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opencc-by-4.0Sep 2021View details →

Quantitative electronic structure and work-function changes of liquid water induced by solute - data

Data set pertaining to the article "Quantitative electronic structure and work-function changes of liquid water induced by solute" | Physical Chemistry Chemical Physics, 24, 1310 (2022). Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07 using the NXmpes user contributed format suggested by the Fairmat consortium, see https://www.nexusformat.org/ https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html A few extensions specific to liquid jet-experiments were added to the standard, and are explained in the notes-group on the top level of each file. NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format: * nexpy (distributed with python) * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium) In each NeXus file-entry, two types of spectra are shown: 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data'). 2. As-measured data ('raw'). Files with extension .txt are comma-separated ascii-files. The following files are provided: Photoemission data pertaining to solute measurements using the cut-off as energy reference: NaI_data.h5 tbai_data.h5 Biased spectra were typically recorded in the following order: [cut-off (fine), cut-off (coarse), (valence band)*(N repeats)]*(M repeats) To avoid the saving of overly complex hdf5-files, these data were saved in a different order, namely: [cut-off (fine)*(M repeats), cut-off (coarse)*(M repeats), (valence band)*(N*M repeats)]. Numeric representations of the traces shown in the article's figures: Figure_1a-data.txt Figure_1b-data.txt Figure_2a-data.txt Figure_2b-data.txt Figure_2c-data.txt Figure_3-data.txt Figure_4-data.txt Figure_5a-data.txt Figure_5b-data.txt Figure_6a-data.txt Figure_6b-data.txt Figure_6c-data.txt Figure_7_diff_spectra-data.txt Figure_8-data.txt Traces shown in several figures are included only in the data file pertaining to the figure in which they occur first.   Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .

opencc-by-4.0Mar 2023View details →

Water quality data from Turbinator

Sensors to monitor water quality are operating in harsh environments since they are in frequent contact with water. The Turbinator turbidity and water level sensor developed by IVL works around this issue by using a laser beam and a camera to measure turbidity and water level without being in contact with water. It can be used for early warning of pollutants or for predictive maintenance of a city's pipeline network for waste- and stormwater. The Turbinator is easy to install or de-install for example when the battery needs to be changed or the sensor is moved to a new location. No drilling is required. The installation and un-installation can be done without entering the drain, thus avoiding otherwise necessary safety measures. The sensors measure distance from the sensor to the water and turbidity. The dataset contains the following columns: <ul> <li>id: identifier of the Turbinator;</li> <li>dateobserved: timestamp of the measurement in UTC;</li> <li>location: Well Known Text representation of the GPS location of the Turbinator;</li> <li>turbidity: turbidity in NTU</li> <li>distance: distance between the sensor (top of the well) and the water in meters.</li> </ul>

opencc-by-4.0Apr 2023View details →

Water quality data from Talkpool sensor

Talkpool installed water quality sensors measuring temperature, conductivity, pH and turbidity in recipients receiving wast water from construction sites to be able to monitor the influence of waste water from construction sites on water quality in those recipients. Sensors are installed both upstream and downstream from the discharge point to be able to measure the effect of the waste water.

opencc-by-4.0May 2023View details →

HYPSTAR hyperspectral water reflectance and derived water quality products (suspended particulate matter and chlorophyll-a concentration) at the Blankaart surface water reservoir (BE)

Hyperspectral Water Leaving Reflectance spectra (2988) measured between 2021-02-03 and 2022-08-03 at the Blankaart Surface Water Reservoir (Belgium, 50.98857N, 2.835213E) with the HYPSTAR® (ID: HYPSTAR_12120241). Detailed description of the data collection, processing and analysis can be found in Goyens et al.- Remote Sens. 2022 - 14(21)- 5607; https://doi.org/10.3390/rs14215607. <ol> <li>HYPSTAR_W_BSBE_L2A_REFL_20210203_20220803_v1.csv</li> </ol> Chlorophyll-a (Chl-a) concentration and Suspended Particulate Matter (SPM) were derived from the above dataset of hyperspectral water reflectance and estimated according to different algorithms found in the litterature, i.e., <ol> <li>HYPSTAR_W_BSBE_CHLA_SIMIS_20210203_20220803_v1.csv: Chlorophyll-a concentration estimated from the HYPSTAR® reflectance measurements and following the algorithm suggested by Simis et al. (2005; https://doi.org/10.4319/lo.2005.50.1.0237) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_CHLA_CRAT_20210203_20220803_v1.csv: Chlorophyll-a concentration estimated with the HYPSTAR® reflectance measurements and following the algorithm suggested by Ruddick et al. (2001; https://doi.org/10.1364/AO.40.003575.) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_SPM_20210203_20220803_v1.csv: Suspended particulate matter estimated with the HYPSTAR® reflectance measurements and following the algorithm suggested by Nechad et al. (2010) at 700 nm</li> </ol>

Cuvette Centrale subset region - monthly water level data

These data were derived using the methods described in the paper: https://www.mdpi.com/2072-4292/15/12/3099 The filenames beginning with 'WL_monthly' contain the monthly minimum, maximum, mean, and standard deviation of the estimated daily water levels for a subset of the Cuvette Centrale region in the Central Congo Basin. The filenames beginning with just 'WL_' contain the minimum, maximum, mean, and standard deviation of the estimated daily water levels over the 20-month study period, March 2019 to October 2020.

Water quality data from s::can sensors in the Barcelona sewer system

Within the frame of the SCOREwater project, BCASA and s::can installed water quality sensors in the sewer system in three neighbourhoods with different socio-economic characteristics (Poblenou, Sant Gervasi and Carmel) in the City Of Barcelona. To prevent vandalism, the exact location of the sensors cannot be disclosed. The sensors are monitoring physico-chemical parameters in the sewer network. These data can be used for multiple purposes, both related to predictive maintenance of the sewer network and life style analysis of inhabitants of Barcelona: <ul> <li>a better operation and maintenance of the sewer network</li> <li>minimizing odor episodes and corrosion from H2S</li> <li>try to prevent blocking from sediments</li> <li>detect spills into the sewer system from a construction site or illegal discharge</li> <li>learn population habits from analyzing the waste water from the three different neighborhoods</li> <li>to learn about how the inhabitants use pharmaceuticals or other compounds (perhaps abusing of it).</li> </ul>

Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation at Lake Garda, GAIT site (Italy)

The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site in Lake Garda in Italy (GAIT). It is a subset of the complete data record which consists of the best quality GAIT measurements which could be used for satellite validation. The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as:$\rho_wnosc=\pi (Lu-\rho_FLd)/E_d$where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance).For the GAIT site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided:$\rho_w=\pi (Lu-\rho_FLd)/E_d-\epsilon$These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included.To obtain this dataset, we start from the full GAIT data record and omit all the data that do not pass all the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation:1. The coefficient of variation in water reflectance is below 10% in the 580-600 nm range2. The water reflectance (after correction for the NIR similarity) at 500 nm is below 0.1

Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation from the LPAR site (Argentina)

The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site in Rio de La Plata, LPAR, in Argentina. It is a subset of the complete data record which consists of the best quality LPAR measurements which could be used for satellite validation.  The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances (without NIR Similarity Correction, see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance (referred to as reflectance_nosc in the file) defined as: ρwnosc=π(Lu−ρFLd)/Ed where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included. To obtain this dataset, we start from the full LPAR data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation: 1. The coefficient of variation in water reflectance is below 10% in the 400-900 nm range 2. The water reflectance at 500 nm is below 0.1

Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation from the VEIT site (Italy)

The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site at Aqua Alta, Venice in Italy (VEIT). It is a subset of the complete data record which consists of the best quality VEIT measurements which could be used for satellite validation.  The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as: $\rho_wnosc=\pi (Lu-\rho_FLd)/E_d$ where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). For the VEIT site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided: $\rho_w=\pi (Lu-\rho_FLd)/E_d-\epsilon$ These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included. To obtain this dataset, we start from the full VEIT data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation: 1. The coefficient of variation in water reflectance is below 10% in the 580-600 nm range 2. The water reflectance (after correction for the NIR similarity) above 800 nm is below 0.01

Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation at Berre coastal lagoon, BEFR site (France)

The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site at Etang de Berre in France (BEFR). It is a subset of the complete data record which consists of the best quality BEFR measurements which could be used for satellite validation.  The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as: ρwnosc=π(Lu−ρFLd)/Ed   where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). For the BEFR site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided: ρw=π(Lu−ρFLd)/Ed−ϵ   These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included. To obtain this dataset, we start from the full BEFR data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation: 1. The coefficient of variation in water reflectance is below 10% in the 500-600 nm range 2. The water reflectance (after correction for the NIR similarity) between 700-900 nm is below 0.01

Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation at the mouth of the Gironde Estuary, MAFR site (France)

The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site at the Gironde Estuary, MAGEST Network, in France (MAFR). It is a subset of the complete data record which consists of the best quality MAFR measurements which could be used for satellite validation.  The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances without NIR Similarity Correction (see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as: ρwnosc=π(Lu−ρFLd)/Ed where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included. To obtain this dataset, we start from the full MAFR data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation: 1. The coefficient of variation in water reflectance is below 10% in the 600-700 nm range

Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation at the measurement tower MOW1, M1BE site (Belgium)

The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site at the measurement pole near the Zeebrugge harbour 3.65km from land, often called MOW1, in Belgium (M1BE). It is a subset of the complete data record which consists of the best quality M1BE measurements which could be used for satellite validation.  The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as: $\rho_wnosc=\pi (Lu-\rho_FLd)/E_d$ where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). For the M1BE site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided: $\rho_w=\pi (Lu-\rho_FLd)/E_d-\epsilon$ These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included. To obtain this dataset, we start from the full M1BE data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation: 1. The coefficient of variation in water reflectance is below 10% in the 500-600 nm range 2. The water reflectance at 500 nm is below 0.1 The data consists of 73 spectra ranging from 20230226T1431 till 20230429T1502. Coordinates of the site are the following: site_latitude = 51.360548 site_longitude = 3.118246 The site is owned by Afdeling Kust (https://www.agentschapmdk.be/nl).

Soil water content before and after rain events, May-July 2014-2016, Hainich, Germany, project AquaDiva

This dataset contains soil water content data used for the analysis published in Fischer-Bedtke et al., (2023).  It gives spatially distributed soil water contents evaluated at a rain event scale covering the following periods: May 5 - July 26 2014 May 13 - July 28 2015 May 25 - July 24 2016 The enclosed files cover two different soil depth: topsoil (soil depth 7 cm) and  subsoil (soil depth 27 cm). Fischer et al. (2023) give details about the included data and should be cited along the with the dataset when using the data. Related datasets Design information on the soil water content measurement points, including position to the next tree, hydraulic soil properties can be found in the follwowing associated dataset Metzger, Johanna Clara, Hildebrandt, Anke, & Filipzik, Janett. (2023). Soil moisture sensor network, design, location attributes and soil properties, Hainich, Germany, project AquaDiva (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8065170   References Fischer, C., Metzger, J. C., Demir, G., Wutzler, T., and Hildebrandt, A.: Throughfall spatial patterns translate into spatial patterns of soil moisture dynamics – empirical evidence, Hydrology and Earth System Sciences, https://doi.org/10.5194/hess-2022-418, 2023.

opencc-by-4.0Apr 2022View details →

Large Ensemble Dataset for Discovering Global Peak Water Limit of Future Groundwater Withdrawals Using 900 GCAM Runs

<h2>Global Groundwater Withdrawals Peak Over the 21st Century </h2> The large ensemble dataset contains groundwater related model outputs from 900 scenarios modeled using <a href="http://jgcri.github.io/gcam-doc/toc.html">Global Change Analysis Model (GCAM)</a>. The scenario ensemble members include five Shared Socioeconomic Pathways (SSPs), four Representative Concentration Pathways (RCPs), five global climate model outputs, three groundwater depletion limits, two surface water storage expansion regimes, and two historical groundwater depletion trends. <h3>Journal Article</h3> Niazi, H., Wild, T.B., Turner, S.W.D., Graham, N.T., Hejazi, M., Msangi, S., Kim, S., Lamontagne, J.R., & Zhao, M. (2024). <a href="https://rdcu.be/dFpb5">Global peak water limit of future groundwater withdrawals</a>. Nature Sustainability, 7(4), 413–422. <a href="https://doi.org/10.1038/s41893-024-01306-w" rel="nofollow">https://doi.org/10.1038/s41893-024-01306-w</a> Read full-text here: <a href="https://rdcu.be/dFpb5">https://rdcu.be/dFpb5</a>  <h3>Data Repository </h3> This data repository is to be used in combination with the main <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">meta-repository</a> containing all scripts and files for reproducing the experiment as well as the analysis and post-processing of the model outputs. Scripts and smaller files are provided in the <a href="https://github.com/JGCRI/niazi-etal_202X_xyz">GitHub meta-repository</a> whereas larger files are provided in this data repository. Please complete the repository by placing the files as described hereunder. Please find the GitHub meta-repository here: <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">https://github.com/JGCRI/niazi-etal_2024_nature-sustainability</a> Descriptions of files: <ol> <li>gcam-5.7z contains the GCAM version used to simulate 900 scenarios of plausible futures. The model folder contains all necessary input files to reproduce the simulations. <ul> <li>The model is to be used in combination with the <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">meta-repository</a> to setup batch runs on cluster.</li> <li>Please navigate to <a href="https://github.com/JGCRI/niazi-etal_202X_xyz/tree/main/model">model/</a> folder for other scenario-specific and model setup folders and files. gcam-5 is to be extracted in the same directory (./model/gcam-5/). </li> <li>For the first-time users of GCAM, please follow guidance on <a href="http://jgcri.github.io/gcam-doc/toc.html">GCAM wiki</a> to setup GCAM or for background knowledge. </li> </ul> </li> <li>crop_yeild.7z: This file contains inputs related to climate impacts on crop yields. This is to be downloaded and extracted in the <a href="https://github.com/JGCRI/niazi-etal_202X_xyz/tree/main/model/combined_impacts">model/combined_impacts/</a> folder. </li> <li>outputs-all.7z: Key model outputs queried and collated from 900 GCAM runs are explained hereunder. The files could be downloaded individually (.csv files) or all at once in .7z format (<a href="../api/files/80b237d3-b22f-499f-8b8e-76c3846720a0/outputs-all.7z">outputs-all.7z</a>). These files are to be placed in the <a href="https://github.com/JGCRI/niazi-etal_202X_xyz/tree/main/model/outputs">model/outputs</a> folder of the <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">meta-repository</a>.  <ul> <li>ag_prod_all_GW_scenarios.csv - Agricultural production across all scenario for 2050 and 2100 (tonnes)</li> <li>prices_water_withdrawal_all.csv - Water prices across all scenarios and years ($/km3)</li> <li>global_irrigated_prod_by_crop.csv - All irrigated agricultural production for each crop across and scenarios all years (tonnes)</li> <li>surface_water_production_all.csv - Runoff across all scenarios and years (km3)</li> <li>groundwater_production_FINAL.csv - Groundwater withdrawals across all scenarios and years (km3)</li> <li>water_withdrawals_desal_all.csv - Water withdrawals from desalination plants across all scenarios and years (km3)</li> </ul> </li> </ol> <h3>Short introduction to the study</h3> Using 900 GCAM runs, this study finds that global groundwater withdrawals are expected to peak around mid-century, followed by a decline through 21st century, exposing about half of the population living in one-third of basins to groundwater stress, with cost and availability of surface water storage being the most significant driver of future groundwater withdrawals. This first-ever robust, quantitative confirmation of the peak-and-decline pattern for groundwater, previously only known for fossil fuels and minerals, raises concerns for basins heavily dependent on groundwater. Niazi, H., Wild, T.B., Turner, S.W.D., Graham, N.T., Hejazi, M., Msangi, S., Kim, S., Lamontagne, J.R., & Zhao, M. (2024). <a href="https://rdcu.be/dFpb5">Global peak water limit of future groundwater withdrawals</a>. Nature Sustainability, 7(4), 413–422. <a href="https://doi.org/10.1038/s41893-024-01306-w" rel="nofollow">https://doi.org/10.1038/s41893-024-01306-w</a> Read full-text here: <a href="https://rdcu.be/dFpb5">https://rdcu.be/dFpb5</a> <h3>Contact </h3> Please reach out to Hassan Niazi at <a href="mailto:hassan.niazi@pnnl.gov">hassan.niazi@pnnl.gov</a> for any questions.

opencc-by-4.0Dec 2022View details →

Water in the terrestrial planet-forming zone of the PDS 70 disk

This release includes the portion of the JWST-MIRI MRS spectrum of the PDS 70 disk analysed in the paper by Perotti et al. (2023). The original observational data are part of the Guaranteed Time Observation (GTO) program 1282 (PI: Th. Henning) with observation number 66 and will become public on 2 August, 2023 on the MAST database (https://archive.stsci.edu/). This release contains: 1) the full rebinned (4.9-22.5 μm) JWST-MIRI MRS spectrum of PDS 70 presented in Fig. 2 of Perotti et al. (2023); 2) the JWST-MIRI MRS spectrum of PDS 70 in the 6.78-7.36 μm region used for the water line analysis shown in Fig. 3 of Perotti et al. (2023); 3) the Spitzer-IRS low-resolution spectrum observed as part of the Spitzer-IRS GTO program 40679 (PI: G. Rieke) shown in Fig.1 of Perotti et al. (2023).  The first dataset consists of one .csv file (1_PDS70_fig2_MIRI_Perotti23.csv) which contains the the 4.9-22.5 μm JWST-MIRI MRS spectrum of PDS 70 presented in Fig. 2 of Perotti et al. (2023). The spectrum is rebinned by averaging 15 spectral points and assign errors σ to the rebinned spectral points assuming a normal error distribution with equal weights for each individual spectral element. The second dataset consists of one .dat file and one python script. One .dat file contains the JWST-MIRI spectrum of PDS 70 in the 6.78-7.36 μm region shown in Fig. 3 of Perotti et al. (2023) where the brightest water emission lines are observed (2_PDS70_fig3_MIRI_Perotti23.dat). The JWST-MIRI continuum-subtracted spectrum and the best-fit water LTE slab model are included. The Python script used to reproduce Figure 3 of Perotti et al. (2023) is also provided (2_script_plot_fig3.py).  The third dataset consists of one .dat file (3_PDS70_fig1_IRS_Perotti23.dat) which represents the Spitzer-IRS low-resolution spectrum of PDS 70 shown in Figure 1 of Perotti et al. (2023).

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2003-2005)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2003–2005</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20030101 = 2003-01-01</li><li>Time reference end time: 20051231 = 2005-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2021-2022)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2021–2022</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20210101 = 2021-01-01</li><li>Time reference end time: 20221231 = 2022-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2015-2017)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2015–2017</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20150101 = 2015-01-01</li><li>Time reference end time: 20171231 = 2017-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2000-2002)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2000–2002</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20000101 = 2000-01-01</li><li>Time reference end time: 20021231 = 2002-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2009-2011)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2009–2011</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20090101 = 2009-01-01</li><li>Time reference end time: 20111231 = 2011-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>