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ERA5 uses weather observations where such observations are available. On top of these observations ERA5 uses a weather forecasting model to produce a spatially and temporally continuous data. Like a weather forecast, the resulting data contains some uncertainty.
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(11) Where is ERA5 more accurate and where less, and how does the uncertainty evolve over time?
Seasonal spread charts are computed for the beginning of each ERA5 decade and for the latest period too (they are available by request from the ERA5 team). These charts give an give an idea about the level of uncertainties for different seasons, regions, periods, levels and variables. For instance, for the summer season of 1980:
For 200 hPa zonal wind the largest uncertainties are in the tropical regions. | For 850 hPa temperature, the uncertainties are generally larger in the Southern Hemisphere (this corresponds well with the fact that we have fewer observations in the Southern Hemisphere). | For MSLP the Antarctic region has the largest spread/uncertainty. | |
For all variables it is clear that the uncertainties are decreasing with time, i.e. the spread values are smaller for recent periods than for older ones.
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MSLP spread | 850 hPa temperature spread | 200 hPa zonal wind spread |
The main reason for this is the limited ensemble size of 10 members that introduces considerable sampling noise. On the other hand, if we consider the mean seasonal (JJA 1980 in this case) spread for the three variables the fields are much smoother and easier to interpret. For seasonal mean fields, this sampling noise is averaged out and as a result will provide smoother spread fields:
MSLP (mean seasonal (JJA) | 850 hPa temperature (mean seasonal (JJA) | 200 hPa zonal wind (mean seasonal (JJA) |
(14) When I look at active systems such as extra-tropical cyclones or tropical cyclones I expect a larger uncertainty, yet I do not see that clearly in the ensemble spread
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Yes. Uncertainty indicators are available on the same model levels (137 model levels, as in the high resolution operational IFS model), pressure, potential temperature and potential vorticity levels as the ensemble members. The raw ensemble data (all members) are available for the users to compute any processed information.
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This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view. |
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