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Ensemble Experiments on Numerical Weather Prediction Error and Uncertainty for a North Pacific Forecast Failure

Ensemble Experiments on Numerical Weather Prediction Error and Uncertainty for a North Pacific... An intense maritime cyclone off the northwest coast of North America during 9–14 February 1999 was remarkable in the repeated inability of numerical weather prediction (NWP) models to forecast its evolution. Each day during that period, the operational and research models of the United States and Canada were initialized with new analysis fields. Each day, the new short-term NWP forecasts predicted the storm to strike the densely populated Lower Mainland (Vancouver) area of southwest British Columbia, Canada, during the next 24–48 h. NWP guidance prompted the local forecast office to issue storm warnings including one or more of the following for Vancouver: heavy snow, heavy rain, and strong winds. Satellite imagery clearly showed the storm off the coast, but the storm did not strike Vancouver until much later and in a decayed state. This synoptic case is studied with an aim to understand the source of the NWP error, and an ensemble of research model runs is made to address three possibilities for failure: 1) initial condition (IC) error, 2) model error for a particularly nonlinear or sensitive event, and 3) sympathetic data denial. To estimate the effect of IC uncertainty, a short-range ensemble system is developed and tested on a limited-area model for a sequence of successive 3-day reforecasts covering the 10-day period surrounding the storm. This IC ensemble shows some correlation between spread and skill and provides one estimate of IC uncertainty. To estimate the effect of model uncertainty, a physics-based ensemble is run for the same period. The effect of data denial is investigated by comparing forecasts made with the same model but from analyses created at different operational centers. Results suggest that if the runs initialized at 0000 UTC 10 February 1999 were used for guidance, model uncertainty was likely responsible for the forecast failure. It had larger-than-average model error but lower-than-average IC error. Subsequent forecast errors were likely dominated by IC uncertainty. An attempt at assessing sympathetic data denial is inconclusive. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Weather and Forecasting American Meteorological Society

Ensemble Experiments on Numerical Weather Prediction Error and Uncertainty for a North Pacific Forecast Failure

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Publisher
American Meteorological Society
Copyright
Copyright © 2001 American Meteorological Society
ISSN
1520-0434
DOI
10.1175/1520-0434(2003)018<0012:EEONWP>2.0.CO;2
Publisher site
See Article on Publisher Site

Abstract

An intense maritime cyclone off the northwest coast of North America during 9–14 February 1999 was remarkable in the repeated inability of numerical weather prediction (NWP) models to forecast its evolution. Each day during that period, the operational and research models of the United States and Canada were initialized with new analysis fields. Each day, the new short-term NWP forecasts predicted the storm to strike the densely populated Lower Mainland (Vancouver) area of southwest British Columbia, Canada, during the next 24–48 h. NWP guidance prompted the local forecast office to issue storm warnings including one or more of the following for Vancouver: heavy snow, heavy rain, and strong winds. Satellite imagery clearly showed the storm off the coast, but the storm did not strike Vancouver until much later and in a decayed state. This synoptic case is studied with an aim to understand the source of the NWP error, and an ensemble of research model runs is made to address three possibilities for failure: 1) initial condition (IC) error, 2) model error for a particularly nonlinear or sensitive event, and 3) sympathetic data denial. To estimate the effect of IC uncertainty, a short-range ensemble system is developed and tested on a limited-area model for a sequence of successive 3-day reforecasts covering the 10-day period surrounding the storm. This IC ensemble shows some correlation between spread and skill and provides one estimate of IC uncertainty. To estimate the effect of model uncertainty, a physics-based ensemble is run for the same period. The effect of data denial is investigated by comparing forecasts made with the same model but from analyses created at different operational centers. Results suggest that if the runs initialized at 0000 UTC 10 February 1999 were used for guidance, model uncertainty was likely responsible for the forecast failure. It had larger-than-average model error but lower-than-average IC error. Subsequent forecast errors were likely dominated by IC uncertainty. An attempt at assessing sympathetic data denial is inconclusive.

Journal

Weather and Forecasting – American Meteorological Society

Published: Jun 27, 2001

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