Atmospheric Model Data Sources

The best analysis is that which allows the IFS models to produce forecasts subsequently that verify nearest to the actual evolution.  The analysis is not necessarily true to the observations in every respect, though of course the analysis processes (4D-Var and LDAS) try to assimilate them to best effect.  For the purposes of the ensemble, the analysis process also tries to quantify the uncertainty in the estimate of the initial state.  Advanced analysis procedures have to be used to assimilate non-conventional observations. 

The ECMWF Land Data Assimilation System (LDAS) provides the land-surface analysis including:

• the screen-level parameters analyses.
• the snow depth analysis.
• the soil moisture analysis.
• the soil temperature.
• the snow temperature analysis.   

When the analysis makes large changes to the background state there are two main possibilities:

• significant differences to forecast values have occurred (e.g. rapid development that is not present in the forecast).
• poor or suspect observations have had an undue influence.  Suspect observations usually have reduced weight attached to them so that they have less influence during the assimilation process - they are rarely rejected.  The impact of suspect observations can be significant only if there is a large number of suspect observations supporting each other.

It is important to inspect closely these areas to help assess shortcomings of evolution in previous forecasts or the effect of the latest observations on the current forecast.

The observations used for the analysis of the atmosphere are available at both synoptic and asynoptic hours and can be divided roughly into direct observations and remote-sensing observations. 

Direct or Ground-based observations 

These consist of observations from:

• Surface weather stations, ships, and buoys.
  • Surface pressure observations are used over land and sea.
  • Wind speed and direction observations from ships and buoys are used.  
  • Wind observations from land stations or even from islands or coastal stations are not used.  
  • Screen temperatures over land are used in the analysis.  Screen temperatures over land are adjusted to the 2m level by taking account of:
    • the vertical distance between the observations and grid point altitude using a modified interpolation function rather than fixed value of stability.
    • land use and land cover with the associated high and low vegetation values which are taken from a new leaf area index (LAI).   Vegetation varies throughout the year but has most influence in spring and summer.
    • surface albedo for forests with underlying snow based on processed satellite data rather than tabulated values.
  • Screen temperature and dew point observations are actively used over land in a separate analysis of soil moisture.
  • Observed temperatures are not used in the analysis where the difference between observed and background temperature is greater than 7.5°C.
  • Some ocean observations are reported from instrumented marine mammals.
  • Cloud cover is not assimilated.

• Upper air stations – radiosondes, dropsondes, profilers and aircraft.
  • The drift of radiosondes and consequent location of the observations is taken into account.  Prior to Cycle 45r1 released in June 2018 the observed values at all altitudes were assigned to the location of the radiosonde station. 
    

Several aspects need to be considered before and during the assimilation process.  Observations may:

• be biased or be reported at incorrect locations or times.  For most observations there are dynamic bias correction mechanisms (e.g. bias correction used on one day may differ from bias correction used the previous day). Incorrect locations are generally flagged out by the monitoring.  Any duplicate observations are filtered out.
• not be representative of the grid resolution or height - perhaps lying in one corner of a grid box or at a different altitude.  Some observations are excluded using certain land/sea mask criteria.  For the other observations, the observation errors include implicitly a representativeness error.
  
• be of non-uniform quality – some radiosondes have good quality, others less so; and absolute calibration can vary with age of the instrument.  For most data types we try to use adapted observation errors.  Radiosondes are also identified by instrumentation type, which facilitates adaptations to the expected data quality.
  

• have insufficient detail - in radiosonde messages, perhaps only significant levels are reported (as in the old style TAC message format) instead of full resolution data (as in the new style BUFR format being gradually introduced around the world).

   

Indirect or Satellite-based observations

These are achieved in two different ways:

• passive technologies sense natural radiation emitted by the earth and atmosphere or solar radiation reflected, refracted or retransmitted by the earth and atmosphere.
  
  • Contamination of atmospheric signals over land and over coastlines can be a problem, although increasingly new ways are being found to utilise such data. For example with cycle 45r1 introduced in June 2018 ECMWF began assimilating non-surface-sensitive infra-red channel data over land, and all sky micro-wave sounding data over coasts.
    
• active technologies emit radiation and sense how much is transmitted, reflected or scattered back.  For example:
  • the GPS radio occultation satellite-to-satellite signal is very sensitive to the temperature and humidity structure of the atmosphere particularly to the sharp moisture and temperature gradients beneath the boundary layer inversion,
  • Scatterometers derive surface-wind vectors from back-scattered radar signals from sea-surface ripples,
  • the Advanced Scatterometer (ASCAT) also enables soil moisture pseudo-observations that observe sub-surface and sub-canopy climate-related features such as water content of sub-canopy and continental surfaces.
    

Radiance assimilation takes the viewing geometry into account, by evaluating the radiative transfer along slantwise paths instead of assuming a nadir (overhead) viewpoint at all times.  This has been introduced recently for some satellite data (e.g. clear sky radiances) but not yet for everything.

Satellite data is important because:

• it is vital for less extensively observed regions (e.g. oceans, deserts, Arctic/Antarctic)
• it has global coverage with high spatial and temporal resolution. It accounts for ~95% of the total observation volume.
• it can help correct small amplitude but large scale errors.

 However, several aspects need to be considered.  Satellite data is an indirect measurement and requires accurate observation operators to translate model quantities into observed ones. Satellite data:

•  has poor vertical resolution for sounding channels.
•  has long term drifts and observation biases on some (but not all) satellite platforms.
•  as with all observations, has a risk of inconsistency in quality – whilst most remotely sensed observations are of very high quality, this can change suddenly.

Geostationary and Polar Orbiter satellite data have different strengths.

• Polar orbiter satellites:
  • Excellent global coverage - Excellent spatial resolution - Moderate temporal resolution.
    
    • are mostly in sun-synchronous orbits (remain in a steady orbit of constant orientation relative to the sun while the earth rotates beneath) and pass northward across the equator at about 100min intervals.
    • the spatial resolution (footprint) beneath the satellite is uniform world wide. 
    • the coverage of the earth's surface is complete but intermittent; no one spot on the Earth's surface can be sensed continuously.  Several hours can elapse between satellite sensing of a location near the equator.  However, the otherwise data-sparse areas around the poles are sensed relatively frequently at each pass of the orbiter.
    • microwave spectrum is observed allowing measurement of temperature and moisture structure of the atmosphere in the vertical, measurements of gases (e.g. ozone) and surface parameters (e.g. waves, snow extent and structure etc).
• Geostationary satellites :
  • Good regional coverage - Varying spatial resolution - Excellent temporal resolution.
    • Remain stationary above an equatorial location because the satellite has an orbital period the same as the Earth's rotation period.
    • The spatial resolution (footprint) varies with distance away from the sub-satellite point on the equator (~5km x ~5km at the sub-satellite point but decreases as the satellite view of of the earth's surface becomes increasingly oblique).  Useful meteorological data is limited to a radius of ~60° away from the sub-satellite point.  Unsuitable for polar regions
    • The coverage of the earth's surface is limited in extent but continuous and frequent at ~15min intervals.

    • No global coverage by a single satellite (collaboration needed)

    • Microwave spectrum is not observed

Satellite data is vital for an effective analysis and the use satellite observations is increasing rapidly.

Fig2.4-1: Pie chart showing the proportion of data types used by the IFC assimilation.  ATMS predominate. Ground-based observations constitute a relatively small proportion.

Users need to be aware of potential problems with the forecast due to deficiencies in coverage of data or conflict of observations with background fields.  Users should inspect:

• the data coverage (use obs drop down menu to select observation type) to show if and where there are deficiencies in data availability. 
• the analysis increments and analysis increment charts to show the location of significant differences between observed and background values.

(FUG Associated with Cy49r1)