Verification of quantitative precipitation forecasts from operational  ensemble prediction systems over India

Abstract. This study aims at evaluating the performances of flash-flood forecasts issued from deterministic and ensemble meteorological prognostic systems. The hydrometeorological modeling chain includes the Weather Research and Forecasting Model (WRF) forcing the rainfall-runoff model MARINE dedicated to flash floods. Two distinct ensemble prediction systems accounting for (i) perturbed initial and lateral boundary conditions of the meteorological state and (ii) mesoscale model physical parameterizations have been implemented on the Agly catchment of the eastern Pyrenees with three subcatchments exhibiting different rainfall regimes. Different evaluations of the performance of the hydrometeorological strategies have been performed: (i) verification of short-range ensemble prediction systems and corresponding streamflow forecasts, for a better understanding of how forecasts behave; (ii) usual measures derived from a contingency table approach, to test an alert threshold exceedance; and (iii) overall evaluation of the hydrometeorological chain using the continuous rank probability score, for a general quantification of the ensemble performances. Results show that the overall discharge forecast is improved by both ensemble strategies with respect to the deterministic forecast. Threshold exceedance detections for flood warning also benefit from large hydrometeorological ensemble spread. There are no substantial differences between both ensemble strategies on these test cases in terms of both the issuance of flood warnings and the overall performances, suggesting that both sources of external-scale uncertainty are important to take into account.

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Present and Future Requirements for Using and Communicating Hydrometeorological Ensemble Prediction Systems for Short-, Medium-, and Long-Term Applications

Handbook of Hydrometeorological Ensemble Forecasting ◽

10.1007/978-3-642-39925-1_39 ◽

2019 ◽

pp. 1047-1092

Author(s):

Geoff Pegram ◽

Damien Raynaud ◽

Eric Sprokkereef ◽

Martin Ebel ◽

Silke Rademacher ◽

...

Keyword(s):

Ensemble Prediction ◽

Prediction Systems

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PostProcessing and Visualization Techniques for Convection-Allowing Ensembles

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-18-0041.1 ◽

2019 ◽

Vol 100 (7) ◽

pp. 1245-1258 ◽

Cited By ~ 11

Author(s):

Brett Roberts ◽

Israel L. Jirak ◽

Adam J. Clark ◽

Steven J. Weiss ◽

John S. Kain

Keyword(s):

Real Time ◽

Data Extraction ◽

Deep Convection ◽

Weather Prediction ◽

Ensemble Forecast ◽

Ensemble Prediction ◽

Data Volume ◽

Explicit Simulation ◽

Prediction Systems ◽

Visualization Techniques

AbstractSince the early 2000s, growing computing resources for numerical weather prediction (NWP) and scientific advances enabled development and testing of experimental, real-time deterministic convection-allowing models (CAMs). By the late 2000s, continued advancements spurred development of CAM ensemble forecast systems, through which a broad range of successful forecasting applications have been demonstrated. This work has prepared the National Weather Service (NWS) for practical usage of the High Resolution Ensemble Forecast (HREF) system, which was implemented operationally in November 2017. Historically, methods for postprocessing and visualizing products from regional and global ensemble prediction systems (e.g., ensemble means and spaghetti plots) have been applied to fields that provide information on mesoscale to synoptic-scale processes. However, much of the value from CAMs is derived from the explicit simulation of deep convection and associated storm-attribute fields like updraft helicity and simulated reflectivity. Thus, fully exploiting CAM ensembles for forecasting applications has required the development of fundamentally new data extraction, postprocessing, and visualization strategies. In the process, challenges imposed by the immense data volume inherent to these systems required new approaches when considering diverse factors like forecaster interpretation and computational expense. In this article, we review the current state of postprocessing and visualization for CAM ensembles, with a particular focus on forecast applications for severe convective hazards that have been evaluated within NOAA’s Hazardous Weather Testbed. The HREF web viewer implemented at the NWS Storm Prediction Center (SPC) is presented as a prototype for deploying these techniques in real time on a flexible and widely accessible platform.

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Intelligent Analysis of Software Maintenance Data

Software Applications ◽

10.4018/978-1-60566-060-8.ch018 ◽

2009 ◽

pp. 189-221

Author(s):

Marek Reformat ◽

Petr Musilek ◽

Efe Igbide

Keyword(s):

Software Engineering ◽

Software Maintenance ◽

Evidence Theory ◽

Data Models ◽

Ensemble Prediction ◽

Software Companies ◽

Single Defect ◽

Engineering Data ◽

Single Data ◽

Prediction Systems

Amount of software engineering data gathered by software companies amplifies importance of tools and techniques dedicated to processing and analysis of data. More and more methods are being developed to extract knowledge from data and build data models. In such cases, selection of the most suitable data processing methods and quality of extracted knowledge is of great importance. Software maintenance is one of the most time and effort-consuming tasks among all phases of a software life cycle. Maintenance managers and personnel look for methods and tools supporting analysis of software maintenance data in order to gain knowledge needed to prepare better plans and schedules of software maintenance activities. Software engineering data models should provide quantitative as well as qualitative outputs. It is desirable to build these models based on a welldelineated logic structure. Such models would enhance maintainers’ understanding of factors which influence maintenance efforts. This chapter focuses on defect-related activities that are the core of corrective maintenance. Two aspects of these activities are considered: a number of software components that have to be examined during a defect removing process, and time needed to remove a single defect. Analysis of the available datasets leads to development of data models, extraction of IF-THEN rules from these models, and construction of ensemble-based prediction systems that are built based on these data models. The data models are developed using well-known tools such as See5/C5.0 and 4cRuleBuilder, and a new multi-level evolutionary-based algorithm. Single data models are put together into ensemble prediction systems that use elements of evidence theory for the purpose of inference about a degree of belief in the final prediction.

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Evaluating U.S. East Coast Winter Storms in a Multimodel Ensemble Using EOF and Clustering Approaches

Monthly Weather Review ◽

10.1175/mwr-d-18-0052.1 ◽

2019 ◽

Vol 147 (6) ◽

pp. 1967-1987 ◽

Cited By ~ 2

Author(s):

Minghua Zheng ◽

Edmund K. M. Chang ◽

Brian A. Colle

Keyword(s):

Lead Time ◽

East Coast ◽

Time Frame ◽

Ensemble Prediction ◽

Multimodel Ensemble ◽

Winter Storms ◽

East Coast Winter Storms ◽

Prediction Systems ◽

Environmental Prediction ◽

The Individual

Abstract Empirical orthogonal function (EOF) and fuzzy clustering tools were applied to generate and validate scenarios in operational ensemble prediction systems (EPSs) for U.S. East Coast winter storms. The National Centers for Environmental Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF), and Canadian Meteorological Centre (CMC) EPSs were validated in their ability to capture the analysis scenarios for historical East Coast cyclone cases at lead times of 1–9 days. The ECMWF ensemble has the best performance for the medium- to extended-range forecasts. During this time frame, NCEP and CMC did not perform as well, but a combination of the two models helps reduce the missing rate and alleviates the underdispersion. All ensembles are underdispersed at all ranges, with combined ensembles being less underdispersed than the individual EPSs. The number of outside-of-envelope cases increases with lead time. For a majority of the cases beyond the short range, the verifying analysis does not lie within the ensemble mean group of the multimodel ensemble or within the same direction indicated by any of the individual model means, suggesting that all possible scenarios need to be taken into account. Using the EOF patterns to validate the cyclone properties, the NCEP model tends to show less intensity and displacement biases during 1–3-day lead time, while the ECMWF model has the smallest biases during 4–6 days. Nevertheless, the ECMWF forecast position tends to be biased toward the southwest of the other two models and the analysis.

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The Skill of Seasonal Ensemble Prediction Systems to Forecast Wintertime Windstorm Frequency over the North Atlantic and Europe

Monthly Weather Review ◽

10.1175/2011mwr3518.1 ◽

2011 ◽

Vol 139 (9) ◽

pp. 3052-3068 ◽

Cited By ~ 15

Author(s):

Dominik Renggli ◽

Gregor C. Leckebusch ◽

Uwe Ulbrich ◽

Stephanie N. Gleixner ◽

Eberhard Faust

Keyword(s):

North Atlantic ◽

Ensemble Prediction ◽

Physical Mechanisms ◽

The North ◽

Predictive Skill ◽

Storm Frequency ◽

The North Atlantic ◽

Temporal And Spatial ◽

Prediction Systems ◽

High Skill

The science of seasonal predictions has advanced considerably in the last decade. Today, operational predictions are generated by several institutions, especially for variables such as (sea) surface temperatures and precipitation. In contrast, few studies have been conducted on the seasonal predictability of extreme meteorological events such as European windstorms in winter. In this study, the predictive skill of extratropical wintertime windstorms in the North Atlantic/European region is explored in sets of seasonal hindcast ensembles from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) and the ENSEMBLE-based predictions of climate changes and their impacts (ENSEMBLES) projects. The observed temporal and spatial climatological distributions of these windstorms are reasonably well reproduced in the hindcast data. Using hindcasts starting on 1 November, significant predictive skill is found for the December–February windstorm frequency in the period 1980–2001, but also for the January–April storm frequency. Specifically, the model suite run at Météo France shows consistently high skill. Some aspects of the variability of skill are discussed. Predictive skill in the 1980–2001 period is usually higher than for the 1960–2001 period. Furthermore, the level of skill turns out to be related to the storm frequency of a given winter. Generally, winters with high storm frequency are better predicted than winters with medium storm frequency. Physical mechanisms potentially leading to such a variability of skill are discussed.

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Ensemble Typhoon Quantitative Precipitation Forecasts Model in Taiwan

Weather and Forecasting ◽

10.1175/waf-d-14-00037.1 ◽

2015 ◽

Vol 30 (1) ◽

pp. 217-237 ◽

Cited By ~ 17

Author(s):

Jing-Shan Hong ◽

Chin-Tzu Fong ◽

Ling-Feng Hsiao ◽

Yi-Chiang Yu ◽

Chian-You Tzeng

Keyword(s):

Model Performance ◽

Precipitation Forecast ◽

Ensemble Prediction ◽

Model Bias ◽

Ensemble Prediction System ◽

Rainfall Forecast ◽

Quantitative Precipitation Forecast ◽

Quantitative Precipitation Forecasts ◽

Typhoon Rainfall ◽

Typhoon Tracks

Abstract In this study, an ensemble typhoon quantitative precipitation forecast (ETQPF) model was developed to provide typhoon rainfall forecasts for Taiwan. The ETQPF rainfall forecast is obtained by averaging the pick-out cases, which are screened using certain criterion based on given typhoon tracks from an ensemble prediction system (EPS). Therefore, the ETQPF model resembles a climatology model. However, the ETQPF model uses the quantitative precipitation forecasts (QPFs) from an EPS instead of historical rainfall observations. Two typhoon cases, Fanapi (2010) and Megi (2010), are used to evaluate the ETQPF model performance. The results show that the rainfall forecast from the ETQPF model, which is qualitatively compared and quantitatively verified, provides reasonable typhoon rainfall forecasts and is valuable for real-time operational applications. By applying the forecast track to the ETQPF model, better track forecasts lead to better ETQPF rainfall forecasts. Moreover, the ETQPF model provides the “scenario” of the typhoon QPFs according to the uncertainty of the forecast tracks. Such a scenario analysis can provide valuable information for risk assessment and decision making in disaster prevention and reduction. Deficiencies of the ETQPF model are also presented, including that the average over the pick-out case usually offsets the extremes and reduces the maximum ETQPF rainfall, the underprediction is especially noticeable for weak phase-locked rainfall systems, and the ETQPF rainfall error is related to the model bias. Therefore, reducing model bias is an important issue in further improving the ETQPF model performance.

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Verification of 24-h Quantitative Precipitation Forecasts over the Pacific Northwest from a High-Resolution Ensemble Kalman Filter System

Weather and Forecasting ◽

10.1175/waf-d-16-0180.1 ◽

2017 ◽

Vol 32 (3) ◽

pp. 1185-1208 ◽

Cited By ~ 5

Author(s):

Phillipa Cookson-Hills ◽

Daniel J. Kirshbaum ◽

Madalina Surcel ◽

Jonathan G. Doyle ◽

Luc Fillion ◽

...

Keyword(s):

Kalman Filter ◽

High Resolution ◽

Data Assimilation ◽

Pacific Northwest ◽

Ensemble Kalman Filter ◽

Ensemble Prediction ◽

Forecast Errors ◽

Ensemble Prediction System ◽

The Pacific ◽

Quantitative Precipitation Forecasts

Abstract Environment and Climate Change Canada (ECCC) has recently developed an experimental high-resolution EnKF (HREnKF) regional ensemble prediction system, which it tested over the Pacific Northwest of North America for the first half of February 2011. The HREnKF has 2.5-km horizontal grid spacing and assimilates surface and upper-air observations every hour. To determine the benefits of the HREnKF over less expensive alternatives, its 24-h quantitative precipitation forecasts are compared with those from a lower-resolution (15 km) regional ensemble Kalman filter (REnKF) system and to ensembles directly downscaled from the REnKF using the same grid as the HREnKF but with no additional data assimilation (DS). The forecasts are verified against rain gauge observations and gridded precipitation analyses, the latter of which are characterized by uncertainties of comparable magnitude to the model forecast errors. Nonetheless, both deterministic and probabilistic verification indicates robust improvements in forecast skill owing to the finer grids of the HREnKF and DS. The HREnKF exhibits a further improvement in performance over the DS in the first few forecast hours, suggesting a modest positive impact of data assimilation. However, this improvement is not statistically significant and may be attributable to other factors.

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