Known IFS forecasting issues

Title	Description	Related activities
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 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.	Resolution upgrade in 2015
3. Snow on the ground takes too long 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 of course be rectified by observational data).	4-year project to address snow issues
4. Multiple snow layers	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.	4-year project to address snow issues
5. Mixed rain/snow leads to snow accumulation	In marginal snow situations, when precipitation at the surface comprises both rain and snow, the snow component accumulates as lying snow. In the vast majority of cases it should melt instantaneously.	4-year project to address snow issues
6. 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.
7. 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.
8. 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.	Physics changes under test
9. 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.	Re-forecast dataset size likely to be increased
10. 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.	Resolution upgrade in 2015
11. Underestimation of orographically-enhanced precipitation	As a consequence of topographical barriers being too low, in general (due to resolution), both the orographic enhancement of precipitation and the rain shadow effect tend to be underestimated in the IFS (more so in ENS than HRES, and more so in ENS after 10 days when resolution changes).	Resolution upgrade in 2015
12. City temperatures too low	Due to the urban heat island effect not being represented, screen temperatures in large urban areas, particularly cities, are commonly too low compared to observations. The problem can be accentuated in winter by snow cover.
13. Sea ice evolution and associated weather	Sea ice cover does not change in the forecast 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.	Sea ice model being developed
14. Sunshine duration	The integrity of this post-processed output parameter is strongly compromised by the radiation timestep in the model, which because of computational cost is longer than the basic model timestep (3 hours in ENS, 1 hour in HRES). This manifests itself in the sunshine duration parameter being (a) an undesirable function of longitude and (b) unreliable.
15. Screen temperatures fall too much near coasts	As a consequence of the radiation grid being larger than the model grid (due to computational constraints) night-time radiative cooling over land near to the coast is often too rapid. This is because cooling progresses according to T⁴, and at near-coast points T is approximately the average temperature of the land and (warmer) ocean. As a result screen temperatures drop too much - related errors can sometimes exceed 10C. The problem is enhanced (i) when there is snow cover, (ii) at high latitudes, and (iii) when the coast has a convex shape.
16. Under-estimation of strong gusts in convective situations	Although there is a helpful convective contribution in the computation of maximum gusts (as used in direct model output and the EFI), experience has shown that extreme gusts are generally under-represented, particularly when vigorous convection is involved, such as one might see with MCSs or squall lines - eg 60kts might be observed when 30-40kts is predicted. This relates to (i) an inability, at current model resolution, to represent the 3-d circulation around convective systems, and (ii) the fact that it is impossible to design an adjustment in the gust computation that will work in all cases.
17. Tropical rainfall extremes greatest on day 1	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 9 above), so the problem arises for the user particularly when referencing the direct model output.

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