Download or read book The Impact of Aerosol-cloud-radiation Interaction on California Weather written by Hsiang-He Lee and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The source-oriented Weather Research and Forecasting chemistry model (SOWC) was modified to include warm/cold cloud processes and applied to investigate 1) how source-oriented aerosols influence fog formation and optical properties in the atmosphere, 2) how aerosol mixing state influences cloud and ice formation and atmospheric optical properties during a winter storm, and 3) the direct, semi-direct, and indirect effects of long-range transport dust on severe weather over California and the Eastern Pacific. SOWC tracks 6-dimensional chemical variables (X, Z, Y, Size Bins, Source Types, Species) through an explicit simulation of atmospheric chemistry and physics. In this study, all aerosol source types can activate to form cloud condensation nuclei (CCN) based on the Köhler theory, but the dust is the only source of ice nuclei (IN). Furthermore, a new source-oriented cloud module in the two-moment Purdue Lin microphysics scheme, and a new module with all source-oriented hydrometeors (cloud, ice, rain, snow and graupel) in the Morrison two-moment microphysics scheme were implemented into the SOWC model to study fog events and winter storm cases, respectively. In Chapter 2, the enhanced SOWC model was used to study a fog event that occurred on January 17th, 2011, in the Central Valley of California. The SOWC reasonably portrays the spatial distribution and duration of the fog event consistent with observations. The source-oriented mixture representation of particles reduced cloud droplet number relative to the internal mixture approach that artificially coats hydrophobic particles with hygroscopic components. The fraction of aerosols activating into CCN at a supersaturation of 0.5% in the Central Valley decreased from 86% in the internal mixture model to 68% in the source-oriented model. This increased the surface energy flux by 3-5 W m-2 and surface temperature by as much as 0.15 K. In Chapter 3, the enhanced SOWC model was used to study a winter storm that occurred on March 6th, 2011, in California. Compared to ground based observations, SOWC with the modified Morrison microphysics scheme and modified Goddard radiation schemes predicted reasonable precipitation, but the onset of precipitation is delayed by 5 hours. Immersion freezing was the main mechanism for ice nuclei formation. Secondary coatings on dust particles increased IN from immersion freezing but decreased IN from contact freezing. Increasing CCN and IN in the internal mixing experiment produced more ice crystals and cloud droplets but did not significantly alter total perception under the conditions studied. However, because of the reducing riming efficiency from snow to graupel in the source-oriented mixing experiment, it resulted more snowfall (less rainfall) on the ground, especially over the mountain area. In Chapter 4, the SOWC model was used to understand the direct, semi-direct, and indirect effects of long-range transport dust on severe weather over Eastern Pacific Ocean. The maximum averaged IN nucleation rate increased 36% after adding long-range transport dust. Because clouds in mid-latitude originate precipitately via the ice phase, an increase in IN can enhance ice formation from supercooled water by heterogenetic freezing (mainly contact freezing) and then to alter hydrometer water amount. Adding long-range transport dust increased the mixing ratio and number concentration for almost all hydrometers. However, the changes of adding local dust in local+LR_dust from LR_dust is more complicated due to the importance of hydrometers in the cumulus scheme. The change in the strength of convection after adding long-range transport dust (or local dust) also produces a noticeable distinction in the precipitation pattern, but the total precipitation did not have major difference after adding long-range transport dust (or local dust).