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22 results for “TOMCAT”

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zenodo52/100

TCOM-CFC11 : TOMCAT CTM and Occultation Mesurement based daily zonal mean CFC-11 stratospheric profiles constructed using machine leaning (2000-2023)

<h3>TOMCAT CTM and Occultation measurement-based Stratospheric CFC11 (TCOM-CFC11) profile data data set  </h3> <h3>Sandip S. Dhomse </h3> School of Earth and Envio, University of Leeds, Leeds, UK National Centre for Earth Observations, University of Leeds, Leeds, UK  email: s.s.dhomse@leeds.ac.uk   <h3>Methodology:  TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated CFC11 (CFCl3)  profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement  differences are calculated for each zonal bins (51 height levels, 10km to 60km). Separate XGBoost regression models are trained for the  differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. Estimated corrections for a given model grid that are added to the original TOMCAT simulated daily (at 1.30 local time) CFC-11 profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values.  For more details see attached presentation.</h3> <h3>Dataset also includes two files containing daily mean zonal mean CFC11  profiles on height (10-60 km) and pressure (300-0.1 hPa) levels (8766 days/64 latitudes):</h3> <h3>zmcfc11_TCOM_hlev_T2Dz_2000-2024_V1.1.nc – height level data (10 to 60 km)</h3> <h3>zmcfc11_TCOM_plev_T2Dz_2000-2024_V1.1.nc – pressure level data (300 to 0.1 hPa)</h3> <h3>Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.</h3> <h3> </h3> <h3> </h3>

opencc-by-4.0Jun 2024View details →

zenodo48/100

TCOM-HF : Daily global gap-free stratospheric hydrogen fluoride (HF) profile data set based on TOMCAT CTM and Occultation Measurements

Methodology:  TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated hydrogen fluoride (HF) profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement  differences are calculated for each zonal bins (51 height levels, 10km to 60km). Note that if enough ACE measurements are not avaliable for a particular level then data is purely based on TOMCAT simulated output field. Separate XGBoost regression models are trained for the  differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids.  TOMCAT output sampled at 1.30 pm local time at the equator. Estimated corrections for a given model grid that are added to the original TOMCAT simulated day and night time hydrogen fluoride profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values.  For more details see attached presentation. Previous version use both HALOE and ACE data. Here only ACE data is used. Dataset also includes two files containing daily mean zonal mean hydrogen fluoride  profiles on height (10-50 km) and pressure (300-0.1 hPa) levels: zmhf_TCOM_hlev_T2Dz_2000_2024.nc – height level data (10 to 50 km) zmhf_TCOM_plev_T2Dz_2000_2024.nc – pressure level data (300 to 0.1 hPa) Daily 3D profiles on height and pressure levels would be made available on request.

opencc-by-4.0Feb 2023View details →

zenodo48/100

ML-TOMCAT V2.0: Machine-Learning-Based Satellite-Corrected Global Stratospheric Ozone Profile Dataset

MLTOMCAT V2 is 46 years (1979-2024) of gap free ozone profile data sets that is created by correcting biases in a TOMCAT Chemical Transport Model (CTM) simulated ozone profiles. We use Random Forest regression model to correct model biases.  Each file contain monthly mean zonal mean ozone profiles. There are 6 data files. <a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_ht_vmr_V2.nc</a> contains ozone profiles on geometric height levels (1 to 60 km)  in mixing ratio units, whereas  <a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_ht_nd_V2.nc</a> contains ozone profile in number density units. Similarly,  <a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_plev_vmr_V2.nc</a> contains ozone profiles on 43 MLS pressure levels  (1000 to 0.1 hPa)  in mixing ratio units, whereas  <a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_plev_nd_V2.nc</a> contains ozone profile in number density units. Please note that data below 300 hPa (~8km) and 1 hPa (~50 km) should be used with caution. There are two straospheric column files ML-TOMCAT-SCO_120ppb_boundary_V2_197901-202412.nc and ML-TOMCAT-SCO_150ppb_boundary_V2_197901-202412.nc Stratospheric column files calculated using 120 ppb and 150 ppb as a chemical ozone boundaries. A manuscript describing MLTOMCAT would be published in EESD (Dhomse et al., 2021).

opencc-by-4.0Jun 2021View details →

zenodo48/100

TOMCAT CTM simulated ozone profiles using NRL2, SATIRE and SORCE solar fluxes

Individual file contain TOMCAT CTM simulated ozone profiles from five model simulations analysed in the following publication. Briefly,  vmro3_T2Mz_TOMCAT_A_NRL2_2005-2020.nc contain ozone profiles from the control simulation that uses ERA5 dynamical forcing fields and NRL V2 solar fluxes vmro3_T2Mz_TOMCAT_B_SATIRE_2005-2020.nc  and vmro3_T2Mz_TOMCAT_C_SORCE_2005-2020.nc contain ozone profiles from a simulations that are  similar to the control simulation but with SATIRE and SORCE solar fluxes vmro3_T2Mz_TOMCAT_D_SFix_2005-2020.nc has ozone profiles from simulation that is similar to the control simulation but with fixed solar fluxes, whereas vmro3_T2Mz_TOMCAT_E_DFix_2005-2020.nc also contain ozone profiles from a simulation where model uses annually repeating dynamical fields.   Dhomse, S. S., Chipperfield, M. P., Feng, W., Hossaini, R., Mann, G. W., Santee, M. L., and Weber, M.: A Single-Peak-Structured Solar Cycle Signal in Stratospheric Ozone based on Microwave Limb Sounder Observations and Model Simulations, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-663, in review, 2021.

opencc-by-4.0Jan 2022View details →

zenodo44/100

TCOM-HCl : Daily global gap-free stratospheric hydrogen chloride profile data set based on TOMCAT CTM and Occultation Measurements

Methodology:   The TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data. <h3>HCl Profile Processing and Bias Correction</h3> Collocated HCl profiles are organized into five distinct latitude bins: <ul> <li> NH polar: 90∘N - 50∘N </li> <li> NH mid-lat: 20∘N - 70∘N </li> <li> Tropics: 40∘S - 40∘N </li> <li> SH mid-lat: 70∘S - 20∘S </li> <li> SH polar: 90∘S - 50∘S </li> </ul> Initially, differences between TOMCAT and satellite measurements (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from 10,km to 60,km). Separate XGBoost regression models are then trained for these HCl differences at each height level within a given latitude bin. These trained models are subsequently used to estimate HCl bias corrections for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles. Height-resolved HCl profile data are then interpolated onto 28 standard pressure levels (from 300,hPa to 0.1,hPa), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions. <h3>Data Files</h3> The dataset includes two files containing daily mean zonal mean HCl profiles: <ul> <li> <code>zmhcl_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</code>: Contains height level data (10,km to 60,km). </li> <li> <code>zmhcl_TCOM_plev_T2Dz_2000-2024_V1.1.nc</code>: Contains pressure level data (300,hPa to 0.1,hPa). </li> </ul> <h3>Reference Publication</h3> This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication: Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, <a title="null" href="https://doi.org/10.5194/essd-15-5105-2023">https://doi.org/10.5194/essd-15-5105-2023</a>, 2023

opencc-by-4.0Feb 2023View details →

zenodo44/100

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

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

opencc-by-4.0May 2023View details →

zenodo44/100

TCOM-O3: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric ozone profile dataset [1991-2021] constructed using machine-learning

Methodology:  TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated Ozone (O3) profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement  differences are calculated for each zonal bins (51 height levels, 10km to 60km). Note that if enough ACE measurements are not avaliable for a particular level then data is purely based on TOMCAT simulated output field. Separate XGBoost regression models are trained for the  differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids.  TOMCAT output sampled at 1.30 pm local time at the equator. Estimated corrections for a given model grid that are added to the original TOMCAT simulated day and night time ozone profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values.  For more details see attached presentation. Previous version use both HALOE and ACE data. Here only ACE data is used (hence starting date is 01 January 2000). PDF file shows comparison between v1.0 and v1.1 as well as TOMCAT data. Dataset also includes two files containing daily mean zonal mean hydrogen fluoride  profiles on height (10-60 km) and pressure (300-0.1 hPa) levels: zmo3_TCOM_hlev_T2Dz_2000_2024.nc – height level data (10 to 60 km) zmo3_TCOM_plev_T2Dz_2000_2024.nc – pressure level data (300 to 0.1 hPa) Daily 3D profiles on height and pressure levels would be made available on request.

opencc-by-4.0Feb 2023View details →

zenodo44/100

TOMCAT CTM and Occultation measurement-based Stratospheric CFC12 (TCOM-CFC12) profile data set

TOMCAT CTM and Occultation measurement-based Stratospheric CFC12 (TCOM-CFC12) profile data set   Sandip S. Dhomse  School of Earth and Enviro, University of Leeds, Leeds, UK National Centre for Earth Observations, University of Leeds, Leeds, UK  email: s.s.dhomse@leeds.ac.uk  Methodology:  TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated CFC12 (CF2Cl2)  profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. Estimated corrections for a given model grid that are added to the original TOMCAT simulated daily (at 1.30 local time) CFC-12 profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values.  For more details see attached presentation. Dataset also includes two files containing daily mean zonal mean CFC-12 profiles on height (10-60 km) and pressure (300-0.1 hPa) levels (9132 days/64 latitudes): zmcfc12_TCOM_hlev_T2Dz_2000-2024_V1.1.nc – height level data (10 to 60 km) zmcfc12_TCOM_plev_T2Dz_2000-2024_V1.1.nc – pressure level data (300 to 0.1 hPa) Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.

opencc-by-4.0Jun 2024View details →

zenodo44/100

Carbonyl Sulfide (OCS) TOMCAT Model Data

These data are monthly mean carbonyl sulfide (OCS) model mixing ratios between 2004 and 2018. We use the TOMCAT/SLIMCAT 3-D off-line chemical transport model (Chipperfield, 2006). The model reads in 6-hourly fields of temperature, humidity, vorticity, divergence and surface pressure from ECMWF ERA-INTERIM meteorological (re)analyses. Horizontal resolution of 2.8° × 2.8° with 60 hybrid σ-pressure levels from the surface to ~60 km. The model dataset TOMCATOCS is defined in the files as 'OCS_SOIL_2_5_mm'. We recommend using this variable for any reproductions of the publication plots. TOMCATCON and TOMCATSOTA available upon request.

opencc-by-4.0Mar 2022View details →

zenodo44/100

Atmospheric Distribution of HCN from Satellite Observations and 3-D Model Simulations - TOMCAT data

This repository contains the model data from the paper "Atmospheric Distribution of HCN from Satellite Observations and 3-D Model Simulations" submitted to ACP. The files contains the monthly mean hydrogen cyanide (HCN) mixing ratios modelled using the TOMCAT 3-D offline chemical transport model with a horizontal resolution of 2.8° × 2.8° with 60 hybrid σ-pressure levels from the surface to ~60 km.

opencc-by-4.0Oct 2022View details →

zenodo44/100

TOMCAT model data & IASI/GOME-2B satellite data of European ozone between 2008 - 2023

Daily mean data of ozone (O3) from the TOMCAT 3D chemical transport model (Chipperfield, 2006) and two satellite products, the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A & B satellites and the Global Ozone Monitoring Experiment-2 (GOME-2) on the MetOp-B satellite. The satellite observations are retrieved using schemes developed by the Rutherford Appleton Laboratory (RAL) see Miles et al. (2015) and Pope et al. (2021). The TOMCAT model data is available for 2017 - 2021, the IASI data is available for 2008 - 2023 and the GOME-2 data for 2015 - 2020.

opencc-by-4.0May 2024View details →

zenodo44/100

TCOM-COF2: TOMCAT CTM and Occultation measurement-based Stratospheric COF2 profile data set

TCOM-COF2: TOMCAT CTM and Occultation measurement-based Stratospheric TCOM-COF2 profile data set   Sandip S. Dhomse  School of Earth and Enviro, University of Leeds, Leeds, UK National Centre for Earth Observations, University of Leeds, Leeds, UK  email: s.s.dhomse@leeds.ac.uk   Methodology:  TOMCAT simulation is performed at T64L32 resolution for the 2000-2023 time period. Collocated COF2  profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. Estimated corrections for a given model grid that are added to the original TOMCAT simulated daily (at 1.30 local time) COF2 profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values.  For more details see attached presentation. Dataset also includes two files containing daily mean zonal mean COF2 profiles on height (10-60 km) and pressure (300-0.1 hPa) levels (8766 days/64 latitudes): zmcof2_TCOM_hlev_T2Dz_2000_2023.nc – height level data (10 to 60 km) zmcof2_TCOM_plev_T2Dz_2020_2023.nc – pressure level data (300 to 0.1 hPa) Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.

opencc-by-4.0Jun 2024View details →

zenodo40/100

Tomcat bug-report

About the Data This dataset is one of the Datasets donated by An Ngoc Lam. Overview of Data The data is present in 2 files: “Tomcat.xlsx” : A spreadsheet with the bug-ids, commits, its summary, files etc. “Tomcat.xml” : An xml file with more detailed information than the above spreadsheet(detailed files changed). Attribute Information The spreadsheet contains a table with the “bug_id”, “summary”, “description”, “time_reported”, “commit associated”, “status of commit” and “files committed”. The xml contains the above information and additionally the lines associated with the commit. Paper Abstract Bug localization refers to the automated process of locating the potential buggy files for a given bug report. To help developers focus their attention to those files is crucial. Several existing automated approaches for bug localization from a bug report face a key challenge, called lexical mismatch, in which the terms used in bug reports to describe a bug are different from the terms and code tokens used in source files. This paper presents a novel approach that uses deep neural network (DNN) in combination with rVSM, an information retrieval (IR) technique. rVSM collects the feature on the textual similarity between bug reports and source files. DNN is used to learn to relate the terms in bug reports to potentially different code tokens and terms in source files and documentation if they appear frequently enough in the pairs of reports and buggy files. Our empirical evaluation on real-world projects shows that DNN and IR complement well to each other to achieve higher bug localization accuracy than individual models. Importantly, our new model, HyLoc, with a combination of the features built from DNN, rVSM, and project’s bug-fixing history, achieves higher accuracy than the state-of-the-art IR and machine learning techniques. In half of the cases, it is correct with just a single suggested file. Two out of three cases, a correct buggy file is in the list of three suggested files.

opencc-by-4.0Dec 2015View details →

zenodo40/100

Tomcat

Overview of Data The data is present in 2 files: “Tomcat.xlsx” : A spreadsheet with the bug-ids, commits, its summary, files etc. “Tomcat.xml” : An xml file with more detailed information than the above spreadsheet(detailed files changed). Attribute Information The spreadsheet contains a table with the “bug_id”, “summary”, “description”, “time_reported”, “commit associated”, “status of commit” and “files committed”. The xml contains the above information and additionally the lines associated with the commit. Paper Abstract Bug localization refers to the automated process of locating the potential buggy files for a given bug report. To help developers focus their attention to those files is crucial. Several existing automated approaches for bug localization from a bug report face a key challenge, called lexical mismatch, in which the terms used in bug reports to describe a bug are different from the terms and code tokens used in source files. This paper presents a novel approach that uses deep neural network (DNN) in combination with rVSM, an information retrieval (IR) technique. rVSM collects the feature on the textual similarity between bug reports and source files. DNN is used to learn to relate the terms in bug reports to potentially different code tokens and terms in source files and documentation if they appear frequently enough in the pairs of reports and buggy files. Our empirical evaluation on real-world projects shows that DNN and IR complement well to each other to achieve higher bug localization accuracy than individual models. Importantly, our new model, HyLoc, with a combination of the features built from DNN, rVSM, and project’s bug-fixing history, achieves higher accuracy than the state-of-the-art IR and machine learning techniques. In half of the cases, it is correct with just a single suggested file. Two out of three cases, a correct buggy file is in the list of three suggested files.

opencc-by-4.0Jan 2016View details →

zenodo40/100

tomcat

This dataset uses the CK OO metrics. More information at http://openscience.us/repo/defect/ck/tomcat.html

opencc-by-4.0Jul 2010View details →

zenodo40/100

TCOM-CH4: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric methane profile dataset [1991-2021] constructed using machine-learning

Methodology:   he TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data. <h3>CH4 Profile Processing and Bias Correction</h3> Collocated CH4 profiles are organized into five distinct latitude bins: <ul> <li> NH polar: 90∘N - 50∘N </li> <li> NH mid-lat: 20∘N - 70∘N </li> <li> Tropics: 40∘S - 40∘N </li> <li> SH mid-lat: 70∘S - 20∘S </li> <li> SH polar: 90∘S - 50∘S </li> </ul> Initially, differences between TOMCAT and satellite measurements (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from 10,km to 60,km). It is important to note that unlike previous versions that might have used both HALOE and ACE measurements, this version exclusively utilizes ACE-FTS data, which is why the dataset starts from 2000. Separate XGBoost regression models are then trained for these CH4 differences at each height level within a given latitude bin. These trained models are subsequently used to estimate CH4 bias corrections for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles. Height-resolved CH4 profile data are then interpolated onto 28 standard pressure levels (from 300,hPa to 0.1,hPa), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions. <h3>Data Files</h3> The dataset includes two files containing daily mean zonal mean CH4 profiles: <ul> <li> <code>zmch4_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</code>: Contains height level data (10,km to 60,km). </li> <li> <code>zmch4_TCOM_plev_T2Dz_2000-2024_V1.1.nc</code>: Contains pressure level data (300,hPa to 0.1,hPa). </li> </ul> <h3>Reference Publication</h3> This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication: Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, <a title="null" href="https://doi.org/10.5194/essd-15-5105-2023">https://doi.org/10.5194/essd-15-5105-2023</a>, 2023.

opencc-by-4.0Nov 2022View details →

zenodo40/100

TCOM-N2O: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric nitrous oxide profile dataset [1991-2021] constructed using machine-learning

Methodology:   The TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data. <h3>N2O Profile Processing and Bias Correction</h3> Collocated N2O profiles are organized into five distinct latitude bins: <ul> <li> NH polar: 90∘N - 50∘N </li> <li> NH mid-lat: 20∘N - 70∘N </li> <li> Tropics: 40∘S - 40∘N </li> <li> SH mid-lat: 70∘S - 20∘S </li> <li> SH polar: 90∘S - 50∘S </li> </ul> Initially, differences between TOMCAT and satellite measurements (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from 10,km to 60,km). Separate XGBoost regression models are then trained for these N2O differences at each height level within a given latitude bin. These trained models are subsequently used to estimate N2O bias corrections for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles. Height-resolved N2O profile data are then interpolated onto 28 standard pressure levels (from 300,hPa to 0.1,hPa), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions. <h3>Data Files</h3> The dataset includes two files containing daily mean zonal mean N2O profiles: <ul> <li> <code>zmn2o_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</code>: Contains height level data (10,km to 60,km). </li> <li> <code>zmn2o_TCOM_plev_T2Dz_2000-2024_V1.1.nc</code>: Contains pressure level data (300,hPa to 0.1,hPa). </li> </ul> <h3>Reference Publication</h3> This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication: Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, <a title="null" href="https://doi.org/10.5194/essd-15-5105-2023">https://doi.org/10.5194/essd-15-5105-2023</a>, 2023.

opencc-by-4.0Nov 2022View details →

zenodo40/100

TOMCAT model data & IASI satellite data of O3, CO, H2O, CH4 and OH/derived OH for 2010 and 2017

Monthly mean data of ozone (O3), carbon monoxide (CO), water vapour (H2O), methane (CH4) and the hydroxyl radical (OH) for 2010 and 2017. Model data is from the 3D chemical transport model TOMCAT (Chipperfield, 2006). Satellite observations are from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite and retrieved using schemes developed by the Rutherford Appleton Laboratory (RAL). The Ch4 is from RAL's CH4 retrieval scheme (Siddans et al. 2020) and the O3, CO and H2O retrievals are from the extended version of RAL’s Infrared and Microwave Sounding (IMS-extended) scheme (Pope et al. 2021).  Full description of the data can be found in Pimlott et al. (2022) (preprint: https://doi.org/10.5194/acp-2022-79) which has now been accepted for publication in ACP.

opencc-by-4.0Jul 2022View details →

zenodo36/100

Case Study - Performance Changes of Apache Tomcat at Code Level

This dataset provides the data of the case study on <a href="https://github.com/apache/tomcat">Apache Tomcat</a> that was conducted as part of the bachelor thesis "Extending Peass to Detect Performance Changes of Apache Tomcat". The data was generated by <a href="https://github.com/DaGeRe/peass">Peass</a> and the <a href="https://github.com/stro18/peass-ant">Peass-Ant plugin</a>. Contents The dataset is divided into case_study_1v-100v.tar.xz and case_study_100v-200v.tar.xz. Both parts contain the following data: <ul> <li>select/ - Results of regression test selection, especially: <ul> <li>results/traceTestSelection_tomcat.json - Tests selected based on static code analysis and trace analysis</li> </ul> </li> <li>3 x measure_p*_100vm_400iter/ - Results of performance measurements, especially: <ul> <li>clean/ - Mean of measurements per VM</li> <li>results/changes.json - Tests containing performance changes, computed with t-test</li> </ul> </li> <li>rca_p3_100vm_400iter/ - Results of root cause analysis, especially: <ul> <li>rca/tree/ - Measurements results per called method</li> <li>results/*/*.html - Visualization of root cause analysis</li> </ul> </li> </ul> Additionally, f1_score.tar.xz is included in this dataset, It contains heatmaps visualizing the F1-score that were used to compare different measurement configurations. Extraction of Data Move all .tar.xz files to an empty directory and execute for each file: <pre><code class="language-bash">tar -Jxf <file></code></pre>

opencc-by-4.0Apr 2022View details →

zenodo32/100

Snow particle number, aerosol concentration and 10 meter windspeed data from MOSAiC, N-ICE, Weddel Sea expeditions and chemistry transport model data (p-TOMCAT) .

The folder contains data for the MOSAiC, N-ICE and Weddell sea expedition for Snow particle counter measurements. Coarse aerosol measurements from the MOSAiC expedition are also included. Simulation data from a chemistry transport model (p-TOMCAT) is also available. This version includes both .mat and .nc files

opencc-by-4.0Jan 2024View details →

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record