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Regional high‐resolution cloud climatology based on MODIS cloud detection data
Author(s) -
Kotarba Andrzej Z.
Publication year - 2015
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.4539
Subject(s) - moderate resolution imaging spectroradiometer , environmental science , climatology , cloud computing , cloud cover , remote sensing , cloud fraction , satellite , meteorology , image resolution , cloud top , scale (ratio) , computer science , geography , geology , cartography , aerospace engineering , artificial intelligence , engineering , operating system
Most satellite cloud climatologies come in the form of global, low‐resolution datasets: so‐ called ‘gridded’ Level 3 products, resulting from the reprojection and spatio‐temporal aggregation of swath (Level 2) data. Their coarse resolution means that global datasets are of limited usefulness in regional studies. In this paper we develop and evaluate a new, regional cloud climatology over Poland and its neighbouring countries (∼10% of the area covered by Europe), based on observations performed with the state‐of‐the‐art cloud imager, the moderate resolution imaging spectroradiometer ( MODIS ). In contrast to the operational, global MODIS cloud climatology, which is delivered as a Level 3 product at a spatial resolution of 1° × 1°, this regional climatology maintains the MODIS nadir spatial resolution of 1 km/pixel. The resulting high‐spatial‐resolution climatology is compared with AVHRR and SEVIRI datasets, and surface‐based ( SYNOP ) observations at the level of monthly and annual means. The results shows that the standard MODIS Level 2 cloud mask product MOD35 / MYD35 can be successfully used to develop a regional, high‐resolution cloud climatology. MODIS provides reliable estimates of cloud amount at the national scale (annual mean: 64.0% or 70.8%, depending on the MODIS data interpretation scheme), and correctly reproduces the annual cloud amount cycle (correlation between monthly means with SEVIRI / AVHRR >0.98). A comparison with monthly mean surface observations reveals a bias ranging from −1.1% up to 5.9%, and a root mean square error of 4.2% − 6.6%. MODIS data also correctly indicates the spatial distribution of clouds. However, local anomalies were detected that were identified as artifacts of the MODIS cloud detection algorithm. Those artifacts covered 9% of the study area, but had no impact on spatially‐averaged metrics.