Known IFS forecasting issues

In products that intrinsically display 2m temperature output in some 'anomaly' form - such as monthly forecast anomalies, seasonal forecast anomalies, and in the shorter ranges EFI and SOT - there is a semi-permanent winter-time 'cold spot' over eastern China. It is not real in the sense that temperatures are not always 'below normal' in this area when they are shown to be. The cold spot owes its existence to incompatibilities between the current forecasting system, and ERA-Interim (ERA-I). ERA-I re-analyses are used to start the re-forecasts which form the 'climatology' against which current forecasts are compared. So whilst these re-forecasts are rightly performed with the latest model version, they also inherit, as a starting point, auxiliary data such as snow depth from ERA-I, in fact from the ERA-I 'offline fields'. In turn this offline snow depth inevitably derives, in part, from what the ERA-I model puts on the ground in the way of snowfall, and this model's climatology is such that there is less snowfall in this area, on average, than in the current HRES. In turn, an intrinsic part of the offline run is a re-scaling using GPCP climatology, and the GPCP climate meanwhile is much drier in winter than either the IFS or ERA-I. So HRES and other IFS components are inclined to have a much deeper snow cover in their analyses through the winter than the re-forecasts have in theirs, which encourages the development of 2m temperatures that are similarly much lower - ie the 'cold spot' anomalies.

The problem is compounded by a historical dearth of observations in this area which might have helped bring things back on track, and also by a snow analysis scheme 'feature' that excludes all observations above 1500m (the said area is around 4000m). This cut-off helps avoid problems in complex terrain, though could be improved by using instead a measure of sub-grid orography. The limited number of observations that do exist suggest that the winter-time snowfall climatology is rather better in IFS than in ERA-I offline, implying that the IFS' absolute 2m temperature forecasts should be more reliable than our anomaly-format products.

6. Marine convection propagation

8. Underestimation of convective precipitation extremes

As a consequence of resolution, and the related parametrisation of convection, localised extreme values in precipitation totals will be systematically "underestimated" in IFS output. Differences equal to about one order of magnitude are possible. However this is not as bad as it seems, because when verified over areas that are the same size as the effective model gridbox size the agreement is generally much better.

If one examines the distribution, in forecasts, of daily rainfall totals for locations in the tropics, the (wet) tails tend to be longer for very short lead times (eg T+0 to T+24), implying that ENS and HRES have a greater propensity to generate extreme rainfall in short range forecasts than they do in medium range forecasts. For example the 99th percentile of daily rainfall at some locations at day 1 is twice what it is at day 3. This would appear to be a 'spin down' issue, of sorts, related to the handling of convection. Formulation of the EFI and SOT is such that they intrinsically account for this (though note item 19 below), so the problem arises for the user particularly when referencing the direct model output.

10. Extreme rainfall at certain gridpoints ("rain bombs")

The model assumes that all snow on the ground has the same density (though that density does vary - e.g. increasing with age). So layers of different density, which arise in the real world, are not catered for. This can impact on several things, such as total snow water content, and upward heat conductivity, which in turn has the potential to adversely affect 2m temperature.

In addition, when new snow falls onto old, the change in snow depth is commonly less that it should be, because the density assigned to the fresh snow depends in part on the density of the pre-existing lying snow, and so tends to be greater than it should. The magnitude of the error (in snow depth change) increases when the pre-existing snow is deeper and/or has a greater density. Example: if 10cm of new snow (ratio 12:1) fell onto 10cm of old lying snow (ratio 2.5), snow depth in the model would increase by only 3.5cm.

There is no coupling with the ocean in HRES. So for relatively slow moving TCs, when fluxes and mixing might in reality lead to a reduction in SST, the SST will remain at an elevated level and this can give the TC extra impetus to deepen too much (provided other factors such as shear remain favourable). For fast moving TCs the affected ocean is left behind, and so the problem is less acute or non-existent. Whilst item 16 above generates errors in the opposite sense that might sometimes fortuitously cancel, there are nonetheless recorded cases where TCs have been over-deepened, by as much as 50mb, because of the lack of coupling.

Sea ice cover does not change in any interactive way in the forecasts as we do not have a sea ice model. So none of the following are represented: sea ice formation due to low air temperatures, break up due to wind effects or melting, and advection by currents and winds. In turn this affects weather that relates, such as 2m temperatures over and downwind of, and convection triggered over water but not over ice. Wave model output will naturally also be affected.

In the twice daily forecasts to day 15 sea ice cover is fixed. At longer ranges, to capture the seasonal cycle, there is relaxation towards the ice cover of the last five years; for monthly forecasts all ENS members use the average value, for seasonal forecasts members are divided into five sets (of 10) each one of which uses the pattern for one of those five years.