Microburst detection with the WRF model: effective resolution and forecasting indices

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Microbursts are meteorological phenomena in the lower troposphere which can produce damaging surface winds and pose a severe risk to aircraft flying close to the ground. As these events usually span less than 4 km and 15 min, the spatiotemporal resolution is a challenge for numerical simulations. Although research of microburst using operative mesoscale models is scarce, the Weather Research and Forecasting (WRF) model has been used in the diagnosis of this phenomenon. In this paper, such model is used to simulate several microburst conducive days using two different boundary conditions. The energy spectra of the simulations are computed to evaluate the effective resolution of the model. The results are in line with previous studies and produce no notable differences among the boundary conditions. Nonetheless, the energy spectra show an overenergetic troposphere at microscale resolutions, rendering the effective resolution inadequate for microburst forecasting using the simulated physics variables. Thus, mesoscale indices are analyzed as a prognostic tool. The wind index, the wet microburst severity index and the microburst windspeed potential index do not show high forecasting performances, even though improving the results of climatology. Also, notable differences among the boundary conditions can be seen. The most consistent results are achieved by the wet microburst severity index.
© 2020. The Authors. The authors declare no conflict of interest. The funding sponsors have no participation in the execution of the experiment, the decision to publish the results, nor the writing of the manuscript. This work is supported by the Interdisciplinary Mathematics Institute of the Complutense University of Madrid and funded by the Spanish Ministry of Economy and Enterprise under the following research projects: PCIN‐2014‐013‐C07‐04, PCIN‐2016‐080 (UE ERANET Plus NEWA Project), CGL2016‐81828‐REDT, FEI‐EU‐17‐16, SAFEFLIGHT (CGL2016‐78702‐C2‐1‐R and CGL2016‐78702‐C2‐2‐R), PID2019‐105306RB‐I00. This work is also supported by the ECMWF special projects SPESMART and SPESVALE.