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opencc-by-4.0Mar 2020View details →

A long term hourly eddy covariance dataset of consistently processed CO2 and H2O Fluxes from the Tibetan Alpine Steppe at Nam Co (2005 - 2019)

The data set contains nearly 15 years of eddy covariance data from an alpine steppe ecosystem on the central Tibetan Plateau. The data was processed following standardized quality control methods to allow for comparability between the different years of our record and with other data sets. To ensure meaningful estimates of ecosystem atmosphere exchange, careful application of the following correction procedures and analyses was necessary: (1) Due to the remote location, continuous maintenance of the eddy covariance (EC) system was not always possible, so that cleaning and calibration of the sensors was performed irregularly. Furthermore, the high proportion of bare soil and high wind speeds led to accumulation of dirt in the measurement path of the infrared gas analyzer (IRGA). The installation of the sensor in such a challenging environment resulted in a considerable drift in CO2 and H2O gas density measurements. If not accounted for, this concentration bias may distort the estimation of the carbon uptake. We applied a modified drift correction procedure following Fratini et al. (2014) which, instead of a linear interpolation between calibration dates, uses the CO2 concentration measurements from the Mt. Waliguan atmospheric observatory as reference time series. (2) We applied rigorous quality filtering of the calculated fluxes to retain only fluxes which represent actual physical processes. (3) During the long measurement period, there were several buildings constructed in the near vicinity of the EC system. We investigated the influence of these obstacles on the turbulent flow regime to identify fluxes with uncertain land cover contribution and exclude them from subsequent computations. (4) We calculated the de-facto standard correction for instrument surface heating during cold conditions (hereafter called sensor self heating correction) following Burba et al. (2008) and a revision of the original method following Frank and Massman (2020). (5) Subsequently, we applied the traditional and widely used gap filling procedure following Reichstein et al. (2005) to provide a more complete overview of the annual net ecosystem CO2 exchange. (6) We estimated the flux uncertainty by calculating the random flux error (RE) following Finkelstein and Sims (2001) and by using the standard deviation of the fluxes used for gap filling (NEE_fsd) as a measure for spatial and temporal variation. References: <ol> <li>Burba, G. G., McDermitt, D. K., Grelle, A., Anderson, D., and XU, L. (2008). Addressing the influence of instrument surface heat exchange on the measurements of CO2 flux from open-path gas analyzers, Global Change Biology, 14, 1854-1876, <a href="https://doi.org/10.1111/j.1365-2486.2008.01606.x">https://doi.org/10.1111/j.1365-2486.2008.01606.x</a>.</li> <li>Finkelstein, P. L. and Sims, P. F. (2001). Sampling error in eddy correlation flux measurements, J. Geophys. Res. Atmos., 106, 3503–3509, doi:10.1029/2000JD900731.</li> <li>Frank, J. M. and Massman, W. J.: A new perspective on the open-path infrared gas analyzer self-heating correction, Agricultural and Forest Meteorology, 290, 107986, doi:10.1016/j.agrformet.2020.107986, 2020.</li> <li>Fratini, G., McDermitt, D. K., and Papale, D. (2004). Eddy-covariance flux errors due to biases in gas concentration measurements: origins, quantification and correction, Biogeosciences, 11, 1037-1051, <a href="https://doi.org/10.5194/bg-11-1037-2014">https://doi.org/10.5194/bg-11-1037-2014</a>.</li> <li>Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-m., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and valentini, R. (20050. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Global Change Biology, 11, 1424-1439, <a href="https://doi.org/10.1111/j.1365-2486.2005.001002.x">https://doi.org/10.1111/j.1365-2486.2005.001002.x</a>.</li> </ol>

opencc-by-sa-4.0May 2022View details →

Soil pH in H2O [-] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe

Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale. The short description of currently available soil properties: soil pH in H2O; Soil properties were predicted at fixed depths:     Surface soil = s0..0cm,     Subsoil 1 = s30..30cm,     Subsoil 2 = s60..60cm,     Subsoil 3 = s100..100cm. To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula. Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;

opencc-by-4.0Mar 2024View details →

Measurements and model simulations of iodine monoxide (IO) radical, water vapor (H2O), nitrogen dioxide (NO2) radical, formaldehyde (HCHO), gaseous elemental mercury (Hg0), and oxidized mercury (HgII) at Storm Peak Laboratory, Colorado, during April 2022

This dataset was compiled to accompany the manuscript Lee et al., titled "Elevated Tropospheric Iodine over the Central Continental United States: Is Iodine a Major Oxidant of Atmospheric Mercury?", submitted to AGU Geophysical Research Letters.   file01 contains two example spectral proofs for iodine monoxide (IO) radical measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Storm Peak Laboratory, CO (SPL; 3220 meters above sea level; 40.455 degrees North; 106.745 degrees West) during April 2022. file02 contains oxygen collision-induced absorption (O2-O2) slant column densities (SCDs) measured in a spectral fit window from 350 to 388 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file03 contains O2-O2 SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file04 contains IO SCDs measured in a spectral fit window from 417.5 to 438 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file05 contains water vapor (H2O) SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file06 contains nitrogen dioxide (NO2) radical SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file07 contains formaldehyde (HCHO) SCDs measured in a spectral fit window from 328,5 to 359 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file08 contains the profiles of pressure, temperature, O2-O2, ozone (O3), NO2, and H2O derived from ECMWF CAMS reanalysis (April 2022 at SPL) and used in the radiative transfer model McArtim3 to calculate weighting functions for the trace gas profile inversions of IO, H2O, NO2, and HCHO. file09 contains the a priori profiles used for the IO profile inversions during April 2022 at SPL. One profile assumes a "flat" profile shape with a constant volume mixing ratio of 0.10 pptv throughout the atmosphere. The other profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average. file10 contains the a priori profile used for the H2O profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average. file11 contains the a priori profile used for the NO2 profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average. file12 contains the a priori profile used for the HCHO profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average. file13 contains the IO tropospheric vertical column densities (VCDtrop; surface to 12 km), volume mixing ratios near instrument altitude (VMRinstr), and degrees of freedom (DoF) measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file14 contains the H2O VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file15 contains the NO2 VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file16 contains the HCHO VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022. file17 contains GEOS-Chem simulated temperature, relative humidity, IO VCDtrop & VMRinstr, H2O VCDtrop & VMRinstr, NO2 VCDtrop & VMRinstr, HCHO VCDtrop & VMRinstr, and bromine monoxide (BrO) radical VCDtrop & VMRinstr at SPL from April 1 to April 30, 2022. file18 contains the gaseous elemental mercury (Hg0) measured by the Utah State University dual-channel mercury system at SPL from April 1 to April 30, 2022. file19 contains the oxidized mercury (HgII) measured by the Utah State University dual-channel mercury system at SPL from April 1 to April 30, 2022. file20 contains the GEOS-Chem simulated Hg0 and HgII at SPL from April 1 to April 30, 2022. file21 contains the profiles of pressure, temperature, relative humidity, BrO, bromine atom (Br), methane (CH4), chlorine monoxide (ClO) radical, chlorine atom (Cl), carbon monoxide (CO), Hg0, peroxy radical (HO2), IO, iodine atom (I), NO2, hydroxyl radical (OH), and O3 used as constraints for the gas-phase mercury box model. All profiles except IO and I are adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average. The IO profile was calculated by scaling the GEOS-Chem April 2022 daytime (SZA < 85) average below 12 km by the average observed IO VCDtrop during April 2022. The I atom profile was calculated by multiplying the scaled IO profile by the ratio of unscaled I / unscaled IO profiles from GEOS-Chem.   file22 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file23 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file24 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file25 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file26 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file27 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file28 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file29 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file30 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file31 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file32 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file33 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file34 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file35 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file36 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file37 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file38 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file39 contains the time-resolved gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient.   file40 contains a profile of the gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file41 contains a profile of the gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file42 contains a profile of the gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file43 contains a profile of the gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file44 contains a profile of the gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file45 contains a profile of the gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file46 contains a profile of the gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file47 contains a profile of the gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file48 contains a profile of the gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgOH as reference for the B-value in the HgI equilibrium coefficient. file49 contains a profile of the gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file50 contains a profile of the gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file51 contains a profile of the gas-phase mercury box model output assuming that HgI forms at half the rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file52 contains a profile of the gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file53 contains a profile of the gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file54 contains a profile of the gas-phase mercury box model output assuming that HgI forms at the same rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file55 contains a profile of the gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 8 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file56 contains a profile of the gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 9.5 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient. file57 contains a profile of the gas-phase mercury box model output assuming that HgI forms at twice the rate as HgBr and that the Hg-I bond strength is 11 kcal / mol, using HgBr as reference for the B-value in the HgI equilibrium coefficient.

opencc-by-sa-4.0Oct 2018View details →

Soil pH in H2O at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution

Soil pH in H2O in × 10 at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Processing steps are described in detail <a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a>. Antarctica is not included. To access and visualize maps use:  <a href="http://www.openlandmap.org/">OpenLandMap.org</a> If you discover a bug, artifact or inconsistency in the maps, 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">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: <ul> <li>sol = theme: soil,</li> <li>ph.h2o = variable: soil pH in H2O,</li> <li>usda.4c1a2a = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>

opencc-by-4.0May 2023View details →

TCOM-H2O: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric H2O profile dataset [1991-2021] constructed using machine-learning.

Methodology:   The TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data. <h3>H2O Profile Processing and Bias Correction</h3> Collocated H2O profiles are organized into five distinct latitude bins: <ul> <li> NH polar: 90∘N - 50∘N </li> <li> NH mid-lat: 20∘N - 70∘N </li> <li> Tropics: 40∘S - 40∘N </li> <li> SH mid-lat: 70∘S - 20∘S </li> <li> SH polar: 90∘S - 50∘S </li> </ul> Initially, differences between TOMCAT and satellite measurements (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from 10,km to 60,km). Note that TOMCAT may not accurately capture H2O evolution post-HTHH eruption due to the sparse spatial coverage of ACE measurements, which limits training data. Separate XGBoost regression models are then trained for these H2O differences at each height level within a given latitude bin. These trained models are subsequently used to estimate H2O bias corrections for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles. Height-resolved H2O profile data are then interpolated onto 28 standard pressure levels (from 300,hPa to 0.1,hPa), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions. We acknowledge the inherent dry biases in the original TOMCAT H2O profiles, largely because the TTL entry mixing ratios are based on a simplistic sinusoidal seasonal cycle, which omits the H2O enhancement contributed by tropical convective clouds. <h3>Data Files</h3> The dataset includes two files containing daily mean zonal mean H2O profiles: <ul> <li> <code>zmh2o_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</code>: Contains height level data (10,km to 60,km). </li> <li> <code>zmh2o_TCOM_plev_T2Dz_2000-2024_V1.1.nc</code>: Contains pressure level data (300,hPa to 0.1,hPa). </li> </ul> <h3>Reference Publication</h3> This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication: Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, <a title="null" href="https://doi.org/10.5194/essd-15-5105-2023">https://doi.org/10.5194/essd-15-5105-2023</a>, 2023.

opencc-by-4.0Jun 2022View details →

Carbonate Chemistry and δ18O-H2O from East and West Greenland fjords, August 2018 and 2016

Greenland’s fjords and coastal waters are highly productive and sustain important fisheries, but retreating glaciers and increasing meltwater supply are changing fjord circulation and biogeochemistry, which may threaten the future productivity of these unique ecosystems. The freshening of Greenland fjords caused by unprecedented melting of the Greenland Ice Sheet has the potential to alter carbonate chemistry in coastal waters, which would influence CO2 uptake as well as have biological consequences from acidification. However, few studies to date explore the current acidification state in Greenland coastal waters. Here we present the first-ever large-scale measurements of carbonate system parameters and δ18O measurements in 16 Greenlandic fjords and by combining datasets from two August cruises. HDMS Lauge Koch and RV Sanna cruises sampled on the East and West coast of Greenland in August 2018 and 2016 respectively. This dataset consists of 52 carbonate chemistry sample sites where dissolved inorganic carbon (DIC), total alkalinity (TA), and δ18O-H2O were measured throughout the water column. Water samples were collected in Niskin bottles and were transferred directly into triplicate 12 ml exetainers with a gas tight Tygon tubes, allowing overflow of at least 3 times the volume of the exetainer. Triplicate exetainers were collected for both DIC and TA at each depth. Samples were preserved with HgCl2 (saturated solution) to a final concentration of 0.02%. TA was measured on an Apollo SciTech AS-ALK2 total alkalinity titrator based on the Gran titration procedure for samples in West Greenland. While samples for East Greenland were measured on automatic titrator (Metrohm 888 Titrando), and a combined Metrohm glass electrode (Unitrode). DIC samples were analyzed on Apollo SciTech's AS-C3 analyzer for both cruises, using a sample volume of 0.5 ml. Routine analysis of Certified Reference Materials (provided by A. G. Dickson, Scripps Institution of Oceanography) verified that the accuracy of DIC and TA measurements. Coalescing this dataset therefore provides the first-ever analysis of wide-scale Greenland Fjord carbonate chemistry.   Environmental variables recorded by CTD instruments are available for cruises from the West and East coasts respectively at the following DOIs: <a href="https://doi.org/10.5281/zenodo.4062024">https://doi.org/10.5281/zenodo.4062024</a>  and <a href="https://doi.org/10.5281/zenodo.5572329">https://doi.org/10.5281/zenodo.5572329</a>.   We would like to thank the crew on board the HDMS Lauge Koch and RV Sanna for their collaboration. The Cruises were funded by Danish Centre for Marine Science (Grants: 2016-05 and 2017-06) and by the EU Horizon2020 funded project INTAROS (grant no. 727890) and the Danish Cooperation for Environment in the Arctic. This dataset is a contribution the project FACE-IT (The Future of Arctic Coastal Ecosystems – Identifying Transitions in Fjord Systems and Adjacent Coastal Areas). FACE-IT has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 869154.

opencc-by-4.0Dec 2023View details →

High-frequency wind (u, w, v, Ts) and gas concentration measurements of CO2 and H2O over an agricultural field in Braunschweig, Germany

This dataset contains high-frequency eddy covariance (EC) measurements over a flat agricultural field at the Thünen Institute in Braunschweig, Germany (52.30° N, 10.45° E). The data collection period spanned 77 days in the year 2020 split into three files   <table> <tbody> <tr> <td>BS2020_06.rds</td> <td>June 11 to July 15</td> </tr> <tr> <td>BS2020_10.rds</td> <td>October 1 to November 10</td> </tr> <tr> <td>BS_2020_07_subset.rds</td> <td>July 11 to July 25</td> </tr> </tbody> </table>   Included in the dataset are 3D wind velocity data, recorded using a uSonic-3 Class A sonic anemometer from Metek GmbH. Additionally, the dataset provides gas concentration measurements for carbon dioxide (CO2) and water vapor (H2O), captured using an LI-7500A open-path infra-red gas analyzer from LI-COR Biosciences GmbH, Germany. Variables in the dataset <table> <tbody> <tr> <th>Variable Name</th> <th>Description</th> <th>Units</th> </tr> </tbody> <tbody> <tr> <td>time</td> <td>Unique time stamp (POSIXct format)</td> <td>Seconds since Unix epoch</td> </tr> <tr> <td>CO2</td> <td>Wet molar density of carbon dioxide</td> <td>µmol m⁻³</td> </tr> <tr> <td>H2O</td> <td>Wet molar density of water vapor</td> <td>mmol m⁻³</td> </tr> <tr> <td>Ts</td> <td>Sonic temperature</td> <td>Kelvin</td> </tr> <tr> <td>u, v, w</td> <td>3D wind velocity components</td> <td>m s⁻¹</td> </tr> </tbody> </table>     <div> <div> <div> The dataset is in RDS format (version 3), compatible with R version 3.5.0 or higher. RDS is a binary file format native to the R programming environment </div> </div> </div>

opencc-by-sa-4.0Jul 2016View details →

Structure of Complement C3(H2O) Revealed By Quantitative Cross-Linking/Mass Spectrometry And Modeling

The slow but spontaneous and ubiquitous formation of C3(H2O), the hydrolytic and conformationally rearranged product of C3, initiates antibody-independent activation of the complement system that is a key first line of antimicrobial defense. The structure of C3(H2O) has not been determined. Here we subjected C3(H2O) to quantitative cross-linking/mass spectrometry (QCLMS). This revealed details of the structural differences and similarities between C3(H2O) and C3, as well as between C3(H2O) and its pivotal proteolytic cleavage product, C3b, which shares functionally similarity with C3(H2O). Considered in combination with the crystal structures of C3 and C3b, the QCMLS data suggest that C3(H2O) generation is accompanied by the migration of the thioester-containing domain of C3 from one end of the molecule to the other. This creates a stable C3b-like platform able to bind the zymogen, factor B, or the regulator, factor H. Integration of available crystallographic and QCLMS data allowed the determination of a 3D model of the C3(H2O) domain architecture. The unique arrangement of domains thus observed in C3(H2O), which retains the anaphylatoxin domain (that is excised when C3 is enzymatically activated to C3b), can be used to rationalize observed differences between C3(H2O) and C3b in terms of complement activation and regulation. For more information about how to reproduce this modeling, see the <a href="https://salilab.org/Complement/">Sali lab website</a> or the README file.

opencc-by-4.0Oct 2019View details →

Lookup tables for H2SO4-H2O binary and H2SO4-H2O-NH3 ternary homogeneous and ion-mediated nucleation

2019/10/10 This directory contains fortran code and lookup tables for calculating nucleation rate for the following four nucleation mechanims: 1. H2SO4-H2O binary homogenous nucleation (BHN), 2. H2SO4-H2O-NH3 ternary homogeneous nucleation (THN), 3. H2SO4-H2O-Ions binary ion-mediated nucleation (BIMN), and 4. H2SO4-H2O-NH3-Ions ternary ion-mediated nucleation (TIMN) References: 1. Yu, F., Nadykto, A. B., Herb, J., Luo, G., Nazarenko, K. M., and  Uvarova, L. A.: H2SO4-H2O-NH3 ternary ion-mediated nucleation (BHN):  Kinetic-based model and comparison with CLOUD measurements, Atmos.  Chem. Phys.,18, 17451-17474,  https://doi.org/10.5194/acp-18-17451-2018, 2018. 2. Yu, F., Nadykto, A. B., Herb, and J., Luo, H2SO4-H2O binary and  H2SO4-H2O-NH3 ternary homogeneous and ion-mediated nucleation: Lookup tables,  in review, GMD, 2019. Files included in the package: YBHN/: BHN code and lookup table YTHN/: THN code and lookup table YBIMN/: BIMN code and lookup table YTIMN/: TIMN code and lookup table nucl_XXX_mod.f (XXX = BHN, THN, BIMN, TIMN): Stand alone module for XXX rate calculation. mainXXX.f: Test program to demonstrate how the lookup table is used. inputXXX.dat: Example input data for testing the code and lookup table. outputXXX.dat: Example output after running the test code Use "f95 *.f" to compile and get executable program a.out To run the test program: >a.out For your reference, output of running the program on my workstation has been saved to outputXXX.dat. I hope that the comments provided in the subroutines make the code self-explanatory. Contact me if you have questions. Enjoy and have fun! Fangqun Yu SUNY-Albany (fyu@albany.edu)

opencc-by-4.0Aug 2024View details →

Data from: Lower-cost eddy covariance for CO2 and H2O fluxes over grassland and agroforestry

This datasets contains eddy covariance fluxes calculated with EddyPro, average (co)spectra from EddyPro and meteorological data from the measurement campaigns conducted in Mariensee, Lower Saxony (Germany) in 2020 and 2021. The results were presented in the paper "Lower-cost eddy covariance for CO2 and H2O fluxes over grassland and agroforestry"; <a href="https://doi.org/10.5194/amt-2024-30">https://doi.org/10.5194/amt-2024-30</a>.  For more information on the data see the accompanying paper. Note that the eddy covariance data are not filtered, quality checked or gap-filled in these files!

opencc-by-4.0Sep 2024View details →

Measurement database and line parameter database for air- and H2O-broadened CO2 in the 4750-5175 cm-1 region including continuum

Air-broadened CO2 measurements in the temperature range 200-294 K and a total pressure range 100-1000 mbar and analysis were carried out within the ESA-funded project ISOGG (Improved Spectroscopy for satellite measurements Of Greenhouse Gases). Additionally, a H2O-broadened measurement at 294 K was recorded and analyzed. The measurement database is in “CO2_air-_and_H2O-broadened_measurements_4750-5175cm-1.zip” and the line parameter database in “CO2_air-_and_H2O-broadening_parameters_4750-5175cm-1_V4.zip”. The CO2/air continuum is given in “CO2_2mu0_RT_foreigncont_polcoeffs_from_scaled_4mu3_N2.txt”. The readme file is “Measurement database and line parameter database for air- and H2O-broadened CO2 in the 4750-5175 cm-1 region including continuum - Readme.docx”. Definitions and units of line parameters are in accordance with the HITRAN database and can be found in the readme file in <a href="https://zenodo.org/records/1009126">ESA SEOM-IAS – Spectroscopic parameters database 2.3 µm region (zenodo.org)</a>. Depletions calculated with the polynomials in temperature have the unit atm-1. Systematic uncertainties which were common for all lines were omitted in the line parameter database and are listed here: Air-broadening parameters 0.03%, speed-dependence of air-broadening 1.5%, air Dicke narrowing 10-20%, air line mixing 0.0001 atm-1, air pressure-induced line shift 3x10-5 cm-1atm-1, temperature exponent of air-broadening parameters 0.0005, temperature dependence of air line mixing 0.1 K-1, temperature dependence of air pressure-induced line shift 5x10-7 cm-1atm-1K-1. In case the speed-dependence was calculated from polynomials of gamma2/gamma0 vs gamma0 the systematic uncertainty is estimated to be 10%.

opencc-by-4.0Sep 2024View details →

Measurement database and line parameter database for air- and H2O-broadened CO2 in the 5970-6575 cm-1 region including continuum

Air-broadened CO2 measurements in the temperature range 210-294 K and a total pressure range 100-1000 mbar and analysis were carried out within the ESA-funded project ISOGG (Improved Spectroscopy for satellite measurements Of Greenhouse Gases). Additionally, a H2O-broadened measurement at 294 K was recorded and analyzed. The measurement database is in “CO2_air-_and_H2O-broadened_measurements_5970-6575cm-1.zip” and the line parameter database in “CO2_air-_and_H2O-broadening_parameters_5970-6575cm-1_V2.zip”. The CO2/air continuum is given in “CO2_1mu6_RT_foreigncont_polcoeffs_from_scaled_4mu3_N2.txt”. The readme file is “Measurement database and line parameter database for air- and H2O-broadened CO2 in the 5970-6575 cm-1 region including continuum - Readme_V2.docx”. Definitions and units of line parameters are in accordance with the HITRAN database and can be found in the readme file in <a href="https://zenodo.org/records/1009126">ESA SEOM-IAS – Spectroscopic parameters database 2.3 µm region (zenodo.org)</a>. Depletions calculated with the polynomials in temperature have the unit atm-1. Systematic uncertainties which were common for all lines were omitted in the line parameter database and are listed here: Air-broadening parameters 0.07%, speed-dependence of air-broadening 1.5%, air line mixing 0.0002 atm-1, air pressure-induced line shift 6x10-5 cm-1atm-1, temperature exponent of air-broadening parameters 0.003, temperature dependence of air line mixing 0.03 K-1, temperature dependence of air pressure-induced line shift 2x10-7 cm-1atm-1K-1. In case the speed-dependence was calculated from polynomials of gamma2/gamma0 vs gamma0 the systematic uncertainty is estimated to be 10%.

opencc-by-4.0Jul 2023View details →

Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) I: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for H2O

We present all of the data across our SNR and abundance study for the molecule H2O for an exoEarth twin. The wavelength range is from 0.515-1 micron, with 25 evenly spaced 20% bandpasses in this range. The SNR ranges from 3-16, and the abundance values range from log10(VMR)  = -3.5 to -1.5 in steps of 0.5 and 0.25 (all presented in VMR in the associated table). We present the lower and upper wavelength per bandpass, the input H2O value (abundance case), the retrieved H2O value (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for H2O. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) I: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for H2O, accepted and currently available on arXiv.  To open this csv as a Pandas dataframe, use the following command: your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input H2O': str})

edi44/100

CO2, CH4, and H2O flux data and associated environmental variables for the BBC collapse scar for 2004

This data set contains flux measurements for the transect from the center of the BBC collapse scar (0m) into the surrounding fire scar (30m) of the Survey Line Fire (burned in June-July 2001). We measured CO2, H2O and CH4 fluxes every one to two weeks throughout the growing season of 2004. We measured fluxes at permanent plots established from the center of the bog into the surrounding burn at 0, 6, 12, 18, 24, and 30 m on the east and west side of the transect. Flux measurements on either side of the transect were treated as replicates. CO2 and H2O fluxes were measured using a Li-840 infrared gas analyzer (Licor Inc., Lincoln, NB, USA). The IRGA was calibrated before each trip to the field using a span of 400 ppm and a zero of N2 gas. We logged data every 0.5 seconds for 2 min. To account for measurement variability, we conducted two measurements in succession at each location, after flushing the chamber for accumulated CO2 and H2O. For the flux measurements, we built plexiglass chambers with pipe insulation bases with dimensions of 61 x 61 x 30.5 cm, 61 x 61 x 61 cm, or 61 x 61 x 122 cm. The shorter chambers were used in the collapse portion of the transect. Chambers included fans for air circulation, inlet and outlet ports for CO2 measurements, or just outlet ports for CH4 measurements (Carroll and Crill 1997). We placed chambers directly on the soil surface and used pipe insulation and plastic sheeting to make a solid seal during the measurement. To estimate the volume for each chamber measurement, we measured the distance to the soil surface from a 6 cm grid suspended 30 cm above each plot, the surface area was then used to calculate the chamber volume for each measurement. Dark measurements were used to determine CO2 derived from soil and root respiration (ecosystem respiration) using a two-layer cloth shroud with a reflective surface to exclude solar radiation. To estimate net ecosystem exchange (NEE) of CO2, we conducted chamber measurements of plant and soi

openOpenNov 2005View details →

opencc-by-4.0Mar 2024View details →

University of Leicester TROPOMI Stable Water Vapour Isotopologue (H2O-ISO) Prototype Product (Vesrion 1.0.0)

This repository holds the prototype level 2 TROPOMI stable isotopologue product for June 2019 used in the study: Thurnherr, I., Sodemann, H., Trent, T., Werner, M., and Boesch, H., 2024. Evaluating TROPOMI δD column retrievals with in situ airborne measurements using expanded collocation criteria, Earth Space Science, in review For further details, please refer to the project website (https://s5pinnovationh2o-iso.le.ac.uk/), which contains the Algorithm Theoretical Baseline Document (ATBD) and Product User Guide (PUG). The final prototype product (version 1.0.2) is freely available from T. Trent (University of Leicester) upon request.

opencc-by-4.0Nov 2021View details →

AutoDock and CB-Dock data for (NPA)6Zn3(H2O)2 in Synthesis, structural analysis, and docking studies with SARS-CoV-2 of a trinuclear zinc complex with N-phenylanthranilic acid ligands

AutoDock 4.2 and CB-Dock data for (NPA)6Zn3(H2O)2 with Mpro from SARS-CoV-2 from PDB Id: 6LU7.

opencc-by-4.0Apr 2021View details →

NMR data for (NPA)6Zn3(H2O)2 in Synthesis, structural analysis, and docking studies with SARS-CoV-2 of a trinuclear zinc complex with N-phenylanthranilic acid ligands

1H, 13C, COSY, HMBC, and HSQC NMR data in fid format for (NPA)6Zn3(H2O)2 (NPA = 2-(phenylamino) benzoate) in DMSO-d6.

opencc-by-4.0Apr 2022View details →

Longitudinal Variation of H2O Ice Absorption on Miranda

Near-infrared reflectance spectra of the Uranian satellite Miranda. Title: Longitudinal Variation of H2O Ice Absorption on Miranda Authors: Riley DeColibus, Nancy Chanover, Richard Cartwright Description of contents: This .tar package is a collection of reduced near-IR (~0.9 to 2.5 um) disk-integrated reflectance spectra of the Uranian moon Miranda. The package includes: <ul> <li>23 spectra acquired with the TripleSpec spectrograph on the ARC 3.5m telescope at Apache Point Observatory.</li> <li>4 spectra acquired with the GNIRS spectrograph on the 8.1m Gemini North telescope on Maunakea.</li> <li>10 spectra acquired with the SpeX spectrograph on the 3m IRTF telescope on Maunakea.</li> <li>5 "grand average" spectra constructed by robustly averaging all TripleSpec data frames acquired covering certain longitude ranges on Miranda. The SpeX and GNIRS data are not included in these grand averages.</li> <li>1 "grand average" TripleSpec spectrum of Uranus, averaged using observations on five nights in fall 2020.</li> </ul> The filenames are in the following format: Mir_[mean sub-observer longitude]_[UT date]_[instrument].txt The files themselves are plain-text, fixed-width ASCII tables. The first several lines of the file header are commented with "#" characters. An example header and explanation is discussed below in the "Additional comments" section. One uncommented line with column titles precedes the data itself. The 'wl' column is wavelength in micrometers (um). The 'flux' column is an arbitrary flux ratio, normalized to unity at 1.72 um. The 'err' column is the 1-sigma errors on the 'flux' column. The process of data acquisition, reduction, and analysis for the TripleSpec and GNIRS spectra are described in the text of the main paper. The SpeX spectra consist of 7 spectra published by previous authors (Gourgeot et al. 2014, Cartwright et al. 2018), and 3 spectra acquired in 2017 that were not previously published, but were reduced via the same procedures as in Cartwright et al. 2018. These data have been corrected for telluric (atmospheric) absorption and divided by the spectrum of an early G dwarf standard star. We did not attempt to correct differing spectral slopes between different standard stars and the Sun. While we only included one 'grand average' Uranus spectrum as an example, we obtained a spectrum of Uranus on every night that we observed Miranda. Users interested in the individual spectra of Uranus from each date should contact the corresponding author. System requirements: No special programs should be required to read these spectra. They were converted to ASCII tables with the Astropy table reading/writing functionality and should be inherently compatible with it. Note that some spectra do include some invalid values represented with 'nan', and these values should be ignored. Additional comments: The commented lines in the file headers include useful metadata regarding observational details for each spectrum. The main text includes more details on how these quantities were determined. Example header and explanation: # PI = DeColibus             [PI of the observing proposal] # INSTRUMENT = TripleSpec       [Instrument/instrument mode, if applicable] # UT_DATE = UT201206            [Date of observation in UTYYMMDD format] # BODY = Miranda                [Observed astronomical body] # MEAN_LAT = 47.66              [Mean sub-observer latitude] # MEAN_LONG = 274.6             [Mean sub-observer longitude (IAU east-positive)] # START_LONG = 241.5            [Sub-obs longitude of first exposure] # END_LONG = 304.7              [Sub-obs longitude of last exposure] # LONG_RANGE = 63.2             [Degrees of longitude spanned by spectrum] # MEAN_PHASE = 1.72             [Mean phase angle (alpha)] # TOT_EXPTIME = 12960           [Total exposure time (seconds)] # N_FRAMES = 72                 [Number of frames contributing to spectrum] # STANDARD = HD 16275           [Star(s) used as telluric standards, typically early G dwarfs] # WAVELENGTH UNITS = micrometers (um) # FLUX UNITS = arbitrary (normalized to unity at 1.72 um) # ERROR UNITS = 1-sigma errors on the above            wl            flux           err           0.910701      -1.165e+00      9.00e+00           0.910824      -9.227e-01      7.27e+00           0.910946      -3.486e+00      8.03e+00                ...           ...           ... The spectra are presented at native resolution and instrumental sampling. The TripleSpec spectrograph has a detector gap between 1.85-1.88 um, and there is no data in this region. As discussed in the main text, the TripleSpec spectra suffer from a "bump" in apparent flux between about 0.95-1.15 micron. This 'bump' is not a real feature in Miranda's spectrum, but results from spurious spectral slopes in different spectral orders. In regions of the spectrum where the wavelength coverage of these spectral orders overlap, merging the spectral orders together results in inflection points that create the resulting 'bump'. Due to its proximity to Uranus, the raw spectra of Miranda are strongly contaminated by scattered light. This contamination (see figure 15 in main text) is strongest between 0.90-0.965 um, 1.02-1.12 um, 1.24-1.32 um, and 1.48-1.63 um. A correction procedure was applied to the TripleSpec spectra to remove this Uranus contamination, described in Appendix A of the paper. However, there may be low-level, residual spectral features resulting from inadequate correction, which may appear as either 'bumps' or 'dips'. We warn the user to be cautious during analysis, as we cannot guarantee that either the spectral slopes or any apparent spectral features at wavelengths shorter than 1.4um are reliable. To a lesser extent, this may also be true of apparent spectral variations <1.4um in the GNIRS and SpeX spectra. For the raw data files, files from intermediate processing steps, more Uranus spectra, or other questions, please contact the corresponding author: Riley DeColibus (contact information omitted, but is included in readme file in download)