Regional Ensemble Prediction System Research Articles

Evaluating Short-Range Forecasts of a 12 km Global Ensemble Prediction System and a 4 km Convection-Permitting Regional Ensemble Prediction System

Evaluating Short-Range Forecasts of a 12 km Global Ensemble Prediction System and a 4 km Convection-Permitting Regional Ensemble Prediction System

Lead‐time‐continuous statistical postprocessing of ensemble weather forecasts

AbstractNumerical weather prediction (NWP) ensembles often exhibit biases and errors in dispersion, so they need some form of postprocessing to yield sharp and well‐calibrated probabilistic predictions. The output of NWP models is usually at a multiplicity of different lead times and, even though information is often required on this range of lead times, many postprocessing methods in the literature are applied either at a fixed lead time or by fitting individual models for each lead time. However, this is (1) computationally expensive because it requires the training of multiple models if users are interested in information at multiple lead times and (2) prohibitive because it restricts the data used for training postprocessing models and the usability of fitted models. This article investigates the lead‐time dependence of postprocessing methods in the idealized Lorenz'96 system as well as temperature and wind‐speed forecast data from the Met Office Global and Regional Ensemble Prediction System (MOGREPS‐G). The results indicate that there is substantial regularity between the models fitted for different lead times and that one can fit models that are lead‐time‐continuous that work for multiple lead times simultaneously by including lead time as a covariate. These models achieve similar and, in small data situations, even improved performance compared with the classical lead‐time‐separated models, whilst saving substantial computation time.

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.

Adaptive selection of members for convective-permitting regional ensemble prediction over the western Maritime Continent

A common issue faced by the downscaled regional ensemble prediction systems is the under-dispersiveness of the ensemble forecasts, often attributed to the lack of spread under the initial conditions from the global ensemble. In this study, a novel method that adopts an adaptive approach to selecting global ensemble members for regional downscaling has been developed. Instead of using a fixed set of pre-selected global ensemble members, the adaptive selection performs a sampling algorithm and selects the global ensemble members, which maximizes a fractions skill score (FSS)-based displacement between ensemble members. The method is applied to a convective-permitting ensemble prediction system over the western Maritime Continent, referred to as SINGV-EPS. SINGV-EPS has a grid spacing of 4.5 km and is a 12-member ensemble that is driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) 51-member global ensemble. Month-long trials were conducted in June 2020 to assess the impact of adaptive selection on the ensemble forecast spread and rainfall verification scores. In both fixed pre-selection and adaptive selection experiments, SINGV-EPS was still under-dispersive. However, adaptive selection improved the ensemble spread and reduced the root-mean-square error (RMSE) of the ensemble mean in wind, temperature, and precipitation fields. Further verification of the rainfall forecasts showed that there was a reduction in the Brier score and a higher hit rate in the relative operating characteristic (ROC) curve for all rainfall thresholds when adaptive selection was applied. Additionally, the ensemble mean forecasts from adaptive selection experiments are more accurate beyond 24 h, with a higher FSS for all rainfall thresholds and neighborhood lengths. These results suggest that the adaptive selection is superior to the fixed pre-selection of global ensemble members for downscaled regional ensemble prediction.

An Ensemble Forecast Wind Field Correction Model with Multiple Factors and Spatio-Temporal Features

Accurate wind speed prediction is significantly important for the full utilization of wind energy resources and the improvement in the economic benefits of wind farms. Because the ensemble forecast takes into account the uncertainty of information about the atmospheric motion, domestic and foreign weather service forecast centers often choose to use the ensemble numerical forecast to achieve the fine forecast of wind speed. However, due to the unavoidable systematic errors of the ensemble numerical forecast model, it is necessary to correct the deviation in the ensemble numerical forecast wind speed. Considering the typical spatio-temporal characteristics of the grid prediction data of the wind field, based on Convolutional Long–Short Term Memory (ConvLSTM) units and attention mechanism, this paper takes the complex and representative North China region as the research area, aiming to reveal the shortcomings of existing deep learning integrated prediction correction models in extracting temporal features of grid prediction data. We propose a new ensemble prediction wind field correction model integrating multi-factor and spatio-temporal characteristics. This model uses reanalyzed land data provided by the European Center for Medium-Range Weather Forecasts as the real data to correct the deviation in the near-surface 10 m wind field data predicted by the regional ensemble numerical prediction model of the China Meteorological Administration. We used the reanalyzed land data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) as the live data to correct the deviation in the near-surface 10 m wind field data predicted by the regional ensemble numerical forecast model of the China Meteorological Administration (CMA). At the same time, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used as the scoring indicators, and the results of the China Meteorological Administration–Regional Ensemble Prediction System (CMA–REPS) ensemble average, multiple linear regression method correction, Long–Short Term Memory (LSTM) method correction, and U-net (UNET) method correction were compared. Compared with the UNET model method, the experimental results show that when processing the 10 m zonal wind data, 10 m meridional wind data, and 10 m average wind speed data of CMA–REPS 24 h forecasts, the correction results of our model can reduce the RMSE score index by 9.15%, 4.83%, and 7.79%. At the same time, when processing the 48 h and 72 h near-surface 10 m wind field data of the CMA–REPS forecast, our model can improve the prediction accuracy of CMA–REPS near-surface wind forecast data. Therefore, the correction effect of the proposed model in a complex terrain area is evidently better compared to other methods.

Hybrid Dynamical–Statistical Forecasts of the Risk of Rainfall in Southeast Asia Dependent on Equatorial Waves

Abstract Equatorial waves are a major driver of widespread convection in Southeast Asia and the tropics more widely, a region in which accurate heavy rainfall forecasts are still a challenge. Conditioning rainfall over land on local equatorial wave phases finds that heavy rainfall can be between 2 and 4 times more likely to occur in Indonesia, Malaysia, Vietnam, and the Philippines. Equatorial waves are identified in a global numerical weather prediction ensemble forecast [Met Office Global and Regional Ensemble Prediction System (MOGREPS-G)]. Skill in the ensemble forecast of wave activity is highly dependent on region and time of year, although generally forecasts of equatorial Rossby waves and westward-moving mixed Rossby–gravity waves are substantially more skillful than for the eastward-moving Kelvin wave. The observed statistical relationship between wave phases and rainfall is combined with ensemble forecasts of dynamical wave fields to construct hybrid dynamical–statistical forecasts of rainfall probability using a Bayesian approach. The Brier skill score is used to assess the skill of forecasts of rainfall probability. Skill in the hybrid forecasts can exceed that of probabilistic rainfall forecasts taken directly from MOGREPS-G and can be linked to both the skill in forecasts of wave activity and the relationship between equatorial waves and heavy rainfall in the relevant region. The results show that there is potential for improvements of forecasts of high-impact weather using this method as forecasts of large-scale waves improve.

Met Office MOGREPS‐G initialisation using an ensemble of hybrid four‐dimensional ensemble variational (En‐4DEnVar) data assimilations

AbstractThe Met Office Global and Regional Ensemble Prediction System–Global (MOGREPS‐G) used an ensemble transform Kalman filter (ETKF) to perturb its initial conditions from its operational implementation in September 2008 until December 2019. In 2019, MOGREPS‐G became the first operational atmospheric ensemble to apply hybrid four‐dimensional ensemble variational data assimilation (En‐4DEnVar) to each of the 44 perturbed ensemble members. Other enhancements have also been added, including to the inflation used to improve ensemble spread. The combined impact of these changes on ensemble forecasts is overwhelmingly positive but initially more neutral for deterministic forecasts, which also use the ensemble to represent flow‐dependent forecast errors in their hybrid data assimilation updates. The latter result is not a surprise, because the deterministic forecast's hybrid data assimilation was initially weighted more strongly to the modelled stationary covariance component and not optimised to take full advantage of the upgraded ensemble. A subsequent operational upgrade in December 2020 has introduced shifting in addition to lagging to exploit the ensemble better in the deterministic forecast's hybrid data assimilation by including ensemble members from a previous cycle and also from adjacent forecast lead times to augment the ensemble without having to run additional forecasts. More weight has since been given to the ensemble in the deterministic forecast's hybrid data assimilation in May 2022. A key motive for adopting hybrid 4DEnVar in MOGREPS‐G is to reduce maintenance overheads by virtue of sharing much of the deterministic forecast system's data assimilation code. This also enables the ensemble to assimilate almost all observation types used by the deterministic forecast. The updated system also exploits parallelism better so as to be fast enough for operational use, despite assimilating more observations and being more computationally expensive than the Met Office's ETKF.

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.

Development of a Hybrid Ensemble–Variational Data Assimilation System over the Western Maritime Continent

Abstract A hybrid three-dimensional ensemble–variational (En3D-Var) data assimilation system has been developed to explore incorporating information from an 11-member regional ensemble prediction system, which is dynamically downscaled from a global ensemble system, into a 3-hourly cycling convective-scale data assimilation system over the western Maritime Continent. From the ensemble, there exists small-scale ensemble perturbation structures associated with positional differences of tropical convection, but these structures are well represented only after the downscaled ensemble forecast has evolved for at least 6 h due to spinup. There was also a robust moderate negative correlation between total specific humidity and potential temperature background errors, presumably because of incorrect vertical motion in the presence of clouds. Time shifting of the ensemble perturbations, by using those available from adjacent cycles, helped to ameliorate the sampling error prevalent in their raw autocovariances. Monthlong hybrid En3D-Var trials were conducted using different weights assigned to the ensemble-derived and climatological background error covariances. The forecast fits to radiosonde relative humidity and wind observations were generally improved with hybrid En3D-Var, but in all experiments, the fits to surface observations were degraded compared to the baseline 3D-Var configuration. Over the Singapore radar domain, there was a general improvement in the precipitation forecasts, especially when the weighting toward the climatological background error covariance was larger, and with the additional application of time-shifted ensemble perturbations. Future work involves consolidating the ensemble prediction and deterministic system, by centering the ensemble prediction system on the hybrid analysis, to better represent the analysis and forecast uncertainties.

A Comparison between 2D and 3D Rescaling Masks of Initial Condition Perturbation in a 3-km Storm-Scale Ensemble Prediction System

Abstract Using a 3-km regional ensemble prediction system (EPS), this study tested a three-dimensional (3D) rescaling mask for initial condition (IC) perturbation. Whether the 3D mask-based EPS improves ensemble forecasts over current two-dimensional (2D) mask-based EPS has been evaluated in three aspects: ensemble mean, spread, and probability. The forecasts of wind, temperature, geopotential height, sea level pressure, and precipitation were examined for a summer month (1–28 July 2018) and a winter month (1–27 February 2019) over a region in North China. The EPS was run twice per day (initiated at 0000 and 1200 UTC) to 36 h in forecast length, providing 56 warm-season forecast cases and 54 cold-season cases for verification. The warm and cold seasons are verified separately for comparison. The study found the following: 1) The vertical profile of IC perturbation becomes closer to that of analysis uncertainty with the 3D rescaling mask. 2) Ensemble performance is significantly improved in all three aspects. The biggest improvement is in the ensemble spread, followed by the probabilistic forecast, and the least improvement is in the ensemble mean forecast. Larger improvements are seen in the warm season than in the cold season. 3) More improvement is in the shorter time range (<24 h) than in the longer range. 4) Surface and lower-level variables are improved more than upper-level ones. 5) The underlying mechanism for the improvement has been investigated. Convective instability is found to be responsible for the spread increment and, thus, overall ensemble forecast improvement. Therefore, using a 3D rescaling mask is recommended for an EPS to increase its utility especially for shorter time range and surface weather elements. Significant Statement A weather prediction model is a complex system that consists of nonlinear differential equations. Small errors in either its inputs or model itself will grow with time during model integration, which will contaminate a forecast. To quantify such contamination (“uncertainty”) of a forecast, the ensemble forecasting technique is used. An ensemble of forecasts is a multiple of model runs at the same time but with slightly “perturbed” inputs or model versions. These small perturbations are supposed to represent true “uncertainty” in inputs or model representation. This study proposed a technique that makes a perturbation’s vertical structure more resemble real uncertainty (intrinsic error) in input data and confirmed that it can significantly improve ensemble forecast quality especially for a shorter time range and lower-level weather elements. It is found that convective instability is responsible for the improvement.

Obtaining mesoscale singular vectors reflecting synoptic‐scale uncertainty by projection in phase space

AbstractThe choice of initial perturbation technique in regional ensemble prediction systems is important for forecasts of heavy rain events. Although the uncertainty at synoptic scale has a strong impact on the forecast performance of mesoscale phenomena, there is no established method for explicitly providing synoptic‐scale uncertainty when generating mesoscale initial perturbations. In this study, a new method of calculating mesoscale singular vectors (SVs) that reflect synoptic‐scale uncertainty by applying a regional targeting technique to a subspace of the model phase space spanned by global ensemble perturbations was developed. Comparison of the results of this new method with those of the original SVs showed that the new SVs with high growth rate corresponded to the original SVs with high growth rate, whereas the new SVs with relatively lower growth rate had a larger contribution from the global ensemble perturbations. The new SVs mitigated the tendency of the original SVs to localize over the southern sea of the computational domain, and the forecast errors were better captured by the new SVs than by the original SVs. Furthermore, the effect on forecast performance of the new SVs as initial perturbations was confirmed by using the operational regional ensemble prediction system at the Japan Meteorological Agency. It was found that the precipitation probability forecast performance was superior in the first half of the forecast period at all rainfall thresholds.

Using a hybrid optimal interpolation–ensemble Kalman filter for the Canadian Precipitation Analysis

Abstract. Several data assimilation (DA) approaches exist to generate consistent and continuous precipitation fields valuable for hydrometeorological applications and land data assimilation. Usually, DA is based on either static or dynamic approaches. Static methods rely on deterministic forecasts to estimate background error covariance matrices, whereas dynamic approaches use ensemble forecasts. Associating the two methods is known as hybrid DA, and it has proven beneficial for different applications as it combines the advantages of both approaches. The present study intends to explore hybrid DA for the 6 h Canadian Precipitation Analysis (CaPA). Based on optimal interpolation (OI), CaPA blends forecasts and observations from surface stations and ground-based radar datasets to provide precipitation fields over the North American domain. The application of hybrid DA to CaPA consisted of finding the optimal linear combination between (i) an OI based on the Regional Deterministic Prediction System (RDPS) and (ii) an ensemble Kalman filter (EnKF) based on the 20-member Regional Ensemble Prediction System (REPS). The results confirmed the known effectiveness of the hybrid approach when low-density observation networks are assimilated. Indeed, the experiments conducted for the summer without radar datasets and for the winter (characterized by very few observations in CaPA) showed that attributing a relatively high weight to the EnKF (50 % and 70 % for summer and winter, respectively) resulted in better analysis skill and a reduction in false alarms compared with the OI method. A deterioration in the moderate- to high-intensity precipitation bias was, however, observed during summer. Reducing the weight attributed to the EnKF to 30 % alleviated the bias deterioration while improving skill compared with the OI-based CaPA.

Quantifying the impact of meteorological uncertainty on emission estimates and the risk to aviation using source inversion for the Raikoke 2019 eruption

Abstract. Due to the remote location of many volcanoes, there is substantial uncertainty about the timing, amount and vertical distribution of volcanic ash released when they erupt. One approach to determine these properties is to combine prior estimates with satellite retrievals and simulations from atmospheric dispersion models to create posterior emission estimates, constrained by both the observations and the prior estimates, using a technique known as source inversion. However, the results are dependent not only on the accuracy of the prior assumptions, the atmospheric dispersion model and the observations used, but also on the accuracy of the meteorological data used in the dispersion simulations. In this study, we advance the source inversion approach by using an ensemble of meteorological data from the Met Office Global and Regional Ensemble Prediction System to represent the uncertainty in the meteorological data and apply it to the 2019 eruption of Raikoke. Retrievals from the Himawari-8 satellite are combined with NAME dispersion model simulations to create posterior emission estimates. The use of ensemble meteorology provides confidence in the posterior emission estimates and associated dispersion simulations that are used to produce ash forecasts. Prior mean estimates of fine volcanic ash emissions for the Raikoke eruption based on plume height observations are more than 15 times higher than any of the mean posterior ensemble estimates. In addition, the posterior estimates have a different vertical distribution, with 27 %–44 % of ash being emitted into the stratosphere compared to 8 % in the mean prior estimate. This has consequences for the long-range transport of ash, as deposition to the surface from this region of the atmosphere happens over long timescales. The posterior ensemble spread represents uncertainty in the inversion estimate of the ash emissions. For the first 48 h following the eruption, the prior ash column loadings lie outside an estimate of the error associated with a set of independent satellite retrievals, whereas the posterior ensemble column loadings do not. Applying a risk-based methodology to an ensemble of dispersion simulations using the posterior emissions shows that the area deemed to be of the highest risk to aviation, based on the fraction of ensemble members exceeding predefined ash concentration thresholds, is reduced by 49 %. This is compared to estimates using an ensemble of dispersion simulations using the prior emissions with ensemble meteorology. If source inversion had been used following the eruption of Raikoke, it would have had the potential to significantly reduce disruptions to aviation operations. The posterior inversion emission estimates are also sensitive to uncertainty in other eruption source parameters and internal dispersion model parameters. Extending the ensemble inversion methodology to account for uncertainty in these parameters would give a more complete picture of the emission uncertainty, further increasing confidence in these estimates.

Comparison between Multi-Physics and Stochastic Approaches for the 20 July 2021 Henan Heavy Rainfall Case

In this study, three model perturbation schemes, the stochastically perturbed parameter scheme (SPP), stochastically perturbed physics tendency (SPPT), and multi-physics process parameterization (MP), were used to represent the model errors in the regional ensemble prediction systems (REPS). To study the effects of different model perturbation schemes on heavy rainfall forecasting, three sensitive experiments using three different combinations (EXP1: MP, EXP2: SPPT + SPP, and EXP3: MP + SPPT + SPP) of the model perturbation schemes were set up based on the Weather Research and Forecasting (WRF)-V4.2 model for a heavy rainfall case that occurred in Henan, China during 20–22 July 2021. The results show that the model perturbation schemes can provide forecast uncertainties for this heavy rainfall case. The stochastic physical perturbation method could improve the heavy rainfall forecast skill by approximately 5%, and EXP3 had better performance than EXP1 or EXP2. The spread-to-root mean square error ratios (spread/RMSE) of EXP3 were closer to 1 compared with those of the EXP1 and EXP2; particularly for the meridional wind above 10 m, the spread/RMSE was 0.94 for EXP3 and approximately 0.85 for EXP1 and EXP2. EXP3 exhibited better performance in Brier score verification. EXP3 had a 5% lower Brier score than EXP1 and EXP2, when the rainfall threshold was 25 mm. The growth of the initial ensemble variances of different model perturbation schemes were explored, and the results show that the perturbation energy of EXP3 developed faster, with a magnitude of 27.22 J/kg, whereas those of EXP1 and EXP2 were only 19.18 J/kg and 20.81 J/kg, respectively. The weak initial perturbation associated with the wind shear north of the heavy rainfall location can be easily developed by EXP3.

Probability forecasts of ice accretion on wind turbines derived from multiphysics and neighbourhood ensembles

AbstractThis study explores the potential of a multiphysics regional ensemble prediction system to improve forecasts of wind turbine icing, examining several error‐representation schemes to capture the forecasting uncertainties of the icing process. An 11‐member multiphysics ensemble based on the Weather Research and Forecasting (WRF) model is run for two winter periods over Europe. Regional verification of surface variables shows that parametrization diversity makes the multiphysics ensemble less underdispersive compared with the European Centre for Medium‐Range Weather Forecasts (ECMWF) global ensemble, without deteriorating the overall forecast accuracy significantly, in particular at forecast ranges below 36 hr. Probability forecasts of active ice growth in the day‐ahead time range (12–36 hr) are derived for two wind farms on hilly terrain in Central Europe and their skill is assessed in terms of relative operating characteristics, reliability, and potential economic value (PEV). Probability forecasts enhance the maximum PEV significantly, but the improvement of the multiphysics ensemble seems modest compared with a simple neighbourhood ensemble approach. Icing forecasts are affected by a considerable degree of overconfidence, meaning that forecast probabilities cannot be used at face value, but require calibration for users to draw benefit from them. The multiphysics ensemble fares slightly better in this regard; however, results point to persistent ensemble underdispersiveness and yet underrepresented forecast uncertainty. Overall, findings show that a large portion of the gain in skill through the use of probabilistic icing forecasts is obtained with a computationally cheap neighbourhood method, a technique easily accessible to forecast users without complex ensemble prediction systems.

A new global ocean ensemble system at the Met Office: Assessing the impact of hybrid data assimilation and inflation settings

AbstractWe have developed a global ocean and sea‐ice ensemble forecasting system based on the operational forecasting ocean assimilation model (FOAM) system run at the Met Office. The ocean model Nucleus for European Modelling of the Ocean (NEMO) and the community ice code (CICE) sea‐ice model are run at 1/4 resolution and the system assimilates data using a three‐dimensional variational assimilation (3DVar) version of NEMOVAR. This data assimilation (DA) system can perform hybrid ensemble/variational assimilation. A 36‐member ensemble of hybrid ensemble variational assimilation systems with perturbed observations (values and locations) has been set up, with each member forced at the surface by a separate member of the Met Office Global–Regional Ensemble Prediction System (MOGREPS‐G). The unperturbed member is forced by atmospheric fields from the Met Office operational numerical weather prediction (NWP) deterministic system. The system includes stochastic model perturbations and a relaxation to prior spread (RTPS) inflation scheme. A control run of the system using an ensemble of 3DVars is shown to be generally reliable for Sea‐Level Anomaly (SLA), temperature, and salinity (the ensemble spread being a good representation of the uncertainty in the ensemble mean), although the ensemble is underspread in eddying regions. The ensemble mean gives a 4% reduction in error in SLA compared with the deterministic 3DVar system currently used operationally. The system was tested with different weights for the ensemble component of the hybrid background‐error covariance matrix and different inflation factors. The best results, in terms of short‐range forecast error and ensemble reliability statistics, were obtained with hybrid three‐dimensional ensemble variational DA (3DEnVar). The RTPS inflation scheme is shown to be beneficial in producing an appropriate ensemble spread in response to hybrid DA. 3DEnVar with an ensemble hybrid weight of 0.8 leads to a reduction of 20% (5%) in the ensemble mean error for SLA (profile temperature and salinity) compared with an ensemble of standard 3DVars.

Forecasting of tropical cyclone using global and regional ensemble prediction systems of NCMRWF : A review

NCMRWF global ensemble prediction system (NEPS-G) is being used for operational forecasting of tropical cyclones (TC) since the year 2016. The NEPS-G was upgraded and its horizontal resolution was increased from 33 km to 12 km in the year 2018. The upgraded NEPS-G has shown improved performance in forecasting tropical cyclones over the North Indian Ocean (NIO). The average 120 hours track forecast errors of NEPS-G for six TCs of 2019 and four TCs of 2020 are about 250 km and 220 km, respectively. The regional ensemble prediction system of NCMWRF (NEPS-R) has 4 km horizontal resolution. Average track forecast error of NEPS-R at 72 hours forecast lead time is less than that of NEPS-G. The strike probability forecast of NEPS-G shows good reliability and the area under the relative operating characteristic curve is greater than 0.5 for four TCs of 2020. The forecasting of peak TC intensity is better in NEPS-R. It could also predict the rapid intensifications of the TCs ‘Phani’ and ‘Amphan’. NEPS-G was unable to predict rapid intensifications. The prediction of time of peak intensity is still an issue with the models. Both NEPS-G and NEPS-R overestimate peak intensity of TC ‘Nisarga’. The overestimation is more in NEPS-R forecast.

Design and Evaluation of Calibrated and Seamless Ensemble Weather Forecasts for Crop Protection Applications

AbstractAgriculture is a highly weather-dependent activity, and climatic conditions impact both directly crop growth and indirectly diseases and pest developments causing yield losses. Weather forecasts are now a major component of various decision-support systems that assist farmers to optimize the positioning of crop protection treatments. However, properly accounting for weather uncertainty in these systems still remains a challenge. In this paper, three global and regional ensemble prediction systems (EPSs), covering different spatiotemporal scales, are coupled to a temperature-driven developmental model for grapevine moths in order to provide probabilistic forecasts of treatment dates. It is first shown that a parametric postprocessing of the EPSs significantly improves the prediction of treatment dates. Anticipating the need for phytosanitary treatments also requires seamless weather forecasts from the next hour to subseasonal time scales. An approach is presented to design seamless ensemble forecasts from the combination of the three EPSs used. The proposed method is able to leverage the increased performance of high-resolution EPS at short ranges, while ensuring a smooth transition toward larger-scale EPSs for longer ranges. The added value of this seamless integration on agronomic predictions is, however, difficult to assess with the current experimental setup. Additional simulations over a larger number of locations and years may be required.

Recent upgrades to the Met Office convective‐scale ensemble: An hourly time‐lagged 5‐day ensemble

AbstractIn this article, we introduce a new configuration of the Met Office convective‐scale ensemble for numerical weather prediction, for the Met Office Global and Regional Ensemble Prediction System over the United Kingdom (MOGREPS‐UK). The new version, which became operational in March 2019, uses an hourly time‐lagged configuration to take advantage of the hourly 4D‐Var data assimilation run in the deterministic UK model with variable horizontal resolution, the UKV. An 18‐member ensemble is created by running three members every hour and time‐lagging these over a 6 hr window. This configuration is compared against the previous operational configuration, a 6‐hourly convective‐scale ensemble running 12 members. The main benefits of the time‐lagged ensemble are to increase the ensemble size, to add small‐scale uncertainties in the initial conditions and to generate more timely forecasts. The time‐lagged configuration is shown to objectively improve the forecast at all lead times, with larger improvements in the first few hours. The improvement is seen in the ranked probability scores and is mainly associated with the improvements in the spread of the ensemble with an increase of about 5 to 10% in both summer and winter seasons. A larger ensemble size is necessary in the time‐lagged configuration for it to outperform or maintain as good a performance against the previous 6‐hourly configuration for all lead times. Alongside the update to an hourly configuration, the forecast length is more than doubled to 120 hr. Objective verification shows that the time‐lagged configuration performs better than the high‐resolution deterministic, UKV, and the global ensemble, MOGREPS‐G, up to T + 120 hr. Increasing the size of the time‐lagged ensemble through lagging over additional cycles leads to small but significant improvements, larger in most cases than those that can be obtained through neighbourhood processing.

Assessment of the Forecast Skill of Multiphysics and Multistochastic Methods within the GRAPES Regional Ensemble Prediction System in the East Asian Monsoon Region

Abstract To more comprehensively and accurately address model uncertainties in the East Asia monsoon region, a single-physics suite, where each ensemble member uses the same set of physics parameterizations as the control member in combination with multiple stochastic schemes, is developed to investigate if the multistochastic schemes that combine different stochastic schemes together can be an alternative to a multiphysics suite, where each ensemble member uses a different set of physics parameterizations (e.g., cumulus convection, boundary layer, surface layer, microphysics, and shortwave and longwave radiation). For this purpose, two experiments are performed for a summer monsoon month over China: one with a multiphysics suite and the other with a single-physics suite combined with multistochastic schemes. Three stochastic schemes are applied: the stochastically perturbed parameterizations (SPP) scheme, consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics, convection, boundary layer, and surface layer parameterization schemes; the stochastically perturbed parameterization tendencies (SPPT) scheme; and the stochastic kinetic energy backscatter (SKEB) scheme. The combination of the three stochastic schemes is compared with the multiphysics suite in the Global and Regional Assimilation and Prediction Enhanced System–Regional Ensemble Prediction System with a horizontal grid spacing of 15 km. Verification results show that, overall, a single-physics suite that combines SPP, SPPT, and SKEB outperforms the multiphysics suite in precipitation verification and verification for upper-air weather variables, 10-m zonal wind, and 2-m temperature in the East Asian monsoon region. The indication is that a single-physics suite combining SPP, SPPT, and SKEB may be an appropriate alternative to a multiphysics suite. This finding lays a foundation for the development and design of future regional and global ensembles.