Page History

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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). 

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• the model forecast temperature at the lowest model level (L137 at 10m) and 
• the model forecast temperature of the underlying surface (the skin temperature).  This is determined using the land surface scheme HTESSEL, or the lake surface scheme FLake, or the sea-surface temperature (from NEMO). 

Stability in the lowest layers is taken into account using an interpolation function (α) derived using Monin-Obukhov similarity theory.  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.

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Considerations in using the forecast values

In land surface modelling (HTESSEL):

• an urban tile models the fluxes of heat, moisture and momentum at the surface and allows a more realistic representation the heat island effects of towns and cities.
• separation of tiles into high and low vegetation gives a more accurate seasonal variability of vegetation and incorporates the differing albedo effects of underlying snow cover.

In soil structure modelling:

• forecasts of the relative availability of water in the soil for plant uptake allows plant roots to better extract water, especially in relatively dry conditions.  This can affect the surface specific humidity.

In the ensemble of data assimilations (EDA):

• the background error estimate still appears too small in the lowest levels although stochastic physics in Cycle 49r1 have partially addressed this.  A larger spread of error estimates near the surface would tend to increase the size of analysis increments from T2m assimilation at the lowest model level and to reduce them a few levels up.  This would be more realistic for winter cases.

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• actual and model station altitude in mountainous areas may differ.
• strong surface inversions, particularly over snow, may not be well modelled.
• extent of low cloud cover may not be captured by the model.

Users should assess the potential for deficiencies in low-level parameters and adjust forecast values as necessary.

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• Read more about improved 2m temperature forecasts in the 2024 upgrade (Cy49r1)

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