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Snow monitoring in the U.K. using active & passive microwave satellite data.
Abstract
Thesis (Ph. D.)--University of Bristol, 1994.
... Basist et al., 1995;Grody and Basist, 1994) and regional scales (e.g. Standley and Barrett, 1994;Kelly, 1994;Rott et al., 1991). A major focus of this paper is the investigation of the value of SSM/I data for monitoring snow at the national level, for the first time in Iran. ...
Although snow is one of the most important parameters in hydrological and climatological studies in Iran, and as an indicator of climate change, the sparse network of ground observing stations and the common problems caused by severe environment make it difficult to monitor. A new passive microwave satellite algorithm based on single channel (V37 GHz) for retrieving snow depth was developed. To help identify appropriate method, a statistical comparison was made between the results of this algorithm and the available in situ data. Data from two sources were obtained: F11-SSM/I data from the Centre for Remote Sensing and ground truth data from the Water Resource Research Organization under the authority of the Iranian Ministry of Energy. The snow depth product compared with in situ data confirmed that passive microwave remote sensing has a great potential (R 2 = 0.92) for the detection of snow depth, especially in regions where few observations are made on the ground. In contrast to earlier work (Chang et al., 1987), which has suggested that passive microwave snow depth algorithm is able to detect snow depths only up to about 50 cm, results from this research evidenced that new algorithm, are able to identify snow depths more than 50 cm in Iran. The results of the research also provide a useful basis for continuous snow depth monitoring and longer term-data in Iran, particularly through real-time or near real-time operations.
... Nevertheless, determination of snow thickness or its water equivalent is still under study in present operational satellite systems. The most widely used satellite images for snow cover identification are the visible and infrared bands of Landsat, SPOT, NOAA and AVHRR (Shi & Dozier, 1995) as well as radar sensors (Bernier & Fortin,1992; Rott & Nagler 1995; Baghdadi et al., 1997; Kelly, 1996). Further, these images cover large areas at short time intervals. ...
At least one-quarter of the Lebanese terrain is covered by snow annually, thus contributing integrally to feeding surface and subsurface water resources. However, only limited estimates of snow cover have been carried out and applied locally. The use of remote sensing has enhanced significantly the delineation of snow cover over the mountains. Several satellite images and sensors are used in this respect. In this study, SPOT-4 (1-km resolution) satellite images are used. They have the capability to acquire consecutive images every 10 days, thus monitoring the dynamic change of snow and its maximum coverage could be achieved. This was applied to Mount Lebanon for the years 2001–2002. The areas covered by snow were delineated, and then manipulated with the slope angle and altitudes in order to classify five major zones of snowmelt potential. The field investigation was carried out in each zone by measuring depths and snow/water ratio. A volume of around 1100 × 10 m of water was derived from snowmelt over the given period. This is equivalent to a precipitation rate of about 425 mm in the region, revealing the considerable portion of water that is derived from snowmelt.
For the purposes of the multilaboratory, EU‐funded, STORM Project, Meteosat IR and SSM/I satellite imagery were used to identify and evaluate moderate to heavy precipitation areas over Western Europe and the Mediterranean region. The Meteosat IR technique used is the Hierarchical Objective Procedure (HOP) technique. This was developed on behalf of the STORM consortium as one possible basis for a future operational flash flood forecasting scheme appropriate for use in southern Europe. Three empirical and one physical SSM/I algorithms were also used in this research. Comparing SSM/I algorithm derived precipitation maps with rain‐gauge data, it was found that the SSM/I algorithms can give reasonably good estimates of precipitation even when this is unusually heavy, but the accuracy varies with algorithms used. Intercomparison between IR HOP and SSM/I derived precipitation maps reveals that generally both techniques agree well but some discrepancies were also was found. In possible future storm monitoring and forecasting operations both techniques should be used together.
Significantly different sea-ice concentrations estimated by the well known Bootstrap and NASA/Team SSM/I algorithms are found to occur when the brightness temperature of horizontally polarized radiation is depressed, possibly as a result of ice layers in the snow cover. Furthermore, discontinuous ice concentrations, which do not reflect real concentration variations, sometimes occur when the Bootstrap algorithm switches between polarization and frequency schemes. The Bristol algorithm is designed to overcome these problems, and is described in this paper. In an initial evaluation against 10 cloud-free Advanced Very High Resolution Radiometer (AVHRR) winter scenes of the Greenland and Barents Seas, the Bristol algorithm, with a correlation coefficient (c corr) of 0·88 and r.m.s. error (e r.m.s.) of 5·2 per cent, outperforms the NASA/Team (c corr = 0·82,e r.m.s. =6·2 per cent), Bootstrap (c corr = 0·74,e r.m.s. = 8·3 per cent) and AES/York (c corr = 0·82,e r.m.s. =6·4 per cent) algorithms, although further work is required to confirm its possible advantages.
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