Auxiliary Albedo Dataset

While APICS was originally developed as a multi-purpose cloud property retrieval, it is especially important in the context of this study that optically thin cirrus clouds are retrieved at the best. To account for that, APICS was modified in a common effort of Luca Bugliaro and myself as described in the following.

The most important sources of uncertainty, considering thin ice clouds, are the a priori assumptions about the ground reflectivity, the aerosol properties and the ice particle shape. Figure 3.1 illustrates that a wrong assumption about the ground reflectivity can lead to large errors in the retrieved cloud properties, especially for clouds withτ <5. Both panels in the figure show excerpts of the lookup tables for albedo value combinations which were actually observed within the considered test sector (Fig. 2.4). The albedo combination in the left panel, for example, was observed for a densely forested region in Spain, just north of Gibraltar (Alcornocales national park), while the albedo combination in the right panel is typically observed for the desert regions in the south-east of the test sector. We adapted APICS such that initially existing problems concerning wrong assumption of the ground reflectivity are reduced.

APICS relies on the albedo to describe the ground reflectivity – that is, isotropic ground reflection is assumed. The albedo must be determined a priori for both SEVIRI channels at every pixel. The original version of APICS used albedo products based on measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard polar orbiting satellites – either the “blacksky” or “whitesky” albedo from the Ambrals processing scheme (Strahler et al., 1999). Now we modified APICS so that it generates its own self-consistent albedo product. This product is based on the SEVIRI “Clear Sky Reflectance Map”, which is the average of the reflectance for a given time of day over the seven preceding days un- der clear sky conditions (EUM OPS DOC 09 5165). The clear sky reflectance contains

3.2 Cloud Property Remote Sensing with the APICS Algorithm 29 0.0 0.2 0.4 0.6 0.8 1.0 0.6 µm Reflectivity 0.0 0.1 0.2 0.3 0.4 0.5 0.6 1.6 µ m Reflectivity 5 µm 10 µm 15 µm 20 µm 25 µm 30 µm 35 µm 40 µm 50 µm 60 µm 90 µm 0.0 0.8 1.6 3.0 5.0 10.0 20.0 40.0 100.0 0.0 0.2 0.4 0.6 0.8 1.0 0.6 µm Reflectivity 0.0 0.1 0.2 0.3 0.4 0.5 0.6 1.6 µ m Reflectivity 5 µm 10 µm 15 µm 20 µm 25 µm 30 µm 35 µm 40 µm 50 µm 60 µm 90 µm 0.0 0.8 1.6 3.0 5.0 10.0 20.0 40.0 100.0

Figure 3.1: Details of the APICS lookup table for “Baum v2” for a sun zenith angle of 30◦, a relative azimuth between sun and satellite of 20◦and a satellite zenith angle of 41.4◦. Blue: Lines of equal effective radius. Dashed red: Lines of equal cloud optical thicknessτ. Left panel: Albedo combination for the 0.6µm and 1.6µm-channels of (0.10, 0.25). Right panel: Albedo combination of (0.35, 0.60). Plotting script courtesy of Luca Bugliaro.

contributions from the ground as well as from the atmosphere. From this combined signal the ground albedo needs to be extracted. The albedo values in both SEVIRI channels are therefore retrieved using the APICS lookup tables. To this end it is evaluated for which albedo the pre-simulated reflectivity values match the clear sky reflectance best, where

τ = 0. This procedure has the advantage of being consistent with the cloud property retrieval concerning atmospheric gas composition, radiative transfer modelling and instru- ment calibration. Furthermore we assume that the thus retrieved albedo also corrects for some of the long term variability in the aerosol properties which may deviate from the constant aerosol properties used in the simulations for the APICS lookup tables. Long term variability here denotes changes on the scale of a few days to one week and which are therefore slow enough to influence the clear sky reflectance averaged over seven days. The “Clear Sky Reflectance Map” is routinely only available for 12 UTC. Therefore the albedo is only retrieved at this time and used for the whole day. Nevertheless the success rates (defined in the following) reached by APICS with this method are superior compared to using the MODIS albedo datasets. In principle one could develop a product alike the

30 3. Tools and Methods

“Clear Sky Reflectance Map” with a day-of-time dependence, but this was assessed to be beyond the scope of this study.

Because of the uncertainties in the a priori assumptions described above and because of other insufficient projections of the reality (e.g. 1D radiative transfer calculations) the cloud property retrieval results can be erroneous. Often the measured reflectivity pair cannot even be reproduced by any parameter combination in the lookup tables. In these cases the retrieval will return a (τ, reff) combination from the “edge” of the LUT andχ2 – although minimized – will differ from zero. Let us call these occurrences “outliers”. Please note that the definition of outliers is very strict. In principle one could add all those data points to the “hits”, which lie within the uncertainty range of the retrieval due to errors in the measurements and in the assumptions. These errors are, however, difficult to quantify. Since the number of outliers is used in the following only as a relative quality criterion, the exact definition is not relevant. The success rate of the cloud property retrieval S is defined as one minus the ratio of the number of outliers to the total number of considered pixels in a scene.

S= 1− outliers

considered pixels (3.4)

The success rate was calculated for four months of SEVIRI measurements within the test sector (see Fig. 2.4), which should be characteristical for the four different seasons (Jun/2011, Sep/2011, Dec/2011 and Mar/2012). The average success rates are 62%, 62% and 81% for the MODIS “blacksky”, MODIS “whitesky” and the APICS-generated albedo respectively when using the “Baum v2” optical properties. Considering the better values with the APICS-generated albedo, APICS was operated with this new albedo product for the retrieval of circumsolar radiation. Note that success means the retrieval obtained values, which need not be correct, though. It was decided to include the “outliers” in the circumsolar radiation calculations with the (τ, reff) combination that APICS returns because they are the best possible result for a given set of measurements.

As one can see from the not perfect success rate this approach attenuates the problems with wrong a priori assumptions but does not solve them completely. The APICS-generated albedo product can adapt to changes of the real surface albedo only on the scale of a few days while the real changes – e.g. due to precipitation – can be on the scale of hours. Further reading on the influence of inhomogeneous surface albedo on Nakajima & King-like cloud property retrievals can be found in Fricke et al. (2012).

3.2 Cloud Property Remote Sensing with the APICS Algorithm 31