Institut fur Physik der Atmosphare Report No. 140
Alpine cloud climatology using long-term NOAA-AVHRR satellite data
by
Martina Kastner and Karl-Theodor Kriebel Diese Berichtsserie des Instituts fur Physik der Atmosphare enthalt Veroffentlichungen, die zu einem spateren Zeitpunkt an anderer Stelle erscheinen sollen, sowie spezielle Einzelergebnisse und erganzende Materialien, die von allgemeinem Interesse sind. .
This series of reports of the Institute of Atmopsheric Physics comprises preprints of publications, specific results and complementary material, which may be of a more general interest.
Herausgeber Ulrich Schumann Deutsches Zentrum fur Luft- und Raumfahrt e.V. (DLR) Institut fur Physik der Atmosphare
Redaktion Ute Lob
Anschrift DLR - Oberpfaffenhofen D-82234 Wessling Germany
Tele fori +49 - 8153 28 1797 Telefax +49-8153 28 1841 WWW http://www.op.dlr.de/ipa/ E-Mail [email protected]
Oberpfaffenhofen Juli 2000
ISSN 0943-4771 DISCLAIMER
Portions of this document may be illegible in electronic image products. Images are produced from the best available original document. DE01G0874
Alpine cloud climatology using long-term NOAA-AVHRR satellite data RECEIVED FEB 0 5 2001 M. Kastner and K.T. Kriebel OSTI
DLR, Institut fur Physik der Atmosphere, Ober pfaffenhofen
DE014976396
ilillullmll IIJ IHIILII JJi &DE014976396* Abstract
Three different climates have been identified by our evaluation of AVHRR (Advanced Very High Resolution Radiometer) data using APOLLO (AVHRR Processing scheme Over Land, cLouds and Ocean) for a five-years cloud climatology of the Alpine region. The cloud cover data from four layers were spatially averaged in boxes of 15 km by 14 km. The study area only comprises 540 km by 560 km, but contains regions with moderate, Alpine and Mediterranean climate. Data from the period July 1989 until December 1996 have been considered. The temporal resolution is one scene per day, the early afternoon pass, yielding monthly means of satellite derived cloud coverages 5% to 10 % above the daily mean compared to conventional surface observation. At non-vegetated sites the cloudiness is sometimes significantly overestimated. Averaging high resolution cloud data seems to be superior to low resolution measurements of cloud properties and averaging is favourable in topographical homogeneous regions only. The annual course of cloud cover reveals typical regional features as foehn or temporal singularities as the so-called Christmas thaw. The cloud cover maps in spatially high resolution show local luff/lee features which outline the orography. Less cloud cover is found over the Alps than over the forelands in winter, an accumulation of thick cirrus is found over the High Alps and an accumulation of thin cirrus north of the Alps.
1 1 Introduction
Climate depends essentially on the radiation balance and the water cycle. Both exchange processes are linked by clouds that are involved in dynamical processes, too. Changes of the distribution of clouds might be an expression or indication of a changing climate. Global cloud cover is monitored by the ISCGP (International Satellite Cloud Climatology Project; Schiffer and Rossow, 1983) for many years. '-Results- are provided in the high temporal resolution of 3 hours, well suited to describe the large-scale circulation and its cloud system evolution cycle. However, the spatial resolution is rather coarse being greater than 100 km. But regional changes in cloud cover may change regional climate, too. This different objective requires the observation of smaller cloud systems with high spatial resolution for longer periods. Surface observationseither don’t provide a high spatial resolution data set (e.g. Warren et ah, 1986), or don’t give homogeneous observations (synoptic ground net). Satellite observations can give high spatial resolution together with homogeneous area coverage. Further, such data are derived by consistant methods instead of subjective eye observations. Until now, there have onlybeen few high spatial resolution cloud climatologies published based on satellite data (e.g. Karlsson, 1997). In this paper, a 5-years cloud climatology in a small area, based on AVHRR (Advanced Very High Resolution Radiometer) data, is described and analyzed which has been initialized several years ago. Its purpose is to pave the way for a 15-years regional European Cloud Climatology from 1986 to 2000 which has already been started at the Deutsches Zentrum fur Luft- und Raumfahrt (DLR). The 5-years cloud climatology has been performed in the Alpine region from 1992 to 1996. Although the study area is small it comprises three climate regions: moderate, Alpine, and Mediterranean climate. One objective of this cloud climatology study is to improve the validity of the threshold tests in different climates used in the APOLLO (AVHRR Processing scheme Over cLouds, Land and Oceans) algorithm package (Saunders and Kriebel, 1988; Kriebel et al., 1989; Gesell, 1989). With the improved cloud detection scheme a unique 15-years cloud climatology is envisaged in near future. This paper concentrates on climatological features of high spatial resolution derived from monthly, seasonal, and annual mean data of different types of cloud cover. Results from other cloud products like optical depth, liquid water path, and IR-emissivity which are derived simultaneously will not be discussed here. Section 2 deals with data sources. In section 3, the APOLLO cloud detection technique is shortly described, together with the thresholds used and the products derived. In section 4 the method of analysis is shown including the remapping of the data, the averaging to boxes, the cloud classification, and the data control. Section 5 presents the achieved results which comprises the comparison with independent data and the identification of seasonal and regional effects. The discussion in section 6 reviews the possibility of detecting trends.
2 Data Sources
This study relies on AVHRR observations made over the Alps and their forelands from the polar orbiting satellites NOAA-9, NOAA-11 and NOAA-14. Data from September 1991 till December 1996 and additionally the midseasonal months July and October of the years 1989 till 1991 are used to reach an 8-years period. The daily afternoon overpass is used for this study, because a high solar elevation allows for an anisotropy correction (Kriebel et al., 1989) which is not too unrealistic.
2 Figure 1: Frame: study area in central Europe. Scene from NOAA-11 AVHRR channels 1, 2, 4 in orthogonal projection, 22 Oct. 1990, 12:53 UTC.
The AVHRR channelshave been chosen to have a maximum response within the atmospheric windows. These five channels in atmospheric windows have weak absorption of atmospheric gases, therefore, the data are appropriate for studying surface and cloud properties. The AVHRR measures in five spectral bandpasses: channel 1 (0.56-0. 68pm), channel 2 (0.73-1.lpm). channel 3 (3.55-3. 93pm), channel 4 (10.3-11.3pm), channel 5 (11.5-12.5pm). The resolution of the subsatellite pixel is about 1 km in all channels. The study area (see Figure 1) extends from 44.25 N (northern Italy) to 49.25 N (southern Germany) and from 6.4 B (eastern France) to 13.6 E (central Austria). The NO A A satellites have an inclination of about 99°, so that the 560 x 550 km 2 study area needs about 800 AVHRR lines to be processed. One image line is sampled in 1/6 s, and the study area is scanned in 2.2 min. The AVHRR raw data are acquired from the DFD (Deutsches Fernerkundungsdatenzentrum) of the DLR.
3 3 Cloud detection
Cloud detection from AVHRR data is performed by means of up to 5 threshold tests applied to each pixel. According to the interpretation of these test results, the pixels are separated into cloud free and not cloud free pixels. From the group of the not cloud free pixels, the fully cloudy pixels un identified by means of 2 more tests which are in fact taken from the first group but with different thresholds and interpretations. The results of these procedures are stored in a cloud mask in full spatial resolution. A combination of 3 tests resets cloudy to snowy pixels if there arc any (Gcscll, 1989). This procedure gives 4 kinds of pixels: cloud free, fully cloudy, snow/ice, and the rest which is called partially cloudy. According to the decision logic applied, the last group contains most of the uncertainties. This algorithm package makes up the first part of APOLLO. The second part allows to determine products from all pixels. Emphasis is put on the derivation of cloud properties. Presently, cloud cover, cloud top temperature, cloud optical thickness, cloud liquid/ice water path and cloud emissivity are implemented. The latter three are derived from channel 1 reflectance, relying on a simple parameterization. Further, each fully cloudy pixel is tested for being from a thick or from a thin cloud and the thick clouds are distinguished into low, medium and high clouds by their cloud top temperature. The cloud cover of the partially cloudy pixels is determined from the nearest cloud free and fully cloudy neighbours by means of a linear approach (cf. section 4.2). The cloud type of an individual partially cloudy pixel is set according to the most frequent cloud type within a 50 by 50 pixels environment. The threshold tests used in APOLLO for daytime data (Table 1) flag a cloud if the reflectance in channel 1 or 2 is higher than a threshold, the temperature in channel 4 is lower than a threshold, the temperature difference in channels 4 and 5 is higher than a threshold (thin cloud), the ratio of the reflectances in channels 2 and 1 is above a threshold over sea or below a threshold over land, and the spatial coherence over ocean in a 3x3 matrix shows a variance which is higher than a threshold. The ratio test gives erroneous results if applied to non-vegetated land surfaces. While vegetation usually has higher values in channel 2 than in channel 1, the ratio has higher values than unity, but bare farmland or central urban areas are definitely darker in channel 2 than grassy sites, yielding ratios near unity, e.g. an erroneous cloud flag is set. This was found not to happen very often in mild and humid grassy regions like the United Kingdom, to which AVHRR data APOLLO had been initially applied, and the same was initially assumed with the study area. However, from the present study it was learnt that this happens more often than tolerable, especially in spring after plowing and in summer after harvesting the fields. Further, a strong aerosol load as it often occurs in broad river valleys (Rhine, Po) will be able to almost completely remove the typical vegetation induced difference between AVHRR channels 1 and 2. In a second round a cloudy pixel is called fully cloudy if either the reflectance ratio is close to the histogram peak within 0.8 and 1.1 determined in a 50x50 pixels working area, or the spatial coherence test shows a variance less than a threshold. All tests are descibcd in detail by Saunders and Kriebel (1988), Kriebel et al. (1989) and Gesell (1989). Calibration has been performed according to the preflight data given in the NOAA Report TM 107 (Planet. 1988). This causes errors in the reflectances due to changes in the sensitivity of the channels after launch and with time. This affects the quality of the cloud products optical depth, liquid/ice water path and emissivity which are based on channel 1 reflectance. Cloud cover, however, depends
4 Table 1: Thresholds used for daytime cloud detection tests in APOLLO (release 2.3). cloud entity thresholdapplied to remarks detection test ocean land T4 = temperature temperature t4< • 265 K 265 K from channel 4 not applied over land, spatial coherence ota > 0.25 K 5 X ;/■■ 1994 /993 7992 1991 1990 1989 Figure 2: Overpass times of NOAA-11 in July. Shift from noon to afternoon in the years 1989 to 1994. on the quality of the cloud detection and, hence, on the thresholds used. In the dynamic brightness test the threshold is determined by means of a histogram technique and, hence, is self-adjusting to changes of the calibration coefficient. The same is true with the ratio test where the ratio of the calibration coefficients in channels 1 and 2 is needed only. Channel 3 reflectance is usedto discriminate between cloud and snow (Gesell, 1989). Assuming the transmittance to be zero channel 3 radiances are transferred into reflectances (Ruff and Gruber, 1983). The thresholds differ over land and sea. This is accounted for by means of a land/sea mask with 0.5 km resolution based on the World Data Base. Sunglint areas are determined geometrically and tests using channels 1, 2 or 3 are not applied in such areas. To determine more realistic sunglint areas the weather state, especially the wind, should be considered. We could neglect this problem because the test area contains only few water surfaces. The daily changes of the sun elevation implicitly affecting-the reflectances of channels 1, 2, and 3 is taken into account. This is necessary, because the overpass time of the sun-synchronous polar orbiter NOAA-11 shifted from noon to afternoon from 1989 to 1994 as shown in Figure 2. Each line shows the daily overpass time in UTC in July. The sharp edges within one line indicate that the eastern overpass has been exchanged by the western overpass, and vice versa, in order to view the study area with the highest possible satellite elevation. 4 Method of Analysis To generate cloud cover data which are complete, homogeneous and continuous in space and time, the cloud mask information has to be remapped, box averaged, and the clouds have to be classified and checked for quality and homogeneity. 6 I "r-it;—’— — r ♦ j l Figure 3: NOAA-14 AVHRR channel 2 scene of the Alpine study area on 18 Jan. 1996, 12:40 UTC (remapped). Thick dashed linerefers to cross section in Fig. 25. 7 4.1 Remapping The collocation of satellite coordinates (line, element) to earth coordinates (latitude, longitude), the navigation, is done interactively using the standard SHARK software from ESA/ESRIN (Arino and Fusco, 1991) which is based on orbit parameters and cloudfree landmarks. The accuracy of the navigation is usually within 1 pixel for a scene of about 800 lines, depending somewhat on the satellite zenith angle. The data in satellite projection are remapped into an orthogonal map by means of resampling. The pixel coordinates are recalculated from the orbit parameters at every 10th row and column, respectively. Figure 3 shows the remapped study area which has 600 lines x 540 columns and a pixel resolution of 930 m in both directions, which is 558 km in north-south and 548 km in west-east direction. The area comprises 5°in latitude and 7.2°in longitude, centered at 46.75 N and 10.00 E near Davos (CH). In Figure 3 the geographical sites are shown to which the following text will refer to. The 11.2 E meridian is indicated by the thick dashed line, Fig. 25 in section 5.4 refers to it. As the subsatellite track of polar orbiting satellites changes daily, remapping is essential to establish consistent time series of the same region in a climatology, to compare with other data, or to plot maps. 4.2 APOLLO Box Averaging In this study, a grid of 1/8° latitude x 1/5° longitude is used, corresponding to boxes of 14 km x 15 km. This grid is of similar size as that of the ’Deutschlandmodell’ of the Deutscher Wetterdienst (DWD) to allow for future comparisons of satellite derived cloud quantities and model outputs. A box value is the average of about 210 pixels, depending somewhat on the location of the box within an AVHRR image, i.e. the distance to the subsatellite track. From each pixel within a box, the cloud cover N is determined which is trivial for fully cloudy pixels (N=l) and cloud free pixels (N=0). The cloud cover of a partially cloudy pixel is derived according to equation 1: the channel 2 to channel 1 reflectance ratio Q of a pixel is equal to the sum of its cloudy (index c) part weighted by the cloud cover N and its cloud free (index e) part weighted with (1-N). This relation is solved for N. Q — Qe Ro with Q = (i) Qc-Qe Ri Cloud free and cloudy reflectance ratios, Qe and Qc, of the partially cloudy pixel are set to those of the nearest cloud free and fully cloudy pixel within a 50 x 50 pixels working area, assuming horizontal homogeneitywithin the two concerning pixels. This procedure can lead to large errors if the reflectance ratios are similar, as e.g. in areas with cloud free snow and non-vegetated pixels. In this case channel 4 temperatures can be used instead of the reflectance ratio. The cloud coverage within a box Nb0x is given by eq. (2), it is the cloud cover of fully cloudy pixels, nc, and the summed cloud cover Nv of nv partially cloudy pixels according to eq. (1) divided by the number of all pixels within a box. Box values of the liquid water path LWP consist of the average liquid water path weighted by Nqox (eq. 3). The latter can be used for direct comparison with model derived values of LWP. This will be discussed in Kriebel et al. (1999). Qi ~ Qe Nbox — (nc T y ' )/(nc + ne + Up) (2) Qc Qe i 8 LWP box — yi LWPi ■ NBox (3) nc , 4.3 Cloud Classification The totally cloudy pixels within a box are classified according to their cloud top temperature in AVHRR channel 4 into low, medium, and high cloud cover. The cloudtop temperature thresholds used are the 700 and 400 hPa temperatures of the standard atmospheres (McClatchey, 1972). The standard atmospheres are used because otherwise a time consuming analysis were necessary to determine the boundaries of the validity of each radiosonde data within the test area. Nowadays, the reanalysis of ECMWF data could be used, but they were not yet available at the time the data analysis was performed. The sum of these three layer cloud coverages is also called thick cloud cover. The partially cloudy pixels of a box are assigned to the most frequent class. Mid latitude winter atmosphere was used from October to March, with T700 = 261.9 K and T400 = 237.5 K, and mid latitude summer atmosphere from April to September with T700 = 278.3 K and T400 = 251.7 K. This definition of the cloud floor is according to the usage at EUMETSAT, but different from the meteorological use, where the cloud base determines the floor. The classification with one standard atmosphere for the whole region from Germany to Italy is rather rough. In case of an actually warmer temperature profile, APOLLO produces too much low cloud cover with the standard atmosphere on cost of medium clouds and too few high clouds on profit of medium clouds, and vice versa in colder atmospheres. But in monthly mean values, which are primarily discussed here, this effect is assumed to vanish except for longer periods of anticyclonic situations as blocking highs. But in cases of the prevailing westerly rapid changing weather the assumption is satisfied. A fourth class of cloud cover is defined, the thin or ice clouds. All totally cloudy pixels are tested, if they have a flag from the temperature difference test, and a reflectance in channel 1 lower than 0.32 (0.36) for ocean (land) surfaces. Thin clouds are dark except over snow and low clouds, and they look warm due to their considerable transmittance which allows for a higher radiative temperature because the underlying surface or cloud layer contributes significantly to the measured signal. This procedure results in the artefact that in case of thin over low clouds they are frequently classified as thick clouds in the medium layer. 4.4 Data check and homogeneity of data For each box the partially cloud covers of low, medium, high, and thin clouds are determined, as well as their sum, the total cloud cover. A simple data check is performed to the box averaged cloud cover data. Ni + Nm + Nh + Nthin = Ntotal ± eps, where eps stands for cut off errors. Notice, that the partial cloud covers are not the true ones, since a great deal of them may be hidden by higher level clouds on account of the satellite view. Thus, they are the satellite-estimated contributions to the total cloud cover from low and medium clouds. If one box does not pass the check, the apparently false data are not used for this cloud climatology. The second simple data check flags out all the days, where the APOLLO region is not completely covered by the AVHRR scan or the raw data are not 9 11 :E 13 : E 48 N - -A /- -) ^7 Figure 4: Gaps in the German network for an assumed visibility of 30 kms around each synoptic station (+: rural, o; urban, A: mountain). The homogeneous and much denser box grid (dotted) is shown in the study area (frame). available. Missing data of only small parts of the study area are replaced by the monthly mean value plus/minus the randomized standard deviation of the respective boxes of the missing day. Nevertheless, there are only a few missing days for this climatology after these two checks. In the 5 years 1992 to 1996 there are 18 days missing in total. Most (9 days) in 1994 and least (1 day) in 1996. Thus, these few missing days show the apparently high continuity of satellite data. Climatologies need continuous and homogeneous data. Meteorological satellites provide very homo geneous data for more than a decade of years, because the same instruments are used and the area coverage is uniform. Geostationary satellite providewith high temporal resolution data for most parts of the earth. Polar orbiters provide with high spatial resolution data, and in polar regions where their swaths overlap a good temporal resolution is reached, too. This is an advantage of satellite data for solving climatological problems, although the problems with harmonizing the sensor calibration within a satellite serie is not negligible. Ground measurements usually have severe sampling errors due to different spacing and network gaps. Table 2 gives the location of those synoptic stations in southern Germany which have been used for cloud cover comparisons (see section 5). In Figure 4 these stations are denoted with crosses for rural sites or little towns with much vegetation, triangles for mountain stations, and circles for bigger cities with less vegetation. The box grid used is shown, too. Each station has a circle with a radius of about 30 km, which is assumed to be the relevant part of the sky seen by an observer. One circle represents about 14 boxes, thus it seems to be justified to speak of a high spatial resolution climatology. Although the network is very dense in Germany, there are apparently gaps. With smaller visibility, the gaps become even larger, while the satellite data keep full spatial coverage. 10 Table 2: List of synoptic stations in southern Germany within the study area. WMO-no. station latitude N longitude E altitude deg:min deg:min m a.s.l. 10704 Berus 49:17 06:41 367 10708 Saarbriicken 49:13 07:07 320 10727 Karlsruhe 49:02 08:22 145 10738 Stuttgart-airport 48:41 09:13 419 10739 Stuttgart-Schn. 48:50 09:12 311 10742 Ohringen 49:13 09:31 277 10761 Weissenburg 49:01 10:58 424 10776 Regensburg 49:03 12:06 371 10803 Freiburg 48:00 07:51 300 10815 Freudenstadt 48:27 08:25 801 10818 Klippeneck . 48:06 08:45 975 10836 Stotten 48:40 09:52 737 10838 Ulm 48:23 09:57 523 10852 Augsburg 48:26 10:56 463 10858 Fxirstenfeldbruck 48:12 11:16 535 10866 Miinchen-Riem 48:08 11:42 529 10875 Miihldorf 48:17 12:30 410 10893 Passau 48:35 13:28 408 10908 Feldberg 47:53 08:00 1493 10929 Konstanz 47:41 09:11 447 10946 Kempten 47:43 10:20 705 10948 Oberstdorf 47:24 10:17 812 10961 Zugspitze 47:25 10:59 2962 10962 Hohenpeissenberg 47:48 11:01 986 10963 Garmisch 47:29 11:04 720 10980 Wendelstein 47:42 12:01 1835 11 Figure 5: Comparison of satellite derived cloud cover (•) with ground observations at Fiirstenfeldbruck airport (o) for each day in July 1990. 5 Results 5.1 Comparison with synoptic data Firstly, a comparison of ground observations with the cloud cover derived from satellite data by means of APOLLO is shown for July 1990. The hourly observationsmade at Fiirstenfeldbruck military airport by trained personnel are used because from synoptic stations only 3-hourly observations are available. The airport is located in a rural countryside. Because the overpass time of the NO A A polar orbiting satellite differs from day to day the hourly airport cloud cover reports have been linearly interpolated to the overpass time. Further, the satellite data have been spatially averaged accordingto the reported horizontal visibility in order to compare that part of the sky which the observer has actually seen. The reported visibility has been assumed within a boundary layer of 1.5 km only. The result is presented in Figure 5. The cloud amounts derived from satellite data (bullets) agree in most cases very well with those from the surface observations (circles). The vertical bars indicate the differences between both data sets. It is suggested to explain the larger deviations as overestimations by the surface observer due to the vertical extent of convective clouds growing to thunderstorms. Further, APOLLO is rather sensitive to thin cirrus which explains the APOLLO cloud cover above zero when the observer reported none. The correlation coefficient r of both data sets is 0.80. If no temporal and spatial interpolations are made it decreases to 0.74. The monthly mean total cloud cover of July 1990 at this rural ground station is 55% and it differs only 7% from the APOLLO cloud cover of 48%. The standard deviation of 33% in each data set indicates the natural variation of monthly cloud cover in southern Germany. In total, this comparison shows a very good agreement within the surface observer’s accuracy of about one octa or 13%. About 15% accuracy in favourable regions is reported of the global 2.5° resolution cloud data of the 12 +o o O+ + °R * 1992 n — 300 n = 300 r = .74 r = .79 observed cloud cover in per cent observed cc (monthly mean) in per cent Figure 6: Comparison of satellite derived Figure 7: Same data as Figure 6 but with the monthly cloud cover (cc) with ground observa stations separated into cities (o), mountains (A) tions at 25 surface stations, figures according to and rural sites (+) and corrected to the daily month. mean cloud cover according to Table 3. ISCCP (Rossow and Garder, 1993a, 1993b; Rossow et al., 1993). In general, the estimation of cloud cover from ground includes some sampling errors. These are caused by hiding the higher cloud layers by lower ones, gaps in the station network and subjective estimation errors: firstly, the clouds near the horizon are weighted too much because they look like moved together as an effect of perspective, and cloud free gaps seem to be covered, secondly, as the result of the apparently flattened sky the cloud covered area of the upper part is overestimated, and thirdly, cloud cover is estimated too low at low visibility or at darkness. The encouraging result of 31 days at one station leads to a comparison with a larger number of stations and for a whole year to show the reliability of the APOLLO derived cloud amounts. Figure 6 shows a comparison of APOLLO total cloud cover and ground observations for monthly means of the year 1992. The monthly means are obtained from 25 synoptic ground stations (cf. Table 2) where eight reports per day are made, while the APOLLO monthly mean is from one measurement per day. Further, for this comparison only that box (14 km x 15 km) is taken into account in which the ground station is situated, e.g. no spatial averaging is applied. In spite of these differences of the two data sets the correlation coefficient is 0.74. The figures in Figure 6 denote one monthly mean value at one synoptic station at each case. In summer the surface observers seem to overestimate the cloud cover due to cumulus clouds, which confirms the findings mentioned above. But in most cases the satellite derived cloud cover is above that of the surface observations. The greatest deviations are found in February and March, where bare soil makes the cloud detection difficult. The APOLLO average of all 300 samples is 15% higher than that of the surface observations which is 65%, the average of all rural sites exceeds the surface observations by only 9%. Figure 7 shows the same data set, but now the stations are separated into cities (circles), mountains 13 (triangles) and rural sites (crosses). A reduction of the daily maximum data to daily means has been performed by using the data set of Auer et al. (1989) according to Table 3 which bases on very long-term hourly data. The dashed line is according to a bias of +10% for comparison. Three reasons are suggested to explain the overestimation of satellite derived cloud data. Firstly, big cities like Munich with little vegetation as well as the bare soil of rural sites are flagged as clouds by APOLLO’s ratio test. Although there is an urban effect on cloudiness, which gives more convective clouds in summer and less fog in winter than in the surrounding areas (Oke, 1978), deviations of APOLLO cloud cover up to 34% from the observed in a single monthly mean cloud cover are not acceptable. Fortunately, there are only some few boxes with urban areas within the considered area. Secondly, we suggest that a part of the considered bias is explained by thin cirrus clouds (optical depth < 1) which are detected by APOLLO but not by an observer (Kastner et al., 1993) and, thirdly, and of same importance, according to the diurnal cycle, the APOLLO cloud cover obtained between 13 and 14 UTC in 1992 must be higher than the observer’s daily mean. But this effect of the daily cycle of the cloud cover in monthly means is often overestimated. In summer the maximum is only about 9% higher than the monthly mean, as confirmed in a case study with half-hourly Meteosat images (Kastner et al., 1995), and in winter only about 5%. This result is supported by Auer et al., (1989) (see Figure 8) who give hourly values of cloud cover at Vienna. The horizontal line indicates the monthly mean. Although Vienna is outside the test area, the very long period of 135 years seems to be rather representative for the average diurnal cycle of cloud cover at least in the northern part of the test area. The maximum of the monthly mean cloud coverage is around 10 UTC in winter and 14 UTC in summer. This means that the shift of the NCAA overpass time from 12 to 16 UTC (cf. Figure 2) adds only less than ±1% uncertainty to the maximum monthly mean cloud coverage reported in this paper. Taking into account these three effects the agreement of satellite derived cloud covers and surface observations is within 5% to 10% in monthly means in non-vegetated sites, in rural sites usually somewhat better. Warren et al. (1986) give cloud amounts over land in a 5°grid. The box which matches our test area best, i.e. to 62%, shows an annual amplitude of 18% in the cloud cover, while the interannual variation of the seasonal means is only ±6%. The APOLLO cloud cover agrees very well with this annual amplitude and with the interannual variations of the seasons. Overall, the monthly mean cloud cover over land derived from NO A A-11 AVHRR data by means of APOLLO is sufficiently reliable except in areas with bare soil or low vegetation as in snow covered high terrain and the centers of big cities. These deficiencies are enhanced if daily values are considered instead of monthly mean values. This gave rise to improve the APOLLO scheme by modifying the way of applying the threshold tests (Kriebel et al., 1999). 5.2 Total cloud cover statistics To achieve a 5-years statistics of monthly mean cloud cover, NO A A data from September 1991 till February 1997 have been processed and the years 1992 to 1996 have been used. The derived cloud cover of the following figures is close to the maximum of the diurnal cycle. The 5-years mean of the total cloud cover (Figure 9) reveals a sharp gradient between land and sea. Within the Adriatic Sea the cloud cover is rather uniform while over land the differences are up to 20%. A minimum of 70% 14 : jui hour Figure 8: Diurnal cycle and mean (horizontal) of total cloud cover in January (solid) and July (dotted) in Vienna (after Auer et al., 1989). 8 E 10 E 12 E 8 E 10 E 12 E Figure 9: Total cloud cover (5-years mean: 1992 Figure 10: Same as Figure 9, but standard de to 1996). viations. 15 yearly mean cloud cover is found north-east of Lake Constance, north of Salzburg, in South Tyrol, and in the Appenino. Overall, the yearly mean does not seem to vary very much either with orography or with different climatic regions within the area considered. These differences will be apparent when seasonal or monthly mean cloud cover is considered, as is shown later. Nevertheless, there are regional effects, e.g. most Alpine river valleys have less clouds than their surroundings while in the forelands it is the opposite. The midday yearly total cloud cover of the whole area increases from 78% in 1992 to 82% in 1996. Such an increase of 0.8% per year does certainly not indicate any climatic change. Much longer time series are necessary, e.g. longer than the sun spot cycle of 10 to 15 years, to make sure that a short and steep change is not extrapolated and misinterpreted. Wylie and Menzel (1999) report in a cloud statistic using HIRS a small increasing trend of 1.1% for a period of eight years for all clouds and an increase of 4.2% for high clouds in the same period. Bucher et al. (1992) report a positive trend in cloudiness of 0.62% in a period of 95 years at the Pic du Midi, a mountain station in the Pyrenees. Thus, there seems to be a slight positive trend, and the longer the observation period, the smaller the trend. Figure 10 shows the standard deviation of the 5-years mean of Figure 9. Maximum variability of the monthly mean cloud cover are found over the Adriatic Sea (±26%), followed by the Mediterranean (±19%), the moderate (±16%) and the Alpine climate (±14%). The latter is as low as at oceanic sites. Thus, different climates do not necessarily have different absolute values of the annual means but different standard deviations allow to distinguish between them. Further, regions with the lowest standard deviations are probably those regions where the cloud detection produced the largest errors, because the natural variability is not met. These regions are in the Alps above about 2800 m a.s.l. (glaciers) and the large cities Munich, Strasbourg and Milan. These findings contributed to the improvements of the APOLLO scheme. Such areas with doubtful cloud detection cover only 3% of the whole area. Therefore, cloud detection seems to be reliable in most parts of the study area. The next four figures treat seasonal means. The winterly total cloud coverage (Figure 11) has rather smooth contours. This indicates large regions of uniform weather with only few convective clouds, the latter show the highest regional effects in cloud cover. The maximum cloud cover is within the big river valleys in the forelands (Rhine, Danube, Po) very likely due to fog (cf. Figure 3: Bachmann mid Bendix, 1993). In winter the cloud cover within the Alps is remarkably less than in the forelands. The Eastern Alps are lee sided of the prevailing westerly flow and they show less clouds than the western Alps. The summer cloud coverage (Figure 12) is much more speckled than the winter cloud coverage due to much more convection. In summer the forelands have less clouds than the Alps, opposite to winter time. The high spatial resolution favours the detection of regional effects in the cloud coverage which can usually not be found in common cloud maps due to their coarse resolution. Low cloud cover is found over the Adriatic Sea and over Italy. Many regions with a cloud cover lower than that of their surroundings can be seen, especially the northern Po-valley. Friuli. South Tyrol, Ticino, the Alpine river valleys, east of lake Constance, east of Munich, and others. Even the double structure of the Black Forest, caused by the mountainous orography is resolved into two separate luff regions with enhanced cloud cover. In summer, the western and the eastern Alps show similar cloud coverage. For both, summer and winter, completely different distributions of the total cloud coverage are found as 16 compared to the annual values. Thus, the annual mean cloud cover is not a meaningful quantity to show similarities and differences of different regimes because inverse effects are smeared out. Spring (Figure 13) and fall (Figure 14) cloud coverage are rather similar to the annual mean values, the Alps are not as striking as in winter or in summer. In spring a continuous band with lower cloud coverage exactly marks the northern Alpine ridge from Switzerland to Austria, most probably due to Foehn from the south. An even larger decrease of cloud cover is observed north of the Appenino ridge in the southern Po-valley. The high cloud cover in the Po-valley in fall (Figure 14) is partly due to fog like in other big river valleys (Rhine, Danube). But especially in the Po-valley there is very often strong haze which has an optical depth at 0.55/xm of higher than 0.5 (Mori et al., 1981; Bachmann und Bendix, 1993). Such pixels are flagged as cloudy by the APOLLO scheme though an observer at the ground would not report clouds. On the other hand, such a ’haze cloud’ has of course radiative impact on the atmosphere. In fall, Foehn from the north causes regions with low cloud cover mainly in southern and eastern Alpine valleys as well as south of the Appenino. In fall Austria has remarkably lower cloud coverage than Switzerland, and the stronger convection causes a more detailed cloud map in southern Germany than in spring, whereas in Italy the convection is in spring already strong enough to show many details. Figure 15 shows the distribution of the total cloud cover based on July data of eight consecutive years, 1989 to 1996. It is the longest period considered here. Although the mean is calculated from eight months only, the cloud distribution is very similar to that of the summer mean derived from five consecutive summer periods of three months each (see Figure 12). In fact, without loosing qualitative information the amount of data required for summer cloud cover can be reduced by a factor of two in the studied area. The maxima and minima of Figure 12 and 15 agree very well. The July mean is typically about 5% less than the summer cloud cover, the highest difference is 11%, which is near the data accuracy. Thus, July is an appropriate month to represent the summer cloud cover. The same holds for October as a representative month in fall. Figure 16 shows the 8-years mean cloud cover in October which shows the same patterns as Figure 14 discussed earlier. 5.3 Cloud floor statistics The APOLLO cloud classification scheme separates thick and thin clouds, mainly according to their reflectance, and distributes the thick clouds into three height classes according to their cloud top temperature. The three floors are identified from the pressure levels 700 hPa and 400 hPa which are converted to temperatures according to the summer (April to September) or winter (October to March) midlatitude standard temperature profiles. On individual days this procedure may result in too many low level clouds if the air is warmer than the assumed temperature profile, or give an underestimation in case of colder air, but within many months this effect should be smoothed out. It must be emphasized that only the top most cloud layer can be identified by means of APOLLO. This means, that APOLLO low clouds have no medium or high clouds above them. The presented cloud covers for each floor in Figures 17 to 20 are three-years means from September 1991 to August 1994. This data set is as homogeneous as possible within this study, because it has been derived from one satellite only, NOAA-11, which AVHRR was operational until 13 September 1994. Figure 17 shows the distribution of low clouds. Typical clouds of this fraction are stratus, stratocu- 17 1.00 49 N 0.95 0.90 0.85 0.80 0.75 48 N 0.70 0.65 0.60 0.55 47 N 0.50 0.45 0.40 46 N 0.35 0.30 0.25 0.20 45 N 0.15 0.10 0.05 0.00 8 E 10 E 12 E 8 E 10 E 12 E Figure 11: Total cloud cover in winter (D.J.F), Figure 12: Total cloud cover in summer (J.J.A), 5-years mean: 1992 to 1996. 5-years mean: 1992 to 1996. .1.00 49 N 48 N 47 N 46 N 45 N 8 E 10 E 12 E Figure 13: Total cloud cover inspring (M,A,M), Figure 14: Total cloud cover in fall (S.O.N). 5 5-years mean: 1992 to 1996. years mean: 1992 to 1996. 18 Figure 15: Total cloud cover in July. 8-years Figure 16: Total cloud clover in October. 8- mean: 1989 to 1996. years mean: 1989 to 1996. mulus and fair weather cumuli. Whereas the annual mean of the total cloud cover does not show a characteristic difference between the three climatic regions, the three-years mean of low cloud cover clearly reveals the moderate climate north of the Alps with about 20% cloud cover (minimum = 15%, maximum = 30%), the Alpine climate with less and the Mediterranean climate with more low cloud cover. Regional effects which are frequently due to topographic structures are mainly expected from low clouds. Broad river valleys in the moderate climate region have locally more clouds than their surroundings which is assigned to fog or thick aerosol layers in the valleys. Big cities like Munich act like a thermal island and show up to 6% more low clouds than the rural countryside. This may result from stronger convection over cities in summer producing more low clouds and thus overcompensating the opposite effect due to less fog in urban areas in winter. However, the increased cloud cover in urban areas may partly be an artefact due to difficult cloud detection there. In the moderate climate the highlands have less low cloud cover and the distribution follows the orography. Vosges, Black Forest. Swabian Alb and Bavarian Forest can be outlined from Figure 17. The lowest low cloud coverage (< 15%) occurs in the Alpine region, this is an artefact. In winter the Alpine mountains are usually colder than the threshold temperature at 700 hPa. Therefore, such clouds would be classified as low clouds by an observer but as medium clouds by the satellite data algorithm without using information on the orography. This has to be taken into account if ground reports are compared with satellite derived cloud cover. In northern Italy low clouds are the most frequent cloud fraction. Typical low cloud cover is 45% at midday. This value may be slightly too high because the midlatitude summer standard atmosphere is colder than the Mediterranean air, at least in summer. A better approach to the real temperature profile could improve the cloud detection noticeably and may be realized in the future by using forecasted or analyzed temperature profiles. A land/water difference in cloud cover is found at the Adriatic Sea but not over big lakes as expected. A possible explanation is the horizontal box averaging over 14 km x 15 km which smears out the postulated land/water difference. 19 Figure 17: Low cloud cover (3-years mean). Figure 18: Medium cloud cover (3-years mean). 0.30 0.24 48 N ■>- .0.18 0.12 0.06 0.00 Figure 19: High cloud cover (3-years mean). Figure 20: Thin cloud cover (3-years mean). 20 The second group are clouds in the medium floor, again with no clouds above but possibly with clouds below. They comprise growing cumulus, nimbostratus, altocumulus and altostratus clouds. Figure 18 shows the 3-years means distribution of medium clouds. Again the three climates show different cloud cover. North of the Alps they are the most frequent fraction of the total cloud cover. A typical value in hilly orography is 30%. Regions with enhanced medium cloud cover usually coincide coarsely with regions of enhanced rainfall rates. The central Alps are clearly marked by a steep gradient in medium cloud cover dueto luff effects from the north as well as from the south. Misclassification of cold snow-covered and cloudfree mountains as clouds by the temperature test is avoided in many cases by resetting the cloud flag within the snow/ice/cloud detection scheme (Gesell, 1989) applied after the two rounds of cloud detection tests. Nevertheless, correct clouddetection in snowy mountainous regions is still an unsolvedproblem. Cloud cover above 50% in medium high clouds over the glaciers are not reasonable and due to the problem of distinguishing clouds from non-vegetated surfaces with this APOLLO release used here. Further, in wet melting snow sunglint effects may occur, which are not considered in this study. In Italy the cloud cover of the medium clouds is distinctively lower than north of the Alps. It ranges from 12% to 18% except for the Alpine region. Figure 19 shows the distribution of the thick high clouds. Dense cirrostratus, cirrocumulus and thun derstorms contribute to this cloud fraction. In satellite derived cloud cover, thunderstorms belong to the high clouds while observers at the ground count them to the low clouds. This makes a comparison of the two types of cloud data intrinsically difficult. Though there is no obvious correspondence to the three regional climates as expected, the maximum of high clouds is situated over the central Alps. This fact can be understood as enhanced convection due to orographical forcing. Figure 20 shows the cloud map of the thin clouds. Thin cirrus (optical depth < 1), contrails and cloud edges contribute to this cloud fraction which is a noticeable portion of the total cloud cover. Clouds with optical depths below 0.5 (at 0.55/zm wavelength) are usually not detected by APOLLO. Only those thin clouds are detected separately that have no other clouds underneath. But just these thin clouds have the greatest potential to contribute to a surface warming by intensifying the greenhouse effect (Liou, 1986). These clouds play an important role in the climate system, since they contribute directly to an increased greenhouse effect by absorbing the infrared radiation emitted from the earth surface and the lower atmospheric layers and emitting itself at lower temperatures. In addition thin clouds and contrails contribute indirectly as comprehensive climate studies with simulated climate models show (Fahey et al., IPCC, 1999). The cloud cover of all real thin clouds is greater than the presented values, because thin clouds aboveother clouds are usually not identified separately, however, they enhance the satellite derived optical depth of the thick clouds below. Anyway, there are more thin clouds north of the Alps than in the south. It is supposed that this fact may be either due to stronger subsidence in the south, dissolving thin clouds and/or due to the more intense aviation traffic north of the Alps producing more contrails which increases the amount of thin clouds and/or there are more cloud edges due to more clouds in the north. The difference in thin cloud cover of about 8% between Germany and Italy seems to be no artefact. Inside the central region of the Alps, the identification of thin clouds with techniques relyingon reflectance contrast and temperature lapse rates is less reliable, mainly due to the high spatial variability. 21 Table 3: Number ofyears used and correction for diurnal cycle per month. month J F M A M J J A S 0 N D number 5a 5 5 5 5 5 8* 5 6 C 8 6 6 ANd in % 3 4 6 7 8 5 5 6 6 4 4 4 ° 5 years: 1992 to 1996, 6 8 years: 1989 to 1996, c 6 years: 1991 to 1996 dAN = N(13 UTC) - N(daily mean), data after Auer et al., 1989. 5.4 Annual course of total cloud cover The next two figures show the annual course of the cloud cover which has been assembled for each day (same sun elevation) from all available data of different years. The presented annual courses are based on about 2100 days processed by means of APOLLO. Within one curve the number of years used changes from month to month as noted in Table 3. Figure 21 shows the annual course of the total cloud cover at Oberpfaffenhofen (48.1 N, 11.3 E), a rural site near Munich, in the following called OP. The dotted vertical lines indicate the last day of the indicated abbreviated month, respectively. The thin solid line shows the daily cloud cover at early afternoon day by day. Though averaged over several years, it changes from day to day frequently more than 20%, showing the most typical feature of cloud cover, its variability. Much more than eight years are necessary to obtain smooth curves. For better interpretation of the cloud information, pentade values are given by the thick solid line. Pentade values are means of five consecutive days assigned to the central day of the interval. Two consecutive pentades do not overlap, their central days are five days apart from each other. Pentade values are used in climatology, because they do not suffer from different numbers of days like monthly means do. In spite of the favourable pentades the curve is rather fidgety, but much smoother than gliding means over 9 days. The horizontal line gives the annual average of the early afternoon cloudiness which is 78% and close to the maximum day time cloudiness over OP. The cloudiness is maximum in winter from November till April and minimum in late July and early August, the yearly mean is met for longer periods in May and June and from mid August to mid September. A well defined second minimum is found in October. It indicates the dry Indian summer, which is one of the best established singularities of the year. A singularity is a distinct weather period at a special time that has a higher probability to occur than other. These singularities do not happen every year, but at distinct times during a year some weather situations are more frequent than others. Other apparent singularities with few clouds are in mid January and in late February caused by winterly high pressure periods, the canicular days around early August within the summer minimum, a period with high pressure weather around mid November, and a fair weather period at mid December, which is stopped by the well-known thaw at Christmas. The annual cloudiness roughly follows the inverse sunset with a phase shift of about one month and it is modulated by singularities and by convection. Convection increases the cloud amount predominantly in early and late summer. Figure 22 again shows the annual course of the cloud cover at OP, but now the cloud coverages in different floors are given as a stacked plot of pentade values. The respective floor contributions to the 22 0 0 100 200 300 day Figure 21: Annual course of total cloud cover Figure 22: Annual course of total cloud cover at 0berpfaffenhofen. Thin: daily data, thick: at Oberpfaffenhofen stacked from low, medium, pentade data. high, and thin cloud cover. annual mean of 78% are 26% for low (CL). 29% for medium (CM), 12% for high (CH), and 11% for thin clouds (Cl). These averaged 11% thin cloud coverage is one order of magnitude greater then the additional cloud cover from contrails (Mannstein et ah, 1999, Kastner et al., 1999). Thin cirrus clouds may warm the surface due to only little attenuation of shortwave radiation but strong absorption and emission of longwave radiation at temperatures much lower than that of the surface (reduced emission to space) but much higher than that of space (increased downward longwave radiation). The annual course of thin clouds is not very distinct, two maxima lie around New Years Day and at the end of May. A speculative suggestion is that these maxima may result from enhanced air traffic at holidays. But the understanding of the transformation of contrails to extended thin cirrus clouds is very poor yet (Fahey et ah, 1999) and still under research. Figure 23 shows the annual cycle of the total cloud cover at OP and six adjacent grid boxes. Two neighbours are left of the 3x3 matrix, because they contain Munich, where the APOLLO version used for this climatology is less reliable. The cloud covers of the neighbours show the same annual course as that at OP which reflects the spatial homogeneity, in the same region the same weather occurs. The band spanned from the neighbours narrows and broadens during the year and is frequently within ±5% in width. So there are some regional effects, but they are in the order of the accuracy of the data. On the other hand, this figure shows that regional averaging seems to be reliable to suppress noise in topographic homogeneous regions. Actually, some peaks seem to be synchronized. These peaks can often be related to singularities as already mentioned. Figure 24 shows the annual courses of cloudiness of the three different climates. The dashed curve represents the distinct cycle of the Mediterranean climate (340 boxes averaged) with strong subsidence 23 100 Mediterranean Alpine moderate IJFMAMJJ AS OND 0 100 200 300 day Figure 24: Annual course of total cloud cover in Figure 23: Annual course of total cloud cover at moderate (solid), Alpine (dotted), and Mediter Oberpfaffenhofen and six adjacent sites. ranean climate (dashed). in summer, causing only few clouds, and predominantly winter rainfall with many clouds. The dotted curveshows the Alpine cloud coverage (562 boxes averaged) with minima in winter, July and October. Contrary to the Mediterranean cloud cover, the Alpine region does not show extreme differences in summer and winter. The solid curve indicates the moderate climate in southern Germany and eastern France (538 boxes averaged) which has been discussed with the three previous figures. Although these annual cycles are not smooth, the typical features of the three involved climates are represented. It is emphasized that there are apparently singularities in the annual cycle which concern the whole test area, displayed by simultaneous peaks in the three cloud cover curves, e.g. in June. Further, the favourable regions with few cloudiness during a year are: from November till mid March within the Alps, mid March till mid September in Italy, and October with equal low cloudiness in the moderate and Alpine climate. The annual means and its standard deviations are 76 ± 17% for the Mediterranean and 80 ± 8% for the Alpine climate, showing a difference of only 4.4% for completely different climates, the annual mean of moderate climate is 80 ± 12%. It differs from the Alpine climate by only 0.1%, indicating how accurate annual cloud cover determination has to be. Notice, that the standard deviations typically differ for each climate, which has already been mentioned with Fig. 10. Figure 25 shows the annual course of the total cloud cover at about midday of the 40 grid boxes in a cross section along the 11.27 E meridian which touches OP at 48.08 N. indicated as the thick dashed line in Fig. 3. The x-axis is the time axis as in the previous figures and the y-axis is a line from 44.25 N to 49.25 N. The grey scale coded annual cloud cover in each of the 40 horizontal lines build up the plot. The cloud cover data are temporally averaged by gliding means over eleven consecutive days and spatially in a 5 x 5 neighbours matrix. This suppresses the "noise" from the rather high 24 J F M A Af J J A 5 0 A' D /oo.o 95 0 90 0 55.9 60 0 75 0 70 0 65 0 60.0 55 0 50 0 ^5 0 Sa ^0.0 Figure 25: Annual course of total cloud cover in per cent along 11.3 E meridian (early afternoon). natural variability of the cloud cover data, which is typically ±30% in monthly means and ±5% in annual means in the moderate climate. On one hand, the same procedure sharpens features like the singularity of the canicular days (low cloudiness) in early August in the moderate climate (north of 47.5 N), but on the other hand, the average procedure smears local effects like lower cloud cover in the Inn-valley (47.3 N) which are found in the original daily highly variable cloud data. The features already mentioned with the previous figure can also be found in this one. Further the steep gradient of the cloud cover is evident at the northern edge of the Alps (47.6 N) in summer caused by orographic convective clouds. The singularity Indian summer (German: Altweibersommer) in October does not cross the Alps and reach the Mediterranean area. A period in early March with clearing up just north of the Alpine ridge around 46.5 N and reaching till 48 N shows accumulated Fdhn situations at that time. During the same period it is totally cloudy south of the Alps (luff). The mentioned regional differences of cloud cover are usually not included in cloud maps because they are not based on spatially such high resolution data as presented here. 6 Discussion and conclusion Clouds are part of the hydrological cycle and changes in the distribution of clouds have a strong impact on the climate. Therefore, observations of cloud cover and their understanding are essential in meteorology and climatology. Ground networks have gaps and automated stations which are now installed everywheregive at best ceilometer measurements of clouds. Where eye observations are made 25 problems arise with incorrectly estimated cloud cover from convective or thin clouds, clouds at the horizon or at low visibility. Satellite data can help to overcome these problems by using consistant algorithms to detect clouds without areal gaps in a homogeneous and continuous way necessary for solving climatological problems. But, viewing geometry defects arise with satellite observations of clouds, if not restricted to near nadir measurements and high sun elevation as in this study. However, in this first quasi operational use the cloud detection algorithm APOLLO showed problems, too: the ratio test over land yielded unrealistic high cloud cover values due to non-vegetated surface, e.g. over big city centers or glaciers. These findings were used to establish a more sophisticated APOLLO version now applied to a 15- years climatology. Satellite derived cloud cover are compared to a large number (>90.000) of surface observations (Kriebel et al., 1999). A further step forward would be the implementation of forecasted or analyzed temperature profiles from numerical weather models. NOAA satellite data (AHVRR) are successfully used in a cloud detection scheme to obtain a regional cloud climatology. The high resolution allowed to identify weaknesses of the cloud detection algorithms and to eliminate them. The presented data are from the Central Alps and their forelands. They have a spatial resolution of 15 km. contain the 5-year period from 1992 to 1996 and for some months 6 and 8 years. They are compared to monthly means of conventional data. The result is an overestimation of 15 % for all data and less for vegetated surfaces. As only one image is processed per day (early afternoon), a positive bias in the monthly means is expected due to the diurnal cycle which is about +5% in winter and +10% in summer in monthly means. In rural countrysides the comparison yielded a very good agreement of about 2% to 7% in monthly mean cloud amounts when taking into account the diurnal cycle. This is much better than the coarser ISCCP data over land, which have errors in the order of 15% (Rossow and Carder, 1993a, 1993b). The natural variation of cloud cover has typical rms values of about ±30% in monthly means which is properly met. The presented data show the same annual variation (18% in the midlatitudes) as reported by Warren et al. (1986). The positive trend of cloud cover during the 5-year period is not significant, as the interannual variation with ±5% is higher. The trend is too small to be recognized by eye observations or with APOLLO or other cloud detection methods, e.g. used at the Swedish Meteorological and Hydrological Institute (Karlsson, 1994). A longer time period is suggested to derive trends in cloud cover, clearly longer than the sun spot cycle of about 11 years. The interannual variability in cloud cover data suggests that observations of 15 to 30 years are necessary depending on the climate (in the Alps less than in northern or southern forelands) to meet a 1%-accuracy of annual cloud cover. As an increase of only 4% of low stratus global cloud cover could more than offset the predicted global warming due to doubling of CO2 content (Slingo, 1990), the accuracy of 1% in the absolute cloud amount is a stringent requirement, and emphasizes the advances needed to monitor the effects of clouds on climate. Further, an annual mean of cloud cover of the participating climate zones (moderate. Alpine. Mediter ranean) does not seem to be meaningful, as features with opposite signs are smeared out. Seasonal means can be qualitatively approximated by one month per season without loosing information of the extrema. This finding opens the possibility of reducing data processing for longer periods. The influence of different climates is shown best in winter/summer contrasts. The most pronounced effect presented in the maps of cloud cover is that less clouds are in winter in the Alps than in both 26 forelands, but in summer less clouds are in both forelands than are in the Alps, and least clouds are over the Adria. The variability of the annual courses of cloud cover decreases from Mediterranean over moderate to Alpine climate. The high resolution of the maps clearly shows that in winter the broad river valleys have lower cloud cover inside the Alps and higher cloud cover outside the Alps than their surroundings. It is emphasized that the occurrance of thin clouds with no clouds underneath, mostly cirrus, is one order of magnitude higher than that of contrails. The temporal variability in cloudiness is rather high. Thus, averaging to achieve annual courses is essential. The spatial variability is not as high, as the same weather usually occurs at neighbouring sites. Annual courses of cloud cover averaged over time and space show that the cloudiness inversely follows the sunset with a phase shift of about one month, modulated by convection in summer and singularities during the whole year. The most famous and best distincted singularity is the Indian summer in southern Germany and in the Alps. The main singularities with less cloud cover are in February, August, November, and December, those with more cloud cover in early June and the well- known Christmas thaw in December. The meridional-time cross section is a comprehensive way to present all mentioned features synoptically. These findings sustain the great potential of a high spatial resolution cloud climatology that is assem bled from satellite data and they pave the way to cloud detection from MSG data. 7 Acknowledgement We thank H. Mannstein and G. Gesell for helping in many discussions and supporting the realisation of an operational use of APOLLO. We thank S. Przybylak, K.-P. Schickel and G. Zangl for their attention and handling of the daily AVHRR data. The NOAA data were received from the Deutsches Fernerkundungsdatenzentrum and the Deutscher Wetterdienst made available their meteorological data. 27 8 References Arino, O., and Fusco, L., 1991: Research tool integrated in the ESA TIROS processing system: A cooperative effort for the generation of high level AVHRR products (SHARK). Frascati: ESA - ESRIN, 17 pp. Auer, I., Bdhm, R., and Mohnl, H., 1989: Klima von Wien., Wien: Astoria Druck, 270 pp. Bachmann, M., und Bendix, J., 1993: Nebel im Alpenraum. Bonn: Bonner Geogr. Abh. 86. Bucher, A., Dessens, J., and Pech, B., 1992: Parametres meteorologiques en relation avec l’augmentation de la temperature a l’observatoire du Pic du Midi, 2862 m, France. Toulouse: XXIInd International Conference on Alpine Meteorology, Toulouse, 7-11 Sept. 1992, Meteo-France, 330-334. Fahey, D. W., Schumann, U., Ackerman, S., Artaxo, P., Boucher, O., Danilin, M. Y., Karcher, B., Minnis, P., Nakajama, T., and Toon, O. B., 1999: IPCC-Report ’Aviation and the Global Atmosphere: Aviation produced aerosols and cloudiness’, IPCC, Geneva, Switzerland, Cambridge University Press, Cambridge and New York, 291-332. Karlsson, K.-G., 1994: Satellite-estimated cloudiness from NO A A AVHRR data in the Nordic area during 1993, SMHI Reports Meteorology and Climatology, 66, SMHI, S-60176 Norrkping, Sweden. Karlsson, K.-G., 1997: Cloud climate investigations in the Nordic region using NOAA AVHRR data. Theor. Appl. Climat., 57, 181-195. Gesell, G., 1989: An algorithm for snow and ice detection using AVHRR data: An extension to the APOLLO software package. Int. J. Remote Sensing, 10, 897-905. Kastner, M., Kriebel, K.T., Meerkotter, R., Ruppersberg, G.H., Wendling, P., 1993: Comparison of cirrus height and optical depth derived from satellite and aircraft measurements. Mon. Wea. Rev., 121, 2708-2717. Kastner, M., Kriebel, K.T., und Mannstein, H., 1995: Regionale Wolkenklimatologie im Alpenraum. Ann. d. Meteor., 34, 60-61. Kastner, M., Meyer, R., and Wendling, P., 1999: Influence of weather conditions on the distribution of persistent contrails. Meteorol. Appl., 6, 261-271. Kriebel, K. T., Saunders, R. W., and Gesell, G., 1989: Optical properties of clouds derived from fully cloudy pixels. Beitr. Phys. Atmosph., 62, 165-171. Kriebel, K.T., Gesell, G., Kastner, M., and Mannstein, H., 1999: The cloud analysis tool APOLLO: improvements and validations. Submitted to Int. J. Remote Sensing. Liou,K., 1986: Influence of cirrus clouds on weather and climate processes: a global perspective. Monthly Weather Review, 114, 1167-1199. Mannstein, H., Meyer, R., and Wendling, P., 1999: Operational detection of contrails from NOAA- AVHRR-data. Int. J. Remote Sensing, 20, 1641-1660. McClatchey, R.A.M., Fenn, R.W., Selby, J.E.A., Volz, F.E., and Garing, J.S., 1972: Optical Properties 28 of the Atmosphere. Bedford, MA, USA: AFCRL-72-0497, 113pp. Mori, P., Reinhardt, E., Renger, W., and Schellhase, R., 1981: The use of the airborne lidar system ALEX F 1 for aerosol tracing in the lower troposphere. Beitr. Phys. Atmosph., 54, 403-410. Oke, T.R., 1978: Urban climate - boundary layer climates. London: Methuen, 372 pp. Planet, W.G. (Ed.), 1988: Data extraction and calibration of TIROS-N/NOAA Radiometers. NO A A Technical Memorandum NESS 107 - Rev. 1. Rossow, W. B., and Carder, L. C., 1993a: Cloud detection using satellite measurements of infrared and visible radiances for ISCCP. J. Glim., 6, 2341-2369. Rossow, W. B., and Carder, L. C., 1993b: Validation of ISCCP cloud detections. J. Clim., 6, 2370 2393. Rossow, W. B., Walker, A.W., and Carder, L. C., 1993: Comparison of ISCCP and other cloud amounts. <7. Clim., 6, 2394-2418. Ruff, I., and Gruber, A., 1983: Multispectral identification of clouds and earth surfaces using AVHRR data. Washington: Proc. 5th Conf. on Atmospheric Radiation, Baltimore, Maryland, 31 Oct.-4 Nov. 1983, Amer. Meteor. Soc., 475-478. Saunders, R.W. and Kriebel, K.T., 1988: An improved method for detecting clear sky and cloudy radiances from AVHRR data. Int. J. Remote Sens., 9, 123-150. Schiffer, R.A., and Rossow, W.B., 1983: The International Satellite Cloud Climatology Project (IS CCP): The first project of the World Climate Research Program. Bull. Amer. Meteor. Soc., 64, 779-784. Slingo, A., 1990: Sensitivity of the earth’s radiation budget to changes in low clowds. Nature, 343, 49-51. Warren, S.G., Hahn, C.J., London, J., Chervin, R.M., and Lenne, R.L., 1986: Global distribution of total cloud and cloud type amounts over land. Boulder: NCAR TN-273 + STR, 299 pp. + 200 maps. Wylie, D.P., and Menzel, W.P., 1999: Eight years of high cloud statistics using HIRS. J. Clim., 12, 170-184. 29 ANNEX Table captions: Table 1: Thresholds used for daytime cloud detection tests in APOLLO (release 2.3). Table 2: List of synoptic stations in southern Germany within the study area. Table 3: Number of years used and correction for diurnal cycle per month. Figure captions: Figure 1 Frame: study area in central Europe. Scene from NO A A-11 AVHRR channels 1, 2, 4 in orthogonal projection, 22 Oct. 1990, 12:53 UTC. Figure 2 Overpass times of NO A A-11 in July. Shift from noon to afternoon in the years 1989 to 1994. Figure 3 NO A A-14 AVHRR channel 2 scene of the Alpine study area on 18 Jan. 1996, 12:40 UTC (remapped). Thick dashed line refers to cross section in Fig. 25. Figure 4 Gaps in the German network for an assumed visibility of 30 kms around each synoptic station (+: rural, o: urban, A: mountain). The homogeneous and much denser box grid (dotted) is shown in the study area (frame). Figure 5 Comparison of satellite derived cloud cover (•) with ground observations (o) at Fiirstenfeldbruck airport for each day in July 1990. Figure 6 Comparison of satellite derived monthly cloud cover (cc) with ground observations at 25 surface stations, figures according to month. Figure 7 Same data as Figure 6, but with the stations separated into cities (o), mountains (A), and rural sites (+) and corrected to the daily mean cloud cover according to Table 3. Figure 8 Diurnal cycle and mean (horizontal) of total cloud cover in January (solid) and July (dotted) in Vienna (after Auer et al., 1989). Figure 9 Total cloud cover (5-years mean: 1992 to 1996). Figure 10 Same as Figure 9, but standard deviations. Figure 11 Total cloud cover in winter (D,J,F), 5-years mean: 1992 to 1996. Figure 12 Total cloud cover in summer (J,J,A), 5-years mean: 1992 to 1996. Figure 13 Total cloud cover in spring (M,A,M), 5-years mean: 1992 to 1996. Figure 14 Total cloud cover in fall (S,0,N), 5-years mean: 1992 to 1996. Figure 15 Total cloud cover in July. 8-years mean: 1989 to 1996. Figure 16 Total cloud cover in October. 8-years mean: 1989 to 1996. Figure 17 Low cloud cover (3-years mean). Figure 18 Medium cloud cover (3-years mean). Figure 19 High cloud cover (3-years mean). Figure 20 Thin cloud cover (3-years mean). 30 Figure 21: Annual course of total cloud cover at Oberpfaffenhofen. Thin: daily data, thick: pentade data. Figure 22: Annual course of total cloud cover at Oberpfaffenhofen stacked from low, medium, high, and thin cloud cover. Figure 23: Annual course of total cloud cover at Oberpfaffenhofen and six adjacent sites. Figure 24: Annual course of total cloud cover in moderate (solid), Alpine (dotted), and Mediterranean climate (dashed). Figure 25: Annual course of total cloud cover in per cent along 11.3 E meridian (early afternoon). Figure 26: January, 5-years mean 1992-1996. Figure 27: February, 5-years mean 1992-1996. Figure 28: March, 5-years mean 1992-1996. Figure 29: April, 5-years mean 1992-1996. Figure 30: May, 5-years mean 1992-1996. Figure 31: June, 5-years mean 1992-1996. Figure 32: July, 8-years mean 1989-1996. Figure 33: August, 6-years mean 1991-1996. Figure 34: September, 6-years mean 1991-1996. Figure 35: October, 8-years mean 1989-1996. Figure 36: November, 6-years mean 1991-1996. Figure 37: December, 6-years mean 1991-1996. Abbreviations: APOLLO = AVHRR Processing scheme Over cLouds, Land and Oceans AVHRR = Advanced Very High Resolution Radiometer DFD = Deutsches Fernerkundungsdatenzentrum des DLR DLR = Deutsches Zentrum fur Luft- und Raumfahrt DWD = Deutscher Wetterdienst EUMETSAT = European Organisation for the Exploitation of Meteorological Satellites ISCCP = International Satellite Cloud Climatology Project LWP = liquid water path NOAA = National Oceanographic and Atmospheric Administration UTC = United Time Coordinated Monthly means: 5- to 8-yrs mean total cloud amounts in the study area for each month at early afternoon derived from NOAA-AVHRR satellite data 31 p.t0192_96 Figure 26: January, 5-years-mean 1992-1996. p.t0292_96 Figure 27: February, 5-years-mean 1992-1996. 32 p.t0392_96 Figure 28: March, 5-years-mean 1992-1996.. p.t0492_96 Figure 29: April, 5-years-mean 1992-1996.. 33 p.t0592_96 1.00 8 E 10 E 12 E Figure 30: May, 5-years-mean 1992-1996.. p.t0692_96 8 E 10 E 12 E Figure 31: June, 5-years-mean 1992-1996. 34 p.t0789_96 49N 48N 47N 46N 45N 8 E 10 E 12 E Figure 32: July, 8-years-mean 1989-1996. p.t0892_96 49 48 47 46 45N 0.10 0.05 Sao.oo 8 E 10 E 12 E Figure 33: August. 5-years-mean 1992-1996.. 35 p.t0991_96 Figure 34: September. 6-yea.rs-mean 1991-1996. p.t1089_96 1.00 ^ 0.95 49 N 48 N 47 N 46 N 45 N 8 E 10 E 12 E Figure 35: October, 8-years-mean 1989-1996. 36 p.t1191 _96 Figure 36: November, 6-years-mean 1991-1996. p.t1291 _96 Figure 37: December, 6-years-mean 1991-1996. 37 List of Reports Report No. 1 - 10 1993 Report No. 11 - 23 1994 Report No. 24- 45 1995 Report No. 46- 72 1996 Report No. 73- 90 1997 Report No. 91-100 1998 Report No. 101 On the transition of contrails into cirrus clouds July 1998 Franz Schroder, Bernd Karcher, Christof Duroure, Johan Strom, Andreas Petzold, Jean- Francois Gayet, Bernhard Strauss and Peter Wendling published in: J. Atmos. Sol. 57, 464-480, 2000. Report No. 102 A diagnostic study of the global coverage by contrails August 1998 Part II: Future air traffic scenarios Klaus Gierens, Robert Sausen and Ulrich Schumann published in: Theor. Appl. Climatol. 63,1-9,1999. Report No. 103 Climate effect of ozonechanges caused by present and future air traffic August 1998 Michael Ponater, Robert Sausen, Brigitte Feneberg and Erich Roeckner published in: Climate Dyn. 15, 631-642,1999. Report No. 104 A new statistical-dynamical downscaling scheme and its application to the Alpine August 1998 precipitation climatology Ursula Fuentes, Dietrich Heimann, Vladimir Sept and Gema Torres published in: Theor. Appl. Climatol. 65,119-135, 2000. Report No. 105 Transport and production of NOx in electrified thunderstorms: Survey of previous August 1998 studies and new observations at mid-latitudes Heidi Huntrieser, Hans Schlager, Christian Feigl and Hartmut Holler published in: J. Geophys. Res. 103, 28247-28264,1998. Report No. 106 Perturbation of the aerosol layer by aviation-produced aerosols: November 1998 A parametrization of plume processes Bernd Karcher and Stefanie Meilinger published in: Geophys. Res. Lett. 25,4465-4468,1998. Report No. 107 In situ measurements of the NOx distribution and variability over the eastern North November 1998 Atlantic Helmut Ziereis, Hans Schlager, Peter Schulte, Ines Kohler, Rainer Marquardt and Christian Feigl • published in: J. Geophys. Res. 104,16021-16032,1999. Report No. 108 Radiative forcing by contrails November 1998 Ralf Meerkotter, Ulrich Schumann, Dave R. Doelling, Patrick Minnis, Teruyuki Nakajima and Yoko Tsushima published in: Ann. Geophys. 17,1080-1094,1999. Report No. 109 Influence of weather conditionson the distribution of persistent contrails November 1998 Martina Kastner, Richard Meyer and Peter Wendling published in: Meteorol. Appl. 6, 261-271,1999. i Report No. 110 A distribution law for relative humidity in the upper troposphere and lower November 1998 stratosphere derived from three years of MOZAIC measurements Klaus Gierens, Ulrich Schumann, Manfred Helten, Herman Smit and Alain Marenco published in: Ann. Geophys. 17,1218-1226,1999. Report No. 111 Space-time correlations of lightning distributions December 1998 Ulli Finke published in: Mon. Wea. Rev. 127,18501861,1999. Report No. 112 Climatic change of summer temperature and precipitation in the Alpine region - a December 1998 statistical-dynamical assessment Dietrich Heimann and Vladimir Sept published in: Theor. Appl. Climatol. 66,1-12, 2000. Report No. 113 In situ observations and model calculations of black carbon emission by aircraft at March 1999 cruise altitude Andreas Petzold, Andreas Dopelheuer, Charles A. Brock and Franz Schroder published in: J. Geophys. Res. 104, 22171-22181,1999. Report No. 114 Contrail cirrus March 1999 Ulrich Schumann Report No. 115 Mountain wave induced record low stratospheric temperatures above Northern April 1999 Scandinavia Andreas Dornbrack, Martin Leutbecher, Rigel Kivi and Esko Kyro published in: Tellus 51 A, 951-963, 1999. Report No. 116 Future changes of the atmospheric composition and the impact of climate change May 1999 Volker Grewe, Martin Dameris, Ralf Hein, Robert Sausen and Benedikt Steil Report No. 117 Model simulations of fuel sulfur conversion efficiencies in an aircraft engine: May 1999 Dependence on reaction rate constants and initial species mixing ratios Hans Georg Tremmel and Ulrich Schumann published in: Aerospace Sci. Techn. 3, 417-430,1999. Report No. 118 On the composition and optical extinction of particles in the tropopause region June 1999 Bernd Karcher and Susan Solomon published in: J. Geophys. Res. 104, 27441-27460,1999. Report No. 119 Two-dimensional wake vortex physics in the stably stratified atmosphere June 1999 Frank Holzapfel and Thomas Gerz published in: Aerospace Sci. Techn. 3, 261-270,1999. Report No. 120 Heavy precipitation in the Alpine region (HERA): August 1999 Three contributions to the final EU-project volume Martin Hagen, Christian Keil, Hans Volkert, Manfred Dorninger and Hans-Heinrich Schiesser published in: Meteorol. Atmos. Phys., 72, 73-85, 87-100,161-173, 2000. Report No. 121 Distributions of NO, NOx and NOv in the upper troposphere and lower stratosphere August 1999 between 28° N and 61° N during POLINAT 2 Helmut Ziereis, Hans Schlager, Peter Schulte, Peter F.J. van Velthoven and Franz Slemr published in: J. Geophys. Res. 105, 3653-3664, 2000. 11 Report No. 122 Pollution from aircraft emissions in the North Atlantic flight corridor: September 1999 Overview on the POLINAT projects Ulrich Schumann, Hans Schlager, Frank Arnold, Joelle Ovarlez, Hennie Kelder, 0ystein Hov, Garry Hayman, Ivar S. A. Isaksen, Johannes Staehelin and Philip D. Whitefield published in: J. Geophys. Res. 105, 3605-3631, 2000. Report No. 123 Ice-supersaturated regions and sub visible cirrus October 1999 Klaus Gierens, Ulrich Schumann, Manfred Helten, Herman Smit, Pi-Huan Wang and Peter Spichtinger Report No. 124 A model-based wind climatology of the eastern Adriatic coast October 1999 Dietrich Heimann Report No. 125 Effects of remote clouds on surface UV irradiance November 1999 Markus Degtinther and Ralf Meerkotter published in: Ann. Geophys. 18, 679-686,2000. Report No. 126 The cloud analysis tool APOLLO : Improvements and validations November 1999 Karl Theodor Kriebel, Gerhard Gesell, Martina Kastner and Hermann Mannstein Report No. 127 Three-dimensional cloud effects and satellite UV mapping November 1999 Ralf Meerkotter and Markus Degtinther Report No. 128 The propagation of mountain waves into the stratosphere : Quantitative evaluation of December 1999 three-dimensional simulations Martin Leutbecher and Hans Volkert Report No. 129 Estimate of the climate impact of cryoplanes January 2000 Susanne Marquart, Robert Sausen, Michael Ponater and Volker Grewe Report No. 130 Results of an interactively coupled atmospheric chemistry - general circulation January 2000 model: comparison with observations Ralf Hein, Martin Dameris, Christina Schnadt, Christine Land, Volker Grewe, Ines Kohler, Michael Ponater, Robert Sausen, Benedikt Steil, Jochen Landgraf and Christoph Brtihl Report No. 131 In-situ studies on volatile jet exhaust particle emissions - Impacts of fuel sulfur February 2000 content and environmental conditions on nuclei-mode aerosols Franz Schroder, Charles A. Brock, Robert Baumann, Andreas Petzold, Reinhold Busen, Peter Schulte and Markus Fiebig Report No. 132 On the unification of aircraft ultrafine particle emission data March 2000 Bernd Karcher, Richard P. Turco, Fangqun Yu, Michael Y. Danilin, Debra K. Weisenstein, Richard C. Miake, Lye and Reinhold Busen Report No. 133 The decay of wake vortices in the convective boundary layer April 2000 Frank Holzapfel, Thomas Gerz, Michael Freeh and Andreas Dornbrack Report No. 134 The turbulent decay of trailing vortex pairs in stably stratified environments April 2000 Frank Holzapfel, Thomas Gerz and Robert Baumann Report No. 135 Aircraft measurements of tracer correlations in the Arctic sub vortex region during May 2000 POLSTAR Helmut Ziereis, Hans Schlager, Christian Feigl, Rainer Marquardt, Horst Fischer, Peter Hoor and Volker Wagner in Report No. 136 A method to extend the temporal validity of nested regional climate simulations June 2000 Udo Busch and Dietrich Heimann Report No. 137 Surface pressure drag for hydrostatic two-layer flow over axisymmetric mountains July 2000 Martin Leutbecher Report No. 138 Shorts term prediction of the horizontal wind vector within a wake vortex warning July 2000 system Michael Freeh, Frank Holzapfel, Thomas Gerz and Jens Konopka Report No. 139 Influence of propulsion efficiency on contrail formation : July 2000 Theory and Experimental Validation Ulrich Schumann, Reinhold Busen and Martin Plohr ReportNo. 140 Alpine cloud climatology using long-term NOAA-AVHRR satellite data July 2000 Martina Kastner and Karl-Theodor Kriebel Bine komplette Liste der Reports ist im Report Nr. 80 oder im Internet unter ‘http://www.op.dlr.de/ipa/pubs.html’ zu linden. Einzelne Exemplare kdnnen unter der E-Mail-Adresse ‘[email protected]’ bestellt werden. Weitere Publikationen des Institutsund des DLR lassen sich linden unter: http://www.dlr.de/lido A complete list of reports can be found in report No. 80 or is available from the internet: ‘http://www.op.dlr.de/ipa/pubs.html’. Single copies can be ordered under the email-address ‘[email protected]’. Further publications of DLR and the institute can be found under: http://www.dlr.de/lido IV