Observation List

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            "uuid": "c6b2f1ca5f8e4c5285fb4f69d1514a03",
            "title": "Lagrangian Dry Point data regarding the sensitivity of stratospheric water vapour to variability in tropical tropopause temperatures and large-scale transport",
            "abstract": "This dataset contains results in support of a publication that investigates processes affecting water vapour entry to the stratosphere. The back trajectories were calculated using the OFFLINE trajectory model. Past publications have shown the key processes are temperatures in the tropical tropopause layer and large-scale transport into the stratosphere using trajectory methods. Lagrangian Dry Points (LDPs) are normally calculated as the minimum water vapour saturation mixing ratio experienced along a back-trajectory that has traversed from troposphere to tropical lower stratosphere in its recorded history. \r\n\r\nThis study separated the two key processes by sampling alternative temperatures. These alternative temperatures are either time-shifted or averaged in time or longitude. This method is applied for two meteorological datasets: ERA-Interim (ERA-I) reanalysis for the period 1999-2009, and the UM-UKCA chemistry-climate model for eleven years of a repeat-year-2000 forcing scenario. The ERA-I trajectories were calculated by S Fueglistaler and S Liu for separate publications. The UM-UKCA climate model scenario was conducted by A Maycock. This dataset contains only the LDPs resulting from alternative-temperature sampling.\r\n\r\nThe directory UM-UKCA/LDP-original-T/ provides a simple view of the original unmodified method to calculate LDPs.\r\n\r\nLDP-alt-T/ directories contain LDPs determined with time-shifted alternative temperature samplings. The time-shift is identified by the alternative initialisation date, denoted in the filename and file metadata.\r\n\r\nLDP-ave-T/ directories contain LDPs determine with averaged alternative temperature samplings. The averaging is identified by the variable name and metadata. In the variable names, shorthand and full-name identifiers include 6h (6 hourly instantaneous), ZM (zonal mean), 30DZM (30-day rolling window mean and zonal mean), 120DM, 90DM, 60DM, 30DM, 15DM, 14DM, 8DM, 7DM, 4DM, 2DM, 1DM (rolling window 120 day mean, 90 day mean, etc.). \r\n\r\nNote that various alternative temperatures are recorded at each LDP calculated from each alternative temperature.\r\n\r\nFor more information on the directory structure, file naming conventions, variable naming conventions and attribute conventions please see the README.txt.",
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            "dataLineage": "Diabatic trajectories subjected to ERA-I meteorology were obtained from S Liu and S Fueglistaler. Kinematic trajectories subjected to UM-UKCA meteorology were calculated using the OFFLINE trajectory model (Methven et al. 1997, Liu et al. 2009) in a from modified by S Liu and by the data provider. Trajectories are initialised every 2 degrees longitude and latitude between 30 N - 30 S on a surface above the tropical tropopause layer (83 hPa surface for ERA-I and 400K potential temperature surface for UM-UKCA). Alternative temperature sampling was also calculated using OFFLINE. Lagrangian Dry Points were calculated using python processing scripts by the data provider. Data has been converted to CF-netCDF using python processing scripts by the data provider, and then passed on to the Centre for Environmental Data Analysis (CEDA).",
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                "title": "Combination of UM-UKCA deployed on unknown computer, OFFLINE deployed on linux machines at DAMTP, University of Cambridge, and python processing scripts deployed on data provider's computer.",
                "abstract": "Combination of Met Office Unified Model with active chemistry components (UM-UKCA) deployed on unknown computer, OFFLINE deployed on linux machines at DAMTP, University of Cambridge, and python processing scripts deployed on data provider's computer. The python scripts for processing and visualisation are not provided."
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                    "title": "iCASE PhD studentship with the UK Met Office: Processes determining stratospheric water vapour",
                    "abstract": "Most air enters the stratosphere in the tropical tropopause region, where temperatures are low, and the resulting dehydration through freeze-drying reduces water vapour concentrations to very small values. Notwithstanding the very low concentrations, stratospheric water vapour is important in the chemistry-climate system through its role in stratospheric ozone chemistry and also through its effects on the radiative balance of the troposphere. Model simulation of past and future changes depend on correct simulation of both the temperature distribution in the tropical tropopause region and the pathways taken by air parcels as they sample this distribution in moving from troposphere to stratosphere. Important aspects of this include both the annual cycle and the longitudinal variation in tropical tropopause temperatures and perhaps variation on intraseasonal and shorter timescales.\r\n\r\nThe co-operating partners in this project will be the University of Cambridge and the Met Office. Improving simulation of stratospheric water vapour remains a challenge for Met Office Earth System Models that are used for climate prediction. There are strong links between the water vapour distribution in the lower stratosphere and the tropopause temperatures which in turn determine water vapour, so positive feedbacks are possible that may significantly enhance the effects of modest errors in model representation of other relevant processes. The project will build on recent work in Cambridge and elsewhere that (a) has exploited trajectory techniques to examine the annual, interannual and longer-term links between tropopause temperatures and stratospheric water vapour and (b) has investigated the radiative coupling between water vapour and temperatures in the tropical tropopause region using a combination of offline radiative calculations and simple dynamical models. The focus of the project will be to analyse the variations of water vapour on monthly, annual, interannual and longer timescales simulated by the Met Office Unified Model (UM) and link these to the corresponding temperature and transport variations. (One component of this analysis would be\r\nthe use of a trajectory code which is already available for the UM.) The results will be compared against corresponding analysis of the recent history of the real atmosphere (some of which is already on record in scientific publications)."
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            "title": "ESA Ozone Climate Change Initiative (Ozone_cci): Total ozone column product (level 2) from the GOME satellite instrument, produced with the GODFIT v4 algorithm, version 0300",
            "abstract": "In Ozone_cci, Level2 total ozone column records from the Global Ozone Monitoring Experiment (GOME) have been processed with the GODFIT (Gome-type Direct FITting) version 4 algorithm developed at the Royal Belgian Institute for Space Aeronomie (BIRA-IASB). \r\n\r\nThe dataset has a global coverage and extends in time from 1995 to 2011.",
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                    "abstract": "The European Space Agency Ozone Climate Change Initiative (Ozone CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs). \r\nOzone_cci aims at generating new high-quality satellite data sets that are essential to assess the fate of atmospheric ozone and better understand its link with anthropogenic activities."
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            "title": "Global model data generated for volcanic halogen simulations: United Kingdom Earth System Model 1.0 (UKESM1.0), Unified Model version 11.2, N96L85, 1990s timeslice.",
            "abstract": "This dataset contains global model data generated for volcanic halogen simulations. Model used was the United Kingdom Earth System Model 1.0 (UKESM1.0), in an atmosphere-only configuration, with Met Office Unified Model version 11.2. The data is on a global N96 grid (192 x 144 points), and is a mid 1990s timeslice. These data were used to study the impact of co-emission of volcanic halogens on atmospheric composition and radiative forcing.\r\n\r\nThere are 6 experimental integrations for sulfur only (u-bw537, u-bw539, u-bw541, u-bs104, u-bs110, u-bs112) and sulfur & halogens (u-bw538, u-bw540, u-bw542, u-bs109, u-bs111, u-bs113), and a control (u-bs034) for each year. The files are labeled using variable codes such as m01s34i001 to determine the model variable field contained. A full description of what these are can be found in the included file variable_codes.txt.\r\n\r\nThe datasets are in NetCDF format, and were generated from the above model suites.",
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            "dataLineage": "Simulations run on Met Office MONSooN2. Data extracted from MASS to JASMIN as PP files. Converted to Cf-compliant NetCDF format using Python 3.7.1. The data were then delivered to the Centre for Environmental Data Anaylsis (CEDA) for archiving.",
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                "abstract": "This computation involved: UK Chemistry Aerosol Community Model - UKCA deployed on Cambridge University computer.  UKCA is a joint NCAS-Met Office programme funded by NCAS, GMR and DEFRA. Project partners are the Hadley Centre and the Universities of Cambridge and Leeds. Our objective is to develop, evaluate and make available a new UK community atmospheric chemistry-aerosol global model suitable for a range of topics in climate and environmental change research."
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            "title": "Ultrafine and Submicron Particles in the Urban Environment in Thailand: PM10 concentration and composition measurements in Lak Si, Bangkok, Thailand",
            "abstract": "This dataset contains PM10 concentration and composition measurements taken in Lak Si, Bangkok, Thailand. Particulate Matter under 10 micrometres diametre in size (PM10) was collected in 24 hour and 72 hour samples in a rooftop site in Lak Si, Bangkok, Thailand using a Sven Leckel LVS3 PM10 sampler. Samples were weighed and analysed for concentrations of the following elements: magnesium (Mg), aluminim (Al), calcium (Ca), vanadium (V), cromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn),  arsenic (As), selenium (Se), molybdenum (Mo), cadmium (Cd), antimony (Sb), barium (Ba) and lead (Pb). The data covered three seasons in Bangkok, hot, cool and rainy, from 5th March 2018 until 15th November 2018. Meteorological data was measured in the same location using a Gill Maximet 501 and Gill Maximet 100 weather station. Weather parameters measured included rainfall, wind speed, wind direction, pressure, relative humidity, temperature, dew point and solar radiation. \r\n\r\nMeasurements were taken by the staff of the Toxicology group in the Chulabhorn Research Institute, Thailand and the Atmospheric Chemistry Research Group in the University of Bristol, UK as part of the NERC grant Ultrafine and Submicron Particles in the Urban Environment in Thailand.",
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                "title": "Ultrafine and Submicron Particles in the Urban Environment in Thailand: PM10 concentration and composition measurements in Lak Si, Bangkok, Thailand",
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                    "title": "Ultrafine and Submicron Particles in the Urban Environment in Thailand - Size, Concentration, Composition and Health Impacts",
                    "abstract": "This project was the first to report ultrafine particle (UFP) number concentration and size distributions in the submicron (smaller than 1 micrometre) size range in urban Bangkok, Thailand.\r\n\r\nIt is well known that particulate matter (PM) poses a significant health risk, especially to urban dwellers, with often the poorest in society most affected. Ultrafine particles (size smaller than 100 nanometres), as a component of PM, are increasingly implicated in disease and mortality. However, much of the research available in the literature is based on data from the developed world, especially for ultrafine particles, and without robust data it is not possible to determine trends in this important pollutant for Thailand and Bangkok in particular, and strategies for health protection therefore lack this vital information. In addition, aerosol particles provide the single largest source of uncertainty in most global climate models, and production of primary particles and gas precursors e.g. Volatile Organic Compounds (VOCs) from the transport and industrial sectors contribute significantly to this. Therefore, in order for the impact of these activities on climate to be assessed and reduced, determination of sources and levels of emission of both gases and particles much be undertaken. NE/P014674/1"
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            "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Monthly climatology of global ocean colour data products, Version 5.0",
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                    "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)."
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            "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection, Version 5.0",
            "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 5.0 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020.  Note, the IOP data is also included in the 'All Products' dataset. \r\n\r\nThe inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320.  (A separate dataset is also available for data on a sinusoidal projection.)\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0",
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                    "uuid": "1dbe7a109c0244aaad713e078fd3059a",
                    "short_code": "coll",
                    "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci):  Version 5.0 Data",
                    "abstract": "This collection contains version 5.0 datasets produced by the Ocean Colour project of the ESA Climate Change Inititative (CCI).  The Ocean Colour CCI is producing long-term multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490 nm.   Information on uncertainties is also provided.\r\n\r\nThis dataset collection refers to the Version 5.0 data products held in the CEDA archive covering the period 1997-2020.   Links to the individual datasets that make up this collection are given in the record below.\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available."
                },
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                    "short_code": "coll",
                    "title": "National Centre for Earth Observation (NCEO) partnered datasets",
                    "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)."
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        {
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            "title": "Iceland Greenland seas Project (IGP): precipitation measurements from the University of Bergen Micro Rain Radar (MRR2) on board the NATO Research Vessel Alliance",
            "abstract": "This dataset contains measurements from the Micro Rain Radar (MRR2), manufactured by Meteorologische Messtechnik GmbH (Metek) installed onboard the NATO Research Vessel Alliance during the Iceland Greenland Seas Project. \r\nThe MRR2 is a frequency modulated (FM), continuous wave (CW) Radar (Radio Detection and Ranging) that obtains doppler spectral density at each range gate with a time resolution of 10 s. The terminal velocity of the precipitation targets (vT) is the primarily retrieved variable from these doppler spectral density observations. Additionally, drop size distribution and the corresponding moments, for example liquid water content (LWC), rain rate (RR) and Radar Reflectivity (Ze) are retrieved with post processing.\r\nThe initial installation location from 03-13 Feb 2018 was midship on the weatherdeck. At Reykjavik harbour the MRR2 worked as expected, while at sea artificial signals at three elevations appeared. The artificial signals were due to an interference on the power cable or power source. On 11 Feb 2018, a separate power source for the MRR2 could be secured, and it subsequently operated without interferences after ~12 UTC that day. For further details and figures on the MRR2 and its operation in the cruise please read the attached documentation.",
            "creationDate": "2022-07-22T09:15:57.183554",
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                    "title": "Iceland Greenland seas Project (IGP) including the Atmospheric Forcing of the Iceland Sea (AFIS)",
                    "abstract": "The Iceland Greenland seas Project (IGP) is an international project involving the UK, US a Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1)\r\n\r\nThe Iceland Sea - to the north and east of Iceland - is arguably the least studied of the North Atlantic's subpolar seas. However new discoveries are forcing a redesign of our conceptual model of the North Atlantic's ocean circulation which places the Iceland Sea at the heart of this system and suggests that it requires urgent scientific focus. The recently discovered North Icelandic Jet is thought to be one of two pathways for dense water to pass through the Denmark Strait - the stretch of ocean between Iceland and Greenland - which is the main route for dense waters from the north to enter the Atlantic. Its discovery suggests a new paradigm for where dense water entering the North Atlantic originates. However at present the source of the North Icelandic Jet remains unknown. It is hypothesized that relatively warm Atlantic-origin water is modified into denser water in the Iceland Sea, although it is unclear precisely where, when or how this happens. \r\n\r\nThis project examined the wintertime atmosphere-ocean processes in the Iceland Sea by characterising its atmospheric forcing, i.e. observing the spatial structure and variability of surface heat, moisture and momentum fluxes in the region and the weather systems that dictate these fluxes. In situ observations of air-sea interaction processes from several platforms (an aircraft; and via project partners an unmanned airborne vehicle, a meteorological buoy and a research vessel)  were made and used to evaluate meteorological analyses and reanalyses from operational weather forecasting centres. \r\n\r\nNumerical modelling experiments investigated the dynamics of selected weather systems which strongly influenced the region, but appear not to be well represented; for example, the boundary layers that develop over transitions between sea ice and the open ocean during cold-air outbreaks; or the jets and wakes that occur downstream of Iceland. The unique observations were used to improve model representation of these systems.\r\n\r\nThe project also carried out new high-resolution climate simulations. A series of experiments covered recent past and likely future situations; as well as some idealised situations such as no wintertime sea ice in the Iceland Sea region. This was done using a state-of-the-art atmospheric model with high resolution over the Iceland Sea to investigate changes in the atmospheric circulation and surface fluxes.   \r\n\r\nFinally, in collaboration with the international partners, the project analysed new ocean observations and establish which weather systems are important for changing ocean properties in this region. The project used a range of ocean and atmospheric models to establish how current and future ocean circulation pathways function.  In short, the project determined the role that atmosphere-ocean processes in the Iceland Sea play in creating the dense waters that flow through Denmark Strait and feed into the lower limb of the AMOC.\r\n\r\nThe subpolar region of the North Atlantic is crucial for the global climate system. It is where coupled atmosphere-ocean processes, on a variety of spatial scales, require an integrated approach for their improved understanding and prediction. This region has enhanced 'communication' between the atmosphere and ocean. Here large surface fluxes of heat and moisture make the surface waters colder, saltier and denser resulting in a convective overturning that contributes to the lower limb of the Atlantic Meridional Overturning Circulation (AMOC). The AMOC is an ocean circulation that carries warm water from the tropics northward with a return flow of cold water southwards at depth; it is instrumental in keeping Europe's climate relatively mild."
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                    "resolvedTerm": "RA-2"
                },
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                    "ob_id": 11091,
                    "vocabService": "clipc_skos_vocab",
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                    "resolvedTerm": "ATSR-2"
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                    "resolvedTerm": "RA"
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                    "resolvedTerm": "H2O Geomatics"
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                    "ob_id": 11136,
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            "ob_id": 32170,
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            "title": "Filter pack and cascade impactor samples of gas and aerosol particulate matter on the Island of Hawai'i (2018, 2019)",
            "abstract": "This dataset reports chemical speciation of airborne gas and aerosol particulate matter (PM) sampled in various locations on the Island of Hawai'i in 2018 and 2019. The 2018 samples were collected during a large eruption of Kilauea volcano.  The 2019 samples were collected during a period of very low volcanic activity. \r\n\r\nSamples were collected in several locations on the Island of Hawai'i, Hawaii, USA. \r\n\r\nTime-series samples were collected at \r\n-Leilani Estates\r\n-Volcano village\r\n-Pahala, Ocean View \r\n-Kailua-Kona\r\n-Mauna Loa Observatory in 2018 and 2019. \r\n\r\nPoint-source samples were collected at the following locations \r\n-The main erupting vent 'Fissure 8' on the Kilauea Volcano in  2018, and repeated in its vicinity post-eruption in 2019\r\n- The lava ocean entry point in 2018 and repeated in its vicinity post-eruption in 2019. \r\n\r\nThe samples were collected using filter packs (FP) and Sioutas cascade impactors (SKC). The instruments were used at ground-level in all cases except for samples FP_08_1, FP_ 09_1, FP_09_2, SKC_08 and SKC_09 which were attached to an Unoccupied Aircraft System (UAS) in order to safely access the erupting vent and the lava ocean entry. \r\n\r\nThe samples were then analysed using inductively-coupled plasma mass spectroscopy (ICP-MS), inductively-coupled plasma optical emission spectroscopy (ICP-OES) and ion chromatography (IC).  Sample analysis was done at the University of Leeds, United Kingdom (2018 samples) and the University of Leeds and Open University, United Kingdom (2019 samples). The results are reported as concentration per volume of air sampled (µg/m^3) to 2 significant figures.  This was done to \r\n- assess the dispersion of major and trace elements in a volcanic plume, and quantify their depletion rates from the source into the far-field (up to ~240 km downwind)\r\n- assess the impact of volcanic emissions on the composition of the local atmosphere.\r\n\r\nThe data were produced as a result of a collaborative project between the Universities of Leeds, Cambridge, Oxford (UK), Hawaiian Volcano Observatory of the United States Geological Survey, and the University of Hawai'i at Manoa (USA).",
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            "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). Data were reformatted by CEDA and approved by Dr Evgenia Ilyinskaya of the University of Leeds",
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                "short_code": "acq",
                "title": "Filter Packs and Cascade Impactors for the collection of sample on the island of Hawaii 2018-2019",
                "abstract": "Filter packs: Filter pack samplers were used to collect simultaneous samples of gas and bulk PM. The filter packs comprised one particle filter followed by 2-4 base-treated gas filters in an all-Teflon cartridge. Gas filters (Whatman Quantitative Filter Papers, Ashless, Grade 41, 55mm diameter) were pre-soaked with a 0.1M K2CO3 (+ glycerol) and dried approximately one week before use in the field. This base treatment of the gas filters capture acidic gases (e.g., SO2, HF and HCl). For some samples, the last gas filter in the filter pack contained >10% of the total captured gas concentration – this is evidence that the gas filters had become saturated; these gas samples are identified in the data file as 'saturated'.  The particle filter collects bulk (non size-resolved) PM. The particle filter used was Whatman PTFE 47 mm diameter, pore size 0.8 µm. The filters were pre-washed with UPA grade nitric acid before use on the 2019 campaign. The filters were not pre-washed before the 2018 campaign. Field and lab filter blanks were used to quantify the level of contamination due to the absence of acid wash. The contamination was found to be negligible in most samples due to the high sampled concentrations in the eruption-affected atmosphere in 2018. Airflow through the filter pack was generated using an external 12 V pump (Charles Austin Capex) running at ~20 l min-1. The flow rate was measured at the start and end of each sampling period. The uncertainty introduced by variations in the flow rate, and by the accuracy of the flow meter are 10%.\r\n\r\nCascade impactors: Cascade impactors size and collect particles through inertial impaction onto a series of stages. A filter is placed onto each stage to collect the PM. We used a 5-stage SKC Inc.  Sioutas impactor with Whatman and Zefluor PTFE filters (25 mm diameter on stages 1-4 and 37 mm diameter on stage 5, 0.2 µm pore size). Filters were acid-washed following the same procedure as described for filter packs. The Sioutas impactor resolves 5 size fractions between >2.5 µm and >0.25 µm at a flow rate of 9 l min-1. Airflow is created by an external pump with inbuilt battery (SKC Leland Legacy). The pumps had been calibrated prior to both campaigns by the manufacturer, with reported accuracy in the flow rate of 5%."
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            "title": "Methane Observations and Yearly Assessments (MOYA): lower troposphere greenhouse gas data taken over Pantanal, Mato Grosso do Sul, Brazil",
            "abstract": "This dataset contains CH4, CO2, CO, N2O and SF6 dry air molar fraction vertical profiles over the Pantanal, Mato Grosso do Sul, Brazil with air sampled using small aircraft and analysed at Laboratório de Gases de Efeito Estufa (LAGEE), Sao Jose dos Campos, Brazil.\r\n\r\nThe air was sampled during ascent of small airplane from 4.4 km above surface down to close to the ground. A series of flasks (17 flasks) were filled sequentially. The flasks were contained in a suitcase. Valves of the flasks were opened and closed by a programmable microcontroller. After sampling the suitcase were sent by mail to the high precision gas analytics laboratory LAGEE at Instituto Nacional de Pesquisas Espaciais (INPE), Sao Jose dos Campos, Brazil where the dry air molar fractions of the air of each flask were measured.\r\n\r\nThese data were collected as part of the Methane Observations and Yearly Assessments (MOYA) project funded by the Natural Environment Research Council (NERC) (NE/N016211/1).",
            "creationDate": "2022-07-22T09:15:57.183554",
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                "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)",
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                "date": "2020-06-05"
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                "short_code": "acq",
                "title": "Methane Observations and Yearly Assessments (MOYA): lower troposphere greenhouse gas data taken over Pantanal, Mato Grosso do Sul, Brazil",
                "abstract": "Methane Observations and Yearly Assessments (MOYA): lower troposphere greenhouse gas data taken over Pantanal, Mato Grosso do Sul, Brazil"
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            "title": "Methane Observations and Yearly Assessments (MOYA): Isotopic d13C methane measurements taken from Llanos de Moxos, Bolivia",
            "abstract": "This dataset contains air sample measurements of isotopic d13C methane. The measurements were collected using regular flask samples at Llanos de Moxos, Bolivia. The samples were analysed by Royal Holloway University of London using continuous flow gas chromatography/isotope ratio mass spectrometry (CF-GC/IRMS).\r\n\r\nDate of campaign:\r\n-31 Mar 2017, location: -15.024 -64.811,  Low to medium forest, with heights up to 7-8 meters, seasonally flooded\r\n-26 May 2017, location: -14.572 -64.869, Open savanah, ocassionally flooded, with palms and scattered trees\r\n-13 July 2017, location: -14.49 -64.86, Open savanah covered by grasses and herbs\r\n-20 Aug 2017, location: -14.49 -64.86, Open savanah covered by grasses and herbs\r\n\r\nThese data were collected as part of the Methane Observations and Yearly Assessments (MOYA) project funded by the Natural Environment Research Council (NERC) (NE/N016211/1).",
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Forecast errors occur primarily because the convective clouds are not accurately linked to the large-scale circulation or to the surface conditions, and these errors persist to long time scales. Worldwide, weather and climate forecast models are gaining resolution, and yet the errors in monsoon rainfall are not diminishing. A lack of detailed observations of the land, ocean and atmospheric parts of the monsoon system, on a range of temporal and spatial scales, is preventing a more thorough understanding of processes in monsoon convective clouds and at the land surface, and their interaction with the large-scale circulation. \r\n\r\nThe project used a programme of new measurements over India and the adjacent oceans to advance monsoon forecasting capability in the Indo-UK community. The first detachment of the FAAM research aircraft to India, in combination with an intensive ground-based observation campaign, will gather new observations of the land surface, the boundary layer structure over land and ocean, and atmospheric profiles. We will institute a new long-term series of measurements of energy and water exchanges at the land surface. Research measurements from one monsoon season will be combined with long-term observations on the Indian operational networks. Observations will be focused on two transects: in the northern plains of India, covering a range of surface types from irrigated to rain-fed agriculture, and wet to dry climatic zones; and across the Western Ghats, with transitions from land to ocean and across orography. The observational analysis will represent a unique and unprecedented characterization of monsoon processes linking the land, ocean and atmospheric patterns which control the rainfall. 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            "title": "APHH: Atmospheric NO, NO2 and NOx measurements made at Indian Institute of Technology (IIT) Delhi",
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            "abstract": "This is the HadISDH blend 1.1.1.2020f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH-blend is a near-global gridded monthly mean surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships and weather stations. The observations have been quality controlled and homogenised / bias adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). These data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2020.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the 1.0.0.2019f version to the end of 2020. It combines HadISDH.land.4.3.1.2020f and HadISDH.marine.1.1.0.2020f and therefore their respective update notes. Users are advised to read the update documents in the Docs section for full details.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775.\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E.,\r\nJones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and\r\ntemperature record for climate monitoring, Clim. Past, 10, 1983-2006,\r\ndoi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more\r\ndetail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de\r\nPodesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface\r\nspecific humidity product for climate monitoring. Climate of the Past, 9, 657-677,\r\ndoi:10.5194/cp-9-657-2013.",
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                "abstract": "HadISDH.blend combines HadISDH.marine and HadISDH.land at the 5 degree by 5 degree gridbox monthly mean level. Gridboxes containing both land and marine data are combined using a weighted average with a minimum and maximum weighting of 25% and 75% respectively. HadISDH.marine utilises simultaneous sub-daily temperature and dew point temperature data from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) ship data. All humidity variables are calculated at hourly resolution. \r\n\r\nQuality control, buddy checking and bias adjustment is applied at hourly resolution to adjust all observations to an observing height of 10 m, accounting for changing ship heights over time, and to adjust all non-ventilated instruments to mitigate the moist bias. Gridded monthly means, monthly mean anomalies and 1981 to 2010 climatologies are created. \r\n\r\nSee Docs 'HadISDH.marine process diagram'. Observation measurement, climatological, whole number presence and bias adjustment uncertainties are estimated for each observation and then gridded. 5° by 5° gridboxes are centred on -177.5°W and -87.5°S to 177.5°E and 87.5°N. Given the uneven distribution of observations over time and space, sampling uncertainty is estimated for each gridbox month. \r\n\r\nFor greater detail please see: Willett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I.: Development of the HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data, doi:10.5194/essd-12-2853-2020, 2020. \r\n\r\nDocs contains links to this publication. \r\n\r\nHadISDH.land utilises simultaneous subdaily temperature and dew point temperature data from over 3000 quality controlled HadISD stations that have sufficiently long records. All humidity variables are calculated at hourly resolution and monthly means are created. Monthly means are homogenised to detect and adjust for features within the data that do not appear to be of climate origin. While unlikely to be perfect, this process does help remove large errors from the data an improve robustness of long-term climate monitoring. The NCEI's Pairwise Homogenisation Algorithm has been used directly on DPD and T. An indirect PHA method (ID PHA) is used whereby changepoints detected in DPD and T are used to make adjustments to q, e, Tw and RH. Changepoints from DPD are also applied to T. Td is derived from homogenised T and DPD. \r\n\r\nSee Docs 'HadISDH.land process diagram'. Station measurement, climatological and homogeneity adjustment uncertainties are estimated for each month. Climatological averages are calculated over 1981-2010 and monthly mean climate anomalies obtained. These anomalies (in addition to climatological mean and standard deviation, actual values and uncertainty components) are then averaged over 5° by 5° gridboxes centred on -177.5°W and -87.5°S to 177.5°E and 87.5°N. Given the uneven distribution of stations over time and space, sampling uncertainty is estimated for each gridbox month. \r\n\r\nFor greater detail please see: Willett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E., Jones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and temperature record for climate monitoring, Clim. Past, 10, 1983-2006, doi:10.5194/cp-10-1983-2014, 2014. \r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface specific humidity product for climate monitoring. Climate of the Past, 9, 657-677, doi:10.5194/cp-9-657-2013. \r\n\r\nDocs contains links to both these publications."
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                "abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) provides high radiometric sensitivity in 36 spectral bands ranging from 0.4 to 14.4 micrometres. Two bands are imaged at a nominal resolution of 250 m at nadir, with five bands at 500 m, and the remaining 29 bands at 1 km.\r\n\r\nThe Terra Satellite is a NASA satellite flying 5 research instruments to study the Earth's atmosphere, ocean and land, including the MODIS instrument.   It was launched in 2003 and is still currently returning data.\r\n\r\nA MODIS instrument is also flown on NASA's AQUA satellite"
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                    "abstract": "Data products from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra and Aqua satellites as part of NASA-led international Earth Observation System (EOS) programme. MODIS provides high radiometric sensitivity (12 bit) in 36 spectral bands ranging in wavelength from 0.4 micrometres to 14.4 micrometres. Two bands are imaged at a nominal resolution of 250 m at nadir, with five bands at 500 m, and the remaining 29 bands at 1 km. A +/- 55-degree scanning pattern at the EOS orbit of 705 km achieves a 2,330-km swath and provides global coverage every one to two days. The main objective of MODIS is to enhance our understandings on global dynamics and processes occurring on the land, in the oceans and in the lower atmosphere, and these data are also essential in the development of validated, global and interactive Earth system models."
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                    "uuid": "3b0630c7fa264164868d4da5c9f90bed",
                    "short_code": "coll",
                    "title": "National Centre for Earth Observation (NCEO) Third Party Data",
                    "abstract": "The National Centre for Earth Observation (NCEO) Third Party data contains a broad range remotely sensed data acquired by satellite for use by the Earth Observation Scientific community supported by NCEO. The Centre for Environmental Data Analysis (CEDA) has archived and provides access to extensive Earth observation datasets under strict licensing conditions. Please see the individual dataset records for conditions of use."
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