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- Marine convection propogation
| 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 varies 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 output in some 'anomaly' form - such as EFI, SOT, monthly forecast anomalies, seasonal forecast anomalies - 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 | | |
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