| Title | Description |
|---|---|
| In reality shower cells have a finite lifetime, so precipitation associated moves with the showers, as one can see on radar. In the IFS showers are instantaneous (as they are parametrised) and the related precipitation does not propogate. So showers triggered over the sea do not generally move inland in the model as they should. This can lead to under-prediction errors of several mm in inland locations, 10mm or more in extremis. The degree to which the error extends inland depends on the windspeed at the steering level for showers. For stronger winds the errors extend further inland. For snow showers the errors can be worse still, compounded by the relatively slow fall speed of snowflakes (up to say one tenth of that of raindrops). So a snowflake starting its descent at the coast might end up landing on the ground 100km inland, if winds are strong, whereas a raindrop in equivalent summer conditions might only propogate 20km before reaching the ground. |
| 2. Tropical cyclone intensity | Resolution limits our ability to fully capture the core of strong winds around many tropical cyclones; likewise depths can be under-estimated, by over 50hPa in extremis. The problems are larger for smaller systems, with a smaller eye - Haiyan was one such example. Often minimum pressure in HRES will be lower than in all the ENS members, and likewise winds stronger; this is because of the higher resolution of HRES. In such situations HRES guidance will often be better, but not always. |
| 3. Time taken for snow on the ground to melt | In both ENS and HRES small amounts of snow on the ground tend to take too long to melt, even if the temperature of the overlying air is well above zero. This is because, for melting purposes, the snow that there is is assumed to be piled up high in one segment of a gridbox. For smaller nominal depths, the pile becomes higher, though at the same time covers a much smaller fraction of the box. The reason this is used is to improve the handling of screen temperature; by confining the snow to gridbox segments the impact on the temperature of that snow is reduced, and on average we find smaller errors and biases in 2m temperature as a result. The main downside is that snow cover pictures can look misleading, particularly at longer leads (when they can not be rectified by observational data). |
| 4. Multiple snow layers on the ground | The model assumes that all snow on the ground has the same density (though that density does vary with age etc.). This is inappropriate when new snow falls on top of old, for example. 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. |
| 5. China cold spot | 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-interim re-analyses are used to drive 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-Interim, in fact the ERA-Interim 'offline fields'. In turn this snow depth inevitably derives, in part, from what the model puts on the ground in the way of snowfall, and unfortunately the ERA-Interim model generates far less snowfall in this area, on average, than the current HRES model, for reasons that are not yet understood. Thus HRES is inclined to have a much deeper snow cover in its analyses through the winter, which encourages the development of 2m temperatures that are much lower than in the re-forecasts. The problem is compounded by the general lack of observations in this area which could in principal help to bring things back on track, and also by a 'feature' of the snow analysis scheme that currently excludes any observations above 1500m (the area in question is at an altitude around 4000m). This cut-off helps avoid problems in topographically complex terrain, though could be improved by using instead a measure of the sub-grid orography. |
| 6. Very poor SST evolution near New York | Due to the lack of resolution in the ocean component of the semi-coupled ENS system we are now running (introduced in Nov 2013 with 40R1), and an associated poor handling of the gulf stream wall, there is a major anomalous upward drift in SSTs over and S and E of the New York Bight (which itself lies just SE of New York city), in the first 10 days of the ENS forecasts. The area affected is about the size of England, and the size of the error that develops in 10 days can exceed 10C. |
| 7. Spurious snowfall in freezing rain situations | In certain winter situations, when snow descends through the atmosphere and melts to rain in a warm layer, before descending again through a cold (sub zero) layer, the model turns the precipitation back to snow far too readily. So surface precipitation in freezing rain situations commonly appears as snow, and that snow also accumulates on the ground. HOWEVER, it seems that where this precipitation is diagnosed as convective, this re-freezing problem does not exist. |
| 8. Jumpiness in EFI and SOT, especially at short lead times | A consequence of the current re-forecast strategy is that extreme events are sometimes not well sampled. Especially at short lead times, say 1 or 2 days, the 5 members that go up to make the re-forecast can be very similar, and so if the re-forecast dates (one per week) happen to be just before certain extreme events there may be over-sampling, whilst if extreme events fall inbetween the re-forecast dates, there may be under-sampling. Thus the tails of the model climate (M-Climate) distribution can be jumpy as we move from one lead time to another, and as EFI and SOT depend heavily on these tails, much more than they depend on solutions around the median, they can be jumpy too. |
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