Find research datasets worth reusing

"A physics-based model for wind turbine wake expansion in the atmospheric boundary layer"

Vahidi, Dara, and Fernando Porté-Agel. "A physics-based model for wind turbine wake expansion in the atmospheric boundary layer." Journal of Fluid Mechanics 943 (2022).

Dataset for "Air-sea interactions on Titan: effect of radiative transfer on the lake evaporation and atmospheric circulation"

These documents are supplements to the "Air-sea interactions on Titan: effect of radiative transfer on the lake evaporation and atmospheric circulation" paper published by the same authors in The Planetary Science Journal in 2022. Are made available: -the Supporting Information document on the performed sensitivity study, "paper_mtWRF_lake_RT_220825_SI.pdf" -the Fortran source code of the radiative transfer module developed for this work, "module_ra_gray.F" -all the netCDF simulation outputs and a list describing their parameters, "run-##.nc.gz" "list_simulations_2D_paper2022_RT_zenodo.pdf" -the Python codes to plot figures from the netCDF output files, "mtwrf_analysis_#D_#.py"

Near-range atmospheric dispersion dataset of an anomalous selenium-75 emission

Description This dataset contains the different measurement data that were obtained during and in the wake of the anomalous selenium-75 (Se-75) emission from the Belgian Reactor 2 (BR2) in 2019. These data were analysed by Frankemölle et al (2022), who showed that they paint a consistent picture of the Se-75 emission on both the time scale of the initial puff as well as on the time scale of the residual release. In the first tab of the Excel file, the different data included in the rest of the excel file are briefly discussed. Included data <ul> <li>On-site meteorological data</li> <li>In-stack measurements of Se-75 source term</li> <li>On-site measurements of ambient dose equivalent rates</li> <li>On-site deposition measurements</li> <li>On-site concentration measurements</li> </ul> Original publication Frankemölle, J.P.K.W., Camps, J., De Meutter, P., Antoine, P., Delcloo, A.W., Vermeersch, F. and Meyers, J. (2022) 'Near-range atmospheric dispersion of an anomalous selenium-75 emission', Journal of Environmental Radioactivity, 255, pp. 107012. DOI:  <a href="https://doi.org/10.1016/j.jenvrad.2022.107012">https://doi.org/10.1016/j.jenvrad.2022.107012</a>

opencc-zeroSep 2022View details →

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Data associated with: Emergence of the physiological effects of elevated CO2 on land-atmosphere exchange of carbon and water

Elevated atmospheric CO2 (eCO2) influences the carbon assimilation rate and stomatal conductance of plants and thereby can affect the global cycles of carbon and water. Yet, the detection of these physiological effects of eCO2 in observational data remains challenging, because natural variations and confounding factors (e.g., warming) can overshadow the eCO2 effects in observational data of real-world ecosystems. In this study, we aim at developing a method to detect the emergence of the physiological CO2 effects on various variables related to carbon and water fluxes. We mimic the observational setting in ecosystems using a comprehensive process-based land surface model QUINCY to simulate the leaf-level effects of increasing atmospheric CO2 concentrations and their century-long propagation through the terrestrial carbon and water cycles across different climate regimes and biomes. We then develop a statistical method based on the signal-to-noise ratio to detect the emergence of the eCO2 effects. The signal in gross primary production (GPP) emerges at relatively low CO2 increase (Δ[CO2] ~ 20 ppm) where the leaf area index is relatively high. Compared to GPP, the eCO2 effect causing reduced transpiration water flux (normalized to leaf area) emerges only at relatively high CO2 increase (Δ[CO2] >> 40 ppm), due to the high sensitivity to climate variability and thus lower signal-to-noise ratio. In general, the response to eCO2 is detectable earlier for variables of the carbon cycle than the water cycle, when plant productivity is not limited by climatic constraints, and stronger in forest-dominated rather than in grass-dominated ecosystems. Our results provide a step toward when and where we expect to detect physiological CO2 effects in in-situ flux measurements, how to detect them and encourage future efforts to improve the understanding and quantification of these effects in observations of terrestrial carbon and water dynamics.

Probing the Extent of Vertical Mixing in Brown Dwarf Atmospheres with Disequilibrium Chemistry

OVERVIEW The substellar atmospheric models described in <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220814317M/abstract">Mukherjee et al. (2022)</a> are presented here. The grid of these 1D radiative-convective atmospheric models was computed using the newly released open-source climate code PICASO 3.0 (<a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a>). The grid consists of four parameters – the effective temperature (Teff), gravity (log(g)), Kzz in the radiative zones, and mixing length in the convective zones. Models with Teff  between 400-1000 K with an increment of 25 K are included. log(g) has been varied from 4.5 to 5.5 with an increment of 0.25 dex. The Kzz in the radiative zone has been varied between 1x, 0.01x, and 100x the parametrization presented in <a href="https://ui.adsabs.harvard.edu/abs/2022ExA....53..279M/abstract">Moses et al. (2021)</a>, whereas the convective mixing length has been between the atmospheric pressure scale height and 0.1x the scale height. There are three types of files released here – atmospheric composition files (TP_chemistry), thermal emission spectra files (spectra), and atmospheric Kzz profile files (TP_kz).  ATMOSPHERIC COMPOSITION The atmospheric composition files are located in the folder TP_chemistry.  These files have the temperature structure of the atmosphere as a function of pressure accompanied by the volume mixing ratio of 37 gases as a function of pressure.  The TP_chemistry files are named following the format “profile_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.dat", where [factor1] denotes the multiplier used for the radiative zone Kzz and can vary between ‘x0pt01’, ‘x1’, and ‘x100’. [factor2] denotes the multiplier for the mixing length and can vary between ‘1’ and ‘0pt1’. [Teff] and [gravity] denote the Teff and gravity of the models used. A simple code snippet to read and plot these files is presented below. SPECTRA The spectra files are located in the folder "spectra_highres_1", "spectra_highres_2", "spectra_highres_3", and "spectra_highres_4". These files have the thermal emission spectra between 0.3-30 microns calculated using the computed models. The native spectral resolution of these calculations is at an R = 500,000, but please be aware that these spectra should always be binned down to a resolution of R = 50,000 or less before usage. This means that these spectra should only be used to interpret datasets with a spectral resolution of 50,000 or less. Please contact the authors if higher resolution spectra are needed. The spectra have been uploaded in three different folders to make the file sizes manageable for transfer. The spectra files are also similarly named using the format “spectra_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.tar.gz". These files can be directly read into a Python pandas dataframe using  <pre><code class="language-python">pd.read_csv(filename, compression='gzip')</code></pre>  The first column of the file is wavenumbers in cm-1, which can be converted to wavelength in microns by wavelength [microns] =10000/wavenumbers[cm-1].   The second column of the file is flux in erg/s/cm2/cm. Note that these fluxes need to be multiplied with R2/D2  before comparing them with the typically observed flux of brown dwarfs/exoplanets. R is the radius of the object, and D is the distance here. A tutorial to convert these fluxes to other units is present in <a href="https://natashabatalha.github.io/picaso/notebooks/6_BrownDwarfs.html#Convert-to-F_\nu-Units-and-Regrid">this link</a>. A binned-down version (R=15,000) of these high-resolution spectra can also be found in the "spectra_lowres" folder. These can be used for datasets that have a maximum spectral resolution of 15,000. Kzz PROFILE The Kzz  as a function of pressure for each model is presented in these files. The Kzz is reported in cm2/s. These files are also similarly named using the format “kz_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.dat". The columns of the files are pressure in bars, the temperature in K, and Kzz in cm2/s.   EXAMPLE PYTHON CODE TO READ AND PLOT COMPOSITION FILES <pre><code class="language-python">import numpy as np import pandas as pd import matplotlib.pyplot as plt grav = np.array([316,562,1000,1780,3160]) Teff=np.array([400,425,450,475,500,525,550,575,600,625,650,675,700,725,750,775,800,825,850,875,900,925,950,975,1000]) factor1 = np.array(['x0pt01','x1','x100']) factor2 = np.array(['1','0pt1']) file ="profile_sc_qt_rz_"+factor1[0]+"_cz_"+factor2[0]+"_"+str(Teff[14])+"_grav_"+str(grav[14])+"_mh_+0.0_sm_NA.dat" df = pd.read_csv(file, sep="\t") # Plot T(P) profile plt.ylim(100,1e-4) plt.semilogy(df['temperature'],df['pressure']) plt.show() # Plot H2O mixing ratio profile plt.ylim(100,1e-4) plt.loglog(df['H2O'],df['pressure']) plt.show()</code></pre> CREDITS If you use these tables, please cite <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220814317M/abstract">Mukherjee et al. (2022, Astrophysical Journal, in press.)</a>

ARtracks - a Global Atmospheric River Catalogue Based on ERA5 and IPART

The ARtracks Atmospheric River Catalogue is based on the ERA5 climate reanalysis dataset, specifically the output parameters "vertical integral of east-/northward water vapour flux". Most of the processing relies on IPART (Image-Processing based Atmospheric River (AR) Tracking, https://github.com/ihesp/IPART), a Python package for automated AR detection, axis finding and AR tracking. The catalogue is provided as a pickled pandas.DataFrame as well as a CSV file. For detailed information, please see <a href="https://github.com/dominiktraxl/artracks">https://github.com/dominiktraxl/artracks</a>. The ARtracks catalogue covers the years from 1979 to the end of the year 2019.

Relevant data for publication 'Atmospheric phosphorus deposition amplifies carbon sinks in simulations of a tropical forest in Central Africa' Goll et al.

Plotting scripts and processed output from ORCHIDEE-CNP. The version of ORCHIDEE is available here: https://doi.org/10.14768/391825ae-d257-4365-9820-30ea1940914c

Exoplanet atmosphere evolution: emulation with neural networks: supplementary data

Supplementary data for 'Exoplanet atmosphere evolution: emulation with neural networks'. Includes MCMC chain for Bayesian Hierarchical Model (BHM) including samples of core mass for all planets, as well 5 hyper parameters (see paper for details). Additionally, a machine readable version of Table 1 is made available.

WRF 3.5km North Atlantic August - October 2020 (TC tracks and atmospheric composites data)

This zip file contains simulated TC tracks and some atmospheric composites for August to October 2020. - TC tracks information: timing, intensity, and location - Composites: vertical wind shear (200 - 850 hPa), potential intensity, 850 hPa absolute vorticity, and 700 hPa specific humidity.

NFFA-Europe|Pilot proporsal "NANO meets ASTRO: simulating the formation of silicon oxide nanoparticles in the atmosphere of dying stars" (PID: 140).

XPS, IRRAS, QMS and OES data of the nanoparticles synthesized within the NFFA-Europe|Pilot proporsal "NANO meets ASTRO: simulating the formation of silicon oxide nanoparticles in the atmosphere of dying stars" (PID: 140).

Reference atmospheres, surface and instrument data used in the test experiments presented in Ridolfi et al. 2022 (doi.org/10.5194/amt-2022-82)

The supplied archive includes a set of ASCII files defining the reference atmospheric and surface states that are the basis of the simulation experiments presented in the paper of Ridolfi et al. 2022 (doi.org/10.5194/amt-2022-82). Along with atmospheric and surface data, we also supply the seasonal variability and mismatch errors used in the test experiments presented in that paper, the noise error covariance matrices anticipated for FORUM and IASI-NG spectra, and some details of the retrieval setup. The archive includes a README file summarizing the contents of the various files provided.

Data for Survival probabilities of atmospheric particles: comparison based on theory, cluster population simulations, and observations in Beijing

Data for Survival probabilities of atmospheric particles: comparison based on theory, cluster population simulations, and observations in Beijing (https://doi.org/10.5194/acp-2022-484) Contact Santeri Tuovinen (santeri.tuovinen@helsinki.fi) for more details.

PICASO 3.0 Atmospheric Models of WASP-39 b for the JWST Transiting Exoplanet Community Early Release Science Program

OVERVIEW The exoplanetary atmospheric models used in the recent <a href="https://www.nature.com/articles/s41586-022-05269-w">discovery of CO2  in WASP- 39 b's atmosphere</a> by the JWST transiting exoplanet community early release science program are presented here. These models are also being used to analyze multiple observations of WASP 39-b obtained using various JWST instruments and observational modes by the transiting exoplanet ERS team. The 1D Radiative-Convective-Thermochemical Equilibrium (RCTE) atmospheric models were computed using the open-source 1D climate model <a href="https://natashabatalha.github.io/picaso/">PICASO 3.0</a> (<a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>). These atmospheric models were then post-processed with condensation clouds using the open-source cloud model <a href="https://natashabatalha.github.io/virga/">VIRGA</a> (<a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...925...33R/abstract">Rooney et al. (2022)</a>). The atmospheric models were also post-processed with the 1D photochemical network code <a href="https://github.com/exoclime/VULCAN">VULCAN</a> (<a href="https://arxiv.org/abs/2108.01790">Tsai et al. (2021)</a>) to explore photochemistry in WASP-39 b's atmosphere. 1D RCTE CLOUD-FREE MODELS The base 1D RCTE grid includes atmospheric metallicity points at 0.1, 0.3, 1.0, 3.0, 10.0, 30.0, 50.0, and 100.0x solar values. The Carbon-to-Oxygen (C/O) ratio value is varied between four values - 0.23, 0.46, 0.69, and 0.92. The intrinsic temperature of the planet has been varied across 100, 200, and 300 K, whereas two values of the heat redistribution factor - 0.4 and 0.5 are included. A heat redistribution factor of 0.5 corresponds to the case of full heat redistribution. With these grid points, the grid includes a total of 8x4x3x2= 192 different models. These models are in the "RCTE_cloud_free.zip" folder. The naming scheme of these files is "profile_eq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_.nc" where [T_int] represents the intrinsic temperature of the planet, [MH] is the log10 of the atmospheric metallicity relative to solar, [CtoO] is the C/O ratio relative to solar, and [rfacv] is the heat-redistribution factor. So, a metallicity value of 0.3xsolar will have a [MH] value of -0.5, and a C/O 0.46 is considered 1xsolar and will correspond to [CtoO]=1. [T_int] and [rfacv] can assume values described in the previous paragraph. 1D RCTE CLOUDY MODELS The base 1D RCTE cloud-free models were post-processed to include condensation cloud species Na2S, MnS, and MgSiO3. The cloud structure and optical property calculations were performed using the VIRGA model where the sedimentation efficiency fsed and the vertical eddy diffusion coefficient (Kzz) are free parameters. For the cloudy models, 5  fsed  values - 0.6, 1, 3, 6, and 10 were used along with 3 different values of log10Kzz - 5, 7, 9, and 11, where Kzz is in cm2/s. These models are included in the "RCTE_cloudy.zip" folder following the naming structure "profile_eq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_kzz_1e[log10Kzz]_fsed_[fsed].cld.nc" where two other variables are added in the name - [log10Kzz] and [fsed]. Both of these variables can take values listed here. PHOTOCHEMICAL CLOUD-FREE MODELS A much smaller subset of the base 1D RCTE models were post-processed with the 1D photochemical network code VULCAN to simulate the effects of vertical mixing and photochemistry in WASP-39 b's atmosphere. log10Kzz  was varied again between the 5, 7, 9, and 11 for this purpose. These files are named as "profile_diseq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_kzz_1e[log10Kzz].nc" and can be found in the "photochem_cloud_free.zip" folder. PHOTOCHEMICAL CLOUDY MODELS The photochemical models were post-processed with clouds to simulate a cloudy atmosphere with disequilibrium chemistry. The fsed  and log10Kzz  grid system for the RCTE cloudy models has been used again for these models as well. These files are in the "photochem_cloudy.zip" folder and are named according to the format "profile_diseq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_kzz_1e[log10Kzz]_fsed_[fsed].cld.nc". FILE FORMATTING AND USAGE All the files are released in the <a href="https://docs.xarray.dev/en/stable/">xarray</a> format. Each model has one single xarray file containing all metadata of that model. This metadata includes the input parameters used to compute the model, for example, the metallicity, C/O ratio, and intrinsic temperature. The temperature-pressure (T(P)) profile and the volume mixing ratio profiles of all the different gases in each model is also included in the metadata. The computed transmission spectrum of the model planet from 0.3-6 microns is included in the same file as well. The spectrum is calculated with resampled opacities at a spectral resolution of 60,000, but they should be re-binned at a spectral resolution of 3000 or less for comparison with observed data. For cloudy models, the wavelength dependant optical depth, asymmetry parameter, and single scattering albedo for each atmospheric layer are included in these xarray files. We refer to this <a href="https://natashabatalha.github.io/picaso/notebooks/codehelp/data_uniformity_tutorial.html#Reading/interpreting-an-xarray-file">PICASO tutorial</a> for reading/writing these xarray files. The spectrum from these xarray files can be easily extracted using the following code. <pre><code class="language-python">import xarray as xr path = "path/to/files" ds_sm = xr.open_dataset(path+"profile_eq_planet_300_grav_4.5_mh_+2.0_CO_2.0_sm_0.0486_v_0.5_.nc") # for spectrum wavelength = ds_sm['wavelength'].values transit_depth = ds_sm['transit_depth'].values # for T(P) profile temperature = ds_sm['temperature'].values pressure = ds_sm['pressure'].values</code></pre> <a href="https://github.com/natashabatalha/picaso/blob/master/docs/notebooks/fitdata/GridSearch.ipynb">This tutorial</a> shows how to use these models to analyze the NIRSpec Prism observations of WASP-39 b, which led to <a href="http://www.nature.com/articles/s41586-022-05269-w">CO2 detection</a>. Please note that the folders must be unzipped before using them with this notebook. CREDITS If you use these modeling products in your work, please cite this zenodo repository along with the following papers depending on the part of the grid being used: 1) RCTE_cloud_free.zip  <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a>  2) RCTE_cloudy.zip <a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...925...33R/abstract">Rooney et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a>  3) photochem_cloud_free.zip  <a href="https://arxiv.org/abs/2108.01790">Tsai et al. (2021)</a> , <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a>  4) photochem_cloudy.zip  <a href="https://arxiv.org/abs/2108.01790">Tsai et al. (2021)</a> , <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...925...33R/abstract">Rooney et al. (2022)</a>

opencc-by-4.0May 2024View details →

Atmospheric aerosol chemical characterization and organic aerosol source apportionment by HR-TOF-AMS in the Po Valley during RHAPS (2021)

Time series of non-refractory submicrometric aerosol (PM1) chemical components (sulfate, nitrate, ammonium, chloride, and organic aerosol, OA) from RHAPS campaigns (winter and summer 2021) at Bologna (BO) and San Pietro Capofiume (SPC), Po Valley, Italy. Time series and profiles of OA source factors derived from PMF.

dryad40/100

Cloud_ICA: A deterministic cloud-overlap algorithm for generating a complete set of independent column atmospheres

In calculating solar radiation, climate models make many simplifications, in part to reduce computational cost and enable climate modeling, and in part from lack of understanding of critical atmospheric information. Whether known errors or unknown errors, the community's concern is how these could impact the modeled climate. The simplifications are well known and most have published studies evaluating them, but with individual studies it is difficult to compare. Here, we collect a wide range of such simplifications in either radiative transfer modeling or atmospheric conditions and assess potential errors within a consistent framework on climate‐relevant scales. We build benchmarking capability around a solar heating code (Solar‐J) that doubles as a photolysis code for chemistry and can be readily adapted to consider other errors and uncertainties. The broad classes here include: use of broad wavelength bands to integrate over spectral features; scattering approximations that alter phase function and optical depths for clouds and gases; uncertainty in ice‐cloud optics; treatment of fractional cloud cover including overlap; and variability of ocean surface albedo. We geographically map the errors in W m−2 using a full climate re‐creation for January 2015 from a weather forecasting model. For many approximations assessed here, mean errors are ∼2 W m−2 with greater latitudinal biases and are likely to affect a model's ability to match the current climate state. Combining this work with previous studies, we make priority recommendations for fixing these simplifications based on both the magnitude of error and the ease or computational cost of the fix.

opencc-zeroMay 2024View details →

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Unveiling the impact of soil methane sink on atmospheric methane concentrations in 2020

In 2020, anthropogenic methane (CH4) emissions decreased due to COVID-19 containment policies, but there was a substantial increase in the concentration of atmospheric CH4. Previous research suggested that this abnormal increase was linked to higher wetland CH4 emissions and a decrease in the atmospheric CH4 sink. However, the impact of changes in the soil CH4 sink remained unknown. To address this, we utilized a process-based model to quantify the alterations in the soil CH4 sink of terrestrial ecosystems between 2019 and 2020. By implementing the model with various datasets, we consistently observed an increase in the global soil CH4 sink, reaching up to 0.35 ± 0.06 Tg in 2020 compared to 2019. This increase was primarily attributed to warmer soil temperatures in northern high latitudes. These findings emphasize the importance of considering the CH4 sink in terrestrial ecosystems, as neglecting it can lead to an underestimation of both emission increases and reductions in atmospheric CH4 sink capacity. Furthermore, they highlight the potential role of increased soil warmth in terrestrial ecosystems in slowing the growth of CH4 concentrations in the atmosphere.

opencc-zeroJun 2024View details →

Atmospheric CO2 simulations over Indian sites using STILT driven by WRF meteorology.

This data contains atmospheric CO2 simulations at a temporal resolution of 3 hours over 15 Indian sites using a Lagrangian transport model, STILT driven by WRF meteorology during May 2017. Note: You are encouraged to contact the creator before using this data in any presentation or publication.  Reference: Jithin Sukumaran, Dhanyalekshmi Pillai, Vishnu Thilakan, Saradambal Lekshmi, Gokul Udayakumar, Thara Anna Mathew, Aparnna Ravi, Manoj M G. How critical is the accuracy of the atmospheric transport modelling to improve the urban CO2 emission in India? - A Lagrangian-based approach. (2024), JGR Atmospheres. [Under review]

Atmospheric CO2 simulations over Indian sites using STILT driven by ECMWF meteorology.

This data contains atmospheric CO2 simulations at a temporal resolution of 3 hours over 15 Indian sites using a Lagrangian transport model, STILT driven by ECMWF meteorology during May 2017. Note: You are encouraged to contact the creator before using this data in any presentation or publication.  Reference: Jithin Sukumaran, Dhanyalekshmi Pillai, Vishnu Thilakan, Saradambal Lekshmi, Gokul Udayakumar, Thara Anna Mathew, Aparnna Ravi, Manoj M G. How critical is the accuracy of the atmospheric transport modelling to improve the urban CO2 emission in India? - A Lagrangian-based approach. (2024), JGR Atmospheres. [Under review]

Data accompanying "Unipolar Quantum Optoelectronics for High Speed Direct Modulation and Transmission in 8-14 µm Atmospheric Window"

This dataset contains measurement data for the results presented in "Unipolar Quantum Optoelectronics for High Speed Direct Modulation and Transmission in 8-14 µm Atmospheric Window".

Atmospheric rivers dataset for machine learning training

A thorough description of the data and how it was created can be found: <a title="http://climate-cms.org/CNN-Atmospheric-Rivers/" href="http://climate-cms.org/CNN-Atmospheric-Rivers/" target="_blank" rel="noopener">http://climate-cms.org/CNN-Atmospheric-Rivers/</a> A Jupyter notebook has also been created where we'll show you how to use this data to train a deep learning model to identify whether an Integrated Vapor Transport map contains an atmosperhic river. it can be found: <a title="CNN_AR_tutorial.ipynb" href="https://github.com/coecms/CNN-Atmospheric-Rivers/blob/main/CNN_AR_tutorial.ipynb" target="_blank" rel="noopener">CNN_AR_tutorial.ipynb</a> and had been published here:  Mesto, M., Hobeichi, S., & Green, S. (2024). CNN-Atmospheric-Rivers (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.12538779" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12538779</a> The data is organised in three folders: <ul> <li>IVT_ERA5_2Deg: Contains global IVT data.</li> <li>AR_Global: Contains polygons representing AR objects identified and hand-labelled in each IVT map.</li> <li>Training_Testing_tiles: Contains tiles of IVT data with an annotation file that classifies each tile as one of the following: ‘Atmospheric River’, ‘Ambiguous’. The ‘Ambiguous’ class refers to objects that are not clearly identifiable as atmospheric rivers.</li> </ul> Integrated Vapor Transport maps: These maps were computed using the magnitude of the vertical integral of northward and eastward water vapour flux variables from ERA5. The IVT values are expressed in units of kg m^-1 s^-1. All the IVT TIFF files were loaded into ArcGIS software and displayed using a colour scheme that allows for the visual identification of atmospheric rivers. Data details: <ul> <li>Folder: IVT_ERA5_2Deg</li> <li>File format: TIFF</li> <li>Spatial resolution: 2 degrees</li> <li>Spatial coverage: Global (longitude: -180 to 180 , latitude: -90 to  90)</li> <li>Geographic Coordinate System: GCS_WGS_1984</li> <li>Temporal coverage: 1st – 5th day of January, April, July, October for 2010, 2013, 2015; these years correspond to La Niña, neutral, and El Niño year respectively</li> <li>Temporal resolution: Daily</li> <li>Naming of files: ivt_2deg_ddmmyyyy.tif</li> <li>Number of files: 60 (5 days × 4 months × 3 years)</li> <li>Number of channels in each file: 1</li> </ul>   Atmospheric Rivers in IVT maps: The annotation tool ‘Label Objects for Deep Learning’ was used to draw polygons to cover the shape of atmospheric rivers on each IVT map. Each polygon was assigned one of two labels: 'Atmospheric Rivers' or 'Ambiguous'. The polygons were drawn based on visual identification of the shape of atmospheric rivers, guided by IVT values close to 500kg m^-1 s^-1 as in Reid et al (2020). The 'Ambiguous' label was assigned to objects that were unclear in their classification as ARs. This ambiguity arose from objects that were shorter, wider, had slightly lower IVT values, or it was hard to tell if they were ARs of tropical cyclones during the early stages of their formation. Data details: <ul> <li>Folder: AR_Global</li> <li>File format: SHP (shapefile)</li> <li>Spatial coverage: Global</li> <li>Geographic Coordinate System: GCS_WGS_1984</li> <li>Temporal coverage: 1st – 5th day of January, April, July, October for 2010, 2013, 2015 (corresponding to La Niña, neutral, and El Niño year respectively)</li> <li>Temporal resolution: Daily</li> <li>Naming of files: ivt_2deg_ddmmyyyy_labelled.shp</li> <li>Number of files: 60 (5 days × 4 months × 3 years)</li> </ul>   Dataset for deep learning training: The tool 'Export Training Data for Deep Learning' uses the IVT maps in the 'AR_Global' folder and the shapefiles in the 'IVT_ERA5_2Deg' folder to create labelled tiles for deep learning training. Each tile in the map is assigned a label: 'Atmospheric River', 'Ambiguous', or no label if it doesn’t contain any AR or ambiguous shape. The generated map chips are stored in folder Training_Testing_tiles/ RCNN_Masks_All_Tiles (no 3-10-15)/images, and the labels are provided in the textfile 'map.txt' file in folder Training_Testing_tiles/ RCNN_Masks_All_Tiles (no 3-10-15)/. Data details: <ul> <li>Folder: Training_Testing_tiles/ RCNN_Masks_All_Tiles (no 3-10-15)/images</li> <li>File format: TIFF</li> <li>Spatial resolution: 2 degrees</li> <li>Spatial coverage: varies. Width of tile = 40 gridcells. Height of tile = 20grid cells</li> <li>Geographic Coordinate System: GCS_WGS_1984</li> <li>Temporal coverage: 1st – 5th day of January, April, July, October for 2010, 2013, 2015. These years correspond to La Niña, neutral, and El Niño year respectively. Please note that data for certain days are missing; these omissions correspond to days with no or only a single atmospheric river detected.</li> <li>Temporal resolution: Daily</li> <li>Number of files: varies</li> <li>Number of channels in each file: 1</li> </ul>