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François Lalaurette

Bio: François Lalaurette is an academic researcher from European Centre for Medium-Range Weather Forecasts. The author has contributed to research in topics: Potential vorticity & Quantitative precipitation forecast. The author has an hindex of 12, co-authored 16 publications receiving 1160 citations.

Papers
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Journal ArticleDOI
TL;DR: In this article, the challenges associated with forecasting extratropical transition are described in terms of the forecast variables (track, intensity, surface winds, precipitation) and their impacts (flooding, bush fires, ocean response).
Abstract: A significant number of tropical cyclones move into the midlatitudes and transform into extratropical cyclones. This process is generally referred to as extratropical transition (ET). During ET a cyclone frequently produces intense rainfall and strong winds and has increased forward motion, so that such systems pose a serious threat to land and maritime activities. Changes in the structure of a system as it evolves from a tropical to an extratropical cyclone during ET necessitate changes in forecast strategies. In this paper a brief climatology of ET is given and the challenges associated with forecasting extratropical transition are described in terms of the forecast variables (track, intensity, surface winds, precipitation) and their impacts (flooding, bush fires, ocean response). The problems associated with the numerical prediction of ET are discussed. A comprehensive review of the current understanding of the processes involved in ET is presented. Classifications of extratropical transition ...

481 citations

Journal ArticleDOI
TL;DR: In this paper, the forecast skill of the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (EPS) in predicting precipitation probabilities is discussed and four seasons are analyzed in detail using signal detection theory and reliability diagrams to define objective measure of predictive skill.
Abstract: The forecast skill of the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (EPS) in predicting precipitation probabilities is discussed. Four seasons are analyzed in detail using signal detection theory and reliability diagrams to define objective measure of predictive skill. First, the EPS performance during summer 1997 is discussed. Attention is focused on Europe and two European local regions, one centered around the Alps and the other around Ireland. Results indicate that for Europe the EPS can give skillful prediction of low precipitation amounts [i.e., lower than 2 mm (12 h)−1] up to forecast day 6, and of high precipitation amounts [i.e., between 2 and 10 mm (12 h)−1] up to day 4. Lower levels of skill are achieved for smaller local areas. Then, the EPS performance during summer 1996 (i.e., prior to the enhancement introduced on 10 December 1996 from 33 to 51 members and to resolution increase from T63 L19 to TL159 L31) and summer 1997 are compared. Results sho...

187 citations

Journal ArticleDOI
TL;DR: An extreme forecast index that ranks the departure between the forecast and the model climate between -1 (forecast given 100% probability that record-breaking low values will be reached) and +1 (record-breaking high values), which is shown to be potentially useful in alerting forecasters to the risk of severe weather up to three or four days in advance.
Abstract: A new method to extract information related to unusual forecast distributions from probabilistic (e.g. ensemble) prediction systems is presented. It consists of an extreme forecast index (EFI) that ranks the departure between the forecast and the model climate between -1 (forecast given 100% probability that record-breaking low values will be reached) and +1 (record-breaking high values). First the new index is derived, it is related to other measures of statistical significance, and a few properties are given. It is then demonstrated how the accumulation of ensemble forecasts every day allows the quick build-up of a model pseudo-climate that is representative enough to detect large departures from normal conditions while being representative of the latest developments in terms of resolution or physical parametrizations. The EFI is then subjected to several severe weather events that have happened in Europe over the last few years, and it is shown to be potentially useful in alerting forecasters to the risk of severe weather up to three or four days in advance. Finally, objective verification over five months in 2001-02 is presented. Although the results confirm that the model pseudo-climate is good enough to set up thresholds for severe weather evenly throughout Europe, the false-alarm rates are much larger than usually expected by forecasters or users. It is argued, however, that operating characteristics in the early medium range for severe weather have largely been ignored in the past. Although the signal is weak in the 3 to five-day range, it is undoubtedly associated with a forecast skill that might be used for setting up pre-alerts to be used either internally by meteorological services, or externally by advanced users aware of its error characteristics.

125 citations

Journal ArticleDOI
TL;DR: The first phase of the FASTEX project as discussed by the authors took place between 5 January and 27 February 1997 with the deployment of a unique set of observing facilities across the North-Atlantic.
Abstract: Summary The eld phase of the FASTEX project took place between 5 January and 27 February 1997 with the deployment of a unique set of observing facilities across the North-Atlantic. The major objective was to document the life-cycle of a representative set of mid-latitude cyclones. Other objectives were to test the practical feasibility of iadaptivei observations with a view to improving the prediction of these same cyclones and to document the internal structure of the associated cloud systems using combined airborne Doppler radars and dropsondes. Another goal of FASTEX was to measure air-sea exchange parameters under conditions of strong winds with high seas. These objectives were successfully achieved. Intensive Observation Periods were conducted on 19 occasions. High-resolution vertical proles through the same cyclones at three dierent stages of their life-cycle have been obtained on more than 10 occasions. Calculation of areas where observations were needed to keep the growth of forecast error under control was undertaken using dierent techniques, and ights were planned and executed in these areas on time. Combined dropsonde and Doppler radar observations of cloud systems are available for 10 cases. A unique air-sea turbulent exchange dataset has been obtained.

117 citations

Journal ArticleDOI
TL;DR: In this article, the performance of the European Centre for Medium-Range Weather Forecasts model in predicting precipitation is discussed, starting from the assumption that model spatial scales have to be verified against data representing similar scales.
Abstract: The demand for verification of forecasting systems to ascertain their strengths and weaknesses is increasing dramatically as models evolve more rapidly. Precipitation forecasts have always been of great interest to forecasters because they influence daily life. The recent flooding over Europe has also shown how important it is to know how models can reproduce these events. The issue of precipitation verification is addressed here, starting from the assumption that model spatial scales have to be verified against data representing similar scales. Only in this way may the skill of forecasting system used herein be determined. The performance of the European Centre for Medium-Range Weather Forecasts model in predicting precipitation is discussed. The study concentrates on the period September to November 1999 during which high-density observations were available for the Alps. The high-resolution observing network over the Alpine region has been used to reconstruct a precipitation analysis that contains smoothed small-scale variability and represents with sufficient accuracy the average behavior of the observed field in the model grid box. The precipitation forecast is verified against both the precipitation analysis and the surface synoptic observations (SYNOP) available in real time via the Global Telecommunication System. Both verification approaches show that for the Alpine region, during autumn 1999, the model overestimates the precipitation amount. Overestimation is smaller when the forecast is compared with the precipitation analysis. It is also shown that verification against irregular and scattered observations (SYNOP data) is highly influenced by the variability of the precipitation in a grid box. A precipitation analysis is, therefore, important if model skill has to be defined.

92 citations


Cited by
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Book
01 Nov 2002
TL;DR: A comprehensive text and reference work on numerical weather prediction, first published in 2002, covers not only methods for numerical modeling, but also the important related areas of data assimilation and predictability.
Abstract: This comprehensive text and reference work on numerical weather prediction, first published in 2002, covers not only methods for numerical modeling, but also the important related areas of data assimilation and predictability. It incorporates all aspects of environmental computer modeling including an historical overview of the subject, equations of motion and their approximations, a modern and clear description of numerical methods, and the determination of initial conditions using weather observations (an important science known as data assimilation). Finally, this book provides a clear discussion of the problems of predictability and chaos in dynamical systems and how they can be applied to atmospheric and oceanic systems. Professors and students in meteorology, atmospheric science, oceanography, hydrology and environmental science will find much to interest them in this book, which can also form the basis of one or more graduate-level courses.

2,240 citations

BookDOI
16 Dec 2011
TL;DR: Jolliffe et al. as mentioned in this paper proposed a framework for verification of spatial fields based on binary and categorical events, and proved the correctness of the proposed framework with past, present and future predictions.
Abstract: List of Contributors. Preface. 1. Introduction (I. Jolliffe & D. Stephenson). 2. Basic Concepts (J. Potts). 3. Binary Events (I. Mason). 4. Categorical Events (R. Livezey). 5. Continuous Variables (M. Deque). 6. Verification of Spatial Fields (W. Drosdowsky & H. Zhang). 7. Probability and Ensemble Forecasts (Z. Toth, et al.). 8. Economic Value and Skill (D. Richardson). 9. Forecast Verification: Past, Present and Future (D. Stephenson & I. Jolliffe). Glossary. References. Author Index. Subject Index.

1,633 citations

Journal ArticleDOI
TL;DR: This monograph is an outstanding monograph on current research on skewelliptical models and its generalizations and does an excellent job presenting the depth of methodological research as well as the breath of application regimes.
Abstract: (2005). Atmospheric Modeling, Data Assimilation, and Predictability. Technometrics: Vol. 47, No. 4, pp. 521-521.

1,580 citations

Journal ArticleDOI
TL;DR: The Ensemble Transform Kalman Filter (ET KF) as discussed by the authors is a suboptimal Kalman filter that uses ensemble transformation and a normalization to obtain the prediction error covariance matrix associated with a particular deployment of observational resources.
Abstract: A suboptimal Kalman filter called the ensemble transform Kalman filter (ET KF) is introduced. Like other Kalman filters, it provides a framework for assimilating observations and also for estimating the effect of observations on forecast error covariance. It differs from other ensemble Kalman filters in that it uses ensemble transformation and a normalization to rapidly obtain the prediction error covariance matrix associated with a particular deployment of observational resources. This rapidity enables it to quickly assess the ability of a large number of future feasible sequences of observational networks to reduce forecast error variance. The ET KF was used by the National Centers for Environmental Prediction in the Winter Storm Reconnaissance missions of 1999 and 2000 to determine where aircraft should deploy dropwindsondes in order to improve 24‐72-h forecasts over the continental United States. The ET KF may be applied to any well-constructed set of ensemble perturbations. The ET KF technique supercedes the ensemble transform (ET) targeting technique of Bishop and Toth. In the ET targeting formulation, the means by which observations reduced forecast error variance was not expressed mathematically. The mathematical representation of this process provided by the ET KF enables such things as the evaluation of the reduction in forecast error variance associated with individual flight tracks and assessments of the value of targeted observations that are distributed over significant time intervals. It also enables a serial targeting methodology whereby one can identify optimal observing sites given the location and error statistics of other observations. This allows the network designer to nonredundantly position targeted observations. Serial targeting can also be used to greatly reduce the computations required to identify optimal target sites. For these theoretical and practical reasons, the ET KF technique is more useful than the ET technique. The methodology is illustrated with observation system simulation experiments involving a barotropic numerical model of tropical cyclonelike vortices. These include preliminary empirical tests of ET KF predictions using ET KF, 3DVAR, and hybrid data assimilation schemes—the results of which look promising. To concisely describe the future feasible sequences of observations considered in adaptive sampling problems, an extension to Ide et al.’s unified notation for data assimilation is suggested.

1,338 citations

Journal ArticleDOI
TL;DR: In this article, the continuous ranked probability score (CRPS) is decomposed into a reliability part and a resolution/uncertainty part, in a way similar to the decomposition of the Brier score.
Abstract: Some time ago, the continuous ranked probability score (CRPS) was proposed as a new verification tool for (probabilistic) forecast systems. Its focus is on the entire permissible range of a certain (weather) parameter. The CRPS can be seen as a ranked probability score with an infinite number of classes, each of zero width. Alternatively, it can be interpreted as the integral of the Brier score over all possible threshold values for the parameter under consideration. For a deterministic forecast system the CRPS reduces to the mean absolute error. In this paper it is shown that for an ensemble prediction system the CRPS can be decomposed into a reliability part and a resolution/uncertainty part, in a way that is similar to the decomposition of the Brier score. The reliability part of the CRPS is closely connected to the rank histogram of the ensemble, while the resolution/ uncertainty part can be related to the average spread within the ensemble and the behavior of its outliers. The usefulness of such a decomposition is illustrated for the ensemble prediction system running at the European Centre for Medium-Range Weather Forecasts. The evaluation of the CRPS and its decomposition proposed in this paper can be extended to systems issuing continuous probability forecasts, by realizing that these can be interpreted as the limit of ensemble forecasts with an infinite number of members.

1,148 citations