Regional Ensemble Prediction System Research Articles

Improving the use of observations to calibrate ensemble spread

AbstractThe Met Office Global and Regional Ensemble Prediction System (MOGREPS) uses an online inflation factor calculation to calibrate the spread of the ensemble in space and time and counteract the tendency of the Ensemble Transform Kalman Filter (ETKF) to underestimate analysis uncertainty. Until 2008, this calibration mechanism relied entirely on sonde and wind profiler data, and was only applied locally in the Extratropics. By producing more appropriate estimates of the error variance of ATOVS brightness temperature observations, it has become possible to include these in the inflation factor calculation. This in turn provides sufficient data to apply localisation uniformly over the globe. The new scheme improves the latitudinal distribution of spread in comparison to forecast error, especially in the Tropics. Issues remain with the vertical distribution of spread, which are addressed by work to be reported in a future paper. © 2011 Crown Copyright, the Met Office. Published by John Wiley & Sons, Ltd.

Environment Canada's Experimental Numerical Weather Prediction Systems for the Vancouver 2010 Winter Olympic and Paralympic Games

The 2010 Winter Olympic and Paralympic Games took place in Vancouver, British Columbia, Canada, on 12–28 February and 12–21 March 2010, respectively. Weather forecasting presents specific challenges at the various Olympic venues, which are located in complex coastal terrain and are often characterized by tricky weather conditions, such as high winds, low visibility, and rapidly varying precipitation types and intensity. In addition to its current operational products, and in order to provide the best possible guidance and support to the Olympic Forecast Team, Environment Canada has developed several experimental numerical weather prediction systems for the games. These include 1) a regional ensemble prediction system (REPS), 2) high-resolution numerical modeling (down to 1-km horizontal grid spacing), and 3) surface modeling at the microscales (100-m grid spacing). The REPS is based on the limited-area version of the Global Environmental Multiscale model (GEM-LAM), with 20 members at 33-km horizontal grid spacing. The high-resolution models include 2.5- and 1-km grid-spacing configurations of the GEM-LAM, with improved cloud microphysics, geophysical fields, and radiation and cloud–radiation interactions. Finally, two innovative approaches are used to adapt and refine forecasts locally and to better predict surface characteristics, such as snow conditions and near-surface air temperatures. A microscale 2D surface system covers the Olympic venues with forcing from the operational regional and global models. Based on a similar strategy, a single-point model uses surface observations as forcing. The configurations of these experimental numerical weather prediction systems are described, together with some examples and verification results from the winters of 2008 and 2009 using the enhanced mesoscale observing network recently set up for the Olympics.

Development and evaluation of an ensemble forecasting system for coastal storm surges

AbstractThe overtopping of flood defences by coastal storm surges is a significant threat to life and property. Like all forecasts, storm surge predictions have an associated uncertainty, but this has not been directly predicted by previous operational systems. The Met Office Global and Regional Ensemble Prediction System (MOGREPS), which has recently become operational, provides an explicit sample of the range of atmospheric evolutions consistent with the latest observations. The storm surge ensemble derives a storm surge forecast for each MOGREPS ensemble member, giving an explicit estimate of the risk of reaching significant water levels. The system has now been running for over two years in trial mode, with positive feedback from both Met Office and Environment Agency forecasters. For the surge on 9 November 2007, which received significant media coverage, it produced a clear signal of an abnormal event at the full 54‐hour lead time, with a useful indication of the range of possible water levels. Statistical verification covering two winters demonstrates that the ensemble spread is indeed a reliable indicator of the significantly increased uncertainty associated with large surge events, although the error in more normal situations is dominated by inaccuracies in the separate harmonic tide prediction which is used to convert surge to water level. Probabilistic verification shows some advantage over climatological error distributions, particularly for larger thresholds and longer lead times. Following this successful trial, the system was made operational in December 2009. Copyright © 2010 Royal Meteorological Society and Crown Copyright.

Ensemble Forecasting of Storm Surges

The overtopping of flood defenses by coastal storm surges constitutes a significant threat to life and property. Like all forecasts, storm surge predictions have an associated uncertainty, but this is not directly predicted by current operational systems. The dominant source of this uncertainty is thought to be uncertainty in the driving atmospheric forecast of conditions at the sea surface, which can vary substantially depending on the meteorological situation. Ensemble prediction is a technique used to assess uncertainty in forecasts of complex nonlinear systems such as weather, where small errors can quickly grow to produce significantly different outcomes. It works by running not one but several forecasts, using slightly different initial conditions, boundary conditions, and/or model physics. These are chosen to sample the range of uncertainty in model inputs and formulation so that the corresponding forecasts will sample the range of possible results that are consistent with those uncertainties. The United Kingdom Met Office has recently developed the Met Office Global and Regional Ensemble Prediction System (MOGREPS), which provides 24 different predictions of meteorological evolution over a North Atlantic and European domain with a 24 km grid length. The aim of the present project is to run a barotropic storm surge prediction for each MOGREPS ensemble member, and thereby estimate the risk of damaging events given the forecast uncertainties which are sampled by the ensemble. The system forecasts 54 hours ahead and runs twice per day. In most situations, the ensemble develops rather little spread, suggesting a fairly predictable situation and a high degree of confidence in the forecast. On some occasions, however, the spread is much larger, suggesting a greater degree of uncertainty. Initial verification results are encouraging, although statistical evaluation suggests the ensemble spread is generally too small.

The local ETKF and SKEB: Upgrades to the MOGREPS short‐range ensemble prediction system

AbstractThe 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. ©Crown Copyright 2009. Reproduced with the permission of HMSO. Published by John Wiley & Sons Ltd.

Ensemble transform Kalman filter perturbations for a regional ensemble prediction system

AbstractThe Met Office has been routinely running a short‐range ensemble prediction system since the summer of 2005. This system consists of two component ensembles, a global ensemble that provides lateral boundary conditions to a regional ensemble. The global ensemble calculates the initial condition perturbations using a local version of the ensemble transform Kalman filter (ETKF). This article details tests of an ETKF run specifically for the regional ensemble.The perturbations using the ETKF for the regional ensemble contain more detail on small scales and less power on large scales. This extra small‐scale detail persists for less than 18 h. These perturbations are overall smaller than the ones derived from the global ensemble, since they have been explicitly scaled to match the average error of the ensemble mean forecast at T + 6 h. Aside from the difference in the overall scaling of the perturbations, the skill of the two ensembles is very similar, with slightly higher skill being seen when the perturbations are derived from the global ensemble. The ETKF for the regional ensemble was implemented during May 2007. This change has recently been reversed, following these results.These comparisons may shed some light on whether the regional ensemble is providing a ‘dynamical downscaling’ of the global ensemble, or unique information of its own. In these tests the ensemble run with perturbations from the global ensemble was slightly more skilful than the regional perturbations ensemble, indicating that improving on dynamical downscaling is likely to be difficult. ©Crown Copyright 2009. Reproduced with the permission of HMSO. Published by John Wiley & Sons Ltd.

A Regional Ensemble Prediction System Based on Moist Targeted Singular Vectors and Stochastic Parameter Perturbations

Abstract A regional ensemble prediction system (REPS) with the limited-area version of the Canadian Global Environmental Multiscale (GEM) model at 15-km horizontal resolution is developed and tested. The total energy norm singular vectors (SVs) targeted over northeastern North America are used for initial and boundary perturbations. Two SV perturbation strategies are tested: dry SVs with dry simplified physics and moist SVs with simplified physics, including stratiform condensation and convective precipitation as well as dry processes. Model physics uncertainties are partly accounted for by stochastically perturbing two parameters: the threshold vertical velocity in the trigger function of the Kain–Fritsch deep convection scheme, and the threshold humidity in the Sundqvist explicit scheme. The perturbations are obtained from first-order Markov processes. Short-range ensemble forecasts in summer with 16 members are performed for five different experiments. The experiments employ different perturbation and piloting strategies, and two different surface schemes. Verification focuses on quantitative precipitation forecasts and is done using a range of probabilistic measures. Results indicate that using moist SVs instead of dry SVs has a stronger impact on precipitation than on dynamical fields. Forecast skill for precipitation is greatly influenced by the dominant synoptic weather systems. For stratiform precipitation caused by strong baroclinic systems, the forecast skill is improved in the moist SV experiments relative to the dry SV experiments. For convective precipitation rates in the range 15–50 mm (24 h)−1 produced by weak synoptic baroclinic systems, all experiments exhibit noticeably poorer forecast skills. Skill improvements due to the Interactions between Soil, Biosphere, and Atmosphere (ISBA) surface scheme and stochastic perturbations are also observed.