This dataset contains Global gas flaring activity from the Along Track Scanning Radiometer (ATSR) (1991-2012) and Sea and Land Surface Temperature Radiometer (SLSTR) (2017-2018) sensors. The data records have been processed to identify persistent thermal anomalies with spectral characteristics consistent with the combustion of natural gas during industrial gas flaring activities. Two different datasets are produced globally for each sensor type, one recording the activity and the other the sampling of these assumed gas flaring sites. The activity dataset records whenever a flare is up, i.e. is actively combusting, and provides a characterisation of the flares behaviour in terms of radiant heat output (in W). The determination of radiant heat output is achieved using the single channel SWIR radiance method of Fisher and Wooster (2018), based on the MWIR radiance method used extensively in the analysis of biomass burning. The sampling dataset records whenever a flaring site is seen by the satellite (irrespective of whether it is up or not) and also provides information on typical levels of cloud cover in the vicinity of the flare. The activity dataset contains information on the point location of the flare (accurate to within approximately ±1km) in the form of a lat/lon coordinate. Also provided is an index lookup that can be used to simply aggregate the flaring activity into arc-minute bins and these are referred to as lat_arcmin and lon_arcmin in the dataset. The sampling dataset is provided at the spatial level of the arc-minute binning only, and when merged onto the activity dataset long-term assessment of a flaring site can be performed. Such estimates of flaring activity over extended time periods for a given flaring site can be made by calculating the flare up-time (times seen actively flaring over time period or the expected number of cloud free overpasses for time period) and the mean radiant heat output of the flare for the same time period. The product of time period (in seconds), the estimated flare up-time and the mean radiant heat output provides an estimate of typical flare activity.

The UK hourly rainfall data contain the rainfall amount (and duration from tilting syphon gauges) during the hour (or hours) ending at the specified time. The data also contains precipitation amounts, however precipitation measured over 24 hours are not stored. Over time a range of rain gauges have been used - see the linked MIDAS User Guide for further details. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSHRLY, DLY3208, SREW and SSER. The data spans from 1915 to 2017. This dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset.

The UK hourly weather observation data contain meteorological values measured on an hourly time scale. The measurements of the concrete state, wind speed and direction, cloud type and amount, visibility, and temperature were recorded by observation stations operated by the Met Office across the UK and transmitted within SYNOP, DLY3208, AWSHRLY and NCM messages. The sunshine duration measurements were transmitted in the HSUN3445 message. The data spans from 1875 to 2017. For details on observing practice see the message type information in the MIDAS User Guide linked from this record and relevant sections for parameter types. This dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Note, METAR message types are not included in the Open version of this dataset. Those data may be accessed via the full MIDAS hourly weather data.

The UK mean wind data contain the mean wind speed and direction, and the direction, speed and time of the maximum gust, all during 1 or more hours, ending at the stated time and date. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, DLY3208, HWNDAUTO and HWND6910. The data spans from 1949 to 2017. For further details on observing practice, including measurement accuracies for the message types, see relevant sections of the MIDAS User Guide linked from this record (e.g. section 3.3 details the wind network in the UK, section 5.5 covers wind measurements in general and section 4 details message type information). This dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record.

This dataset contains wind speed and direction, air temperature, relative humidity, barometric pressure, nitric oxide, nitric dioxide, nitric oxides, sulphur dioxide, carbon dioxide, ozone and pm2.5 based on a newly built-up rural site at Xibaidian, Pinggu district, Beijing in winter 2016 and summer 2017. The data were taken for the APHH-Beijing campaign for the Effects of air pollutions on cardiopulmonary disease in urban and peri-urban residents in Beijing (AIRLESS) project as part of the Atmospheric Pollution & Human Health in a Chinese Megacity (APHH) programme. Instruments were deployed on the roof of a one-story building in the far north end of a village, where most of the subjects resided nearby. Northern winds tend to bring relatively clean background air. In contrast, winds from the south are often contaminated by emissions from traffic and industries. The following instruments were used: 1. Meteorological parameter: TH16A meteorological station 2. NOx: TEI 42 trace level chemiluminescence NOx Analyzer; 3. SO2: Ecotech EC9850 Sulfur Dioxide Analyzer 4. CO: Ecotech EC9830 Carbon Monoxide Analyzer 5. O3: Ecotech EC9810 Ozone Analyzer 6. PM2.5: Met One BAM 1020 The dataset was collected in Xibaidian, Pinggu district, Beijing for the Effects of air pollutions on cardiopulmonary disease in urban and peri-urban residents in Beijing (AIRLESS) project can provide ambient level of air pollutant in rural Beijing, enabling better understanding of the exposure level for local residents and potential examination for the related health effects.

This dataset contains PM2.5 and meteorology measurements taken from Temuco and Padre Las Casas, Chile from June 2017 to July 2018. This data was collected for the NERC funded project Impact of Wood Burning Air Pollution on Preeclampsia and other Pregnancy Outcomes in Temuco which aimed to determine whether exposure to air pollutants (specifically PM2.5 and wood burning tracer) have an impact on preeclampsia and other pregnancy outcomes (low birth weight, birth weight, small of gestational age, preterm birth). The purpose of this data is to predict the spatio-temporal PM2.5 concentrations and wood tracers using land use regression models. The campaign included sampling at 40 fixed sites in parallel with sampling at a central site located at a government monitoring station to control for background levels. Sites tried to maximize the spatial distribution of likely predictors such as number of residential dwellings, number of wood-stoves, PM2.5 concentrations and traffic impact. Two-weeks PM2.5 samples were collected at each site and repeated in 4 sessions covering a whole year. Samples were analyzed for mass and the wood-burning tracers levoglucosan and soluble potassium.

This dataset contains tracks generated using a bespoke tracking algorithm developed within the BITMAP (Better understanding of Interregional Teleconnections for prediction in the Monsoon And Poles) project, identifying and linking upper-tropospheric vortices (described in Hunt et al, 2018, QJRMS - see linked documentation). This utilised data derived from from various simulation output for the WCRP Coupled Model Intercomparison Project, Phase 5 (CMIP5) 'RCP45' experiment. Similar datasets were produced using various model output from the WRCP CMIP5 'Historical' and 'RCP85' experiments and the ECMWF ERA-Interim reanalysis model output, also available within the parent dataset collection. Western disturbances (WDs) are upper-level vortices that can significantly impact the weather over Pakistan and north India. This is a catalogue of the tracks of WDs passing through the region (specifically 20-36.5N, 60-80E) on the 500 hPa layer. This differs from those tracks from the ECMWF Era-Interim data which were carried out on the 450-300 hPa layer. See linked documentation for details of the algorithms used. BITMAP was an Indo-UK-German project (NERC grant award NE/P006795/1) to develop better understanding of processes linking the Arctic and Asian monsoon, leading to better prospects for prediction on short, seasonal and decadal scales in both regions. Recent work had suggested that the pole-to-equator temperature difference is an essential ingredient driving variations in the monsoon. For further details on the project itself see the linked Project record.

This dataset contains tracks generated using a bespoke tracking algorithm developed within the BITMAP (Better understanding of Interregional Teleconnections for prediction in the Monsoon And Poles) project, identifying and linking upper-tropospheric vortices (described in Hunt et al, 2018, QJRMS - see linked documentation). This utilised data derived from from various simulation output for the WCRP Coupled Model Intercomparison Project, Phase 5 (CMIP5) 'RCP85' experiment. Similar datasets were produced using various model output from the WRCP CMIP5 'Historical' and 'RCP45' experiments and the ECMWF ERA-Interim reanalysis model output, also available within the parent dataset collection. Western disturbances (WDs) are upper-level vortices that can significantly impact the weather over Pakistan and north India. This is a catalogue of the tracks of WDs passing through the region (specifically 20-36.5N, 60-80E) on the 500 hPa layer. This differs from those tracks from the ECMWF Era-Interim data which were carried out on the 450-300 hPa layer. See linked documentation for details of the algorithms used. BITMAP was an Indo-UK-German project (NERC grant award NE/P006795/1) to develop better understanding of processes linking the Arctic and Asian monsoon, leading to better prospects for prediction on short, seasonal and decadal scales in both regions. Recent work had suggested that the pole-to-equator temperature difference is an essential ingredient driving variations in the monsoon. For further details on the project itself see the linked Project record.

The UK soil temperature data contain daily and hourly values of soil temperatures at depths of 5, 10, 20, 30, 50, and 100 centimetres. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM or DLY3208 messages. The data spans from 1900 to 2017. At many stations temperatures below the surface are measured at various depths. The depths used today are 5, 10, 20, 30 and 100cm, although measurements are not necessarily made at all these depths at a station and exceptionally measurements may be made at other depths. When imperial units were in general use, typically before 1961, the normal depths of measurement were 4, 8, 12, 24 and 48 inches. Liquid-in-glass soil thermometers at a depth of 20 cm or less are unsheathed and have a bend in the stem between the bulb and the lowest graduation. At greater depths the thermometer is suspended in a steel tube and has its bulb encased in wax. This dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record.

The UK daily temperature data contain maximum and minimum temperatures (air, grass and concrete slab) measured over a period of up to 24 hours. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM, DLY3208 or AWSDLY messages. The data span from 1853 to 2017. For details on measurement techniques, including calibration information and changes in measurements, see section 5.2 of the MIDAS User Guide linked to from this record. Soil temperature data may be found in the UK soil temperature datasets linked from this record. This dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily temperature observations within the full MIDAS collection.