Authors: Thierry Phulpin, CNES Lydie Lavanant, Meteo France Claude Camy-Peyret, LPMAA/CNRS. Date: 15 June 2005

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1 Comments on the number of cloud free observations per day and location- LEO constellation vs. GEO - Annex in the final Technical Note on geostationary mission concepts Authors: Thierry Phulpin, CNES Lydie Lavanant, Meteo France Claude Camy-Peyret, LPMAA/CNRS Date: 15 June 2005 In the Annex to the Technical Note on Geostationary Mission Concepts of the Capacity study, it is quantified, with respect to the cloud cover frequency, what is the advantage of a continuous monitoring from a UV/VIS instrument on a GEO (geostationary) satellite in comparison with several instruments on LEO (low Earth orbit). The assumptions are: UV/VIS instrument on GEO having a 5 km 5 km SSP (sub-satellite point) resolution. UV/VIS instruments on LEO are GOME-2, OMPS and OMI only daylight acquisitions are considered. It is implicitly admitted that integration time for the GEO instrument is of the same order than for MVIRI, i.e. a couple of minutes. The method quantifies the number of cloud free pixels hour, which is quite different from a probability of having a cloud free scene. The notion of a cloud free pixel is not defined. To compute this quantity, the method is to multiply the probability of having a cloud free pixel, given the size of a pixel of N N km 2, with the number of daylight hours. For the LEO option, the statistics used are those provided in a study by A. Stevens (RAL) for ACOR. For the GEO option, they are those provided in the Eumetsat Technical Memorandum N 13 by S. Tjemkes et al. 1. Comparison of statistics sources We have plotted the results of the latter on the figure of the former, for the case of Europe and for the four seasons in the year (Fig 3.4 from S. Tjemkes et al). The points added in Fig 1 of the present note are given in Table 1. A very strong difference in the percentage of cloud free scenes is observed. Clearly, the results from the Eumetsat study appear to be very optimistic, when the results from RAL may look pessimistic. Table 1 Pixel size DJF MAM JJA SON 5 km 5 km km 21 km

2 2 MAM JJA + SON MAM DJF SON DJF Figure 1: Percentage of scenes with a given cloud fraction as a function of ground pixel size according to the ATSR-2 cloud scheme of A. Stevens (RAL). The largest pixel size corresponds to the GOME-2 ground pixel. The fraction of cloud-free scenes for a 1 km 2 pixel is inferred by extrapolation. This figure appears in ACECHEM SP-1257 as well as in the following ACOR study final report. It was also used in the TROC proposal (COM2-35), where the parameterisation P = log A was proposed for the probability P of clear pixels (cloud fraction = 0) and for an area A < 400 km 2. The values obtained from a seasonal analysis for several semesters over Europe have been added for 2 pixel areas: 25 km 2 and 440 km 2 corresponding to possible GEO sounders in UV-vis and in IR respectively. Actually, the causes of such a discrepancy can be assigned, in our point of view, to: a definition of cloud coverage that is not the same in both studies the characteristics of the instruments the method used for the cloud analysis. a) Definition of cloud cover. It is well recognised that every air parcel may potentially be considered as cloudy. This difficulty has been encountered in many projects using satellite data (see for instance the ISCCP results of Rossow et al). The question is quite complex as some clouds are almost fully transparent in some regions of the spectrum. Moreover, the cloud cover is sometimes calculated as the number of cloud contaminated pixels. The definition is depending on the use of the data. For cloud statistics, a false alarm (meaning a pixel classified as cloudy when it is actually clear) is not acceptable. That is why in some sense, when based on high resolution data (the best statistics is certainly the one given by very high resolution satellite images like those of SPOT, for which on the whole globe the statistics is about 20% cloud free), the values for cloud free pixels are more optimistic. As for us, based on the experience on inversion of atmospheric profiles from atmospheric sounder (e.g. HIRS, AIRS or IASI), all pixels departing from the expected brightness

3 3 temperature (meaning all outliers ) have to be screened out. On this basis, the number of AIRS cloud free pixels (14 km 14 km) as detected by MODIS is only 5% (e.g. Goldberg, 2005 or G. Aumann, 2003). For this reason, the analysis based on ATSR looks more reliable for application. b) Characteristics of the instrument b.1 Spatial resolution. In that case, MVIRI with a 5 km 5 km SSP pixel, that is to say 7 to 8 km resolution over Europe, is compared to results obtained with a 1 km 1 km pixel. In surface this is about 60 km 2 compared to 1 km 2. This will obviously influence the results. Small cumulus or thin cirrus averaged over large surface pixels will not modify strongly the apparent radiance and will not be accounted in this statistics. b.2 Spectral channels. ATSR analysis is probably based on all the channels in the visible, near IR, SWIR and the thermal windows. Many studies have shown the interest of channels at 1.6 µm, 3.7 µm, 11 and 12 µm, for cirrus, stratus, small cumulus, delineation with bright backgrounds etc. As the Eumetsat analysis is based on MVIRI with only one broadband Visible channel and one broadband IR channel, this will certainly make a difference. c) Methods for cloud analysis Different methods (more or less refined) can be used, based on a statistical approach, on thresholds etc The best method will combine all these techniques. 2. Pixel surface The results are computed for GEO with a 5 km 5 km SSP for a UV/VIS sounder (really optimistic as the corresponding LEO instruments have a much coarser spatial resolution). This leads to an area of about 60 km 2 over Europe. In this GEO analysis, no result was given for an IR sounder, which is supposed to have a coarser ground resolution. Based on the hypothesis of a 15 km 15 km SSP resolution, this leads to a 440 km 2 surface pixel over Europe. 3. Time for integration. In the method applied for LEO, only one overpass for each instrument is considered. Actually, a wide field of view instrument like AVHRR (OMI has about the same field of view) covers Europe with three or four successive orbits and a large overlap (see Fig. 2), which must be taken into account for a fair comparison. The increase of cover for one day is about 50%. For the GEO, some integration time is needed to get spectra with good signal to noise ratio. For instance, for a Fourier transform infrared spectrometer with a specification of NeDT around 0.05 K, a one hour integration time is to be considered. During this time, the cloud cover may change significantly. In that case, either the pixels are discarded or the radiometric accuracy is strongly reduced. This analysis is not possible with MVIRI data, but it can be done with SEVIRI that has twice the data rate (every 15 min).

4 4 NOAA 16 on 02 May :11 UTC 02:53 UTC 04:34 UTC 06:14 UTC Figure 2: Consecutive overpasses over Europe of the polar satellite NOAA16 for 2 May A number of ground pixels are revisited several times in daylight Discussion Considering the restrictions of the study, a) We have re-computed the occurrence day, as defined above, with the same statistics, adequate resolution for the infrared and accounting for overlap of successive swaths for LEO orbits Considering GEO pixels of 64 km 2 for UV/VIS and 440 km 2 for IR, ACOR statistics, and an average daylight of 10 hours leads to: GEO UV/VIS : = 2.60 GEO IR : = 1.50 Considering the overlap of consecutive orbits LEO UV/VIS or IR : ( ) 1.5 = 0.57

5 5 This shows the advantage of GEO over LEO, but not as strongly as stated in the Annex. In addition, the integration time has to be considered in the statistics. b) We started a study based on a more robust cloud mask, elaborated by H. Legléau et al ( used operationally at the Nowcasting SAF to process SEVIRI data. This cloud mask is described in the reference above and an output is given hereafter. Figure 3: Meteosat Second Generation composite image with surface/cloud classification The study is based on SEVIRI, with a pixel size at SSP that is 3 km 3 km (meaning about 5 5 km 2 on the average over Europe). A set of data has been selected in Summer Five scenes representative of the cloud cover over the full period are considered. For each scene, a subset of 800 lines corresponding to North Atltantic and Europe is extracted. In this analysis, only two classes are considered : clear (indices < 5) or cloudy (index >5). The pixels are assembled in superpixels of 2 2, 3 3, 4 4 and 5 5 pixels. A superpixel is considered as cloudy if only one pixel is cloudy. This is done every 15 minutes for one day.

6 6 Then, time integration is performed by merging pixels acquired in 30 minute, 60 minute or 120 minute periods. A pixel in any of these periods is considered as cloudy if only one pixel has been cloudy at a given time of the overall period. As a first output, the results for one single date are given in Fig. 4. 0,5 0,45 0,4 0,35 0,3 0,25 0,2 0,15 0,1 0, :00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 00:00 1 pixel 3x3 3x3_30min 3x3_60min 3x3_120min 5x5 5x5_30min 5x5_60min 5x5_120min Figure 4 : Daytime variation of the cloud fraction as a function of pixel merging and time integration. A square superpixel is obtained by merging n n adjacent pixels (n = 1, 3 and 5 in this figure) for periods of 30 min, 60 min and 120 min. The area covered is North Atlantic and Europe for Summer A decrease of resolution from 3 3 km 2 to 5 5 km 2 leads to a decrease of 0.06 in the probability of getting a cloud free pixel. Getting information over an integration time of 60 minute instead of 15 minute has the same effect. Thus for a 5 5 km 2 (15 km 15 km SSP) superpixel integrated over one hour, the probability of being cloud free is 0.24 on the average instead of 0.29 (0.43 in Tjemkes et al). SUMMARY To summarize, the results shown in the Anenx of the report by H. Bovensmann et al. strongly overestimate the potential benefits of a geostationary orbit with respect to the holes in the cloud cover. A more elaborate study is being carried out based on SEVIRI data and a well-referenced cloud mask. The first results show less optimistic results and that averaging in time is degrading the information as much as averaging in space.

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