Step  Proc  Process Description  i) Basic Principles  ii) General Options  iii) Specific Class: IR Vis MW InterSatellite/InterSensor InterSatellite/IntraSensor GEOLEO LEOLEO GEOGEO  iv) Specific Instruments: SEVIRIIASI GOESAIRS 
1  1a  Select Orbit  A first roughcut to:
 Reduce data volume
 Include only relevant portions (channels, area, time, viewing geometry)

 v0.1
 Select data on perorbit or perimage basis
 Need to know how often to do intercalibration – based on observed rate of change
(Defined iteratively with 2c & 2d.) 
 v0.1
 Define GEO Region of Interest: within 60° of GEO SSP
 Subset GEO data to RoI
 Select LEO data within GEO RoI for each intercal period
 Subset LEO data to GEO RoI

 v0.1
 GEO RoI = ±30° lat/lon of SSP
 Take 1 Metop overpass with nighttime equator crossing closest to GEO SSP
 Subset IASI data to GEO RoI
 Select SEVIRI image closest in time to LEO Equator crossing
 v0.2
 Select fixed GEO frame at nominal LEO local equator crossing time (21:30)
 Extend RoI to ±35°

1b  Collocate Pixels  Define which pixels to compare:
 Define FoV of all pixels
 and environment around pixels
 Identify pixels for both instruments within these areas meeting collocation criteria for time, space and geometry

 v0.1
 Search for all pixels within FoV and environment
 v0.3
 Grid observations using 2Dhistogram in lat/lon space

 v0.1
 Geometric alignment: Select GEO/LEO pixels where secant of zenith angle is within 0.01
 Temporal alignment: Select GEO/LEO pixels with time differences <300s

 v0.1
 IASI FoV=12km at nadir
 SEVIRI FoV=3km at SSP
 Time difference <900s
 Select 5x5 SEVIRI pixels closest to centre of IASI FoV
 v0.3  IR1b4eeGLSIv0.3
 Select SEVIRI and IASI pixels in same bin of 2D histrogram with 0.125° lat/lon grid

1c  Preselect Channels 
 Select only broadly comparable channels from both instruments (to reduce data volume)

 v0.1
 Selection based on predetermined criteria for each instrument pair

 v0.1
 Select IR channels (315µm)

 v0.1
 Select IR channels of SEVIRI
 Select all channels for IASI

2  2a  Collect Radiances  Convert observations from both instruments to a common definition of radiance to allow direct comparison. 
 v0.1
 Convert instrument Level 1.5/1b/1c data to radiances, accounting for channel Spectral Response Functions

 v0.1
 Perform comparison in radiance units: mW/m2/st/cm1

 v0.1
 Account for Meteosat radiance definition applicable to level 1.5 dataset

2b  Spectral Matching  Basic Principles:
 Identify which channel sets provide sufficient common information to allow meaningful intercalibration.
 Transform these into comparable channels
 Account for deficiencies in channel matches

 v0.1
 Define SRFs for all channels
 Coaverage comparable channels
 Use Radiative Transfer Model to account for differences
 Estimate uncertainty due to spectral mismatches

 v0.1
 Transform spectral response functions to common grid
 Spectral Convolution to synthesise GEO channels
 Account for spectral sampling and stability in error budget

 v0.1
 Assume IASI channels are spectrally stable and contiguously sampled
 Use published SRFs for MSG at 95K, interpolated to IASI grid.
 Estimate radiance missing from IASI’s coverage of SEVIRI IR3.9 channel by assuming a uniform brightness temperature

2c  Spatial Matching  Basic Principles:
 Transform observations from each instrument to comparable spatial scales
 Estimate uncertainty due to spatial variability

 v0.1
 Identify Point Spread Functions of each instrument
 Specify the target area and identify the pixels within it
 Specify the ‘environment’ around target area
 Average pixel radiances within specified target areas and Calculate their variance

 v0.1
 Define target area as LEO FoV
 Average GEO pixels within target area and calc variance
 Define environment as GEO pixels within 3x radius of target area

 v0.1
 Assume IASI FoV circular near nadir with diameter of 12km
 Assume SEVIRI pixels are contiguous, independent samples: 3km spacing @SSP
 Calculate mean and variance of radiance in 5x5 SEVIRI pixels closest to centre of IASI FoVs
 v0.2 as v0.1, except:
 Select SEVIRI and IASI pixels in same bin of 2D histogram with 0.125° lat/lon grid

2d  Temporal Matching  Basic Principles:
 Establish timing difference between instruments’ observations
 Assign uncertainty based on (expected or observed) variability over this timescale.

 v0.1
 Identify each instruments’ sample timings

 v0.1
 Select GEO image closest to time of LEO Equator crossing
 Calculate time difference for each target
 v0.2

 v0.1
 Select only targets with time difference <900s

3  3a  Uniformity Test  Basic Principles:
 Only compare observations in homogenous scenes to reduce uncertainty in comparison due to spatial/temporal mismatches

 v0.1
 Compare spatial/temporal variability of scene within target area to predefined threshold and exclude scenes with greater variance from analysis
 Performed on a perchannel basis

 v0.1
 Calculate variance of GEO radiances with each LEO FoV
 v0.2
 Include interpolation between sequential GEO images

 v0.0
 Not implemented as found to not change results significantly. (Results rely instead on inhomogeneous scenes having lower weighting in regression and include the full range of scene radiances.)
 v0.2
 Reject any targets with scene variance >5% of reference radiance

3b  Outlier Rejection  Basic Principles:
 To prevent anomalous observations having undue influence on results
 Identify and reject 'outliers' on a statistical basis

 v0.1
 Compare the radiances in the target area with those in the surrounding environment
 Reject targets which are significantly different from the environment (3σ)

 v0.1
 Compare difference between mean GEO radiances within LEO FoV and 'environment'
 Reject targets where this difference is >3 times the variance of the environment's radiances


3c  Auxiliary Datasets  Basic Principles:
 To allow analysis of statistics in terms of other geophysical variables  e.g. land/sea/ice, cloud cover




4  4a  Regression  Systematically compare collocated radiances from 2 instruments. (This comparison may also be done in counts or brightness temperature.) This allows:
 Investigating how biases depend on various geophysical variables
 Providing statistics of any significant dependences
 Investigating the cause of these dependences

 v0.1
 Simple averaging of differences between collocated radiances
 v0.2
 Weighted linear regression of collocated radiances, using estimated uncertainty on each point as a weighting
 v0.3
 Perform stepwise multiple linear regression to investigate dependence of various geophysical variables

 v0.2
 Repeat intercalibration daily
 Use only nighttime LEO overpasses
 Include only incidence angles <30°
 Weight collocations in regression by the inverse variance of target radiances
This allows the investigation of the sensitivity of the differences to Latitude, Longitude, Incidence angle/LEO scan angle, Time of day 
 v0.1
 Select only pixels with incidence angle ~15°±1°
 Repeat intercalibration every 10 days (nights)
 v0.2
 Extend range of incidence angles to <40°
 Intercalibrations may be averaged over periods of ~1 week. (Longer periods are subject to drift due to ice contamination.)
 Reset statistics following Meteosat decontaminations.

4b  Define reference radiances  Basic Principles:
 Provide standard scene radiances at which instruments' intercalibration bias can be directly compared
 Because biases can be scenedependent, it is necessary to define channelspecific reference scene radiances
 More than one reference scene radiance may be needed for different applications  e.g. clear/cloudy, day/night

 v0.1
 Select representative Region of Interest (RoI)
 Construct histogram of observed radiances within ROI
 Identify peaks of histogram corresponding to clear/cloudy scenes to define reference scene radiances
 These are determined a priori from representative sets of observations

 v0.1
 Limit target area to within 30° of GEO subsatellite point
 Limit target times to nighttime LEO overpasses

 v0.1
 Find mode of histogram of each channels' brightness temperature for collocated pixels in 5 K wide bins from 200 to 300 K
 For bimodal distributions, the mean of the modes is used
 Define low reference radiance scene for high cloud of 200 K for all channels

4c  Calculate biases  Basic Principles:
 Perform direct comparison of intercalibration biases for representative scenes in a way easily understood by users

 v0.1
 Apply regression coefficients to estimate expected bias and uncertainty for reference scenes in radiances
 Account for correlation between regression coefficients, when calculating uncertainty on the fitted radiances
 Results may be expressed in absolute or percentage bias in radiance, or brightness temperature differences

 v0.1
 Convert biases (and uncertainties) from radiances to brightness temperatures

 v0.1
 Use effective radiances definition to covert to brightness temperature

4d  Test nonlinearity  Basic Principles:
 Characterise any nonlinearity in the relative differences between instruments, or place limits on their maximum magnitude
 May be used to account for detector nonlinearity, calibration errors or inaccurate spectral response functions

 v0.1
 Compare results of linear and quadratic regression of collocated radiances from different instruments
 Estimate maximum departure from linearity, the scene radiance at which it occurs and uncertainty associated with it

 v0.1
 Combine multiple LEO overpasses need to produce enough data to identify relative instrument linearity to the level of the instruments' noise. (Any nonlinearity is likely to be relatively constant in time.)


4e  Recalculate calibration coefficients  Basic Principles:
 Produce revised set of calibration coefficients for one instrument following its intercalibration against a reference instrument
 Allow users to recalibrate data from the target instrument to be consistent with the reference instrument
 Generate uncertainties with the calibration coefficients to allow users to specify the error bars on recalibrated data

 v0.1
 Read original calibration coefficients and calculate the changes required to reproduce observed relative biases
 v0.2
 Read original counts observed by the target instrument and fit these to the collocated radiances observed by the reference instrument



4f  Report Results  Quantify the magnitude of relative biases by intercalibration. This allows users to:
 Monitor changes in instrument calibration in time,
 Recalibrate observations,
 Specify the uncertainty on observations,
 Derive relative biases and uncertainties between various different instruments.

 v0.1
 Produce plots and tables of relative biases and uncertainties for reference scene radiances.
 Show evolution of these in time and dependence on geophysical variables.
 Produce tables of recalibration coefficients for nearrealtime and archive data.

 v0.1
 Plot relative brightness temperature bias for clear sky reference scenes as time series with uncertainties
 Calculate trend line in above time series (with uncertainties)
 Calculate monthly mean bias from time series

 v0.1
 Reset trends and statistics when decontamination procedures performed on MSG

5  5a  Operational Corrections  Basic Principles:




5  5b  Process Description  Basic Principles:



