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This dataset contains a record of daily mean air temperature for each of the U.S. Great Lakes from January 1, 1897 to October 22, 2023. These temperatures were derived using the following method. Daily maximum and minimum air temperature data were obtained from the Global Historical Climatology Network-Daily (GHCNd, Menne, et al. 2012) and the Great Lakes Air Temperature/Degree Day Climatology, 1897-1983 (Assel et al. 1995). Daily air temperature was calculated by taking a simple average of daily maximum and minimum air temperature. Following Cohn et al. (2021), a total of 24 coastal locations along the Great Lakes were selected. These 24 locations had relatively consistent station data records since the 1890s. Each of the selected locations had multiple weather stations in their proximity covering the historical period from 1890s to 2023, representing the weather conditions around the location. For most of the locations, datasets from multiple stations in the proximity of each location were combined to create a continuous data record from the 1890s to 2023. When doing so, data consistency was verified by comparing the data during the period when station datasets overlap. This procedure resulted in almost continuous timeseries, except for a few locations that still had temporal gaps of one to several days. Any temporal data gap less than 10 days in the combined timeseries were filled based on the linear interpolation. This resulted in completely continuous timeseries for all the locations. Average daily air temperature was calculated from by simply making an average of timeseries data from corresponding locations around each lake. This resulted in daily air temperature records for all five Great Lakes (Lake Superior, Lake Huron, Lake Michigan, Lake Erie, and Lake Ontario).
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Derived daily timeseries of weather, soil moisture and temperature, flow and nitrogen species (nitrate and nitrite, ammonium) concentrations data for the North Wyke Farm Platform National Biosciences Research Infrastructure, England
For a selection of catchments from the North Wyke Farm Platform in southwest England, where land use conversions have been introduced, daily time series data covering weather conditions (minimum temperature, maximum temperature, total rainfall, wind speed and solar radiation), near-surface soil status (moisture content and temperature), flow and concentrations of key nitrogen species (nitrate and nitrite, ammonium) have been filtered based on attached data quality tags . The datasets run between 2013 and March 2024. For the main climate variables, data gaps were infilled with preceding- and following-on daily data, observations from a nearby weather station or existing national datasets to generate a continuous data series for modelling. For the other data series, annual and seasonal summary statistics on data coverage are provided. Information on significant field events, such as ploughing, drilling and harvest, fertiliser applications and manure spreading were also tabulated.
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- Award ID(s):
- 2330502
- PAR ID:
- 10647348
- Publisher / Repository:
- Zenodo
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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As part of its long-term climate data core collection, the Niwot Ridge LTER has collected daily air temperature at the Saddle site since 1981. The Saddle station is located at 3525 m.a.s.l. and is an important point location to capture local, ambient meteorological conditions for many biological and environmental datasets collected nearby. The location of the Saddle station has also presented challenges to its operation. Freezing temperatures, snow deposition from strong winds following storms, and exposure to lightning are some elements that have disrupted instrument functionality, affected data quality, and made access for research staff difficult over time, especially in winter months. These interruptions have led to missing or faulty data at times and inconsistent data gap-filling. Additionally, a mixture of mechanical hygrothermograph chart and temperature sensors with electronic data loggers have been used since the inception of the Saddle station to measure and record air temperature. Thus, a close inspection of potential influence from instrument turnover and relevant notes from research staff is required for a quality, daily air temperature time series for Saddle. Here we present a quality-controlled, gap-filled, daily time series of maximum, average, minimum, and diurnal air temperatures that accounts for instrument turnover at the Saddle. Methods follow those used to gap-fill long-term daily air temperature at the Niwot Ridge LTER D1 and C1 stations so there is consistency among core collection daily air temperature datasets. Metadata for this data package centralizes the most complete station history for Saddle air temperature and includes notes to data users on aspects and limitations of the dataset to consider when using these data in scientific analyses.more » « less
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Abstract: Load forecasting plays a very crucial role in many aspects of electric power systems including the economic and social benefits. Previously, there have been many studies involving load forecasting using time series approach, including weather-load relationships. In one such approach to predict load, this paper investigates through different structures that aim to relate various daily parameters. These parameters include temperature, humidity and solar radiation that comprises the weather data. Along with natural phenomenon as weather, physical aspects such as traffic flow are also considered. Based on the relationship, a prediction algorithm is applied to check if prediction error decreases when such external factors are considered. Electricity consumption data is collected from the City of Tallahassee utilities. Traffic count is provided by the Florida Department of Transportation. Moreover, the weather data is obtained from Tallahassee regional Airport weather station. This paper aims to study and establish a cause and effect relationship between the mentioned variables using different causality models and to forecast load based on the external variables. Based on the relationship, a prediction algorithm is applied to check if prediction error decreases when such external factors are considered.more » « less
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