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156 results for “troposphere”
GRM: A Novel Stochastic Model for Real-time GNSS Tropospheric Delay Estimation
<p>The dataset includes the proposed RWPN model (Cal_rwpn_new.m) and related files. The model is built based on ERA5 ZWD products from 2010 to 2019, which can be accessed at (<a>ftp://ftp.gfz-potsdam.de/pub/home/GNSS/products/gfz-vmf1/</a>). The proposed GRM model can contribute greatly by providing an efficient RWPN value to real-time GNSS ZTD estimation with an accuracy improvement of over 10% compared to fixed RWPN results. In addition, GRM also shows the superiorities of saving computation cost significantly since a large volume of the ERA5-derived RWPN values is modeled with only several parameters.</p>
Dataset for mammatus-like radar echoes along the bases of upper-tropospheric outflow-layer clouds of typhoons.
<p>PPI and RHI data of radar observations of mammatus-like echoes at the cloud bases of upper-level clouds of typhoons observed by the Nagoya-University cloud radar. The observations were carried out at Okinawa, Japan in 2016 and 2019, and Kobe, Japan in 2018 This dataset includes raw data at polar coordinates and grid data interpolated to Cartesian coordinates. This dataset also includes upper-level sounding observation data during the appearance of mammatus-like echoes at Okinawa, Japan in 2016.</p> <p>In this version 2, all RHI data obtained in 3 October 2016 were added.</p>
MAX-DOAS tropospheric NO2 column measurements in Islamabad, Pakistan (33°N, 73°E) from 2015 to 2019 and comparisons with OMI and TROPOMI satellite data
<p>This data presents an intercomparison of NO<sub>2</sub> retreival settings using Differential Optical Absorption Spectroscopy (DOAS) and those based on literature published over last 20 years. Moreover, it presents comparison of NO<sub>2</sub> Vertical Column Densities(VCD) obtained from ground based MAX-DOAS in Islamabad, Pakistan with satellite data from 2015-2019. MAX-DOAS has retrieved data at seven elevation angles i.e., 2, 4, 5, 10, 15, 30, 45. On the other hand, VCDs are in molecules per cm<sup>2</sup>. However, in order to collect NO2 dataset, DOASIS was used was used to obtain data from MAX-DOAS and further analyzed using QDOAS. Then geometric approximation was applied to obtain VCDs that are presented in this data set.</p>
Data and Code for figures of "Long-range transport and fate of DMS-oxidation products in the free troposphere derived from observations at the high-altitude research station Chacaltaya (5240 m a.s.l.) in the Bolivian Andes"
<p>This database includes the material to create the figures in "Measurement Report: Long-range transport and fate of DMS-oxidation products in the free troposphere derived from observations at the high-altitude research station Chacaltaya (5240 m a.s.l.) in the Bolivian Andes" and the analyzed time series of all atmospheric variables presented.</p>
Data accompanying Using Neural Networks to Learn the Forced Response of the Jet-Stream to Tropospheric Temperature Tendencies
<p>Data used to train and evaluate a CNN. Details about data and the preprocessing can be found in the citation given below</p> <p>Charlotte Connolly, Elizabeth A. Barnes, Pedram Hassanzadeh, and Mike Pritchard: Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability, accepted to Artificial Intelligence for the Earth Systems 03/2023. Preprint available at <a href="https://arxiv.org/abs/2301.00496">https://arxiv.org/abs/2301.00496</a>.</p> <p>Code found at https://doi.org/10.5281/zenodo.7796266.</p> <p> </p>
Animations of Tropospheric Signatures Preceding and Following Sudden Stratospheric Warmings in Extended-Range Ensemble Forecasts
<p>Animations of Tropospheric Signatures Preceding and Following Sudden Stratospheric Warmings in Extended-Range Ensemble Forecasts</p>
First observational evidence of the relationship between the tropospheric temperature and cirrus cloud occurrence over India
<p>This dataset contains the normalized relative backscatter (NRB) coefficients and cloud fractions observed using Micro Pulse Lidar over Kattankulathur (12.82<sup>o</sup>N, 80.04<sup>o</sup>E ) presented in the paper.</p> <p> </p> <p> </p>
Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
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Modelling system for computing the tropospheric O3 and CH4 perturbations from South Korean Emissions (KORUS-AQ period)
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Lifetimes and timescales of tropospheric ozone: Ozone emission experiments
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Clumped-isotope constraint on upper-tropospheric cooling during the Last Glacial Maximum
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Deconstruction of tropospheric chemical reactivity using aircraft measurements: the Atmospheric Tomography Mission (ATom) data
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OCO-2 lowermost troposphere partial column
<p>This archive contains daily netcdf files with two vertically resolved partial columns from OCO-2: The LMT (lowermost troposphere) partial column contains the 5 levels nearest the Earth’s surface, and the U (upper) partial column contains the upper 15 levels (described in Kulawik et al., 2017). The bias correction of LMT was done by comparisons to aircraft observations using a similar process to the OCO-2 XCO2 bias correction (described in O’Dell et al., 2018). The LMT bias correction is described in detail in Kulawik et al. (2017, 2019). The U partial column is set by subtracting the bias-corrected LMT from the bias corrected XCO2 (with appropriate airmass factors). The LMT product contains screening additionally to XCO2. The LMT quality flag is found in lmt -> quality_flag, and all values contain good quality (0).</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>
An LES perturbed parameter ensemble of free-tropospheric cloud-controlling factors on stratocumulus
<p>This dataset contains a perturbed parameter ensemble of large-eddy simulations to assess the effect of two free-tropospheric cloud-controlling factors on stratocumulus clouds properties. The simulations were run on the UK Met Office/NERC cloud model (MONC) for the DYCOMS-II RF01 nocturnal stratocumulus case (Stevens et. al., 2005). Each simulation had the same initial conditions except for the two perturbed parameters, which were the jumps in moisture and temperature at the temperature inversion at cloud top. The corresponding analysis code can be found at this <a href="https://github.com/eers1/dycoms_analysis">dycoms_analysis Github page</a>. </p><p> </p><p>Stevens, B., Moeng, C. H., Ackerman, A. S., Bretherton, C. S., Chlond, A., de Roode, S., . . . Zhu, P. (2005). Evaluation of large-eddy simulations via observations of nocturnal marine stratocumulus. Monthly Weather Review , 133 (6), 1443–1462. doi: 10.1175/MWR2930.1</p>
Data presented in figures of "Measurement Report: Insights into the chemical composition and origin of molecular clusters and potential precursor molecules present in the free troposphere over the Southern Indian Ocean: observations from the Maïdo observatory (2150 m a.s.l., Reunion Island)"
<p>This dataset includes the data shown in the figures of "Measurement Report: Insights into the chemical composition and origin of molecular clusters present in the free troposphere over the Southern Indian Ocean: observations from the Maïdo observatory (2150 m a.s.l., Reunion Island)". Read me files containing information on the reported data can be found in the different folders. </p>
Data to support: Anthropogenic influence on tropospheric reactive bromine since the pre-industrial: Implications for ice-core bromine trends
<p>Tropospheric reactive bromine (Br<sub>y</sub>) influences the oxidation capacity of the atmosphere by acting as a sink for ozone and nitrogen oxides. Aerosol acidity plays a crucial role in Br<sub>y</sub> abundances through acid-catalyzed debromination from sea-salt-aerosol, the largest global source. Bromine concentrations in a Russian Arctic ice-core, Akademii Nauk, show a 3.5-fold increase from pre-industrial (PI) to the 1970s (peak acidity, PA), and decreased by half to 1999 (present day, PD). Ice-core acidity mirrors this trend, showing robust correlation with bromine, especially after 1940 (<em>r</em>=0.9). Model simulations considering anthropogenic emission changes alone show that atmospheric acidity is the main driver of Br<sub>y</sub> changes, consistent with the observed relationship between acidity and bromine. The influence of atmospheric acidity and Br<sub>y</sub> should be considered in interpretation of ice-core bromine trends.</p>
Stratosphere-Troposphere wind profiler radar data
<p>Horizontal wind profiles from ST radar data at Cochin (10.04N, 76.33 E) during mosoon seasons (June to September) for three years (2019-2021)</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>
Northern hemispheric atmospheric ethane trends in the upper troposphere and lower stratosphere (2006-2016) with reference to methane and propane
<p>Datasets for northern hemispheric atmospheric ethane, propane and methane (2006-2016) from airborne measurements by IAGOS-CARIBIC project. </p>
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International Brain Laboratory public data
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OpenNeuro
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