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Urban influence on cloud cover estimated from satellite data
Atmospheric Environment 33 (1999) 4163}4172
Urban in#uence on cloud cover estimated from satellite data Peter Romanov* Research Centre **Planeta++ RPA, ROSHYDROMET 7, B. Predtechensky, Moscow 123242, Russia
Abstract Quantitative assessment of the in#uence of a big city on the cloud cover was made using time series of satellite measurements. Data from the advanced very high resolution radiometer (AVHRR) on board NOAA satellites received during 1993}1996 over the Central European part of Russia have been processed to derive statistics on spatial distribution of cloudiness for a territory of approximately 7000 km covering Moscow (55.7 N, 37.6 E) and adjacent areas. Two basic cloud cover characteristics were studied: the total fractional cloud cover and the frequency of occurrence of cloudless scenes (or the clear-sky frequency). Results of the study show that the urban e!ect is most pronounced during spring and summer periods when a considerable increase in the cloud amount over most of the built-up city area is observed. The winter period presents only a slight evidence of the urban-induced modi"cation of the cloud amount spatial distribution pattern. The average clear-sky frequency in the centre of the city was found to be 5.4% lower than in the nearest suburbs. No well-de"ned seasonal variations of the urban}rural di!erence in the clear-sky frequency were detected. 1999 Published by Elsevier Science Ltd. All rights reserved. Keywords: Cloud amount; Satellite remote sensing; Urban e!ects
1. Introduction Industrial activity and urbanization cause signi"cant changes in land surface physical characteristics and energy budget, enhance heat release and air pollution and thus result in a distinct modi"cation of local climate in big cities. Some aspects of this modi"cation such as changes in temperature, precipitation, air humidity and wind regime have been actively studied during the last decades. Urban e!ects regarding these meteorological parameters are well documented both qualitatively and quantitatively (Landsberg, 1981; Changnon and Hu!, 1986; Ackerman, 1987; Roth et al., 1989; Changnon et al., 1991; Gallo et al., 1993). The current state of investigation of the urban in#uence on the cloudiness is quite di!erent. Apparently the above-mentioned factors, peculiar to big cities, may also a!ect the cloud cover. However, to assess the urban e!ect * Present address: NOAA/NESDIS/ORA, WWB 712, 5200 Auth Road, Camp Springs, MD 20746, USA. Tel.: #1-301-7638042; fax: #1-301-763-8108. E-mail address: [email protected] (P. Romanov)
on the cloudiness appears more di$cult. First, there are no objective quantitative methods for measuring cloud cover parameters and most of the studies in this area are based on the analysis of subjective visual observations data. Second, cloudiness is a highly variable component of the atmosphere which means that long-term sets of observations are required to derive reliable statistics on cloud cover characteristics. In general, two basic techniques can be applied to estimate the urban in#uence on the cloud cover. The "rst one relies on the use of long-term records of observations made at sites located in big cities (e.g. Stief, 1991; Gomez-Rojas et al., 1992; Matushko, 1992). Trends revealed from the analysis of these time series are attributed to urban or industrial factors. The other approach, aimed at a direct estimation of the urban}rural cloud cover di!erence, employs simultaneous observations at paired stations located in the city and in the nearby suburb or rural area (e.g. Bulat, 1992; Chow, 1992). Both methods have serious limitations. The application of the "rst one is reasonable only for homogeneous time series of observations which are rarely available. Also, it cannot help in separating large-scale variability and local changes in
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cloud cover. If the second approach is used, the requirements for the duration of time series involved are not so strict, however insu$cient accuracy of observations because of their subjectiveness hinders precise determination of di!erences in the cloud cover over urban and rural areas. Observations from NOAA polar orbiting satellites have proved to be an e!ective tool for global climatological studies and, in particular, for the global cloud cover monitoring (Stowe et al., 1991). Due to the high spatial resolution of the advanced very high resolution radiometer (AVHRR) measurements, equal to 1.1 km at nadir, it is also possible to employ these data for the determination of instantaneous cloud cover characteristics, in particular to estimate the total cloud amount, at a spatial scale close to the one of ground-based observations (Karlson, 1993; Romanov, 1994). The possibility of regular and dense sampling, continuous area coverage and spatial and temporal homogeneity of remote sensing data also make satellite measurements one of the most appropriate if not the only source of information for developing and improving the local area cloud climatology and in particular for a quantitative assessment of cloud cover parameters and their variability over big cities and their nearest surroundings. In this study, AVHRR/NOAA data were used to derive statistical characteristics of the cloud cover spatial distribution over the Moscow region and thus to investigate the impact of a big city on cloudiness. Two parameters were of primary interest: the average cloud amount and the frequency of observations of cloud-free scenes (or the clear-sky frequency). The study area comprised a square with a side of 80 km centred on Moscow (55.7 N 37.6 E); the spatial resolution of derived cloud cover characteristics was equal to approximately 3.5 km. Moscow was chosen as a test site for the investigation of the urban e!ect on the cloud cover because of the following considerations. First, it is one of the world's biggest cities with a population exceeding 10 million people and a built-up area covering more than 1500 km. There are over 1600 industrial enterprises in Moscow including several big electric power plants, oil re"nery, iron and steel works, chemical and timber processing industries. Obviously such a huge urbanized and industrialized area strongly in#uences the environment, and its possible e!ect on the cloud cover should be much more pronounced than the one of smaller cities. Second, Moscow lies on an almost #at terrain in the central part of the European Russian Plane with no big bodies of water in the vicinity of the city. Thus, the impact of the other local factors, besides urban ones, on the air #ow and on the cloud cover over the city and its nearest surroundings city is expected to be minimal. Third, the city built-up area has a nearly circular shape with a well de"nedboundary. The latter factors could facilitate the analysis of the spatial distribution of the cloud cover character-
istics and could help to trace the urban-induced modi"cation of cloudiness.
2. Data and method The initial set of satellite data consisted of daytime local area coverage (LAC) images of the AVHRR channels 1}5 covering the Central European part of Russia. The data were collected during the three-year time period between April 1993 and March 1996. Only afternoon passes of NOAA-11 (from April 1993 to September 1994) and NOAA-14 (from February 1995 to March 1996) were considered. All the received images were transformed onto a regular 1.5' longitude and 1' latitude grid and geometrically corrected using ground control points, with the resulting accuracy of the images navigation of $1.5 km. The AVHRR measurements were calibrated in equivalent albedo units for channels 1 and 2 (denoted further by A and A , correspondingly) and in brightness temperature for channels 3, 4 and 5 (¹ , ¹ , and ¹ ). In addition, channel 3 albedo (A ) was estimated from ¹ and ¹ following Stowe et al. (1991). It should be noted that due to several reasons we did not succeed in the accumulation of a complete set of daily satellite images over the study period. No data were collected during October 1994 } January 1995, because of the failure of NOAA-13. Data of December 1993 } January 1994 and partially of November 1993 were omitted from consideration. During this period satellite observations from NOAA-11 were made mostly at low sun elevation angles (less than 103) or after the sunset which made the AVHRR visible and near-infrared measurements unusable for the accurate determination of the cloud cover parameters. In general, due to technical problems with the high resolution picture transmission (HRPT) receiving station operated by the Hydrometeorological Centre (HMC) of Russia in Moscow, the satellite data acquisition was not performed on a daily basis. Moreover, approximately one out of 20 orbits acquired had bad scan time or date information and thus was unsuitable for further processing. In order to diminish possible e!ects of the decreasing spatial resolution of the AVHRR measurements at the edges of the instrument scan on the accuracy of the cloud cover parameters retrieval we only used those satellite overpasses where the study area was observed by the satellite sensor with a viewing angle of 453 and less. The total number of successfully processed satellite orbits amounted to 412 in 30 months of observations, thus making an average of 13}14 orbits per month. Due to the satellite orbital drift and because of the switch from NOAA-11 to NOAA-14 the time of observations varied throughout the study period. The nominal time of the NOAA-11 afternoon passes over Moscow in 1993}1994 changed gradually from about 1600 to 1715
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local standard time (LST). During February 1995 } March 1996 the nominal time of the NOAA-14 overpasses was around 1400 LST, whereas the orbital drift of this satellite during that period resulted in only a 15 min shift to a later time. The procedure used to derive cloud cover distribution statistics included three stages: (1) cloud detection, (2) estimation of cloud cover parameters in every individual AVHRR image, and (3) accumulation and subsequent averaging of the results on a monthly basis. Cloud detection in AVHRR images was performed on a pixel-bypixel basis using a threshold algorithm. The algorithm included sequential threshold testing and employed spectral features derived from AVHRR measurements in channels 1, 3, 4 and 5. Three tests were applied: infrared test, employing channel 4 temperature measurements, split window temperature di!erence test (¹ !¹ ) and visible/infrared re#ectance test based on channels 1 and 3 albedo data. Thresholding for AVHRR channel 4 measurements was used to detect low temperature pixels corresponding to medium- and high-level clouds. The threshold value for ¹ was determined using the air temperature estimate at 850 mb level. Thresholds for the temperature di!erence ¹ !¹ to detect low-level cloudiness were established through radiative transfer modelling with LOWTRAN-7 code (Kneizys et al., 1988) and using appropriate temperature and humidity vertical pro"les. The information on the vertical structure of atmosphere used both in the "rst and the second tests was inferred from the nearest in time actual analysis data produced at the HMC of Russia. Two-dimensional thresholds employing A and A were used at the third step to separate the cloudy pixels remaining undetected after the "rst two tests were applied. Three di!erent sets of these thresholds were established to process AVHRR data obtained over di!erent land surface cover types, i.e. snow covered, snow free and partially snow covered land surfaces. The "rst and the second sets were used correspondingly for satellite measurements taken from December to March and from May to October, while the third set was applied during November and April. All the threshold values for the visible}infrared re#ectance test were determined and adjusted by way of a visual imagery analysis and by comparison of the AVHRR-based cloud amounts derived at the following stage of the processing procedure with the satellite-synchronous ground-based meteorological observations. The surface observations data in the form of synoptic weather reports containing information on the cloud cover were obtained from the HMC of Russia. Cloud amount estimates were produced for each grid point of the AVHRR image through straightforward calculation of cloudy and clear pixels within cells of 6 latitude and 9 longitude (or approximately 11;10 km). The size of a cell was selected to achieve a better "t of spatial scales of satellite-based cloud amount estimates
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and of surface cloud cover observations and thus to have possibilities for a quantitative comparison of these data. Data on the cloud cover distribution derived from all acquired AVHRR images within a month period were used to produce monthly statistics on cloud cover characteristics. Accumulation of data was performed within 2 latitude and 3 longitude grid boxes covering a square with approximately 80 km side centered on Moscow. Estimates of cloud amount were produced in tenths (1 tenth corresponds to 10% of the full cloud cover) and the frequency of occurrence of cloudless scenes was determined in percent.
3. Comparison of satellite-derived cloud amount with surface observations The accuracy of satellite-derived cloud amount estimates was tested using satellite-synchronous groundbased cloud cover observations taken at six meteorological stations located within the study area (see Fig. 1). Four of six stations (WMO numbers 27514, 27527, 27524 and 27613) are positioned in the rural area whereas the other two (WMO 27612 and the Moscow State University meteorological observatory) are located within the limits of the built-up area. The maximum time gap allowed between surface and satellite observations compared was equal to 30 min. Ground-based total fractional cloud cover data originally reported in oktas were
Fig. 1. The study area and the location of the meteorological stations within the Moscow area and its nearest surroundings. MU stands for the Moscow State University meteorological observatory. Thin lines show major highways, thick line contours the urbanized area.
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converted into tenths (1 okta is equal to 1.25 tenths or 12.5% of the full cloud cover). Satellite estimates of the total fractional cloud cover were obtained in a way described above for the squares of 11;10 km centred on each of the meteorological stations. The total statistics used in the study amounted to 2080 coincident satellite}surface observations collected during 1993}1996. The results of a comparison of satellite- and groundbased measurements of the instantaneous cloud amount presented in Fig. 2 show that in approximately 73% of all cases the discrepancy does not exceed 2 tenths. Since the accuracy of the surface observations of the total cloud amount comprises around 2.2 tenths (see appendix), the correspondence of estimates within 2 tenths may be considered as acceptable. In 90% of all cases the di!erence in satellite and surface cloud amount remains less than 5 tenths. Severe errors of cloud detection in satellite images resulting in underestimation or overestimation of the cloud amount of over 5 tenths occurred in approximately 4% of all cases considered. The major part of the latter errors originates from a di$culty in detecting semitransparent cirrus clouds in AVHRR images. If only low- and middle-level cloudiness are observed the correspondence between satellite- and ground- based data on the total fractional cloud cover improves (see Fig. 2). In this case around 79% of all the coincident ground-based and satellite observations provide estimates of the cloud amount with less than 2 tenths di!erence. The standard deviation between satellite and surface estimates of the total cloud amount comprised 1.97 tenths for all cases considered and 1.67 tenths for the cases when no cirrus clouds were observed. Fig. 3 illustrates the correspondence of time-averaged satellite and surface estimates of the total fractional cloud cover. Average cloud amount values for each of the sites were calculated using 285 cases when satellite and corresponding surface observations at all six stations were available at the same time. It is seen that the satellite}surface di!erence in cloud amount ranges from 0.1 to 0.5 tenths for di!erent sites, i.e. it is approximately of the same magnitude as the uncertainty in the monthly and yearly averaged ground-based estimate of the cloud amount (see Appendix A). As a whole satellite estimates of the cloud amount have a slight negative bias of 0.15 tenths relative to surface observations. There is another important inference that can be made from the results presented in Fig. 3. Since the considered meteorological stations are located not far apart (more precisely, within a circle of 35 km radius) there should be observed a close correspondence between estimated average cloud amount values for these sites. It is seen that both satellite and surface samples of cloud amount means exhibit approximately the same scatter with 0.4}0.45 di!erence between minimum and maximum values. However, if we assume that urban factors have a pronounced e!ect on the cloud cover characteristics
Fig. 2. Correspondence between satellite- and ground-based estimates of the instantaneous cloud amount.
Fig. 3. Average total fractional cloud cover estimated for a sample of coincident satellite measurements and ground-based observations. Sample size: 285 synchronous satellite and surface observations for each station included. (*) MU is the Moscow University Observatory.
and consider rural and urban sites separately, a better agreement between satellite-derived mean values within these sub-samples becomes obvious. The scatter in satellite mean cloud amount estimates does not exceed 0.2 tenths for the four rural stations and is less than 0.1 tenth for the two urban sites, whereas the corresponding values for the surface sample are 0.35 and 0.2 tenths. The better site-to-site correspondence of satellite-averaged cloud amount estimates is indicative of a better consistency of these data in terms of the derived spatial distribution of the cloud cover characteristics. Summarizing the results presented in this section and in the appendix it can be concluded that the accuracy of satellite retrievals of the cloud amount is comparable with the accuracy of surface observations with respect to the instantaneous and time-averaged characteristics. It means, in particular, that for an arbitrary selected site the errors of 2 and 0.5 tenths should be expected respectively
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in satellite estimates of an instantaneous cloud amount and of a cloud amount value averaged on a monthly basis. A better spatial consistency of satellite cloud cover retrievals than that of the ground-based observations suggests that local inhomogeneities in the cloud amount spatial distribution of even lesser magnitudes can be e!ectively detected. However, a more precise quantitative estimate of satellite cloud cover characteristics retrieval errors is hardly possible.
4. Results and discussion Fig. 4 illustrates a seasonal dependence of the di!erence in the total fractional cloud cover and in the frequency of clear-sky observations over the centre of the urbanized area (within the 4 km radius) compared to the rural areas located within radii of 48 and 52 km away from the centre. As seen from Fig. 4a, year-to-year variations of urban}rural di!erence in monthly averaged cloud amounts for the same months are substantial. However, the average plot clearly indicates a considerable increase of the cloud cover fraction over the centre of the city relative to the suburb during the warm period, from March to October. The average di!erence in the monthly averaged total fractional cloud cover for this period comprises 0.76 tenth, with maximum values reaching 0.8}1.3 tenths observed in spring (from March to May). During the cold season, the urban}rural contrast noticeably decreases and even becomes negative in November and December. However, it should be noted that for these months the statistics available are the least extensive. Student's t-test for the di!erence in the means was applied to verify whether the observed urban}rural di!erence in the cloud amount value is statistically significant for di!erent seasons. The null hypothesis was that the di!erence was equal to zero. The testing showed that the detected positive di!erence during spring and summer seasons (from March to May and from June to August, correspondingly) was signi"cant at the 99% con"dence level. The di!erence for autumn and winter seasons (from September to November and from December to February, correspondingly) tested at the 95% con"dence level was found to be insigni"cant. The scatter in estimates of monthly mean di!erence in frequency of occurrence of cloudless scenes for di!erent years is also high (see Fig. 4b), yet mean values averaged over all three years of observations do not present distinct seasonal variations. The probability of observing clear sky in the centre of Moscow is on average 5.4% less than that in the nearby rural areas. The detected negative urban}rural di!erence in the clear-sky frequency proved to be statistically signi"cant at the 99% con"dence level. The urban}rural cloud cover di!erence is one of the parameters re#ecting urban in#uence on cloudiness. To
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investigate urban e!ects in more detail, spatial distributions of the cloud cover over the study area have to be involved. In the paper, we analysed data for the two most interesting cases, namely for winter and spring seasons, presenting correspondingly the highest and the lowest urban}rural cloud cover di!erences. Fig. 5 illustrates the spatial distribution of the average cloud amount over the Moscow region for the two selected seasons. Results are given in terms of the di!erence between the current value and the minimum value over the study area. Since the major factor causing the daytime increase of the total cloud amount over the city area during warm seasons is the enhanced convection, the most pronounced e!ect should be expected over the centre of the city which has usually the highest surface temperature as compared to the nearby areas. This hypothesis is supported by the results presented in Fig. 5a, which shows however that the excessive cloudiness covers not only the centre of the city but almost the whole built-up area (shown with a thick contour on the picture) and the nearest surroundings up to about 40 km away from the city centre. The shift of the excessive cloud cover zone to the southeast relative to the urbanized area most probably is the consequence of advection for the predominant wind directions in this region are northerly, northwesterly and westerly. The chart for the winter season (Fig. 5b) at "rst glance gives no clear indication of the urban e!ect on the average cloud amount distribution during the cold period. However, there are areas in the surroundings of Moscow where the average cloud amount is several percent higher than in the city. The region with the highest cloud amount is located to the northwest of the city, i.e. in the direction corresponding on average to the up-wind one. The other such region is observed approximately to the southeast of the city. The substantial changes in the cloud cover appearing just along the predominant wind direction suggest that this may be a result of the urban in#uence. Yet the data available are not quite su$cient to ultimately justify the correlation detected and a more detailed study is needed. The spatial distribution of the di!erence in clear-sky frequency in spring (Fig. 6a) presents features similar to the ones of the distribution of the total fractional cloud cover di!erence for the same period. Cloudless sky in the centre of the city is observed 8}10% less often than to the north and approximately 6% less often than to the south of Moscow. In the winter season the region with lower clear-sky frequency appears in the southeast part of the city. Since east and southeast parts of Moscow are most industrialized, it is reasonable to suggest that this decrease is due to the industrial activity. The particular sources of the excessive cloudiness can be moisture-laden plumes from cooling towers of large industries and power plants (Hanna, 1977; Guan and Reuter, 1996). However, the detected reduction in clear-sky frequency is rather
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Fig. 4. Di!erence in the total fractional cloud cover (a) and in the clear-sky frequency (b) over the Moscow centre (within the 4 km radius) and the city suburb (48}52 km). Bars present the total number of NOAA passes processed for each month.
small (2}6%) and the area with lower values does not sharply stand out against the whole picture. The results of satellite monitoring of cloudiness over Moscow and adjacent areas presented above reveal in general a well-pronounced in#uence of a big city on the cloud cover distribution which can be traced up to at least 20 km away from the city limits. Stimulation of convection by the urban heat island along with the injection of additional moisture and aerosol particles from industrial sources into the atmosphere must be considered as the main processes responsible for the positive urban}rural di!erence in the total fractional cloud cover
as well as for the lower frequency of occurrence of cloudless scenes observed over the city during the warm season. Apparently, these are the e!ects which also cause an enhancement of convective precipitation over and downwind of the urbanized areas (Changnon et al., 1991; Landsberg, 1981). Considering the peculiarities of the observed cloud cover modi"cations in the winter season, one should remember that during this period stratiform clouds prevail in Moscow region, the atmosphere is usually stable and advection is the major mechanism determining the cloud cover spatial distribution. On the other hand, the
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Fig. 5. Spatial distribution of the average fractional cloud cover, in tenths, (a) for the spring season (March } May) and (b) for the winter season (December } February). Results are presented in terms of di!erence between the current value (N) and the minimum value for the study area (N ).
Fig. 6. Spatial distribution of the clear-sky frequency (in percents) (a) for the spring season (March } May) and (b) for the winter season (December } February). Results are presented in terms of di!erence between the current value (F ) and the maximum value for the study area (F ).
excessive heat release over urbanized areas is responsible for a considerable air temperature increase which under certain conditions can extend for up to the height of several hundred meters (Oke, 1987). The temperature rise may cause the uplift of the level of condensation which in turn leads to the increase of the cloud base height. As far as the height of the top of stratiform clouds is controlled mainly by large-scale atmospheric processes and hardly varies signi"cantly over the city area, the in#uence of the urban heat island may result in a reduced cloud thickness and eventually in a reduced total fractional cloud cover. Obviously the process described above can in#uence the stratiform cloud cover distribution throughout the year, however its e!ect should be more pronounced during the cold season since winter stratiform clouds have on the average lower cloud bases and lesser thickness than summer stratiform clouds. This latter conclusion is supported by the observed winter cloud cover distribution over Moscow (Fig. 5b) which presents an indication of the reduced total cloud amount over the urbanized area.
In general, the development of speci"c meteorological conditions is necessary for the urban e!ect on cloudiness could manifest itself against a background of large-scale atmospheric processes. Only a weak air #ow makes possible a considerable transformation of a local environmental airmass over the urbanized territory. The urban heat island contribution to the vertical motions can provide a noticeable increase of the cloud amount only if the local airmass is stable or convection is shallow. The foregoing implies that the urban}rural di!erence in cloud cover is a strongly weather-dependent feature and that the long-term average e!ect of a big city on cloudiness is determined to a great extent by the weather-type statistics in the particular area. This means also that the variations in the frequency of occurrence of di!erent weather situations should be considered as one of the major reasons for the observed year-to-year variability in the monthly averaged urban}rural di!erence in the total cloud amount and clear-sky frequency.
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One of the major limitations of the present research stems from the fact that satellite measurements have not been collected on a daily basis and thus the sample of the cloud cover observations used in the analysis was not complete. On average only 13}14 daily observations per month were utilized. Therefore, the results obtained in general can be treated only as having statistical but not climatological meaning. However, it is known that atmospheric motions and corresponding cloud cover patterns and weather types in midlatitudes are correlated over scales of 500}2000 km and 2}3 d. It follows, that within a month only about 10}15 observations of the cloud cover and weather type at each particular location are independent (Rossow et al., 1993). Since the particular urban e!ect on the cloud cover is to a great extent determined by the weather conditions, the feature similar to the one of the cloud cover observation time series mentioned above should be also peculiar to the time series of the urban}rural cloud cover di!erence. This suggests that even by using a limited monthly sample of 13}14 daily cloud cover observations we might obtain reliable quantitative estimates of the monthly mean urban}rural di!erence in the total cloud amount and clear-sky frequency. The other shortcoming of the study is that the presented results refer to one particular period of the day, from 1400 to 1700 local time. The daily variations of the atmosphere boundary layer, the changes in the urban}rural di!erence in temperature and cooling/ heating rates could a!ect the cloud cover distribution pattern and change the observed urban}rural di!erence in cloud cover characteristics. It is expected that at night the decreased advection, weakened convection and the enhanced urban}rural temperature contrast would emphasize an urban-induced transformation of the cloud cover over a big city. In the morning, when the temperature distribution is smoothed out, the urban e!ect on cloudiness should be minimal. These empirical conclusions, however, require experimental quantitative con"rmation.
5. Conclusions Three-year time series of AVHRR/NOAA daytime data were applied to study peculiarities of the local area cloud cover distribution and to quantitatively assess the urban e!ect on cloudiness. The results of satellite soundings over Moscow and adjacent areas revealed a well-pronounced in#uence of a big city on cloud cover statistical characteristics which manifested itself in E up to 1.3 tenth (or 13%) increase in the averaged total fractional cloud cover over urbanized areas as compared to rural ones during the warm period and a small decrease in winter;
E 5.4% average decrease in the frequency of occurrence of cloudless scenes over the built-up area. No distinct seasonal variation was observed. The spring and summer increase of the total cloud amount and the decrease of the clear-sky frequency over the built-up city area as compared to the nearby rural areas were found to be statistically signi"cant at 99% con"dence level. It was shown that during the warm season the excessive cloudiness is observed not only over the city centre but it covers the whole built-up area and extends to at least 20 km over the boundaries of the city along the predominant directions of wind, i.e., to the east, southeast and south of Moscow. Only slight evidence of the direct in#uence of the industrial activity on the cloud cover characteristics was found.
Acknowledgements The author would like to thank Drs. D. Tarpley and G. Gutman of NOAA/NESDIS and also anonymous reviewers for critical reading of the manuscript and valuable comments. The manuscript has been revised while the author held postdoctoral position at Centre d'Applications et de Recherches en TeH leH deH tection (CARTEL), UniversiteH de Sherbrooke, Sherbrooke, QueH bec, J1K 2R1, Canada.
Appendix A. Accuracy of ground-based observations of the total cloud amount Uncertainty in the total fractional cloud cover data reported from the ground-based meteorological stations is an important factor that should be accounted for when using these data to validate satellite retrievals of the total cloud amount. Since no direct instrumental measurements of the cloud amount are available, the most obvious approach to evaluate the accuracy of surface observations consists in the comparison of synchronous cloud cover observations made at sites located at close proximity to each other. The revealed discrepancy between cloud amount estimates made at neighbouring stations may be treated as an error typical to this type of measurements. In this study we used the data set of Hahn et al. (1996) containing the routine cloud cover observation data reported from ground-based meteorological stations over the globe. To investigate the consistency in the total cloud amount estimates we extracted and examined synchronous measurements taken at paired stations located at less than 100 km apart from each other. Only day time observations (900}1500 UTC) collected over Europe (within 5}603E and 20}603N) in 1991 were considered.
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Fig. 7. Scatter of the root mean square di!erence in estimates of the instantaneous total cloud amount reported from paired meteorological stations vs. distance between stations. Daytime observations (9}15 UTC) made over Europe in 1991 were considered. Bold line shows the average root mean square di!erence for the sites located at less than 15 km apart.
Fig. 8. Scatter of the root mean square di!erence in monthly mean values of the total fractional cloud cover for paired ground-based meteorological stations vs. distance between stations. Observations made during 1991 at 1200 UTC over Europe were considered. Bold line shows the average root mean square di!erence for the sites located at less than 15 km apart.
Fig. 7 presents the scatter in the root mean square di!erence in instantaneous cloud amounts reported from paired meteorological stations. Each value was obtained using from 1000 to 1500 synchronous observations taken during 1991. The graph clearly shows that the discrepancies between the estimates of the instantaneous cloud amount tend to decrease with the decrease of the distance between the stations. However, a lower limit of the deviation, with only few exceptions, comprises 1 okta, which is equal to 1.25 tenths or 12.5% of the full cloud cover. The analysis of synchronous observations made at most closely spaced pairs of sites, located at less than 15 km apart, showed that the average root mean square di!erence in the reported cloud amount values comprises 1.75 oktas or 2.2 tenths. Apparently, this di!erence is determined predominantly by the subjectiveness of individual estimates of the cloud amount, since, "rst cloud amounts vary on much larger space scales of at least several hundred kilometers (Rossow, 1993) and, second, the observers being at a distance of 15 km in most of the cases estimate the state of a cloud cover using much the same portion of the sky dome. Though there exist no obvious reasons for large systematic errors in the visual estimates of the cloud amount, the absolute bias in yearly averaged cloud amount estimates for the stations located within 15 km and less ranged within 0}0.5 okta (0}0.6 tenths) with a mean value of 0.25 okta (0.3 tenths). Fig. 8 illustrates the scatter in the root mean square di!erence in monthly averaged values of the total frac-
tional cloud cover reported from paired stations during 12 months of the year of 1991. Only observations taken at 12 UTC were considered. Comparing Figs. 7 and 8 we see that the averaging of cloud cover observations on a monthly basis results in a better "t of cloud amount estimates at neighbouring sites. The consistency in estimated cloud amount monthly mean values does not noticeably improve with a decrease of the distance between stations and even for sites located within 15 km apart, the root mean square di!erence comprises 0.45 oktas or 0.56 tenths. The discrepancies in the estimates of the instantaneous and monthly mean cloud cover parameters can be treated as the errors typical to the surface observations of the cloud cover. The error of the instantaneous cloud amount estimate is 2.2 tenths, whereas for the monthly and yearly average values it decreases to approximately 0.56 and 0.25 tenths, correspondingly. It should be noted that the results obtained are based on the analysis of the visual synoptic observations made over the territory of Europe, where the ground-based meteorological network is one of the densest and the data are most likely to be of high quality. Therefore, the di!erence in the total cloud amount reported from the stations located at a very close proximity can be considered as an indicator of an uncertainty in the data on the cloud cover typical to surface observations. The presented results show that only limited possibilities exist to control the validity of the satellite-derived cloud amount with surface observation data.
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In particular, the correspondence of the estimates of the instantaneous cloud amount within 2 tenths as well as the correspondence of the cloud amount averaged on a monthly or yearly basis within 0.2}0.6 tenths should be considered as a perfect "t.
References Ackerman, B., 1987. Climatology of Chicago area urban}rural di!erences in humidity. Journal of Climate and Applied Meteorology 26, 427}430. Bulat, A., 1992. Cloud cover in city and suburb by the example of Krakow and Gaik Bzhezuv. Zeszyty Naukowe Universytetu Jajioellonskiego Prace Geogralicze 90, 35}51. Changnon, S.A., Hu!, F.A., 1986. The urban related nocturnal rainfall anomaly at St. Louis. Journal of Climate and Applied Meteorology 25, 1985}1995. Changnon, S.A., Shealy, R.T., Scott, R.W., 1991. Precipitation changes in fall, winter and spring caused by St. Louis. Journal of Applied Meteorology 30, 126}134. Chow, S.D., 1992. The urban climate of Shanghai. Atmospheric Environment B26, 9}15. Gallo, K.P., McNab, A.L., Karl, T.R., Brown, J.F., Hood, J.J., Tarpley, J.D., 1993. The use of AVHRR/NOAA data for assessment of the urban heat island e!ect. Journal of Applied Meteorology 32, 899}908. Gomez-Rojas, J., Carlos, I., Nacubaga, D., 1992. Climatic changes in Mexico city. Technical Program and Abstracts, 27th International Geographical Congress, August 9}14, 1992, Washington, DC, pp. 221}222. Guan, S., Reuter, G.W., 1996. Numerical simulation of an industrial cumulus a!ected by heat, moisture and CCN released form an oil re"nery. Journal of Applied Meteorology 35, 1257}1264. Hahn, C.J., Warren, S.G., London, J., 1996. Edited synoptic cloud reports from ships and land stations over the globe,
1982}1991. NDP026B, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, TN. Hanna, S.R., 1977. Predicted climatology of cooling tower plumes from energy centers. Journal of Applied Meteorology 16, 880}887. Karlson, K.-G., 1993. Comparison of operational AVHRRbased cloud analysis with surface observations. Proceedings of the sixth AVHRR Data Users' Meeting, Belgirate, Italy, 29th June}2nd July 1993, EUM P12, pp. 223}230. Kneizys, F.X., Shettle, E.P., Abreau, L.W, Anderson, G.P., Chetwynd, J.H., Gallery, W.O., Shelby, J.E.A., Clough, S.A., 1988. User's guide to LOWTRAN-7. AFGL TR No. 880177, 137pp. Landsberg, H.E., 1981. The Urban Climate. Academic Press, New York, 277pp. Matushko, D., 1992. Century variations of cloud cover in Krakov. Zeszyty Naukowe Universytetu Jajioellonskiego Prace Geogralicze 88, 147}156. Oke, T.R., 1987. Boundary Layer Climates. Methuen, London, 435pp. Romanov, P.Yu., 1994. Cloud parameters and precipitation intensity analysis using AVHRR images classi"cation scheme. Proceedings of the Seventh Australoasian Remote Sensing Conference, 28th February}5th March 1994, Melbourne, Australia, pp. 219}225. Rossow, W.B., Walker, A.N., Garder, L.C., 1993. Comparison of ISCCP and other cloud amounts. Journal of Climate 6, 2394}2418. Roth, M., Oke, T.R., Emery, W.J., 1989. Satellite derived urban heat island from three coastal cities and the utilization of such data in urban climatology. International Journal of Remote Sensing 10, 1699}1720. Stief, G., 1991. Trends in cloud-cover, temperatures and global irradiation in Central Italy since 1957. Possible evidence of jet contrails e!ects. Annals of Geophysics 9, 245}250 (Suppl). Stowe, L.L., McClain, E.P., Carey, R., Pellegrino, P., Gutman, G.G., Davis, P., Long, C., Hart, S., 1991. Global distribution of cloud cover derived from NOAA/AVHRR operational satellite data. Advances in Space Research 11, 51}54.
Urban in#uence on cloud cover estimated from satellite data Peter Romanov* Research Centre **Planeta++ RPA, ROSHYDROMET 7, B. Predtechensky, Moscow 123242, Russia
Abstract Quantitative assessment of the in#uence of a big city on the cloud cover was made using time series of satellite measurements. Data from the advanced very high resolution radiometer (AVHRR) on board NOAA satellites received during 1993}1996 over the Central European part of Russia have been processed to derive statistics on spatial distribution of cloudiness for a territory of approximately 7000 km covering Moscow (55.7 N, 37.6 E) and adjacent areas. Two basic cloud cover characteristics were studied: the total fractional cloud cover and the frequency of occurrence of cloudless scenes (or the clear-sky frequency). Results of the study show that the urban e!ect is most pronounced during spring and summer periods when a considerable increase in the cloud amount over most of the built-up city area is observed. The winter period presents only a slight evidence of the urban-induced modi"cation of the cloud amount spatial distribution pattern. The average clear-sky frequency in the centre of the city was found to be 5.4% lower than in the nearest suburbs. No well-de"ned seasonal variations of the urban}rural di!erence in the clear-sky frequency were detected. 1999 Published by Elsevier Science Ltd. All rights reserved. Keywords: Cloud amount; Satellite remote sensing; Urban e!ects
1. Introduction Industrial activity and urbanization cause signi"cant changes in land surface physical characteristics and energy budget, enhance heat release and air pollution and thus result in a distinct modi"cation of local climate in big cities. Some aspects of this modi"cation such as changes in temperature, precipitation, air humidity and wind regime have been actively studied during the last decades. Urban e!ects regarding these meteorological parameters are well documented both qualitatively and quantitatively (Landsberg, 1981; Changnon and Hu!, 1986; Ackerman, 1987; Roth et al., 1989; Changnon et al., 1991; Gallo et al., 1993). The current state of investigation of the urban in#uence on the cloudiness is quite di!erent. Apparently the above-mentioned factors, peculiar to big cities, may also a!ect the cloud cover. However, to assess the urban e!ect * Present address: NOAA/NESDIS/ORA, WWB 712, 5200 Auth Road, Camp Springs, MD 20746, USA. Tel.: #1-301-7638042; fax: #1-301-763-8108. E-mail address: [email protected] (P. Romanov)
on the cloudiness appears more di$cult. First, there are no objective quantitative methods for measuring cloud cover parameters and most of the studies in this area are based on the analysis of subjective visual observations data. Second, cloudiness is a highly variable component of the atmosphere which means that long-term sets of observations are required to derive reliable statistics on cloud cover characteristics. In general, two basic techniques can be applied to estimate the urban in#uence on the cloud cover. The "rst one relies on the use of long-term records of observations made at sites located in big cities (e.g. Stief, 1991; Gomez-Rojas et al., 1992; Matushko, 1992). Trends revealed from the analysis of these time series are attributed to urban or industrial factors. The other approach, aimed at a direct estimation of the urban}rural cloud cover di!erence, employs simultaneous observations at paired stations located in the city and in the nearby suburb or rural area (e.g. Bulat, 1992; Chow, 1992). Both methods have serious limitations. The application of the "rst one is reasonable only for homogeneous time series of observations which are rarely available. Also, it cannot help in separating large-scale variability and local changes in
1352-2310/99/$ - see front matter 1999 Published by Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 2 - 2 3 1 0 ( 9 9 ) 0 0 1 5 9 - 4
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cloud cover. If the second approach is used, the requirements for the duration of time series involved are not so strict, however insu$cient accuracy of observations because of their subjectiveness hinders precise determination of di!erences in the cloud cover over urban and rural areas. Observations from NOAA polar orbiting satellites have proved to be an e!ective tool for global climatological studies and, in particular, for the global cloud cover monitoring (Stowe et al., 1991). Due to the high spatial resolution of the advanced very high resolution radiometer (AVHRR) measurements, equal to 1.1 km at nadir, it is also possible to employ these data for the determination of instantaneous cloud cover characteristics, in particular to estimate the total cloud amount, at a spatial scale close to the one of ground-based observations (Karlson, 1993; Romanov, 1994). The possibility of regular and dense sampling, continuous area coverage and spatial and temporal homogeneity of remote sensing data also make satellite measurements one of the most appropriate if not the only source of information for developing and improving the local area cloud climatology and in particular for a quantitative assessment of cloud cover parameters and their variability over big cities and their nearest surroundings. In this study, AVHRR/NOAA data were used to derive statistical characteristics of the cloud cover spatial distribution over the Moscow region and thus to investigate the impact of a big city on cloudiness. Two parameters were of primary interest: the average cloud amount and the frequency of observations of cloud-free scenes (or the clear-sky frequency). The study area comprised a square with a side of 80 km centred on Moscow (55.7 N 37.6 E); the spatial resolution of derived cloud cover characteristics was equal to approximately 3.5 km. Moscow was chosen as a test site for the investigation of the urban e!ect on the cloud cover because of the following considerations. First, it is one of the world's biggest cities with a population exceeding 10 million people and a built-up area covering more than 1500 km. There are over 1600 industrial enterprises in Moscow including several big electric power plants, oil re"nery, iron and steel works, chemical and timber processing industries. Obviously such a huge urbanized and industrialized area strongly in#uences the environment, and its possible e!ect on the cloud cover should be much more pronounced than the one of smaller cities. Second, Moscow lies on an almost #at terrain in the central part of the European Russian Plane with no big bodies of water in the vicinity of the city. Thus, the impact of the other local factors, besides urban ones, on the air #ow and on the cloud cover over the city and its nearest surroundings city is expected to be minimal. Third, the city built-up area has a nearly circular shape with a well de"nedboundary. The latter factors could facilitate the analysis of the spatial distribution of the cloud cover character-
istics and could help to trace the urban-induced modi"cation of cloudiness.
2. Data and method The initial set of satellite data consisted of daytime local area coverage (LAC) images of the AVHRR channels 1}5 covering the Central European part of Russia. The data were collected during the three-year time period between April 1993 and March 1996. Only afternoon passes of NOAA-11 (from April 1993 to September 1994) and NOAA-14 (from February 1995 to March 1996) were considered. All the received images were transformed onto a regular 1.5' longitude and 1' latitude grid and geometrically corrected using ground control points, with the resulting accuracy of the images navigation of $1.5 km. The AVHRR measurements were calibrated in equivalent albedo units for channels 1 and 2 (denoted further by A and A , correspondingly) and in brightness temperature for channels 3, 4 and 5 (¹ , ¹ , and ¹ ). In addition, channel 3 albedo (A ) was estimated from ¹ and ¹ following Stowe et al. (1991). It should be noted that due to several reasons we did not succeed in the accumulation of a complete set of daily satellite images over the study period. No data were collected during October 1994 } January 1995, because of the failure of NOAA-13. Data of December 1993 } January 1994 and partially of November 1993 were omitted from consideration. During this period satellite observations from NOAA-11 were made mostly at low sun elevation angles (less than 103) or after the sunset which made the AVHRR visible and near-infrared measurements unusable for the accurate determination of the cloud cover parameters. In general, due to technical problems with the high resolution picture transmission (HRPT) receiving station operated by the Hydrometeorological Centre (HMC) of Russia in Moscow, the satellite data acquisition was not performed on a daily basis. Moreover, approximately one out of 20 orbits acquired had bad scan time or date information and thus was unsuitable for further processing. In order to diminish possible e!ects of the decreasing spatial resolution of the AVHRR measurements at the edges of the instrument scan on the accuracy of the cloud cover parameters retrieval we only used those satellite overpasses where the study area was observed by the satellite sensor with a viewing angle of 453 and less. The total number of successfully processed satellite orbits amounted to 412 in 30 months of observations, thus making an average of 13}14 orbits per month. Due to the satellite orbital drift and because of the switch from NOAA-11 to NOAA-14 the time of observations varied throughout the study period. The nominal time of the NOAA-11 afternoon passes over Moscow in 1993}1994 changed gradually from about 1600 to 1715
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local standard time (LST). During February 1995 } March 1996 the nominal time of the NOAA-14 overpasses was around 1400 LST, whereas the orbital drift of this satellite during that period resulted in only a 15 min shift to a later time. The procedure used to derive cloud cover distribution statistics included three stages: (1) cloud detection, (2) estimation of cloud cover parameters in every individual AVHRR image, and (3) accumulation and subsequent averaging of the results on a monthly basis. Cloud detection in AVHRR images was performed on a pixel-bypixel basis using a threshold algorithm. The algorithm included sequential threshold testing and employed spectral features derived from AVHRR measurements in channels 1, 3, 4 and 5. Three tests were applied: infrared test, employing channel 4 temperature measurements, split window temperature di!erence test (¹ !¹ ) and visible/infrared re#ectance test based on channels 1 and 3 albedo data. Thresholding for AVHRR channel 4 measurements was used to detect low temperature pixels corresponding to medium- and high-level clouds. The threshold value for ¹ was determined using the air temperature estimate at 850 mb level. Thresholds for the temperature di!erence ¹ !¹ to detect low-level cloudiness were established through radiative transfer modelling with LOWTRAN-7 code (Kneizys et al., 1988) and using appropriate temperature and humidity vertical pro"les. The information on the vertical structure of atmosphere used both in the "rst and the second tests was inferred from the nearest in time actual analysis data produced at the HMC of Russia. Two-dimensional thresholds employing A and A were used at the third step to separate the cloudy pixels remaining undetected after the "rst two tests were applied. Three di!erent sets of these thresholds were established to process AVHRR data obtained over di!erent land surface cover types, i.e. snow covered, snow free and partially snow covered land surfaces. The "rst and the second sets were used correspondingly for satellite measurements taken from December to March and from May to October, while the third set was applied during November and April. All the threshold values for the visible}infrared re#ectance test were determined and adjusted by way of a visual imagery analysis and by comparison of the AVHRR-based cloud amounts derived at the following stage of the processing procedure with the satellite-synchronous ground-based meteorological observations. The surface observations data in the form of synoptic weather reports containing information on the cloud cover were obtained from the HMC of Russia. Cloud amount estimates were produced for each grid point of the AVHRR image through straightforward calculation of cloudy and clear pixels within cells of 6 latitude and 9 longitude (or approximately 11;10 km). The size of a cell was selected to achieve a better "t of spatial scales of satellite-based cloud amount estimates
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and of surface cloud cover observations and thus to have possibilities for a quantitative comparison of these data. Data on the cloud cover distribution derived from all acquired AVHRR images within a month period were used to produce monthly statistics on cloud cover characteristics. Accumulation of data was performed within 2 latitude and 3 longitude grid boxes covering a square with approximately 80 km side centered on Moscow. Estimates of cloud amount were produced in tenths (1 tenth corresponds to 10% of the full cloud cover) and the frequency of occurrence of cloudless scenes was determined in percent.
3. Comparison of satellite-derived cloud amount with surface observations The accuracy of satellite-derived cloud amount estimates was tested using satellite-synchronous groundbased cloud cover observations taken at six meteorological stations located within the study area (see Fig. 1). Four of six stations (WMO numbers 27514, 27527, 27524 and 27613) are positioned in the rural area whereas the other two (WMO 27612 and the Moscow State University meteorological observatory) are located within the limits of the built-up area. The maximum time gap allowed between surface and satellite observations compared was equal to 30 min. Ground-based total fractional cloud cover data originally reported in oktas were
Fig. 1. The study area and the location of the meteorological stations within the Moscow area and its nearest surroundings. MU stands for the Moscow State University meteorological observatory. Thin lines show major highways, thick line contours the urbanized area.
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converted into tenths (1 okta is equal to 1.25 tenths or 12.5% of the full cloud cover). Satellite estimates of the total fractional cloud cover were obtained in a way described above for the squares of 11;10 km centred on each of the meteorological stations. The total statistics used in the study amounted to 2080 coincident satellite}surface observations collected during 1993}1996. The results of a comparison of satellite- and groundbased measurements of the instantaneous cloud amount presented in Fig. 2 show that in approximately 73% of all cases the discrepancy does not exceed 2 tenths. Since the accuracy of the surface observations of the total cloud amount comprises around 2.2 tenths (see appendix), the correspondence of estimates within 2 tenths may be considered as acceptable. In 90% of all cases the di!erence in satellite and surface cloud amount remains less than 5 tenths. Severe errors of cloud detection in satellite images resulting in underestimation or overestimation of the cloud amount of over 5 tenths occurred in approximately 4% of all cases considered. The major part of the latter errors originates from a di$culty in detecting semitransparent cirrus clouds in AVHRR images. If only low- and middle-level cloudiness are observed the correspondence between satellite- and ground- based data on the total fractional cloud cover improves (see Fig. 2). In this case around 79% of all the coincident ground-based and satellite observations provide estimates of the cloud amount with less than 2 tenths di!erence. The standard deviation between satellite and surface estimates of the total cloud amount comprised 1.97 tenths for all cases considered and 1.67 tenths for the cases when no cirrus clouds were observed. Fig. 3 illustrates the correspondence of time-averaged satellite and surface estimates of the total fractional cloud cover. Average cloud amount values for each of the sites were calculated using 285 cases when satellite and corresponding surface observations at all six stations were available at the same time. It is seen that the satellite}surface di!erence in cloud amount ranges from 0.1 to 0.5 tenths for di!erent sites, i.e. it is approximately of the same magnitude as the uncertainty in the monthly and yearly averaged ground-based estimate of the cloud amount (see Appendix A). As a whole satellite estimates of the cloud amount have a slight negative bias of 0.15 tenths relative to surface observations. There is another important inference that can be made from the results presented in Fig. 3. Since the considered meteorological stations are located not far apart (more precisely, within a circle of 35 km radius) there should be observed a close correspondence between estimated average cloud amount values for these sites. It is seen that both satellite and surface samples of cloud amount means exhibit approximately the same scatter with 0.4}0.45 di!erence between minimum and maximum values. However, if we assume that urban factors have a pronounced e!ect on the cloud cover characteristics
Fig. 2. Correspondence between satellite- and ground-based estimates of the instantaneous cloud amount.
Fig. 3. Average total fractional cloud cover estimated for a sample of coincident satellite measurements and ground-based observations. Sample size: 285 synchronous satellite and surface observations for each station included. (*) MU is the Moscow University Observatory.
and consider rural and urban sites separately, a better agreement between satellite-derived mean values within these sub-samples becomes obvious. The scatter in satellite mean cloud amount estimates does not exceed 0.2 tenths for the four rural stations and is less than 0.1 tenth for the two urban sites, whereas the corresponding values for the surface sample are 0.35 and 0.2 tenths. The better site-to-site correspondence of satellite-averaged cloud amount estimates is indicative of a better consistency of these data in terms of the derived spatial distribution of the cloud cover characteristics. Summarizing the results presented in this section and in the appendix it can be concluded that the accuracy of satellite retrievals of the cloud amount is comparable with the accuracy of surface observations with respect to the instantaneous and time-averaged characteristics. It means, in particular, that for an arbitrary selected site the errors of 2 and 0.5 tenths should be expected respectively
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in satellite estimates of an instantaneous cloud amount and of a cloud amount value averaged on a monthly basis. A better spatial consistency of satellite cloud cover retrievals than that of the ground-based observations suggests that local inhomogeneities in the cloud amount spatial distribution of even lesser magnitudes can be e!ectively detected. However, a more precise quantitative estimate of satellite cloud cover characteristics retrieval errors is hardly possible.
4. Results and discussion Fig. 4 illustrates a seasonal dependence of the di!erence in the total fractional cloud cover and in the frequency of clear-sky observations over the centre of the urbanized area (within the 4 km radius) compared to the rural areas located within radii of 48 and 52 km away from the centre. As seen from Fig. 4a, year-to-year variations of urban}rural di!erence in monthly averaged cloud amounts for the same months are substantial. However, the average plot clearly indicates a considerable increase of the cloud cover fraction over the centre of the city relative to the suburb during the warm period, from March to October. The average di!erence in the monthly averaged total fractional cloud cover for this period comprises 0.76 tenth, with maximum values reaching 0.8}1.3 tenths observed in spring (from March to May). During the cold season, the urban}rural contrast noticeably decreases and even becomes negative in November and December. However, it should be noted that for these months the statistics available are the least extensive. Student's t-test for the di!erence in the means was applied to verify whether the observed urban}rural di!erence in the cloud amount value is statistically significant for di!erent seasons. The null hypothesis was that the di!erence was equal to zero. The testing showed that the detected positive di!erence during spring and summer seasons (from March to May and from June to August, correspondingly) was signi"cant at the 99% con"dence level. The di!erence for autumn and winter seasons (from September to November and from December to February, correspondingly) tested at the 95% con"dence level was found to be insigni"cant. The scatter in estimates of monthly mean di!erence in frequency of occurrence of cloudless scenes for di!erent years is also high (see Fig. 4b), yet mean values averaged over all three years of observations do not present distinct seasonal variations. The probability of observing clear sky in the centre of Moscow is on average 5.4% less than that in the nearby rural areas. The detected negative urban}rural di!erence in the clear-sky frequency proved to be statistically signi"cant at the 99% con"dence level. The urban}rural cloud cover di!erence is one of the parameters re#ecting urban in#uence on cloudiness. To
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investigate urban e!ects in more detail, spatial distributions of the cloud cover over the study area have to be involved. In the paper, we analysed data for the two most interesting cases, namely for winter and spring seasons, presenting correspondingly the highest and the lowest urban}rural cloud cover di!erences. Fig. 5 illustrates the spatial distribution of the average cloud amount over the Moscow region for the two selected seasons. Results are given in terms of the di!erence between the current value and the minimum value over the study area. Since the major factor causing the daytime increase of the total cloud amount over the city area during warm seasons is the enhanced convection, the most pronounced e!ect should be expected over the centre of the city which has usually the highest surface temperature as compared to the nearby areas. This hypothesis is supported by the results presented in Fig. 5a, which shows however that the excessive cloudiness covers not only the centre of the city but almost the whole built-up area (shown with a thick contour on the picture) and the nearest surroundings up to about 40 km away from the city centre. The shift of the excessive cloud cover zone to the southeast relative to the urbanized area most probably is the consequence of advection for the predominant wind directions in this region are northerly, northwesterly and westerly. The chart for the winter season (Fig. 5b) at "rst glance gives no clear indication of the urban e!ect on the average cloud amount distribution during the cold period. However, there are areas in the surroundings of Moscow where the average cloud amount is several percent higher than in the city. The region with the highest cloud amount is located to the northwest of the city, i.e. in the direction corresponding on average to the up-wind one. The other such region is observed approximately to the southeast of the city. The substantial changes in the cloud cover appearing just along the predominant wind direction suggest that this may be a result of the urban in#uence. Yet the data available are not quite su$cient to ultimately justify the correlation detected and a more detailed study is needed. The spatial distribution of the di!erence in clear-sky frequency in spring (Fig. 6a) presents features similar to the ones of the distribution of the total fractional cloud cover di!erence for the same period. Cloudless sky in the centre of the city is observed 8}10% less often than to the north and approximately 6% less often than to the south of Moscow. In the winter season the region with lower clear-sky frequency appears in the southeast part of the city. Since east and southeast parts of Moscow are most industrialized, it is reasonable to suggest that this decrease is due to the industrial activity. The particular sources of the excessive cloudiness can be moisture-laden plumes from cooling towers of large industries and power plants (Hanna, 1977; Guan and Reuter, 1996). However, the detected reduction in clear-sky frequency is rather
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Fig. 4. Di!erence in the total fractional cloud cover (a) and in the clear-sky frequency (b) over the Moscow centre (within the 4 km radius) and the city suburb (48}52 km). Bars present the total number of NOAA passes processed for each month.
small (2}6%) and the area with lower values does not sharply stand out against the whole picture. The results of satellite monitoring of cloudiness over Moscow and adjacent areas presented above reveal in general a well-pronounced in#uence of a big city on the cloud cover distribution which can be traced up to at least 20 km away from the city limits. Stimulation of convection by the urban heat island along with the injection of additional moisture and aerosol particles from industrial sources into the atmosphere must be considered as the main processes responsible for the positive urban}rural di!erence in the total fractional cloud cover
as well as for the lower frequency of occurrence of cloudless scenes observed over the city during the warm season. Apparently, these are the e!ects which also cause an enhancement of convective precipitation over and downwind of the urbanized areas (Changnon et al., 1991; Landsberg, 1981). Considering the peculiarities of the observed cloud cover modi"cations in the winter season, one should remember that during this period stratiform clouds prevail in Moscow region, the atmosphere is usually stable and advection is the major mechanism determining the cloud cover spatial distribution. On the other hand, the
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Fig. 5. Spatial distribution of the average fractional cloud cover, in tenths, (a) for the spring season (March } May) and (b) for the winter season (December } February). Results are presented in terms of di!erence between the current value (N) and the minimum value for the study area (N ).
Fig. 6. Spatial distribution of the clear-sky frequency (in percents) (a) for the spring season (March } May) and (b) for the winter season (December } February). Results are presented in terms of di!erence between the current value (F ) and the maximum value for the study area (F ).
excessive heat release over urbanized areas is responsible for a considerable air temperature increase which under certain conditions can extend for up to the height of several hundred meters (Oke, 1987). The temperature rise may cause the uplift of the level of condensation which in turn leads to the increase of the cloud base height. As far as the height of the top of stratiform clouds is controlled mainly by large-scale atmospheric processes and hardly varies signi"cantly over the city area, the in#uence of the urban heat island may result in a reduced cloud thickness and eventually in a reduced total fractional cloud cover. Obviously the process described above can in#uence the stratiform cloud cover distribution throughout the year, however its e!ect should be more pronounced during the cold season since winter stratiform clouds have on the average lower cloud bases and lesser thickness than summer stratiform clouds. This latter conclusion is supported by the observed winter cloud cover distribution over Moscow (Fig. 5b) which presents an indication of the reduced total cloud amount over the urbanized area.
In general, the development of speci"c meteorological conditions is necessary for the urban e!ect on cloudiness could manifest itself against a background of large-scale atmospheric processes. Only a weak air #ow makes possible a considerable transformation of a local environmental airmass over the urbanized territory. The urban heat island contribution to the vertical motions can provide a noticeable increase of the cloud amount only if the local airmass is stable or convection is shallow. The foregoing implies that the urban}rural di!erence in cloud cover is a strongly weather-dependent feature and that the long-term average e!ect of a big city on cloudiness is determined to a great extent by the weather-type statistics in the particular area. This means also that the variations in the frequency of occurrence of di!erent weather situations should be considered as one of the major reasons for the observed year-to-year variability in the monthly averaged urban}rural di!erence in the total cloud amount and clear-sky frequency.
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One of the major limitations of the present research stems from the fact that satellite measurements have not been collected on a daily basis and thus the sample of the cloud cover observations used in the analysis was not complete. On average only 13}14 daily observations per month were utilized. Therefore, the results obtained in general can be treated only as having statistical but not climatological meaning. However, it is known that atmospheric motions and corresponding cloud cover patterns and weather types in midlatitudes are correlated over scales of 500}2000 km and 2}3 d. It follows, that within a month only about 10}15 observations of the cloud cover and weather type at each particular location are independent (Rossow et al., 1993). Since the particular urban e!ect on the cloud cover is to a great extent determined by the weather conditions, the feature similar to the one of the cloud cover observation time series mentioned above should be also peculiar to the time series of the urban}rural cloud cover di!erence. This suggests that even by using a limited monthly sample of 13}14 daily cloud cover observations we might obtain reliable quantitative estimates of the monthly mean urban}rural di!erence in the total cloud amount and clear-sky frequency. The other shortcoming of the study is that the presented results refer to one particular period of the day, from 1400 to 1700 local time. The daily variations of the atmosphere boundary layer, the changes in the urban}rural di!erence in temperature and cooling/ heating rates could a!ect the cloud cover distribution pattern and change the observed urban}rural di!erence in cloud cover characteristics. It is expected that at night the decreased advection, weakened convection and the enhanced urban}rural temperature contrast would emphasize an urban-induced transformation of the cloud cover over a big city. In the morning, when the temperature distribution is smoothed out, the urban e!ect on cloudiness should be minimal. These empirical conclusions, however, require experimental quantitative con"rmation.
5. Conclusions Three-year time series of AVHRR/NOAA daytime data were applied to study peculiarities of the local area cloud cover distribution and to quantitatively assess the urban e!ect on cloudiness. The results of satellite soundings over Moscow and adjacent areas revealed a well-pronounced in#uence of a big city on cloud cover statistical characteristics which manifested itself in E up to 1.3 tenth (or 13%) increase in the averaged total fractional cloud cover over urbanized areas as compared to rural ones during the warm period and a small decrease in winter;
E 5.4% average decrease in the frequency of occurrence of cloudless scenes over the built-up area. No distinct seasonal variation was observed. The spring and summer increase of the total cloud amount and the decrease of the clear-sky frequency over the built-up city area as compared to the nearby rural areas were found to be statistically signi"cant at 99% con"dence level. It was shown that during the warm season the excessive cloudiness is observed not only over the city centre but it covers the whole built-up area and extends to at least 20 km over the boundaries of the city along the predominant directions of wind, i.e., to the east, southeast and south of Moscow. Only slight evidence of the direct in#uence of the industrial activity on the cloud cover characteristics was found.
Acknowledgements The author would like to thank Drs. D. Tarpley and G. Gutman of NOAA/NESDIS and also anonymous reviewers for critical reading of the manuscript and valuable comments. The manuscript has been revised while the author held postdoctoral position at Centre d'Applications et de Recherches en TeH leH deH tection (CARTEL), UniversiteH de Sherbrooke, Sherbrooke, QueH bec, J1K 2R1, Canada.
Appendix A. Accuracy of ground-based observations of the total cloud amount Uncertainty in the total fractional cloud cover data reported from the ground-based meteorological stations is an important factor that should be accounted for when using these data to validate satellite retrievals of the total cloud amount. Since no direct instrumental measurements of the cloud amount are available, the most obvious approach to evaluate the accuracy of surface observations consists in the comparison of synchronous cloud cover observations made at sites located at close proximity to each other. The revealed discrepancy between cloud amount estimates made at neighbouring stations may be treated as an error typical to this type of measurements. In this study we used the data set of Hahn et al. (1996) containing the routine cloud cover observation data reported from ground-based meteorological stations over the globe. To investigate the consistency in the total cloud amount estimates we extracted and examined synchronous measurements taken at paired stations located at less than 100 km apart from each other. Only day time observations (900}1500 UTC) collected over Europe (within 5}603E and 20}603N) in 1991 were considered.
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Fig. 7. Scatter of the root mean square di!erence in estimates of the instantaneous total cloud amount reported from paired meteorological stations vs. distance between stations. Daytime observations (9}15 UTC) made over Europe in 1991 were considered. Bold line shows the average root mean square di!erence for the sites located at less than 15 km apart.
Fig. 8. Scatter of the root mean square di!erence in monthly mean values of the total fractional cloud cover for paired ground-based meteorological stations vs. distance between stations. Observations made during 1991 at 1200 UTC over Europe were considered. Bold line shows the average root mean square di!erence for the sites located at less than 15 km apart.
Fig. 7 presents the scatter in the root mean square di!erence in instantaneous cloud amounts reported from paired meteorological stations. Each value was obtained using from 1000 to 1500 synchronous observations taken during 1991. The graph clearly shows that the discrepancies between the estimates of the instantaneous cloud amount tend to decrease with the decrease of the distance between the stations. However, a lower limit of the deviation, with only few exceptions, comprises 1 okta, which is equal to 1.25 tenths or 12.5% of the full cloud cover. The analysis of synchronous observations made at most closely spaced pairs of sites, located at less than 15 km apart, showed that the average root mean square di!erence in the reported cloud amount values comprises 1.75 oktas or 2.2 tenths. Apparently, this di!erence is determined predominantly by the subjectiveness of individual estimates of the cloud amount, since, "rst cloud amounts vary on much larger space scales of at least several hundred kilometers (Rossow, 1993) and, second, the observers being at a distance of 15 km in most of the cases estimate the state of a cloud cover using much the same portion of the sky dome. Though there exist no obvious reasons for large systematic errors in the visual estimates of the cloud amount, the absolute bias in yearly averaged cloud amount estimates for the stations located within 15 km and less ranged within 0}0.5 okta (0}0.6 tenths) with a mean value of 0.25 okta (0.3 tenths). Fig. 8 illustrates the scatter in the root mean square di!erence in monthly averaged values of the total frac-
tional cloud cover reported from paired stations during 12 months of the year of 1991. Only observations taken at 12 UTC were considered. Comparing Figs. 7 and 8 we see that the averaging of cloud cover observations on a monthly basis results in a better "t of cloud amount estimates at neighbouring sites. The consistency in estimated cloud amount monthly mean values does not noticeably improve with a decrease of the distance between stations and even for sites located within 15 km apart, the root mean square di!erence comprises 0.45 oktas or 0.56 tenths. The discrepancies in the estimates of the instantaneous and monthly mean cloud cover parameters can be treated as the errors typical to the surface observations of the cloud cover. The error of the instantaneous cloud amount estimate is 2.2 tenths, whereas for the monthly and yearly average values it decreases to approximately 0.56 and 0.25 tenths, correspondingly. It should be noted that the results obtained are based on the analysis of the visual synoptic observations made over the territory of Europe, where the ground-based meteorological network is one of the densest and the data are most likely to be of high quality. Therefore, the di!erence in the total cloud amount reported from the stations located at a very close proximity can be considered as an indicator of an uncertainty in the data on the cloud cover typical to surface observations. The presented results show that only limited possibilities exist to control the validity of the satellite-derived cloud amount with surface observation data.
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In particular, the correspondence of the estimates of the instantaneous cloud amount within 2 tenths as well as the correspondence of the cloud amount averaged on a monthly or yearly basis within 0.2}0.6 tenths should be considered as a perfect "t.
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