CONTRAILS FROM (A)ATSR(2) DATA

Transcription

1 CONTRAILS FROM (A)ATSR(2) DATA Hermann Mannstein and Rüdiger Büll Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen, Wessling, Germany ABSTRACT/RESUME The DLR contrail detection algorithm identifies linear contrails using the infrared split window channels of moderate resolution (~ 1km) satellite data. Within the ESA DUE project contrails this algorithm was adapted to ATSR2 and AATSR. Data from the years 1995, 2000 and 2004 is processed and evaluated for the North Atlantic and European region in order to retrieve information on occurence, extent and properties of linear contrails. First results are shown here. 1 INTRODUCTION A contrail forms in the wake of an aircraft if the ambient air is cold and moist enough. The Schmidt-Appleman criterion quantifies the necessary atmospheric conditions [1]. If the ambient air is not supersaturated with respect to ice, the contrail disappears quickly. Otherwise the ice crystals within the contrail grow by uptake of ambient moisture while the contrail is spreading due to turbulence and vertical wind shear [2,3], forming contrail cirrus that may last several hours. Young, linear contrails can be visually identified by their linear appearance from thermal infrared images of the NOAA AVHRR instrument [4] and detected also by using the thermal infrared split window channels of this instrument [5,6]. An operational automated contrail detection algorithm was developed and applied to NOAA14 - AVHRR data over Europe by [5]. This algorithm was also used by [8] over the Continental US and by [9] over the Northern Pacific area to estimate the coverage by linear contrails and their optical properties. Recently [10] has shown, that the linear contrails contribute only to approximately 10% of the cirrus clouds produced by air traffic: The spectral and morphological appearance of ageing contrails approaches that of natural cirrus. Therefore a distinction between linear contrails and contrail cirrus is highly subjective. Nevertheless the identification of linear contrails in satellite data is important, as it is a direct evidence for the impact of air traffic on cirrus cloud coverage. This additional, man made cloud cover adds to the climate impact of air traffic [11, 12]. In order to assess the contrail coverage for large parts of Europe and the North Atlantic processing chains for AVHRR, (A)ATSR(2) and METEOSAT-8 data have been set up within the ESA Data User Element project contrails. The results from the (A)ATSR(2) data of the years 1995, 2000 and 2004 are shown and discussed in the following. Figure 1: detected contrails from NOAA - AVHRR

2 2 THE CONTRAIL DETECTION ALGORITHM The contrail detection scheme presented here is an adaptation of the algorithm described in [7] to data of the (A)ATSR(2) instruments. Lines in the temperature and split-window temperature difference images are selected by rotational symmetric line filters in 16 different directions. Several tests are applied to the resulting linear objects and combined to avoid misdetections. Independence from the properties of a single scene is achieved by normalizing the data on a regional scale and avoiding scene dependent operators like the Hough transform. The main differences to the AVHRR version described in [7] result from the higher radiometrical resolution which reduces the noise in the split window temperature difference of the (A)ATSR(2) instruments and the higher effective spatial resolution, as the ATSR scan is confined to a 512 km wide ground swath. Figure 2: the contrail detection algorithm applied to a section of the AATSR nadir scan from May :06 showing the Thyrrenian Sea with Corsica and Sardegna at the left of the images and parts of Central Italy at the top. Top left: False colour image from visible and thermal infrared channels, top right: split window temperature difference, bottom left: detected contrails, bottom right: contrail mask in grey over the false colour image.

3 3 ATSR2 and AATSR DATA 3.1 ATSR2 data (1995 and 2000) The global data set of ATSR2 for the years 1995 and 2000 has been delivered from the UK-PAC on large (1 Terabyte) portable disks in the ungridded SADIST UBT format. As this data is partitioned into slices of 512 scan lines, we had to stitch the single slices covering the region of interest (ROI) defined for the contrails project ranging from 20 o to 75 o N and from 80 o W to 50 o E into overpasses stored in an internal format for further processing. This was necessary to extract the maximum of information from the data set, as the contrail detection algorithm cannot provide results at the edges of scenes due to the use of large filers (see Figure 2). With this stitching into overpasses of more than 6000 scan lines the northern and southern edges where outside of the ROI. A comparison of the results of the contrail detection between the mapped gridded brightness temperature (GBT) data and the UBT data showed no significant differences. Therefore we decided to use the more basic UBT format. The available datasets per day are shown in figure 3 and 4. overppasses per day overppasses per day ATSR2 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1995 Figure 3: Data availability of ATSR2 data in 1995 ATSR2 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2000 Figure 4: Data availability of ATSR2 data in AATSR data (2004) AATSR2 data where delivered from the UK-PAC as full orbit GBT (ATS_TOA_1P) NetCDF products on DVD ROMs. The data for one day was distributed on 3 or 4 DVD ROMs. In order to speed up the reading process, only the data for the ROI was extracted and stored on disk for further processing in the same way as the ATSR2 data

4 overppasses per day AATSR Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2004 Figure 5: Data availability of AATSR data in RESULTS The processing of the data for contrail analyses results in masks as shown in figure 2 for a small section of an overpass. These masks are sampled for further evaluation onto the ROI in 0.25x0.25 degree grid boxes. As the climate impact of contrails strongly depends on the sunlight conditions, we discriminate between daylight and no daylight on a pixel by pixel basis. The results for one day (31 May 2004) are shown as an example in figures 6 and 7. Figure 6: Results for 31 May 2004, daylight conditions: useful data points per 0.25x0.25 degree grid box (left) and contrail coverage (right) Figure 7: Results for 31 May 2004, no sunlight: useful data points per 0.25x0.25 degree grid box (left) and contrail coverage (right) Even over Central Europe linear contrails are in a statistical sense a rare event. They cover less then 1% of area and time. Therefore it is necessary to analyze huge volumes of data to reach statistical significant results. Looking at all analyzed data (9948 overpasses), we still see a noisy image in the 0.25x0.25 degree resolution data (figure 7). Smoothing this data with a gaussian filter to an effective resolution of apr. 2 degree results in the more homogeneous display in figure 8, right. Several features are clearly visible from these figures: a) the concentration of detected contrails to regions with air traffic, like North American east coast, the flight routes over the Atlantic, Western and Northern Europe and north-east of Moskow b) a low detection efficiency over mountain regions like the Alps, the Pyrenees and Greenland. c) A relatively high false alarm rate over hot areas in Northern Africa and the Arabian Peninsula.

5 d) Lower data coverage over the hot areas. This is due to saturation of the thermal infrared channels of ATSR. The saturated parts of the data are automatically excluded from the analyses. A further evaluation and correction for false alarm rate and detection efficiency as performed in [13] for AVHRR data over Europe is still missing. The directly measured contrail coverage of ~ 0.5 % over Western Europe and the Northern Atlantic is in good agreement with previous analyses [1, 13]. This can be interpreted as an indication for a high detection efficiency even without further corrections. As ERS2 and ENVISAT, the satellites carrying the ATSR instruments, are on sun synchronous orbits, the observations are concentrated to relatively small time windows, which move from East to West through the ROI. In order to relate the detected contrails to air traffic, the daily cycle of it has to be taken into consideration. The high contrail coverage SE of Iceland (see figure 8) might result from the match of observation time with the eastbound North Atlantic traffic. Figure 7: Results for all ATSR data from 1995, 2000 and 2004: contrail coverage Figure 8: Results for all ATSR data from 1995, 2000 and 2004: useful data points per 0.25x0.25 degree grid box (left) and smoothed contrail coverage (right) 5 CONCLUSIONS The quality of the ATSR instruments allowed for tuning the contrail detection algorithm to quite low false alarm rate while keeping the detection efficiency relatively high. The advantages of the ATSR data are the homogeneous spatial coverage and the global availability, which has not been exploited within the contrails project. A disadvantage compared to the AVHRR HRIT data is the low coverage, which prohibits the interpretation of the data with a higher temporal resolution. With the contrail detection applied to the whole ATSR data set a homogeneous global view on contrail coverage is possible.

6 6 ACKNOWLEDGEMENT The data analysis was performed within the ESA DUE project contrails. 7 REFERENCES 1. Schumann, U.: On conditions for contrail formation from aircraft exhausts. Meteorol. Z., N.F.., 5, 4-23, Minnis, P., et al. : Transformation of contrails into cirrus during SUCCESS. Geophys. Res. Lett., 25, , Schumann, U.: Contrail cirrus. In D. K. Lynch, et al. (Eds.): Cirrus. Oxford University Press, , Bakan, S. et al.: Contrail frequency over Europe from NOAA-satellite images. Ann. Geophys., 12, , Carleton, A. M. and P. J. Lamb. Jet contrails and cirrus clouds..:. BAMS, 67: , Engelstad, M. et al.: Automated detection of jet contrails using the AVHRR split window. IJRS 13(8): , Mannstein, H., Meyer, R. and Wendling, P., 1999: Operational detection of contrails from NOAA-AVHRR-data. IJRS, 20, Palikonda,R. et al.: Contrail coverage derived from 2001 AVHRR data over the continental United States of America and surrounding Areas, Meteorol. Z.,14,4, , Minnis,P. et al.: Contrail properties over the eastern North Pacific from AVHRR data, Meteorol. Z., 14, 4, , Mannstein, H. and U. Schumann : Aircraft induced contrail cirrus over Europe, Meteorol. Z., 14, 4, , Intergovernmental Panel on Climate Change:: Aviation and the global atmosphere, Cambridge University Press ( Sausen, R., et al.: Aviation radiative forcing in 2000: An update on IPCC (1999), Meteorol. Z., 14, 4, , Meyer, R., et al.: Regional Radiative Forcing by Line-Shaped Contrails Derived from Satellite Data. JGR, 107, D10, S. ACL 17-1-ACL 17-15, 2002