The purpose of the research reported in this paper is to develop a variational data analysis system that can be used to assimilate data from one or more Doppler radars. In the first part of this two-part study, the technique used in this analysis system is described and tested using data from a simulated warm rain convective storm. The analysis system applies the 4D variational data assimilation technique to a cloud-scale model with a warm rain parameterization scheme. The 3D wind, thermodynamical, and microphysical fields are determined by minimizing a cost function, defined by the difference between both radar observed radial velocities and reflectivities (or rainwater mixing ratio) and their model predictions. The adjoint of the numerical model is used to provide the sensitivity of the cost function with respect to the control variables. Experiments using data from a simulated convective storm demonstrated that the variational analysis system is able to retrieve the detailed structure of wind, thermodynamics, and microphysics using either dual-Doppler or single-Doppler information. However, less accurate velocity fields are obtained when single-Doppler data were used. In both cases, retrieving the temperature field is more difficult than the retrieval of the other fields. Results also show that assimilating the rainwater mixing ratio obtained from the reflectivity data results in a better performance of the retrieval procedure than directly assimilating the reflectivity. It is also found that the system is robust to variations in the Z-q r relation, but the microphysical retrieval is quite sensitive to parameters in the warm rain scheme. The technique is robust to random errors in radial velocity and calibration errors in reflectivity.

No abstract

Data from the Convection and Precipitation/Electrification (CaPE) project, as well as results from numerical simulations, are used to study horizontal convective rolls. The environmental conditions necessary for sustaining rolls and for influencing the aspect ratio, ratio of roll wavelength to convective boundary layer (CBL) depth, and orientation are examined. Observations and numerical model simulations both suggest that a moderate surface sensible heat flux and some vertical wind shear are necessary for roll existence. Unlike some previous studies, however, it is shown that rolls occurred within very low CBL shear conditions (ϳ2 ϫ 10 Ϫ3 s Ϫ1). In addition, the low-level (i.e., ϳ200 m) shear seems to be more important than the shear through the depth of the CBL in roll sustenance. The aspect ratio is shown to be proportional to the CBL instability, measured in terms of the Monin-Obukhov length. The roll orientation is similar to the wind direction at 10 m AGL, the CBL wind direction, the inversion-level wind direction, and the CBL shear direction. This is not surprising since there was very little directional shear observed within the CBL during CaPE.

The variational Doppler radar analysis system developed in part I of this study is tested on a Florida airmass storm observed during the Convection and Precipitation/ Electrification Experiment. The 3D wind, temperature, and microphysical structure of this storm are obtained by minimizing the difference between the radar-observed radial velocities and rainwater mixing ratios (derived from reflectivity) and their model predictions. Retrieval experiments are carried out to assimilate information from one or two radars. The retrieved fields are compared with measurements of two aircraft penetrating the storm at different heights. The retrieved wind, thermodynamical, and microphysical fields indicate that the minimization converges to a solution consistent with the input velocity and rainwater fields. The primary difference between using single-Doppler and dual-Doppler information is the reduction of the peak strength of the storm on the order of 10% when information from only one radar is provided. The comparison with aircraft data shows good agreement for the vertical velocity, buoyancy, and the water vapor mixing ratio in terms of the general structure and strength of the fields, but less agreement for the cloud water and rainwater field. The sensitivities of the retrieval system to the neglect of the time difference at each grid point in a radar volume and to the inclusion of the background information at the initial time of the assimilation period are examined. Both show rather sensitive response. The experiments also show that the microphysical retrieval is quite sensitive to the relation used to derive the rainwater mixing ratio from reflectivity observations.

This paper reviews the status of forecasting convective precipitation for time periods less than a few hours (nowcasting). Techniques for nowcasting thunderstorm location were developed in the 1960s and 1970s by extrapolating radar echoes. The accuracy of these forecasts generally decreases very rapidly during the first 30 min because of the very short lifetime of individual convective cells. Fortunately more organized features like squall lines and supercells can be successfully extrapolated for longer time periods. Physical processes that dictate the initiation and dissipation of convective storms are not necessarily observable in the past history of a particular echo development; rather, they are often controlled by boundary layer convergence features, environmental vertical wind shear, and buoyancy. Thus, successful forecasts of storm initiation depend on accurate specification of the initial thermodynamic and kinematic fields with particular attention to convergence lines. For these reasons the ability to improve on simple extrapolation techniques had stagnated until the present national observational network modernization program. The ability to observe small-scale boundary layer convergence lines is now possible with operational Doppler radars and satellite imagery. In addition, it has been demonstrated that high-resolution wind retrievals can be obtained from single Doppler radar. Two methods are presently under development for using these modern datasets to forecast thunderstorm evolution: knowledge-based expert systems and numerical forecasting models that are initialized with radar data. Both these methods are very promising and progressing rapidly. Operational tests of expert systems are presently taking place in the United Kingdom and in the United States.