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Alpine Cloud Climatology Using Long-Term NOAA-AVHRR Satellite Data

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Alpine Cloud Climatology Using Long-Term NOAA-AVHRR Satellite Data Institut fur Physik der Atmosphare Report No. 140 Alpine cloud climatology using long-term NOAA-AVHRR satellite data by Martina Kastner and Karl-Theodor Kriebel Diese Berichtsserie des Instituts fur Physik der Atmosphare enthalt Veroffentlichungen, die zu einem spateren Zeitpunkt an anderer Stelle erscheinen sollen, sowie spezielle Einzelergebnisse und erganzende Materialien, die von allgemeinem Interesse sind. This series of reports of the Institute of Atmopsheric Physics comprises preprints of publications, specific results and complementary material, which may be of a more general interest. Herausgeber Ulrich Schumann Deutsches Zentrum fur Luft- und Raumfahrt e.V. (DLR) Institut fur Physik der Atmosphare Redaktion Ute Lob Anschrift DLR - Oberpfaffenhofen D-82234 Wessling Germany Tele fori +49 - 8153 28 1797 Telefax +49-8153 28 1841 WWW http://www.op.dlr.de/ipa/ E-Mail [email protected] Oberpfaffenhofen Juli 2000 ISSN 0943-4771 DISCLAIMER Portions of this document may be illegible in electronic image products. Images are produced from the best available original document. DE01G0874 Alpine cloud climatology using long-term NOAA-AVHRR satellite data RECEIVED FEB 0 5 2001 M. Kastner and K.T. Kriebel OSTI DLR, Institut fur Physik der Atmosphere, Ober pfaffenhofen DE014976396 ilillullmll IIJ IHIILII JJi &DE014976396* Abstract Three different climates have been identified by our evaluation of AVHRR (Advanced Very High Resolution Radiometer) data using APOLLO (AVHRR Processing scheme Over Land, cLouds and Ocean) for a five-years cloud climatology of the Alpine region. The cloud cover data from four layers were spatially averaged in boxes of 15 km by 14 km. The study area only comprises 540 km by 560 km, but contains regions with moderate, Alpine and Mediterranean climate. Data from the period July 1989 until December 1996 have been considered. The temporal resolution is one scene per day, the early afternoon pass, yielding monthly means of satellite derived cloud coverages 5% to 10 % above the daily mean compared to conventional surface observation. At non-vegetated sites the cloudiness is sometimes significantly overestimated. Averaging high resolution cloud data seems to be superior to low resolution measurements of cloud properties and averaging is favourable in topographical homogeneous regions only. The annual course of cloud cover reveals typical regional features as foehn or temporal singularities as the so-called Christmas thaw. The cloud cover maps in spatially high resolution show local luff/lee features which outline the orography. Less cloud cover is found over the Alps than over the forelands in winter, an accumulation of thick cirrus is found over the High Alps and an accumulation of thin cirrus north of the Alps. 1 1 Introduction Climate depends essentially on the radiation balance and the water cycle. Both exchange processes are linked by clouds that are involved in dynamical processes, too. Changes of the distribution of clouds might be an expression or indication of a changing climate. Global cloud cover is monitored by the ISCGP (International Satellite Cloud Climatology Project; Schiffer and Rossow, 1983) for many years. '-Results- are provided in the high temporal resolution of 3 hours, well suited to describe the large-scale circulation and its cloud system evolution cycle. However, the spatial resolution is rather coarse being greater than 100 km. But regional changes in cloud cover may change regional climate, too. This different objective requires the observation of smaller cloud systems with high spatial resolution for longer periods. Surface observationseither don’t provide a high spatial resolution data set (e.g. Warren et ah, 1986), or don’t give homogeneous observations (synoptic ground net). Satellite observations can give high spatial resolution together with homogeneous area coverage. Further, such data are derived by consistant methods instead of subjective eye observations. Until now, there have onlybeen few high spatial resolution cloud climatologies published based on satellite data (e.g. Karlsson, 1997). In this paper, a 5-years cloud climatology in a small area, based on AVHRR (Advanced Very High Resolution Radiometer) data, is described and analyzed which has been initialized several years ago. Its purpose is to pave the way for a 15-years regional European Cloud Climatology from 1986 to 2000 which has already been started at the Deutsches Zentrum fur Luft- und Raumfahrt (DLR). The 5-years cloud climatology has been performed in the Alpine region from 1992 to 1996. Although the study area is small it comprises three climate regions: moderate, Alpine, and Mediterranean climate. One objective of this cloud climatology study is to improve the validity of the threshold tests in different climates used in the APOLLO (AVHRR Processing scheme Over cLouds, Land and Oceans) algorithm package (Saunders and Kriebel, 1988; Kriebel et al., 1989; Gesell, 1989). With the improved cloud detection scheme a unique 15-years cloud climatology is envisaged in near future. This paper concentrates on climatological features of high spatial resolution derived from monthly, seasonal, and annual mean data of different types of cloud cover. Results from other cloud products like optical depth, liquid water path, and IR-emissivity which are derived simultaneously will not be discussed here. Section 2 deals with data sources. In section 3, the APOLLO cloud detection technique is shortly described, together with the thresholds used and the products derived. In section 4 the method of analysis is shown including the remapping of the data, the averaging to boxes, the cloud classification, and the data control. Section 5 presents the achieved results which comprises the comparison with independent data and the identification of seasonal and regional effects. The discussion in section 6 reviews the possibility of detecting trends. 2 Data Sources This study relies on AVHRR observations made over the Alps and their forelands from the polar orbiting satellites NOAA-9, NOAA-11 and NOAA-14. Data from September 1991 till December 1996 and additionally the midseasonal months July and October of the years 1989 till 1991 are used to reach an 8-years period. The daily afternoon overpass is used for this study, because a high solar elevation allows for an anisotropy correction (Kriebel et al., 1989) which is not too unrealistic. 2 Figure 1: Frame: study area in central Europe. Scene from NOAA-11 AVHRR channels 1, 2, 4 in orthogonal projection, 22 Oct. 1990, 12:53 UTC. The AVHRR channelshave been chosen to have a maximum response within the atmospheric windows. These five channels in atmospheric windows have weak absorption of atmospheric gases, therefore, the data are appropriate for studying surface and cloud properties. The AVHRR measures in five spectral bandpasses: channel 1 (0.56-0. 68pm), channel 2 (0.73-1.lpm). channel 3 (3.55-3. 93pm), channel 4 (10.3-11.3pm), channel 5 (11.5-12.5pm). The resolution of the subsatellite pixel is about 1 km in all channels. The study area (see Figure 1) extends from 44.25 N (northern Italy) to 49.25 N (southern Germany) and from 6.4 B (eastern France) to 13.6 E (central Austria). The NO A A satellites have an inclination of about 99°, so that the 560 x 550 km 2 study area needs about 800 AVHRR lines to be processed. One image line is sampled in 1/6 s, and the study area is scanned in 2.2 min. The AVHRR raw data are acquired from the DFD (Deutsches Fernerkundungsdatenzentrum) of the DLR. 3 3 Cloud detection Cloud detection from AVHRR data is performed by means of up to 5 threshold tests applied to each pixel. According to the interpretation of these test results, the pixels are separated into cloud free and not cloud free pixels. From the group of the not cloud free pixels, the fully cloudy pixels un­ identified by means of 2 more tests which are in fact taken from the first group but with different thresholds and interpretations. The results of these procedures are stored in a cloud mask in full spatial resolution. A combination of 3 tests resets cloudy to snowy pixels if there arc any (Gcscll, 1989). This procedure gives 4 kinds of pixels: cloud free, fully cloudy, snow/ice, and the rest which is called partially cloudy. According to the decision logic applied, the last group contains most of the uncertainties. This algorithm package makes up the first part of APOLLO. The second part allows to determine products from all pixels. Emphasis is put on the derivation of cloud properties. Presently, cloud cover, cloud top temperature, cloud optical thickness, cloud liquid/ice water path and cloud emissivity are implemented. The latter three are derived from channel 1 reflectance, relying on a simple parameterization. Further, each fully cloudy pixel is tested for being from a thick or from a thin cloud and the thick clouds are distinguished into low, medium and high clouds by their cloud top temperature. The cloud cover of the partially cloudy pixels is determined from the nearest cloud free and fully cloudy neighbours by means of a linear approach (cf. section 4.2). The cloud type of an individual partially cloudy pixel is set according to the most frequent cloud type within a 50 by 50 pixels environment. The threshold tests used in APOLLO for daytime data (Table 1) flag a cloud if the reflectance in channel 1 or 2 is higher than a threshold, the temperature in channel 4 is lower than a threshold, the temperature difference in channels 4 and 5 is higher than a threshold (thin cloud), the ratio of the reflectances in channels 2 and 1 is above a threshold over sea or below a threshold over land, and the spatial coherence over ocean in a 3x3 matrix shows a variance which is higher than a threshold. The ratio test gives erroneous results if applied to non-vegetated land surfaces.
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