optimal cloud analysis: product guide

© EUMETSAT

Doc.No. : EUM/TSS/MAN/14/770106

Issue : v2A e-signed

Date : 21 April 2016

WBS/DBS :

Optimal Cloud Analysis: Product Guide

EUMETSAT Eumetsat-Allee 1, D-64295 Darmstadt, Germany

Tel: +49 6151 807-7 Fax: +49 6151 807 555 http://www.eumetsat.int

EUM/TSS/MAN/14/770106 v2A e-signed, 21 April 2016


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Document Change Record

Issue / Revision

Date DCN. No

Changed Pages / Paragraphs

1 06/10/2010 Document creation

1A 09/12/2010 Update to current OCA status

2 13/05/2015 Update to operational version: 2-Layer capability

2A 21/04/2016 Added Appendix with WMO GRIB-2 Code table 4.2 Changes. Amended the Product Component.



Table of Contents 1 Product Description .................................................................................................................. 5

1.1 Multi-layer clouds .............................................................................................................. 5 1.2 Product structure ............................................................................................................... 6 1.3 Product Component List .................................................................................................... 7

2 Product Specifications ............................................................................................................. 8 2.1 Product Change Record .................................................................................................... 8 2.2 Known Operations Limitations ........................................................................................... 9

2.2.1 Anomalous CRE in water clouds .......................................................................... 9 3 Product Illustration ................................................................................................................. 10

3.1 Processed Image ............................................................................................................ 11 3.2 Cloud Phase Determination ............................................................................................ 13 3.3 Measurement Cost .......................................................................................................... 14 3.4 Layer-One Cloud Optical Thickness (COT-1) .................................................................. 15 3.5 Layer-One Cloud Top Pressure (CTP-1) ......................................................................... 17 3.6 Layer-One Cloud Effective Radius (CRE-1) .................................................................... 19 3.7 Error in Layer-One Cloud Optical Thickness (ErrCOT-1) ................................................. 21 3.8 Error in Layer-One Cloud Top Pressure (ErrCTP-1) ........................................................ 23 3.9 Error in Layer-One Cloud Effective Radius (ErrCRE-1) ................................................... 24 3.10 Layer-Two Cloud Optical Thickness (COT-2) .................................................................. 25 3.11 Lower-layer Cloud Top Pressure CTP (CTP-2) ............................................................... 26 3.12 Error in Layer-Two CTP (ErrCTP-2) ............................................................................... 27 3.13 Error in Layer-Two COT (ErrCOT-2) ............................................................................... 28

4 Basic Structure of the OCA Algorithm .................................................................................. 29 5 ReferenceS .............................................................................................................................. 30

Online Resources and Assistance ............................................................................................. 30 Appendix A: WMO GRIB-2 Code table 4.2 Changes ....................................................................... 31

A.1 Code Table 4.2-3-1 ........................................................................................................... 31 A.2 Code Table 4.218 ............................................................................................................. 31

Table of Figures Figure 1 Layer notation in the OCA Product. ......................................................................................... 6 Figure 2 Visualisation of some of the OCA retrieved parameters along a tropical region transect. ...... 10 Figure 3 Visualisation of OCA CTP parameters, including error estimates. ......................................... 11 Figure 4: Panel 4a shows IR and Vis RGB. ........................................................................................ 12 Figure 5: Retrieved cloud phase (minus 110). Legend: ....................................................................... 13 Figure 6: Measurement cost. ............................................................................................................... 14 Figure 7: Cloud Optical Thickness, Layer-1. ........................................................................................ 15 Figure 8: Histograms of COT-1.. .......................................................................................................... 16 Figure 9: Cloud top pressure of layer-1 (upper layer). ......................................................................... 17 Figure 10: Histograms of CTP-1:. ....................................................................................................... 18 Figure 11: Histogram of CTP-1. ........................................................................................................... 18 Figure 12: Layer-one cloud effective radius (CRE-1). .......................................................................... 19 Figure 13: Histograms of CRE: ............................................................................................................ 20 Figure 14: Error in COT-1, the left upper panel map in (Log10) COT-1.. .............................................. 22 Figure 15: Error in CTP-1, with frequency scale histogram at bottom. ................................................. 23 Figure 16: Error in CRE-1. Artificial constant “prior” is applied in the histogram. ................................ 24 Figure 17: Cloud optical thickness, double-layer; COT-2. .................................................................... 25 Figure 18: Cloud Top pressure, double layer, CTP-2. ......................................................................... 26 Figure 19: Error in cloud top pressure of layer-two. ............................................................................. 27 Figure 20: Percent fractional error in Cloud Optical Thickness of second layer. .................................. 28



1 PRODUCT DESCRIPTION

The Optimal Cloud Analysis is an algorithm used to extract the principal cloud parameters (phase, pressure, optical thickness and effective radius) using an optimal estimation (OE) method and, simultaneously, all the SEVIRI spectral measurements. This combination allows for robust quality control that checks that the scene corresponds to the cloud model, and systematic parameter error analysis. As part of the quality control, multi-layer clouds are identified and reprocessed with an adjusted model setup that permits the retrieval of two-layer quantities. In the current configuration, the IR 3.9 µm and IR 9.6 µm measurements are made passive because of difficulties in the radiative transfer modelling.

This Optimal Cloud Analysis scheme was first developed as a research study awarded to the Rutherford Appleton Laboratory (RAL) in 1997 and was coded as a prototype system in 2001. The product was developed by EUMETSAT with the aim of providing potential Day-2 products from the MSG SEVIRI instrument. The OCA Product has been operational, although with demonstration status, at hourly frequency since June, 2013. Recently, the product has been extended and it now identifies multi-layer cloud situations and retrieves two-layer properties found there.

When compared to alternative retrieval methods, particularly those based on only a few spectral channels, the OCA product has a singular advantage: the cost function value (a scalar) at the solution minimum is an indicator of consistency of the modelling and the reality. When issues like calibration, error modelling, and accuracy of auxiliary data sets like surface albedo are stable and well understood, the most important modelling issue is that of the cloud itself. Multi-layer clouds are the most frequent deviations from the single-layer, plane parallel cloud model.

1.1 Multi-layer clouds

A major enhancement to the OCA product was developed when external studies and validation information from the A-Train satellite constellation became available. These showed that multi-layer clouds, when interpreted with a single layer cloud model, tended to give products with values that were an average of the individual layer values. Thus the Cloud Top Pressure (CTP) of a multi-layer—two-layer—system would be greater than that of the top layer, often cirrus, and less than that of the bottom layer.

To mitigate this, the OCA product retrieval algorithm retrieves two-layer properties when multi-layer cloud is detected. The retrieved layer cloud top pressures (CTPs) have been validated using values from the Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Until A-train satellite data was available, however, the validation of cloud optical thickness (COT) was made by comparison to MODIS-derived values and restricted to total COT.

The availability of DARDAR (raDAR/liDAR) project data has allowed a first validation of the SEVIRI OCA COTs for the upper ice layer in multi-layer clouds. The validating DARDAR product used by the OCA product is the visible optical depth–defined as the integral of the ice cloud visible extinction along a vertical path through the entire atmosphere. The results indicate that the OCA scheme generally detects the presence of multi-layer cloud when the overlying cloud layer is of optical thickness less than 4 to 6 and greater than approximately 0.1 to 0.3 in the tropics and greater than approximately 0.5 at high latitudes. The higher sensitivity to thin cirrus in the tropics may be related to the greater thermal contrast found with the higher / colder tropical clouds. The upper limit likely arises because the detection, based on goodness of fit to a single layer cloud model, is highly



dependent on the infrared channels for which a cloud with COT greater than 4 to 6 is essentially opaque and therefore radiatively equivalent to a single layer. OCA COTs for the upper layer clouds apparently compare well with DARDAR values, however the agreement is often outside their

respective 3 error estimates and an OCA bias of negative 20% to 30% is indicated.

1.2 Product structure

The OCA product is structured according to layers numbered in a top-down notation as shown in Figure 1. This structure makes it as consistent as possible with similar (but single–layer) cloud datasets as the layer–1 CTP is comparable to the CTP from single–layer products. Thus, “Layer-1” is the first cloud layer encountered from the top of atmosphere, i.e. the highest layer. “Layer-2” is the lower cloud layer. Layer-2 properties only exist (and have values in the product file) when the two-layer model has been invoked. Layer-2 properties are also more restricted than layer-1 properties. There is no reported effective radius because of limitations with the simple two-layer radiative transfer model used and the physical lack of information; by definition, the lower layer is always somewhat obscured from the satellite.

Figure 1 Layer notation in the OCA Product.

Additionally, each product / pixel is accompanied by an error estimate and the final fit to the measurements. This is summarised by the solution cost which is also reported. The complete product component list is shown in Table 1.



1.3 Product Component List

Product Description Units Range

Phase Cloud type:

111 – single layer water cloud

112 – single layer ice cloud

113 – two - layer (ice over undetermined)

- 111 to 113

Jm Solution measurement cost - 0 to 2000* (capped)

COT-1 Layer-1: Log10 optical thickness (referenced to 0.55 microns)

- –1.3 to 2.5

CTP-1 Layer-1 pressure hPa ˟ 1e2 (Pa) 5000 to 106000

CRE-1 Layer-1 particle effective radius m ˟ 1e-6 (meters) 0.000001 to 0.000092

ErrCOT-1 Error in Log10 COT-1 - 0 to 3.0*

ErrCTP-1 Error in CTP-1 hPa ˟ 1e2 (Pa) 0 to 100000*

ErrCRE-1 Error in CRE-1 m ˟ 1e-6 (meters) 0 to 0.000001*

COT-2 Layer-2 Log10 optical thickness (referenced to 0.55 microns)

- –1.3 to 2.5

CTP-2 Layer-2 pressure hPa ˟ 1e2 (Pa) 5000 to 106000

ErrCOT-2 Error in Log10 COT-2 - 0 to 3.0*

ErrCTP-2 Error in CTP-2 hPa ˟ 1e2 (Pa) 0 to 100000*

Table 1: Product component description. Range maximums in cost and product errors marked with an asterisk (*) are capped at a large value to facilitate smaller product GRIB file size; in all cases, the capping value can be considered “very large”’ in that a product with such a value can be considered to have no value.



2 PRODUCT SPECIFICATIONS

Category Specification

Product uses Weather forecasting,

Numerical weather prediction,

Climate research and monitoring

Input satellite data Reflectances from the SEVIRI Level 1.5 image data for the VIS0.6 µm, the VIS0.8 µm and the VIS1.6 µm channels;

Radiances from the SEVIRI Level 1.5 image data for these channels: IR3.9 µm, IR6.2 µm, IR7.3 µm, IR8.7 µm, IR9.6 µm, IR10.8 µm, IR12.0 µm and IR13.4 µm.

Note: The IR3.9 µm and the IR12.0 µm channels are used only passively.

Future: Radiances from the SEVIRI Level 1.5 image data for the IR3.9 µm and IR9.6 µm channels to be made active;

Auxiliary input: ECMWF forecast, CIMSS IR land emissivity, CRM (surface reflectance maps), CLM (cloud mask product).

Product Distribution

EUMETCast EUMETSAT Data Centre

Product Area Full Earth Scanning Area (FES)

Product Resolution Pixel

Product Distribution F

EUMETCast: hourly for the 00:00, 01:00, .... and 23:00 UTC products EUMETSAT Data Centre: hourly for the

00:00, 01:00, .... and 23:00 UTC products

Product Names

EUMETCast FES Area: L-000-MSG3__-MPEF________-OCAE_____-000010___-201306110100-__

Product Format GRIB format

Product Size About 45 MB (variable)

2.1 Product Change Record

Date Event

18 June 2013 Product first available from the EUMETSAT Data Centre.

10 July 2013 Product available via EUMETCAST but limited to member state met services.

8 August 2013 Product available via EUMETCAST to all users.

22 April 2015 New MPEF Release (2.1) included significant upgrades for the OCA product:

1. Shortwave channel (0.6, 0.8 and 1.6 micron) atmospheric transmittances previously obtained from latitude interpolated fixed atmosphere values calculated using LOWTRAN7 are replaced by values are obtained from RTTOV-11 based on forecast atmospheric temperature, water vapour and ozone, i.e. consistently with the longwave channels. Results of change: lower measurement cost values

(better fit to measurements), larger (mainly liquid phase) effective radii (~1 m) and slightly larger optical depths.

2. Cloud radiative property look-up tables for (liquid) water phase have been extended in effective radius range from 1 – 23 microns to 1 – 31 microns, however, currently the fixed limit of 23 microns on the retrieval remains in place.



2.2 Known Operations Limitations

The current operational status of the product is demonstration.

2.2.1 Anomalous CRE in water clouds

Approximately 10 % of daytime water cloud retrievals have CRE values equal to 23 m, which is the original upper limit of the water cloud look-up Table (LUT). For reasons more fully discussed in Section 3.6, we have not permitted water cloud CRE to go above this value despite the introduction of new LUTs. Until the reasons and characteristics of these high CRE values are better established, the

limit stays within 23 m.



3 PRODUCT ILLUSTRATION

Interpreting the results of the OCA product can be a challenge at first due to the layer structure it incorporates. This layer structure is explained and diagrammed in Section 1.2. At the bottom of Figure 2 below, CTPs of one or two layers are plotted with small symbols. The layer phase is plotted in one of two colours: water (green) or ice (blue). An indication of the layer optical depths is plotted by using a proportionally long vertical bar. This geometrical depth representation is purely illustrative, not figurative. The image at the top in Figure 2 has a dotted line running through it. This line can be used to match the product to obvious cloud features. Note also that in the remaining cloud product presented in this document, the effective radius is not indicated like this.

Figure 2 Visualisation of some of the OCA retrieved parameters along a tropical region transect.

A second presentation of the product in Figure 3 is similar but more clearly demonstrates the error estimates that accompany the product parameters. Here, CTP errors are shown.



Figure 3 Visualisation of OCA CTP parameters, including error estimates.

The remainder of this section illustrates and characterises OCA outputs and diagnostics from MSG SEVIRI based on an example image taken predominantly during daytime. These will support an understanding of the OCA method and characteristics.

3.1 Processed Image

The processed image for demonstration is from Meteosat-10 and is taken at 1300 UT on 23 April

2015 and shown in Figure 4. In the left frame, IR and Vis RGB is intended to show ice cloud (blue where thin and white where thick) and water cloud (yellow). The frame at the bottom shows the

standard natural colour composite from the 0.6, 0.8 and 1.6 m channels. The full disk scene contains most types and combinations of land and sea pixels, vegetated and desert surfaces, semi-transparent and thick cirrus, cumulus and stratiform low clouds. The sections that follow in this document illustrate some of the characteristics of the OCA output:

measurement cost

cloud phase

cloud optical thickness

top pressure

cloud effective radius for one layer

cloud effective radius for two layers



of 31

4a. 4b.

Figure 4: Panel 4a shows IR and Vis RGB. Panel 4b shows natural colour RGB, Meteosat-10 at 1300 UT 23 April 2015. The text in white on the RGB image (ML, SLice) in 4a is referred to in the descriptions that follow this page.



3.2 Cloud Phase Determination

Figure 5: Retrieved cloud phase (minus 110). Legend:

Value Colour Description

0 black off-disk or cloud-free

1 blue single-layer water cloud

2 cyan single layer ice cloud

3 green double-layer cloud

Figure 5 shows the cloud phase determination. Large areas of single layer (SL) water cloud correspond to yellow areas in Figure 4a or white areas in Figure 4b. Single-layer ice (cyan) appears at the centre of tropical convection in the extra-tropical frontal systems and, occasionally, where there is thin cirrus–the

area marked SLice in Figure 4a. Double-layer results are present often as the outflow to tropical convection or as cirrus overlying stratus / stratocumulus decks. Look at the places

marked ML in Figure 4a. The fraction of the various phases present is shown in the accompanying histogram at left. Single-layer water phase dominates, but single-layer ice and two–layer combined (both are ice phases in the top layer) are nearly as frequent.



3.3 Measurement Cost

Figure 6: Measurement cost.

The measurement cost, Jm, as shown in Figure 6, is a very “noisy” field. However, the lowest values can clearly be matched to the homogeneous clouds. High values are often found at cloud edges and in this map there are regions of high values associated with the Arabian desert. The histogram at left shows a peak value of Jm ~ 18. This suggests the mean cost per degree of freedom (number of channels used) is about 2918 .

As a result of a squared calculation, cost values become very large when the solution obtained by the OCA minimisation does not have a good fit to the SEVIRI measurements. This can be for a variety of reasons: shadowing, inhomogeneous cloud structure, or poor auxiliary data (like surface temperature, albedo). In

general, high-cost retrievals cannot be trusted in the sense that the accompanying error estimate has no value; and this is with respect to all retrieved parameters. There is no definitive cut-off to be recommended as the tolerance depends on the application. However, we suggest that pixels with a cost greater than ~100 should be treated with caution.



3.4 Layer-One Cloud Optical Thickness (COT-1)

Figure 7: Cloud Optical Thickness, Layer-1.

The retrieved COT for layer-one is shown in Figure 7. Normally, there should be a clear and direct correlation to the visible channel brightness; this can be seen to some extent in the true colour image in Figure 4b, but the presence of the

1.6 m channel with high ice absorption in the RGB partially hides this. The histogram at left is useful for spotting anomalies in COT. In this case, we can see minor discontinuities at the Look-up Table (LUT) discretisation values and a collection of saturated high and low values. Saturated high values are those equal to 256–the LUT limit– and are probably due to clouds being illuminated from the side. The low value spike is at the Cloud Property Lookup

Table lower limit and represents thin clouds at or around the detection limit. Physical properties are poorly defined for these thin clouds. OCA diagnostics demonstrate this.



Figure 8: Histograms of COT-1. The blue plots are obtained using OCA diagnostic quality control. At left, the measurement cost Jm is less than 500. At right, the COT-1 fractional error is less than 25%.

The histograms in Figure 8 illustrate how the spike of low values can be removed in COT-1 data. In this model, limits are set on values of both the solution measurement cost, Jm, and the expected error in COT-1. The Jm limit is less than 500 and the expected error in COT-1 is less than 25% fractional. Both quality measures also remove a significant number of the thinner clouds [Log10 COT<0, (=COT<1)]; the fractional error filtering also removes thicker clouds, including the spike at the upper LUT limit.



3.5 Layer-One Cloud Top Pressure (CTP-1)

Figure 9: Cloud top pressure of layer-1 (upper layer).

Figure 9 shows the retrieved CTP of the (upper) layer-one. The image has familiar characteristics and areas of stratocumulus can, for example, be matched to the high CTP areas. If you compare this map to the map in Figure 4a, you can see the areas labelled ML on Figure 4a have sharp boundaries between cirrus and the stratus decks below them in Figure 9. The histogram at left shows a typical distribution with broad peaks around the planetary boundary layer and the tropopause (approximately 100 hPa to 400 hPa over the disk) and lower frequencies in the middle atmosphere. You see a spike at 222 to 224 hPa because optically thin cloud has very little corresponding information about its height.

The CTP in these cases remains at the guess value used in the OCA algorithm. For double-layer retrievals, this is the tropopause level + 100 hPa. Note: The tropopause level is calculated and lies on an internal fixed pressure level. As there are a restricted



number (30) of fixed pressure levels, the tropopause frequently lies at 123.73 hPa, especially at lower

latitudes) at 123.73 hPa and, hence the CTP first guess at 223.73 hPa.

The two histograms in Figure 10 illustrate this case. At left in Figure 10, in blue, the remaining CTP distribution is plotted when only single-layer results are included.. The histogram at right re-introduces double-layer results but only for COT-1 value greater than one; this result shows that if the upper layer is moderately thick, the retrieval does move the CTP-1 value away from the first-guess value.

Figure 10: Histograms of CTP-1: The blue plots are obtained using OCA diagnostic quality control: At left: only single- layer results; at right: Single- layer or COT-1 is greater than 1.0.

Figure 11 shows that the thin clouds making up the members of the spike also have associated a large CTP-1 error; here the blue plotted points exclude pixels with an estimated error greater than 20 hPa and results in the spike being significantly reduced in size.

Note: We must emphasise that the clustering in the spike is an artefact of the processing as explained. Nevertheless, the clouds do exist and the pressure estimate is as valid as other pixels and within the accuracy as specified by the estimated error.

Figure 11: Histogram of CTP-1. Blue plots exclude pixels with estimated CTP-1 error greater than 20 hPa.



3.6 Layer-One Cloud Effective Radius (CRE-1)

The retrieved cloud effective radius shows mode values of 6 m to 7 m for water phase and

18 m to 20m for ice phase. LUT table effects are seen in the water phase distribution. In

Figure 13, at left, the spike at 5.0 m is related to thin clouds and, again, represents pixels for which there is little information in the measurements. This is further demonstrated by the added blue plots

where pixels with expected error in CRE-1 greater than 1 m are excluded.

Figure 12: Layer-one cloud effective radius (CRE-1).

In Figure 13, at left, the spike at 5.0 m is related to thin clouds and, again, represents pixels for which there is little information in the measurements. This is further demonstrated by the added blue

plot points where pixels with expected error in CRE-1 greater than 1 m are excluded.



Figure 13: Histograms of CRE: Left- water phase pixels with (blue) threshold of 1.0 microns on estimated error applied; at right – ice phase.

The spike and truncation at 23 m is present because, although new LUTs with range 1 m to 31 m

were introduced in Release 2.1 (22 April, 2015), the limit checking within OCA remains at 23 m at

present. The 1 to 31 m range was introduced with the aim to remove this anomalous truncation under the observation that liquid water clouds do at least occasionally exist with effective radii larger than

23 m. However, current investigation indicates that most of the population contributing to the 23 m spike are probably a result of one of the following:

broken or sub-pixel clouds

highly heterogeneous (horizontally) clouds, and

mixed phase clouds.

The question of whether to permit the OCA reff to exceed the current limit of 23 m is therefore somewhat complicated. On the one hand, it does result in lower solution costs in the affected pixels and therefore produces cloud parameters which are more consistent overall with the measurements. On the other hand, this introduces a significant population of reff with high values, a significant fraction of which are probably due to deficiencies in modelling as described.

At this stage, since the in situ measurements of cloud reff indicate that values of greater than 23 m are

rare, we recommend to keep the limit fixed at 23 m, while recognising that this is not entirely satisfactory. We will plan to resolve some of the modelling issues. We will attempt to categorise events where reff

attempts to exceed the 23 m LUT boundary and take some type of informed decision. It might be possible, for example, to establish that a warm horizontally homogeneous cloud– insofar as can be established from SEVIRI–should be permitted to take larger reff values. Similarly, cold but horizontally homogeneous clouds might be allowed to take large reff values but be flagged as likely “mixed phase” rather than pure liquid.



3.7 Error in Layer-One Cloud Optical Thickness (ErrCOT-1)

The COT errors in the OCA product are given in Log10(COT) that correspond to the form of the

variable in the retrieval. The Log10(COT) error, LT, can be converted to a percent of fractional error

value in COT, %, either as )110(100% LT if the error is assumed a plus contribution to the

COT, or as )101(100%LT if the error is assumed as a negative contribution.

The nature of the logarithmic function means that the plus formulation gives much higher values than

the minus formulation unless the errors are reasonably small, where LT is less than 0.2. The

Log10(COT) error, LT is shown in the top left map in Figure 14. with the % shown in the bottom left

map. Perhaps the most distinctive characteristics are as follows:

Characteristic due to...

Very high errors in the southeastern edge of the disk

lack of visible channel information at “night”, where solar angles are greater than 70o.

High errors in deep clouds, either tropical convection or extra-tropical frontal. High errors are in the 40% to 60% range and are coloured cyan to green.

saturation of visible channel signal in thick clouds.

Scattered very high errors in some very thin cirrus and at cloud edges, which tend to return low optical thicknesses.

Note: Most % errors however lie in the 10 % to 20 % region.



Figure 14: Error in COT-1, the left upper panel map in (Log10) COT-1. The left lower panel map is expressed

as percent fractional.



3.8 Error in Layer-One Cloud Top Pressure (ErrCTP-1)

The CTP-1 error is in the main part a function of COT-1. As COT-1 reaches a value of four to five, the cloud becomes close to opaque in the infrared channels and the CTP-1 error becomes largely a function of the temperature (based on ECMWF forecast) lapse rate at that pressure. The lapse rate behaviour is not dramatic, but higher errors will be seen in some cases where the CTP coincides with a weak to near isothermal layer around the tropopause. Low CTP-1 errors can be seen in frontal thick cloud, the centres of tropical convective cells, and solid stratocumulus regions.

Higher CTP-1 errors are seen where COT-1 is less than four to five and, as most two-layer retrievals imply this situation–they are not “seen” as multi-layer if the upper layer is thick. CTP-1 in these cases are generally higher.

Figure 15: Error in CTP-1, with frequency scale histogram at bottom.



3.9 Error in Layer-One Cloud Effective Radius (ErrCRE-1)

Like the CTP-1 error, the error in the CRE-1 estimate is similarly higher for optically thin clouds

where the signal from the absorbing solar channel (1.6 m) becomes more dependent on COT and

less orthogonal to the signals from the non-absorbing (0.6 and 0.8 m) channels. In the case of two-layer retrievals, the current infrared-only capability means that CRE-1 retrieval is based only on infrared and not solar channels as in the single-layer (daytime) case. Normally, this would lead to a high error rate, especially near the IR saturation COT of four to five. However, in the OCA

two-layer algorithm, an artificial constraint of 5 m (called prior) is applied to the background value.

This means that CRE-1 errors in these cases cannot exceed 5 m, a feature clearly shown in the histogram at the bottom of Figure 16.

Figure 16: Error in CRE-1. Artificial constant “prior” is applied in the histogram.



3.10 Layer-Two Cloud Optical Thickness (COT-2)

Figure 17: Cloud optical thickness, double-layer; COT-2.

COT-2 is available in two-layer retrievals and is obtained by subtracting the layer-two COT-1 from the stored single-layer COT result, which can be considered the “total” result. COT-2 values range up to only about 101.5 (COT~30), which indicates that there are few, if any, detectable cirrus layers overlying very thick clouds.



3.11 Lower-layer Cloud Top Pressure CTP (CTP-2)

Figure 18: Cloud Top pressure, double layer, CTP-2.

Cloud-top pressure (CTP) of the lower layer in two-layer retrievals ranges from the surface pressure to about 300 hPa, with typical values in the lower troposphere. The map in Figure 18 shows several coherent areas that can be matched to the areas marked with ML in Figure 4a. In these areas, the overlying cloud in layer-one is thin and the underlying layer-two cloud is of a layer nature. Otherwise, the field appears quite “noisy”. This will be a combination of genuine variability (cumulus growing under tropical anvils for example) and errors in the retrieval. Errors in the retrieval should be stressed: information available to the estimation of the lower layer is highly dependent on the upper layer COT. As the upper COT-1 approaches four to five, both the ability to estimate the lower CTP–and to detect it in the first place is significantly reduced. This ability is reflected in the product’s error as explained in Section 3.12. It is important to take note of this.



3.12 Error in Layer-Two CTP (ErrCTP-2)

Figure 19: Error in cloud top pressure of layer-two.

As defined in Section 0, the CTP-2 error is primarily sensitive to the COT-1 of the overlying cloud. Again, we compared the areas marked ML in Figure 4a to the image in Figure 19 . In these areas of low COT-1, we can see errors of approximately 10 hPa to 30 hPa for CTP-2. Elsewhere, errors tend to be higher and highly variable. A good example of the effect of COT-1 is seen in the round convection in the centre of Figure 19. Away from the centre, the error is moderate (~30 hPa) but it quickly grows towards the centre as the anvil thickens and obscures the underlying cloud. CTP-2 errors are significantly higher than CTP-1 errors; compare the histogram at left with the histogram for Figure 15.



3.13 Error in Layer-Two COT (ErrCOT-2)

Figure 20: Percent fractional error in Cloud Optical Thickness of second layer.

Like the COT-2 itself, the error in layer-two COT is not directly estimable from the two- algorithm but inferred in combination with the single–layer result. For the error, no correction is made for the thickness of COT-1 in the obscuring layer for two reasons: First, it is not clear how this could be done and it is assumed that as COT-1 is less than four to five then the error in COT-2 would be more or less the same as that in the single-layer error for the combined COTs. Hence, for the majority of the disk, the error (shown as percent fractional in Figure 20) is small–generally less than 15%. Note: the thin night-time strip at the south-west edge has considerably larger errors.



4 BASIC STRUCTURE OF THE OCA ALGORITHM

The Optimal Cloud Algorithm (OCA) product was so named to describe the approach taken to estimation of cloud properties from the MSG SEVIRI instrument, for which it was originally developed. The approach justifies the description optimal from two of its characteristics:

all measurements and all important cloud parameters are dealt with simultaneously

the formal technique of optimal estimation (OE) is employed to obtain a solution. The OCA algorithm is based on the following components:

A model of a cloudy atmosphere defined by a set of variable ‘state’ parameters: x = COT, CRE, CTP, CFR, CPHS, TS (the cloud phase, CPHS, is not a continuous variable and takes one of only two values.)

and a set of fixed “model” parameters: b = Atmospheric temperature and gaseous constituents and surface properties (from ECMWF forecasts) and radiative properties derived from these using RTTOV-11.

A fast radiative-transfer model (RTM) which, when operated on the state and model parameters, estimates the values of the imager measurements y. This operation is denoted by y(x,b). Fast radiative transfer for the cloudy atmosphere is obtained using look up tables.

Models of errors in the measurements and the ‘prior to retrieval’ values of the state parameters.

A penalty or “cost” function, J, which describes the “distance” (a weighted mismatch) between measurements of the prior state and the state estimate.

A technique to minimise the penalty function.

Diagnostic checks on the cost and other parameters to establish the presence of multi-layer cloud.

A “two-layer” re-process method operated when multi-layer cloud is suspected. The variable state parameters are re-initialised and re-estimated under new constraints with infrared channels only such that a simple two-layer system is modelled. A combination of the two-layer parameters and the original single-layer result (including solar channel information) enables more parameters to be extracted than those given by the two-layer inversion alone.



5 REFERENCES

Type Document Name Reference

Algorithm Specification ATBD for Optimal Cloud Analysis Product

EUM/MTG/DOC/10/0229

Product Validation OCA Product Validation EUM/TSS/DOC/13/706263

Study

RAL, 2001: Study on Cloud Properties derived from Meteosat Second Generation Observations

ITT 97/181

Published Research Watts, P. D., R. Bennartz, and F. Fell (2011), Retrieval of two‐layer cloud properties from multispectral observations using optimal estimation,

J. Geophys. Res., 116, D16203, doi:10.1029/2011JD015883

Online Resources and Assistance

All of the reference documents listed above are in the EUMETSAT Technical Documents page.

www.eumetsat.int > Satellites > Technical Documents > Meteosat Services > 0° Meteosat Meteorological Products

To register for data delivery from this product, go to the Data Registration page on the EUMETSAT web page:

www.eumetsat.int > Data > Data Delivery > Data Registration

GRIB (GRIdded Binary) is the WMO standard binary format for exchanging gridded data. GRIB Edition 2 is an extension of GRIB, with a much higher degree of flexibility and expandability. For complete details on the format, see the WMO web page:

http://www.wmo.int/pages/prog/www/WMOCodes.html

For this Optimal Cloud Analysis product, there are some changes in Code table 4.2 of the WMO description as detailed in Appendix A to this document. These changes are also in this document: OCA local GRIB descriptors on the EUMETSAT web page:

www.eumetsat.int > Data > Products > Formats > LOCAL GRIB DESCRIPTOSTS

Information about the service status of EUMETSAT satellites and the data they deliver is this EUMETSAT web page:

www.eumetsat.int > Data > Service Status

To get answers to questions about data delivery, registration or documentation, contact the EUMETSAT User Service Help Desk:

Telephone: +49 6151 807 3660/3770 e-mail: [email protected]



APPENDIX A: WMO GRIB-2 CODE TABLE 4.2 CHANGES

A.1 Code Table 4.2-3-1

In order to encode the optimal cloud analysis quantitative parameters in GRIB, the following changes to Code Table 4.2 have been made.

1. Added these parameter numbers (24-34) to code table 4.2, Product Discipline 3 – Space products, Parameter category 1: Quantitative products.

2. Changed the reserved parameter numbers due to the addition of the new parameter numbers.

GRIB2 ‐ TABLE 4.2‐3‐1

Number Parameter Name Units Abbrev

24 Measurement Cost ‐ JM

25 Upper Layer Cloud Optical Thickness ‐ ULCOT

26 Upper Layer Cloud Top Pressure Pa ULCTP

27 Upper Layer Cloud Effective Radius m ULCRE

28 Error in Upper Layer Cloud Optical Thickness ‐ ERR‐ULCOT

29 Error in Upper Layer Cloud Top Pressure Pa ERR‐ULCTP

30 Error in Upper Layer Cloud Effective Radius m ERR‐ULCRE

31 Lower Layer Cloud Optical Thickness ‐ LLCOT

32 Lower Layer Cloud Top Pressure Pa LLCTP

33 Error in Lower Layer Cloud Optical Thickness ‐ ERR‐LLCOT

34 Error in Lower Layer Cloud Top Pressure Pa ERR‐LLCTP

35 ‐ 191 Reserved ‐ ‐

A.2 Code Table 4.218

In order to encode optimal cloud analysis image format parameters in GRIB, the following changes to Code Table 4.2 have been made:

1. Added the following parameter numbers (111-113) to code table 4.2, Product Discipline 3 - Space products, Parameter category 0: Image format products, Number 8: Pixel scene type - code table 4.218.

2. Changed the reserved parameter numbers due to the addition of the new parameter numbers.

GRIB2 ‐ CODE TABLE 4.218 (PIXEL SCENE TYPE)

Number Parameter Name

111 Single Layer Water Cloud

112 Single Layer Ice Cloud

113 Multi Layer Cloud

114 ‐ 191 Reserved