81 research outputs found
The Accumulated Winter Season Severity Index (AWSSI)
The character of a winter can be defined by many of its features, including temperature averages and extremes, snowfall totals, snow depth, and the duration between onset and cessation of winter-weather conditions. The accumulated winter season severity index incorporates these elements into one site-specific value that defines the severity of a particular winter, especially when examined in the context of climatological values for that site. Thresholds of temperature, snowfall, and snow depth are assigned points that accumulate through the defined winter season; a parallel index uses temperature and precipitation to provide a snow proxy where snow data are unavailable or unreliable. The results can be analyzed like any other meteorological parameter to examine relationships to teleconnection patterns, determine trends, and create sector-specific applications, as well as to analyze an ongoing winter or any individual winter season to place its severity in context
The Accumulated Winter Season Severity Index (AWSSI)
The character of a winter can be defined by many of its features, including temperature averages and extremes, snowfall totals, snow depth, and the duration between onset and cessation of winter-weather conditions. The accumulated winter season severity index incorporates these elements into one site-specific value that defines the severity of a particular winter, especially when examined in the context of climatological values for that site. Thresholds of temperature, snowfall, and snow depth are assigned points that accumulate through the defined winter season; a parallel index uses temperature and precipitation to provide a snow proxy where snow data are unavailable or unreliable. The results can be analyzed like any other meteorological parameter to examine relationships to teleconnection patterns, determine trends, and create sector-specific applications, as well as to analyze an ongoing winter or any individual winter season to place its severity in context
Spatial Accuracy of Climate Networks: A Case Study in Nebraska
Climate data are increasingly scrutinized for accuracy because of the need for reliable input for climaterelated decision making and assessments of climate change. Over the last 30 years, vast improvements to U.S. instrumentation, data collection, and station siting have created more accurate data. This study explores the spatial accuracy of daily maximum and minimum air temperature data in Nebraska networks, including the U.S. Historical Climatology Network (HCN), the Automated Weather Data Network (AWDN), and the more recent U.S. Climate Reference Network (CRN). The spatial structure of temperature variations at the earth’s surface is compared for timeframes 2005–09 for CRN and AWDN and 1985–2005 for AWDN and HCN. Individual root-mean-square errors between candidate station and surrounding stations were calculated and used to determine the spatial accuracy of the networks. This study demonstrated that in the 5-yr analysis CRN and AWDN were of high spatial accuracy. For the 21-yr analysis the AWDN proved to have higher spatial accuracy (smaller errors) than the HCN for both maximum and minimum air temperature and for all months. In addition, accuracy was generally higher in summer months and the subhumid area had higher accuracy than did the semiarid area. The findings of this study can be used for Nebraska as an estimate of the uncertainty associated with using a weather station’s data at a decision point some distance from the station
Climate Change: What Does It Mean for Nebraska?
Because Nebraska’s location on the North American continent is far removed from large bodies of water, Nebraskans experience a strong continental type climate. As such, residents do not benefit from the moderating influence of the ocean, and temperatures can have wide swings from day to day and season to season. Typical characteristics for a continental climate at this latitude are large temperature variability with warm summers dominated by convective thunderstorms, and cold winters influenced by snow and wind from mid-latitude cyclones
Development of a Long-Term (1884-2006) Serially Complete Dataset of U.S. Temperatures and Precipitation for Climate Services
Serially complete climate datasets with no missing data are necessary for a diverse group of users working in many economic sectors. In this article we describe the procedures used to create a Serially Complete Data set (SCD) for the U.S. We include the selection criterion applied to potential SCD stations, the various procedural steps and the details applied to each step. A few observations that were not previously digitized were obtained from observers official paper reports. The methods used to estimate missing data are the Spatial Regression Test and the Inverse Distance Weighting technique. Using the criterion for selecting stations we were able to include 2144 stations for the SCD that had at least 1 element (maximum/minimum temperature and/or precipitation) for a continuous period of at least 40 years. In addition, the quality control procedure assigned confidence intervals to all observations and many of the estimates. We continue to explore the options for estimating any missing data that remain after our 3 step approach and we look forward to changing the base data set form TD 3200 to GHCN
Regional Climate Trends and Scenarios for the U.S. National Climate Assessment Part 4. Climate of the U.S. Great Plains
This document is one of series of regional climate descriptions designed to provide input that can be used in the development of the National Climate Assessment (NCA). As part of a sustained assessment approach, it is intended that these documents will be updated as new and well-vetted model results are available and as new climate scenario needs become clear. It is also hoped that these documents (and associated data and resources) are of direct benefit to decision makers and communities seeking to use this information in developing adaptation plans.
There are nine reports in this series, one each for eight regions defined by the NCA, and one for the contiguous U.S. The eight NCA regions are the Northeast, Southeast, Midwest, Great Plains, Northwest, Southwest, Alaska, and Hawai‘i/Pacific Islands.
These documents include a description of the observed historical climate conditions for each region and a set of climate scenarios as plausible futures – these components are described in more detail below.
While the datasets and simulations in these regional climate documents are not, by themselves, new, (they have been previously published in various sources), these documents represent a more complete and targeted synthesis of historical and plausible future climate conditions around the specific regions of the NCA.
There are two components of these descriptions. One component is a description of the historical climate conditions in the region. The other component is a description of the climate conditions associated with two future pathways of greenhouse gas emissions
Theology, News and Notes - Vol. 31, No. 03
Theology News & Notes was a theological journal published by Fuller Theological Seminary from 1954 through 2014.https://digitalcommons.fuller.edu/tnn/1085/thumbnail.jp
Factors Associated with Preoperative Magnetic Resonance Imaging Use among Medicare Beneficiaries with Nonmetastatic Breast Cancer
Preoperative breast magnetic resonance imaging (MRI) use among Medicare beneficiaries with breast cancer has substantially increased from 2005 to 2009. We sought to identify factors associated with preoperative breast MRI use among women diagnosed with ductal carcinoma in situ (DCIS) or stage I-III invasive breast cancer (IBC)
Breast MRI in the Diagnostic and Preoperative Workup Among Medicare Beneficiaries With Breast Cancer
We compared the frequency and sequence of breast imaging and biopsy use for the diagnostic and preoperative workup of breast cancer according to breast MRI use among older women
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