Articles

Authors:	Jerald Brotzge, Steven Eddy, Zachary Suriano
Volume:	Volume 2026, No. 001
DOI:	https://doi.org/10.46275/JOASC.2026.03.001
Abstract:	Weather and climate impact every locale uniquely. Local weather hazards, climate extremes, socioeconomic vulnerability, and the sensitivity of local economic activities vary widely from one location to the next. As such, the weather-sensitive decisions that need to be made, and the subsequent need for local weather and climate information to support those decisions, vary as a function of local threat hazards and vulnerability. To better understand the decision-making, needs and gaps in weather and climate services across Kentucky, the Kentucky Climate Center hosted a 1.5-day workshop to better quantify these needs and gaps. The workshop was organized around six specific weather-sensitive sectors: (1) energy and transportation; (2) water and agriculture; (3) environment and conservation; (4) industry and commerce; (5) media and forecast providers; and (6) hazard mitigation and emergency management. The primary needs expressed by all sectors included more local, more frequent observations and more precise, longer-range forecasts. In addition, much greater organization of all available data and products would enable much broader adoption of these tools. This paper summarizes the findings from this workshop in the hope of providing an example and template for others to follow.
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Authors:	Michael T. Kiefer, Jeffrey A. Andresen, William J. Baule
Volume:	Volume 2025, No. 001
DOI:	https://doi.org/10.46275/JOASC.2025.09.001
Abstract:	In this study, three gridded climate datasets are examined for their ability to accurately reproduce temperature climatologies at observing sites across Michigan, United States during the period 1981-2018: Parameter-elevation Regressions on Independent Slopes Model (PRISM, versions D1 and D2), Gridded Surface Meteorological (gridMET), and Topography Weather (TopoWx). Michigan is the focus of this study since a comprehensive assessment of gridded climate datasets in a non-mountainous region with otherwise complex physiography is lacking. Two observational networks are utilized, one not assimilated into any of the gridded datasets (Enviroweather), and the other assimilated into all three datasets (Global Historical Climatology Network-Daily). Overall, TopoWx is found to exhibit the smallest deviation from observed daily temperatures, PRISMD2 is found to exhibit the smallest deviations from observed annual extreme minimum temperatures, and gridMET is found to exhibit the largest estimate–observation differences across all timescales. Maps of contoured PRISM-version temperature differences reveal distinct spatiotemporal patterns. Generally speaking, differences are maximized along the lakeshores, in areas with low station density, and at airport station sites. Despite the challenges involved in assessing gridded climate datasets, results of such studies are critical sources of user information. Knowledge of dataset strengths and weaknesses can help inform users as to which datasets and variables are likely to be most accurate or appropriate for a given application and timescale. Study results are expected to be applicable to other regions where terrain gradients, complex coastlines, and land use patterns are important climate controls.
Link:	https://stateclimate.org/pdfs/journal-articles/2025_1-Kiefer.pdf

Authors:	Annie Britton, Garrett Graham, Molly Woloszyn
Volume:	Volume 2024, No. 005
DOI:	https://doi.org/10.46275/JOASC.2024.09.001
Abstract:	Rangeland ecosystems in the United States hold great ecological, economic, and cultural value. However, the increasing frequency and severity of droughts pose potential threats to these ecosystems. This study used remotely sensed data, machine learning, and explainable artificial intelligence (XAI) to explore relationships between drought indices and vegetation health in the Cheyenne River Basin, USA. The study employed XGBoost Regressor and Extra Trees Regressor models in conjunction with SHapley Additive exPlanations (SHAP) to identify the most influential drought indices and environmental variables for predicting Normalized Difference Vegetation Index (NDVI), thereby uncovering indicators of vegetation stress in the basin. The XGBoost regressor was moderately successful at predicting NDVI, making the model suitable for subsequent XAI analysis using SHAP. SHAP results revealed that the Palmer Drought Severity Index (PDSI), the 90-day Standardized Precipitation Index (SPI), and snow water equivalent (SWE) were the most influential predictors of NDVI, indicating their strong association with changes in vegetation health in the Cheyenne River Basin. This study demonstrates the feasibility and value of applying XAI methods to investigate both the strength and directionality of ecological drought indicators—an approach that has been underutilized in drought research. These insights can inform future drought research, improve monitoring efforts, and help anticipate ecological drought impacts in the region.
Link:	https://stateclimate.org/pdfs/journal-articles/2024_5-Britton.pdf

Authors:	Jacob L. Fields, Geddy R. Davis, Zachary T. Leasor, Jason Cervenec
Volume:	Volume 2024, No. 004
DOI:	https://doi.org/10.46275/JOASC.2024.07.001
Abstract:	In recent years, advancements in the field of artificial intelligence have increased exponentially, culminating in widely available user-based tools such as ChatGPT. Although fairly new, these tools have as of yet been underutilized by the scientific community, including climatology. As a large language model, ChatGPT’s strong ability to accurately respond to prompts allows it to be used as a comprehensive tool with a variety of applications in climatology, which this article groups into practical and conceptual applications. Practically, ChatGPT excels in the assistance of code creation and troubleshooting, allowing for efficient automation of data collection, as well as the process of basic data sorting. Conceptually, the tool gives a foundation for researchers to “fill the knowledge gap” by gaining a basic understanding of supplementary information presented in the literature review portion of a research project. While ChatGPT is powerful, it contains significant limitations that hinder its status as a standalone tool, such as occasional inaccurate responses, lack of transparency, and absence of data protection. Despite these setbacks, use of ChatGPT in a responsible and ethical manner with awareness of its limitations can be efficient, dynamic, and adherent to the principles of scientific integrity.
Link:	https://stateclimate.org/pdfs/journal-articles/2024_4-Fields.pdf

Authors:	Russ S. Schumacher, Rebecca A. Bolinger, Jeffrey J. Lukas
Volume:	Volume 2024, No. 003
DOI:	https://doi.org/10.46275/JOASC.2024.06.002
Abstract:	The official climate divisions for the contiguous United States are used for a wide range of purposes, including ongoing climate monitoring, and through NOAA’s long-standing nClimDiv dataset. In Colorado, the climate divisions are based around the basins of the large rivers that flow out of the state. However, considering the complex topography and climate of the state, these divisions do not always represent key climate variations and changes. This study builds upon an approach first developed by Wolter and Allured to establish alternate climate divisions that more closely reflect observed climate variability across Colorado. Hierarchical cluster analysis is applied to gridded temperature and precipitation data (NOAA’s nClimGrid) from 1950-2021 to identify areas with similar climate variability, then manual inspection is used to establish 11 divisions. These resulting divisions are being used in an updated state-level climate change assessment. The method is flexible and uses open-source tools that could be extended to other regions or datasets.
Link:	https://stateclimate.org/pdfs/journal-articles/2024_3-Schumacher.pdf

Authors:	Kevin R. Brinson, Tracy L. DeLiberty, Daniel J. Leathers
Volume:	Volume 2024, No. 002
DOI:	https://doi.org/10.46275/JOASC.2024.06.001
Abstract:	Irrigation is an important agricultural management practice and the second largest consumer of fresh-water resources in Delaware. As more farmland is converted to irrigated agriculture, it is crucial that water resource managers be able to determine reasonable estimates of irrigation water usage in order to protect the resource. This study used a soil water balance approach to simulate irrigation for corn and soy-beans in Delaware (USA). The simulations were divided into four scenarios to determine which irrigation management method best represents agricultural irrigation water usage in Delaware. Two scenarios utilized an evapotranspiration-based (ET-based) approach using meteorological data from the Delaware Environmental Observing System (DEOS) with a soil water availability threshold to determine when and how much to irrigate. A second set of scenarios used a calendar-based approach and a rain gauge trigger to simulate irrigation. Analyses were performed to examine the seasonal and spatial variability of irrigation in Delaware and to compare simulated irrigation data to reported water use data provided by the Delaware Department of Natural Resources and Environmental Control’s Water Allocation Permit program. Seasonal irrigation varied due to environmental as well as irrigation decision making factors. Spatially, irrigation varied primarily as a result of the soil water holding properties. The ET-based scenario with a fixed amount of irrigation had the best agreement with the reported irrigated water use data. This study demonstrated the utility of high-resolution, environmental data with a soil water balance approach to improve estimates of irrigated water usage at the state and regional scale.
Link:	https://stateclimate.org/pdfs/journal-articles/2024_2-Brinson.pdf

Authors:	Joel Lisonbee, John Nielsen-Gammon, Blair Trewin, Gretel Follingstad, and Britt Parker
Volume:	Volume 2024, No. 001
DOI:	https://doi.org/10.46275/JOASC.2024.05.001
Abstract:	Should drought be considered an extreme dry period based on the entire record of available data? Or, should drought be considered a low in precipitation variability within the context of a present, contemporary climate? The two most common reference periods are the full period of record (all observed data or as much as possible) and a 30-year reference climatology. However, climate non-stationarity may render the "all-data" approach an inaccurate or obsolete comparison unless a trend is factored in. The aim of this review is to explore the literature for approaches to addressing these issues. The World Meteorological Organization (WMO) has recommended a 30-year reference period for most climatological applications since 1935, but for drought assessments and drought indices the modus operandi has been to use as much data as possible. However, in the literature, the “all data” approach has been challenged by evident impacts from climate change-induced non-stationarity. Over the past several years, as potential errors in drought assessments became more apparent due to a stationarity assumption when applying drought indices, several studies have adopted shorter reference periods, with 30-years being the most common. Furthermore, several recent papers have recommended using short reference periods with more frequent data updates for drought assessments to be representative of a contemporary climate. Additionally, at least 18 non-stationary drought indices have been proposed in efforts to retain long datasets and account for non-stationarity in the climate system.
Link:	https://stateclimate.org/pdfs/journal-articles/2024_1-Lisonbee.pdf

Authors:	Peter E. Goble, Horst W. Caspari, and Russ S. Schumacher
Volume:	Volume 2023, No. 1
DOI:	https://doi.org/10.46275/JOASC.2023.04.001
Abstract:	Western Colorado’s warm, dry summers and access to mountain river water for irrigation create ideal conditions for the growth of wine grapes, specifically cultivars of the European grape species Vitis vinifera. The largest limiting factor to Vitis vinifera production is nocturnal temperatures cold enough to damage crops, or Low Temperature Injury Events (LTIEs). LTIEs require producers to undergo the time-and-cost prohibitive venture of retraining vines. Eastern Mesa County Colorado has sustained large-scale grape production due to the area’s relatively mild cold season weather. Areas with similarly hospitable conditions may exist elsewhere within western Colorado. Parameter-elevation Regressions on Independent Slopes Model (PRISM) temperatures (1981-2020) were used to estimate the frequency of LTIEs across Colorado, and identify trends associated with a warming climate. In the interest of comparing PRISM temperatures to observations over actual vineyards, thermometers were placed on current vineyards in Montezuma County from 2016 to 2020. Findings suggest additional areas of opportunity for Vitis vinifera production exist in Colorado, particularly western Montezuma County, and western Mesa and Montrose Counties. Like eastern Mesa County, these areas experience a LTIE in fewer than 20 % of years. PRISM data also suggest southeast Colorado is becoming more hospitable for Vitis vinifera growth over time. Temperature measurements in Montezuma County during potentially lethal weather events compared closely with PRISM data, with a mean absolute difference of 1.8 ˚C. This comparison suggests PRISM is a reliable tool for identifying areas of opportunity in spite of western Colorado’s complex terrain.
Link:	https://stateclimate.org/pdfs/journal-articles/2023_1-Goble.pdf

Authors:	Olivia G. Campbell, Natalie A. Umphlett, and Crystal J. Stiles
Volume:	Volume 2022, No. 1
DOI:	http://doi.org/10.46275/JOASC.2022.01.001
Abstract:	Sufficient soil temperatures at the time of planting are essential for a well-established stand in both large-scale agriculture and recreational home gardening. Planting too early in the season increases the risk for frost damage and slow seedling growth while planting too late risks not reaching the required growing degree days (GDD) for plant maturity. In this study, a climatology of the date in which soils reach critical temperature thresholds for crops was developed for the Northern and Central Plains. At least 15 years of soil temperature data from 155 automated stations from six different networks were utilized in this study. Results showed that Minnesota consistently reached each soil temperature threshold last, while south-central Colorado reached each threshold first, with differences in air temperature and soil moisture likely playing a role. These results were incorporated into an online tool that both professional and recreational agriculturists can use to determine when soil temperatures are best for planting. It will also help put soil temperatures into context based on a climatological average
Link:	https://stateclimate.org/pdfs/journal-articles/2022_1-Campbell.pdf

Authors:	Nancy E. Westcott, Jason Cooper, Karen Andsager, Leslie A. Stoecker, and Karsten Shein
Volume:	Volume 2021, No. 2
DOI:	http://doi.org/10.46275/JOASC.2021.09.001
Abstract:	The Climate Data Modernization Program Forts and Volunteer Observer Database (CDMP-Forts) currently consists of 450 keyed and 355 quality-controlled stations for the period 1788–1892, reaching across the United States. In conjunction with the Global Historical Climate Network (GHCN) daily data, this resource is invaluable for examining 19th century weather and climate in the United States. CDMP-Forts is incomplete, however, with a considerable amount of data remaining to be digitally transcribed and quality controlled. It is the intent of this paper to provide an overview of the processes involved in rescuing these data and to show important ways these data can be used and the considerations that may have to be taken to create meaningful analyses. Finally, the dataset is placed in the context of other global datasets and efforts to rescue historical weather data.
Link:	https://stateclimate.org/pdfs/journal-articles/2021_2-Westcott.pdf