SEV06-CLD Project

SEVIRI CLOUD Data and Services at ICARE

Background

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 modis-atmos.gsfc.nasa.gov/products_C006update.html).

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).


Annexe

Table 1:
Legend of values stored in SEVIRI cloud mask product and their definitions as per Satellite Meteorology Centre of Meteo-France (SATMOS) website. www.satmos.meteo.fr/html_en/Diffusion_CT_MSG.html

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


Relative_Azimuth_Angle


Can be calculated from solar and sensor azimuth angles

Above_Cloud_Water_Vapor


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.