Cloud Mask /Cloud Products

A second dataset includes macro- and microphysical cloud properties. It contains information on:

• Cloud Fractional Coverage
• Cloud Optical Depth + Uncertainty
• Cloud Top Effective Radius + Uncertainty
• Cloud Water Path + Uncertainty
• Cloud Top Temperature + Uncertainty
• Cloud Top Phase
• Precipitation Likelihood
• Cloud Property Retrieval Quality Flag

The TIMELINE cloud products are processed with a further development of the cloud analysis tool APOLLO (AVHRR Processing Over cLouds, Land and Ocean), which has been in use for more than 25 years now. It has been developed for cloud detection from Advanced Very High Resolution Radiometer (AVHRR) observations (Saunders and Kriebel, 1988). It has been expanded by a snow/clouds-separation (e.g., Gesell et al., 1989) and updated towards a quantitative retrieval of physical cloud properties (Kriebel et al., 2003). Necessary requirements like the introduction of a cloud droplet effective radius retrieval along with the optical depth estimation (Nakajima and King, 1990), the use of modern representations of ice cloud optical properties (Baum et al., 2014) and the requirement for more flexible cloud detection (Merchant et al., 2005; Holzer-Popp et al., 2013) lead to a new development. A new probabilistic cloud detection scheme has been developed on the basis of the APOLLO physics which is called APOLLO Next Generation (APOLLO_NG). It aims to evaluate the probability of cloud occurrence in a given observation x. Its analysis based on the well-known APOLLO principles is now displayed as a probabilistic interpretation of the results. For the cloud property retrieval the mathematics in the original APOLLO follow the approach outlined in Stephens (1978). The general approach and the mathematical treatment have widely been conserved, but a couple of improvements and innovations have been realized. A complete and detailed description of APOLLO_NG is given in Klüser et al, 2015.

The AVHRR cloud detection with APOLLO has been evaluated a couple of times (Kriebel et al., 2003; Meerkötter et al., 2004). An initial comparison of APOLLO_NG with the traditional APOLLO cloud detection scheme showed that 79 % of cloud fraction retrievals from APOLLO_NG fall within ±12.5% of APOLLO (Klüser et al, 2015). Evaluations of APOLLO_NG have also been done in the framework of the COPERNICUS Atmosphere Monitoring Service (CAMS) (Killius et al, 2016).

APOLLO_NG facilitates the possibility to continue and expand the use of the APOLLO principle in a wide range of applications (e.g., Gesell, 1989; Meerkötter et al., 2004; Holzer-Popp et al., 2008; Klüser and Holzer-Popp, 2010; Qu et al., 2012). All of these applications require a well-understood error characterization as well as clearly documented sensitivities of the APOLLO_NG cloud products.

Most of the APOLLO_NG parameters are so-called “essential climate variables (ECV)”. The ECVs must be known for running climate models and predicting climate change.

References:

Baum, BA; Yang, P; Heymsfield, AJ; Bansemer, A; Cole, BH; Merrelli, A; Schmitt, C & Wang, C (2014) Ice cloud singlescattering property models with the full phase matrix at wavelengths from 0.2 to 100 μm. J. Quant. Spectrosc. Ra., 146, 123– 139.

Gesell, G (1989) An algorithm for snow and ice detection using AVHRR data an extension to the APOLLO software package. Int. J. Remote Sens., 10, 897–905.

Holzer-Popp, T; Schroedter-Homscheidt, M; Breitkreuz, H; Martynenko, D; and Klüser, L (2008) Improvements of synergetic aerosol retrieval for ENVISAT. Atmos. Chem. Phys., 8, 7651–7672.

Holzer-Popp, T; de Leeuw, G; Griesfeller, J; Martynenko, D; Klüser, L; Bevan, S; Davies, W; Ducos, F; Deuzé, JL; Graigner, RG; Heckel, A; von Hoyningen-Hüne, W; Kolmonen, P; Litvinov, P; North, P; Poulsen, CA; Ramon, D; Siddans, R; Sogacheva, L; Tanre, D; Thomas, GE; Vountas, M; Descloitres, J; Griesfeller, J; Kinne, S; Schulz, M; & Pinnock, S (2013) Aerosol retrieval experiments in the ESA Aerosol_cci project. Atmos. Meas. Tech., 6, 1919–1957.

Killius, N; Klüser, L; Schroedter-Homscheidt, M & Blanc, P (2016) APOLLO_NG - A new cloud retrieval for the CAMS Radiation service. EUMETSAT Meteorological Satellite Conference, 26.9.2016-30.9.2016, Darmstadt, Deutschland.

Klüser, L & Holzer-Popp, T (2010) Relationships between mineral dust and cloud properties in the West African Sahel. Atmos. Chem. Phys., 10, 6901–6915.

Klüser, L; Killius, N & Gesell, G (2015) APOLLO_NG – a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels. Atmospheric Measurement Techniques, 8, 4155-4170.

Kriebel, KT; Gesell, G; Kästner, M & Mannstein, H (2003) The cloud analysis tool APOLLO: improvements and validations. Int. J. Remote Sens., 24, 2389–2408.
Meerkötter, R; König, C;Bissoli, P; Gesell, G & Mannstein, H (2004) A 14-year European Cloud Climatology from NOAA/AVHRR data in comparison to surface observations. Geophys. Res. Lett., 31, L15103.

Merchant, CJ; Harris, AR; Maturi, E & Maccallum, S (2005) Probabilistic physically based cloud screening of satellite infrared imagery for operational sea surface temperature retrieval. Q. J. Roy. Meteorol. Soc., 131, 2735–2755.

Nakajima, T & King, MD (1990) Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements – Part I: Theory. J. Atmos. Sci., 47, 1878–1893.

Qu, Z; Oumbe, A; Blanc, P; Lefevre, M; Wald, L; Schroedter - Homscheidt, M; Gesell, G & Klüser, L (2012) Assessment of Heliosat-4 surface solar irradiance derived on the basis of SEVIRI-APOLLO cloud products, Proceedings of the 2012 EUMETSAT Meteorological Satellite Conference, Sopot, Poland, 3–7 September 2012, EUMETSAT, 61, s2–06.

Saunders, RW & Kriebel, KT (1988) An improved method for detecting clear sky and cloudy radiances from AVHRR data. Int. J. Remote Sens., 9, 123–150.