Aspects of short‐term probabilistic blending in different weather regimes

Abstract

This study explores the potential of regime‐dependent approaches to improve short‐range precipitation forecasts. Probabilistic forecasts have been generated from the radar nowcaster Radar Tracking and Monitoring (Rad‐TRAM) and the convection‐permitting weather prediction model of the Consortium for Small‐scale Modeling, Deutscher Wetterdienst (COSMO‐DE) using the neighbourhood method for a 99 day period during summer 2009. The convective adjustment time‐scale was used to classify the days of the investigated period into stratiform, equilibrium and non‐equilibrium convection regimes. The COSMO‐DE forecasts were calibrated using the reliability diagram method and blended with the nowcasts using an additive weighting, where the weighting function varies with lead time according to the time evolution of the conditional square root of ranked probability score (CSRR). The examination of two case studies showed large differences in the calibration and weighting functions for different regimes. Over the entire period, regime‐dependent calibration was found to produce large improvements in reliability in comparison with the uncalibrated forecasts; however, the results were only modestly better than with a single calibration function. The blending procedure successfully combined the nowcast and forecast information, in the sense that the blended forecast was as good as either of the two components, but there was no further gain expected from regime‐dependent weighting if regime‐dependent calibration had already been performed.

Short‐range probabilistic forecasting of convective risks for aviation based on a lagged‐average‐forecast ensemble approach

“…A blending of nowcasts based on extrapolated radar data with probabilistic high resolution forecasts for precipitation forecasts has already been used by, for example, Kober (), Kober et al . (, ) and Scheufele et al . ().…”

“…A blending of nowcasts based on extrapolated radar data with probabilistic high resolution forecasts for precipitation forecasts has already been used by, for example, Kober (2010), Kober et al (2012Kober et al ( , 2014 and Scheufele et al (2014). The UK Met Office combines extrapolated radar data with downscaled NWPs to their nowcasting tool called STEPS (Bowler et al, 2006).…”

Extreme precipitation events over northern Italy. Part I: A systematic classification with machine‐learning techniques

“…In the equilibrium regime the generation of CAPE is balanced by widespread convective heating associated with synoptic forcing, while in the non-equilibrium regime CAPE can rise to larger values since convection is limited by high convection inhibition (CIN) and its initiation is associated with local circulations in the boundary layer (weak large-scale forcing). Values between 3 and 12 h can be used as a threshold to discriminate between these regimes with a value of 6 h mostly used (Molini et al, 2011;Keil et al, 2014;Kober et al, 2014). Following Zimmer et al (2011), Tau is computed as…”

Investigation of Sickle‐Cell Haemoglobin Polymerisation under Electrochemical Control

“…The measure of the convective adjustment time‐scale τ has already been successfully applied to examine ensemble bias and spread of precipitation (Done et al. , 2006, 2011; Keil and Craig, 2011), to assess the length of impact of radar data assimilation in COSMO‐DE (Craig et al , 2012), to distinguish the skill of probabilistic forecasts (Kober et al , 2013), to study severe precipitation events in the Mediterranean using re‐analysis data (Molini et al , 2011) and to classify observed summertime precipitation (Zimmer et al , 2011).…”