Cloud-Resolving Model Applied to Nowcasting: An Evaluation of Radar Data Assimilation and Microphysics Parameterization

Abstract

This research explores the benefits of radar data assimilation for short-range weather forecast in Southeastern Brazil using the Weather Research and Forecasting (WRF) model’s three-dimensional variational data assimilation (3DVAR) system. Different data assimilation options are explored, including the cycling frequency, the number of outer loops and the use of null-echo assimilation. Initially, four microphysics parameterizations are evaluated (Thompson, Morrison, WSM6 and WDM6). The Thompson parameterization produces the best results, while the other parameterizations generally overestimate the precipitation forecast, especially WDSM6. Additionally, the Thompson scheme tends to overestimate snow, while the Morrison scheme overestimates graupel. Regarding the data assimilation options, the results deteriorate and more spurious convection occurs when using a higher cycling frequency, i.e., 30 minutes instead of 60 minutes. The use of two outer loops produces worse precipitation forecasts than the use of one outer loop, and the null-echo assimilation is shown to be an effective way to suppress spurious convection. However, in some cases, the null-echo assimilation also removes convective clouds that are not observed by the radar and/or are still not producing rain, but have the potential to grow into an intense convective cloud with heavy rainfall. Finally, a cloud convective mask was implemented using ancillary satellite data to prevent null-echo assimilation from removing potential convective clouds. The mask demonstrated to be beneficial in some circumstances, but it needs to be carefully evaluated in more cases to have a more robust conclusion regarding its use.

Impact of Explicitly Parameterized Mid-to-Low Level Latent Heating on the Simulation of a Squall Line in South China

“…Previously, most numerical simulation researches on the effect of different LH altitudes were about large-scale circulations [22,23]. The fact that CRMs tend to create top-heavier reflectivity and overestimate MCS updrafts in the middle and upper troposphere compared to the observation in many cases [24][25][26], indicate that uncertainties of certain vertical scale may exist in these simulations. In their ideal simulation study, Pandya and Durran [5] indicated that differences in LH profiles may result in different development of the circulation within MCSs.…”

Quick Predictions of Onset Times and Rain Amounts from Monsoon Showers over Urban Built Environments

“…Newer studies are emerging to illustrate data assimilation techniques to better configure cloud microphysical schemes, particularly when both droplet sizes and numbers are used to compute cloud conversion. A recent paper by Vendrasco et al [11] recommended the use of radar data assimilation for short-range local predictions using the widely used Weather Research and Forecasting (WRF) model configured with a double-moment microphysical scheme. At this stage, we wish to point out that the present paper used droplet spectral attributes within the remit of an optimised Kessler scheme for warm rain processes through the WRF double-moment scheme.…”