DOI: 10.17028/rd.lboro.28704884.v1
title: Loughborough University East Midlands campus meteorological data, 2008-2021

description: The weather station on the campus of Loughborough University, in the East Midlands of the UK, had fallen into disuse and disrepair by the mid-2000s, but in 2007 the availability of infrastructure funding made it possible to re-establish regular weather observation with new equipment. The meteorological dataset subsequently collected at this facility between 2008 and 2021 is archived here. The dataset comes as fourteen Excel (.xlsx) files of annual data, with explanatory notes in each.<b>Site description</b>The campus weather station is located at latitude 52.7632°, longitude -1.235° and 68 m a.s.l., in a dedicated paddock on a green space near the centre-east boundary of the campus. A cabin, which houses power and network points, sits 10 m to the northeast of the main meteorological instrument tower. The paddock is otherwise mostly open on an arc from the northwest to the northeast, but on the other sides there are fruit trees (mainly varieties of <i>prunus domestica</i>) at distances of 13–16 m, forming part of the university's "Fruit Routes" biodiversity initiative.<b>Data collection</b>Instruments were fixed to a 3 m lattice mast which is concreted into the ground in the centre of the paddock described above. Up to late July 2013, the instruments were controlled by a solar-charged, battery-powered Campbell Scientific CR1000 data logger, and periodically manually downloaded. From early November 2013, this logger was replaced with a Campbell Scientific CR3000, run from the mains power supply from the cabin and connected to the campus network by ethernet. At the same time, the station's Young 01503 Wind Monitor was replaced by a Gill WindSonic ultrasonic anemometer. This combination remained in place for the rest of the measurement period described here. Frustratingly, the CS215 temperature/relative humidity sensor failed shortly before the peak of the 2018 heatwave, and had to be replaced with another CS215. Likewise, the ARG100 rain gauge was replaced in 2011 and 2016. The main cause of data gaps is the unreliable power supply from the cabin, particularly in 2013 and 2021 (the latter leading to the complete replacement of the cabin and all other equipment). Furthermore, even though the post-2013 CR3000 logger had a backup battery, it sometimes failed to restart after mains power was lost, yielding data gaps until it was manually restarted. Nevertheless, out of 136 instrument-years deployment, only 36 are less than 90% complete, and 21 less than 75% complete.<b>Data processing</b>Data retrieved manually or downloaded remotely were filtered for invalid measurements. The 15-minute data were then processed to daily and monthly values, using the pivot table function in Microsoft Excel. Most variables could be output simply as midnight-to-midnight daily means (e.g. solar and net radiation, wind speed). However, certain variables needed to be referred to the UK and Ireland standard ‘Climatological Day’ (Burt, 2012:272), 0900-0900: namely, air temperature minimum and maximum, plus rainfall total. The procedure for this follows Burt (2012; https://www.measuringtheweather.net/) and requires the insertion of additional date columns into the spreadsheet, to define two further, separate ‘Climate Dates’ for maximum temperature and rainfall total (the 24 hours commencing at 0900 on the date given, ‘ClimateDateMax’), and for minimum temperatures (24 hours ending at 0900 on the date given, ‘ClimateDateMin’). For the archived data, in the spreadsheet tabs labelled ‘Output - Daily 09-09 minima’, the pivot table function derives daily minimum temperatures by the correct 0900-0900 date, given by the ClimateDateMin variable. Similarly, in the tabs labelled ‘Output - Daily 09-09 maxima’, the pivot table function derives daily maximum temperatures and daily rainfall totals by the correct 0900-0900 date, given by the ClimateDateMax variable. Then in the tabs labelled ‘Output - Daily 00-00 means’, variables with midnight-to-midnight means use the unmodified date variable. To take into account the effect of missing data, the tab ‘Completeness’ again uses a pivot table to count the numbers of daily and monthly observations where the 15-minute data are not at least 99.99% complete. Values are only entered into the ‘Daily data’ tab of the archived spreadsheets where 15-minute data are at least 75% complete; values are only entered into ‘Monthly data’ tabs where daily data are at least 75% complete.Wind directions are particularly important in UK meteorology because they indicate the origin of air masses with potentially contrasting characteristics. But wind directions are not averaged in the same way as other variables, as they are measured on a circular scale. Instead, 15-minute wind direction data in degrees are converted to 16 compass points (the formula is included in the spreadsheets), and a pivot table is used to summarise these into wind speed categories, giving the frequency and strength of winds by compass point.In order to evaluate the reliability of the collected dataset, it was compared to equivalent variables from the HadUK-Grid dataset (Hollis et al., 2019). HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations, which have been interpolated from meteorological station data onto a uniform grid to provide coherent coverage across the UK at 1 km x 1 km resolution. Daily and monthly air temperature and rainfall variables from the HadUK-Grid v1.1.0.0 Met Office (2022) were downloaded from the Centre for Environmental Data Analysis (CEDA) archive (https://catalogue.ceda.ac.uk/uuid/bbca3267dc7d4219af484976734c9527/). Then the grid square containing the campus weather station was identified using the Point Subset Tool of the NOAA Weather and Climate Toolkit (https://www.ncdc.noaa.gov/wct/index.php) in order to retrieve data from that specific location. Daily and monthly HadUK-grid data are included in the spreadsheets for convenience.Campus temperatures are slightly, but consistently, higher than those indicated by HadUK-grid, while HadUK-Grid rainfall is on average almost 10% higher than that recorded on the campus. Trend-free statistical relationships between campus and HadUK-grid data implies that there is unlikely to be any significant temporal bias in the campus dataset.<b>References</b>Burt, S. (2012). <i>The Weather Observer's Handbook</i>. Cambridge University Press, https://doi.org/10.1017/CBO9781139152167.Hollis, D, McCarthy, M, Kendon, M., Legg, T., Simpson, I. (2019). HadUK‐Grid—A new UK dataset of gridded climate observations. <i>Geoscience Data Journal</i> 6, 151–159, https://doi.org/10.1002/gdj3.78.Met Office; Hollis, D.; McCarthy, M.; Kendon, M.; Legg, T. (2022). <i>HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.1.0.0 (1836-2021)</i>. NERC EDS Centre for Environmental Data Analysis, https://dx.doi.org/10.5285/bbca3267dc7d4219af484976734c9527.