Page History

...

The analysis process seeks to realistically represent in the model the actual state of the atmosphere.  However, inconsistencies in time and space of the observations mean that this aim will never be actually attained and the .  The best that can be done is to approximate (hopefully closely) the actual state of the atmosphere while maintaining the numerical stability (in the numerical sense) of the model atmosphere both horizontally and vertically.  The assimilation process is carried out by 4D-Var (see below).  In simple terms the previous analysis step has used model processes (e.g. dynamics, radiation, etc) to reach a forecast first guess value at a given location.  This will usually differ from an observation at that location and time. The difference between them is the "departure".  The analysis scheme now adjusts the value at the location towards the observed value while retaining stability in the model atmosphere. This adjustment is the "Analysis Increment".  The  If the magnitude , if is large , it suggests the model is not capturing the state of the atmosphere well (e.g. a jet is displaced, a trough is sharper, large scale active convection has not been completely captured).  However, a large Analysis Increment may also suggest poor data.  Analysis Increment charts are a powerful tool for identifying areas of uncertainty which might propagate downstream. 

...

Fig2.5-2: Schematic of the data assimilation process (from a diagnostic perspective).  All the model forecast parameters (dynamics, radiation, etc.) are used in the model forecast to deliver the first guess forecast (red).  The observations (grey) however will normally differ to a greater or lesser extent from the first guess forecast (red) and the .  The analysis increment (yellow) is evaluated to bring the the evolution more into line with the observations while maintaining model stability.  The resultant value (blue) becomes the first guess for the next analysis.

...

The four-dimensional variational analysis (4D-Var) system uses an optimisation procedure whereby to adjust the initial condition is adjusted to obtain:

• an optimal fit

...

• of all the observations in the assimilation time window and
• at the same time tries to stay as close as possible to the first guess.

  4D-Var uses the concept of a continuous feedback between observations and IFS model data analysis.  The impact of the observations is determined by three considerations:

• the assumed accuracy of the observations, .  These are considered to be more or less static.  However, consistently poor observations can be blacklisted.
• the representativeness of the observation relative to the IFS model's depiction (i.e. taking .  For example, this takes into account:
• the differences between the altitude of the observation and the corresponding altitude of that the corresponding point within in the IFS orography.
• the within the IFS, or the differences between the location of the observation and the grid point).
  
• the accuracy of the short-range forecasts, which are flow-dependent.  Hence the uncertainty may be larger in a developing baroclinic depression than in a subtropical high-pressure system.

The 4D-Var analysis uses the IFS dynamics and physics to create a sequence of states that fits as closely as possible to the available observations.  These states are consistent with the dynamics and physics of the atmosphere, as expressed by the equations of the IFS model.  4D-Var compares the actual observations with that which would be expected at the time and position of the observation given the model fields - it .  It is in effect a short-range forecast that serves to bring information forward from the previous cycle.  The

The analysis essentially uses data within a time window, currently 09–21UTC for the 12UTC model run; 21–09UTC for the 00UTC model run.  However in  In practice for the main forecasts of the ensemble use the data cut-off used (i.e. the last time) is brought back to 15UTC (for the 12UTC run) and 03UTC respectively (for the 12UTC and 00UTC runs, 00UTC run).  This is in order to be able to deliver forecasts to customers in a timely manner (see the continuing sequence of analyses).  All observational data are processed similarly, including radiances from satellites.  A weakly coupled sea-ice atmosphere assimilation is used in the surface analyses of the 4D-Var, and in the ensemble of data assimilations (EDA).  Before the introduction of cycle 45r1 in June 2018  a remotely-generated sea ice cover analysis (OSTIA) had been used directly.

...

Suppose a forecaster wants to create a sequence of manually analysed hourly synoptic charts for their region, which evolve smoothly, continuously and realistically from one time to the next, throughout.  To achieve this one approach would be to draw up one chart, using all available data, surface observations, imagery etc, then another for a subsequent time in the same way. Then the forecaster might go back to the first chart, rub something out and re-draw it in a rather different fashion , that still fits the available observations reasonably well, but  that allows the next chart to follow on better.  Then they might draw up other charts for other times, and repeat this process many times, rubbing out and re-drawing, probably all of the charts in some way or other, to achieve the final goal of sensible continuity.  Each time a chart is readjusted the changes needed will become smaller and smaller, until finally the forecaster is happy that they have a full and consistent sequence.  This is the forecaster's equivalent of 4D-Var.  Of course in 4D-Var there is the additional constraint of full vertical consistency also, though in the forecaster world soundings and imagery may indeed be contributing in an analogous way.

...