# Documentation Index for Hierarchical GSICS Algorithms

Step Proc Process Description i) Basic Principles ii) General Options iii) Specific Class:
IR Vis MW Inter-Satellite/Inter-Sensor Inter-Satellite/Intra-Sensor GEO-LEO LEO-LEO GEO-GEO
iv) Specific Instruments:
SEVIRI-IASI GOES-AIRS
1 1a Select Orbit A first rough-cut to:
• Reduce data volume
• Include only relevant portions (channels, area, time, viewing geometry)
• v0.1
• Select data on per-orbit or per-image basis
• Need to know how often to do inter-calibration – 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 inter-cal period
• Subset LEO data to GEO RoI

• v0.1
• GEO RoI = ±30° lat/lon of SSP
• Take 1 Metop overpass with night-time 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 2D-histogram 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 Pre-select Channels
• Select only broadly comparable channels from both instruments (to reduce data volume)
• v0.1
• Selection based on pre-determined criteria for each instrument pair
• v0.1
• Select IR channels (3-15µm)
• 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/cm-1
• 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 inter-calibration.
• Transform these into comparable channels
• Account for deficiencies in channel matches
• v0.1
• Define SRFs for all channels
• Co-average 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
• Interpolate GEO images
• 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 pre-defined threshold and exclude scenes with greater variance from analysis
• Performed on a per-channel 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
• v0.0
• Not implemented.
3c Auxiliary Datasets Basic Principles:
• To allow analysis of statistics in terms of other geophysical variables - e.g. land/sea/ice, cloud cover
• v0.0
• Not yet implemented
• v0.0
• Not yet implemented
• v0.0
• Not yet implemented
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 inter-calibration daily
• Use only night-time 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 inter-calibration every 10 days (nights)
• v0.2
• Extend range of incidence angles to <40°
• Inter-calibrations 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' inter-calibration bias can be directly compared
• Because biases can be scene-dependent, it is necessary to define channel-specific 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 sub-satellite point
• Limit target times to night-time 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 inter-calibration 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 non-linearity Basic Principles:
• Characterise any non-linearity in the relative differences between instruments, or place limits on their maximum magnitude
• May be used to account for detector non-linearity, 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 non-linearity is likely to be relatively constant in time.)
• v0.0
• Not implemented yet
4e Recalculate calibration coefficients Basic Principles:
• Produce revised set of calibration coefficients for one instrument following its inter-calibration 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
• v0.0
• Not implemented yet
• v0.0
• Not implemented yet
4f Report Results Quantify the magnitude of relative biases by inter-calibration. 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 near-real-time 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:
• Defined as bullets
• v0.1
• 1
• 2
• v0.2
• 1
• 2
• v0.1
• 1
• 2
• v0.2
• 1
• 2
• v0.1
• 1
• 2
• v0.2
• 1
• 2
5 5b Process Description Basic Principles:
• Defined as bullets
• v0.1
• 1
• 2
• v0.2
• 1
• 2
• v0.1
• 1
• 2
• v0.2
• 1
• 2
• v0.1
• 1
• 2
• v0.2
• 1
• 2

-- TimHewison - 13 Jan 2009
Topic revision: r1 - 13 Jan 2009, TimHewison

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