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Roberto Buizza and Piero Chessa

Abstract

Between 24 and 26 January 2000 explosive cyclogenesis along the U.S. east coast caused serious economic disruption and loss of lives. The performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) high-resolution TL319 model and of the TL159 Ensemble Prediction System (EPS) in predicting the storm evolution is investigated.

The most critical time period to predict was the rapid intensification of the storm between 24 and 25 January. Single deterministic forecasts based on the TL319 model gave skillful predictions only 36 h before the event. By contrast, the EPS indicated the possibility that the storm would hit the affected region 2 days before the event, consistently enhancing the indications present in forecasts issued 3 and 4 days before the event. This suggests that the ECMWF EPS, suitably used, could be a valuable support tool for critical issues as alerts for extreme winter weather.

Sensitivity studies indicate that the interaction of initial perturbations and stochastic perturbations added to the model tendencies was a necessary ingredient to have some EPS members correctly predicting the storm.

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Roberto Buizza and Franco Molteni

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Linear instability analysis is applied to study the role of barotropic dynamics in the evolution of blocking events during winter 1990/91. Finite-time interval instabilities (i.e., nonnormal-mode structures defined as the singular vectors of the tangent propagator) growing over periods of 4 days have been computed using adjoint methods. Correlation between large values of the singular vector amplification rate and the occurrence of blocking onset in the real atmosphere is studied.

A correspondence is found between periods with the largest singular vector amplification rates and periods either leading to blocking formation or covering the mature phase of blocks. It is shown that at final time the singular vectors tend to have largest amplitude in the same regions of planetary wave ridging where blocks develop. On average, singular vectors developing on the Pacific have larger growth rates than those in the Euro–Atlantic region.

The analysis of some case studies indicates a qualitative similarity between observed tendencies and their projections onto the five leading singular vectors, although correlation coefficients between actual and projected fields are small. The cases with largest tendency correlation are associated with the formation of blocking dipoles from preexisting planetary-scale ridges of larger meridional scale. Overall, our results indicate that barotropic instability is mostly driven by planetary wave amplification rather than being the cause of it, and mainly contributes to a rather mature stage of blocking development.

Energetics of barotropic perturbations indicate that dipole structures similar to blocking patterns can efficiently gain energy from the planetary-scale flow provided that the longitudinal gradient of the basic-state zonal wind ub in the jet exit has a comparable magnitude to the meridional gradient of ub near the jet core. It is shown that an anomaly reinforcing the basic-state ridge on the eastern side of the Pacific and/or Atlantic Ocean (therefore increasing the magnitude of the longitudinal wind gradient) is necessary for a dipole structure to emerge as the fastest growing perturbation.

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Steven L. Mullen and Roberto Buizza

Abstract

The performance of the ECMWF Ensemble Prediction System (EPS) is assessed for probabilistic forecasts of 24-h accumulated precipitation over the eastern United States. Daily forecasts for the period 1 January 1997 to 31 January 1999 are verified for projections of 1–10 days. Verification is performed separately for the cool and warm seasons, and the impact of changes to the EPS that occurred during the study period is assessed. Analyses of rain gauge data from the River Forecast Centers of NOAA are used for verification. Skill is measured relative to long-term climatic frequencies, and the statistical significance of differences in the accuracy and skill among forecasts is estimated.

Overall, EPS forecasts are more skillful during the winter than the summer. The EPS produces significantly skillful forecasts to past 1 week for a threshold of 1 mm in both seasons. Accuracy decreases as the threshold increases, until forecasts of 50 mm are not significantly skillful at 1 day. The implementation of evolved singular vectors in the EPS appears to have minimal impact on skill during the summer. The addition of both evolved singular vectors and stochastic processes in the EPS appears to improve short-range performance for thresholds between 1 and 20 mm during the winter, but results for higher thresholds (50 mm) are equivocal.

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Steven L. Mullen and Roberto Buizza

Abstract

The effect of horizontal resolution and ensemble size on the ECMWF Ensemble Prediction System (EPS) is assessed for probabilistic forecasts of 24-h accumulated precipitation. Two sets of experiments are analyzed. The primary experiment compares two spectral truncations (total wavenumbers 159 and 255) for 30 summer and 57 winter dates. An auxiliary experiment compares three truncations (total wavenumbers 159, 255, and 319) for 16 initial dates (8 cool- and 8 warm-season events) during which heavy precipitation (>50 mm) occurred over the eastern United States at day 5 of the forecast. Rain gauge data from the River Forecast Centers of NOAA are used for verification. Skill is measured relative to long-term climatic frequencies, and the statistical significance of differences in the accuracy among the forecasts is estimated. Finer model resolution produces statistically significant improvements in EPS performance for ensemble configurations with the same number of members, especially for lighter thresholds (1 and 10 mm day−1). Performance changes somewhat when ensemble configurations with different resolutions and ensemble sizes, but equivalent computational costs, are compared for the heavier amounts (20 and 50 mm day−1). Coarser-resolution, larger-member ensembles can outperform higher-resolution, smaller-member ensembles in terms of ability to predict rare (in terms of climatic frequency of occurrence) precipitation events. The overall conclusion is that probabilistic forecasts of precipitation from large ensemble sizes at lower resolution can be more valuable to users and decision makers than probabilistic forecasts from smaller ensemble sizes at higher resolution, particularly when heavy precipitation occurs.

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Isla Gilmour, Leonard A. Smith, and Roberto Buizza

Abstract

Day-to-day variations in the growth of uncertainty in the current state of the atmosphere have led to operational ensemble weather predictions in which an ensemble of different initial conditions, each perturbed from the best estimate of the current state and yet still consistent with the observations, is forecast. Contrasting competing methods for the selection of ensemble members is a subject of active research; the assumption that the ensemble members represent sufficiently small perturbations so as to evolve within the “linear regime” is implicit to several of these methods. This regime, in which the model dynamics are well represented by a linear approximation, is commonly held to extend to 2 or 3 days for operational forecasts. It is shown that this is rarely the case. A new measure, the relative nonlinearity, which quantifies the duration of the linear regime by monitoring the evolution of “twin” pairs of ensemble members, is introduced. Both European and American ensemble prediction systems are examined; in the cases considered for each system (87 and 25, respectively), the duration of the linear regime is often less than a day and never extends to 2 days. The internal consistency of operational ensemble formation schemes is discussed in light of these results. By decreasing the optimization time, a modified singular vector–based formation scheme is shown to improve consistency while maintaining traditional skill and spread scores in the seven cases considered. The relevance of the linear regime to issues regarding data assimilation, adaptive observations, and model sensitivity is also noted.

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Nedjeljka Žagar, Roberto Buizza, and Joseph Tribbia

Abstract

A new methodology for the analysis of ensemble prediction systems (ENSs) is presented and applied to 1 month (December 2014) of ECMWF operational ensemble forecasts. The method relies on the decomposition of the global three-dimensional wind and geopotential fields onto the normal-mode functions. The ensemble properties are quantified in terms of the 50-member ensemble spread associated with the balanced and inertio-gravity (IG) modes for forecast ranges every 12 h up to 7 days. Ensemble reliability is defined for the balanced and IG modes comparing the ensemble spread with the control analysis in each scale.

Modal analysis shows that initial uncertainties in the ECMWF ENS are largest in the tropical large-scale modes and their spatial distribution is similar to the distribution of the short-range forecast errors. Initially the ensemble spread grows most in the smallest scales and in the synoptic range of the IG modes but the overall growth is dominated by the increase of spread in balanced modes in synoptic and planetary scales in the midlatitudes. During the forecasts, the distribution of spread in the balanced and IG modes grows toward the climatological spread distribution characteristic of the analyses. In the 2-day forecast range, the global IG spread reaches 60% of its asymptotic value while the same percentage of the global balanced spread is reached after 5 days of forecasts. An underdispersiveness of the system is suggested to be associated with the lack of tropical variability, primarily the Kelvin waves.

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Ervin Zsoter, Roberto Buizza, and David Richardson

Abstract

This work investigates the inconsistency between forecasts issued at different times but valid for the same time, and shows that ensemble-mean forecasts are less inconsistent than corresponding control forecasts. The “jumpiness” index, the concepts of different forecast jumps—the “flip,” “flip-flop,” and “flip-flop-flip”—and the inconsistency correlation between time series of inconsistency indices are introduced to measure the consistency/inconsistency of consecutive forecasts. These new measures are used to compare the behavior of the ECMWF and the Met Office control and ensemble-mean forecasts for an 18-month period over Europe. Results indicate that for both the ECMWF and the Met Office ensembles, the ensemble-mean forecast is less inconsistent than the control forecast. However, they also indicate that the ensemble mean follows its corresponding control forecast more closely than the controls (or the ensemble means) of the two ensemble systems following each other, thus suggesting weaknesses in both ensemble systems in the simulation of forecast uncertainty due to model or analysis error. Results also show that there is only a weak link between forecast jumpiness and forecast error (i.e., forecasts with lower inconsistency do not necessarily have, on average, lower error).

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Čedo Branković, Blaženka Matjačić, Stjepan Ivatek-Šahdan, and Roberto Buizza

Abstract

Dynamical downscaling has been applied to global ensemble forecasts to assess its impact for four cases of severe weather (precipitation and wind) over various parts of Croatia. It was performed with the Croatian 12.2-km version of the Aire Limitée Adaptation Dynamique Développement International (ALADIN) limited-area model, nested in the ECMWF TL255 (approximately 80 km) global ensemble prediction system (EPS). The 3-hourly EPS output was used to force the ALADIN model over the central European/northern Mediterranean domain.

Results indicate that the identical clustering algorithm may yield differing results when applied to either global or to downscaled ensembles. It is argued that this is linked to the fact that a downscaled, higher-resolution ensemble resolves more explicitly small-scale features, in particular those strongly influenced by orographic forcing. This result has important implications in limited-area ensemble prediction, since it implies that downscaling may affect the interpretation or relevance of the global ensemble forecasts; that is, it may not always be feasible to make a selection (or a subset) of global lower-resolution ensemble members that might be representative of all possible higher-resolution evolution scenarios.

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John C. Schaake, Thomas M. Hamill, Roberto Buizza, and Martyn Clark

The Hydrological Ensemble Prediction Experiment (HEPEX) is an international project to advance technologies for hydrological forecasting. Its goal is “to bring the international hydrological and meteorological communities together to demonstrate how to produce and utilize reliable hydrological ensemble forecasts to make decisions for the benefit of public health and safety, the economy, and the environment.” HEPEX is an open group composed primarily of researchers, forecasters, water managers, and users. HEPEX welcomes new members.

In the first workshop, held in the spring of2004, HEPEX participants formulated scientific questions that, once addressed, should help produce valuable hydrological ensemble prediction to serve users' needs. During the second HEPEX workshop, held in the summer of 2005, a series of coordinated test-bed demonstration projects was set up as a method for answering these questions. The test beds are collections of data and models for specific hydrological basins or subbasins, where relevant meteorological and hydrological data have been archived. The test beds will facilitate the intercomparison of various hydrological prediction methods and linkages to users. The next steps for HEPEX are to complete the work planned for each test bed and to use the results to engineer more valuable automated hydrological prediction systems.

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Roberto Buizza, P. L. Houtekamer, Gerald Pellerin, Zoltan Toth, Yuejian Zhu, and Mozheng Wei

Abstract

The present paper summarizes the methodologies used at the European Centre for Medium-Range Weather Forecasts (ECMWF), the Meteorological Service of Canada (MSC), and the National Centers for Environmental Prediction (NCEP) to simulate the effect of initial and model uncertainties in ensemble forecasting. The characteristics of the three systems are compared for a 3-month period between May and July 2002. The main conclusions of the study are the following:

  • the performance of ensemble prediction systems strongly depends on the quality of the data assimilation system used to create the unperturbed (best) initial condition and the numerical model used to generate the forecasts;
  • a successful ensemble prediction system should simulate the effect of both initial and model-related uncertainties on forecast errors; and
  • for all three global systems, the spread of ensemble forecasts is insufficient to systematically capture reality, suggesting that none of them is able to simulate all sources of forecast uncertainty.
The relative strengths and weaknesses of the three systems identified in this study can offer guidelines for the future development of ensemble forecasting techniques.

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