An Assessment of the Subseasonal Predictability of Severe Thunderstorm title Environments and Activity using the Climate Forecast System Version 2

The prospect for skillful long-term predictions of atmospheric conditions known to directly contribute to the onset and maintenance of severe convective storms remains unclear. A thorough assessment of the capability for a global climate model such as the Climate Forecast System Version 2 (CFSv2) to skillfully represent parameters related to severe weather has the potential to significantly improve medium- to longrange outlooks vital to risk managers. Environmental convective available potential energy (CAPE) and deep-layer vertical wind shear (DLS) can be used to distinguish an atmosphere conducive to severe storms from one supportive of primarily nonsevere ordinary convection. As such, this research concentrates on the predictability of CAPE, DLS, and a product of the two parameters (CAPEDLS) by the CFSv2 with a specific focus on the subseasonal timescale. Individual month-long verification periods from the Climate Forecast System reanalysis (CFSR) dataset are measured against a climatological standard using cumulative distribution function (CDF) and area-under-the-CDF (AUCDF) techniques designed mitigate inherent model biases while concurrently assessing the entire distribution of a given parameter in lieu of a threshold-based approach. Similar methods imposed upon the CFS reforecast (CFSRef) and operational CFSv2 allow for comparisons elucidating both spatial and temporal trends in skill using correlation coefficients, proportion correct metrics, Heidke skill score (HSS), and root-meansquare- error (RMSE) statistics. Key results show the CFSv2-based output often demonstrates skill beyond a climatologically-based threshold when the forecast is notably anomalous from the 29-year (1982-2010) mean CFSRef prediction (exceeding one standard deviation at grid point level). CFSRef analysis indicates enhanced skill during the months of April and June (relative to May) and for predictions of DLS. Furthermore, years exhibiting skill in terms of RMSE are shown to possess certain correlations with El Ni˜no-Southern Oscillation conditions from the preceding winter and concurrent Madden Julian Oscillation activity. Applying results gleaned from the CFSRef analysis to the operational CFSv2 (2011-16) indicates predictive skill can be increased by isolating forecasts meeting multiple parameter-based relationships

Assessment of VAS soundings in the analysis of a preconvective environment

Retrievals from the VISSR Atmospheric Sounder (VAS) are combined with conventional data to assess the impact of geosynchronous satellite soundings upon the analysis of a preconvective environment. VAS retrievals of temperature, dewpoint, equivalent potential temperature, precipitable water, and lifted index are derived with 60 km resolution at 3 hour intervals. When VAS fields are combined with analyses from conventional data sources, mesoscale regions with convective instability are more clearly delineated prior to the rapid development of the thunderstorms. The retrievals differentiate isolated areas in which air extends throughout the lower troposphere from those regions where moisture is confined to a thin layer near the Earth's surface. The analyses of the VAS retrievals identify significant spatial gradients and temporal changes in the thermal and moisture fields, especially in the regions between radiosonde observations

GIS Modeling of the Prominent Geohazards in Arkansas

The State of Arkansas is prone to numerous geohazards. This thesis is a twofold study of prominent geohazards in Arkansas: the first fold includes a novel triggerless approach for mass wasting susceptibility modeling applied to the Boston Mountains in NW Arkansas, and the second fold is a GIS-based regression modeling of the extreme weather patterns at the state level. Each study fold is presented in this thesis as a separate chapter embracing a published peer-reviewed paper. In the first paper, I have used the analytical hierarchy process to assign preliminary statistical weights to the most cogent variables influencing mass wasting in the central Boston Mountains. These most significant variables are then incorporated in Fuzzy modeling of mass wasting susceptibility within the 1200 km2 study area. For comparison and accuracy assessment, a second model has been established using a conventional weighted overlay (WO) approach. Results indicate that the developed novel approach is superior, with approximately 83% accuracy, to the traditional WO approach that has a marginal success of about 28% accuracy. Road related mass wasting events recorded by the Arkansas Department of Transportation have been used to validate both models. In the second paper, I have conducted a systematically gridded analysis of severe weather events, including tornadoes, derechos, and hail, during 1955-2015. The study examines and statistically determines the most significant explanatory variables contributing to the spatial patterns of severe weather events between 1955 and 2015, consequently it identifies severity indices for the entire state. These weather-related hazards and their associated risk will always abide; therefore, the best defense is employ geospatial technologies to plan for hazard mitigation. The mass wasting model developed in this study contributes pivotal information for identifying zones of high risk along roadways in NW Arkansas, which definitely can be adapted to avoid disastrous road failures. In addition, the weather-related severity indices determined at the state level can profoundly benefit state and federal agencies focused on increasing the availability of public and private storm shelters in previously under-represented zones of high risk. This undoubtedly will save lives from unavoidable catastrophic events across the entire state

An evaluation of a severe smog episode in the Eastern U.S. using regional modeling and satellite measurements

An ensemble of regional chemical modeling (WRF/Chem with RADM2) simulations, satellite, ozonesonde, and surface observations during July 7-11, 2007 was used to examine the horizontal and vertical signature of one of the worst smog events in the eastern U.S. in the past decade. The general features of this event -- a broad area of high pressure, weak winds and heavy pollution, terminated by the passage of a cold front -- were well simulated by the model. Average 8-hr maximum O3 has a mean (±Σ) bias of 0.59 (±11.0) ppbv and a root mean square error of 11.0 ppbv. WRF/Chem performed the best on poor air quality days, simulating correctly the spatial pattern of surface O3. Yet the model underpredicted O3 maxima by 5-7 ppbv in the Northeast and overpredicted by 8-11 ppbv in the Southeast. High O3 biases in the Southeast are explained by overpredicted temperatures in the model (>1.5°C). Sensitivity simulations with 1) accelerated O3 dry deposition velocity and 2) suppressed multiphase nitric acid formation pushed the model closer to observations. Simulated O3 vertical profiles over Beltsville, MD showed good agreement with ozonesonde measurements, but the modeled boundary layer depth was overpredicted on July 9, contributing to the low bias over this region. During this severe smog episode, space-borne TES detected high total tropospheric column ozone (TCO) over the Western Atlantic Ocean off the coast near North and South Carolina. The standard product (OMI/MLS) missed the magnitude of these local maxima, but the level-2 ozone profile (OMI) confirmed the TES observations. HYSPLIT back trajectories from these O3 maxima intersected regions of strong convection over the Southeast and Great Lakes regions. When lightning NO emissions were implemented in WRF/Chem, the high concentrations of NOx and O3 off the coast were well reproduced, showing that the exported O3 was produced by a combination of natural NO and pollutants lofted from the lower atmosphere. Lastly, WINTER MONEX O3 data from 1978 are presented for the first time here in discussion of open cell convection over Indonesia

A Regime-Based Evaluation of Southern and Northern Great Plains Warm-Season Precipitation Events in WRF

A competitive neural network known as the self-organizing map (SOM) is used to objectively identify synoptic patterns in the North American Regional Reanalysis (NARR) for warm-season (April-September) precipitation events over the Southern and Northern Great Plains (SGP/NGP) from 2007 to 2014. Classifications for both regions demonstrate contrast in dominant synoptic patterns ranging from extratropical cyclones to subtropical ridges, all of which have preferred months of occurrence. Precipitation from deterministic Weather Research and Forecasting (WRF) Model simulations run by the National Severe Storms Laboratory (NSSL) are evaluated against National Centers for Environmental Prediction (NCEP) Stage IV observations. The SGP features larger observed precipitation amount, intensity, and coverage, as well as better model performance than the NGP. Both regions' simulated convective rain intensity and coverage have good agreement with observations, whereas the stratiform rain (SR) is more problematic with weaker intensity and larger coverage. Further evaluation based on SOM regimes shows that WRF bias varies with the type of meteorological forcing, which can be traced to differences in the diurnal cycle and properties of stratiform and convective rain. The higher performance scores are generally associated with the extratropical cyclone condition than the subtropical ridge. Of the six SOM classes over both regions, the largest precipitation oversimulation is found for SR dominated classes, whereas a nocturnal negative precipitation bias exists for classes featuring upscale growth of convection.Climate Model Development and Validation (CMDV) program - Office of Biological and Environmental Research in the U.S. Department of Energy Office of Science under University of Arizona [DE-SC0017015]; NOAA R2O project at the University of North Dakota [NA15NWS468004]; Climate Model Development and Validation program; Water Cycle and Climate Extreme Modeling science focus area - Office of Biological and Environmental Research in the U.S. Department of Energy Office of Science; U.S. Department of Energy (DOE) [DE-AC05-76RL01830]6 month embargo; published online: 2 July 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]