{'fraction_non_alphanumeric': 0.019246861924686193, 'fraction_numerical': 0.015899581589958158, 'mean_word_length': 5.328042328042328, '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': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
New Developments in the Regional Atmospheric Modeling System Suitable for Simulations of Snowpack Augmentation over Complex Terrain

The Colorado State University Regional Atmospheric Modeling System (RAMS) has been used to emulate cloud seeding operations in the Colorado Rocky Mountains for the winter of 2003-2004 in a previous study (Cotton et al. 2006). This paper documents new developments in RAMS since that study using a winter storm simulation that occurred in Colorado from 3-4 November 2003 as an illustration. The authors reported the advantages and disadvantages in the precipitation prediction of the innermost grid (Δx=3 km) by using the Kain-Fritsch convective parameterization scheme (CPS) in the outer grids in order to reduce the excessive precipitation in the innermost grid. Also, we examined the impacts of the bin-emulation approach to riming on supercooled liquid water prediction and precipitation. The bin-emulation approach alleviated a negative bias in the prediction of cloud liquid water content that occurred in the older microphysics package without this feature. With this new feature, there should be improvements in emulating cloud seeding operations in RAMS.