ABSTRACT:The Met Office has recently introduced a short-range ensemble prediction system known as MOGREPS. This system consists of global and regional ensembles, with the global ensemble providing the boundary conditions and initial-condition perturbations for the regional ensemble. Perturbations to the initial conditions are calculated using the ensemble transform Kalman filter, which is a computationally-efficient version of the ensemble Kalman filter. Model uncertainties are represented in the system through a series of schemes designed to tackle the structural and subgrid-scale sources of model error.This paper describes the set-up of the system, and provides justification for the initial-condition and model perturbation schemes chosen. An outline of the structure of the perturbations generated by the system is presented, along with performance results, including verification from case studies and routine running.MOGREPS has been on trial within the operational suite at the Met Office since August 2005. On 20 October 2006 it was decided that this system should be made fully operational, with implementation expected in summer 2008. Results show a good performance. The regional ensemble is more skilful than the global ensemble, and compares favourably to the ECMWF ensemble for the forecast variables examined in this study.  Crown

Leading NWP centers have agreed to create a database of their operational ensemble forecasts and open access to researchers to accelerate the development of probabilistic forecasting of high-impact weather.Objectives and cOncept. During the past decade, ensemble forecasting has undergone rapid development in all parts of the world. Ensembles are now generally accepted as a reliable approach to forecast confidence estimation, especially in the case of high-impact weather. Their application to quantitative probabilistic forecasting is also increasing rapidly. In addition, there has been a strong interest in the development of multimodel ensembles, whether based on a set of single (deterministic) forecasts from different systems, or on a set of ensemble forecasts from different systems (the so-called superensemble). The hope is that multimodel ensembles will provide an affordable approach to the classical goal of increasing the hit rate for prediction of high-impact weather without increasing the false-alarm rate. This is being taken further within The Observing System Research and Predictability Experiment (THORPEX), a major component of the World Weather Research Programme (WWRP) under the World Meteorological Organization (WMO). A key goal of THORPEX is to accelerate improvements in

A series of tracer experiments studying concentration fluctuations in a dispersing plume of pollutant in the atmosphere at ranges of between SO m and 1000 m is described. Experiments were conducted on three different field sites in near-neutral or slightly convective meteorological conditions. The results show time series which are characterised by the intermittent occurrence of periods of fluctuating non-zero concentrations, interspersed by periods of esscntially zero concentration. The spectrum of concentration fluctuations is found to display inertial subrange behaviour, characterised by a -2/3 power law when nS(n) is plotted against frequency n, where X(n) is the variance (of the fluctuation) per unit frequency interval. The spectral peak frequency varies with distance from the source. In all cases the clipped-normal probability density function (PDF) provides a reasonable fit to the concentration PDF. Thc exponential PDF is less flexihle in fitting a wide range of experimental conditions, but is slightly superior for some short range examples. In the alongwind direction it is found that, although there is a rapid initial decrease in fluctuation intensity with distance, the intensity seems to approach an approximately constant non-zero value at long range. In cross-sections of the plume the variation of fluctuation statistics is dominated by the varying proportion of time during which the concentration is essentially zero. Conditional statistics, calculated from significantly non-zero concentrations only, show only slight variations across the plume.

Quantifying forecast uncertainty with an ensemble approach can improve the users' bottom line D uring the past decade, due to increased computer resources, the development of more realistic atmospheric models, and the recognition of the importance of atmospheric predictability in general, ensemble forecasting became a major component of Numerical Weather Prediction (NWP). NWP centers around the globe [European Centre for Medium-Range Weather Forecasts (ECMWF),

ABSTRACT:The Met Office has been routinely running a short-range global and regional ensemble prediction system (EPS) since the summer of 2005. This article describes a major upgrade to the global ensemble, which affected both the initial condition and model uncertainty perturbations applied in that ensemble. The change to the initial condition perturbations is to allow localization within the ensemble transform Kalman filter (ETKF). This enables better specification of the ensemble spread as a function of location around the globe. The change to the model uncertainty perturbations is the addition of a stochastic kinetic energy backscatter scheme (SKEB). This adds vorticity perturbations to the forecast in order to counteract the damping of small-scale features introduced by the semi-Lagrangian advection scheme. Verification of ensemble forecasts is presented for the global ensemble system. It is shown that the localization of the ETKF gives a distribution of the spread as a function of latitude that better matches the forecast error of the ensemble mean. The SKEB scheme has a substantial effect on the power spectrum of the kinetic energy, and with the scheme a shallowing of the spectral slope is seen in the tail. A k −5/3 slope is seen at wavelengths shorter than 1000 km and this better agrees with the observed spectrum. The local ETKF significantly improves forecasts at all lead times over a number of variables. The SKEB scheme increases the rate of growth of ensemble spread in some variables, and improves forecast skill at short lead times.