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Remarks on the Extended Range
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Sea-surface temperatures have an important influence upon the atmospheric evolution. There is oceanOcean-atmosphere coupling (is made at hourly intervals ) throughout the extended range forecast period. This high-frequency coupling:
- can influence the development of synoptic-scale systems (e.g. tropical cyclones)
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- influences meteorological evolution and predictability in the extra-tropics
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- helps
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- capture the propagation of Madden-Julian Oscillation (MJO) events, notably in the equatorial Indian Ocean and western Pacific ocean.
The LIM2 subprogram (within the NEMO) forecasts changes in the sea-surface temperature and sea-ice evolution. Note: ECMWF uses LIM2 which is an earlier version of the Louvain-la-Neuve sea ice model currently available (Version 3.6).
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The strategy for dealing with model drift is straightforward.:
- For all forecasts
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- , the ocean, atmosphere and land surface are initialised to be as close to reality as possible. Then the coupled models calculate the evolution of the atmospheric and oceanic systems.
- No "artificial" terms are introduced to try to reduce the drift of the model.
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- No steps are taken to remove or reduce any imbalances in the coupled model initial state.
The The effect of model drift can be estimated from re-forecasts and thus may be "removed" from the latest model solution during the post-processing. This is not an expedient rather than a perfect solution, but rather an expedient one. Model drift characteristics may also depend somewhat on the prevailing synoptic pattern, and will not be accounted for fully.
The extended range model climate (ER-M-climate) drifts towards becoming rather too cold at longer lead-times in wintertime high latitudes. ????? Hence the anomaly in forecast temperatures against ER-M-climate temperatures may be too large. The magnitude of the drift is not uniform. At longer lead-times the trend in northern China is towards colder values but less so in Siberia and Canada. The variation may be due to the analysed initial snowpack conditions and/or snowmelt in marginal snow cover areas in these areas. Issues regarding this are being addressed. A multi-layer snow scheme is incorporated.
After about 10 days of forecasts, the spread of the ensemble can become very large. A significant shift can be detected by comparing probability distribution functions of the latest model and the ER-M-climate.
Fig5.2.1: Example of plumes for Dublin. Extended Range forecast, DT00UTC 1 January 2018. The plumes show increasing spread of forecasted values of 850hPa temperatures and 500hPa geopotential height within the extended range period.
The re-forecasts are created twice a week (Mondays and Thursdays) and are ready a week before the real-time forecasting suite starts. Real-time forecasts are calibrated using all the re-forecasts available in a one week window centred on the forecast start day and month. ?????
Fig5.2.1: Example of plumes for Dublin. Extended Range forecast, DT00UTC 1 January 2018. The plumes show increasing spread of forecasted values of 850hPa temperatures and 500hPa geopotential height within the extended range period.
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