Probabilistic precipitation nowcasting based on an extrapolation of radar reflectivity and an ensemble approach

0476481 - ÚFA 2018 RIV NL eng J - Journal Article
Sokol, Zbyněk - Mejsnar, Jan - Pop, Lukáš - Bližňák, Vojtěch
Probabilistic precipitation nowcasting based on an extrapolation of radar reflectivity and an ensemble approach.
Atmospheric Research. Roč. 194, 15 September (2017), s. 245-257. ISSN 0169-8095. E-ISSN 1873-2895
Institutional support: RVO:68378289
Keywords : scale-dependence * forecast * predictability * images * model * rainfall * weather * analogs * motion
OECD category: Meteorology and atmospheric sciences
Impact factor: 3.817, year: 2017
http://www.sciencedirect.com/science/article/pii/S0169809516305142

A new method for the probabilistic nowcasting of instantaneous rain rates (ENS) based on the ensemble technique and extrapolation along Lagrangian trajectories of current radar reflectivity is presented. Assuming inaccurate forecasts of the trajectories, an ensemble of precipitation forecasts is calculated and used to estimate the probability that rain rates will exceed a given threshold in a given grid point. Although the extrapolation neglects the growth and decay of precipitation, their impact on the probability forecast is taken into account by the calibration of forecasts using the reliability component of the Brier score (BS).

ENS forecasts the probability that the rain rates will exceed thresholds of 0.1, 1.0 and 3.0 mm/h in squares of 3 km by 3 km. The lead times were up to 60 min, and the forecast accuracy was measured by the BS. The ENS forecasts were compared with two other methods: combined method (COM) and neighbourhood method (NEI). NEI considered the extrapolated values in the square neighbourhood of 5 by 5 grid points of the point of interest as ensemble members, and the COM ensemble was comprised of united ensemble members of ENS and NEI.

The results showed that the calibration technique significantly improves bias of the probability forecasts by including additional uncertainties that correspond to neglected processes during the extrapolation. In addition, the calibration can also be used for finding the limits of maximum lead times for which the forecasting method is useful. We found that ENS is useful for lead times up to 60 min for thresholds of 0.1 and 1 mm/h and approximately 30 to 40 min for a threshold of 3 mm/h. We also found that a reasonable size of the ensemble is 100 members, which provided better scores than ensembles with 10, 25 and 50 members. In terms of the BS, the best results were obtained by ENS and COM, which are comparable. However, ENS is better calibrated and thus preferable.
Permanent Link: http://hdl.handle.net/11104/0272970