The Rocky Mountain Biological Laboratory (RMBL; Colorado, USA) is the site for many research projects spanning decades, taxa, and research fields from ecology to evolutionary biology to hydrology and beyond. Climate is the focus of much of this work and provides important context for the rest. There are five major sources of data on climate in the RMBL vicinity, each with unique variables, formats, and temporal coverage. These data sources include (1) RMBL resident billy barr, (2) the National Oceanic and Atmospheric Administration (NOAA), (3) the United States Geological Survey (USGS), (4) the United States Department of Agriculture (USDA), and (5) Oregon State University's PRISM Climate Group. Both the NOAA and the USGS have automated meteorological stations in Crested Butte, CO, ~10 km from the RMBL, while the USDA has an automated meteorological station on Snodgrass Mountain, ~2.5 km from the RMBL. Each of these data sets has unique spatial and temporal coverage and formats. Despite the wealth of work on climate‐related questions using data from the RMBL, previous researchers have each had to access and format their own climate records, make decisions about handling missing data, and recreate data summaries. Here we provide a single curated climate data set of daily observations covering the years 1975–2022 that blends information from all five sources and includes annotated scripts documenting decisions for handling data. These synthesized climate data will facilitate future research, reduce duplication of effort, and increase our ability to compare results across studies. The data set includes information on precipitation (water and snow), snowmelt date, temperature, wind speed, soil moisture and temperature, and stream flows, all publicly available from a combination of sources. In addition to the formatted raw data, we provide several new variables that are commonly used in ecological analyses, including growing degree days, growing season length, a cold severity index, hard frost days, an index of El Niño‐Southern Oscillation, and aridity (standardized precipitation evapotranspiration index). These new variables are calculated from the daily weather records. As appropriate, data are also presented as minima, maxima, means, residuals, and cumulative measures for various time scales including days, months, seasons, and years. The RMBL is a global research hub. Scientists on site at the RMBL come from many countries and produce about 50 peer‐reviewed publications each year. Researchers from around the world also routinely use data from the RMBL for synthetic work, and educators around the United States use data from the RMBL for teaching modules. This curated and combined data set will be useful to a wide audience. Along with the synthesized combined data set we include the raw data and the R code for cleaning the raw data and creating the monthly and yearly data sets, which facilitate adding additional years or data using the same standardized protocols. No copyright or proprietary restrictions are associated with using this data set; please cite this data paper when the data are used in publications or scientific events.
Adverse weather conditions are responsible for millions of vehicular crashes, thousands of deaths, and billions of dollars per year in economic and congestion costs. Many transportation agencies utilize a performance or mobility metric to assess how well they maintain road access; however, there is only limited consideration of meteorological impacts to the success of their operations. This research develops the Nebraska winter severity index (NEWINS), which is a daily event-driven index derived for the Nebraska Department of Transportation (NDOT). The NEWINS includes a categorical storm classification framework to capture atmospheric conditions and possible road impacts across diverse spatial regions of Nebraska. A 10-yr (2006–16) winter season database of meteorological variables for Nebraska was obtained from the National Centers for Environmental Information. The NEWINS is based on a weighted linear combination applied to the collected storm classification database to measure severity. The NEWINS results were compared to other meteorological variables, many used in other agencies’ winter severity indices. This comparison verified the NEWINS robustness for the observed events for the 10-yr period. An assessment of the difference between days with observed snow versus days with accumulated snow revealed 39% fewer snow-accumulated days than snow-observed days. Furthermore, the NEWINS results highlighted the greater number of events during the 2009/10 winter season and the lack of events during the 2011/12 winter season. It is expected that the NEWINS could help transportation personnel allocate efficiently resources during adverse weather events. Moreover, the NEWINS framework can be used by other agencies to assess their weather sensitivity.more » « less
- NSF-PAR ID:
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of Applied Meteorology and Climatology
- Page Range / eLocation ID:
- p. 1779-1798
- Medium: X
- Sponsoring Org:
- National Science Foundation
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