The radiative characteristics of stratocumulus clouds are dependent upon their microphysical properties, primarily the liquid water content and effective radius of the drop population. Aircraft observations of droplet spectra in warm stratocumulus over the North Atlantic and around the British Isles by the Hercules C-130 aircraft of the U.K. Meteorological Office Meteorological Research Flight have been used to calculate the radar reflectivity, liquid water content, and effective radius. Empirically derived relationships, found from more than 4000 km of flight data on 11 separate days, that link reflectivity with either liquid water content or effective radius have been derived. These empirical relationships are significantly different from those predicted if the cloud droplet spectrum is modeled as a gamma function. Occasional drizzle-sized drops are frequently present within the cloud, and even though their concentration is very low, they dominate the reflectivity and these empirical relationships fail. However, although the drizzle drops increase the reflectivity, they have a negligible effect on the liquid water content and effective radius of the cloud. As these drops have a significant fall velocity in comparison to the cloud droplets, it is suggested that a ground-based Doppler radar could separate the components of the reflectivity due to bimodal drop spectra and the vertical structure of the cloud properties that determine radiative transfer could be retrieved.

Rainfall events in the United Kingdom during the twentieth century have been surveyed and those identified as extreme by the Flood Studies Report (1975)

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Object-oriented verification methodology is becoming more and more common in the evaluation of model performance on high-resolution grids. The research herein describes an advanced version of an object-oriented approach that involves a combination of object identification on multiple scales with Procrustes shape analysis techniques. The multiscale object identification technique relies heavily on a novel Fourier transform approach to associate the signals within convection to different spatial scales. Other features of this new verification scheme include using a weighted cost function that can be user defined for object matching using different criteria, delineating objects that are more linear in character from those that are more cellular, and tagging object matches as hits, misses, or false alarms. Although the scheme contains a multiscale approach for identifying convective objects, standard minimum intensity and minimum size thresholds can be set when desirable. The method was tested as part of a spatial verification intercomparison experiment utilizing a combination of synthetic data and real cases from the Storm Prediction Center (SPC)/NSSL Weather Research and Forecasting (WRF) model Spring Program 2005. The resulting metrics, including error measures from differences in matched objects due to displacement, dilation, rotation, and intensity, from these cases run through this new, robust verification scheme are shown.

Statistical and case study-oriented comparisons of the quantitative precipitation nowcasting (QPN) schemes demonstrated during the first World Weather Research Programme (WWRP) Forecast Demonstration Project (FDP), held in Sydney, Australia, during 2000, served to confirm many of the earlier reported findings regarding QPN algorithm design and performance. With a few notable exceptions, nowcasting algorithms based upon the linear extrapolation of observed precipitation motion (Lagrangian persistence) were generally superior to more sophisticated, nonlinear nowcasting methods. Centroid trackers [Thunderstorm Identification, Tracking, Analysis and Nowcasting System (TITAN)] and pattern matching extrapolators using multiple vectors (Auto-nowcaster and Nimrod) were most reliable in convective scenarios. During widespread, stratiform rain events, the pattern matching extrapolators were superior to centroid trackers and wind advection techniques (Gandolf, Nimrod). There is some limited case study and statistical evidence from the FDP to support the use of more sophisticated, nonlinear QPN algorithms. In a companion paper in this issue, Wilson et al. demonstrate the advantages of combining linear extrapolation with algorithms designed to predict convective initiation, growth, and decay in the Auto-nowcaster. Ebert et al. show that the application of a nonlinear scheme [Spectral Prognosis (S-PROG)] designed to smooth precipitation features at a rate consistent with their observed temporal persistence tends to produce a nowcast that is superior to Lagrangian persistence in terms of rms error. However, the value of this approach in severe weather forecasting is called into question due to the rapid smoothing of high-intensity precipitation features.

To relate observed rainfall rates (R) to the kinetic energy flux (E) that affects soil erosion it is necessary to develop relationships between the two. This paper explores theoretical ER relationships based on gamma distributions of drop size. The relationship is poorly defined unless assumptions are made about changes in the shape of the drop-size distribution (DSD) with rainfall rate. The study suggests that the assumption of an exponential DSD leads to overestimation of kinetic energy flux. Further, incorporation of a horizontal component of kinetic energy allows for a clearer relationship between kinetic energy and rainfall intensity to be defined, but a question remains regarding the most appropriate definition of the horizontal component of drop velocity.