@article{runkle_matthews_sparks_mcnicholas_sugg_2022, title={Racial and ethnic disparities in pregnancy complications and the protective role of greenspace: A retrospective birth cohort study}, volume={808}, ISSN={["1879-1026"]}, DOI={10.1016/j.scitotenv.2021.152145}, abstractNote={Greenspace may positively impact pregnancy health for racially and economically minoritized populations; few studies have examined local availability and accessibility of green/park space in reducing maternal morbidity. The objective of this retrospective birth cohort study was to examine the association between residential exposure to greenspace and adverse pregnancy health outcomes in a Southern US state characterized by high poverty and racial disparities in maternal health (2013-2017). National data from the Protected Area database - United States (PAD-US) and ParkServe estimated three publicly available and accessible residential greenspace measures-a more direct proxy than using remotely-sensed greenness indicators (e.g., normalized difference vegetation index (NDVI))-(a) percent area of greenspace (M1), (b) area of available greenspace per person (M2), (c) total population within a 10-minute walk (M3). Generalized Estimating Equations with logistic regression were used to examine the association between individual greenspace metrics and South Carolina hospital deliveries (n = 238,922 deliveries) for women with correlated maternal health outcomes for gestational hypertension (GHTN), gestational diabetes (GD), severe maternal morbidity (SMM), preeclampsia (PRE), mental disorders (MD), depressive disorders (DD), and preterm birth (PTB). Lowest compared to highest tertiles of all three metrics were associated with increased risk for MD, DD, and a monotonic increase in GD, particularly for black women. Women with the lowest access to M2 and M3 were more at risk for PRE, PTB, and MD. We observed that women in low-income, majority-black communities in the lowest versus highest tertile of M2 were more likely to experience a DD, MD, SMM, or PTB compared to primarily high-income majority-white communities. Available and accessible green/park space may present as an effective nature-based intervention to reduce maternal complications, particularly for gestational diabetes and other pregnancy health risks for which there are currently few known evidence-based primary prevention strategies.}, journal={SCIENCE OF THE TOTAL ENVIRONMENT}, author={Runkle, Jennifer D. and Matthews, Jessica L. and Sparks, Laurel and McNicholas, Leo and Sugg, Margaret M.}, year={2022}, month={Feb} }
 @article{lasky_parsons_schuttler_hess_sutherland_kalies_clark_olfenbuttel_matthews_clark_et al._2021, title={Carolina critters: a collection of camera-trap data from wildlife surveys across North Carolina}, volume={6}, ISSN={["1939-9170"]}, DOI={10.1002/ecy.3372}, abstractNote={Abstract}, journal={ECOLOGY}, author={Lasky, Monica and Parsons, Arielle W. and Schuttler, Stephanie G. and Hess, George and Sutherland, Ron and Kalies, Liz and Clark, Staci and Olfenbuttel, Colleen and Matthews, Jessie and Clark, James S. and et al.}, year={2021}, month={Jun} }
 @article{leeper_matthews_cesarini_bell_2021, title={Evaluation of Air and Soil Temperatures for Determining the Onset of Growing Season}, volume={126}, ISSN={["2169-8961"]}, DOI={10.1029/2020JG006171}, abstractNote={Abstract}, number={8}, journal={JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES}, author={Leeper, Ronald D. and Matthews, Jessica L. and Cesarini, Maria S. and Bell, Jesse E.}, year={2021}, month={Aug} }
 @article{cheng_konomi_matthews_karagiannis_kang_2021, title={Hierarchical Bayesian nearest neighbor co-kriging Gaussian process models; an application to intersatellite calibration}, volume={44}, ISSN={["2211-6753"]}, DOI={10.1016/j.spasta.2021.100516}, abstractNote={Recent advancements in remote sensing technology and the increasing size of satellite constellations allow for massive geophysical information to be gathered daily on a global scale by numerous platforms of different fidelity. The auto-regressive co-kriging model provides a suitable framework for the analysis of such data sets as it is able to account for cross-dependencies among different fidelity satellite outputs. However, its implementation in multifidelity large spatial data sets is practically infeasible because the computational complexity increases cubically with the total number of observations. In this paper, we propose a nearest neighbor co-kriging Gaussian process (GP) that couples the auto-regressive model and nearest neighbor GP by using augmentation ideas. Our model reduces the computational complexity to be linear with the total number of spatially observed locations. The spatial random effects of the nearest neighbor GP are augmented in a manner which allows the specification of semi-conjugate priors. This facilitates the design of an efficient MCMC sampler involving mostly direct sampling updates. The good predictive performance of the proposed method is demonstrated in a simulation study. We use the proposed method to analyze High-resolution Infrared Radiation Sounder data gathered from two NOAA polar orbiting satellites.}, journal={SPATIAL STATISTICS}, author={Cheng, Si and Konomi, Bledar A. and Matthews, Jessica L. and Karagiannis, Georgios and Kang, Emily L.}, year={2021}, month={Aug} }
 @article{matthews_peng_meier_brown_2020, title={Sensitivity of Arctic Sea Ice Extent to Sea Ice Concentration Threshold Choice and Its Implication to Ice Coverage Decadal Trends and Statistical Projections}, volume={12}, ISSN={["2072-4292"]}, url={https://doi.org/10.3390/rs12050807}, DOI={10.3390/rs12050807}, abstractNote={Arctic sea ice extent has been utilized to monitor sea ice changes since the late 1970s using remotely sensed sea ice data derived from passive microwave (PM) sensors. A 15% sea ice concentration threshold value has been used traditionally when computing sea ice extent (SIE), although other threshold values have been employed. Does the rapid depletion of Arctic sea ice potentially alter the basic characteristics of Arctic ice extent? In this paper, we explore whether and how the statistical characteristics of Arctic sea ice have changed during the satellite data record period of 1979–2017 and examine the sensitivity of sea ice extents and their decadal trends to sea ice concentration threshold values. Threshold choice can affect the timing of annual SIE minimums: a threshold choice as low as 30% can change the timing to August instead of September. Threshold choice impacts the value of annual SIE minimums: in particular, changing the threshold from 15% to 35% can change the annual SIE by more than 10% in magnitude. Monthly SIE data distributions are seasonally dependent. Although little impact was seen for threshold choice on data distributions during annual minimum times (August and September), there is a strong impact in May. Threshold choices were not found to impact the choice of optimal statistical models characterizing annual minimum SIE time series. However, the first ice-free Arctic summer year (FIASY) estimates are impacted; higher threshold values produce earlier FIASY estimates and, more notably, FIASY estimates amongst all considered models are more consistent. This analysis suggests that some of the threshold choice impacts to SIE trends may actually be the result of biased data due to surface melt. Given that the rapid Arctic sea ice depletion appears to have statistically changed SIE characteristics, particularly in the summer months, a more extensive investigation to verify surface melt impacts on this data set is warranted.}, number={5}, journal={REMOTE SENSING}, author={Matthews, Jessica L. and Peng, Ge and Meier, Walter N. and Brown, Otis}, year={2020}, month={Mar} }
 @article{runkle_sugg_leeper_rao_matthews_rennie_2020, title={Short-term effects of specific humidity and temperature on COVID-19 morbidity in select US cities}, volume={740}, ISSN={["1879-1026"]}, url={https://doi.org/10.1016/j.scitotenv.2020.140093}, DOI={10.1016/j.scitotenv.2020.140093}, abstractNote={Little is known about the environmental conditions that drive the spatiotemporal patterns of SARS-CoV-2. Preliminary research suggests an association with meteorological parameters. However, the relationship with temperature and humidity is not yet apparent for COVID-19 cases in US cities first impacted. The objective of this study is to evaluate the association between COVID-19 cases and meteorological parameters in select US cities. A case-crossover design with a distributed lag nonlinear model was used to evaluate the contribution of ambient temperature and specific humidity on COVID-19 cases in select US cities. The case-crossover examines each COVID case as its own control at different time periods (before and after transmission occurred). We modeled the effect of temperature and humidity on COVID-19 transmission using a lag period of 7 days. A subset of 8 cities were evaluated for the relationship with meteorological parameters and 5 cities were evaluated in detail. Short-term exposure to humidity was positively associated with COVID-19 transmission in 4 cities. The associations were small with 3 out of 4 cities exhibiting higher COVID19 transmission with specific humidity that ranged from 6 to 9 g/kg. Our results suggest that weather should be considered in infectious disease modeling efforts. Future work is needed over a longer time period and across different locations to clearly establish the weather-COVID19 relationship.}, journal={SCIENCE OF THE TOTAL ENVIRONMENT}, publisher={Elsevier BV}, author={Runkle, Jennifer D. and Sugg, Margaret M. and Leeper, Ronald D. and Rao, Yuhan and Matthews, Jessica L. and Rennie, Jared J.}, year={2020}, month={Oct} }
 @article{peng_matthews_wang_vose_sun_2020, title={What Do Global Climate Models Tell Us about Future Arctic Sea Ice Coverage Changes?}, volume={8}, ISSN={["2225-1154"]}, url={https://doi.org/10.3390/cli8010015}, DOI={10.3390/cli8010015}, abstractNote={The prospect of an ice-free Arctic in our near future due to the rapid and accelerated Arctic sea ice decline has brought about the urgent need for reliable projections of the first ice-free Arctic summer year (FIASY). Together with up-to-date observations and characterizations of Arctic ice state, they are essential to business strategic planning, climate adaptation, and risk mitigation. In this study, the monthly Arctic sea ice extents from 12 global climate models are utilized to obtain projected FIASYs and their dependency on different emission scenarios, as well as to examine the nature of the ice retreat projections. The average value of model-projected FIASYs is 2054/2042, with a spread of 74/42 years for the medium/high emission scenarios, respectively. The earliest FIASY is projected to occur in year 2023, which may not be realistic, for both scenarios. The sensitivity of individual climate models to scenarios in projecting FIASYs is very model-dependent. The nature of model-projected Arctic sea ice coverage changes is shown to be primarily linear. FIASY values predicted by six commonly used statistical models that were curve-fitted with the first 30 years of climate projections (2006–2035), on other hand, show a preferred range of 2030–2040, with a distinct peak at 2034 for both scenarios, which is more comparable with those from previous studies.}, number={1}, journal={CLIMATE}, publisher={MDPI AG}, author={Peng, Ge and Matthews, Jessica L. and Wang, Muyin and Vose, Russell and Sun, Liqiang}, year={2020}, month={Jan} }
 @article{matthews_shi_2019, title={Intercomparisons of Long-Term Atmospheric Temperature and Humidity Profile Retrievals}, volume={11}, ISSN={["2072-4292"]}, DOI={10.3390/rs11070853}, abstractNote={This study builds upon a framework to develop a climate data record of temperature and humidity profiles from high-resolution infrared radiation sounder (HIRS) clear-sky measurements. The resultant time series is a unique, long-term dataset (1978–2017). To validate this long-term dataset, evaluation of the stability of the intersatellite time series is coupled with intercomparisons with independent observation platforms as available in more recent years. Eleven pairs of satellites carrying the HIRS instrument with time periods that overlap are examined. Correlation coefficients were calculated for the retrieval of each atmospheric pressure level and for each satellite pair. More than 90% of the cases examining both temperature and humidity have correlation coefficients greater than 0.7. Very high correlation is demonstrated at the surface and two meter levels for both temperature (>0.99) and specific humidity (>0.93). For the period of 2006–2017, intercomparisons are performed with four independent observations platforms: radiosonde (RS92), constellation observing system for meteorology ionosphere and climate (COSMIC), global climate observing system (GCOS) reference upper-air network (GRUAN), and infrared atmospheric sounding interferometer (IASI). Very close matching of surface and two meter temperatures over a wide domain of values is depicted in all presented intercomparisons: intersatellite matches of HIRS retrievals, HIRS vs. GRUAN, and HIRS vs. IASI.}, number={7}, journal={REMOTE SENSING}, author={Matthews, Jessica L. and Shi, Lei}, year={2019}, month={Apr} }
 @article{peng_matthews_yu_2018, title={Sensitivity Analysis of Arctic Sea Ice Extent Trends and Statistical Projections Using Satellite Data}, volume={10}, ISSN={["2072-4292"]}, url={http://www.mdpi.com/2072-4292/10/2/230}, DOI={10.3390/rs10020230}, abstractNote={An ice-free Arctic summer would have pronounced impacts on global climate, coastal habitats, national security, and the shipping industry. Rapid and accelerated Arctic sea ice loss has placed the reality of an ice-free Arctic summer even closer to the present day. Accurate projection of the first Arctic ice-free summer year is extremely important for business planning and climate change mitigation, but the projection can be affected by many factors. Using an inter-calibrated satellite sea ice product, this article examines the sensitivity of decadal trends of Arctic sea ice extent and statistical projections of the first occurrence of an ice-free Arctic summer. The projection based on the linear trend of the last 20 years of data places the first Arctic ice-free summer year at 2036, 12 years earlier compared to that of the trend over the last 30 years. The results from a sensitivity analysis of six commonly used curve-fitting models show that the projected timings of the first Arctic ice-free summer year tend to be earlier for exponential, Gompertz, quadratic, and linear with lag fittings, and later for linear and log fittings. Projections of the first Arctic ice-free summer year by all six statistical models appear to converge to the 2037 ± 6 timeframe, with a spread of 17 years, and the earliest first ice-free Arctic summer year at 2031.}, number={2}, journal={REMOTE SENSING}, publisher={MDPI AG}, author={Peng, Ge and Matthews, Jessica L. and Yu, Jason T.}, year={2018}, month={Feb} }
 @article{claverie_matthews_vermote_justice_2016, title={A 30+Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation}, volume={8}, ISSN={["2072-4292"]}, DOI={10.3390/rs8030263}, abstractNote={In- land surface models, which are used to evaluate the role of vegetation in the context of global climate change and variability, LAI and FAPAR play a key role, specifically with respect to the carbon and water cycles. The AVHRR-based LAI/FAPAR dataset offers daily temporal resolution, an improvement over previous products. This climate data record is based on a carefully calibrated and corrected land surface reflectance dataset to provide a high-quality, consistent time-series suitable for climate studies. It spans from mid-1981 to the present. Further, this operational dataset is available in near real-time allowing use for monitoring purposes. The algorithm relies on artificial neural networks calibrated using the MODIS LAI/FAPAR dataset. Evaluation based on cross-comparison with MODIS products and in situ data show the dataset is consistent and reliable with overall uncertainties of 1.03 and 0.15 for LAI and FAPAR, respectively. However, a clear saturation effect is observed in the broadleaf forest biomes with high LAI (>4.5) and FAPAR (>0.8) values.}, number={3}, journal={REMOTE SENSING}, author={Claverie, Martin and Matthews, Jessica L. and Vermote, Eric F. and Justice, Christopher O.}, year={2016}, month={Mar} }
 @article{shi_matthews_ho_yang_bates_2016, title={Algorithm development of temperature and humidity profile retrievals for long-term HIRS observations}, volume={8}, number={4}, journal={Journal of Remote Sensing}, author={Shi, L. and Matthews, J. L. and Ho, S. P. and Yang, Q. and Bates, J. J.}, year={2016} }
 @article{peng_shi_stegall_matthews_fairall_2016, title={An Evaluation of HIRS Near-Surface Air Temperature Product in the Arctic with SHEBA Data}, volume={33}, ISSN={["1520-0426"]}, DOI={10.1175/jtech-d-15-0217.1}, abstractNote={Abstract}, number={3}, journal={JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY}, publisher={American Meteorological Society}, author={Peng, Ge and Shi, Lei and Stegall, Steve T. and Matthews, Jessica L. and Fairall, Christopher W.}, year={2016}, month={Mar}, pages={453–460} }
 @article{knapp_matthews_kossin_hennon_2016, title={Identification of Tropical Cyclone Storm Types Using Crowdsourcing}, volume={144}, ISSN={["1520-0493"]}, DOI={10.1175/mwr-d-16-0022.1}, abstractNote={Abstract}, number={10}, journal={MONTHLY WEATHER REVIEW}, author={Knapp, Kenneth R. and Matthews, Jessica L. and Kossin, James P. and Hennon, Christopher C.}, year={2016}, month={Oct}, pages={3783–3798} }
 @article{smith_matthews_2015, title={Quantifying uncertainty and variable sensitivity within the US billion-dollar weather and climate disaster cost estimates}, volume={77}, ISSN={["1573-0840"]}, DOI={10.1007/s11069-015-1678-x}, number={3}, journal={NATURAL HAZARDS}, author={Smith, Adam B. and Matthews, Jessica L.}, year={2015}, month={Jul}, pages={1829–1851} }
 @article{lattanzio_schulz_matthews_okuyama_theodore_bates_knapp_kosaka_schueller_2013, title={LAND SURFACE ALBEDO FROM GEOSTATIONARY SATELLITES: A Multiagency Collaboration within SCOPE-CM}, volume={94}, ISSN={["1520-0477"]}, DOI={10.1175/bams-d-11-00230.1}, abstractNote={Climate has been recognized to have direct and indirect impact on society and economy, both in the long term and daily life. The challenge of understanding the climate system, with its variability and changes, is enormous and requires a joint long-term international commitment from research and governmental institutions. An important international body to coordinate worldwide climate monitoring efforts is the World Meteorological Organization (WMO). The Global Climate Observing System (GCOS) has the mission to provide coordination and the requirements for global observations and essential climate variables (ECVs) to monitor climate changes. The WMO-led activity on Sustained, Coordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM) is responding to these requirements by ensuring a continuous and sustained generation of climate data records (CDRs) from satellite data in compliance with the principles and guidelines of GCOS. SCOPE-CM represents a new partnership between operational space agencies to coordinate the generation of CDRs. To this end, pilot projects for different ECVs, such as surface albedo, cloud properties, water vapor, atmospheric motion winds, and upper-tropospheric humidity, have been initiated. The coordinated activity on land surface albedo involves the operational meteorological satellite agencies in Europe [European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)], in Japan [the Japan Meteorological Agency (JMA)], and in the United States [National Oceanic and Atmospheric Administration (NOAA)]. This paper presents the first results toward the generation of a unique land surface albedo CDR, involving five different geostationary satellite positions and approximately three decades of data starting in the 1980s, and combining close to 30 different satellite instruments.}, number={2}, journal={BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY}, author={Lattanzio, Alessio and Schulz, Joerg and Matthews, Jessica and Okuyama, Arata and Theodore, Bertrand and Bates, John J. and Knapp, Kenneth R. and Kosaka, Yuki and Schueller, Lothar}, year={2013}, month={Feb}, pages={205–214} }
 @article{matthews_fiscus_smith_heitman_2013, title={Quantifying Plant Age and Available Water Effects on Soybean Leaf Conductance}, volume={105}, ISSN={0002-1962}, url={http://dx.doi.org/10.2134/agronj2012.0263}, DOI={10.2134/agronj2012.0263}, abstractNote={Given the ever‐present threat of drought and the knowledge that water availability is the strongest limiting factor in vegetation growth, it is important to characterize the effect of water limitations on agricultural production. In this study, a small field plot technique for controlling soil moisture content suitable for physiological research in moist, humid areas was tested. We characterized the effect of water stress on total leaf conductance (gl) for two distinct determinate soybean [Glycine max (L.) Merr.] genotypes. Based on these findings, a model of gl as a function of plant age and soil moisture content was formulated and validated. The dependency of gl on plant age was well represented by a parabolic function that increased throughout the vegetative period, peaked around anthesis, and decreased throughout the reproductive period and senescence. A sigmoidal function explained the relation of gl to plant‐available soil water content. This new empirical model effectively quantifies the response of gl to plant‐available soil water and plant age with a functional form similar to the abscisic acid related Tardieu–Davies model.}, number={1}, journal={Agronomy Journal}, publisher={Wiley}, author={Matthews, Jessica L. and Fiscus, Edwin L. and Smith, Ralph C. and Heitman, Joshua L.}, year={2013}, month={Jan}, pages={28–36} }