Global Ensemble Prediction System Research Articles

A comparison of two statistical postprocessing methods for heavy‐precipitation forecasts over India during the summer monsoon

AbstractAccurate ensemble forecasts of heavy precipitation in India are vital for many applications and essential for early warning of damaging flood events, especially during the monsoon season. In this study we investigate to what extent Quantile Mapping (QM) and Ensemble Model Output Statistics (EMOS) statistical postprocessing reduce errors in precipitation ensemble forecasts over India, in particular for heavy precipitation. Both methods are applied to day‐1 forecasts at 12‐km resolution from the 23‐member National Centre for Medium Range Weather Forecasting (NCMRWF) global ensemble prediction system (NEPS‐G). By construction, QM leads to distributions close to the observed ones, while EMOS optimizes the ensemble spread, and it is not a priori clear which is better suited for practical applications. The methods are therefore compared with respect to several key aspects of the forecasts: local distributions, ensemble spread, and skill for forecasting precipitation amounts and the exceedance of heavy‐precipitation thresholds. The evaluation includes rank histograms, Continuous Ranked Probability Skill Scores (CRPSS), Brier Skill Scores (BSS), reliability diagrams, and receiver operating characteristic. EMOS performs best not only with respect to correcting under‐ or overdispersive ensembles, but also in terms of forecast skill for precipitation amounts and heavy precipitation events, with positive CRPSS and BSS in most regions (both up to about 0.4 in some areas), while QM in many regions performs worse than the raw forecast. QM performs best with respect to the overall local precipitation distributions. Which aspects of the forecasts are most relevant depends to some extent on how the forecasts are used. If the main criteria are the correction of under‐ or overdispersion, forecast reliability, match between the forecasted distribution for individual days and observations (CRPSS), and the skill in forecasting heavy‐precipitation events (BSS), then EMOS is the better choice for postprocessing NEPS‐G forecasts for short lead times.

Relative economic value of global ensemble prediction system of NCMRWF, India, for extreme weather events

AbstractAn ensemble prediction system quantifies the uncertainty in the forecast by a numerical weather prediction model and helps in decision‐making. The global ensemble prediction system (NEPS‐G) of NCMRWF, India, has 1 control and 22 perturbed ensemble members. In the present study, the relative economic value of NEPS‐G forecast has been investigated based on a simple decision‐analytic model. Temperature at 850 hPa (T850), maximum surface temperature (Tmax) and precipitation (rf) over the Indian domain have been studied. Ensemble mean forecast offers a larger value to the users than the control forecast. For rare extreme events, the area under the curve (T‐AUC) of a relative operating characteristic (ROC) obtained using the trapezoidal approximation for 22‐member NEPS‐G (NEPS‐22) is significantly smaller than the area under the curve (Z‐AUC) of the full ROC obtained using binormal model and Z‐transformation. As a result, the discrimination ability and relative economic value of NEPS‐G are underestimated. A method (SUB‐EM) which subdivides the lowest probability category into several subcategories and uses ensemble mean forecast as the secondary decision variable to enhance T‐AUC and improve relative economic value of NEPS‐G has been investigated. Tmax > 45°C and rf > 65 mm·day−1 have been considered as extreme events. The impact of increasing the number of perturbed ensemble members from 22 to 33 (NEPS‐33) by running additional 11 perturbed members from 0000 UTC initial condition has also been investigated and compared with the results obtained using SUB‐EM. SUB‐EM offers larger economic value to the users with small cost–loss ratio (α) without engaging any extra computational resources. An experiment is also carried out to study the impact of using different ensemble summary statistics as a secondary decision variable. Ensemble Maximum provides maximum value and benefits the largest range of users.

Comparison of different global ensemble prediction systems for tropical cyclone intensity forecasting

AbstractMany meteorological centers have operationally implemented global model‐based ensemble prediction systems (GEPSs), making tropical cyclone (TC) forecasts from these systems available. The relatively low resolution of these GEPSs means that limits previous studies primarily focused on TC track forecasting. However, recent GEPS upgrades mean that TC intensity predictions from GEPSs are now also becoming of interest. This study focuses on the verification and comparison of the latest generation of GEPSs for TC intensity forecasts, particularly during the rapid intensification (RI) period over the western North Pacific (WP), eastern North Pacific (EP), and North Atlantic (NA) basins in 2021–2022. On average, the National Centers for Environmental Prediction (NCEP) GEPS performed best in predicting both TC intensity and RI across all three basins. Nevertheless, the exact timing of RI remains highly uncertain for these GEPS, indicating significant limitations in using GEPSs to forecast RI.

Development of flood forecasting and warning system using hybrid approach of ensemble and hydrological model for Dharoi Dam

Abstract The most frequent natural disaster is flooding. Advanced forecasting systems are lacking in developing countries. The majority of urban areas are located close to flood plains for rivers. Accurate flood forecasting is necessary for reservoir planning and flood management. The Sabarmati River's atmospheric-hydrologic ensemble flood forecasting model has been developed using TIGGE data. Precipitation can be reliably predicted by TIGGE's global ensemble numerical weather prediction (NWP) systems. By using NWP data, flood forecasting systems may be extended from hours to days. Ensemble weather forecasts are produced using the European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction together with 5-day lead times from TIGGE. The flood occurrences from 2015, 2017, and 2020 were used for the calibration and validation of the ensemble flood forecasting model. Bias was corrected using Bayesian model averaging (BMA), heterogeneous extended linear regression, censored non-homogeneous linear regression (cNLR), and other statistical downscaling techniques. Forecasted and downscaled precipitation data were checked using the Brier score and rank likelihood score. For cNLR, Brier's score performed admirably. The specificity vs. sensitivity performance of the cNLR and BMA approaches is 91.87 and 91.82%, respectively, according to receiver operating characteristic and area under the curve diagrams. Models with the hybrid hydrologic coupling approach accurately predict floods. Users may predict peak time and peak discharge hazard likelihood with reliability using peak time and flood warning probability distributions.

Stepwise correction of ECMWF ensemble forecasts of severe rainfall in China based on segmented hierarchical clustering

Ensemble forecast plays a vital role in numerical weather prediction. Hence, effectively extracting useful information from ensemble members to improve precipitation forecasting skills has always been an important issue. Using the ensemble forecast data on precipitation from the ECMWF-GEPS (Global Ensemble Prediction System), we propose a stepwise correction method, based on segmented hierarchical clustering (SHC), for forecast of daily precipitation. This method employs a segmented correction scheme, thereby generating more probabilistic forecast information and improving forecasts. Validations of the SHC method have been performed by comparison with two other methods, namely the ensemble-mean (EM) method and the direct hierarchical clustering (HC) method. Our results showed that deterministic forecast via SHC improved the ability to forecast heavy precipitation in short- and medium-range forecast timeframes. Therefore, SHC performed better than either EM or HC by effectively extending lead time to impending severe rainfall by 2–3 days relative to the other two methods. SHC also demonstrated better performance than the other methods through continuous forecast verification in summer 2021, and even had better effects in the forecast of multiple heavy-precipitation cases, including the Zhengzhou extreme rainfall on 20 July 2021. Overall, the SHC method has great potential for improving ensemble rainfall forecasts in the current operational system.

The Forecast Skill of Tropical Cyclone Genesis in Two Global Ensembles

Abstract Tropical cyclone (TC) genesis forecasts during 2018–20 from two operational global ensemble prediction systems (EPSs) are evaluated over three basins in this study. The two ensembles are from the European Centre for Medium-Range Weather Forecasts (ECMWF-EPS) and the MetOffice in the United Kingdom (UKMO-EPS). The three basins include the northwest Pacific, northeast Pacific, and the North Atlantic. It is found that the ensemble members in each EPS show a good level of agreement in forecast skill, but their forecasts are complementary. Probability of detection (POD) can be doubled by taking all the member forecasts in the EPS into account. Even if an ensemble member does not make a hit forecast, it may predict the presence of cyclonic vortices. Statistically, a hit forecast has more nearby disturbance forecasts in the ensemble than a false alarm. Based on the above analysis, we grouped the nearby forecasts at each model initialization time to define ensemble genesis forecasts, and verified these forecasts to represent the performance of the ensemble system. The PODs are found to be more than twice that of the individual ensemble members at most lead times, which is about 59% and 38% at the 5-day lead time in UKMO-EPS and ECMWF-EPS, respectively; while the success ratios are smaller compared with that of the ensemble members. In addition, predictability differs in different basins, and genesis events in the North Atlantic basin are the most difficult to forecast in EPS, and its POD at the 5-day lead time is only 46% and 23% in UKMO-EPS and ECMWF-EPS, respectively. Significance Statement Operational forecasting of tropical cyclone (TC) genesis relies greatly on numerical models. Compared with deterministic forecasts, ensemble prediction systems (EPSs) can provide uncertainty information for forecasters. This study examined the predictability of TC genesis in two operational EPSs. We found that the forecasts of ensemble members complement each other, and the detection ratio of observed genesis will be doubled by considering the forecasts of all members, as multiple simulations conducted by the EPS partially reflect the inherent uncertainties of the genesis process. Successful forecasts are surrounded by more cyclonic vortices in the ensemble than false alarms, so the vortex information is used to group the nearby forecasts at each model initialization to define ensemble genesis forecasts when evaluating the ensemble performance. The results demonstrate that the global ensemble models can serve as a valuable reference for TC genesis forecasting.

Stochastic perturbation of tendencies and parameters of parameterizations in the global ensemble prediction system based on the SL-AV model

AbstractAlgorithms for stochastic perturbation of parameters and tendencies of physical parameterizations for subgrid-scale processes are implemented into the ensemble prediction system. This system is based on the global semi-Lagrangian atmospheric model SL-AV with the resolution of 0.9 × 0.72 degrees in longitude and latitude, respectively, 96 vertical levels, and our implementation of the Local Ensemble Tranform Kalman Filter (LETKF). The use of stochastically perturbed parameterizations allows to generate ensembles with a significantly larger spread compared to one obtained with the method of static parameter perturbation. An improvement in the probabilistic estimates of the ensemble forecast for different seasons is shown.

Using Stochastically Perturbed Parameterizations to Represent Model Uncertainty. Part II: Comparison with Existing Techniques in an Operational Ensemble

Abstract The ability of a stochastically perturbed parameterization (SPP) approach to represent uncertainties in the model component of the Canadian Global Ensemble Prediction System was demonstrated in Part I of this investigation. The goal of this second step in SPP evaluation is to determine whether the scheme represents a viable alternative to the current operational combination of a multiphysics configuration and stochastically perturbed parameterization tendencies (SPPT). An assessment of the impact of each model uncertainty estimate in isolation reveals that, although the multiphysics configuration is highly effective at generating ensemble spread, it is often the result of differing biases rather than a reflection of flow-dependent error growth. Moreover, some of the members of the multiphysics ensemble suffer from large errors on regional scales as a result of suboptimal configurations. The SPP scheme generates a greater diversity of member solutions than the SPPT scheme in isolation, and it has an impact on forecast performance that is similar to that of current operational uncertainty estimates. When the SPP framework is combined with recent upgrades to the model physics suite that are only applicable in the stochastic perturbation context, the quality of global ensemble guidance is significantly improved. Significance Statement The stochastically perturbed parameterization (SPP) technique was introduced in Part I to represent model uncertainties in forecasts generated by an operational global ensemble prediction system. We focus here on the viability of this technique as a replacement for the system’s current uncertainty estimates: multiphysics and stochastic perturbations of physics tendencies. Despite the practical success of this combination, it suffers from physical inconsistencies and poor conservation properties. The adoption of SPP allows the ensemble to benefit from a recent set of model updates that couple with this new representation of model uncertainty to yield significant improvements in the quality of forecasts generated by the system.

Forecast Dropouts in the NAVGEM Model: Characterization with Respect to Other Models, Large-Scale Indices, and Ensemble Forecasts

Abstract A forecast “bust” or “dropout” can be defined as an intermittent but significant loss of model forecast performance. Deterministic forecast dropouts are typically defined in terms of the 500-hPa geopotential height (Φ500) anomaly correlation coefficient (ACC) in the Northern Hemisphere (NH) dropping below a predefined threshold. This study first presents a multimodel comparison of dropouts in the Navy Global Environmental Model (NAVGEM) deterministic forecast with the ensemble control members from the Environment and Climate Change Canada (ECCC) Global Ensemble Prediction System (GEPS) and the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS). Then, the relationship between dropouts and large-scale pattern variability is investigated, focusing on the temporal variability and correlation of flow indices surrounding dropout events. Finally, three severe dropout events are examined from an ensemble perspective. The main findings of this work are the following: 1) forecast dropouts exhibit some relation between models; 2) although forecast dropouts do not have a single cause, the most severe dropouts in NAVGEM can be linked to specific behavior of the large-scale flow indices, that is, they tend to follow periods of rapidly escalating volatility of the flow indices, and they tend to occur during intervals where the AO and Pacific North American (PNA) indices are exhibiting unusually strong interdependence; and 3) for the dropout events examined from an ensemble perspective, the NAVGEM ensemble spread does not provide a strong signal of elevated potential for very large forecast errors.

Using Stochastically Perturbed Parameterizations to Represent Model Uncertainty. Part I: Implementation and Parameter Sensitivity

Abstract Accurately representing model-based sources of uncertainty is essential for the development of reliable ensemble prediction systems for NWP applications. Uncertainties in discretizations, algorithmic approximations, and diabatic and unresolved processes combine to influence forecast skill in a flow-dependent way. An emerging approach designed to provide a process-level representation of these potential error sources, stochastically perturbed parameterizations (SPP), is introduced into the Canadian operational Global Ensemble Prediction System. This implementation extends the SPP technique beyond its typical application to free parameters in the physics suite by sampling uncertainty both within the dynamical core and at the formulation level using “error models” when multiple physical closures are available. Because SPP perturbs components within the model, internal consistency is ensured and conservation properties are not affected. The full SPP scheme is shown to increase ensemble spread to keep pace with error growth on a global scale. The sensitivity of the ensemble to each independently perturbed “element” is then assessed, with those responsible for the bulk of the response analyzed in more detail. Perturbations to surface exchange coefficients and the turbulent mixing length have a leading impact on near-surface statistics. Aloft, a tropically focused error model representing uncertainty in the advection scheme is found to initiate growing perturbations on the subtropical jet that lead to forecast improvements at higher latitudes. The results of Part I suggest that SPP has the potential to serve as a reliable representation of model uncertainty for ensemble NWP applications. Significance Statement Ensemble systems account for the negative impact that uncertainties in prediction models have on forecasts. Here, uncertain model parameters and algorithms are subjected to perturbations representing impact on forecast errors. By initiating error growth within the model calculations, the equally skillful members of the ensemble remain physically realistic and self-consistent, which is not guaranteed by other depictions of model error. This “stochastically perturbed parameterization” technique (SPP) comprises many small error sources, each analyzed in isolation. Each source is related to a limited set of processes, making it possible to determine how the individual perturbations affect the forecast. We conclude that SPP in the Canadian Global Ensemble Forecasting System produces realistic estimates of the impact of model uncertainties on forecast skill.

Randomized Subensembles: An Approach to Reduce the Risk of Divergence in an Ensemble Kalman Filter Using Cross Validation

Abstract In an ensemble Kalman filter, when the analysis update of an ensemble member is computed using error statistics estimated from an ensemble that includes the background of the member being updated, the spread of the resulting ensemble systematically underestimates the uncertainty of the ensemble mean analysis. This problem can largely be avoided by applying cross validation: using an independent subset of ensemble members for updating each member. However, in some circumstances cross validation can lead to the divergence of one or more ensemble members from observations. This can culminate in catastrophic filter divergence in which the analyzed or forecast states become unrealistic in the diverging members. So far, such instabilities have been reported only in the context of highly nonlinear low-dimensional models. The first known manifestation of catastrophic filter divergence caused by the use of cross validation in an NWP context is reported here. To reduce the risk of such filter divergence, a modification to the traditional cross-validation approach is proposed. Instead of always assigning the ensemble members to the same subensembles, the members forming each subensemble are randomly chosen at every analysis step. It is shown that this new approach can prevent filter divergence and also brings a cycling ensemble data assimilation system containing divergent members back to a state consistent with Gaussianity. The randomized subensemble approach was implemented in the operational global ensemble prediction system at Environment and Climate Change Canada on 1 December 2021.

Stochastically Perturbed Parameterizations for the Process-Level Representation of Model Uncertainties in the CMA Global Ensemble Prediction System

Stochastically Perturbed Parameterizations for the Process-Level Representation of Model Uncertainties in the CMA Global Ensemble Prediction System

Contribution of lagged members to the performance of a global ensemble prediction system

National Centre for Medium Range Weather Forecasting (NCMRWF), India runs operationally a global ensemble prediction system (NEPS-G) which has 23 ensemble members. The control and 11 perturbed ensemble members have the initial conditions of 00 UTC. The start time of the other 11 perturbed members is 12 UTC of the previous day. The objective of the present paper is to study the effect of the lagged ensemble members on the forecast quality of NEPS-G. For this, we have carried out an inter-comparison of the relative skills of (i) 11-member ensemble starting from 00 UTC i.e., E00_11, (ii) 11 lagged ensemble members starting from 12 UTC i.e., E12_11 and the (iii) operational 23-member ensemble i.e. E23 using the verification of temperature, wind and geopotential height forecasts for 7-day forecast lead times for April–May-June 2019 (AMJ) of summer and December 2019–January 2020 (DJ) of winter. The temperature at 850 hPa and geopotential height at 500 hPa are verified over the northern hemisphere. The zonal and meridional winds at 850 hPa and 200 hPa are verified over the region 0oN-40oN, 60°E-110°E. The metrics used for validation are ensemble-spread and root-mean-square error (RMSE) relationship, brier skill score (BSS), reliability diagram, outliers statistics, relative operating characteristics (ROC) score and ranked probability score (RPS). For brevity, only the results for Z500, T850 and U850 have been discussed in detail here.The results show that in both seasons, the RMSE-spread relationship is better for E23 than for E00_11 and E12_11. For T850 and Z500 over NH, both E23 and E00_11 have higher RMSE and higher ensemble spread in DJ season as compared to AMJ season. In case of wind over the Indian region, both RMSE and ensemble spread exhibit lower values during DJ as compared to AMJ for both ensemble configurations. For both seasons, the BSS of E23 is positive and the ROC score is >0.5 beyond 7 days forecast times for all the variables for all three ensembles and the percentage of outliers in E23 is also less. Overall, the contribution of 11 lagged ensemble members improves the skill of NEPS-G in both seasons and skill is better during winter (DJ season). The error in the mean forecast track for tropical cyclone ‘Fani’ for E23 is found to be less than for E00_11.We have also compared the forecast skill of E23 with that of the ensemble formed by 22 members, all running from 00 UTC i.e. E00_22 for the same set of variables as well as precipitation for one month forecast data from each season, namely June 2019 from summer and January 2020 from winter. Results show that the RMSE-spread relationship is very much similar for both configurations and both seasons. For E00_22 and E23, the ensemble spread is closer to the RMSE of ensemble mean for all the variables in January compared to June. BSS was slightly higher for E00_22 but was not significant at all lead time for all the variables except Z500 in January which shows significant improvement in BSS of E00_22 from day 2 to day 5. In precipitation forecast verification, RPS for E00_22 is slightly less (though significant) than E23 at all forecast lead times in June while in January there was no significant difference. So, E23 dataset of NEPS-G which demands less computational resources at the same time offers more skilful forecast than E00_11 over a longer forecast lead time and is nearly as skilful as E00_22.

Development of Moist Singular Vectors in GRAPES-GEPS and a Preliminary Evaluation

ABSTRACT In this study, moist singular vector (MSV) was developed based on GRAPES-GEPS (Global/Regional Assimilation and Prediction System – Global Ensemble Prediction System), the adjoint model of large-scale condensation and cumulus deep convection in GRAPES-4DVar (Four-dimensional variational assimilation). Five consecutive days of numerical experiments were performed for a preliminary evaluation of MSV. The singular values, horizontal distribution structure, spread of MSVs perturbation and its influence on the ensemble prediction were compared for each group of tests. The results showed that in the middle and high latitudes of the northern and southern hemispheres, the addition of both linearized moist physical processes increased the spread of the mid- and low-level SVs, but the linearized large-scale condensation (LC) process plays a leading role in the structure of MSV. The analysis of ensemble forecast shows the inclusion of moist linearized physical processes led to a greater effect of MSV on the rainfall levels of 10 and 25 mm and a slight improvement in anomaly correlation coefficient (ACC) of the atmospheric circulation field, and more obvious improvement due to linearized large-scale condensation. In the future, continuous multi-year testing and tropical-specific analyses are required for operation.

Calibration of subseasonal sea‐ice forecasts using ensemble model output statistics and observational uncertainty

AbstractIn response to a growing demand for improved sea‐ice forecast guidance at shorter timescales and higher spatial resolutions, this study investigates the predictive skill of daily subseasonal sea‐ice forecasts from two state‐of‐the‐art prediction systems: SEAS5 from the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the Global Ensemble Prediction System (GEPS) of Environment and Climate Change Canada (ECCC). Based on hindcast records from 1998–2017, we find that probabilistic forecasts of sea‐ice concentration (SIC) throughout the marginal ice zone of the Canadian Arctic in June and November are no more skillful than simple benchmark forecasts based on climatology and damped persistence. At short lead times, the lack of skill arises from overconfident ensemble spread and errors in forecast initial conditions. At longer lead times, the development of model drift also plays a role. To improve the forecasts, we develop the nonhomogeneous censored Gaussian regression for SIC (NCGR‐sic) calibration procedure, which uses the forecast ensemble mean and ensemble standard deviation (both locally and from neighboring locations) as predictors in an ensemble model output statistics framework. Importantly, NCGR‐sic incorporates observational uncertainty directly during model parameter fitting, leading to enhanced calibration. NCGR‐sic improves the spatial probability score by , on average, half of which we infer results from the removal of climatological bias and half from the improvement of forecast uncertainty. Calibrated forecasts from SEAS5 (GEPS) exceed the skill of climatology throughout the marginal ice zone over at least the first 33 (26) days in June and the first 32 (15) days in November. Generally, better skill occurs outside the Canadian Archipelago and for low‐ to mid‐range SIC compared with high SIC. While calibrated forecasts also outperform damped persistence after the first 3–10 days, we postulate that initial condition inconsistencies must be resolved in order to obtain skill at shorter timescales. Finally, the robustness of NCGR‐sic is demonstrated by effectively improving an operational forecast from June 2020.

Understanding sources of Northern Hemisphere uncertainty and forecast error in a medium‐range coupled ensemble sea‐ice prediction system

AbstractThe Global Ensemble Prediction System (GEPS) of Environment and Climate Change Canada was recently upgraded to a coupled atmosphere, ocean, and sea‐ice version from an uncoupled atmosphere‐only system. This has been operational since July 2019, with over a year of forecasts now available to evaluate the system throughout all seasons. Using metrics that score the forecast error in ice‐edge position, the spatial probability score and the integrated ice‐edge error, we investigate the spread–error relationship in probabilistic Arctic sea‐ice forecasts from the system and compare this with the skill of the system relative to persistence and a companion Global Deterministic Prediction System (GDPS). Within this ensemble framework, we explore the advantages of having a probabilistic forecast and probe its usefulness in addressing the errors in the system. Both the ensemble GEPS and the deterministic GDPS systems show enhanced sea‐ice prediction over persistence in all months except May and June, when significant biases exist in the systems in shallow‐sea and shelf regions. We attribute a significant portion of these biases to problems modelling landfast ice, but other sources of bias, including significant uncertainties in initializing and verifying sea‐ice analysis, also contribute. The lowest errors in the systems are found during September and continue at reasonably low levels through much of the boreal winter. The minimum and maximum extent periods, along with the early freeze‐up period, are shown to be periods for which the ensemble system offers enhanced benefits over a single deterministic forecast. For these periods, the errors are low and strongly correlated spatially with the ensemble spread. Nevertheless, we find that the ensemble system would likely still benefit from further improvement of the spread/error relationship in the system, currently hampered due to ensemble perturbations that are produced solely in the atmospheric component.

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.

Global Ensemble Forecast System (GEFS T1534) evaluation for tropical cyclone prediction over the North Indian Ocean

The high-resolution global ensemble prediction system was implemented at IITM (Indian Institute of Tropical Meteorology) in June 2018 and handed over to India Meteorological Department (IMD) for operational running. During the Tropical Cyclone (TC) season over the North Indian Ocean, real time forecasts of ensemble tracks, intensity and strike probability of TCs are provided to IMD. This paper evaluates the skill of the model in predicting TCs during 2018, 2019 and two cases in 2020. A total of 13 cases are considered and various skill scores are calculated for the TC predictions. This study evaluates and highlights the importance of the Ensemble Prediction System for Tropical Cyclone forecasting. The key finding from this analysis is that, the higher skill of ensemble prediction system based on GEFS at longer lead time compared with the deterministic prediction. This is particularly beneficial for operational forecaster for issuing early warnings.

Evaluation of Tropical Cyclone Intensity Forecasts from Five Global EnsemblePrediction Systems During 2015-2019

This study presented an evaluation of tropical cyclone (TC) intensity forecasts from five global ensemble prediction systems (EPSs) during 2015-2019 in the western North Pacific region. Notable error features include the underestimation of the TC intensity by ensemble mean forecast and the under-dispersion of the probability forecasts.The root mean square errors (brier scores) of the ensemble mean(probability forecasts) generally decrease consecutively at long lead times during the five years, but fluctuate between certain values at short lead times.Positive forecast skill appeared in the most recent two years (2018-2019) at 120 h or later as compared with the climatology forecasts. However, there is no obvious improvement for the intensity change forecasts during the 5-year period, with abrupt intensity change remaining a big challenge. The probability forecasts show no skill for strong TCs at all the lead times. Among the five EPSs, ECMWF-EPS ranks the best for the intensity forecast, while NCEPGEFS ranks the best for the intensity change forecast, according to the evaluation of ensemble mean and dispersion.As for the other probability forecast evaluation, ECMWF-EPS ranks the best at lead times shorter than 72 h, while NCEP-GEFS ranks the best later on.

Incorporating the North Atlantic Oscillation into the post‐processing of MOGREPS‐G wind speed forecasts

AbstractChanges in the North Atlantic Oscillation (NAO) heavily influence the weather across the UK and the rest of Europe. Due to an incorrect representation of the polar jet stream and its associated physical processes, it is reasonable to believe that errors in numerical weather prediction models may also depend on the prevailing behaviour of the NAO. To address this, information regarding the NAO is incorporated into statistical post‐processing methods through a regime‐dependent mixture model, which is then applied to wind speed forecasts from the Met Office's global ensemble prediction system, MOGREPS‐G. The mixture model offers substantial improvements upon conventional post‐processing methods when the local wind speed depends strongly on the NAO, but the additional complexity of the model can hinder forecast performance otherwise. A measure of regime dependency is thus defined that can be used to differentiate between situations when the numerical model output is, and is not, expected to benefit from regime‐dependent post‐processing. Implementing the regime‐dependent mixture model only when this measure exceeds a certain threshold is found to further improve predictive performance, while also producing more accurate forecasts of extreme wind speeds.