@article{subedi_walls_barichivich_boyles_ross_hogan_tupy_2022, title={Future changes in habitat availability for two specialist snake species in the imperiled rocklands of South Florida, USA}, ISSN={["2578-4854"]}, DOI={10.1111/csp2.12802}, abstractNote={Abstract}, journal={CONSERVATION SCIENCE AND PRACTICE}, author={Subedi, Suresh C. and Walls, Susan C. and Barichivich, William J. and Boyles, Ryan and Ross, Michael S. and Hogan, J. Aaron and Tupy, John A.}, year={2022}, month={Aug} }
 @article{maudlin_mcneal_dinon-aldridge_davis_boyles_atkins_2020, title={Website Usability Differences between Males and Females: An Eye-Tracking Evaluation of a Climate Decision Support System}, volume={12}, ISSN={["1948-8335"]}, DOI={10.1175/WCAS-D-18-0127.1}, abstractNote={ABSTRACT}, number={1}, journal={WEATHER CLIMATE AND SOCIETY}, author={Maudlin, Lindsay C. and McNeal, Karen S. and Dinon-Aldridge, Heather and Davis, Corey and Boyles, Ryan and Atkins, Rachel M.}, year={2020}, month={Jan}, pages={183–192} }
 @article{bhardwaj_misra_mishra_wootten_boyles_bowden_terando_2018, title={Downscaling future climate change projections over Puerto Rico using a non-hydrostatic atmospheric model}, volume={147}, ISSN={["1573-1480"]}, url={http://dx.doi.org/10.1007/s10584-017-2130-x}, DOI={10.1007/s10584-017-2130-x}, number={1-2}, journal={CLIMATIC CHANGE}, author={Bhardwaj, Amit and Misra, Vasubandhu and Mishra, Akhilesh and Wootten, Adrienne and Boyles, Ryan and Bowden, J. H. and Terando, Adam J.}, year={2018}, month={Mar}, pages={133–147} }
 @article{wootten_terando_reich_boyles_semazzi_2017, title={Characterizing Sources of Uncertainty from Global Climate Models and Downscaling Techniques}, volume={56}, ISSN={["1558-8432"]}, DOI={10.1175/jamc-d-17-0087.1}, abstractNote={Abstract}, number={12}, journal={JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY}, author={Wootten, A. and Terando, A. and Reich, B. J. and Boyles, R. P. and Semazzi, F.}, year={2017}, month={Dec}, pages={3245–3262} }
 @article{cumbie-ward_boyles_2016, title={Evaluation of a High-Resolution SPI for Monitoring Local Drought Severity}, volume={55}, ISSN={["1558-8432"]}, url={http://dx.doi.org/10.1175/jamc-d-16-0106.1}, DOI={10.1175/jamc-d-16-0106.1}, abstractNote={Abstract}, number={10}, journal={JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY}, publisher={American Meteorological Society}, author={Cumbie-Ward, Rebecca V. and Boyles, Ryan P.}, year={2016}, month={Oct}, pages={2247–2262} }
 @article{wootten_bowden_boyles_terando_2016, title={The Sensitivity of WRF Downscaled Precipitation in Puerto Rico to Cumulus Parameterization and Interior Grid Nudging}, volume={55}, ISSN={["1558-8432"]}, url={http://dx.doi.org/10.1175/jamc-d-16-0121.1}, DOI={10.1175/jamc-d-16-0121.1}, abstractNote={Abstract}, number={10}, journal={JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY}, author={Wootten, A. and Bowden, J. H. and Boyles, R. and Terando, A.}, year={2016}, month={Oct}, pages={2263–2281} }
 @article{coopersmith_cosh_bell_boyles_2016, title={Using machine learning to produce near surface soil moisture estimates from deeper in situ records at US Climate Reference Network (USCRN) locations: Analysis and applications to AMSR-E satellite validation}, volume={98}, ISSN={["1872-9657"]}, DOI={10.1016/j.advwatres.2016.10.007}, abstractNote={Surface soil moisture is a critical parameter for understanding the energy flux at the land atmosphere boundary. Weather modeling, climate prediction, and remote sensing validation are some of the applications for surface soil moisture information. The most common in situ measurement for these purposes are sensors that are installed at depths of approximately 5 cm. There are however, sensor technologies and network designs that do not provide an estimate at this depth. If soil moisture estimates at deeper depths could be extrapolated to the near surface, in situ networks providing estimates at other depths would see their values enhanced. Soil moisture sensors from the U.S. Climate Reference Network (USCRN) were used to generate models of 5 cm soil moisture, with 10 cm soil moisture measurements and antecedent precipitation as inputs, via machine learning techniques. Validation was conducted with the available, in situ, 5 cm resources. It was shown that a 5 cm estimate, which was extrapolated from a 10 cm sensor and antecedent local precipitation, produced a root-mean-squared-error (RMSE) of 0.0215 m3/m3. Next, these machine-learning-generated 5 cm estimates were also compared to AMSR-E estimates at these locations. These results were then compared with the performance of the actual in situ readings against the AMSR-E data. The machine learning estimates at 5 cm produced an RMSE of approximately 0.03 m3/m3 when an optimized gain and offset were applied. This is necessary considering the performance of AMSR-E in locations characterized by high vegetation water contents, which are present across North Carolina. Lastly, the application of this extrapolation technique is applied to the ECONet in North Carolina, which provides a 10 cm depth measurement as its shallowest soil moisture estimate. A raw RMSE of 0.028 m3/m3 was achieved, and with a linear gain and offset applied at each ECONet site, an RMSE of 0.013 m3/m3 was possible.}, journal={ADVANCES IN WATER RESOURCES}, author={Coopersmith, Evan J. and Cosh, Michael H. and Bell, Jesse E. and Boyles, Ryan}, year={2016}, month={Dec}, pages={122–131} }
 @article{wootten_boyles_2014, title={Comparison of NCEP Multisensor Precipitation Estimates with Independent Gauge Data over the Eastern United States}, volume={53}, ISSN={["1558-8432"]}, DOI={10.1175/jamc-d-14-0034.1}, abstractNote={Abstract}, number={12}, journal={JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY}, author={Wootten, Adrienne and Boyles, Ryan P.}, year={2014}, month={Dec}, pages={2848–2862} }
 @article{pan_boyles_white_heitman_2012, title={Characterizing Soil Physical Properties for Soil Moisture Monitoring with the North Carolina Environment and Climate Observing Network}, volume={29}, ISSN={["0739-0572"]}, DOI={10.1175/jtech-d-11-00104.1}, abstractNote={Abstract}, number={7}, journal={JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY}, publisher={American Meteorological Society}, author={Pan, Weinan and Boyles, R. P. and White, J. G. and Heitman, J. L.}, year={2012}, month={Jul}, pages={933–943} }
 @article{misra_michael_boyles_chassignet_griffin_o'brien_2012, title={Reconciling the Spatial Distribution of the Surface Temperature Trends in the Southeastern United States}, volume={25}, ISSN={["1520-0442"]}, DOI={10.1175/jcli-d-11-00170.1}, abstractNote={Abstract}, number={10}, journal={JOURNAL OF CLIMATE}, author={Misra, V. and Michael, J. -P. and Boyles, R. and Chassignet, E. P. and Griffin, M. and O'Brien, J. J.}, year={2012}, month={May}, pages={3610–3618} }
 @article{kehoe_raman_boyles_2010, title={Characteristics of Landfalling Tropical Cyclones in North Carolina}, volume={33}, ISSN={["1521-060X"]}, DOI={10.1080/01490419.2010.518059}, abstractNote={Trends in the Atlantic tropical cyclones and the cyclones that had tracks through North Carolina were analyzed for more than 100 years. From about 1970, there appears to be an increase in the mean number of storms developing. The number of storms affecting North Carolina each decade has been increasing since the 1960s. In the 1980s, 1990s, and into the 2000s, there was an increase in the number of landfalling storms in North Carolina. Although August and September are the most active months of the Atlantic hurricane season, the hurricane season for North Carolina peaks in September. Wind distribution and frictional convergence associated with landfalling hurricanes in North Carolina are discussed. Convection and precipitation patterns of landfalling hurricanes are presented. Two examples of the effect of spatial surface moisture distribution on intensification of tropical cyclones over land after landfall are discussed.}, number={4}, journal={MARINE GEODESY}, author={Kehoe, Jennifer and Raman, Sethu and Boyles, Ryan}, year={2010}, pages={394–411} }
 @article{carbone_rhee_mizzell_boyles_2008, title={A regional-scale drought monitoring tool for the Carolinas}, volume={89}, ISSN={["1520-0477"]}, DOI={10.1175/BAMS-89-1-20}, number={1}, journal={BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY}, author={Carbone, Gregory J. and Rhee, Jinyoung and Mizzell, Hope P. and Boyles, Ryan}, year={2008}, month={Jan}, pages={20–28} }
 @article{childs_raman_boyles_2007, title={High-resolution numerical simulations of hurricane Isabel (2003) over North Carolina}, volume={41}, ISSN={["0921-030X"]}, DOI={10.1007/s11069-006-9050-9}, number={3}, journal={NATURAL HAZARDS}, author={Childs, Peter and Raman, Sethu and Boyles, Ryan}, year={2007}, month={Jun}, pages={401–411} }
 @article{boyles_raman_sims_2007, title={Sensitivity of mesoscale surface dynamics to surface soil and vegetation contrasts over the carolina sandhills}, volume={164}, ISSN={["0033-4553"]}, DOI={10.1007/s00024-007-0227-2}, number={8-9}, journal={PURE AND APPLIED GEOPHYSICS}, author={Boyles, Ryan and Raman, Sethu and Sims, Aaron}, year={2007}, month={Sep}, pages={1547–1576} }
 @article{holder_boyles_robinson_raman_fishel_2006, title={Calculating a daily normal temperature range that reflects daily temperature variability}, volume={87}, ISSN={["1520-0477"]}, DOI={10.1175/BAMS-87-6-769}, abstractNote={Normal temperatures, which are calculated by the National Climatic Data Center for locations across the country, are quality-controlled, smoothed 30-yr-average temperatures. They are used in many facets of media, industry, and meteorology, and a given day's normal maximum and minimum temperatures are often used synonymously with what the observed temperature extremes “should be.” However, allowing some leeway to account for natural daily and seasonal variations can more accurately reflect the ranges of temperature that we can expect on a particular day—a “normal range.” Providing such a range, especially to the public, presents a more accurate perspective on what the temperature “usually” is on any particular day of the year. One way of doing this is presented in this study for several locations across North Carolina. The results yield expected higher variances in the cooler months and seem to well represent the varied weather that locations in North Carolina tend to experience. Day-to-day variations in t...}, number={6}, journal={BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY}, author={Holder, Christopher and Boyles, Ryan and Robinson, Peter and Raman, Sethli and Fishel, Greg}, year={2006}, month={Jun}, pages={769-+} }
 @article{holder_boyles_syed_niyogi_raman_2006, title={Comparison of collocated automated (NCECONet) and manual (COOP) climate observations in North Carolina}, volume={23}, ISSN={["0739-0572"]}, DOI={10.1175/jtech1873.1}, abstractNote={Abstract}, number={5}, journal={JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY}, author={Holder, C and Boyles, R and Syed, A and Niyogi, D and Raman, S}, year={2006}, month={May}, pages={671–682} }
 @article{childs_qureshi_raman_alapaty_ellis_boyles_niyogi_2006, title={Simulation of convective initiation during IHOP_2002 using the flux-adjusting surface data assimilation system (FASDAS)}, volume={134}, ISSN={["1520-0493"]}, DOI={10.1175/MWR3064.1}, abstractNote={Abstract}, number={1}, journal={MONTHLY WEATHER REVIEW}, author={Childs, PP and Qureshi, AL and Raman, S and Alapaty, K and Ellis, R and Boyles, R and Niyogi, D}, year={2006}, month={Jan}, pages={134–148} }
 @article{raman_sims_ellis_boyles_2005, title={Numerical simulation of mesoscale circulations in a region of contrasting soil types}, volume={162}, ISSN={["1420-9136"]}, DOI={10.1007/s00024-005-2689-4}, number={8-9}, journal={PURE AND APPLIED GEOPHYSICS}, author={Raman, S and Sims, A and Ellis, R and Boyles, R}, year={2005}, month={Aug}, pages={1689–1714} }
 @article{boyles_raman_2003, title={Analysis of climate trends in North Carolina (1949-1998)}, volume={29}, ISSN={["0160-4120"]}, DOI={10.1016/S0160-4120(02)00185-X}, abstractNote={North Carolina has one of the most complex climates in the United States (U.S.). Analysis of the climate in this state is critical for agricultural and planning purposes. Climate patterns and trends in North Carolina are analyzed for the period 1949-1998. Precipitation, minimum temperature, and maximum temperature are analyzed on seasonal and annual time scales using data collected from the National Weather Service Cooperative Observer Network. Additionally, changes in patterns of occurrence of the last spring freeze and first fall freeze are investigated. Linear time series slopes are analyzed to investigate the spatial and temporal trends of climate variability in North Carolina. Spatial analysis of climate variability across North Carolina is performed using a geographic information system. While most trends are local in nature, there are general statewide patterns. Precipitation in North Carolina has increased over the past 50 years during the fall and winter seasons, but decreased during the summer. Temperatures during the last 10 years are warmer than average, but are not warmer than those experienced during the 1950s. The warm season has become longer, as measured by the dates of the last spring freeze and first fall freeze. Generally, the last 10 years were the wettest of the study period. These conclusions are consistent with earlier studies that show that the difference between the maximum and minimum temperatures is decreasing, possibly due to increased cloud cover and precipitation. Similarly, these results show that temperature patterns are in phase with the North Atlantic Oscillation and precipitation patterns appear to be correlated with the Pacific Decadal Oscillation.}, number={2-3}, journal={ENVIRONMENT INTERNATIONAL}, author={Boyles, RP and Raman, S}, year={2003}, month={Jun}, pages={263–275} }