Met Office Global And Regional Ensemble Prediction System Research Articles

Comparison of probabilistic forecasts of extreme precipitation for a global and convection‐permitting ensemble and hybrid statistical–dynamical method based on equatorial wave information

AbstractRecent work has demonstrated that skilful hybrid statistical–dynamical forecasts of heavy rainfall events in Southeast Asia can be made by combining model forecasts of the phases and amplitudes of Kelvin, Rossby, and westward‐moving Rossby gravity waves with climatological rainfall statistics conditioned on these waves. This study explores the sensitivity of this hybrid forecast to its parameter choices and compares its skill in forecasting extreme rainfall events in the Philippines, Malaysia, Indonesia, and Vietnam to that of the Met Office Global and Regional Ensemble Prediction System (MOGREPS). The hybrid forecast is found to outperform both the global and convection‐permitting ensemble in some regions when forecasting the most extreme events; however, for less extreme events, the ensemble is found more skilful. A weighted blend of the MOGREPS forecasts and the hybrid forecast was found to have the highest skill of all for almost all definitions of extreme event and in most regions. To quantify the influence of errors in the predicted wave state on the skill of the hybrid forecast, the skill of a hypothetical best‐case forecast was also calculated using reanalysis data to specify the wave amplitudes and phases. This best‐case forecast indicates that errors in the forecasts of all wave types reduce the skill of hybrid forecast; however, the reduction in skill is largest for Kelvin waves. The skill in convection‐permitting models is greater than for global models in the regions where Kelvin waves dominate, but the added value of limited‐area high‐resolution forecasts is hampered by the poor representation of Kelvin waves in the parent global model.

Impact of land surface processes on convection over West Africa in convection‐permitting ensemble forecasts: A case study using the MOGREPS ensemble

AbstractSoil moisture (SM) affects weather through its impact on surface flux partitioning, influencing vertical atmospheric profiles and circulations driven by differential surface heating. In West Africa, observational studies point to a dominant negative SM‐precipitation feedback, where dry soils help to initiate and maintain convection. In this context, serious concerns exist about the ability of models with parameterised convection to simulate this observed sensitivity of daytime convection to SM. Here, we evaluate the effect of initial SM perturbations in a short‐range ensemble forecast over West Africa, comparing the UK Met Office Global and Regional Ensemble Prediction System (MOGREPS) with parameterised convection (GLOB‐ENS) to its regional convection‐permitting counterpart (CP‐ENS). Results from both models suggest SM perturbations introduce considerable spread into daytime evaporative fraction (EF) and near‐surface temperatures. This spread is still evident on Day 3 of the forecast. Both models also show a tendency to increased afternoon rainfall frequency over negative EF anomalies, reproducing the observed feedback. However, this effect is more pronounced in CP‐ENS than GLOB‐ENS, which illustrates the potential for process‐based forecast improvements at convection‐permitting scales. Finally, we identify persistent biases in rainfall caused by land cover mapping issues in the operational GLOB‐ENS setup, emphasising the need for careful evaluation of different mapping strategies for land cover.

Medium‐range global ensemble prediction system at 12 km horizontal resolution and its preliminary validation

AbstractForecasts of high‐impact weather systems require sufficiently high resolution of state‐of‐the‐art numerical models in order to resolve the small‐scale features. At the National Centre for Medium Range Weather Forecasting (NCMRWF) in India, a global ensemble prediction system (EPS—called the NEPS) has been implemented operationally at 12 km horizontal grid size. The NEPS configuration is based on the UK Met Office Global and Regional Ensemble Prediction System (MOGREPS). Initial condition perturbations are generated by the ensemble transform Kalman filter (ETKF) method. Model uncertainties are taken care of by stochastic kinetic energy backscatter (SKEB) and random parameters (RP) schemes. Forecast perturbations obtained from 6 hr short forecasts of 22 ensemble members are updated by the ETKF four times a day (0000, 0600, 1200 and 1800 UTC). Perturbations of surface parameters such as sea‐surface temperature, soil moisture content and soil temperature are also included in the new NEPS. The NEPS aims to provide 10 day probabilistic long forecasts using 23 ensemble members (22 perturbed plus one control). The long forecast provided at 0000 UTC is the combination of 11 members from the 0000 UTC cycle and lagged 11 members from the 1200 UTC cycle. The new NEPS shows improvements in terms of forecast agreement among the members in comparison with the previously operational NEPS that was running at 33 km horizontal grid size with 44 perturbed members. The ratio between the root mean square error of the ensemble mean and ensemble spread as a function of lead time has improved in both the Northern and Southern Hemispheres in the new NEPS.

A moisture‐incrementing operator for the assimilation of humidity‐ and cloud‐sensitive observations: formulation and preliminary results

Being able to assimilate satellite radiances that are affected by cloud and precipitation is a key goal for the Met Office, as it is expected to provide improvements to forecast skill both at global scale and over the UK, at locations that are meteorologically important. The set of ‘control’ variables currently used for the minimization of the data assimilation cost function includes a single moist variable that accounts for total water (i.e. from all its phases) increments arising from moisture‐sensitive observations. The control variable defines a total water increment, which is partitioned by a suitably designed operator into specific humidity and cloud (liquid or frozen) water increments to be added to the forecast model's short‐range predictions. These incremented values are fitted to the observations during the minimization procedure and used to start the next forecast. This article discusses the design and initial testing of a new moisture‐incrementing operator with the following characteristics: (i) it is linearly related to the perturbation forecast (PF) model state; (ii) its physically based formulation is consistent with that of the cloud scheme operating within the PF model; and (iii) it is trained statistically to fit cloud‐water perturbations from the Met Office Global and Regional Ensemble Prediction System (MOGREPS), when the operator is fed with specific humidity and temperature perturbations also from MOGREPS. The moisture‐incrementing operator's formulation described in this article is relevant to meteorological centres that (plan to) make use of moist control variables that are linearly dependent on total (i.e. vapour plus cloud) water data‐assimilation increments. The initial validation results of the new operator show a neutral impact, arising mostly from changes in wind forecasts in the Tropics. Future work will test the performance of the new moisture‐incrementing operator with a set of observations that include cloud‐affected microwave radiances.

Evaluating the performance of two global ensemble forecasting systems in predicting rainfall over India during the southwest monsoons

ABSTRACTEnsemble prediction systems help in quantifying the inherent uncertainties in numerical weather prediction models. Verification of forecasts is essential, in order to monitor and improve forecast quality. Also, verification can be used to compare the capabilities of different numerical weather prediction models in predicting the weather. This study deals with a comparison of probabilistic rainfall forecasts obtained from the National Centre for Medium Range Weather Forecasting (NCMRWF) Global Ensemble Forecast System (NGEFS) and the UK Met Office Global and Regional Ensemble Prediction System (MOGREPS) for four monsoon seasons, June–September 2012–2015. Verification is done based on the Brier score, the Brier skill score, the reliability diagram, the relative operating characteristic (ROC) curve and the area under the ROC curve (AROC). The NMSG (India Meteorological Department NCMRWF merged satellite and gauge) observation dataset is used for verification.The Brier score values for verification of the MOGREPS are lower by approximately 6–14% across all rainfall thresholds and lead times, indicating that these forecasts match better with observations than the NGEFS. This is further reiterated by Brier skill score values of MOGREPS forecasts which are approximately 13–47% higher than the NGEFS. Furthermore, the reliability diagram shows that forecast probabilities are closer to observed frequencies for the MOGREPS than the NGEFS. The AROC for the MOGREPS is also higher than for the NGEFS, hence indicating a better skill of the MOGREPS. From this study, it can be concluded that the MOGREPS showed a better capability in predicting rainfall during the southwest monsoons of 2012–2015 over the Indian region compared to the NGEFS.

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.

Developments in aviation meteorology

Developments in aviation meteorology

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.

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

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.

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.

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.