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Frédéric Fabry

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.

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Frédéric Fabry

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.

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Frederic Fabry

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.

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Frédéric Fabry

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.

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David Themens and Frédéric Fabry

Abstract

The ability of different ground-based measurement strategies for constraining thermodynamic variables in the troposphere, particularly at the mesoscale, is investigated. First, a preliminary assessment of the capability of pure-vertical sounders for constraining temperature and water vapor fields in clear-sky conditions to current accuracy requirements is presented. Using analyses over one month from the Rapid Refresh model as input to an optimal estimation technique, it is shown that the horizontal density of a network of nonexisting, ideal vertical profiling instruments must be greater than 30 km in order to achieve accuracies of 0.5 g kg−1 for water vapor and 0.5 K for temperature. Then, an assessment of a scanning microwave radiometer’s capability for retrieving water vapor and temperature fields in a cloud-free environment over two- and three-dimensional mesoscale domains is also presented. The information content of an elevation and azimuthal scanning microwave radiometer is assessed using the same optimal estimation framework. Even though, in any specific pointing direction, the scanning radiometer does not provide much information, it is capable of providing considerably more constraints on thermodynamic fields, particularly water vapor, than a near-perfect vertical sounder. These constraints on water vapor are largely located within 80 km of the radiometer and between 1000- and 7000-m altitude, while temperature constraints are limited to within 35 km of the instrument at altitudes between the ground and 1500 m. The findings suggest that measurements from scanning radiometers will be needed to properly constrain the temperature and especially moisture fields to accuracies needed for mesoscale forecasting.

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Frederic Fabry and Isztar Zawadzki

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.

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Aldo Bellon and Frédéric Fabry

Abstract

An algorithm based on the self-consistency between the horizontal reflectivity Z H and the specific differential phase K DP has been devised for the calibration of the reflectivity measurements of the McGill S-band dual-polarization radar and implemented in real time in the fall of 2012. By combining pairs of measured and theoretical differential propagation phases (ΦDP) along rain paths from several azimuths, elevation angles, and radar cycles, a robust calibration estimate is obtained even in relatively light precipitation, provided the number of pairs is of the order of at least 103. It confirmed the stability of the radar system as further corroborated by disdrometer and ground echo comparisons. However, the two-parameter Z H –K DP technique proved to be inadequate in convective situations because it overestimates ΦDP differences of paths with heavy precipitation. An ex post facto analysis has revealed that a three-parameter (Z H –K DP –Z DR) relationship provides a much better agreement with the measured ΦDP differences regardless of the intensity of the precipitation along the rain paths. The main usefulness of the technique remains its ability to derive a reliable calibration correction factor even in light precipitation; thus, it is readily applicable in climate regimes and/or at times of the year characterized by the absence of strong convection capable of providing the large ΦDP differences previously thought necessary for such a technique to be successful.

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Shinju Park and Frédéric Fabry

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.

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Frédéric Fabry and Véronique Meunier

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.

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Frédéric Fabry and Juanzhen Sun

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.

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