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Cloud Model for operational Retrievals from MSG SEVIRI PM2, RAL, 17 Feb 2009 Phase III Plan

Cloud Model for operational Retrievals from MSG SEVIRI PM2, RAL, 17 Feb 2009 Phase III Plan. Original study schedule. Phase III: Application to real data. WP3110 Transfer of fast CFM Implement fast CFM in non-linear retrieval scheme

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Cloud Model for operational Retrievals from MSG SEVIRI PM2, RAL, 17 Feb 2009 Phase III Plan

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  1. Cloud Model for operational Retrievals from MSG SEVIRI PM2, RAL, 17 Feb 2009 Phase III Plan

  2. Original study schedule

  3. Phase III: Application to real data WP3110 Transfer of fast CFM • Implement fast CFM in non-linear retrieval scheme • Check consistency of fast retrieval with linear & non-linear sims based on reference code. • Due 4 weeks after PM2+4 (but now essentially done) WP3120 Non-linear simulations from model field • Apply scheme to 1d & 3d radiances from model cloud field • Develop / Test application of context dependent FM errors • Statistical assessment of (i) benefit of multi-layer scheme (ii) impact of 3-d errors • Conclusions wrt severity of 3d error on OCA and prospects for mitigating in future • Due 3 months after PM2 but many aspects done

  4. Phase III: Application to real data WP3130 Application to real data • Apply multi-layer scheme to real SEVIRI data for sub-set of scenes (over sea) • Implement use of HRV channel to define cloud fraction • Assess benefit of HRV channel • Approach to quantitatively assess improvement may make use of validation study code ? • Control experiment (single-layer scheme) done WP3200 Study findings • Produce Final Report • Informal code delivery • Due 1 week before RM3

  5. Issues to be investigated before full application to real data • Use of H2O channels looks very promising but clearly difficult to model. • Want to fit cold radiances precisely and ~ignore warm clear radiances • Best approach would be fit H2O profile with suitable prior covariance (ECMWF background cov ?) • Automatically means clear channels will be mainly used to fit H2O and cold channels to fit cloud. • But larger state vector & more RTTOV calls • May be possibly do define scene radiance dependent measurement error ?

  6. Use of 3.9 micron channel • Residuals 10-20K found with operational OCA retrievals so 3.9 not fitted • Did not occur for ATSR ? • Definitely not calibration from IASI vs SEVIRI comparisons. • An issue: Wide SEVIRI channel includes very thick CO2 band • Quasi monochromatic approach may not work. • Can conveniently test by running same code (RTTOV+LUT FM) to simulate IASI channels, convolve & compare to standard model • Currently assume measurement error fixed in BT – better fixed in radiance ? Revisit measurement error in general ?

  7. Detailed definition of horizontal constraint: • Looks reasonable to constrain only upper cloud ZC and RE • Horizontal length scales difficult to determine in completely satisfactory way, so will probably proceed with • fully correlated gross error (e.g. 5km ± 100 km) • tight random pixel to pixel variation (e.g. 1km) • Implementation via process a region and fit full state vector to full measurement vector initially but vectors get too large if region > 100x100km • Subsample pixels within region • Occasional pixels may not be consistent with the cloud model leading to failure of retrieval in entire region • Sequential approach would fix this • Construct a priori for individual pixelfrom previous good retrievals including correlation • Still need to handle large matrices ?

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