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 (

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 / µm 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 (<0.2) or noise around cloud edges (easily excluded by filtering) Identifies thick (big) ash where simple BT difference does not Identifies far more ash than CM-SAF flag Sometimes prefers liquid where BT difference quite high (but successfully eliminates many points with negative BT difference but presumably not ash) Scenes often mixed (usually with lower liquid cloud but also ice) so can either get ash-with-high-cost or water-with-high-cost, leading to blocky results Could consider using cost to provide more continuous ash "probability" NB may well have problems distinguishing ash and desert dust (not tested)

Practical points on running OCA with ash Running all scenes with ash would double computational cost so... Want to start with flag which identifies scenes which might be ash – assume false positives will be eliminated by OCA Number of scenes to be processed reduced by first running standard retrieval and only try ash if flag raised and liquid cloud a poor fit Using BT difference < 0.1K as a flag this would lead to 10% of scenes to be processed with ash (in section of disk analysed here). If only scenes with high liquid cost are processed then this reduces to 5%, without changing the number of retrievals then identified as ash. However this will miss some ash which seems not to have negative BT difference – Flag using 8.7 and/or 3.9 might (Pavolonis 2009 and 2006, respectively) seems neceasary and should work...

Two-layer retrievalswith OCA Earlier Eumetsat "cloud model" study, investigated potential to retrieve jointly information about 2 cloud layers (ice over water). Retrieval of opt.depth, size and height for two layers leads to a problem which is not well determined under all conditions – suitable prior constraints needed. but not yet clear which(!) However, quick "two-layer" scheme implemented at Eumetsat: If single-layer retrieval has high cost, then – only fit IR channels – let retrieved surface temperature represent lower cloud – retrieved cloud parameters represent upper layer – constrain effective radius of upper layer This shown to have some success for ice cloud-height. Here we apply a similar IR only method, but using an explicit 2-layer FM: Instead of effective surface T, retrieve height of lower liquid cloud layer which is assumed to be optically thick – Test with tight (0.4K) and loose (3 & 2K) fit to 6.2 & 7.3 channels

Two layer scheme

Two layer scheme (weak constraint on H2O channels)

Single layer scheme

Two layer scheme

Single layer scheme

Two layer scheme

Single layer scheme

Two layer scheme

Single layer scheme

Two layer scheme

Fa Two layer scheme

Comparison with GOES-R scheme Pavolonis has independently compared results from applying GOES-R ABI scheme to SEVIRI with Calipso – Based on flagging ash using 8.7,11,12 – Then use 11,12,13 to retrieve (with OE) cloud effective temperature, effective emissivity and size – Derive height from effective temperature by searching T- profile. Have results for 5 cases (one case on 6 May overlooked in runs conducted here)

Conclusions OCA scheme applied to analyse ash from the Eyja eruption in May 2010 “Standard” single layer scheme often correctly identifies ash – sees much more ash than NWCSAF flag – capable of eliminating false detections from difference caused by BL cloud under T-inversion – correctly identifies thick ash where would not detect – but min-cost approach can “flip” to other class (with high cost) in mixed scenes other approaches probably able to clearly identify presence of ash in some of these cases may be able to do this from OCA's cost function Approach of only processing scenes flagged as possible ash and which have poor water cloud fit should enable ash to run without increasing CPU time by more than ~5% – simple test not good enough to find all ash, would want to use 8.7 and/or 3.7 as well

Conclusions Ash from Eyja frequently optically thin, over thick liquid cloud – Retrieval results from single layer scheme not reliable in this case (optical depth too high, height too low, radius ?) – However this situation is identifiable using the fit-cost. 2-layer scheme seems extremely promising Heights which often agree well with Calipso and Pavolonis scheme Water vapour channels definitely valuable, but cannot always get consistent fit, presumably due to errors in the met fields – May be partly due to interpolating 6 hour, ~1deg gridded data – Approach to reduce error using clear-sky scenes proposed Very little time in study to optimise the 2-layer scheme – Have tools to bring in also solar channels but more work needed to construct suitable prior constraint Capability to jointly fit SO2 also developed (RTTOV coefs done) but no conclusive results for Eyja (not enough SO2?)

Fast 2-layer solar forward model Single layer model: LUTs of direct and diffuse cloud reflection & transmissions coupled to surface BRDF and clear-sky transmission model R_BRDF R_BlackR_White... R_White Two layer model is nested single layer-model: Compute BRDF of lower layer and use as surface for upper layer Errors < 0.02 in reflectance Except solar term in 3.9 micron channel, subject to larger errors due to optically thick CO2 + quasi-monochromatic assumption for clear-sky transmission

Fast 2-layer thermal forward model Errors < 1K, usually < 0.5K LUTs of direct and diffuse cloud reflection & transmissions Clear-sky transmissions from RTTOV Reflection between cloud layers and cloud / surface neglected