A method to generate surface IN radiation maps over Europe using GOME, Meteosat, and ancillary geophysical data

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 105, NO. D4, PAGES , FEBRUARY 27, 2000 A method to generate surface IN radiation maps over Europe using GOME, Meteosat, and ancillary geophysical data Jean Verdebout European Commission, Joint Research Centre, Space Applications Institute, Ispra, Italy Abstract. This paper presents a method for generating surface ultraviolet (UV) radiation maps over Europe, with a spatial resolution of 0.05 ø, and potentially on a half-hour basis. The UV irradiance is obtained by interpolation in a look-up table (LUT), the entries of which are solar zenith angle, total column ozone amount, cloud liquid water thickness, near-surface horizontal visibility, surface elevation, and UV albedo. Both satellite (Meteosat, GOME) and nonsatellite (synoptic observations, meteorological model results, digital elevation model) data are exploited to assign values to the influencing factors. With the help of another LUT simulating the visible signal, Meteosat data are processed to retrieve the cloud liquid water thickness. The radiative transfer calculations are performed with the UVspec code. A preliminary step consists in generating an effective surface Meteosat albedo map from a series of t0 consecutive days. In this process the well-known difficulty of distinguishing clouds from snow-covered surfaces is encountered. An attempt is made to partially resolve the ambiguity by using the Meteosat infrared channel and modeled snow cover data. After additional empirical cloud filtering, the effective albedo map is used as a baseline to estimate the cloud liquid water thickness. The UV surface albedo is assigned uniform values for land and sea/ocean, except in the presence of snow. In this case it is given a value proportional to the Meteosat effective albedo. The total column ozone is extracted from the level 3 GOME products. The aerosol optical thickness is mapped by gridding the daily measurements performed by ---too0 ground stations. The digital elevation model is the GTOPO30 data set from the U.S. Geological Survey. European wide UV dose rate maps are presented for one day in April 1997, and the influence of the various factors is illustrated. A daily integrated dose map was also generated using 27 Meteosat acquisitions at half-hour intervals on the same day. The dose map produced in this way takes into accounthe evolution of the cloud field and is thoughto be more accurate than if it were estimated from one data take, in particular at the relatively high spatial resolution of the product. Finally, a preliminary comparison of modeled dose rate and daily dose with measurements performed with a ground instrument is discussed. 1. Introduction The increase in the intensity of the UV radiation reaching the Earth surface is one of the most important direct consequences of ozone depletion. Exposure to higher levels of UV radiation is potentially harmful to human health (skin cancer, cataract, immunodepression) and could induce modifications in natural systems. It is therefore important to monitor the changes that occur in this environmental parameter. Not directly linked with ozone depletion, there is also increasing awareness that exposure to solar radiation plays a role in the risk of contracting some diseases or simply on skin ageing. The reader interested in UV impacts is referred to Tevini [1993], where he will find a comprehensive discussion of these effects. The development presented here aims at providing geographically and temporally extended information on the surface UV radiation to support environmental impact assessments and epidemiological studies. Surface UV is classically measured with ground spectroradiometers. However, these are and will remain too few to provide a synoptic view. Modeling and the exploitation of Copyright 2000 by the American Geophysical Union. Paper number 1999JD /00/1999JD information derived from satellite data can help in this respect. At global scale, this need for extended information drove the development of the Total Ozone Monitoring Spectrometer (TOMS) UV climatology [Eck et al., 1995; Herman et al., 1996], which is now being extended with the Global Ozone Monitoring Experiment (GOME) (onboard the ERS-2 satellite of the European Space Agency) [Peeters et al., 1998]. Some applications such as impact studies on the environment or skin cancer risk change studies [Slaper et al., 1998] would benefit from information at higher spatial resolution. UV irradiance values in high-resolution maps are also more easily compared with the ground measurements, which will remain the reference in terms of accuracy. The maps can then in turn be used to assess the accuracy of the global products. An example of such a development is the modeling of surface UV at the 1 km spatial resolution of the advanced very high resolution radiometer (AVHRR) [Meerk6tter et al., 1997]. The method presented here uses Meteosat data to estimate the attenuation of the radiation by clouds. Meteosat is the generic name of the geostationary meteorological satellites operated by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). They have been operational since 1978 and the present satellite is the seventh in the series; the Meteosat visible and infrared imager (MVIRI) designates the main optical sensor on these platforms. All

2 5050 VERDEBOUT: SURFACE UV MAPS FROM GOME AND METEOSAT TOTAL COLUMN OZONE VISIBILITY DIGITAL ELEVATION HIRLAM MVIRI I METEOSAT (GOME) OBSERVATIONS MODEL SNOW COVER DATA (VlS& IR) TRIANGULATION >OLATION ON OUTPUT GRID LAND/SEA [ GEOMETRIC MASK CALCULATIONS (ANGLES) METEOSAT DATA PROCESSING EFFECTIVE SURFACE METEOSAT ALBEDO CLOUD LIQUID WATER THICKNESS INTERPOLATION IN THE UV LOOK UP TABLE ASSIGNMENT OF UV ALBEDO VALUE SURFACE RADIATION UV MAPS Figure 1. Overall structure of the algorithm to produce surface UV radiation maps. MVIRI instruments have a broad visible channel ( nm) and a thermal infrared channel centered at 11.5/xm; the most recent ones also have a channel in the absorption band of water vapor at 6.4 /xm(not used in this work). The signal is coded on 8 bits (digital count ranging from 0 to 255). The data can be received in near real time from the satellite itself or ordered from the Meteorological Archive and Retrieval Facility (MARF). The images are acquired every half hour and identified by a slot number from 1 to 48. Acquisition of slot 1 begins at 0000 UTC and takes 25 min to complete. The acquisition times in the center of the UV maps presented here are -22 and 52 min past the hour for odd- and even-numbered slots, respectively. The orbital platform is positioned at 0 ø latitude, 0 ø longitude. The instantaneous field of view results in a spatial resolution 2.5 x 2.5 km and 5 x 5 km at the subsatellite point, for the visible and infrared images. The pixel size increases with viewing zenith angle and its shape is also distorted. Over the central part of western Europe the Meteosat pixel in the visible image is approximately 6 x 6 km large. The Meteosat spatial resolution is coarser than that of AVHRR, but the high temporal acquisition frequency allows to documenthe evolution of the cloud field during the day and to better estimate daily integrated doses. For this reason, Meteosat-derived UV maps are complementary to both the global and the higher-resolution products. The method also takes into account as many influencing factors as possible by exploiting other satellite (GOME) and nonsatellite data (ground observations of visibility, data sets generated with meteorological models, digital elevation model). The present algorithm should be considered as a first version that can certainly be improved, following the assessment of the accuracy and as new sources of information on the influencing factors become available. In this respect, the UV processing scheme is not fundamentally bound to any particular sensor or data set as the UV maps are ultimately obtained by interpolating in a look-up table, the entries of which are physical parameters. These are solar zenith angle, total column ozone amount, cloud liquid water thickness, near-surface horizontal visibility, surface elevation, and albedo. Most of the processing actually consists in estimating the values of these parameters in each pixel of the desired UV map. This work focuses on western Europe. The maps cover an area extending from 34 ø to 74 ø north and from 12 ø west to 32 ø east. A regular grid in latitude and longitude with a 0.05 ø resolution was chosen. It roughly corresponds to the typical resolution of the Meteosat images over Europe. 2. Radiative Transfer Model and Its Use for Generating UV Radiation Maps The look-up tables (LUTs)were generated with the UVspec code [Mayer et al., 1997], which is part of the freely available libradtran (version 13) software package (ftp.geophysikk. uio.no,/pub/outgoing/arvkey). This program offers several options for somng the radiative transfer equations; the well-known DISORT routine [Stamnes et al., 1988] was systematically used, in its classical plane parallel version and with six streams. Two look-up tables were built, one containing ultraviolet dose rates at the surface and the other simulating the visible band radiance at the input of the Meteosat/MVIRI sensor. The entries of these tables are a selection of the UVspec parameters de- scribing the radiative characteristics of the atmosphere and the Earth surface. All calculations have been performed in 0.5 nm steps, before integrating on wavelength UV Look-Up Table The retained entries for the ultraviolet LUT are solar zenith angle, total column ozone amount, cloud liquid water thickness (CLWT), horizontal visibility at the surface, LambertJan equivalent surface albedo in the ultraviolet, and surface elevation. These parameters were chosen because they significantly influence the surface UV radiation intensity and/or because it is possible to assign them geographically and temporally resolved values. Figure 1 shows the overall scheme for producing UV maps using this LUT. Solar zenith angle, horizontal visibility, and LambertJan equivalent surface albedo are explicit input parameters of the

3 VERDEBOUT: SURFACE UV MAPS FROM GOME AND METEOSAT 5051 radiative transfer code. The visibility parameter modulates the tropospheric aerosol optical thickness (the code equates the extinction coefficient at 550 nm to -3 times the inverse of the visibility expressed in kilometers). A background standard aerosol model is used [Shettle, 1989]; the method is therefore unable to take into account the variability in the spectral radiative properties of different aerosol species or in vertical aerosol profiles. Changing the total column ozone amount and/or surface elevation requires modifying the atmospheric profiles. A standard atmosphere (U.S. standard) is used as the basis [Anderson et al., 1986]. The profiles are truncated in their lower part, up to the surface altitude. The resulting total column ozone amount is then computed and set to the desired value by multiplying the ozone concentration by the adequate scaling factor. This procedure obviously neglects the variability in the ozone profile shape. UVspec uses four parameters to describe low clouds: thickness, altitude, effective water droplet radius, and density. As the cloud thickness information is later derived solely from the MVIRI visible band image, the cloud description must be reduced to a single variable parameter. Fixed values are chosen for the altitude, thickness, and droplet radius (1 km, i km and 7 /am). The density is left variable, which then determines the cloud liquid water thickness. The value for the effective drop radius is a climatological value adapted to clouds over land [Rogers et al., 1989]. The LUT does not explicitly contain the Sun-Earth distance correction, which is applied during the processing and according to the day. Linear scales are chosen for the solar zenith angle (up to 78 ø) and the Lambertian equivalent surface albedo. For high solar zenith angles a spherical version of DISORT could be used, but because the main purpose of the UV maps is to represent daily, monthly, and/or annual doses, a good precision in the high solar zenith angle case is not crucial. The other portionality factor necessary to convert the radiance in MVIRI digital counts is determined by empirical calibration on the thickest clouds identified in the images. For practical reasons, the Meteosat LUT does not yet take into account some atmospheric constituents (ozone, water vapor, other gases). These are admittedly drastic simplifications. However, the LUT is used only to compare the cloudy and noncloudy signals. This should reduce the impact on the results as the absolute values are of second-order importance. 3. Retrieval of Values for the Cloud Liquid Water Path Although EUMETSAT corrects the images for the oscillations of the satellite around its nominal position, the Meteosat images (visible and infrared) are first coregistrated with the digital elevation model. For this purpose, land/sea masks in both the visible and the infrared Meteosat grids were generated from the original GTOPO30 digital elevation model (see section 4.4). A number of vertical (15) and horizontal (23) coastlines are then used to determine column and line shifts. When the area is cloud free, a land/sea boundary is clearly detectable in the Meteosat images. Because of the lower reflectivity of water, the signal in the visible band abruptly changes, typically from 50 to 20 digital counts. In the infrared channel, which is sensitive to the surface temperature, the amplitude and sign of the transition varies during the day (the sea is warmer than the land in the morning and inversely in the afternoon). As the control points are distributed in the images, enough cloud free land/sea boundaries are always identified. Checks of the results indicate that the coregistration is of subpixel quality on the average. April 1997 images (Meteosat 6 satellite) were actually found to be geometrically very stable, influencing parameters are discretised on a logarithmic scale to reduce the errors resulting from the interpolation. For each combination of the parameters, the UV LUT contains the values of the following dose rates: UV-B, UV-A, erythemal (Commission Internationale de l'eclairage (CIE87)/International Commission on Illumination) [MacKinlay and Diffey, 1987]), and SCUP-h (Skin Cancer Utrecht Philadelphiam humans) [de Gruijl and van der Leun, 1994]. The dose rate is intended here as the downwelling spectral irradiance (direct plus diffuse) integrated from 280 to 315 nm for UV-B, from 315 to 400 nm for UV-A, and from 280 to 400 nm weighted by the respective action spectra for erythemal and SCUP-h. and the coregistration step is almost superfluous in this case. The overall scheme for retrieving the cloud liquid water thickness is illustrated in Figure 2. Given the date and time slot, the first step is to reconstruct a map of effective surface albedo. To find a cloudless situation over each pixel, the algorithm uses a 10 day series of images, centered on the considered date and for the same time slot. For each pixel, the illumination and observation angles are computed. Using also the elevation data, the Meteosat LUT is reduced to two entries: effective surface albedo and cloud liquid water thickness. For low values of the surface albedo (vast majority of cases) the visible signal increases with cloud thickness. A first guess is therefore to assign the lowest observed signal to the cloudless 2.2. Meteosat Look-Up Table situation and to compute the corresponding surface albedo. This second LUT is used to retrieve a cloud liquid water thickness from the MVIRI/Meteosat images. This time, the top of the atmosphere radiance is computed as a function of solar zenith angle, viewing zenith angle, difference between the illumination and the viewing azimuth angles, cloud liquid water The Meteosat signal dependence on CLWT is then estimated from the LUT, for the retrieved effective albedo. If the signal is indeed found to increase monotonically with CLWT, the effective albedo value is accepted. However, when the surface is bright (e.g., when snow covered), the presence of clouds does thickness, effective surface albedo in the MVIRI visible band, not always increase the signal. Figure 3 shows the calculated and surface elevation. The three angles are explicit input pa- top of atmosphere radiance in the viewing direction of MVIRI, rameters of UVspec, and the other parameters are treated in as a function of CLWT and surface effective albedo. As the the same way as for the UV LUT. The MVIRI visible channel has a broad spectral band extending from 500 to 900 nm. It therefore takes quite a large CPU time to perform the calculations over the entire band. To keep the computing time reasonable, a narrower band centered on the maximal spectral surface effective albedo increases, the signal dependence on CLWT shows a minimum in the presence of a thin cloud. For effective albedo values close to 1, the function even becomes monotonically decreasing with CLWT. Intuitively, this behavior is attributed to the changing balance between phenomena sensitivity (650 nm) is used. The Sun-Earth distance correction with opposite effects. On one hand, the cloud backscatters a is not included in the LUT but applied during the processing. fraction of the photons before they enter the lowest part of the The LUT contains the top of atmosphere radiance. The pro- atmosphere where they have a higher probability of being

4 5052 VERDEBOUT: SURFACE UV MAPS FROM GOME AND METEOSAT TEN DAYS OF METEOSAT IMAGES (VIS & IR) FOR THE GIVEN TIME SLOT SNOW HIRLAM COVER LAND / SEA MASK I,I PROCESSING USING THE METEOSAT LOOK UP TABLE GEOMETRIC CALCULATIONS (ANGLES) DIGITAL ELEVATION MODEL M ETEOSAT IMAGES (VlS & IR) FOR THE GIVEN DAY AND TIME SLOT INTERPOLATION OUTPUT INTERPOLATION IN THE METEOSAT LOOK UP TABLE GRID ON FIRST GUESS OF THE EFFECTIVE METEOSAT SURFACE ALBEDO ADDITIONAL CLOUD EFFECTIVE METEOSAT CLOUD LIQUID WATER FILTERING SURFACE ALBEDO THICKNESS Figure 2. Overall scheme for estimating the cloud liquid water thickness from Meteosat Visible and Infrared Imager (MVIRI)/Meteosat data. absorbed by gases and the surface. On the other hand (1) the purpose, the pixels are divided in four categories depending on cloud enhances the absorption by the surface by backscattering whether they are land or sea/ocean pixels and on whether they toward it a fraction of the reflected photons, (2) the multiple are potentially snow/ice covered. The latter distinction is perscattering in the cloud increases the mean optical path in the formed using HIRLAM snow cover data. The HIRLAM lower atmosphere and thereby the gaseous absorption. In practice, when the surface albedo is high, the discrimination of clouds on the basis of the visible signal becomes ambiguous. (High-Resolution Limited Area Model) [Eerola, 1995; Ki llen, 1996] project provides six hourly maps of snow thickness with a spatial resolution of 0.4 ø. With this lower resolution the An attempt is made to partially resolve this ambiguity by using HIRLAM data do not allow to decide whether an individual the infrared channel. The infrared signal is primarily sensitive to the pixel brightness temperature. The temperature is norpixel is snow covered, but they permit to identify the areas where it may be the case. The histograms of effective surface mally higher at the surface than at the cloud top. A cloudless pixel therefore appears as "warm." In the case of partial cloud cover, the apparent brightness temperature is intermediate between that of the surface and of the cloud top. To summa- 1.0 rize, a high infrared signal is indicative of a cloudless or near- cloudless situation. When the discrimination on the basis of the visible signal is ambiguous, the day when the pixel is the warmest is chosen as a candidate to represent the cloudless case, and the corresponding effective albedo is retrieved. As before, the visible MVIRI signal dependence on the cloud liquid water thickness is then interpolated from the LUT. The solution is 0.6 ' 0.5 _ accepted only if the observed variability in the visible signal over the 10 days lies in the predicted range. In the opposite case, the next warmest day is evaluated, and so on. If no solution is found in this way, the pixel is flagged and the value 0.04 of the effective visible albedo is not assigned at this stage. Basing the choice on the highest infrared signal only leads to inconsistencies when estimating the cloud liquid water thickness. The variability of both the visible and the infrared signals during 10 consecutive days is illustrated in Figure 4. Three t pixels differing by the surface brightness are represented; as suming that a high infrared signal indicates low cloudiness, the Cloud Liquid Woter Thickness [cm] observed variability of the visible signal is consistent with the Figure 3. Top of atmosphere (TOA) radiance (at 650 nm) in radiative transfer calculations presented in Figure 3. the direction of the MVIRI sensor, as a function of the cloud Although for most pixels a cloudless or near-cloudlessituliquid water thickness and surface effective albedo (values ination can be identified within the 10 days, some areas are dicated next to the curves). The conditions are typical of Norpermanently cloud covered, in particular in the northern At- way on April 15 around 1200 UTC: the solar zenith angle is lantic. The effective albedo map is therefore further processed 58ø; the viewing angle is 65 ø, and the difference between solar to eliminate the resulting anomalously high values. For this and viewing azimuths is 180 ø.

5 . VERDEBOUT: SURFACE UV MAPS FROM GOME AND METEOSAT 5053 albedo for the four categories are then generated, a typical example is shown in Figure 5. The histograms have a peak usually centered between 0.2 and 0.3 (land) or between 0.05 and 0.1 (sea/ocean), with a trailing edge toward the higher values. For the potentially snow-/ice-covered pixels, a second maximum is observed above 0.7. There is unfortunately no easy way of deciding whether a high value is due to the surface or clouds. The following logic has been adopted: (1) the potentially snow-/ice-covered pixels are not filtered, (2) for snow/ice free pixels, a threshold is applied above which the value is rejected and substituted by the effective albedo found in the acceptable geographically nearest pixel of the same category. The latter operation is also performed on the pixels previously flagged as unresolved in the processing with the LUT. The thresholds have been chosen after a number of tries by visualizing the resulting albedo image. For land pixels the threshold is positioned at the peak value plus 1.5 times the full width at half maximum (FWHM) of the main peak. This places it around 0.4 and preserves the relative high effective albedo found in North. ffrica. For sea/ocean pixels it is positioned at the peak value plus the FWHM. Dynamic thresholds have := 80 o ro _ 60 or, 0,,, i, i i ' 20 - very bright surfoce : 80 o o.? 4.o O i i i i i i ' VlS... IR I _ bright surfoce ' VIS i i i i i i i ,,,, ß, i, i i dork surfoce O i i i! i.i Dote (April 1997) Figure 4. Typical behavior of the MVIRI visible and infrared signals for pixels with varying surface brightness; the day-today variations are due to the presence of clouds. When the surface is dark, the two signals vary in opposition; they vary in concordance when the surface is very bright; in the intermediate case there is no obvious correlation between the signs of the variation. This behavior is consistent with the radiative transfer calculations in the visible. i [ 80 ii I I :1ond õ so - ' o o.o 0.2 o.s o.s ' '1' ' ' ' ' ' ' ' ' ' ' ' ' ' ' I- t II potentiolly snow/ice o [ '"' oo,r,. 80 ' ii :land -. [ii /... :,.o,/oo.on-.> o 20 n,' :,..,\ 0,0 0,2 0,4 0,6 0,8 1,0 L rnbertion equivalent lbedo Figure 5. Histograms of the effective Meteosat surface albedo before the additional cloud filtering is applied. The histograms correspond to four categories of pixels; they have been normalized to be presented on the same scale. With respect to the total number of pixels, the snow/ice free pixels represent 25.5% (land) and 50% (sea/ocean); the potentially snow/ice covered pixels represent 23.7% (land) and 0.8% (sea/ocean). been preferred to fixed ones to allow for the observed variability of the effective albedo with changing illumination geometry when early morning or late afternoon slots are processed. This dependence is attributed to the non-lambertian character of the Earth natural surfaces. It should be mentioned that for land pixels, the fraction of rejected values is quite small (typically less than 2%); this fraction can reach 30% for sea/ocean pixels. In the latter case however, the high values are definitely due to clouds, and as the surface albedo is quite uniform, the substitution will not strongly impact the cloud thickness retrieval. Nevertheless, the filtering procedure described above is far from being satisfactory in principle. It could certainly be improved with the help of a climatology of the Meteosat visible effective albedo. Unfortunately, this climatology is not available and generating it implies processing a very large amount (several years) of data. The above procedure further assumes that the surface reflectivity is stable during the 10 day period. This should be reasonably true with respect to phenomena like plant growth but may not be respected in case of short-term meteorological events (e.g., snowfall). It should also be stressed that the el-.

6 ,,,,,. ß 5054 VERDEBOUT: SURFACE UV MAPS FROM GOME AND METEOSAT D 2D 3D.7Qi...' :... ;.:. ',. ß ß ß ß ß..;,..'...' % '.. ß ß..'-}. i " i ".' ' '.-:.-",-:' :.' '. 'i ß :...:.. :.: ß '.s r i-..:...::..'..½ M;.i:..:;...:5i.:::."..'.. -:..:.:/...,,'. :.'...'C,.. ::..::,.i...'; ½..:. :i %-2..'. i. ::.':.-.:. :::.... ß """ "" '.1... ' 'i,' '.-',.' ' "ß ' ' ' i.,. ß. ß o,... ß.. ß ;...'.:o,., I '. ß ' ß.' ß -." t ';I. ß % '- '".I I Figure 6. Location of the stations contributing to the meteorological database used to estimate the surface atmospheric horizontal visibility. fective albedo does not pretend in any way to represent the real surface albedo. It is only the value of the albedo parameter, in the modeling scheme, which reproduces the visible signal intensity for the given date and time slot. Finally, the cloud liquid water thickness for the selected day and slot is retrieved from the corresponding Meteosat image and the effective albedo map generated as described above. For each pixel the LUT is used to determine the value of CLWT that reproduces the Meteosat visible signal. For each pixel the visible signal dependence on CLWT is determined by successive interpolation on the elevation, solar zenith angle, viewing zenith angle, difference between the illumination and the viewing azimuths, and surface effective albedo. The value of the observed signal then determines the cloud liquid water thickness. However, as Figure 3 shows, the solution is not always unique. In this case, the "thin cloud solution" is retained if the infrared signal is above the median for the 10 day period, the "thick cloud solution" otherwise. 4. Assignment of Values to the Other Influencing Parameters and Operational Aspects 4.1. Ultraviolet Surface Reflectivity For surfaces not covered by ice or snow, the UV Lambertian equivalent reflectivity typically varies between (land) and (water). These ranges of value are supported both by ground and airborne measurements [Madronich, 1993] and by estimation from satellite observations [Herman and Celarier, 1997]. Several sensitivity studies have shown that for these low values of surface reflectivity, the influence on the surface downwelling UV intensity is very limited [Madronich, 1993; Krotkov et al., 1998]. The bright surfaces that make the exception are sand deserts and, more important for Europe, snow-covered areas. The UV effective reflectivity of a snowcovered surface depends on a number of factors such as grain size, purity, illumination angle, layer thickness. A range of values from 0.6 to close to 1 are reported in the literature [Warren, 1982; Feister and Grewe, 1995]. Furthermore, for mapping the UV radiation the effective reflectivity of an area corresponding to a map pixel is the significant parameter. While the reflectivity of pure clean snow may approach 1, partial snow cover, the presence of trees, and other similar factors will very often significantly reduce the effective pixel reflectivity. The detailed information necessary to correctly assign the value to the UV albedo is clearly not available at the chosen spatial resolution and scale. For generating the maps, a value of 0.06 is assigned to sea/ocean pixels and 0.03 to land pixels, provided they are identified as snow/ice free. A value equal to 70% of the effective Meteosat albedo is assigned to the potentially snow-/ice-covered pixels (as indicated by the HIRLAM data) and which have a Meteosat effective albedo above 0.4. The threshold was chosen by visual examination of the resulting UV albedo map. The rationale for proportionality between the albedos in the two spectral ranges is that partial snow cover should affect them in a similar way. The value of 70% was chosen to set the values of the UV albedo in the reported experimental range. It is intended to later optimize this factor, using the results of the validation by comparison with ground measurements Aerosol Optical Depth or Visibility Tropospheric aerosols are an important factor in determining the intensity of the surface UV radiation. They backscatter and absorb the radiation, leading to a diminution of the surface irradiance. Different types of aerosols vary considerably in their UV single-scattering albedo: between 0.9 and 1 for maritime aerosols and anthropogenic sulfate, down to 0.6 for urban aerosols and smoke [Krotkov et al., 1998]. Their efficiency in reducing the surface radiation varies accordingly. In any case, the aerosols should be taken into account for mapping UV radiation, in particular if the results are exploited for change studies. An increase in the aerosol load could indeed reduce the upward trend due to stratospheric ozone depletion [WMO, 1995]. However, historical and geographically resolved maps of aerosol load and optical thickness are not readily available. In most UV mapping processors, this constrains to assume a constant background aerosol characterization. One exception is the use of the TOMS UV aerosol index [Herman et al., 1997]. This data set is definitely pertinent for global studies but is not ideally adapted for high spatial resolution maps. To our knowledge, satellite-derived maps of the aerosol optical thickness over land, extending over the whole of Europe and with a spatial resolution of a few kilometers, do not exist. In the present version of the processor, an attempt is made to use the observations of horizontal visibility reported by ground meteorological stations. More precisely, the information is obtained from a database of meteorological observations compiled by the Monitoring Agriculture with Remote Sensing (MARS) project at the Joint Research Centre (JRC). This data set is made of measurements obtained from national meteorological services, either directly or via the Global Telecommunication System. The data are preprocessed to convert them in a standard format. The database spans a period from 1932 to present. The mean daytime visibility is used, expressed in kilometers. Figure 6 shows the location of the 3144 stations contributing to the database and positioned inside the area covered by the UV maps. On a given day, about a third of these report visibility values. The meteorological observations are interpolated on the UV map grid. This is performed in a

7 VERDEBOUT: SURFACE UV MAPS FROM GOME AND METEOSAT 5055 simple way by finding the Voronoi polygon of stations associated to each node of a 0.5 ø grid in latitude and longitude, following a spherical Delaunay triangulation. The visibility value at each node is then computed by weighting the contributing station measurements in inverse proportion to their distance to the considered grid node. Finally, the values are bilinearly interpolated to the 0.05 ø grid. Because there are very few stations at sea, the aerosol distribution over seas and oceans is obviously poorly described Total Column Ozone The method described here only uses the total column ozone amount, which can be obtained from various sources. The present version uses the level 3 GOME data available from the ATMOS User Center ( at the Deutsche Zentrum ftir Luft und Raumfarht (DLR). These daily maps are obtained by interpolating the level 2 data on the basis of an atmospheric planetary wave model (the level 2 data provide global coverage in 3 days). The data delivered by DLR are gridded with a 0.36 ø resolution. They are interpolated to the UV map grid with a simple bilinear algorithm Surface Elevation The surface elevation comes from the GTOPO30 world digital elevation model (DEM) elaborated by and available from the U.S. Geological Survey EROS data center ( edcwww'cr'usgs'gøv/erøs-høme'html)' This truly global data set contains the elevation for land surfaces, on a regular grid in latitude and longitude, with a spatial resolution of 30 arc sec. For the purpose of this work, the DEM was resampled both on the Meteosat grid and on the grid chosen for the UV maps. In both cases the resampled DEMs contain the mean elevation inside the pixel. Masks of land and sea/ocean were also generated and are used in the processing scheme Operational Aspects The amount of input data is significant. A subwindow of 1600 columns x 1005 lines of the full Meteosat visible images is used; the corresponding files are 1.8 Mb large (per time slot). The infrared image files are smaller due to the lesser resolution (565 kb). Per day, the other input data represent 140 kb (HIRLAM), 80 kb (GOME), and 30 kb (visibility observations). One produced UV map (i.e., one dose rate or one dose, 880 columns x 800 lines) amounts to 2.8 Gb in uncompressed, floating binary format. The method is still under development and the associated code is not yet optimized. It presently takes ---1 hour on a SUN Ultral-mod 170 to generate a set of UV dose rate maps (the various doses are generated simultaneousbetter estimate the day dose in such conditions. A very preliminary comparison has been performed between ly). With the above parameters, it is evident that the processing the results obtained with the mapping methodology and of long time series would be a significant effort, but it is feasible. Also, the application of the method on more restricted ground measurements taken at the Environment Institute of the JRC, in Ispra. The spectral measurements were performed areas of interest considerably fastens the process. with a Brewer instrument and extend from 290 to 325 nm. 5. Example of Results and Preliminary Comparison With Ground Measurements Although the processor also generates other dose rates, the erythemally weighted UV radiation only is presented. Plate 1 shows the results obtained when the influencing factors are taken into account step by step. In the first image (top left), only the solar zenith angle and total column ozone are included, the other parameters are given uniform values (0.03 Lambertian equivalent UV albedo, 20 km visibility, 0 m elevation). The image corresponds to 1122 UTC when the solar zenith angle is minimal at ø of longitude east. In the image however, the maximal surface UV radiation is shifted to the west because of lower values of the total ozone column, which on this day typically varied from 300 to 350 Dobson units (DU) over the Iberian Peninsula, France, and the British Isles, and between 370 and 400 DU over the rest of Europe. In the next image (top right) the elevation and the surface Lambertian equivalent UV albedo are introduced. The increase in surface UV radiation is due to both elevation and snow cover in Scan- dinavia and over the Alps, while the effect in Spain and northern Africa, for instance, is solely due to the altitude. The procedure assigned typical UV Lambertian equivalent albedo values between 0.5 and 0.7 to snowy pixels in Norway and between 0.3 and 0.6 in the Alps. The typical increase in surface erythemal dose rate is from 55 to 70 mw/m 2 and from 150 to 200 mw/m 2, respectively. In the highest pixel in the Sierra Nevada (37.6øN-4.25øW, snow free), for which the mean altitude given by the resampled DEM is 1420 m, the erythemal dose rate increases to 190 mw/m 2 from 177 mw/m 2 when calculated as if at the sea level. In the following image (bottom right), the horizontal visibility interpolated from ground observations is used instead of the relatively high uniform value of 20 km. Areas most affected are Austria and Wales, but as these are also heavily covered by clouds, the low visibility value could result from rain. A decrease is also visible in Cataluna, where a cloud free condition prevails. Choosing the pixel containing Barcelona (41.37øN-2.12øE), the visibility is found to be 13.8 km, and the erythemal dose rate is reduced from 179 to 166 mw/m 2. Plate 2 shows a map of the erythemal daily dose on April 15, The dose rate was estimated at half-hour intervals from 0452 to 1752 UTC, using 27 Meteosat acquisitions and integrated over this time range to obtain the dose. A correction factor was applied to take into account the fact that the fraction of the full daily cycle covered by the 27 time slots varies according to the geographical position. It should be remembered that only the attenuation by clouds (and solar zenith angle) is actually temporally sampled in this way, the other influencing factors are documented only once per day. The attenuation by clouds is however the most dynamic influencing factor. As expected, the cloud features are smoother than in the dose rate map of Plate 1. It can also be noticed that some patterns over Spain are more visible in the daily dose map. This is due to rapidly evolving meteorological conditions over this area, with clouds forming in the afternoon. This illustrates the interest of using the half-hour sampling rate of Meteosat to Because longer wavelengths still significantly contribute to the erythemally weighted dose, the comparison is made on the unweighted UV-B ( nm). Figure 7 shows the dose rate on two days of July 1996; the measurements were taken about every 20 min; the time associated with the modeled values correspond to the Meteosat acquisition time. On July 31, a cloudless day, the agreement is quite good. This is not the case for July 28 when a broken cloud field situation is encountered. It can be seen that the model does not reproduce the high dynamic of the measurement. This is expected as the satellite-

8 5056 VERDEBOUT: SURFACE UV MAPS FROM GOME AND METEOSAT 0 =:

9 VERDEBOUT: SURFACE UV MAPS FROM GOME AND METEOSAT 5057 derive dose rate averages over an area of about 25 km 2, while the ground instrument reacts to the passing of a single cloud. The broken cloud field is also a case where plane parallel modeling, which considers a horizontally homogeneous atmosphere, is in principle not valid. The same is true, in general, wherever a strong horizontal gradient in any of the influencing factors is present. Another example is the fact that high-albedo surfaces (snow covered) increase the surface irradiance in snow free adjacent areas [Deganther et al., 1998]. These "3-D" effects are significant for high spatial resolution UV maps such as those presented here. On the other hand, the implied CPU time still makes three-dimensional modeling impracticable for generating maps. However, in the case of a broken cloud field and because of ergodicity, it can be expected that the errors will partly average out when performing the time integration to obtain the daily dose. This is confirmed by Figure 8, which shows quite encouraging agreement on the results for the daily dose of 10 days in July To illustrate the relative importance of the influencing factors, the figure also shows the doses obtained for absolutely clear sky and constant ozone amount and when introducing step by step the observed ozone amount and estimated 31/07/96... meas. : rood. 2.0f...,...,...,...''''t? E 6O 5O E 40 õ 30 '5 m I I I I I I I I a Dote (July 1996) Figure 8. Comparison between modeled and measured UV-B ( m) daily dose at Ispra (45.8øN-8.63øE-220 m altitude) during a 10 day period from July 22 to 31, The dotted line (curve e) represents the measurement. The solid lines represent the modeled values; (curve a) only the solar zenith angle is variable (total column ozone = 290 DU, no aerosols and no clouds); (curve b) the actual total column ozone is additionally taken into account; (curve c) the visibility observations are used to modulate the attenuation by aerosols; (curve d) after inclusion of cloud attenuation. D 0 "0 : ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' 28/07/96... meos. ß moclo visibility. Over these 10 days, total column ozone varied between 303 and 323 DU and visibility between 9.7 and 19.5 km. This single comparison is certainly not sufficient to draw a conclusion on the quality of the UV maps. A systematic validation is planned to assess the map accuracy by comparing the results with a comprehensive set of ground measurements. These will be chosen to be representative of the variability in influencing factors encountered in Europe during a full year (geographical, meteorological, and environmental). This exercise as a vital step to determine the usefulness of satellitederived maps with regard to various potential applications. 1.o ' ' ' i i i I i i i UTC time [hour] Figure 7. Comparison between modeled and measured UV-B ( m) dose rate at Ispra (45.8øN-8.63øE, 220 m altitude) for (top) a cloudless day and (bottom) a broken cloud field situation. The modeled value corresponds to the solid line, the measurement to the dotted line. 6. Conclusions and Future Work The technical capability of generating surface UV irradiance maps over Europe which take into account the geographical variability of solar zenith angle, total column ozone, attenuation by clouds and tropospheric aerosols, surface elevation, and albedo with a spatial resolution of 0.05 ø has been demonstrated. These maps could be produced on a half-hour basis and used to compute the UV dose integrated on a day. All included influencing factors have been found to significantly affect the surface UV irradiance in the context of the geographical and temporal variability encountered in Europe. The accuracy of the produced information has not yet been systematically assessed, but a preliminary comparison with a single set of ground measurements in Ispra is encouraging for the quality of the daily dose estimation. The next steps are to establish the error budget for the method and to systematically assess the accuracy of the results

10 5058 VERDEBOUT: SURFACE UV MAPS FROM GOME AND METEOSAT by comparison with ground measurements. These studies are indispensable to enable the use of the data for impact or trend studies. In this respect, the potential for applying the method to historical data should be mentioned. Meteosat data exist since 1978, and the MARS meteorological database starts in TOMS products could substitute GOME column ozone. HIRLAM snow cover starts in 1990, but coarser resolution ECMWF (European Centre for Medium-Range Weather Forecast) data are available from Obviously, the consistency of the information during the period would be a key issue to examine. Acknowledgments. The author thanks F. Cappellani and C. Koechler of the Environment Institute of the JRC for providing the ground measurements of UV-B. He also thanks M. Amsellem and C. Attardo of the MARS project for the meteorological synoptic data. P. Taalas and S. 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