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202 results for “water vapor”
Composite geostationary weather satellite images (second time derivative of water vapor channel) for visualizing Lamb waves
<p>Second time derivative of water vapor channel (6.2 micrometer) brightness temperature from geostationary weather satellites (Units: K s<sup>-2</sup>)</p> <p>Himawari-8 (original data obtained from NICT Science Cloud)</p> <p>GOES-16/17 (original data obtained from Amazon AWS)</p> <p>Meteosat-8/9/10/11 (original data obtained from EUMETSAT)</p> <p> </p> <p>Time interval of the files: 5 minutes</p> <p> </p> <p>Time interval of each satellite, dt for time derivative:</p> <p>Himaawri-8, GOES-16/17: 10 minutes, 10 minutes</p> <p>Meteosat-8/9/11: 15 minutes, 15 minutes</p> <p>Meteosat-10: 5 minutes, 10 minutes</p> <p> </p> <p>Each file contains the latest images from those satellites at that time. The time stamp for each satellite represents the beginning of each full-disk scan.</p> <p> </p> <p>Bias correction:</p> <p>Himawari-8: bias removal for each swath</p> <p>GOES-16/17, Meteosat-11: bias removal for each east-west line</p> <p>Meteosat-8/9/10: bias removal for each east-west line (note: satellite attitude was not stable)</p> <p> </p> <p>Smoothing:</p> <p>Band-pass filter for each full-disk image separately: 2-40 degrees on lat-lon coordinate</p> <p>Stronger smoothing at latitudes higher than 60 degrees north/south</p> <p> </p> <p>Down-sampling:</p> <p>Full-disk images were mapped to a 0.04-degree lat-lon coordinate.</p> <p>Then, composite images were produced at the 0.2-degree resolution.</p> <p> </p> <p>Version 2:</p> <p>Improved interpolation algorithm</p> <p>Himawari-8: improved geolocation</p> <p>Meteosat-8/9: improved treatment of noise near the edge of full disk images</p>
Modeling abrupt excursions in water vapor isotopic variability during cold fronts at the Pointe Benedicte observatory in Amsterdam Island / Model dataset
<p>Water vapor mixing ratios, isotopic composition of water vapor and precipitations associated with the manuscript:</p> <div> <div>Landais, A., Agosta, C., Vimeux, F., Magand, O., Solis, C., Cauquoin, A., Dutrievoz, N., Risi, C., Leroy-Dos Santos, C., Fourré, E., Cattani, O., Jossoud, O., Minster, B., Prié, F., Casado, M., Dommergue, A., Bertrand, Y., and Werner, M.: Abrupt excursions in water vapor isotopic variability at the Pointe Benedicte observatory on Amsterdam Island, Atmos. Chem. Phys., 24, 4611–4634, https://doi.org/10.5194/acp-24-4611-2024, 2024.</div> </div>
Data and code from: Long-term climate impacts of large stratospheric water vapor perturbations
<p>The amount of water vapor injected into the stratosphere after the eruption of Hunga Tonga-Hunga Ha'apai (HTHH) was unprecedented, and it is therefore unclear what it might mean for surface climate. We use chemistry climate model simulations to assess the long-term surface impacts of stratospheric water vapor (SWV) anomalies similar to those caused by HTHH, but neglect the relatively minor aerosol loading from the eruption. The simulations show that the SWV anomalies lead to strong and persistent warming of Northern Hemisphere landmasses in boreal winter, and austral winter cooling over Australia, years after eruption, demonstrating that large SWV forcing can have surface impacts on a decadal timescale. We also emphasize that the surface response to SWV anomalies is more complex than simple warming due to greenhouse forcing and is influenced by factors such as regional circulation patterns and cloud feedbacks. Further research is needed to fully understand the multi-year effects of SWV anomalies and their relationship with climate phenomena like El Nino Southern Oscillation.</p>
WV-TTL: water vapor mixing ratio from GEOSCCM and trajectory model simulations in tropical tropopause layer
<p>This dataset includes 100 hPa water vapor mixing ratio simulated from a trajectory transport model and a climate-chemistry model in the tropical tropopause layer from 2005 to 2016 in the format of netCDF. The data are monthly and have three dimensions as lon/lat/time in the unit of parts per million by volume.</p> <p>Also included the tropical average time series of indices for Brewer-Bobson circulation (BDC), tropospheric temperature and/or Quasi-biennial Oscillation (QBO) from ERAi/MERRA-2/GEOSCCM. These indices are used in a multivariate regression.</p>
Data and code from: Long-term climate impacts of large stratospheric water vapor perturbations (Part 1)
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Eddy flux measurements and transfer velocities of momentum, water vapor, and sulfur dioxide over the coastal Atlantic ocean
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A 4.5 year-long record of Svalbard water vapor isotopic composition documents winter air mass origin
<p>Isotopic data from the article in JGR A: A 4.5 year-long record of Svalbard water vapor isotopic composition documents winter air mass origin</p> <p><strong>CLDS_2020_ground_iso_vapor_1h.dat</strong></p> <p><strong>CLDS_2020_zeppelin_iso_vapor_1h.dat </strong></p> <p>first column: date in matlab format</p> <p>second column: humidity in ppmv</p> <p>thrid column: d18O in per mil</p> <p>forth column: dD in per mil</p> <p>fifth column: not to take into account</p> <p><strong>CLDS_2020_iso_precip.dat </strong></p> <p>first column: date in matlab format</p> <p>second column: temperature at noon in degre C</p> <p>thrid column: d18O in per mil</p> <p>forth column: dD in per mil</p> <p>fifth column:type of precip 1: water & 2 : snow & 3 : other (melt, etc.)</p>
Data for "Potential of mid-tropospheric water vapor isotopes to improve large-scale circulation and weather predictability"
<p>This contains simulation results of "Potential of mid-tropospheric water vapor isotopes to improve large-scale circulation and weather predictability".</p>
Aerosol, Temperature and Water Vapor profiling during the BIOSPHERE Athens Campaign - NTUA - Level 3 - June 2023
<p>June 2023 - Level 3 of the lidar data obtained by the EOLE and DEPOLE lidar systems in the National Technical University of Athens (NTUA), during the EURAMET European Partnership on Metrology (EPM) project BIOSPHERE.</p>
Aerosol, Temperature and Water Vapor profiling during the BIOSPHERE Athens Campaign - NTUA - Level 3 - July 2023
<p>July 2023 - Level 3 of the lidar data obtained by the EOLE and DEPOLE lidar systems in the National Technical University of Athens (NTUA), during the EURAMET European Partnership on Metrology (EPM) project BIOSPHERE.</p>
Aerosol, Temperature and Water Vapor profiling during the BIOSPHERE Athens Campaign - NTUA - Level 3 - August 2023
<p>August 2023 - Level 3 of the lidar data obtained by the EOLE and DEPOLE lidar systems in the National Technical University of Athens (NTUA), during the EURAMET European Partnership on Metrology (EPM) project BIOSPHERE.</p>
Recent changes in hemispheric asymmetry of stratospheric water vapor
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The lag and cumulative response of vegetation WUE to water vapor pressure deficit on the Shiyang River Basin, northwest of China_data description
<p>Data description</p> <p>This document provides details about the data description in the manuscript. The variables are described below along with their collection methods and calculation processes:</p> <p>the lag effect: The zip file of“The lag effect” contains three files: "rET_lag_result_z", "rGPP_lag_result"_, and" rWUE_lag_result_z".</p> <p>rET_lag_result_z: "rET_lag_result_z" database is a dataset used to analyze the lag effect of water vapor pressure deficit (VPD) on Evapotranspiration (ET)</p> <p>rGPP_lag_result_z: "rGPP_lag_result_z" database is a dataset used to analyze the lag effect of water vapor pressure deficit (VPD) on Gross Primary Productivity (GPP) </p> <p>rWUE_lag_result_z: "rWUE_lag_result_z" database is a dataset used to analyze the lag effect of water vapor pressure deficit (VPD) on Water Use Efficiency (WUE)</p> <p> </p> <p><br>the Cumulative effect: The zip file of“The Cumulative effect” contains three files: "rET_acc_result_z", "rGPP_acc_result"_, and" rWUE_acc_result_z".</p> <p>rET_acc_result_z: "rET_acc_result_z" database is a dataset used to analyze the Cumulative effect of water vapor pressure deficit (VPD) on Evapotranspiration (ET)</p> <p>rGPP_acc_result_z: "rGPP_acc_result_z" database is a dataset used to analyze the Cumulative effect of water vapor pressure deficit (VPD) on Gross Primary Productivity (GPP) </p> <p>rWUE_acc_result_z: "rWUE_acc_result_z" database is a dataset used to analyze the Cumulative effect of water vapor pressure deficit (VPD) on Water Use Efficiency (WUE)</p> <p> </p> <p><br>the Slop_Partition: The zip file of“The Slop_Partition”contains three files: "Slop_ET_Partition", "Slop_GPP_Partition"_, and" Slop_WUE_Partition".</p> <p>Slop_ET_Partition: "Slop_ET_Partition" database is a dataset used to calculate the temporal and spatial dynamic change·trend of Evapotranspiration (ET)</p> <p>Slop_GPP_Partition: "Slop_GPP_Partition" database is a dataset used to calculate the temporal and spatial dynamic change·trend of Gross Primary Productivity (GPP)</p> <p>Slop_WUE_Partition: " Slop_WUE_Partition" database is a dataset used to calculate the temporal and spatial dynamic change·trend of Water Use Efficiency (WUE)</p> <p> </p> <p> </p>
Simulations of FLEXPART-WRF used for testing TRansport Of water VApor (TROVA) software (Part II)
<p>Necessary data for carrying out the tests using TRansport Of water VApor (TROVA) software for backward in time. The TROVA software, was developed in Python and Fortran for the study of moisture sources and sinks. <span><span><span>In addition, the Python code is provided for the representation of the results.</span></span></span></p>
Tropospheric delays and precipitable water vapor retrieved from global radiosonde observations from 2014 to 2019
<p>The data consists of a set of meteorological quantities including tropospheric delays (zenith wet delay, zenith hydrostatic delay, and zenith total delay), precipitable water vapor, and surface temperature and pressure. The data is retrieved from the observations of 414 globally distributed radiosonde stations from 2014 to 2019. In addition to the geographic information of radiosonde stations, the profiles of tropospheric delays and precipitable water vapor are contained in the data file. This data has a wide range of applications, e.g., validating the tropospheric delays and precipitable water vapor derived from other techniques, investigating the spatial-temporal variations of water vapor, and acting as training data of machine learning to build tropospheric delay models.</p> <p>In the manuscript "Machine Learning-based Model for Real-time GNSS Precipitable Water Vapor Sensing", this data is used to train a machine learning model to map the zenith total delays to precipitable water vapor. The data is split into training data and test data, where the data from 2014 to 2018 are employed for model training, and the data of 2019 are used for testing. The developed models and the results for the manuscript are saved in the directories of Models and Results, respectively.</p>
Dataset of synergistically retrieved water vapor from SPICAM and PFS nadir observations
<p>Compilation of processed data files used for the creation of the figures presented in the manuscript "Constraining near-surface water vapor on Mars: a spectral synergy climatological survey applied to PFS and SPICAM nadir observations".</p>
Water vapor vertical distribution on Mars during perihelion season of MY 34 and MY 35 with ExoMars-TGO/NOMAD observations [Dataset]
<p>1. Description of methods used for collection/generation of data:<br> NOMAD SO channel acquires transmittance spectra at different diffraction orders sounding the limb of the Martian atmosphere in solar occultation. It uses an echelle grating with a density of ∼4 lines/mm in a litrow configuration. An Acousto-Optical Tunable Filter (AOTF) is used to select different spectral windows (with a width that varies from 20 to 35 cm−1). Each window corresponds to the desired diffraction order to be used during the atmospheric scan. The spectral resolution of the SO channel is λ/∆λ=20,000.<br> After spectral calibration, the inversion problem is solved by fitting the data with a forward model and the vertical profiles are obtained.</p> <p>2. Methods for processing the data:<br> For the H2O inversion we use the Retrieval Control Program (RCP) developed at Institut für Meteoriologie und Klimaforschung (IMK), which incorporates the Karlsruhe Optimized and Precise Radiative transfer Algorithm (KOPRA) forward model. After providing an a priori, a first-guess and the measured spectra, RCP solves the inversion problem iteratively until the convergence of the solution. The IMK-IAA level-2 processor relies on multi-parameter non-linear least squares fitting of measured and modeled spectra (von Clarmann et al., 2003). Further information about RCP and the inversion problem can be found in (Jurado Navarro et al., 2016).<br> Retrievals of NOMAD diffraction orders 134 (3011-3035 cm−1) and 168 (3775-3805 cm−1) have been obtained and merged when collocated.</p>
Raw Picarro L2140i data - Support to "A versatile water vapor generation module for vapor isotope calibration and liquid isotope measurements"
<p>Support data to reproduce Figure 3 - Figure 10 from the article:</p> <p><em>A versatile water vapor generation module for vapor isotope calibration and liquid isotope measurements</em></p> <p>by Hans Christian Steen-Larsen and Daniele Zannoni</p> <p>Accepted in Atmospheric Measurement Techniques on 25/05/2024 (Preprint available at <a href="https://doi.org/10.5194/amt-2023-160" rel="nofollow">https://doi.org/10.5194/amt-2023-160</a>)</p> <p>Figure numbers refer to the final (peer-reviewed and accepted) version of the manuscript.</p> <p>To reproduce the figures, the data can be used in conjunction with the code available at <a href="https://github.com/danielez83/AMT-2023-160" target="_blank" rel="noopener">https://github.com/danielez83/AMT-2023-160 </a>(https://zenodo.org/doi/10.5281/zenodo.12741980)</p> <p><strong>File description</strong></p> <p>Figure numbers are referring to the final (peer-reviewd and accepted) version of the manuscript.</p> <ul> <li>ADEV_BER17k_withmemory_CALIBRATED_R1.csv <ul> <li>Allan Deviation data without removing memory effect, used in FIgure 3</li> </ul> </li> <li>Cal_Pulses_MultiOven_new20230609.csv <ul> <li>Data obtained with the multioven configuration, used in Figure 9</li> </ul> </li> <li>Cal_Pulses_Selector.csv<br> <ul> <li>Data obtained with the VICI selector configuration (only one oven working), used in Figure 9</li> </ul> </li> <li>HKDS2092.zip<br> <ul> <li>Compressed archive of the Picarro L2140i (HKDS2092) raw data.</li> </ul> </li> <li>HKDS2156.zip<br> <ul> <li>Compressed archive of the Picarro L2140i (HKDS2156) raw data.</li> </ul> </li> <li>HKDS2156_IsoWater_20221116_165037.csv<br> <ul> <li>Results of liquid injections with Picarro vaporizer, used in Figure 4 and Figure 10</li> </ul> </li> <li>SP_BER_inj_time.csv <ul> <li>Date and times of injections, used in Figure 4 and Figure 10</li> </ul> </li> <li>Timings_Picarro.xlsx <ul> <li>Excel spreadsheet with time and dates of experiment. It is used as a lookup table to retrieve the raw data correctly</li> </ul> </li> </ul>
Geostatistical data of summertime rainfall and water vapor in Korea during 2013-2015
<p>The dataset includes spatial/temporal autocorrelation and histogram for composite precipitation and Himawari-8 water vapor bands, and Moran’s I and general G for precipitation, with ASCII format. It is produced for the precipitation cases shown in cases.xlsx. Composite precipitation data covers 1153 x 1441 over the Korean Peninsula (118.826-133.581 °E, 30.125-43.566 °N), with a grid size of 1 km and a time resolution of 1 hr. Himawari-8 satellite data covers the East Asia but we selected the domain (120.132-134.243 °E, 30.436-44.068 °N; 600 X 770) similarly to the precipitation data area. The spatial and temporal resolutions are 2 km and 1 hr, respectively. More information about each data can be found in 0_README.txt.</p>
Supporting data and code for "Water vapor estimation using wireless two-way interferometry (Wi-Wi)"
<p>Notes on generating figures for Radio Science paper.<br> Nobuyasu Shiga<br> 2019/3/9</p> <p>Fig. 4<br> File name: PhaseShifter.eps<br> Matlab file: PhaseShifter.m<br> Raw Data: 20181002124143PhaseShifter.csv</p> <p>Fig. 7<br> File name: WMRain091418.eps<br> Matlab file: WaterVaporAnalysis180913paper.m<br> Raw Data: avg20180913162410.csv %WiWi<br> WXT520_M_20180914_0000.txt %meteorological equipment @NICT<br> ...<br> WXT520_M_20180918_2350.txt<br> 01_min1_20180914_20180918Mod.csv %rain gauge</p> <p>Fig. 8<br> File name: WMRainDec.eps<br> Matlab file: WaterVaporAnalysis1202.m<br> Raw Data: avg20181130184636.csv %WiWi<br> WXT520_M_20181202_0000.txt %meteorological equipment @NICT<br> ...<br> WXT520_M_20181208_2350.txt<br> WeatherAVG20181202-20181208.csv %meteorological equipment @SWT<br> 01_min1_20181202_20181208Mod.csv %precipitation</p> <p>Fig. 9<br> File name: WMR0917.eps<br> Matlab file: WaterVaporAnalysis180913paper.m<br> Raw Data: avg20180913162410.csv %WiWi<br> WXT520_M_20180917_0000.txt %meteorological equipment @NICT<br> ...<br> WXT520_M_20180917_2350.txt<br> North15_MP3000.txt<br> South15_MP3000.txt</p> <p>Fig. 10<br> File name: WMGR0917.eps<br> Matlab file: WaterVaporAnalysis180913paper.m<br> Raw Data: avg20180913162410.csv %WiWi<br> WXT520_M_20180917_0000.txt %meteorological equipment @NICT<br> ...<br> WXT520_M_20180917_2350.txt<br> KGN3_ZWD.txt<br> Zenith_MP3000.txt</p> <p> </p>
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