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Supplementary Information: Exoplanet atmosphere retrievals in 3D using phase curve data with ARCiS: application to WASP-43b. Chubb and Min, A&A (2022).

Supplementary information containing additional figures of the journal article 'Exoplanet atmosphere retrievals in 3D using phase curve data with ARCiS: application to WASP-43b' by K. L. Chubb and M. Min, published in Astronomy & Astrophysics (2022).

Training and Testing Data, Associated Code, and WRF Code for ML-based nonhydrostatic alternative scheme in dynamical core of atmosphere

Data and codes for a nonhydrostatic alternative scheme (NAS) in dynamical core of atmosphere based on machine learning. In this new version, the randomly sampled training data samples testing data samples from nonhydrostatic simulations in WRF baraclinic wave test are provided. They are processed into a new data structure, which can be directly utilized in training and testing.  Follow the instructions in README.txt and download the training and testing data, and the associated codes. Here we provide 3 parts of data and codes: 1, Training and testing data from WRF; 2, Training and testing codes for two machine learning emulators: machine learning and neural network 3, WRF application.

Simulation data on the growth of atmospheric molecular clusters and particles

This data set contains output data from cluster population simulations performed with Atmospheric Cluster Dynamics Code (ACDC) model, which simulates the formation of clusters from atmospheric vapors and the growth of these clusters by further molecular and cluster-cluster collisions. The data can be used for investigating the formation and growth of atmospheric particles from inorganic and organic vapors. The data is output of a computational process model, and hence does not represent a specific time period or location. Simulation sets are calculated for a one or two-component system containing a quasi-unary inorganic compound representing a mixture of sulfuric acid and ammonia (SA) and/or oxidized organic vapors corresponding to a low volatility organic compound (LVOC) and an extremely-low volatility organic compound (ELVOC). The external conditions in the simulations correspond to those in the CLOUD (Cosmics Leaving Outdoor Droplets) chamber at temperature of 5 C°. Data are provided for 14 simulations. References Kontkanen J, Stolzenburg D, Olenius T, Yan C, Dada L, Ahonen L, Simon M, Lehtipalo K, Riipinen I (2022) What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?. Environ. sci. Atmos. <a href="https://doi:10.1039/d1ea00103e">https://doi:10.1039/d1ea00103e</a>     Olenius T, Riipinen I (2017) Molecular-resolution simulations of new particle formation: Evaluation of common assumptions made in describing nucleation in aerosol dynamics models. Aerosol Sci. Tech. 51:397⁠ – ⁠408. <a href="https://doi.org/10.1080/02786826.2016.1262530">https://doi.org/10.1080/02786826.2016.1262530</a> Olenius T, Atmospheric Cluster Dynamics Code. <a href="https://github.com/tolenius/ACDC">https://github.com/tolenius/ACDC</a>  McGrath MJ et al. (2012) Atmospheric Cluster Dynamics Code: a flexible method for solution of the birth-death equations. Atmos. Chem. Phys. 12:2345⁠ – ⁠2355. <a href="https://doi.org/10.5194/acp-12-2345-2012">https://doi.org/10.5194/acp-12-2345-2012</a> Data description The data is in the form of text files. The provided data files (total compressed size ~10GB) correspond to simulation output from the ACDC model. Simulation sets are shown in the table below and further described in Kontkanen et al. (2022). For the interpretation of the model output, the interested user is referred to the manual of ACDC model (<a href="https://github.com/tolenius/ACDC">https://github.com/tolenius/ACDC</a>).   <table align="left"> <tbody> <tr> <td> Simulation set </td> <td> Model compounds </td> <td> Vapor concentrations (cm-3) </td> <td> Method to retrieve evaporation rates </td> </tr> <tr> <td> 1 </td> <td> SA </td> <td> CSA = 8.0*106, 2.0*107, 4.7*107, 1.1*108 </td> <td> Kelvin eq. (classical evaporation rates) </td> </tr> <tr> <td> 2 </td> <td> SA </td> <td> CSA = 2.0*107, 4.7*107, 1.1*108 </td> <td> QC data and Kelvin eq. (non-classical evaporation rates) </td> </tr> <tr> <td> 3 </td> <td> LVOC </td> <td> CLVOC = 5.0*107, 1*108 </td> <td> Kelvin eq. (classical evaporation rates) </td> </tr> <tr> <td> 4 </td> <td> LVOC, ELVOC </td> <td> CLVOC = 5.0*107, 1*108 CELVOC = 1.0 *107 </td> <td> Kelvin eq. (classical evaporation rates) </td> </tr> <tr> <td> 5 </td> <td> LVOC, SA </td> <td> CLVOC = 2.0*107, 5.0*107, 1*108 CSA = 8.0*106 </td> <td> Kelvin eq. (classical evaporation rates) </td> </tr> </tbody> </table>

Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon (Open)

This work was carried out in the scope and with the support of the project: Climate Services Through Knowledge Co-Production: A Euro-South American Initiative for Strengthening Societal Adaptation Response to Extreme Events (CLIMAX). The project consortium includes the following institutions: Centre National de la Recherche Scientifique CNRS/Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI-IFAECI) (Argentina-France); General Coordination of Earth Sciences /National Institute  for Space Research (INPE) (Brazil); Institut de Recherche pour le Développement (IRD)/ Unité Mixte de Recherche (UMR 245) (France);  Le Laboratoire des Sciences du Climat et de l'Environnement (LSCE) (France); Potsdam Institute for Climate Impact Research (PIK) (Germany); Technical University of Munich (TUM) (Germany) and Wageningen University and Research (WUR) Netherlands). The project is sponsored by the Collaborative Research Action (CRA) on “Climate Predictability and Inter-Regional Linkages” of the Belmont Forum, launched in 2015. Climate variability patterns linking the South American Monsoon region, including Amazonia, with southeastern South America influence climate extremes and impact several societal sectors. More than 200 million people live in the study region, which is also one of the largest agricultural production regions of the world and home to the world’s second largest hydroelectric power plant. The objectives of CLIMAX  include  better understanding the combined role of remote and local drivers on South American climate variability from sub-seasonal to decadal timescales, and its impact on the occurrence and intensity of extreme events. Special focus is given to an improved understanding of the effects of land use changes from the Amazon to the subtropics and their impact on climate. <ol> <li> EXPERIMENT DESIGN </li> </ol> We used four models that are classified as Dynamic Global Vegetation Models (DGVMs) (Prentice et al., 2007; Rezende et al., 2015): Integrated Model of Land Surface Processes (INLAND) (Tourigny, 2014); Lund-Potsdam-Jena managed Land model version 4 (LPJmL4) (Schaphoff et al., 2018), Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) (Smith et al. 2001, Hickler et al., 2012), and Organising Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE) (Krinner et al., 2005). We used three forcings with climate data (GLDAS, GSWP3, and WATCH+WFDEI), Land Use Change (LUC) data and validation data (FLUXCOM (Remote sensor+meteorological data+artificial neural network approach), FLUXCOM (eddy covariance), MODIS (Light Use Efficiency), GLEAM, and TerraClimate (Rezende et al., 2022). We conducted two sets of simulation experiments with different values of CO2: 1) increasing CO2 from the pre-industrial period to 2010 named historical CO2 (hist CO2); 2) constant concentration of 278 ppm of (pre-industrial) atmospheric CO2 named constant CO2 (const CO2). We ran both CO2 experiments under Land Use Change (LUC) and Potential Natural Vegetation (PNV) conditions. All combinations of CO2 and land use change resulted in four sets of simulation experiments per climate input: 1. LUC historical CO2; 2. LUC constant CO2; 3. PNV historical CO2; 4. PNV constant CO2  (Rezende et al., 2022). 2. DATA DESCRIPTION The complete description of the data, including the climate forcing, LUC, the validation datasets, methodology, simulations, discussion and conclusion is in Rezende et al. (2022). This archive contains only the data description from the simulations (outputs) by the DGVMs. 2.1 SOFTWARE The data were manipulated, worked, standardized, converted using the software: Climate Data Operators (cdo) version 1.7.0, Grid Analysis and Display System (Grads) (<a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a>) version 2.0.2, and RStudio Desktop version 1.3.1093 (R Core Team, 2020), through command lines and several scripts developed for this purpose. The figures were generated  with Grads, and RStudio, and some images were enhanced  with Gimp version 2.8.22. All the software used is freeware.    2.2 PRIMARY DATA FROM SIMULATIONS Data originating from the simulations are in monthly resolution, covering South America, with all the forcings. Despite data spanning  over 1948-2010 or 1950-2010 our study focuses on the period 1981-2010. Variables: Gross Primary Productivity (GPP) (kg m-2 month-1), evaporation  (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment). The naming of the files is according to the following rules: DGVM_forcing_vegetation cover_CO2 concentration_attribute DGVMs;     InLand (INLAND)     LPJ-G (LPJ-GUESS)     LPJmL (LPJmL4)     ORCHI (ORCHIDEE) forcings:      gld - GLDAS     gsw – GSWP3     wat – WATCH+WFDEI   vegetation cover:     LU – Land Use Change     PNV – Potential Natural Vegetation   CO2 concentration:     CO2 – historical CO2     noCO2 – constant CO2 = 278 ppm   variables:     E – evaporation     Et – transpiration     gpp – Gross Primary Productivity     npp - Net Primary Productivity (not used in the experiment)   Example:     inLand_gld_LU_noCO2_E.nc    2.3 SUPPLEMENTARY DATA These interception loss data (mm month-1) were requested by a reviewer to complement the analysis and are available only for the LUC CO2 scenario and for the study region: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude). Files are named  according to the following rules: variable_season_forcing_DGVM_vegetation cover CO2 concentration_region variable:     inter – loss by interception   season:     D – dry season     R – rainy season   forcings:                 gl - GLDAS                gs – GSWP3                wa – WATCH+WFDEI   DGVMs:     in - INLAND      lg - LPJ-GUESS             lm - LPJmL4             or – ORCHIDEE   vegetation cover      L – Land Use Change     P – Potential Natural Vegetation   CO2 concentration     C – historical CO2     N – constant CO2 = 278 ppm   region     SA – southern Amazon   Example:     inter_D_gl_in_LC_SA.nc 2.4 PROCESSED DATA Processed data cover all scenarios and input data sets and are restricted to the study area: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).They are in seasonal resolution with averages for January-February-March-April (JFMA) (rainy season) and averages for June-July-August-September (JJAS). Each of the files contains the Gross Primary Productivity variables (GPP) (kg m-2 month-1), evaporation  (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).   Files are named according to the following rules:   season_DGVM_forcing_vegetation cover CO2 concentration_region season     D – dry season     R – rainy season   DGVMs:     in - INLAND      lg - LPJ-GUESS            lm - LPJmL4            or - ORCHIDEE   forcings:             Gl - GLDAS            Gs – GSWP3            Wa – WATCH+WFDEI      vegetation cover      L – Land Use Change     P – Potential Natural Vegetation   CO2 concentration     C – historical CO2     N – constant CO2 = 278 ppm   Example: D_in_Gs_LN.nc 2.5 DERIVED DATA   The variable Water Use Efficiency (WUE) (kg m-2 mm-1 month-1) results from rate: GPP / Tr (transpiration) (Eq. 1).    <table> <tbody> <tr> <td>                                   WUE = GPP / Tr </td> <td> (Eq. 1) </td> </tr> </tbody> </table>   These data refer to the study region: southern Amazon and apply to only one scenario:  Land Use Change and historic CO2. Files are named  naming of according to the following rules:   Variable:     WUE – Water Use Efficiency   season     D – dry season     R – rainy season   DGVMs:     in - INLAND      lg - LPJ-GUESS            lm - LPJmL4            or - ORCHIDEE   forcings:                 Gl - GLDAS                Gs – GSWP3                Wa – WATCH+WFDEI      vegetation cover      L – Land Use Change     P – Potential Natural Vegetation   CO2 concentration     C – historical CO2     N – constant CO2 = 278 ppm   region     SA – southern Amazon   Example: wue_D_lm_Gl_LC_SA.nc   How to cite this work: Rezende, Luiz F. C., Aline Castro, Celso Von Randow, Romina Ruscica, Boris Sakschewski, Phillip Papastefanou, Nicolas Viovy, Kirsten Thonicke, Anna Sörensson, Anja Rammig, Iracema F. A. Cavalcanti. Impacts of Land Use Change and atmospheric CO2 on  Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022. References <a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a>. Center for Ocean-Land-Atmosphere Studies, Institute of Global Environment and Society,<a href="https://en.wikipedia.org/wiki/George_Mason_University"> George Mason University</a>. Archived from<a href="http://www.iges.org/grads/gadoc/"> the original</a> on 7 April 2015. Retrieved 14 March 2015. Hickler T. et al., 2012. Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species based dynamic vegetation model. Glob Ecol Biogeograp 21:50–63, <a href="https://doi.org/10.1111/j.1466-8238.2010.00613.x">https://doi.org/10.1111/j.1466-8238.2010.00613.x</a> Krinner, G.et al., 2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochemical Cycles, 19, GB1015, doi:10.1029/2003GB002199. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.               Prentice IC (2007) Dynamic global vegetation modeling: quantifying terrestrial ecosystem responses to large-scale environmental change. In: Canadell J, Pataki D, Pitelka L (eds) Terrestrial ecosystems in a changing world. Springer, Berlin Heidelberg. Rezende, Luiz F. C. et al., 2015. Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2. Int J Biometeorol., 2015, doi: 10.1007/s00484-015-1087-6. Rezende, Luiz F. C. et al., 2022. Impacts of Land Use Change and atmospheric CO2 on  Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research - Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022. Schaphoff, S. et al., 2018. LPJmL4 – a dynamic global vegetation model with managed land –Part 1: Model description. Geosci. Model Dev., 11, 1343–1375, 2018, <a href="https://doi.org/10.5194/gmd-11-1343-2018">https://doi.org/10.5194/gmd-11-1343-2018</a>. Smith B. et al (2001) Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Glob Ecol Biogeograp 10:621–637 Tourigny, E. (2014). Multi-scale fire modeling in the neotropics: coupling a land surface model to a high resolution fire spread model, considering land cover heterogeneity. Phd dissertation, Meteorology. INPE. Retrieved from http://urlib.net/sid.inpe.br/mtc-m21b/2014/05.30.00.36

opencc-by-4.0Feb 2022View details →

Raw and analyzed data for manuscript "Dielectric barrier discharge plasma reduction of oxidized copper surfaces in an Ar/SiH4 atmosphere"

Abstract: Nowadays, cold plasma techniques like dielectric barrier discharge (DBD) plasmas have attracted considerable interest in view of high deoxidation efficiencies as well as relative simplicity of setups. Although DBD plasma deoxidation of copper has been mainly studied in Ar/H2 mixtures, there is no information on reduction performance of such methods in other protective atmospheres. In this study, the reduction of natively oxidized copper surfaces using a DBD plasma in an Ar/SiH4 atmosphere at 100 hPa and 20 °C was investigated. The influence of a silane gas on the deoxidation performance was studied by varying the SiH4 concentration from 0.0 to 0.5 vol%. An addition of a SiH4 gas to an Ar atmosphere results in the increase of the deoxidation effect of a DBD plasma, so almost all Cu2O was reduced after around 10 s of treatment in 0.1 vol% silane. Surface morphology analysis showed formation of particles after Ar/SiH4 plasma treatments, which can be cleaned from the surfaces by wiping. Additionally, characterization of the plasma phase indicated the presence of SiH* radicals, which likely play a role in the deoxidation effect. Moreover, an elimination of residual oxygen and nitrogen species in Ar by addition of SiH4 was observed.

opencc-by-4.0Apr 2022View details →

Modeling output for "The dynamic atmospheric and aeolian environment of Jezero crater, Mars"

This dataset contains meso- and microscale numerical modeling output supporting the findings presented in the paper, "Newman et al., The dynamic atmospheric and aeolian environment of Jezero crater, Mars, Science Advances"

opencc-by-4.0Apr 2022View details →

Data supporting the study "The impact of molecular self-organisation on the atmospheric fate of a cooking aerosol proxy" by Milsom et al.

Model and experimental data from the study "The impact of molecular self-organisation on the atmospheric fate of a cooking aerosol proxy" to be published in Atmospheric Chemistry and Physics.

opencc-by-4.0Dec 2012View details →

The dataset by "Spectrally Consistent Scattering, Absorption, and Polarization Properties of Atmospheric Ice Crystals at Wavelengths from 0.2 to 100 μm"

This is the ice crystal single-scattering property dataset introduced in the paper "Yang, Ping, et al. "Spectrally consistent scattering, absorption, and polarization properties of atmospheric ice crystals at wavelengths from 0.2 to 100 μ m." Journal of the Atmospheric Sciences 70.1 (2013): 330-347.".

Assets for 'Phase correlation on the edge for estimating cloud motion' submitted to Atmospheric Measurement Techniques

1. CMV-26-07-2016_ARM-SGP.gif  Sample cloud motion vectors from TSI camera images over the United States Atmospheric Radiation Measurement user facility’s Southern Great Plains site. 2. raindrop_02-01-2017_ARM-SGP.gif Rotation of cloud motion vectors from raindrop contaminated TSI camera.

Gravity waves in Titan's atmosphere: A comparison between linearized wave model calculations and HASI observations

The data for the article "Gravity waves in Titan's atmosphere: A comparison between linearized wave model calculations and HASI observations" (GWTA).    <ol> <li>"Titan_CJP_std_chem.dat" is the background atmosphere data of Titan's atmosphere from Strobel's model. It is used in Figure 1 of the article.</li> <li>"HASI_T_p_rho_vsZ_2008.dat" is the data for Cassini-Huygens observations in Titan's atmosphere. It is used in Figure 1 of the article.</li> <li>"Mma-Program-for-GW-on-Titan.txt" is the main Mathematica program to simulate the gravity waves on Titan.</li> <li>"solutions-fun.rar" is the simulation result. This RAR file includes 174 gravity wave samples simulated with different periods and horizontal wavelengths (can be read from the subfile names after uncompressing). These gravity wave solutions are stored as InterpolatingFunction of Mathematica. The solution describes the gravity wave temperature, velocity, and density perturbations profiles from altitude 300km to 2000km. However, they are plain texts and can easily be read by any software. Figures from 2-10 are based on these data.</li> </ol>

opencc-by-4.0Jun 2022View details →

Analysis of regional CO2 contributions at the high Alpine observatory Jungfraujoch by means of atmospheric transport simulations and δ13C

The data set complementary to manuscript "Analysis of regional CO2 contributions at the high Alpine observatory Jungfraujoch by means of atmospheric transport simulations and δ13C" in Atmospheric Chemistry and Physics (<a href="https://acp.copernicus.org">https://acp.copernicus.org</a>).

Pressure data used in 'Surface-to-space atmospheric waves from Hunga Tonga-Hunga Ha'apai eruption' (Wright et al., 2022)

Pressure data used in 'Surface-to-space atmospheric waves from Hunga Tonga-Hunga Ha’apai eruption'  (Wright et al., 2022).    Phase speed estimates by station: Author: Fred Prata, AIRES Pty Ltd Description: distances, locations, arrival times and phase speed estimates for the Hunga Tonga Lamb wave from pressure stations used in our study.     Pressure time series data (19 stations): Lauder (1 station): Author: Dan Smale/NIWA, State Highway 85, Omaku, New Zealand Description: Data sourced from a CO2 eddy-covariance instrument operated and maintained by NIWA.  Values were provided as an image file of pressure anomaly versus time (NZST) which was digitized at approximately 90 s time resolution and 0.1 hPa. Mt Eliza / HRO (1 station): Author: Fred Prata/AIRES Pty Ltd, 116 Humphries Road, Mount Eliza, Vic 3930, Australia Description: Data derived from an ecowitt weather station (Easyweather-WIFIA 19E) operated and maintained by AIRES Pty Ltd.  The measurements are logged every 5 minutes with a pressure resolution of 0.1 hPa. Tonga (1 station): Author: Malo e Leilei Taaniela/Fua'amotu Domestic Airport, Tonga and Shane Cronin/University of Auckland, School of Environment, New Zealand. Description: Data derived from a barometer operated by the Tongan meteorological office located at Nukualofa port (met.gov.to).  Sampling interval is 1 minute and the pressure resolution is 0.1 hPa Weatherlink (3 stations): Author: Fred Prata/AIRES Pty Ltd, 116 Humphries Road, Mount Eliza, Vic 3930, Australia Description: Data downloaded from http://weatherlink.com The time resolution is 5 minutes for Davis and Boston and 15 minutes for Travis.  The pressure resolution is 0.01 in Hg. PurpleAir (13 stations):  Author: citizen science project - https://map.purpleair.com/ (free for non-commercial use) Description: PNG images of pressure traces from each station: American Samoa, Anchorage, Auckland, Brisbane, Colorado Springs, Concepcion, Glenn Dale, Kahuko, Manhattan Beach, Papeete, Solvang, Sydney, Tokyo. See table, described above, for latitude/longitude of each site.   Other pressure data used in the paper already archived elsewhere, and associated licensing (11 stations): AIMS (10 stations): https://apps.aims.gov.au/metadata/search?term=Weather%20Stations (CC BY 3.0 AU) Wegenernet (1 station): https://wegenernet.org/portal/v7.1/2021/1 ("openly available to all and free of charge except for commercial usage")     Not included (6 stations): Due to licensing terms, we do not include 6 pressure time series obtained from the Australian Bureau of Meteorology in their raw form, specifically those at Mt Isa Aero, Learmonth Airport,  Broome Airport, Alice Springs Airport, Adelaide Airport and Perth Airport. Derived products made from these data are permitted to be shared, and accordingly phase speed estimates from these stations are included in the table described above. A graphical representation of the data from Broome is also included in the scientific paper these data support as Extended Data Figure 1e.

opencc-by-4.0May 2021View details →

Correlated k coefficients for H2-He atmospheres; 196 spectral windows and 1060 pressure-temperature points

There are 72 correlated k-coefficients datasets, using the naming convention m-xxx_coyyy.data.196.tar.zip, where xxx is the metallicity in dex relative to solar, and yyy is the C/O ratio relative to solar, as a multiplication factor. For example a metallicity of 0.0 and a C/O ratio of 1.0 indicates solar abundances. There are an additional 5 datasets that do not include the TiO and VO opacities, as indicated by the "_noTiOVO" designation in the file name. We use the Lodders et al. 2010 value for the solar C/O=0.458. The spectral windows are listed in the file 196_windows.txt (intervals defined as starting at lambda1 and ending at lambda2), and the k-coefficients can be read in and checked using the script in the IDL code read_k_coefficients.pro. The k-coefficients are calculated for a grid of 1060 pressure-temperature points listed in the file PT_list_1060. The correlated-k coefficients are calculated using pre-mixed opacities, where the abundances for each metallicity-C/O combination have been calculated using equilibrium chemistry, as described in Marley et al. 2021. There are 12 Fe/H values: 0.0, 0.5, 0.7, 1.0, 1.5, 1.5, 1.7, 2.0, -0.25, -0.3, -0.5, -0.75, and -1.0; and 6 C/O values: 0.25, 0.5, 1.0, 1.5, 2.0 and 2.5.  The opacity sources included in the calculations are: C2H2, C2H4, C2H6, CH4, CO, CO2, CrH, FeH, H2O, H2S, HCN, LiCl, MgH, N2,  NH3, OCS, PH3, SiO, TiO, and VO, in addition to alkali metals (Li, Na, K, Rb, Cs). The references for the line lists and broadening parameters used in these opacity calculations can also be found in Marley et al 2021, and are included here for convenience in the file Table_2_Marley_et.al.2021.png Each dataset contains the following files: ascii_data: the correlated k coefficients file in ascii format. This can be read by the included IDL code. binary_data: the correlated k coefficients file in binary format cp_all: contains the mean molecular weight for each layer. The head capacity values do not include the proper H2 heat capacity and should not be used. full_abunds: the relative abundances for all the species from the chemistry files, on the 1060-point pressure-temperature grid. sum_in_atoms: relative abundances for the alkali metals sum_in_cia: relative abundances for the species that could be used for calculating collision-induced absorption (CIA) sum_in_layer: relative abundances for all molecules included in the correlated k-coefficients calculations Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.

opencc-by-4.0May 2021View details →

Correlated k coefficients for H2-He atmospheres; 11 spectral windows and 1060 pressure-temperature points

There are 72 correlated k-coefficients datasets, using the naming convention m-xxx_coyyy.data.11.tar.zip, where xxx is the metallicity in dex relative to solar, and yyy is the C/O ratio relative to solar, as a multiplication factor. For example a metallicity of 0.0 and a C/O ratio of 1.0 indicates solar abundances. There are an additional 10 datasets that do not include the TiO and VO opacities, as indicated by the "_noTiOVO" designation in the file name. We use the Lodders et al. 2010 value for the solar C/O=0.458. The spectral windows are listed in the file 11_windows.txt (intervals defined as starting at lambda1 and ending at lambda2), and the k-coefficients can be read in and checked using the script in the IDL code read_k_coefficients.pro. The k-coefficients are calculated for a grid of 1060 pressure-temperature points listed in the file PT_list_1060. The correlated-k coefficients are calculated using pre-mixed opacities, where the abundances for each metallicity-C/O combination have been calculated using equilibrium chemistry, as described in Marley et al. 2021. There are 12 Fe/H values: 0.0, 0.5, 0.7, 1.0, 1.5, 1.5, 1.7, 2.0, -0.25, -0.3, -0.5, -0.75, and -1.0; and 6 C/O values: 0.25, 0.5, 1.0, 1.5, 2.0 and 2.5.  The opacity sources included in the calculations are: C2H2, C2H4, C2H6, CH4, CO, CO2, CrH, FeH, H2O, H2S, HCN, LiCl, MgH, N2,  NH3, OCS, PH3, SiO, TiO, and VO, in addition to alkali metals (Li, Na, K, Rb, Cs). The references for the line lists and broadening parameters used in these opacity calculations can also be found in Marley et al 2021, and are included here for convenience in the file Table_2_Marley_et.al.2021.png Each dataset contains the following files: ascii_data: the correlated k coefficients file in ascii format. This can be read by the included IDL code. binary_data: the correlated k coefficients file in binary format cp_all: contains the mean molecular weight for each layer. The head capacity values do not include the proper H2 heat capacity and should not be used. full_abunds: the relative abundances for all the species from the chemistry files, on the 1060-point pressure-temperature grid. sum_in_atoms: relative abundances for the alkali metals sum_in_cia: relative abundances for the species that could be used for calculating collision-induced absorption (CIA) sum_in_layer: relative abundances for all molecules included in the correlated k-coefficients calculations Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.

opencc-by-4.0Jun 2022View details →

CEDAR Project: A Whole-Atmospheric Perspective on Connections between Intra-Seasonal Variations in the Troposphere and Thermosphere

This collaborative award is aimed at studying the relationship between the variability of thermospheric winds to the variability caused by wave structures generated in the tropical troposphere. This coupling is driven by wave excitation by deep convection in the tropical troposphere that can propagate vertically into the thermosphere. Tropospheric convection associated with the Madden‐Julian Oscillation (MJO), the dominant mode of intra-seasonal variability in tropical convection and circulation, is known to modulate the intensity of upward‐propagating gravity and Kelvin waves. Previous work demonstrated that a 90-day oscillation in tropospheric convection during 2009-2010 was imprinted on both thermospheric mean winds and the eastward propagating wavenumber 3 diurnal (DE3) tidal amplitudes. This modulation was observed by the GOCE and CHAMP satellites and modeled with the TIME-GCM. The research effort would broaden participation by involving and training two undergraduate student interns through the University of Colorado BOLD internship program that focuses on promoting the recruitment, retention, and development of traditionally underrepresented engineering students. The new research will follow up on the results obtained in recent studies that demonstrated that strong coupling between the troposphere and the thermosphere occurs on intra-seasonal timescales. The award will address the following questions: Q1: How frequent, prevalent, and persistent are correlations between 30 to 100-day variations in the three regions of troposphere, mesosphere, and thermosphere, during the past two decades? Q2: What plausible roles do large-scale upward propagating waves play in dynamically coupling tropical tropospheric intra-seasonal variability into the thermosphere? Q3: Is there any observational evidence suggesting a connection between this troposphere-thermosphere intra-seasonal coupling and MJO, Quasi-Biennial Oscillation (QBO) and El Niño-Southern Oscillation (ENSO)? The combination of available upper atmosphere satellite data with ground-, and model-based datasets would be studied to provide insight into whether the intra-seasonal variations in the waves are caused by variability in the tropospheric sources or by wave-mean flow interactions. In the case of the latter, the study would determine at which heights these interactions are occurring. This study will determine the contribution of global-scale wave coupling between the troposphere and the thermosphere, thus addressing outstanding issues of fundamental importance to the CEDAR community. This research primarily involves performing correlation analyses and extracting wave information from satellite (CHAMP, GOCE, Swarm-C, TIMED, OLR), ground (Kauai, Christmas Island, and Adelaide, Maui, Urbana, and Chile), and model (MERRA-2, TIE-GCM, and WACCM-X) -based datasets and processing, plotting, data produced in standard ways to draw scientific conclusions.  This project does not generate any new physical or observational data. The Findable, Accessible, Interoperable and Reusable (FAIR) principles are followed by making data resources (e.g. code/software and metadata) resulting from this project publicly available. GOCE, CHAMP, Swarm-C data (V01) are available at ftp://anonymous@thermosphere.tudelft.nl/. SABER data (V2.0, L2B) are available at http://saber.gats-inc.com/data.php. Tl DI data (V3.7) are available at http:// timed.hao.ucar.edu/tidi/. OLR data are available at https://psl.noaa.gov/data/gridded/ data.interp_OLR.html. F10.7 data are available at http://www.swpc.noaa.gov/content/data-access. kp/ap data are available at ftp:// ftp.gfz-potsdam.de/pub/home/obs/ kp-ap/.

opencc-by-4.0Jul 2022View details →

Datasets for "Evaluating the performance of a Picarro G2207-i analyser for high-precision atmospheric O2 measurements"

These data files contain the data used in the manuscript "Evaluating the performance of a picarro G2207-i analyser for high-precision atmospheric O2 measurements" submitted to Atmospheric Measurement Techniques.  WAO_G2207i_O2_calibrated_AM_all : Calibrated O2 measurements from the G2207-i during the no-drying, partial-drying, and full-drying periods at WAO, for both the water-corrected and non-water corrected outputs CRAM_lab_run1_noRT : calibrated O2 measurements for the first run of cylinder gases in the CRAM lab, UEA, without reference tank correction CRAM_lab_run1_wRT    : calibrated O2 measurements for the first run of cylinder gases in the CRAM lab, UEA, with reference tank correction applied CRAM_lab_run2_noRT : calibrated O2 measurements for the second run of cylinder gases in the CRAM lab, UEA, without reference tank correction CRAM_lab_run2_wRT : calibrated O2 measurements for the second run of cylinder gases in the CRAM lab, UEA, with reference tank correction applied

opencc-by-4.0Jul 2022View details →

Data used in a manuscript entitled "Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model" submitted to Geophysical Research Letters

This include a dataset used in a manuscript entitled “Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model” by Yamada and co-authors, which is submitted to Geophysical Research Letters. Contact: Yohei Yamada (yoheiy@jamstec.go.jp)

opencc-by-4.0Jul 2022View details →

The Tracing Convective Momentum Transport in Complex Cloudy Atmospheres Experiment - Level 2

The first field campaign from the Tracing Convective Momentum Transport in Complex Cloudy Atmospheres experiment project (CMTRACE) took place in Cabauw, the Netherlands, between September 13th and October 3rd 2021. During this field campaign, two cloud radars and one wind lidar were operated with a similar scanning strategy for deriving wind speed and direction profiles from near the surface up to cloud tops. Here we provide the daily Level 2 data from the campaign. At this level, several processing steps were applied to the Level 1 data from each instrument to minimize the differences between the sampled volumes resampled and temporal and spatial resolution to generate merged profiles of wind speed and direction. The raw dataset is available for the users on request from the corresponding author.

opencc-by-4.0Aug 2022View details →

Supplementary files for paper: "Design and fabrication of an electrostatic precipitator for infrared spectroscopy" in Atmospheric Measurement Techniques, 2022.

<ol> <li>File of absorbance spectra and hypothetical thickness for each sample.</li> <li>MATLAB function to perform clean crystal spectrum subtraction and baseline correction (described in the paper).</li> </ol>

Dataset for Smaller_Sensitivity_of_Precipitation_to_Surface_Temperature_under_Massive_Atmosphere

This file is the dataset for "Smaller Sensitivity of Precipitation to Surface Temperature under Massive Atmosphere". Uploaded as 5 groups (1-D radiative transfer model, GCM fixsst simulations, GCM aqua planet simulations, GCM present continent simulations, and cloud-resolving simulations), The data is time average of balanced state. For our article, GCM fixsst simulations are designed for group A, H and sensitivity test 1; GCM aqua planet simulations are designed for group B, C, D, and sensitivity test 2; GCM present continent simulations for group E, F, G; and RCE simulations for sensitivity test 4.