The use of simulated radar reflectivity fields in the diagnosis of mesoscale phenomena from high-resolution WRF model forecasts

The use of composite radar reflectivity fields (i.e., the maximum reflectivity in the grid column) as a model output product has become increasingly popular recently as a means for display of high-resolution numerical model fields, mainly for convective weather scenarios. This past winter, simulated radar reflectivity fields were produced for 5-km WRF model forecasts during the DTC (Developmental Testbed Center) Winter Forecast Experiment (DWFE). In addition, model reflectivity fields from 2-km and 4-km WRF forecasts were utilized during the annual Storm Prediction Center/National Severe Storms Laboratory (NSSL) Spring Program. The reflectivity product offers significant advantages over traditional precipitation forecast displays, including the obvious fact that radar reflectivity is easier to verify in real time by directly comparing with readily available, observed composite reflectivity products. It has also recently become possible to compare model forecast radar reflectivity fields to a high quality, three-dimensional, national radar reflectivity mosaic product on a 1-km Cartesian grid being developed at NSSL. The chief advantage of the model reflectivity product appears to be that it allows one to more easily see detailed mesoscale and near-stormscale structures capable of being forecast by finer resolution models, such as lake-effect snowbands, the structure of deep convection, and frontal precipitation bands. Examples demonstrating this advantage will be presented at the conference for a variety of mesoscale phenomena.

Before one can have confidence in the meaning of simulated reflectivity factor fields for interpretation of mesoscale models, it is important to understand how they are determined. The equivalent reflectivity factor is computed from the forecast mixing ratios of grid-resolved hydrometeor species, assuming Rayleigh scattering by spherical particles of known density and an exponential size distribution. During the DWFE, perceptible differences appeared in the general nature of the simulated reflectivity fields from the two WRF models, most notably a greater coverage of reflectivity below ~25 dBZ and higher maximum reflectivities in the case of the NMM compared to the ARW for winter storms. However, when attention focused on severe convective weather regions during the Spring Program, the NMM produced noticeably lower values of maximum reflectivity compared to the ARW versions, with the NMM values limited to less than 50 dBZ. These differences are mostly explained by the differences in physics packages, particularly the way various liquid water and ice species are treated in the model microphysics schemes. The WRF Single-Moment 5-class (“WSM5”) microphysics scheme used for the WRF-ARW model during DWFE treats the cloud condensate in the form of cloud water and cloud ice as a combined category, and precipitation in the form of rain and snow also as a combined category. The WRF-NMM used the Ferrier microphysics scheme, which accounts for four classes of hydrometeors. The most important difference between the two microphysical parameterizations concerns the assumed size distributions for snow: for the same snow mass content, differences in radar reflectivity will scale with differences in parameterized snow number concentrations between the two microphysical schemes.

It is also important to understand that it is not possible to make a strictly valid comparison between composite reflectivity computed from a model grid point and that measured by scanning radar. Owing to the fact that the radar resolution degrades with distance from the transmitter, that scanning radars cannot detect hydrometeors in the lower atmosphere due to the earth's curvature effect, and numerous other considerations (including ground clutter near the radar, anomalous propagation, etc.), any attempt to make direct comparisons between the model simulated reflectivity fields and radar measurements is replete with problems, though the NSSL product is experimenting with novel ways to overcome these problems.