Regional Ensemble Prediction System Research Articles

A Comparison of ETKF and Downscaling in a Regional Ensemble Prediction System

Based on the operational regional ensemble prediction system (REPS) in China Meteorological Administration (CMA), this paper carried out comparison of two initial condition perturbation methods: an ensemble transform Kalman filter (ETKF) and a dynamical downscaling of global ensemble perturbations. One month consecutive tests are implemented to evaluate the performance of both methods in the operational REPS environment. The perturbation characteristics are analyzed and ensemble forecast verifications are conducted; furthermore, a TC case is investigated. The main conclusions are as follows: the ETKF perturbations contain more power at small scales while the ones derived from downscaling contain more power at large scales, and the relative difference of the two types of perturbations on scales become smaller with forecast lead time. The growth of downscaling perturbations is more remarkable, and the downscaling perturbations have larger magnitude than ETKF perturbations at all forecast lead times. However, the ETKF perturbation variance can represent the forecast error variance better than downscaling. Ensemble forecast verification shows slightly higher skill of downscaling ensemble over ETKF ensemble. A TC case study indicates that the overall performance of the two systems are quite similar despite the slightly smaller error of DOWN ensemble than ETKF ensemble at long range forecast lead times.

Multivariate Probabilistic Analysis and Predictability of Medium-Range Ensemble Weather Forecasts

Abstract Ensemble weather forecasting has been operational for two decades now. However, the related uncertainty analysis in terms of probabilistic postprocessing still focuses on single variables, grid points, or stations. Inevitable dependencies in space and time and between variables are often ignored. To address this problem, two probabilistic postprocessing methods are presented, which are multivariate versions of Gaussian fit and kernel dressing, respectively. The multivariate case requires the estimation of a full rank, invertible covariance matrix. For this purpose, a Graphical Least Absolute Shrinkage and Selection Operators (GLASSO) estimator has been employed that is based on sparse undirected graphical models regularized by an L1 penalty term in order to parameterize the full rank inverse covariance. In all cases, the result is a multidimensional probability density. The forecasts used to test the approach are station forecasts of 2-m temperature and surface pressure from four main global ensemble prediction systems (EPS) with medium-range weather forecasts: the NCEP Global Ensemble Forecast System (GEFS), the Met Office Global and Regional Ensemble Prediction System (MOGREPS), the Canadian Meteorological Centre (CMC) Global Ensemble Prediction System (GEPS), and the ECMWF EPS. To evaluate the multivariate probabilistic postprocessing, especially the uncertainty estimates, common verification methods such as the analysis rank histogram and the continuous ranked probability score (CRPS) are applied. Furthermore, a multivariate extension of the CRPS, the energy score, allows for the verification of a complete medium-range forecast as well as for determining its predictability. It is shown that the predictability is similar for all of the examined ensemble prediction systems, whereas the GLASSO proved to be a useful tool for calibrating the commonly observed underdispersion of ensemble forecasts during the first few lead days by using information from the full covariance matrix.

A Spatial View of Ensemble Spread in Convection Permitting Ensembles

Abstract With movement toward kilometer-scale ensembles, new techniques are needed for their characterization. A new methodology is presented for detailed spatial ensemble characterization using the fractions skill score (FSS). To evaluate spatial forecast differences, the average and standard deviation are taken of the FSS calculated over all ensemble member–member pairs at different scales and lead times. These methods were found to give important information about the ensemble behavior allowing the identification of useful spatial scales, spinup times for the model, and upscale growth of errors and forecast differences. The ensemble spread was found to be highly dependent on the spatial scales considered and the threshold applied to the field. High thresholds picked out localized and intense values that gave large temporal variability in ensemble spread: local processes and undersampling dominate for these thresholds. For lower thresholds the ensemble spread increases with time as differences between the ensemble members upscale. Two convective cases were investigated based on the Met Office United Model run at 2.2-km resolution. Different ensemble types were considered: ensembles produced using the Met Office Global and Regional Ensemble Prediction System (MOGREPS) and an ensemble produced using different model physics configurations. Comparison of the MOGREPS and multiphysics ensembles demonstrated the utility of spatial ensemble evaluation techniques for assessing the impact of different perturbation strategies and the need for assessing spread at different, believable, spatial scales.

Impact of EOS MLS ozone data on medium‐extended range ensemble weather forecasts

AbstractAs the stratosphere is largely characterized by its ozone abundance, the quality of the ozone field is important for a realistic representation of the stratosphere in weather and climate models. While the stratosphere is directly affected by radiative heating from ozone photodissociation, ozone abundance might also impact the representation of the troposphere since the stratosphere and troposphere are dynamically linked. In this paper, we examine the potential benefits of using ozone data from the Earth Observing System (EOS) Microwave Limb Sounder (MLS) for medium‐extended range tropospheric forecasts in a current numerical weather prediction system. The global component of the Met Office Global and Regional Ensemble Prediction System is used, which is run at a resolution of N216 L85 with 24 ensemble members. We compare two scenarios of 31 day forecasts covering the same period, one with the current operational ozone climatology and the other with a monthly mean zonally averaged ozone field computed from the MLS data set. In the extreme case of the Arctic “ozone hole” of March 2011, our results show a general reduction in stratospheric forecast errors in the tropics and Southern Hemisphere as a result of the improved representation of ozone. However, even in such a scenario, where the MLS ozone field is much superior to that of the control, we find that tropospheric forecast errors in the medium‐extended range are dominated by the spread of ensemble members and no significant reduction in the root‐mean‐square forecast errors.

Characteristics of Initial Perturbations in the Ensemble Prediction System of the Korea Meteorological Administration

AbstractIn this study, the initial ensemble perturbation characteristics of the new Korea Meteorological Administration (KMA) ensemble prediction system (EPS), a version of the Met Office Global and Regional Ensemble Prediction System, were analyzed over two periods: from 1 June to 31 August 2011, and from 1 December 2011 to 29 February 2012. The KMA EPS generated the initial perturbations using the ensemble transform Kalman filter (ETKF). The observation effect was reflected in both the transform matrix and the inflation factor of the ETKF; it reduced (increased) uncertainties in the initial perturbations in regions with dense observations via the transform matrix (inflation factor). The reduction in uncertainties is generally governed by the transform matrix but locally modulated by the inflation factor. The sea ice significantly affects the initial perturbations near the lower boundary layer. The large perturbation energy in the lower stratosphere of the tropics was related to the dominant zonal wind, whereas the perturbation energy in the upper stratosphere of the winter hemispheres was related to the dominant polar night jet. In the early time-integration stage, the initial perturbations decayed in the lower troposphere but grew rapidly in the mid- to upper troposphere. In the meridional direction, the initial perturbations grew greatest in the northern polar region and smallest in the tropics. The initial perturbations maintained a hydrostatic balance, especially during the summer in both hemispheres and during both the summer and winter in the tropics, associated with the smallest growth rates of the initial perturbations. The initial perturbations in the KMA EPS appropriately describe the uncertainties associated with atmospheric features.

A New Method for Generating Initial Condition Perturbations in a Regional Ensemble Prediction System: Blending

Abstract A blending method for generating initial condition (IC) perturbations in a regional ensemble prediction system is proposed. The blending is to combine the large-scale IC perturbations from a global ensemble prediction system (EPS) with the small-scale IC perturbations from a regional EPS by using a digital filter and the spectral analysis technique. The IC perturbations generated by blending can well represent both large-scale and small-scale uncertainties in the analysis, and are more consistent with the lateral boundary condition (LBC) perturbations provided by global EPS. The blending method is implemented in the regional ensemble system Aire Limitée Adaptation Dynamique Développement International-Limited Area Ensemble Forecasting (ALADIN-LAEF), in which the large-scale IC perturbations are provided by the European Centre for Medium-Range Weather Forecasts (ECMWF-EPS), and the small-scale IC perturbations are generated by breeding in ALADIN-LAEF. Blending is compared with dynamical downscaling and breeding over a 2-month period in summer 2007. The comparison clearly shows impact on the growth of forecast spread if the regional IC perturbations are not consistent with the perturbations coming through LBC provided by the global EPS. Blending can cure the problem largely, and it performs better than dynamical downscaling and breeding.

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

Abstract. In this paper ensembles of forecasts (of up to six hours) are studied from a convection-permitting model with a representation of model error due to unresolved processes. The ensemble prediction system (EPS) used is an experimental convection-permitting version of the UK Met Office's 24-member Global and Regional Ensemble Prediction System (MOGREPS). The method of representing model error variability, which perturbs parameters within the model's parameterisation schemes, has been modified and we investigate the impact of applying this scheme in different ways. These are: a control ensemble where all ensemble members have the same parameter values; an ensemble where the parameters are different between members, but fixed in time; and ensembles where the parameters are updated randomly every 30 or 60 min. The choice of parameters and their ranges of variability have been determined from expert opinion and parameter sensitivity tests. A case of frontal rain over the southern UK has been chosen, which has a multi-banded rainfall structure. The consequences of including model error variability in the case studied are mixed and are summarised as follows. The multiple banding, evident in the radar, is not captured for any single member. However, the single band is positioned in some members where a secondary band is present in the radar. This is found for all ensembles studied. Adding model error variability with fixed parameters in time does increase the ensemble spread for near-surface variables like wind and temperature, but can actually decrease the spread of the rainfall. Perturbing the parameters periodically throughout the forecast does not further increase the spread and exhibits "jumpiness" in the spread at times when the parameters are perturbed. Adding model error variability gives an improvement in forecast skill after the first 2–3 h of the forecast for near-surface temperature and relative humidity. For precipitation skill scores, adding model error variability has the effect of improving the skill in the first 1–2 h of the forecast, but then of reducing the skill after that. Complementary experiments were performed where the only difference between members was the set of parameter values (i.e. no initial condition variability). The resulting spread was found to be significantly less than the spread from initial condition variability alone.

Developments in aviation meteorology

Developments in aviation meteorology

Probabilistic Weather Prediction with an Analog Ensemble

Abstract This study explores an analog-based method to generate an ensemble [analog ensemble (AnEn)] in which the probability distribution of the future state of the atmosphere is estimated with a set of past observations that correspond to the best analogs of a deterministic numerical weather prediction (NWP). An analog for a given location and forecast lead time is defined as a past prediction, from the same model, that has similar values for selected features of the current model forecast. The AnEn is evaluated for 0–48-h probabilistic predictions of 10-m wind speed and 2-m temperature over the contiguous United States and against observations provided by 550 surface stations, over the 23 April–31 July 2011 period. The AnEn is generated from the Environment Canada (EC) deterministic Global Environmental Multiscale (GEM) model and a 12–15-month-long training period of forecasts and observations. The skill and value of AnEn predictions are compared with forecasts from a state-of-the-science NWP ensemble system, the 21-member Regional Ensemble Prediction System (REPS). The AnEn exhibits high statistical consistency and reliability and the ability to capture the flow-dependent behavior of errors, and it has equal or superior skill and value compared to forecasts generated via logistic regression (LR) applied to both the deterministic GEM (as in AnEn) and REPS [ensemble model output statistics (EMOS)]. The real-time computational cost of AnEn and LR is lower than EMOS.

Ensemble‐based analysis and sensitivity of mesoscale forecasts of a vortex over southwest China

AbstractIn this study, an ensemble analysis and sensitivity experiment of a mesovortex over southwest China (Southwest Vortex, SWV) are performed with a regional ensemble prediction system in order to demonstrate: (i) the usefulness of an ensemble prediction system for diagnosing the dynamical and thermodynamical characteristics leading to the SWV's eastward movement and growth; and (ii) the potential importance of adopting targeted observations in improving short‐range weather forecasts by applying a simple technique. Results show significant variations in forecast performance, ranging from good to poor, among 11 ensemble members. By examining two distinct clusters (‘moving’ and ‘staggering’) of the ensemble members it is found that a strong upper‐level trough with positive vorticity advection near the vortex centre, its westward vertical tilt and lower‐level downstream wet and upstream dry moisture configuration favour the eastward movement and growth of the SWV. Ensemble sensitivity analysis shows that: (i) a simple correlation‐based ensemble sensitivity method works well in identifying sensitive areas of mesoscale forecasts of the SWV; (ii) the forecast of the SWV movement and intensity is especially sensitive to initial conditions of horizontal wind and moisture fields; and (iii) unlike the medium‐range forecast in which a sensitive region is often traced back to an area far upstream in association with a Rossby‐wave packet, sensitive regions in short‐range forecasts appear to be directly related to the meteorological system of interest located not far upstream. Based on the results, we suggest that field experiments of targeted observations potentially can be designed using this simple ensemble sensitivity approach to improve short‐range weather forecasts associated with SWVs.

Effect of doubling the ensemble size on the performance of ensemble prediction in the warm season using MOGREPS implemented at the KMA

Using the Met Office Global and Regional Ensemble Prediction System (MOGREPS) implemented at the Korea Meteorological Administration (KMA), the effect of doubling the ensemble size on the performance of ensemble prediction in the warm season was evaluated. Because a finite ensemble size causes sampling error in the full forecast probability distribution function (PDF), ensemble size is closely related to the efficiency of the ensemble prediction system. Prediction capability according to doubling the ensemble size was evaluated by increasing the number of ensembles from 24 to 48 in MOGREPS implemented at the KMA. The initial analysis perturbations generated by the Ensemble Transform Kalman Filter (ETKF) were integrated for 10 days from 22 May to 23 June 2009. Several statistical verification scores were used to measure the accuracy, reliability, and resolution of ensemble probabilistic forecasts for 24 and 48 ensemble member forecasts. Even though the results were not significant, the accuracy of ensemble prediction improved slightly as ensemble size increased, especially for longer forecast times in the Northern Hemisphere. While increasing the number of ensemble members resulted in a slight improvement in resolution as forecast time increased, inconsistent results were obtained for the scores assessing the reliability of ensemble prediction. The overall performance of ensemble prediction in terms of accuracy, resolution, and reliability increased slightly with ensemble size, especially for longer forecast times.

New schemes to perturb sea‐surface temperature and soil moisture content in MOGREPS

AbstractThis article investigates two schemes that perturb sea‐surface temperatures (SSTs) and soil moisture content (SMC) in the Met Office Global and Regional Ensemble Prediction System (MOGREPS), to address a known deficiency of a lack of ensemble spread near the surface. Results from a two‐month‐long trial during the Northern Hemisphere summer show positive benefits from these schemes. These include a decrease in the spread deficit of surface temperature and improved probabilistic verification scores. SST perturbations exhibit a stronger impact than SMC perturbations but, when combined, the increased spread from the two schemes is cumulative. A regional ensemble system driven by the global ensemble members largely reflects the same changes seen in the global ensemble but cycling fields, like SMC, between successive regional forecasts does show some benefit.

Impact of Surface Parameter Uncertainties within the Canadian Regional Ensemble Prediction System

Abstract The aim of this study is to assess the impact of uncertainties in surface parameter and initial conditions on numerical prediction with the Canadian Regional Ensemble Prediction System (REPS). As part of this study, the Canadian version of the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface scheme has been coupled to Environment Canada’s numerical weather prediction model within the REPS. For 20 summer periods in 2009, stochastic perturbations of surface parameters have been generated in several experiments. Each experiment corresponds to 20 simulations differing by the perturbations at the initial time of one or several surface parameters or prognostic variables. The sensitivity to these perturbations is quantified especially for 2-m temperature, 10-m wind speed, cloud fraction, and precipitation up to 48-h lead time. Spatial variability of these sensitivities over the North American continent shows that soil moisture, albedo, leaf area index, and SST have the largest impacts on the screen-level variables. The temporal evolution of these sensitivities appears to be closely linked to the diurnal cycle of the boundary layer. The surface perturbations are shown to increase the ensemble spread of the REPS for all screen-level variables especially for 2-m temperature and 10-m wind speed during daytime. A preliminary study of the impact on the ensemble forecast has shown that the inclusion of the surface perturbations tends to significantly increase the 2-m temperature and 10-m wind speed skill.

An ensemble based turbulence forecasting system

ABSTRACTTurbulence is a major source of weather‐related aviation incidents. There are many different indicators used to try and predict where turbulence is likely to occur. The indicators are derived from deterministic models although they are often quoted as probabilities. This paper proposes the use of ensemble forecasts from the Met Office Global and Regional Ensemble Prediction System (MOGREPS) to produce a probabilistic indicator of wind shear and convectively induced moderate or greater turbulence. An objective verification scheme using high‐resolution automated aircraft observations from the Global Aircraft Data Set is used to compare the skill with the routinely available World Area Forecast Centre gridded turbulence forecasts. The forecasts are assessed globally over a 12 month period from November 2010 to October 2011 looking at the skill, reliability and economic value of the forecasts.

Ensemble based first guess support towards a risk-based severe weather warning service

This paper describes an ensemble-based first guess support tool for severe weather, which has evolved over time to support changing requirements from the UK National Severe Weather Warning Service (NSWWS). This warning tool post-processes data from the regional component of the Met Office Global and Regional Ensemble Prediction System (MOGREPS), and is known as MOGREPS-W (‘W’ standing for ‘warnings’). The original system produced area-based probabilistic first guess warnings for severe and extreme weather, providing forecasters with an objective basis for assessing risk and making probability statements. The NSWWS underwent significant changes in spring 2011, removing area boundaries for warnings and focusing more on a risk-based approach. Warnings now include details of both likelihood and impact, whereby the higher the likelihood and impact, the greater the risk of disruption. This paper describes these changes to the NSWWS along with the corresponding changes to MOGREPS-W, using case studies from both the original and new systems. Calibration of the original MOGREPS-W system improves forecast accuracy of severe wind gust and rainfall warnings by reducing under-forecasting. In addition, verification of forecasts from different groups of areas of different sizes shows that larger areas have better forecast accuracy than smaller areas. © 2013 British Crown copyright, the Met Office. Published by John Wiley & Sons Ltd.

On‐line calibration of the vertical distribution of ensemble spread

AbstractThe Met Office Global and Regional Ensemble Prediction System (MOGREPS) uses an on‐line 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. Sonde measurements are augmented with ATOVS brightness temperature observations to provide sufficient coverage across the globe. This paper describes how the scheme has been extended to improve the vertical distribution of spread. Separate inflation factors are derived for each of three ‘bands’ of model levels, loosely representing the boundary layer, troposphere and stratosphere. A continuous profile is ensured by linearly interpolating between the band centres. The error/spread relationship observed in each ATOVS channel is mapped onto the three bands using a set of weights obtained by integrating the appropriate Jacobians over the range of pressures covered by each band. These weights are found to be approximately constant, thanks to the small number of bands and the fact that near‐surface channels are not used over land. The revised initial perturbations lead to increased near‐surface spread which persists throughout the 72 h forecast and carries through to improved probabilistic scores. However, the near‐surface spread remains too small due to insufficient growth of spread by the forecast model. This suggests perturbation of sea‐surface temperatures, soil initial conditions, soil properties and boundary‐layer physics as priorities for future work.

Breakdown of hydrostatic balance at convective scales in the forecast errors in the Met Office Unified Model

AbstractFor data assimilation in numerical weather prediction, the initial forecast‐error covariance matrix Pf is required. For variational assimilation it is particularly important to prescribe an accurate initial matrix Pf, since Pf is either static (in the 3D‐Var case) or constant at the beginning of each assimilation window (in the 4D‐Var case). At large scales the atmospheric flow is well approximated by hydrostatic balance and this balance is strongly enforced in the initial matrix Pf used in operational variational assimilation systems such as that of the Met Office. However, at convective scales this balance does not necessarily hold any more. Here we examine the extent to which hydrostatic balance is valid in the vertical forecast‐error covariances for high‐resolution models in order to determine whether there is a need to relax this balance constraint in convective‐scale data assimilation. We use the Met Office Global and Regional Ensemble Prediction System (MOGREPS) and a 1.5 km resolution version of the Unified Model for a case study characterized by the presence of convective activity. An ensemble of high‐resolution forecasts valid up to three hours after the onset of convection is produced. We show that at 1.5 km resolution hydrostatic balance does not hold for forecast errors in regions of convection. This indicates that in the presence of convection hydrostatic balance should not be enforced in the covariance matrix used for variational data assimilation at this scale. The results show the need to investigate covariance models that may be better suited for convective‐scale data assimilation. Finally, we give a measure of the balance present in the forecast perturbations as a function of the horizontal scale (from 3–90 km) using a set of diagnostics. Copyright © 2012 Royal Meteorological Society and British Crown Copyright, the Met Office

Study of perturbing method in regional BGM ensemble prediction system

Study of perturbing method in regional BGM ensemble prediction system

Evaluation of the NMC regional ensemble prediction system during the Beijing 2008 Olympic Games

Based on the B08RDP (Beijing 2008 Olympic Games Mesoscale Ensemble Prediction Research and Development Project) that was launched by the World Weather Research Programme (WWRP) in 2004, a regional ensemble prediction system (REPS) at a 15-km horizontal resolution was developed at the National Meteorological Center (NMC) of the China Meteorological Administration (CMA). Supplementing to the forecasters’ subjective affirmation on the promising performance of the REPS during the 2008 Beijing Olympic Games (BOG), this paper focuses on the objective verification of the REPS for precipitation forecasts during the BOG period. By use of a set of advanced probabilistic verification scores, the value of the REPS compared to the quasi-operational global ensemble prediction system (GEPS) is assessed for a 36-day period (21 July–24 August 2008). The evaluation here involves different aspects of the REPS and GEPS, including their general forecast skills, specific attributes (reliability and resolution), and related economic values. The results indicate that the REPS generally performs significantly better for the short-range precipitation forecasts than the GEPS, and for light to heavy rainfall events, the REPS provides more skillful forecasts for accumulated 6- and 24-h precipitation. By further identifying the performance of the REPS through the attribute-focused measures, it is found that the advantages of the REPS over the GEPS come from better reliability (smaller biases and better dispersion) and increased resolution. Also, evaluation of a decision-making score reveals that a much larger group of users benefits from using the REPS forecasts than using the single model (the control run) forecasts, especially for the heavy rainfall events.

Using a Stochastic Kinetic Energy Backscatter Scheme to Improve MOGREPS Probabilistic Forecast Skill

Abstract An improved stochastic kinetic energy backscatter scheme, version 2 (SKEB2) has been developed for the Met Office Global and Regional Ensemble Prediction System (MOGREPS). Wind increments at each model time step are derived from a streamfunction forcing pattern that is modulated by a locally diagnosed field of likely energy loss due to numerical smoothing and unrepresented convective sources of kinetic energy near the grid scale. The scheme has a positive impact on the root-mean-square error of the ensemble mean and spread of the ensemble. An improved growth rate of spread results in a better match with ensemble-mean forecast error at all forecast lead times, with a corresponding improvement in probabilistic forecast skill from a more realistic representation of model error. Other examples of positive impact include improved forecast blocking frequency and reduced forecast jumpiness. The paper describes the formulation of the SKEB2 and its assessment in various experiments.