Effect of circulation anomalies on the heavy precipitation predictability

0508678 - ÚFA 2020 DE eng A - Abstract
Kašpar, Marek - Bližňák, Vojtěch - Müller, Miloslav - Zacharov, Petr, jr.
Effect of circulation anomalies on the heavy precipitation predictability.
EMS Annual Meeting Abstracts, Vol. 16. Berlin: European Meteorological Society, 2019.
[EMS Annual Meeting 2019. 09.09.2019-13.09.2019, Copenhagen]
Institutional support: RVO:68378289
Keywords : extreme precipitation events * atmospheric circulation conditions * numerical weather prediction * COSMO model * reanalysis
OECD category: Meteorology and atmospheric sciences
https://meetingorganizer.copernicus.org/EMS2019/EMS2019-661.pdf

Extreme precipitation events (EPEs) belong to the most studied natural hazards because of their high impacts on the
human society. The demand for reliable quantitative precipitation forecast is echoed by many user communities.
However, the forecast skill is often insufficient to predict the amount, location, and timing of extreme precipitation
accurately and consistently. Our research is motivated by the fact that the identification of factors affecting the
predictability following by the quantification of their effect may significantly contribute to better assessment of the
forecast uncertainty. We present results of the analysis of the dependence between the extremeness of circulation
conditions and forecast skill using representative, mostly recent case studies in the territory of Czechia.
Reference EPEs are selected by the Weather Extremity Index which takes into account the rarity of 1-day totals,
the affected area and duration of the events. EPEs are re-forecasted with the horizontal resolution of 2.8 km by the
NWP model COSMO, the non-hydrostatic, compressible model which is able to resolve and explicitly simulate
larger elements of organized convection. In order to evaluate the forecast skill, various spatial verification methods
are used. Verification data comprise daily totals from rain gauge measurements from approximately 700 stations,
precipitation intensities from a couple of pluviographs (since 1960) and radar measurements (since 2002).
The extremeness of circulation conditions is quantified by the method of anomalies using the NCEP/NCAR Reanalysis dataset. Frequency analysis of selected thermodynamic parameters is carried out in each grid box. An
anomaly in a parameter is determined as a contiguous space where probabilities of exceeding are nearing 0 or 1.
First, we determine the anomalies including their location and extent typical of the set of EPEs. Next, for each
EPE, we compute magnitudes of individual typical anomalies. This enables to separate EPEs into several circulation variants and to evaluate the measure of the extremeness of circulation conditions by combining magnitudes of
the anomalies. We assume that this measure would be significant explanatory variable of the forecast uncertainty.
Permanent Link: http://hdl.handle.net/11104/0299518