Dynamical Extended Range Forecasting (DERF) at the National Meteorological Center

M. Steven Tracton Climate Analysis Center, National Meteorological Center, NWS/NOAA, Wasington, D.C

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Kingtse Mo Climate Analysis Center, National Meteorological Center, NWS/NOAA, Wasington, D.C

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Wilbur Chen Climate Analysis Center, National Meteorological Center, NWS/NOAA, Wasington, D.C

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Eugenia Kalnay Development Division National Meteorological Center. NWS/NOAA, Washington, D.C.

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Robert Kistler Development Division National Meteorological Center. NWS/NOAA, Washington, D.C.

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Glenn White Development Division National Meteorological Center. NWS/NOAA, Washington, D.C.

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Abstract

Early results are presented of an experimental program in Dynamical Extended Range Forecasting at the National Meteorological Center. The primary objective of this program is to assess the feasibility of extending operational numerical weather prediction beyond the medium range to the monthly outlook problem. Additionally, the extended integrations provide greater insight into systematic errors and climate drift and thereby feedback to model development. In this paper the principal focus is upon assessment of a contiguous set of 108 thirty-day integrations generated with the then operational Medium Range Forecast model from initial conditions 24 hours apart between 14 December 1986 and 31 March 1987.

Results indicate some serious model deficiencies such as the tendency for zonalization, i.e., systematically stronger midlatitude zonal flow than observed, and a stratospheric cold bias, which continues to grow through the 30--day integrations.

In the 1–30 day mean Northern Hemisphere 500 mb height fields the dynamical model is almost always more skillful than persistence. Most of this skill, however, is concentrated in the earlier time ranges so that on average the best estimate of the 30-day mean circulation is not the forecast 30-day mean, but the average of only the first 7–10 days. Beyond 10 days the average skill is low, but the variability in skill is large with many individual cases of skillful predictions.

We consider the problem of enhancing the forecast skill by statistical postprocessing, including time averaging Legged Average Forecasting (LAF), correction of systematic errors and Empirical Orthogonal Function filtering. A main finding is that these procedures separately or in combination, can significantly enhance the skill of already skillful predictions but do not have a significant effect on poor forecasts.

Four potential predictors of skill have been examined. Forecast agreement, the degree of consistency between members of LAF ensembles, explains on average about 10% of the regional skill variance, and forecast persistence an additional 5%. The magnitude of the forecast anomaly has virtually no relationship with skill except for small anomalies for which the skill also becomes very small. lie Pacific North American (PNA) teleconnection index of the initial circulation regime is an extremely good indicator of forecast skill at midranges where the correlation between the PNA index and skill reaches 0.77.

Finally, a major finding is that a large component of the variability in forecast results from the inability to predict the evolution of blocking events beyond a few days into the forecast. We also found a relationship between blocking episodes and the antecedent PNA index, but whether this relationship is more than coincidental has not yet been established.

Abstract

Early results are presented of an experimental program in Dynamical Extended Range Forecasting at the National Meteorological Center. The primary objective of this program is to assess the feasibility of extending operational numerical weather prediction beyond the medium range to the monthly outlook problem. Additionally, the extended integrations provide greater insight into systematic errors and climate drift and thereby feedback to model development. In this paper the principal focus is upon assessment of a contiguous set of 108 thirty-day integrations generated with the then operational Medium Range Forecast model from initial conditions 24 hours apart between 14 December 1986 and 31 March 1987.

Results indicate some serious model deficiencies such as the tendency for zonalization, i.e., systematically stronger midlatitude zonal flow than observed, and a stratospheric cold bias, which continues to grow through the 30--day integrations.

In the 1–30 day mean Northern Hemisphere 500 mb height fields the dynamical model is almost always more skillful than persistence. Most of this skill, however, is concentrated in the earlier time ranges so that on average the best estimate of the 30-day mean circulation is not the forecast 30-day mean, but the average of only the first 7–10 days. Beyond 10 days the average skill is low, but the variability in skill is large with many individual cases of skillful predictions.

We consider the problem of enhancing the forecast skill by statistical postprocessing, including time averaging Legged Average Forecasting (LAF), correction of systematic errors and Empirical Orthogonal Function filtering. A main finding is that these procedures separately or in combination, can significantly enhance the skill of already skillful predictions but do not have a significant effect on poor forecasts.

Four potential predictors of skill have been examined. Forecast agreement, the degree of consistency between members of LAF ensembles, explains on average about 10% of the regional skill variance, and forecast persistence an additional 5%. The magnitude of the forecast anomaly has virtually no relationship with skill except for small anomalies for which the skill also becomes very small. lie Pacific North American (PNA) teleconnection index of the initial circulation regime is an extremely good indicator of forecast skill at midranges where the correlation between the PNA index and skill reaches 0.77.

Finally, a major finding is that a large component of the variability in forecast results from the inability to predict the evolution of blocking events beyond a few days into the forecast. We also found a relationship between blocking episodes and the antecedent PNA index, but whether this relationship is more than coincidental has not yet been established.

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