SEVIRI is the main imager on the EUMETSAT Meteosat Second Generation platform. As part of our participation in the international CREW (Cloud Retrieval Evaluation Workshop) product intercomparison and assessment effort, we have adapted portions of the operational Collection 6 MODIS (MOD06/MYD06) cloud optical and microphysical algorithm and the GOES-R cloud top properties algorithm to run on SEVIRI. The overall retrieval package is referred to as CHIMAERA (Cross-platform HIgh resolution Multi-instrument AtmosphEric Retrieval Algorithms); it was designed for flexibility in ingesting data from a variety of satellite and airborne imaging instruments (MODIS, VIIRS, SEVIRI, eMAS, etc.). The CHIMAERA package utilizes a shared-core concept where the same core code and algorithm-specific ancillary data sources are used for all instrument retrievals. Lookup tables (LUTs) such as cloud reflectance/emissivity and absorbing gas transmittances for atmospheric corrections are developed on an instrument-specific basis. Figure 1 illustrates the processing chain for both MOD06_L2 and SEV06-CLD products.

SEVIRI-specific Algorithm Details

There are some important differences between the implementation of the MODIS and SEVIRI optical property retrieval products (optical thickness, effective particle radius, and derived water path). SEVIRI lacks the MODIS 1.2µm and 2.1µm channels, which compromises SEVIRI’s ability to retrieve clouds over snow/ice surfaces (Platnick et al., 2003). Similarly, the optional 1.6 and 2.1µm MODIS retrieval over snow/ice surfaces (Platnick et al., 2001) is not available. However SEVIRI’s spatial coverage is such that snow/ice surfaces typically cover a very small fraction of the observable area and thus are unlikely to be an issue except for users interested in wintertime northern hemisphere scenes and/or mountain regions.

The CO2 emissive band coverage on SEVIRI consists of a single broadband CO2 channel instead of the four narrow-band CO2 channels on MODIS. Therefore the MODIS CO2 slicing algorithm cannot be used in SEVIRI processing to obtain cloud top properties of high clouds. In lieu of being able to implement a full MODIS cloud-top properties algorithm, the SEVIRI algorithm utilizes a hybrid algorithm. A GOES-R Algorithm Working Group (AWG) style optimal estimation cloud-top properties retrieval, described in Heidinger and Pavolonis (2009) and Heidinger et al. (2010), is used for retrievals of low emissivity high clouds with good success (Hamann et al., 2014). For low clouds, a MODIS-heritage IR Window retrieval is used. An IR cloud thermodynamic phase algorithm is implemented using the same method utilized in MODIS Collection 6 (Baum et al., 2012).

The cloud algorithms ingest the well-established and documented SAFNWC cloud mask product developed by the Météo France Nowcasting and Weather Prediction Satellite Application Facility. This cloud mask algorithm is described in detail in Derrien and Le Gleau (2005) and Derrien and Le Gleau (2010). We also rely on the SAFNWC cloud mask to identify broken clouds/partly cloudy pixels for PCL (Partly CLoudy, see Table 3) discrimination. We do not perform a MODIS-like multilayer cloud retrieval as SEVIRI does not have the requisite spectral channels. However, unlike the MODIS cloud mask, the SAFNWC cloud mask does provide a multilayer mask. We also do not perform separate visible/near-infrared/shortwave-infrared cloud thermodynamic phase tests to supplement the IR phase algorithm; only the IR cloud thermodynamic phase algorithm (mentioned above) is used.

The impact of cloud mask and phase differences relative to MODIS can be important for ambiguous scenes (broken clouds, heavy aerosol/dust, supercooled cloud temperatures). For example, any difference in the phase decision will result in potentially strong differences in the retrieved optical thickness and effective radius due simply to the different microphysical assumptions. Regardless, the cloud optical thickness and effective radius retrieval algorithms that are implemented for both the visible/near-infrared (VNIR) -1.6µm and VNIR-3.8µm SEVIRI channel combinations are identical to MODIS Collection 6 as are the QA bit assignments (see

The SEVIRI nadir resolution is 3 km and degrades away from nadir as the view angle becomes more oblique. Like MODIS, retrievals are limited to where the solar zenith angle is less than 81.36° (µ0>0.15). In consideration of the SEVIRI wide field of view, a limit of the same 81.36 degrees is also applied to the sensor zenith angle. Note that baseline retrieval uncertainties are provided in the data file (see Table 3) and can increase substantially at the extreme solar and view zenith angles. Further, the impact of SEVIRI’s coarser spatial resolution (3km vs. 1km) is expected to impact retrievals in heterogeneous cloud scenes (Zhang and Platnick, 2011; Zhang et al., 2012).


Table 1 :
Legend of values stored in SEVIRI cloud mask product and their definitions as per Satellite Meteorology Centre of Meteo-France (SATMOS) website.

Result Value Description
0 No retrieval
1 Clear sky, land surface
2 Clear sky, ocean surface
3 Snow / ice on land, no cloud
4 Snow / ice on ocean, no cloud
5 Cloud, very low, cumuliform
6 Cloud, very low, other
7 Cloud, low, cumuliform
8 Cloud, low, other
9 Cloud, medium, cumuliform
10 Cloud, medium, other
11 Cloud, high, cumuliform
12 Cloud, high, other
13 Cloud, very high, cumuliform
14 Cloud, very high, other
15 Cloud, semi-transparent, thin
16 Cloud, semi-transparent, meanly thick
17 Cloud, semi-transparent, thick
18 Cloud, semi-transparent, above medium cloud
19 Cloud, broken
20 Undetermined

Table 2 :
SEVIRI channels and their MODIS equivalents

SEVIRI channel number and central wl (µm) SEVIRI band-pass (µm) MODIS channel number and central wl (µm) MODIS band-pass (µm)
1: 0.635 0.590-0.698 1: 0.658 0.620-0.670
2: 0.810 0.768-0.854 2: 0.863 0.841-0.876
3: 1.640 1.539-1.729 6: 1.625 1.628-1.652
4: 3.920 3.550-4.360 20: 3.851 3.660-3.840
5: 6.250 5.746-6.862 27: 6.766 6.535-6.895
6: 7.350 7.010-7.730 28: 7.282 7.175-7.475
7: 8.700 8.444-8.972 29: 8.642 8.400-8.700
8: 9.660 9.500-9.839 30: 9.673 9.580-9.880
9: 10.800 10.080-11.600 31: 10.984 10.780-11.280
10: 12.000 11.360-12.560 32: 11.897 11.770-12.270
11: 13.400 12.48-14.320 33-36: N/A 13.185-14.385

Table 3 :
SEV06-CLD SDS list and equivalent MOD06 SDSs

SEV06-CLD SDS name Equivalent MOD06 SDS name Notes
MSG_Latitude Latitude
MSG_Longitude Longitude
Can be calculated from solar and sensor azimuth angles
This water vapor amount is from an integrated ancillary profile and is not a direct retrieval
Cloud_Optical_Thickness_16 Cloud_Optical_Thickness_16 Except over snow/ice surfaces where MODIS is able to use 1.2µm channel.
Cloud_Optical_Thickness_16_PCL Cloud_Optical_Thickness_16_PCL Except for different PCL definition as stated earlier
Cloud_Optical_Thickness_38 Cloud_Optical_Thickness_37 Except over snow/ice surfaces where MODIS is able to use 1.2µm channel.
Cloud_Optical_Thickness_38_PCL Cloud_Optical_Thickness_37_PCL Except for different PCL definition as stated earlier
Cloud_Effective_Radius_16 Cloud_Effective_Radius_16 See COT note
Cloud_Effective_Radius_16_PCL Cloud_Effective_Radius_16_PCL See COT PCL note
Cloud_Effective_Radius_38 Cloud_Effective_Radius_37 See COT note
Cloud_Effective_Radius_38_PCL Cloud_Effective_Radius_37_PCL See COT PCL note
Cloud_Water_Path_16 Cloud_Water_Path_16 See COT note
Cloud_Water_Path_16_PCL Cloud_Water_Path_16_PCL See COT PCL note
Cloud_Water_Path_38 Cloud_Water_Path_37 See COT note
Cloud_Water_Path_38_PCL Cloud_Water_Path_37_PCL See COT PCL note
Cloud_Effective_Radius_Uncertainty_16 Cloud_Effective_Radius_Uncertainty_16 Calibration uncertainty of flat 5% used for SEVIRI because there is no L1B uncertainty index
Cloud_Effective_Radius_Uncertainty_38 Cloud_Effective_Radius_Uncertainty_38 See CER_Unc16 note
Cloud_Optical_Thickness_Uncertainty_16 Cloud_Optical_Thickness_Uncertainty_16 See CER_Unc16 note
Cloud_Optical_Thickness_Uncertainty_38 Cloud_Optical_Thickness_Uncertainty_38 See CER_Unc16 note
Cloud_Water_Path_Uncertainty_16 Cloud_Water_Path_Uncertainty_16 See CER_Unc16 note
Cloud_Water_Path_Uncertainty_38 Cloud_Water_Path_Uncertainty_38 See CER_Unc16 note
Cloud_Phase_Optical_Properties Cloud_Phase_Optical_Properties SEVIRI CPOP SDS at this time is identical to SEVIRI Cloud_Phase_Infrared SDS
Single_Scatter_Albedo_Ice Single_Scatter_Albedo_Ice
Asymmetry_Parameter_Ice Asymmetry_Parameter_Ice
Extinction_Efficiency_Ice Extinction_Efficiency_Ice
Single_Scatter_Albedo_Liq Single_Scatter_Albedo_Liq
Asymmetry_Parameter_Liq Asymmetry_Parameter_Liq
Extinction_Efficiency_Liq Extinction_Efficiency_Liq
Failure_Metric_16 Failure_Metric_16
Failure_Metric_38 Failure_Metric_37
Quality_Assurance Quality_Assurance_1km Full QA bit description is present in file metadata just like MOD06
Cloud_Mask Cloud_Mask_1km SEVIRI Cloud_Mask SDS does not require any bit decoding, values are as listed in Table 1
Cloud_Top_Temperature cloud_top_temperature_1km SEVIRI uses the AWG algorithm, but use of data is same as for MOD06
Cloud_Top_Height cloud_top_height_1km
Surface_Temperature surface_temperature_1km Interpolated model surface temperature
Cloud_Top_Pressure cloud_top_pressure_1km See CTT note
Cloud_Top_Method cloud_top_method_1km
Cloud_Phase_Infrared cloud_phase_infrared_1km SEVIRI uses an identical algorithm to MODIS, but 13.2 µm instead of 7.2 µm for absorbing IR channel.

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