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Assimilation of 2m temperature
Screen level temperature (T2m) observations are assimilated into the model analyses. Assimilation is done using 4D-Var as for observations of other variables. Observations of screen level temperature are accepted at higher spacial and temporal density than in Cy49. Screen level temperatures were not assimilated in Cy48 and earlier. The model atmosphere uses sea surface temperatures (SSTs) directly from the ocean model.
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- The observed screen level temperature is adjusted using a lapse rate of 5.5°C/km to take account of the height difference between station height and model height in 4D-Var. This fits the data slightly better than the standard lapse rate of 6.5°C/km.
- Only stations between 400 m below and 200 m above the model height are used. The lower height limit is because, on average, stations are slightly lower than the model height as they are more likely to be in valleys.
- No bias correction is applied, because of the complexity of observation–background T2m biases, and in many cases the background biases are larger than observation biases.
- Large differences of the adjusted temperature from the background T2m temperature field are given a lower weighting. Temperature differences of more than 7.5°C are not used.
- Observations are clustered into 15 minute 'timeslots' before assimilation over a 12‑hour 4D‑Var window. This allows a more accurate comparison between the model and the observations and enables more localised increments. ('Timeslots' were 30min in 6hr window in Cy49).
Assimilation of 2m specific humidity
Screen level specific humidity (q2m) observations, both day and night, are assimilated into the model analyses in a similar way to the assimilation of screen level temperature observations.
Forecast of 2m temperatures
Model output of forecast of screen level temperatures (T2m) is not a direct output from the atmospheric model.
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- in some winter regions 850 hPa temperatures are degraded. This is partly due to unrealistic coupling in stable conditions.
Forecast of 2m humidity
Model output of forecast of screen level humidity (q2m) is not a direct output from the atmospheric model. It is interpolated in a similar way to screen level temperature.
Fig2A.1.4.9-1: 2m temperatures are interpolated between model forecast temperature at the lowest atmospheric model level (level 137, ~10m) and surface skin temperature. 2m dew points are interpolated in a similar way between from model forecast specific humidities. The nature of the interpolation profile used depends on other factors, such as stability and/or wind speed. (See Fig9.1.4.9-2).
Fig2A.1.4.9-2: The interpolation function (α) shown as a function of stability. The stability measure is taken as the ratio of height above ground (z) to the Monin-Obukhov length (L). The Monin-Obukhov length (L) is itself a function of, among other parameters, horizontal wind speed and upward ground heat flux. α=1 implies that T2m equals the temperature at the lowest model level (TL137 at 10m); α=0 implies that T2m equals the surface (skin) temperature. For practical purposes, the orange line shows the function that is used to interpolate between the temperature at 10m and the skin temperature.
Fig2A.1.4.9-3: Illustration of temperature observations which are accepted for use in analysis of T2m by 4D-Var. Temperatures are adjusted by 5°C/km from station height up or down to the model orography height. Temperatures at stations >200m higher or >400m lower than model orography height are not used. Stations on mountain tops and in deep valleys are thus excluded while retaining the majority of observations, including those in shallow valleys where many stations are located.
Assimilation of other surface variables
Winds from ships and moored buoys are assimilated. However, 10 m wind observations over land are not assimilated as it has proved difficult to get a positive impact on forecasts.
Surface pressure observations remain the most important surface variable for global NWP.
Considerations in using the forecast values
In land surface modelling (HTESSEL):
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Users should assess the potential for deficiencies in low-level parameters and adjust forecast values as necessary.
Additional sources of information
(Note: In older material there may be references to issues that have subsequently been addressed)
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