Rutherford Appleton Laboratory Towards Detection and Retrieval of Volcanic Ash from SEVIRI using the OCA Processor R.Siddans, C. Poulsen Eumetsat, 17 March 2011 Background Eumetsat's Optimal Cloud Analysis scheme OCA is an optimal estimation scheme to retrieve cloud properties from visible / near-ir / thermal-ir imagery Originally developed by P. Watts while at RAL Now moving towards operational implementation at Eumetsat for SEVIRI Developed in parallel at RAL and University of Oxford ("ORAC") Applied mainly to analyse (A)ATSR Version for aerosol developed (ESA GlobAerosol) Candidate algorithm for ESA CCI aerosol and cloud. Following Icelandic eruption, scheme was applied by RAL to AATSR and SEVIRI scenes, using new ash optical properties measured by Oxford Demonstrated sufficient potential for Eumetsat commission small project to further explore capability of the scheme (reported here) Content of study A nalyse SEVIRI scenes during Eyja eruption focussed on May 6-18, during daytime for short study avoid complication (not necessarily fundamental problem) with land reflectance / emissivity and need to adapt scheme to work well at night. Comment on importance of individual channels for ash, noting potential issues Assess capability of OCA to identify ash Suggest strategy for deciding when to apply the ash scheme without significantly increasing the CPU requirements for operational processing Validate retrieved properties by comparison to Calipso. used Calipso orbits identified by F. Prata (most in May) Optimal Cloud Analysis (OCA) Optimal Estimation (OE) scheme Fit explicit cloud forward model (FM) to observations in 0.6,0.8,1.6,6.2, 7.3, 8.7,10.8,12,13 micron channels. 9.7 not used because of ozone 3.7 not used because often difficult to fit (...) Fits state vector consisting of cloud optical depth, effective radius, height and surface temperature assumes cloud is plane-parallel, geometrically thin layer. Solution is the state which minimises the cost function Cost = (y-F(x)) t S y -1 (y-F(x)) + (x-a) t S a -1 (x-a) In standard scheme prior constraint is negligible except for surface temperature (which has realistic error) OCA forward model (FM) Fast FM using look-up-tables (LUTs) to describe cloud Trace-gas absorption above and below cloud modeled using RTTOV applied to ECMWF fields. Land surface reflectance and emissivity is taken from MODIS Cox and Munck model is used over sea. LUTs Consist of cloud direct and diffuse reflection and transmission computed using DISORT based on defined spectral optical properties (extinction coef, single-scatter albedo, phase function) These are computed as a function of view/solar geometry, optical depth and effective radius. Standard LUTs: Liquid cloud based on Mie calculations (gamma distribution) Ice cloud based on Baran or Baum models of crystal shape OCA is here applied to retrieve ash by running with new LUTs based on Mie calculations (E. Carboni) based on new refractive index measurements (D. Peters) Plot from E. Carboni showing Volc Ox refractive indices measured by Dan Peters (http://www.atm.ox.ac.uk/project/aerosol/spectra.html) Relevance of individual channels 11,12 fundamental (usually gives distinct -ve BT difference for ash) Adding 8.7 further captures spectral shape can give signature of ash when > 0 gives some size information 6.2,7.3, 13.4 useful for height (resolving window channel ambiguity of height vs optical thickness) 0.6, 0.8, 1.6 sensitive to total optical depth and size (water phase) Ash afton more reflecting at 3.7 than liquid cloud (helps confirm type/size) Potential problems: 3.7 difficult to model, especially consistently with 1.6 6.2, 7.3 particularly sensitive to correct H2O 7.3, 8.7 sensitive to SO2 Fitting all channels together in OE method resolves co-dependencies of channels on cloud/ash parameters, testing suitability of cloud model. Identification of cloud type with OCA Basic method is to attempt to fit a given scene with each type in turn and then choose the result which gives the lowest cost (best fit) In standard OCA, for water cloud only, a faster way to achieve this is to switch type during the retrieval iteration based on current estimate of state This not practical now 3 types being modeled. In tests here the minimum-cost method applied here but applying this in practice is considered prohibitively expensive for operations Look for approach to reduce additional cost General Pros and Cons of OCA Advantages All channels contribute information to all state variables. Cost function provides measure of quality of fit to observations: A consistent fit to all channels gives high confidence in appropriateness of retrieved values (and assumed cloud model) If this is achieved then reliable estimates of the error on state variables readily obtained Disadvantages Single-layer model is often not correct ! (but cost identifies this) When model not correct retrieved result is compromise which best matches all channels, but results may not be "good" In particular, multi-layer cloud is known to be common and often problematic for OCA (but is being addressed...). For Eyja eruption, a major issue is the ubiquity of thin layers of ash over thick liquid water cloud (often under a pronounced T-inversion). 0.6, 0.8, 1.6 composite. Scene at 12:12 9 May , 11, 12 composite. Altitude / km Particle typeEffective Radius / m Optical Depth Scene at 12:12 9 May 2010 Simulated 8.7/11/12 Particle typeMeasured 8.7/11/12 Final Cost Scene at 12:12 9 May 2010 Summary of ash detection behaviour Ash often seems to be correctly detected Outside of plume, false detections either at low optical depth (