skill of measurements to pull out a useful signal for data assimilation systems to use. Using model runs of the evolution of convective storms in the Great Plains over an active 6-day period, simulated measurements from a variety of instruments are evaluated in terms of their ability to detect various initial condition errors and to provide a signal above and beyond measurement errors. The usability of the signal for data assimilation is also investigated. Imaging remote sensing systems targeting cloud and precipitation properties such as radars and thermal IR imagers provided both the strongest signals and the hardest ones to assimilate to recover fields other than clouds and precipitation because of the nonlinear behavior of the sensors combined with the limited predictability of the signal observed. The performance of other sensors was also evaluated, leading to several unexpected results. If used with caution, these findings can help determine assimilation priorities for improving mesoscale forecasting.
Abstract
The ability of data assimilation to correct for initial conditions depends on the presence of a usable signal in the variables observed as well as on the capability of instruments to detect that signal. In , the nature, properties, and limits in the usability of signals in model variables were investigated. Here, the focus is on studying the skill of measurements to pull out a useful signal for data assimilation systems to use. Using model runs of the evolution of convective storms in the Great Plains over an active 6-day period, simulated measurements from a variety of instruments are evaluated in terms of their ability to detect various initial condition errors and to provide a signal above and beyond measurement errors. The usability of the signal for data assimilation is also investigated. Imaging remote sensing systems targeting cloud and precipitation properties such as radars and thermal IR imagers provided both the strongest signals and the hardest ones to assimilate to recover fields other than clouds and precipitation because of the nonlinear behavior of the sensors combined with the limited predictability of the signal observed. The performance of other sensors was also evaluated, leading to several unexpected results. If used with caution, these findings can help determine assimilation priorities for improving mesoscale forecasting.
Abstract
A technique to obtain wind profiles from conventional scanning radars is proposed. This technique uses the slope of precipitation trails and their velocity to infer lower- and midtropospheric winds. Details of the implementation as well as the possibilities and limitations of the method are described in this article. Comparisons with wind profiles from radiosondes and model output for the 12 January 1991 snowstorm and for the 26 September 1991 rainstorm give satisfactory results The combination of the results of this technique with additional information from Doppler radar is also discussed.
Abstract
A technique to obtain wind profiles from conventional scanning radars is proposed. This technique uses the slope of precipitation trails and their velocity to infer lower- and midtropospheric winds. Details of the implementation as well as the possibilities and limitations of the method are described in this article. Comparisons with wind profiles from radiosondes and model output for the 12 January 1991 snowstorm and for the 26 September 1991 rainstorm give satisfactory results The combination of the results of this technique with additional information from Doppler radar is also discussed.
Abstract
Many characteristics of radar echoes from ground targets vary with time as the properties of the atmosphere in which the radar waves propagate evolve. For example, if the phase of a target varies with time and the target is known to be stationary, that phase variation is related to changes in the refractive index of air between the radar and that target. These changes are themselves caused by variations in the pressure, temperature, and especially the humidity of air. The changing phase and intensity of ground targets are hence records of evolving atmospheric conditions and could therefore in theory be used to retrieve parameters of meteorological value.
In this paper, the various ways meteorological conditions can affect radar returns from ground targets are explored and quantified. In particular, the extent with which the changes in the phase of ground targets can be used to extract weather-related information is investigated. While wind, precipitation, and the vertical structure of temperature and humidity all affect ground echo appearance, it is demonstrated that the most promising quantity to be obtained is the refractive index of air near the ground. The process and the uncertainty of refractive index measurements by radar are then described. Comparisons with surface stations indicate that near-surface refractive index can be obtained reasonably accurately by radar in very flat terrain. In more complex topography, sensitivity analyses show that radar-based refractive index measurements will be strongly affected by the vertical profile of refractive index.
Abstract
Many characteristics of radar echoes from ground targets vary with time as the properties of the atmosphere in which the radar waves propagate evolve. For example, if the phase of a target varies with time and the target is known to be stationary, that phase variation is related to changes in the refractive index of air between the radar and that target. These changes are themselves caused by variations in the pressure, temperature, and especially the humidity of air. The changing phase and intensity of ground targets are hence records of evolving atmospheric conditions and could therefore in theory be used to retrieve parameters of meteorological value.
In this paper, the various ways meteorological conditions can affect radar returns from ground targets are explored and quantified. In particular, the extent with which the changes in the phase of ground targets can be used to extract weather-related information is investigated. While wind, precipitation, and the vertical structure of temperature and humidity all affect ground echo appearance, it is demonstrated that the most promising quantity to be obtained is the refractive index of air near the ground. The process and the uncertainty of refractive index measurements by radar are then described. Comparisons with surface stations indicate that near-surface refractive index can be obtained reasonably accurately by radar in very flat terrain. In more complex topography, sensitivity analyses show that radar-based refractive index measurements will be strongly affected by the vertical profile of refractive index.
Abstract
Although radar is our most useful tool for monitoring severe weather, the benefits of assimilating its data are often short lived. To understand why, we documented the assimilation requirements, the data characteristics, and the common practices that could hinder optimum data assimilation by traditional approaches. Within storms, radars provide dense measurements of a few highly variable storm outcomes (precipitation and wind) in atmospherically unstable conditions. However, statistical relationships between errors of observed and unobserved quantities often become nonlinear because the errors in these areas tend to become large rapidly. Beyond precipitating areas lie large regions for which radars provide limited new information, yet whose properties will soon shape the outcome of future storms. For those areas, any innovation must consequently be projected from sometimes distant precipitating areas. Thus, radar data assimilation must contend with a double need at odds with many traditional assimilation implementations: correcting in-storm properties with complex errors while projecting information at unusually far distances outside precipitating areas. To further complicate the issue, other data properties and practices, such as assimilating reflectivity in logarithmic units, are not optimal to correct all state variables. Therefore, many characteristics of radar measurements and common practices of their assimilation are incompatible with necessary conditions for successful data assimilation. Facing these dataset-specific challenges may force us to consider new approaches that use the available information differently.
Abstract
Although radar is our most useful tool for monitoring severe weather, the benefits of assimilating its data are often short lived. To understand why, we documented the assimilation requirements, the data characteristics, and the common practices that could hinder optimum data assimilation by traditional approaches. Within storms, radars provide dense measurements of a few highly variable storm outcomes (precipitation and wind) in atmospherically unstable conditions. However, statistical relationships between errors of observed and unobserved quantities often become nonlinear because the errors in these areas tend to become large rapidly. Beyond precipitating areas lie large regions for which radars provide limited new information, yet whose properties will soon shape the outcome of future storms. For those areas, any innovation must consequently be projected from sometimes distant precipitating areas. Thus, radar data assimilation must contend with a double need at odds with many traditional assimilation implementations: correcting in-storm properties with complex errors while projecting information at unusually far distances outside precipitating areas. To further complicate the issue, other data properties and practices, such as assimilating reflectivity in logarithmic units, are not optimal to correct all state variables. Therefore, many characteristics of radar measurements and common practices of their assimilation are incompatible with necessary conditions for successful data assimilation. Facing these dataset-specific challenges may force us to consider new approaches that use the available information differently.
Abstract
Data assimilation is used among other things to constrain the initial conditions of weather forecasting models by fitting the model fields to observations made over a certain time interval. In particular, it tries to tie incomplete data with model constraints to detect and correct for initial condition errors. This is possible only if initial condition errors leave their signature on the data assimilated and if the model is capable of faithfully reproducing such signatures. Using simulations of the evolution of convective storms in the Great Plains over an active 6-day period, the propagation of initial condition errors to other variables as well as their effect on the accuracy of the forecasts were investigated. Increasing the assimilation time window boosts the ability of assimilation systems to detect a variety of initial condition errors; however, limits to the predictability of convective events impose a maximum assimilation period that is a function of the type of measurements assimilated as well as of the type of errors one tries to correct for. These findings are then used to suggest changes in assimilation approaches to take into account the different predictability times of the model fields constrained by assimilation.
Abstract
Data assimilation is used among other things to constrain the initial conditions of weather forecasting models by fitting the model fields to observations made over a certain time interval. In particular, it tries to tie incomplete data with model constraints to detect and correct for initial condition errors. This is possible only if initial condition errors leave their signature on the data assimilated and if the model is capable of faithfully reproducing such signatures. Using simulations of the evolution of convective storms in the Great Plains over an active 6-day period, the propagation of initial condition errors to other variables as well as their effect on the accuracy of the forecasts were investigated. Increasing the assimilation time window boosts the ability of assimilation systems to detect a variety of initial condition errors; however, limits to the predictability of convective events impose a maximum assimilation period that is a function of the type of measurements assimilated as well as of the type of errors one tries to correct for. These findings are then used to suggest changes in assimilation approaches to take into account the different predictability times of the model fields constrained by assimilation.
Abstract
The vertical gradient of refractivity (dN/dh) determines the path of the radar beam; namely, the larger the negative values of the refractivity gradient, the more the beam bends toward the ground. The variability of the propagation conditions significantly affects the coverage of the ground echoes and, thus, the quality of the scanning radar measurements. The information about the vertical gradient of refractivity is usually obtained from radiosonde soundings whose use, however, is limited by their coarse temporal and spatial resolution. Because radar ground echo coverage provides clues about how severe the beam bending can be, we have investigated a method that uses radar observations to infer propagation conditions with better temporal resolution than the usual soundings.
Using the data collected during the International H2O Project (IHOP_2002), this simple method has shown some skill in capturing the propagation conditions similar to these estimated from soundings. However, the evaluation of the method has been challenging because of 1) the limited resolution of the conventional soundings in time and space, 2) the lack of other sources of data with which to compare the results, and 3) the ambiguity in the separation of ground from weather echoes.
Abstract
The vertical gradient of refractivity (dN/dh) determines the path of the radar beam; namely, the larger the negative values of the refractivity gradient, the more the beam bends toward the ground. The variability of the propagation conditions significantly affects the coverage of the ground echoes and, thus, the quality of the scanning radar measurements. The information about the vertical gradient of refractivity is usually obtained from radiosonde soundings whose use, however, is limited by their coarse temporal and spatial resolution. Because radar ground echo coverage provides clues about how severe the beam bending can be, we have investigated a method that uses radar observations to infer propagation conditions with better temporal resolution than the usual soundings.
Using the data collected during the International H2O Project (IHOP_2002), this simple method has shown some skill in capturing the propagation conditions similar to these estimated from soundings. However, the evaluation of the method has been challenging because of 1) the limited resolution of the conventional soundings in time and space, 2) the lack of other sources of data with which to compare the results, and 3) the ambiguity in the separation of ground from weather echoes.
Abstract
In this study, 600 h of vertically pointing X-band radar data and 50 h of UHF boundary layer wind profiler data were processed and analyzed to characterize quantitatively the structure and the causes of the radar signature from melting precipitation. Five classes of vertical profiles of reflectivity in rain were identified, with three of them having precipitation undergoing a transition between the solid and liquid phase. Only one of them, albeit the most common, showed a radar brightband signature.
In-depth study of the bright band and its dependence on precipitation intensity reveals that the ratio of the brightband peak reflectivity to the rainfall reflectivity is constant at 8 dB below 0.5 mm h−1 and then increases to reach 13 dB at 2.5 mm h−1 and 16 dB at 5 mm h−1. The equivalent reflectivity factor of snow just above the melting layer is on average 1–2 dB below the reflectivity of rain just below the melting layer, independent of precipitation intensity. The classical brightband explanation accounts for less than half of the observed reflectivity enhancement; the difference could be explained by effects associated with the shape and density of melting snowflakes and, to a smaller extent, by precipitation growth in the melting layer and aggregation in the early stages of the melting followed by breakup in the final stages. The brightband statistics were also significantly different for reflectivities in rain above 2.5 dBZ when observations were made with an X-band radar as opposed to the wind profiler because of the combination of attenuation in the melting layer and the fact that scattering from some of the large hydrometers above and within the melting layer depart from the Rayleigh approximation usually used to compute reflectivity. The bright band is often capped by a thin and faint dark layer, which tends to be more evident at weak precipitation intensifies.
Abstract
In this study, 600 h of vertically pointing X-band radar data and 50 h of UHF boundary layer wind profiler data were processed and analyzed to characterize quantitatively the structure and the causes of the radar signature from melting precipitation. Five classes of vertical profiles of reflectivity in rain were identified, with three of them having precipitation undergoing a transition between the solid and liquid phase. Only one of them, albeit the most common, showed a radar brightband signature.
In-depth study of the bright band and its dependence on precipitation intensity reveals that the ratio of the brightband peak reflectivity to the rainfall reflectivity is constant at 8 dB below 0.5 mm h−1 and then increases to reach 13 dB at 2.5 mm h−1 and 16 dB at 5 mm h−1. The equivalent reflectivity factor of snow just above the melting layer is on average 1–2 dB below the reflectivity of rain just below the melting layer, independent of precipitation intensity. The classical brightband explanation accounts for less than half of the observed reflectivity enhancement; the difference could be explained by effects associated with the shape and density of melting snowflakes and, to a smaller extent, by precipitation growth in the melting layer and aggregation in the early stages of the melting followed by breakup in the final stages. The brightband statistics were also significantly different for reflectivities in rain above 2.5 dBZ when observations were made with an X-band radar as opposed to the wind profiler because of the combination of attenuation in the melting layer and the fact that scattering from some of the large hydrometers above and within the melting layer depart from the Rayleigh approximation usually used to compute reflectivity. The bright band is often capped by a thin and faint dark layer, which tends to be more evident at weak precipitation intensifies.
Abstract
To complement the meteorological modeling of the melting layer, a model of the scattering properties at microwave frequencies for snow, melting snow, and rain is implemented. The scattering model, running in tandem with a meteorological model, generates the reflectivity fields associated with the hydrometeors in the model to facilitate comparisons with available observations. Several existing and a few new approaches for the scattering of melting snow are attempted. In addition, the models are run using several relationships for the density of snowflakes as a function of their size.
A large variability in the prediction of the brightband intensity is observed as a function of the scattering model. However, the scattering model whose melting snow morphology resembles most the one of real snowflakes reproduces the available observations with the highest accuracy. Sensitivity to the snowflake density relationship used is found to be less important. Other features like the melting-layer thickness, brightband peak position, and Doppler velocity are also correctly predicted.
Abstract
To complement the meteorological modeling of the melting layer, a model of the scattering properties at microwave frequencies for snow, melting snow, and rain is implemented. The scattering model, running in tandem with a meteorological model, generates the reflectivity fields associated with the hydrometeors in the model to facilitate comparisons with available observations. Several existing and a few new approaches for the scattering of melting snow are attempted. In addition, the models are run using several relationships for the density of snowflakes as a function of their size.
A large variability in the prediction of the brightband intensity is observed as a function of the scattering model. However, the scattering model whose melting snow morphology resembles most the one of real snowflakes reproduces the available observations with the highest accuracy. Sensitivity to the snowflake density relationship used is found to be less important. Other features like the melting-layer thickness, brightband peak position, and Doppler velocity are also correctly predicted.
Abstract
The radar refractivity retrieval algorithm applied to radar phase measurements from ground targets can provide high-resolution, near-surface moisture estimates in time and space. The reliability of the retrieval depends on the quality of the returned phase measurements, which are affected by factors such as 1) the vertical variation of the refractive index along the ray path and 2) the properties of illuminated ground targets (e.g., the height and shape of the targets intercepted by radar rays over complex terrain). These factors introduce ambiguities in the phase measurement that have not yet been considered in the refractivity algorithm and that hamper its performance.
A phase measurement simulator was designed to better understand the effect of these factors. The results from the simulation were compared with observed phase measurements for selected atmospheric propagation conditions estimated from low-level radio sounding profiles. Changes in the vertical gradient of refractivity coupled with the varying heights of targets are shown to have some influence on the variability of phase fields. However, they do not fully explain the noisiness of the real phase observations because other factors that are not included in the simulation, such as moving ground targets, affect the noisiness of phase measurements.
Abstract
The radar refractivity retrieval algorithm applied to radar phase measurements from ground targets can provide high-resolution, near-surface moisture estimates in time and space. The reliability of the retrieval depends on the quality of the returned phase measurements, which are affected by factors such as 1) the vertical variation of the refractive index along the ray path and 2) the properties of illuminated ground targets (e.g., the height and shape of the targets intercepted by radar rays over complex terrain). These factors introduce ambiguities in the phase measurement that have not yet been considered in the refractivity algorithm and that hamper its performance.
A phase measurement simulator was designed to better understand the effect of these factors. The results from the simulation were compared with observed phase measurements for selected atmospheric propagation conditions estimated from low-level radio sounding profiles. Changes in the vertical gradient of refractivity coupled with the varying heights of targets are shown to have some influence on the variability of phase fields. However, they do not fully explain the noisiness of the real phase observations because other factors that are not included in the simulation, such as moving ground targets, affect the noisiness of phase measurements.
