Blending a probabilistic nowcasting method with a high‐resolution numerical weather prediction ensemble for convective precipitation forecasts

Abstract

A seamless prediction of convective precipitation for a continuous range of lead times from 0-8 h requires the application of different approaches. Here, a nowcasting method and a high-resolution numerical weather prediction ensemble are combined to provide probabilistic precipitation forecasts. For the nowcast, an existing deterministic extrapolation technique was modified by the local Lagrangian method to calculate the probability of exceeding a threshold value in radar reflectivity. Numerical forecasts were obtained from an experimental high-resolution ensemble that provides 20 different deterministic forecasts of synthetic radar reflectivity. Probabilistic information was calculated by different approaches from the ensemble output. The probabilistic forecasts based on the ensemble were calibrated with the reliability diagram statistics method. The skill of the probabilistic nowcasts and forecasts was evaluated using three quality measures. Finally, a seamless probabilistic forecast was generated as an additive combination of nowcast and forecast, using a weighting function based on their relative skills. The skill of the seamless forecast was greater than or equal to that of the nowcast or ensemble forecast in all quality measures and at all lead times. Copyright c 2011 Royal Meteorological Society Key Words: short-range forecasting; blending; ensemble prediction; forecast calibration

Examination of a Stochastic and Deterministic Convection Parameterization in the COSMO Model

“…The fraction method that derives probabilities by calculating at every grid point the fraction of members exceeding a threshold (e.g., Kober et al 2012) is not suitable for two reasons. First, the number of ensemble members is different in the PC08 and the Tiedtke ensembles.…”

“…The neighborhood is taken to be a square with a 63-km side length that corresponds to a spatial window of 9 grid points in the neighborhood verification. This value is chosen following Kober et al (2012), and it is large enough that the results are not sensitive to the exact value.…”

Quantitative precipitation estimation based on high-resolution numerical weather prediction and data assimilation with WRF – a performance test

“…However, recent skill score analyses demonstrate that convectionpermitting mesoscale models start to outperform extrapolation-based nowcasting methods at forecast lead times of 2Á3 hours (Tafferner et al, 2008;Kober et al, 2010). With an accurate analysis, the model acts as a dynamically and physically consistent rainfall interpolator in combination with multi-sensor observations.…”

Improvement of forecast skill for severe weather by merging radar-based extrapolation and storm-scale NWP corrected forecast

“…Optimal predictions could be obtained by assigning the primary weight to extrapolation forecasts for the first lead times, while the NWP forecasts are weighted to increase with lead time (Golding, 1998;Wong and Lai, 2006;Wong et al, 2009). The merged quantity was either rainfall rate (Golding, 1998;Pierce et al, 2000;Wong et al, 2009), radar reflectivity factor (Wilson and Xu, 2006), or a probabilistic forecast of precipitation (Bowler et al, 2006;Kober et al, 2012). Additionally, a bias-correction was applied for NWP model forecast before merging was performed (Wong et al, 2009).…”