problems and promises of deterministic extended range forecasting1

Smagorinsky, Joseph

doi:10.1175/1520-0477-50.5.286

Cited by 142 publications

(82 citation statements)

References 5 publications

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“…A few years later, Smagorinsky (1969), using a more refined, nine-level primitive equation model that contained also moist processes, gave a lower estimate of the forecast-error doubling time of 3 days. Lorenz (1982), analysing 500 hPa geopotential height forecasts from the operational model used at the European Centre Copyright c 2010 Royal Meteorological Society for Medium-Range Weather Forecasts (ECMWF) at that time, a 15-level primitive equation model with moist processes and orography, further reduced the estimate to 1.85 days for the Northern Hemisphere (NH) winter.…”

Section: Forecast-error Growth and Predictability Limits Of A Numericmentioning

confidence: 99%

Horizontal resolution impact on short- and long-range forecast error

Buizza

2010

Q.J.R. Meteorol. Soc.

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The impact of horizontal resolution increases from spectral truncation T95 to T799 on the error growth of ECMWF forecasts is analysed. Attention is focused on instantaneous, synoptic-scale features represented by the 500 and 1000 hPa geopotential height and the 850 hPa temperature. Error growth is investigated by applying a three-parameter model, and improvements in forecast skill are assessed by computing the time limits when fractions of the forecast-error asymptotic value are reached. Forecasts are assessed both in a realistic framework against T799 analyses, and in a perfect-model framework against T799 forecasts.A strong sensitivity to model resolution of the skill of instantaneous forecasts has been found in the short forecast range (say up to about forecast day 3). But sensitivity has shown to become weaker in the medium range (say around forecast day 7) and undetectable in the long forecast range. Considering the predictability of ECMWF operational, high-resolution T799 forecasts of the 500 hPa geopotential height verified in the realistic framework over the Northern Hemisphere (NH), the long-range time limit τ (95%) is 15.2 days, a value that is one day shorter than the limit computed in the perfect-model framework. Considering the 850 hPa temperature verified in the realistic framework, the time limit τ (95%) is 16.6 days for forecasts verified in the realistic framework over the NH (cold season), 14.1 days over the SH (warm season) and 20.6 days over the Tropics.Although past resolution increases have been providing continuously better forecasts especially in the short forecast range, this investigation suggests that in the future, although further increases in resolution are expected to improve the forecast skill in the short and medium forecast range, simple resolution increases without model improvements would bring only very limited improvements in the long forecast range.

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Section: Forecast-error Growth and Predictability Limits Of A Numericmentioning

confidence: 99%

Horizontal resolution impact on short- and long-range forecast error

Buizza

2010

Q.J.R. Meteorol. Soc.

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“…The evolution of error growth in numerical weather prediction (NWP) from the analysis state to the extended forecast has been of interest since the early development of atmospheric models (Thompson, 1957;Lorenz, 1963Lorenz, , 1993Charney et al, 1966;Smagorinsky, 1969). Understanding the nature of error growth helps provide guidance for the best methods of reducing forecast error, as well as quantification of the uncertainty of predictions.…”

Section: Introductionmentioning

confidence: 99%

Spectral analysis of forecast error investigated with an observing system simulation experiment

Privé¹,

Errico

2015

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C TThe spectra of analysis and forecast error are examined using the observing system simulation experiment framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office. A global numerical weather prediction model, the Global Earth Observing System version 5 with Gridpoint Statistical Interpolation data assimilation, is cycled for 2 months with once-daily forecasts to 336 hours to generate a Control case. Verification of forecast errors using the nature run (NR) as truth is compared with verification of forecast errors using self-analysis; significant underestimation of forecast errors is seen using self-analysis verification for up to 48 hours. Likewise, self-analysis verification significantly overestimates the error growth rates of the early forecast, as well as mis-characterising the spatial scales at which the strongest growth occurs. The NR-verified error variances exhibit a complicated progression of growth, particularly for low wavenumber errors. In a second experiment, cycling of the model and data assimilation over the same period is repeated, but using synthetic observations with different explicitly added observation errors having the same error variances as the control experiment, thus creating a different realisation of the control. The forecast errors of the two experiments become more correlated during the early forecast period, with correlations increasing for up to 72 hours before beginning to decrease.

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“…The study of atmospheric predictability was initiated by Thompson [8] and Lorenz [6,9] more than 50 years ago and was extensively explored theoretically using various numerical and statistical models since then (e.g. [10][11][12][13][14][15][16][17]). One of the obvious measures of predictability that can be used to verify a weather forecast is the mean-squared error (the average of the squared differences between forecasts and observations).…”

Section: Introductionmentioning

confidence: 99%

Predictability in Deterministic Dynamical Systems with Application to Weather Forecasting and Climate Modelling

Soldatenko¹,

Yusupov²

2017

Dynamical Systems - Analytical and Computational Techniques

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Climate system consisting of the atmosphere, ocean, cryosphere, land and biota is considered as a complex adaptive dynamical system along with its essential physical properties. Since climate system is a nonlinear dissipative dynamical system that possesses a global attractor and its dynamics on the attractor are chaotic, the prediction of weather and climate change has a finite time horizon. There are two kinds of predictability of climate system: one is generated by uncertainties in the initial conditions (predictability of the first kind) and another is produced by uncertainties in parameters that describe the external forcing (predictability of the second kind). Using the concept of the 'perfect' climate model, two kinds of predictability are considered from the standpoint of the mathematical theory of climate.

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problems and promises of deterministic extended range forecasting1

Cited by 142 publications

References 5 publications

Horizontal resolution impact on short- and long-range forecast error

Horizontal resolution impact on short- and long-range forecast error

Spectral analysis of forecast error investigated with an observing system simulation experiment

Predictability in Deterministic Dynamical Systems with Application to Weather Forecasting and Climate Modelling

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