Ensemble prediction for nowcasting with a convection-permitting model—I: description of the system and the impact of radar-derived surface precipitation rates

Abstract

A B S T R A C TA key strategy to improve the skill of quantitative predictions of precipitation, as well as hazardous weather such as severe thunderstorms and flash floods is to exploit the use of observations of convective activity (e.g. from radar). In this paper, a convection-permitting ensemble prediction system (EPS) aimed at addressing the problems of forecasting localized weather events with relatively short predictability time scale and based on a 1.5 km grid-length version of the Met Office Unified Model is presented. Particular attention is given to the impact of using predicted observations of radar-derived precipitation intensity in the ensemble transform Kalman filter (ETKF) used within the EPS. Our initial results based on the use of a 24-member ensemble of forecasts for two summer case studies show that the convectivescale EPS produces fairly reliable forecasts of temperature, horizontal winds and relative humidity at 1 h lead time, as evident from the inspection of rank histograms. On the other hand, the rank histograms seem also to show that the EPS generates too much spread for forecasts of (i) surface pressure and (ii) surface precipitation intensity. These may indicate that for (i) the value of surface pressure observation error standard deviation used to generate surface pressure rank histograms is too large and for (ii) may be the result of non-Gaussian precipitation observation errors. However, further investigations are needed to better understand these findings. Finally, the inclusion of predicted observations of precipitation from radar in the 24-member EPS considered in this paper does not seem to improve the 1-h lead time forecast skill.

Representation of model error in a convective-scale ensemble prediction system

“…An inflation factor is applied to scale the perturbations. This ensures that the innovation variance is consistent with observations at T + 1 of the forecast (Migliorini et al, 2011;Caron, 2013). A spatially and temporally fixed inflation factor value is used throughout the forecast.…”

“…The 1.5 km EPS is described in detail by Migliorini et al (2011). Subsequent improvements to the 1.5 km EPS setup using the so-called scale-selective ETKF to avoid ensemble perturbation mismatches between the ICs and LBCs are described by Caron (2013).…”

“…Here we investigate a proposed representation of model error that can influence the forecast skill at convective scale. We use an experimental convection-permitting version of the UK Met Office's 24-member Global and Regional Ensemble Prediction System (EPS) (MOGREPS) (Bowler et al, 2008): the southern UK 1.5 km EPS (Migliorini et al, 2011;Caron, 2013). We focus on the effect of model error resulting from the parameterisation of unresolved processes; specifically microphysics and turbulent boundary layer processes.…”

Data-driven exploration of orographic enhancement of precipitation

“…The data-driven modelling of small scale precipitation enhancement patterns in complex topography provides observational support to operational NWP including the convection-permitting models (Migliorini et al, 2011). Radar-based susceptibility maps of orographic precipitation could be used to correct the windward and leeward quantitative precipitation estimation biases present in many NWP models (Bauer et al, 2011).…”

“…where ∇z(x) is the gradient vector of elevation evaluated at the (X, Y) spatial coordinates x, u(x,t) is the flow vector with (u,v) components estimated at x at time t. Several algorithms are available to detect precipitation cells from radar imagery (Lakshmanan et al, 2003;Wilson et al, 2004). In this study cells were identified by a simple method that finds the points of maxima of a smoothed precipitation field.…”