ECMWF Ensemble Prediction System Research Articles

Event attribution of a midlatitude windstorm using ensemble weather forecasts

The widespread destruction incurred by midlatitude storms every year makes it an imperative to study how storms change with climate. The impact of climate change on midlatitude windstorms, however, is hard to evaluate due to the small signals in variables such as wind speed, as well as the high resolutions required to represent the dynamic processes in the storms. Here, we assess how storm Eunice, which hit the UK in February 2022, was impacted by anthropogenic climate change using the ECMWF ensemble prediction system. This system was demonstrably able to predict the storm, significantly increasing our confidence in its ability to model the key physical processes and their response to climate change. Using modified greenhouse gas concentrations and changed initial conditions for ocean temperatures, we create two counterfactual scenarios of storm Eunice in addition to the forecast for the current climate. We compare the intensity and severity of the storm between the pre-industrial, current, and future climates. Our results robustly indicate that Eunice has become more intense with climate change and similar storms will continue to intensify with further anthropogenic forcing. These results are consistent across forecast lead times, increasing our confidence in them. Analysis of storm composites shows that this process is caused by increased vorticity production through increased humidity in the warm conveyor belt of the storm. This is consistent with previous studies on extreme windstorms. Our approach of combining forecasts at different lead times for event attribution enables combining event specificity and a focus on dynamic changes with the assessment of changing risks from windstorms. Further work is needed to develop methods to adjust the initial conditions of the atmosphere for the use in attribution studies using weather forecasts but we show that this approach is viable for reliable and fast attribution systems.

Evaluating GFS and ECMWF Ensemble Forecasts of Integrated Water Vapor Transport along the U.S. West Coast

Abstract Atmospheric rivers (ARs) are long and narrow regions in the atmosphere of enhanced integrated water vapor transport (IVT) and can produce extreme precipitation and high societal impacts. Reliable and skillful forecasts of landfalling ARs in the western United States are critical to hazard preparation and aid in decision support activities, such as Forecast-Informed Reservoir Operations (FIRO). The purpose of this study is to compare the cool-season water year skill of the NCEP Global Ensemble Forecast System (GEFS) and ECMWF Ensemble Prediction System (EPS) forecasts of IVT along the U.S. West Coast for 2017–20. The skill is analyzed using probability-over-threshold forecasts of IVT magnitudes ≥ 250 kg m−1 s−1 (P250) using contingency table skill metrics in coastal Northern California and along the west coast of North America. Analysis of P250 with lead time (dProg/dt) found that the EPS provided ∼1 day of additional lead time for situational awareness over the GEFS at lead times of 6–10 days. Forecast skill analysis highlights that the EPS leads over the GEFS with success ratios 0.10–0.15 higher at lead times > 6 days for P250 thresholds of ≥25% and ≥50%, while event-based skill analysis using the probability of detection (POD) found that both models were largely similar with minor latitudinal variations favoring higher POD for each model in different locations along the coast. The relative skill of the EPS over the GEFS is largely attributed to overforecasting by the GEFS at longer lead times and an increase in the false alarm ratio. Significance Statement The purpose of this study is to evaluate the efficacy of the NCEP Global Ensemble Forecast System (GEFS) and the ECMWF Ensemble Prediction System (EPS) in forecasting enhanced water vapor transport along the U.S. West Coast commonly associated with landfalling atmospheric rivers and heavy precipitation. The ensemble models allow us to calculate the probability that enhanced water vapor transport will occur, thereby providing situational awareness for decision-making, such as in hazard mitigation and water resource management. The results of this study indicate that the EPS model is on average more skillful than the GEFS model at lead times of ∼6–10 days with a higher success ratio and lower false alarm ratio.

Fully coupled high‐resolution medium‐range forecasts: Evaluation of the hydrometeorological impact in an ensemble framework

AbstractFully coupled atmospheric‐hydrological models allow a more realistic representation of the land surface–boundary layer continuum, representing both high‐resolution land‐surface/subsurface water lateral redistribution and the related feedback towards the atmosphere. This study evaluates the potential contribution of the fully coupled approach in medium‐range mesoscale hydrometeorological ensemble forecasts. Previous studies have shown, for deterministic simulations that the effect of fully coupling for short‐range forecasts is minor compared to other sources of uncertainty. However, it becomes not negligible when increasing the forecast period. The paper presents a proof‐of‐concept consisting of a 7‐day ensemble (50 members from the ECMWF Ensemble Prediction System) forecast of a high impact event affecting the Calabrian peninsula (southern Italy, Mediterranean basin) on November 2019. The scope is to investigate how the impact of improved representation of the terrestrial water lateral transport in the weather research and forecasting (WRF)—hydro modelling system affects the water balance, focusing on the precipitation and the hydrological response, in terms of both soil moisture dynamics and streamflow in 14 catchments spanning over 42% of the region. The fully coupled approach led to an increase of surface soil moisture and latent heat flux from land in the days preceding the event, affecting the lower Planetary Boundary Layer. However, in cases when shoreward moisture transport from surrounding sea is the dominant process, only a weak signature of soil moisture contribution could be detected, resulting in only slightly higher precipitation forecast and not clear variation trend of peak flow, even though the latter variable increased up to 10% in some catchments. Overall, this study highlighted a remarkable performance of the medium‐range ensemble forecasts, suggesting a profitable use of the fully coupled approach for forecasting purposes in circumstances in which soil moisture dynamics is more relevant and needs to be better addressed.

Machine learning in calibrating tropical cyclone intensity forecast of ECMWF EPS

AbstractIntensity prediction of tropical cyclones (TC) has been one of the major challenges for the operational forecast and warning service, as well as consequential assessment of impacts including high winds, storm surge and heavy rainfall caused by TC. With the advances in global numerical weather prediction (NWP) modelling systems, TC track and intensity forecasts for medium range are available every 6 or 12 h, and ensemble prediction system (EPS) outputs provide various scenarios for producing probabilistic forecasts. The TC intensity forecast from the EPS of the European Centre for Medium‐Range Weather Forecasts (ECMWF) has shown systematic negative biases, although the performance is better than other global models in general. A machine learning model based on XGBoost, a decision‐tree‐based machine learning algorithm, is introduced in this paper to post‐process ECMWF EPS outputs and generate an improved forecast of TC intensity. The predictors such as selected percentiles of ensemble members in maximum wind and minimum pressure of previous TC cases were applied in the XGBoost model to generate a calibrated forecast for the maximum wind of TCs. Verification of the XGBoost model was made using TCs over the western North Pacific during 2016–2019. It is found that the negative biases of the intensity forecast from ECMWF EPS and HRES can be reduced with improvement in the overall accuracy.

Evaluating the added value of multi-input atmospheric transport ensemble modeling for applications of the Comprehensive Nuclear Test-Ban Treaty organization (CTBTO)

The Comprehensive Nuclear Test-Ban Treaty Organization (CTBTO) runs to date operationally an atmospheric transport modeling chain in backward mode based on operational deterministic European Centre for Medium-Range Weather Forecasts-Integrated Forecasting System (ECMWF-IFS) and on National Centers for Environmental Prediction-Global Forecast System (NCEP-GFS) input data. Meanwhile, ensemble dispersion modeling is becoming more and more widespread due to the ever increasing computational power and storage capacities. The potential benefit of this approach for current and possible future CTBTO applications was investigated using data from the ECMWF-Ensemble Prediction System (EPS). Five different test cases - among which are the ETEX-I experiment and the Fukushima accident - were run in backward or forward mode and - in the light of a future operational application - special emphasis was put on the performance of an arbitrarily selected 10- versus the full 51-member ensemble. For those test cases run in backward mode and based on a puff release it became evident that Possible Source Regions (PSRs) can be meaningfully reduced in size compared to results based solely on the deterministic run by applying minimum and probability of exceedance ensemble metrics. It was further demonstrated that a given puff release of 4E10 Bq of Se-75 can be reproduced within the meteorological uncertainty range [1.9E9 Bq,1.7E13 Bq] including a probability for not exceeding an assumed upper limit source term using simple scaling of a measurement with the corresponding ensemble metrics of backward fields. For the test cases run in forward mode it was found that the control run as well as 10- and 51-member medians all exhibit similar performance in time series evaluation. Maximum rank difference adds up to less than 10% with reference to possible rank values [0,4]. The maximum difference in the Brier score for both ensembles is less than 3%. The main added value of the ensemble lies in producing meteorologically induced concentration uncertainties and thus explaining observed measurements at specific sites. Depending on the specific test case and on the ensemble size between 27 and 74% of samples all lie within concentration ranges derived from the different meteorological fields used. In the future uncertainty information per sample could be used in a full source term inversion to account for the meteorological uncertainty in a proper way. It can be concluded that a 10-member meteorological ensemble is good enough to already benefit from useful ensemble properties. Meteorological uncertainty to a large degree is covered by the 10-member subset because forecast uncertainty is largely suppressed due to concatenating analyses and short term forecasts, as required in the operational CTBTO procedure, on which this study focuses. Besides, members from different analyses times are on average unrelated. It was recommended to Working Group B of CTBTO to implement the ensemble system software in the near future.

The AEMET-<i>γ</i>SREPS over the Antarctic Peninsula and the impact of kilometric-resolution EPS on logistic activities on the continent

Abstract. Kilometric-resolution Ensemble Prediction Systems (EPSs) will be the new state-of-the-art forecasting tools for short-range prediction in the following decade. Their value will be even greater in Antarctica due to the increasingly demanding weather forecasts for logistic services. During the 2018–2019 austral summer (1 December–31 March), coinciding with the Southern Hemisphere Special Observation Period of the Year of Polar Prediction (YOPP), the 2.5 km AEMET-γSREPS was operationally integrated over the Antarctic Peninsula. In particular, the Antarctic version of γSREPS comes up with crossing four non-hydrostatic convection-permitting NWP models at 2.5 km with three global NWP driving models as boundary conditions. The γSREPS forecasting system has been validated in comparison with ECMWF EPS. It is concluded that γSREPS has an added value to ECMWF EPS due to both its higher resolution and its multi-boundary conditions and multi-NWP model approach. γSREPS performance has a positive impact on logistic activities at research stations and its design may contribute to polar prediction research.

Potential skill of continental-scale, medium-range ensemble streamflow forecasts for flood prediction in South America

Recent scientific and computational developments have pushed hydrological forecasting systems up to continental and global scales. In this study, we evaluate the potential skill of ensemble streamflow forecasts for flood prediction in South America (SA), with a closer look on some of the large basins of the continent. Medium-range precipitation forecasts from the ECMWF Ensemble Prediction System were used to force a continental-scale, fully coupled hydrologic-hydrodynamic model to produce streamflow forecasts up to 15 days in advance. Forecasts were evaluated over a 4-year period against a reference simulation, derived from the hydrological model forced with a gridded precipitation dataset that merges data from multiple sources (MSWEP). To provide a benchmark for assessing the potential skill, forecasts based on the Ensemble Streamflow Prediction (ESP) technique were produced by running the hydrological model with precipitation data resampled from past MSWEP observations. Overall, ECMWF-based forecasts were skillful mainly at the eastern and southeastern regions of the continent (e.g., São Francisco, Parnaíba, Upper Paraná, Tocantins-Araguaia and Uruguay basins), showing little or no added skill in large rivers draining the Andes such as the Magdalena, Orinoco, Madeira (Amazon) and right-bank tributaries of the Paraguay. In the latter basins, ECMWF-based forecasts largely overestimated the exceedance probabilities of critical thresholds due to a positive bias in the predicted precipitation (in relation to MSWEP), visible mostly during the wet season. In addition, forecasts were found to perform better than ESP in terms of both overall quality and discrimination skill at regions characterized by a warm temperate, fully-humid climate. A direct relation between forecast skill and basin size was not always valid, since the former can vary geographically along tributaries. Limitations and future developments needed for continental-scale hydrological forecasting in SA are also discussed.

Hydrometeorological Ensemble Forecast of a Highly Localized Convective Event in the Mediterranean

The uncertainties that affect hydrometeorological modelling chains can be addressed through ensemble approaches. In this paper, a convection-permitting ensemble system was assessed based on the downscaling of all members of the ECMWF ensemble prediction system through the coupled atmospheric-hydrological WRF-Hydro modelling system. An exemplary highly localized convective event that occurred in a morphologically complex area of the southern Italian coast was selected as a case study, evaluating the performance of the system for two consecutive lead times up to the hydrological forecast on a very small (11.4 km2) catchment. The proposed approach accurately downscales the signal provided by the global model, improving up to almost 200% the quantitative forecast of the accumulated rainfall peak in the area affected by the event and supplying clear information about the forecast uncertainty. Some members of the ensemble simulations provide accurate results up to the hydrological scale over the catchment, with unit peak discharge forecasts up to 3 m3∙s−1∙km−2. Overall, the study highlights that for highly localized convective events in coastal Mediterranean catchments, ensemble approaches should be preferred to a classic single-based simulation approach, because they improve the forecast skills and provide spatially distributed information about the forecast uncertainty, which can be particularly useful for operational purposes.

Improving sub-seasonal forecast skill of meteorological drought: a weather pattern approach

Abstract. Dynamical model skill in forecasting extratropical precipitation is limited beyond the medium-range (around 15 d), but such models are often more skilful at predicting atmospheric variables. We explore the potential benefits of using weather pattern (WP) predictions as an intermediary step in forecasting UK precipitation and meteorological drought on sub-seasonal timescales. Mean sea-level pressure forecasts from the European Centre for Medium-Range Weather Forecasts ensemble prediction system (ECMWF-EPS) are post-processed into probabilistic WP predictions. Then we derive precipitation estimates and dichotomous drought event probabilities by sampling from the conditional distributions of precipitation given the WPs. We compare this model to the direct precipitation and drought forecasts from the ECMWF-EPS and to a baseline Markov chain WP method. A perfect-prognosis model is also tested to illustrate the potential of WPs in forecasting. Using a range of skill diagnostics, we find that the Markov model is the least skilful, while the dynamical WP model and direct precipitation forecasts have similar accuracy independent of lead time and season. However, drought forecasts are more reliable for the dynamical WP model. Forecast skill scores are generally modest (rarely above 0.4), although those for the perfect-prognosis model highlight the potential predictability of precipitation and drought using WPs, with certain situations yielding skill scores of almost 0.8 and drought event hit and false alarm rates of 70 % and 30 %, respectively.

Bias Correction of Tropical Cyclone Parameters in the ECMWF Ensemble Prediction System in Australia

Abstract Global ensemble prediction systems have considerable ability to predict tropical cyclone (TC) formation and subsequent evolution. However, because of their relatively coarse resolution, their predictions of intensity and structure are biased. The biases arise mainly from underestimated intensities and enlarged radii, in particular the radius of maximum winds. This paper describes a method to reduce this limitation by bias correcting TCs in the ECMWF Ensemble Prediction System (ECMWF-EPS) for a region northwest of Australia. A bias-corrected TC system will provide more accurate forecasts of TC-generated wind and waves to the oil and gas industry, which operates a large number of offshore facilities in the region. It will also enable improvements in response decisions for weather sensitive operations that affect downtime and safety risks. The bias-correction technique uses a multivariate linear regression method to bias correct storm intensity and structure. Special strategies are used to maintain ensemble spread after bias correction and to predict the radius of maximum winds using a climatological relationship based on wind intensity and storm latitude. The system was trained on the Australian best track TC data and the ECMWF-EPS TC data from two cyclone seasons. The system inserts corrected vortices into the original surface wind and pressure fields, which are then used to estimate wind exceedance probabilities, and to drive a wave model. The bias-corrected system has shown an overall skill improvement over the uncorrected ECMWF-EPS for all TC intensity and structure parameters with the most significant gains for the maximum wind speed prediction. The system has been operational at the Australian Bureau of Meteorology since November 2016.

Constrained Quantile Regression Splines for Ensemble Postprocessing

Abstract Statistical postprocessing of ensemble forecasts is widely applied to make reliable probabilistic weather forecasts. Motivated by the fact that nature imposes few restrictions on the shape of forecast distributions, a flexible quantile regression method based on constrained spline functions (CQRS) is proposed and tested on ECMWF Ensemble Prediction System (ENS) wind speed forecasting data at 125 stations in Norway. First, it is demonstrated that constraining quantile functions to be monotone and bounded is preferable. Second, combining an ensemble quantile with the ensemble mean proved to be a good covariate for the respective quantile. Third, CQRS only needs to be applied to about 10 equidistant quantiles, while those between can be obtained by interpolation. A comparison of CQRS versus a mixture model of truncated and lognormal distributions showed slight overall improvements in quantile score (less than 1%), reliability, and to some extent also sharpness. For strong wind speed forecasts the quantile score was improved by up to 4.5% depending on lead time.

Investigating the Factors That Contribute to African Easterly Wave Intensity Forecast Uncertainty in the ECMWF Ensemble Prediction System

Abstract Although there have been numerous studies documenting the processes/environments that lead to the intensification of African easterly waves (AEWs), only a few of these studies investigated the effect of those processes or the environment on the predictability of AEWs. Here, the large-scale modulation of AEW intensity predictability is evaluated using the 51-member ECMWF ensemble prediction system (EPS) during an active AEW period (July–September 2011–13). Forecasts are stratified based on the 72-h AEW intensity standard deviation (SD) to evaluate hypotheses for how different processes contribute to large forecast SD. While large and small SD forecasts are associated with similar baroclinic and barotropic energy conversions, forecasts with large SD are characterized by higher relative humidity values downstream of the AEW trough. These areas of higher humidity are also associated with higher precipitation and precipitation SD, suggesting that uncertainty associated with diabatic processes could be linked with large AEW intensity SD. Although water vapor is a strong function of longitude and phase of convectively coupled equatorial waves, the cases with large and small SD are characterized by similar longitude and wave phase, suggesting that AEWs occurring in certain locations or convectively coupled equatorial wave phases are not more or less predictable.

Selective ensemble mean technique for severe European windstorms

We show that ensemble forecasts of extreme European windstorms can be improved up to a lead time of 36–48 hr by sub‐selecting ensemble members based on their performance at very short lead times (up to 12 hr). This applies to both the ensemble mean position of the cyclone centre and the ensemble windstorm footprint over the continent. A number of ensemble forecasts, including those from the ECMWF Ensemble Prediction System, are initialised every 12 hr and disseminated several hours after initialisation; therefore our approach has the potential to provide improved forecasts in an operational context. The analysis is performed on GEFS reforecast data.

The ECMWF ensemble prediction system: Looking back (more than) 25 years and projecting forward 25 years

This paper has been written to mark 25 years of operational medium‐range ensemble forecasting. The origins of the ECMWF Ensemble Prediction System are outlined, including the development of the precursor real‐time Met Office monthly ensemble forecast system. In particular, the reasons for the development of singular vectors and stochastic physics – particular features of the ECMWF Ensemble Prediction System ‐ are discussed. The author speculates about the development and use of ensemble prediction in the next 25 years.

Use of Ensemble Forecasts to Investigate Synoptic Influences on the Structural Evolution and Predictability of Hurricane Alex (2016) in the Midlatitudes

Abstract Hurricane Alex was an extremely rare hurricane event, the first North Atlantic hurricane to form in January since 1938. Alex developed from an extratropical low pressure system that formed over the western North Atlantic basin, and then underwent tropical transition after moving to the eastern basin. It subsequently underwent anomalous extratropical transition (ET) just north of the Azores Islands. We examine the factors affecting Alex’s structural evolution and the predictability of that evolution. Potential scenarios of structural development are identified from a 51-member forecast ensemble from the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF-EPS), initialized at 0000 UTC 10 January 2016. The EPS forecasts are clustered using a regression mixture model based on the storm’s path through the cyclone phase space. Composite maps constructed from these clusters are used to investigate the role of synoptic-scale features on the evolving structure of Hurricane Alex as it interacted with the midlatitude flow. Results suggest that the crucial factor affecting this interplay was the behavior of a large extratropical cyclone and its associated cold front and likely warm conveyor belt upstream of Alex; the intensity of these structures determined whether Alex underwent a typical cold-core ET (as observed) or a warm-seclusion ET. The clustering and compositing methodology proposed not only provides a nuanced analysis of the ensemble forecast variability, helping forecasters to analyze the predictability of future complex tropical–midlatitude interactions, but also presents a method to investigate probable causes of different processes occurring in cyclones.

The diagonal score: Definition, properties, and interpretations

The diagonal score is proposed as a new scoring rule for the assessment of univariate probabilistic forecasts. First, a general framework for the definition of proper user‐oriented scores is set up using the concept of elementary scoring rules. Building on this fundamental notion, a complete overview of forecast skill is provided by a new verification tool. The forecast skill card introduced displays the forecast value as a function of two decision parameters, a probability level and an event threshold, simultaneously. The diagonal score emerges as a summary score focusing on the diagonal of the skill card, that is, by fixing the relationship between the event base rate and forecast probability level. The properties of the new score, as well as interpretations from the perspectives of users and developers, are discussed based on synthetic datasets and 2 m temperature forecasts of the operational ECMWF ensemble prediction system.

African Easterly Wave Forecast Verification and Its Relation to Convective Errors within the ECMWF Ensemble Prediction System

Abstract African easterly waves (AEWs) are the primary synoptic-scale weather feature found in sub-Saharan Africa during boreal summer, yet there have been few studies documenting the performance of operational ensemble prediction systems (EPSs) for these phenomena. Here, AEW forecasts in the 51-member ECMWF EPS are validated against an average of four operational analyses during two periods of enhanced AEW activity (July–September 2007–09 and 2011–13). During 2007–09, AEW position forecasts were mainly underdispersive and characterized by a slow bias, while intensity forecasts were characterized by an overintensification bias, yet the ensemble-mean errors generally matched the forecast uncertainty. Although 2011–13 position forecasts were still underdispersive with a slow bias, the ensemble-mean error is smaller than for 2007–09. In addition, the 2011–13 intensity forecasts were overdispersive and had a negligible intensity bias. Forecasts from 2007 to 2009 were characterized by higher precipitation in the AEW trough center and high correlations between divergence errors and intensity errors, suggesting the intensity bias is associated with errors in convection. By contrast, forecasts from 2011 to 2013 have smaller precipitation biases than those from 2007 to 2009 and exhibit a weaker correlation between divergence errors and intensity errors, suggesting a weaker connection between AEW forecast errors and convective errors.

Gridded probabilistic weather forecasts with an analog ensemble

This study extends the analog ensemble (AnEn) technique to generate probabilistic forecasts of 10 m wind speed over a grid. The AnEn has been widely used to generate short‐term probabilistic predictions of meteorological variables, air quality, wind and solar power, and to effectively downscale reanalysis fields. It is based on a historical dataset including observations paired with corresponding deterministic predictions. For each forecast lead time and location, the AnEn is generated using the observations corresponding to the past deterministic predictions that are more similar to the current forecast. So far, the AnEn has been used to generate predictions at specific locations with available observations. Here, it is extended over a two‐dimensional grid, where each grid point is treated independently, using meteorological analysis instead of observations. The forecast range is extended up to 6 days ahead to evaluate the performance of the AnEn in both the short and medium range. The proposed approach allows to extend the AnEn to applications requiring gridded probabilistic products that are often generated with dynamical ensemble models. The AnEn forecasts are generated using the European Centre for Medium‐Range Weather Forecasts (ECMWF) deterministic and analysis model. Also, the ECMWF Ensemble Prediction System (EPS) is used for comparison. Given that the AnEn predictions are generated independently at any location and lead time, the resulting spatial and temporal correlation may be degraded by noise. A reordering technique called Schaake Shuffle (SS) is then applied to the ensemble members to recover the spatio‐temporal correlation. The AnEn outperforms a calibrated version of EPS for the first two days of the prediction. During the third forecast day, the AnEn remains competitive with the calibrated EPS, while the latter is more skilful from 72 to 144 h ahead. The AnEn uses about one sixth of the computational resources necessary to generate the real‐time EPS prediction.

Ensemble superparameterization versus stochastic parameterization: A comparison of model uncertainty representation in tropical weather prediction

AbstractStochastic schemes to represent model uncertainty in the European Centre for Medium‐Range Weather Forecasts (ECMWF) ensemble prediction system has helped improve its probabilistic forecast skill over the past decade by both improving its reliability and reducing the ensemble mean error. The largest uncertainties in the model arise from the model physics parameterizations. In the tropics, the parameterization of moist convection presents a major challenge for the accurate prediction of weather and climate. Superparameterization is a promising alternative strategy for including the effects of moist convection through explicit turbulent fluxes calculated from a cloud‐resolving model (CRM) embedded within a global climate model (GCM). In this paper, we compare the impact of initial random perturbations in embedded CRMs, within the ECMWF ensemble prediction system, with stochastically perturbed physical tendency (SPPT) scheme as a way to represent model uncertainty in medium‐range tropical weather forecasts. We especially focus on forecasts of tropical convection and dynamics during MJO events in October–November 2011. These are well‐studied events for MJO dynamics as they were also heavily observed during the DYNAMO field campaign. We show that a multiscale ensemble modeling approach helps improve forecasts of certain aspects of tropical convection during the MJO events, while it also tends to deteriorate certain large‐scale dynamic fields with respect to stochastically perturbed physical tendencies approach that is used operationally at ECMWF.

The Dynamics of an Extreme Precipitation Event in Northeastern Vietnam in 2015 and Its Predictability in the ECMWF Ensemble Prediction System

Abstract A record-breaking rainfall event occurred in northeastern Vietnam in late July–early August 2015. The coastal region in Quang Ninh Province was hit severely, with station rainfall sums in the range of 1000–1500 mm. The heavy rainfall led to flooding and landslides, which resulted in an estimated economic loss of $108 million (U.S. dollars) and 32 fatalities. Using a multitude of data sources and ECMWF ensemble forecasts, the synoptic–dynamic development and practical predictability of the event is investigated in detail for the 4-day period from 1200 UTC 25 July to 1200 UTC 29 July 2015, during which the major portion of the rainfall was observed. A slowly moving upper-level subtropical trough and the associated surface low in the northern Gulf of Tonkin promoted sustained moisture convergence and convection over northeastern Vietnam. The humidity was advected in a moisture transport band lying across the Indochina Peninsula and emanating from a tropical storm over the Bay of Bengal. Analyses of the ECMWF ensemble forecasts clearly showed a sudden emergence of the predictability of the extreme event at lead times of 3 days that was associated with the correct forecasts of the intensity and location of the subtropical trough in the 51 ensemble members. Thus, the Quang Ninh event is a good example in which the predictability of tropical convection arises from large-scale synoptic forcing; in the present case it was due to a tropical–extratropical interaction that has not been documented before for the region and season.