{'fraction_non_alphanumeric': 0.01032258064516129, 'fraction_numerical': 0.0, 'mean_word_length': 5.466666666666667, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Management of Gridded Climate Data for National Scale Integrated Assessment Models

Computer model analyses of climate change impacts are data intensive due to the spatial and temporal dimensions over which climate operates. Data intensity proves a major constraint in the design of such climate models. For policy oriented climate models this constraint proves critical, given the lower specification computer hardware readily available to decision makers. This paper discusses the use of spatial data orderings in combination with run-length encoding to spatially compress climate data. Experiments have been conducted which test the application of various data ordering schemes to the storage of climate data for New Zealand, Australia and Bangladesh. The results of these