The Singular-Vector Structure of the Atmospheric Global Circulation

Buizza, Roberto; Palmer, T. N.

doi:10.1175/1520-0469(1995)052<1434:tsvsot>2.0.co;2

Cited by 596 publications

(449 citation statements)

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“…Ferranti et al, 2002). The presence 1332 T. JUNG AND M. LEUTBECHER 19601965197019751980198519901995 Figure 3, however, are representative for the polar regions as a whole. In order to understand whether there are regional differences in the influence of resolution on forecast skill, high-resolution deterministic and lower-resolution control forecasts have been assessed on a grid-point basis (Figure 4).…”

Section: Deterministic Forecast Skillmentioning

confidence: 97%

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Performance of the ECMWF forecasting system in the Arctic during winter

Jung

Leutbecher

2007

Quart J Royal Meteoro Soc

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ABSTRACT:The performance of the ECMWF forecasting system in the Arctic during boreal winter is investigated. A comparison between different analysis products suggests that synoptic-scale features in the Arctic are relatively well represented by state-of-the-art analysis systems. Furthermore, it is shown that the improvement in deterministic forecast error in the Arctic since the early 1980s closely follows that reported in previous studies for the Northern Hemisphere as a whole. One of the biggest 'jumps' in deterministic forecast skill, both in the troposphere and in the stratosphere, occurred in autumn 2000 when the high-resolution deterministic system (T L 511, about 40 km) was introduced at ECMWF. Our results suggest that 10-day forecasts of the stratospheric circulation have been very skilful, particularly since the introduction of the high-resolution system. Adjoint computations reveal pronounced flow-dependence of the sensitivity of short-range forecast error to initial perturbations at high northern latitudes (north of 70°N); this highlights the importance of using ensemble prediction systems (EPSs) for weather forecasting in the Arctic. Verification of the ECMWF EPS in the Arctic reveals substantial improvements in probabilistic predictive skill since the mid-1990s. The most pronounced increase in probabilistic forecast skill, particularly on synoptic scales, occurred in autumn 2000 when the high-resolution EPS (T L 255, about 80 km) was introduced. Finally, it is shown that the observed stratosphere-troposphere link ('downward propagation' of stratospheric anomalies), which constitutes a potential source of extended-range predictability in high latitudes, is realistically represented in seasonal integrations with the ECMWF model -even at the relatively low horizontal resolution of T L 95 (about 180 km).

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Section: Deterministic Forecast Skillmentioning

confidence: 97%

“…Initial perturbations are constructed using the singular-vector (SV) approach (e.g. Buizza and Palmer, 1995). Since October 1998, model uncertainty has been taken into account through the use of a simple stochastic physics scheme (Buizza et al, 1999).…”

Section: Datamentioning

confidence: 99%

Performance of the ECMWF forecasting system in the Arctic during winter

Jung

Leutbecher

2007

Quart J Royal Meteoro Soc

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“…The Singular Vector (SV) method (Buizza and Palmer 1995) computes new perturbations at each analysis time. The method identifies the fastest growing perturbations over a given time period (e.g.…”

Section: Initial Condition Uncertaintiesmentioning

confidence: 99%

Medium- and Extended-Range Ensemble Weather Forecasting

Richardson

2018

Weather &Amp; Climate Services for the Energy Industry

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The chapter provides an overview of ensemble weather forecasting for the medium-and extended-range (days to weeks ahead). It reviews the methods used to account for uncertainties in the initial conditions and in the forecast models themselves. The chapter explores the challenges of making useful forecasts for the sub-seasonal timescale, beyond the typical limit for skilful day-to-day forecasts, and considers some of the sources of predictability such as the Madden-Julian oscillation (MJO) that make this possible. It then introduces some of the ensemble-based forecast products and concludes with a case study for a European heat wave that demonstrates how ensemble weather forecasts can be used to guide decision making for weather-dependent activities.

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“…These systems have been designed to simulate the effect on initial and model uncertainties on forecast states. When first implemented in 1992 (note that at the time of writing, ECMWF is celebrating 15 years of operational ensemble prediction), the ECMWF Ensemble Prediction System (EPS, Molteni et al, 1996) was based on 33 forecasts produced with a T63L19 (spectral triangular truncation Buizza and Palmer, 1995). Between December 1992 and September 2006, the EPS was upgraded several times, benefiting both from changes of the ECMWF data assimilation and forecasting system, and from modifications of the EPS configuration designed to improve the simulation of initial and model uncertainties.…”

Section: The Meteorological and Hydrological Ensemble Approaches To Pmentioning

confidence: 99%

The value of probabilistic prediction

Buizza

2008

Atmospheric Science Letters

117

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Probabilistic forecasts designed to estimate the probability density function of forecast states are potentially more valuable than single forecasts because (1) they can predict not only the most likely outcome but also the probability of occurrence of extreme and rare events and (2) probabilistic forecasts issued on consecutive days are usually more consistent than corresponding single forecasts. In this communication, the potential economic value of probabilistic and single forecasts are compared, and single and probabilistic forecasts of precipitation for a flooding event are analyzed to illustrate the potential value of consistency between successive forecasts.

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The Singular-Vector Structure of the Atmospheric Global Circulation

Cited by 596 publications

References 0 publications

Performance of the ECMWF forecasting system in the Arctic during winter

Performance of the ECMWF forecasting system in the Arctic during winter

Medium- and Extended-Range Ensemble Weather Forecasting

The value of probabilistic prediction

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