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Data and Code Accompanying "Retrieving and Analyzing Taste Colexifications from Lexibank"
<p>Data and Code accompanying the study "Retrieving and analyzing taste colexifications from Lexibank" by Olena Shcherbakova and Johann-Mattis List (see <a href="https://calc.hypotheses.org/6398">https://calc.hypotheses.org/6398</a>).</p><p>Information on how to run the code can be found in the study itself.</p>
Retrieved snow depth in Mainland Norway (2018.10-2022.10) based on ICESat-2 ATL08 and DEMs
<h3><strong>Introduction</strong></h3> <p>This dataset's snow depth data was derived using elevation differencing, which is simply the snow surface elevation (ICESat-2 ATL08) minus the reference surface elevation (obtained from Digital Elevation Models):</p> <ol> <li><strong>DEM Co-registration</strong>: DEMs are co-registered to ICESat-2 ATL08 snow-off reference without vertical bias adjustment.</li> <li><strong>Elevation Bias Correction</strong>: The elevation bias between the DEMs and ICESat-2 is corrected using ICESat-2 ATL08 snow-off segments.</li> <li><strong>Snow Depth Calculation</strong>: Determining snow depth by subtracting <strong>the bias-free reference ground elevation(from Step 2)</strong> from ICESat-2 ATL08 snow-on segments.</li> </ol> <p>This dataset is presented in a tabular format, which simplifies the preprocess for machine learning models. While co-registration has been done (1), users have the flexibility to train a bias correction model again (2) and retrieve snow depth measurements anew (3). Alternatively, the snow depth can be directly used for various analytical purposes. Detailed methodologies for the co-registration, bias correction, and snow depth determination are thoroughly documented in the paper (under submission) to support users in leveraging this dataset for their research needs.<br> </p> <h3><strong>Meta Information</strong></h3> <ul> <li><strong>Study Area</strong>: Mainland Norway</li> <li><strong>Acquisition Period (ICESat-2)</strong>: October 2018 to October 2020</li> <li><strong>ICESat-2 data source</strong>: ATL08 (level3, version 5)</li> <li><strong>Reference DEMs</strong>: Norway DTM1, Norway DTM10, Copernicus GLO30, FABDEM. (see reference links)</li> <li><strong>Reference snow depth: </strong>ERA5 Land (hourly), ERA5 Land (monthly).</li> <li><strong>Snow condition</strong>: The dataset contains snow depth retrieved (snow_on_alt08_segments_and_snow_depth.csv) and snow-free observations (snow_free_alt08_segments_and_dems.csv).</li> <li><strong>Data Cleaning</strong>: No, this is a raw dataset that may contain outliers.</li> <li><strong>Mask</strong>: Excluded water surface and permanent ice at a spatial resolution of 100 m. </li> </ul> <h3><strong>Description</strong></h3> <p>This dataset encapsulates a wide array of attributes derived from ICESat-2 observations, alongside measurements pertinent to snow depth, terrain, and environmental conditions across Mainland Norway. For detailed attribute descriptions, refer to the <a href="https://nsidc.org/data/atl08/versions/5#anchor-2">ICESat-2 ATL08 documentation</a>. The dataset is structured into several columns, each representing a specific attribute:</p> <ol> <li>'latitude': Latitude coordinates of the data points in WGS 84.</li> <li>'longitude': Longitude coordinates of the data points in WGS 84.</li> <li>'segment_landcover': Land cover classification for each segment.</li> <li>'segment_snowcover': Snow cover classification for each segment.</li> <li>'h_te_best_fit': Best-fit elevation of the terrain.</li> <li>'h_te_std': Standard deviation of terrain elevation.</li> <li>'n_te_photons': Number of photons used for terrain elevation estimation.</li> <li>'subset_te_flag': Quality flag (5 = all geosegments available, 4 = four geosegments...).</li> <li>'segment_cover': Woody vegetation fractional cover derived from the 2019 Copernicus 100m shrub and forest fractional cover data product.</li> <li>'h_canopy': Canopy height above terrain from ICESat-2 (only for snow-off segments).</li> <li>'h_mean_canopy': Mean canopy height ICESat-2 (only for snow-off segments).</li> <li>'canopy_openness': Canopy openness from ICESat-2 (only for snow-off segments).</li> <li>'h_canopy_winter': Canopy height above terrain from ICESat-2 (only for snow-on segments).</li> <li>'h_mean_canopy_winter':Canopy mean height from ICESat-2 (only for snow-on segments).</li> <li>'canopy_openness_winter':Canopy openness from ICESat-2 (only for snow-on segments).</li> <li>'tree_presence': the presence of trees in the segment (1 = tree, 0 = no tree, binary of h_canopy).</li> <li>'pair': Pair flag for ICESat-2.</li> <li>'beam': Beam flag for ICESat-2.</li> <li>'p_b': Pair and beam flag for ICESat-2.</li> <li>'region': Region identifier for ICESat-2.</li> <li>'cloud_flag_atm': Atmospheric cloud flag for ICESat-2.</li> <li>'urban_flag': Urban area flag for ICESat-2.</li> <li>'h_te_skew': Skewness of terrain elevation of segments.</li> <li>'snr': Signal-to-noise ratio for ICESat-2.</li> <li>'terrain_slope': Slope of the terrain from ICESat-2.</li> <li>'h_te_uncertainty': Uncertainty in terrain elevation estimation.</li> <li>'night_flag': Flag indicating nighttime data.</li> <li>'brightness_flag': Brightness flag for ICESat-2.</li> <li>'h_te_interp': Interpolated terrain elevation.</li> <li>'E': Easting coordinate in EPSG 32633.</li> <li>'N': Northing coordinate in EPSG 32633.</li> <li>'slope': Terrain slope computed from DTM10.</li> <li>'aspect': Terrain aspect computed from DTM10.</li> <li>'planc': Plan curvature computed from DTM10.</li> <li>'profc': Profile curvature computed from DTM10.</li> <li>'curvature': Overall terrain curvature computed from DTM10.</li> <li>'tpi': Terrain Position Index computed from DTM10.</li> <li>'tpi_9': TPI with a 90-meter radius.</li> <li>'tpi_27': TPI with a 270-meter radius.</li> <li>'wf_positive': Positive wind aspect index.</li> <li>'wf_negative': Negative wind aspect index.</li> <li>'smlt_acc': Snowmelt accumulation calculated from ERA5 Land monthly snow melting (currently not in use).</li> <li>'sf_acc': Snowfall accumulation calculated from ERA5 Land monthly snowfall (currently not in use).</li> <li>'sd_era': Snow depth from ERA5 Land reanalysis, coupled with ICESat-2 measurements at daily resolution,</li> <li>'sde_era': Snow depth linear interpolated from ERA5 Land reanalysis.</li> <li>'date': Date of data acquisition.</li> <li>'date_': Date in Pandas Datatime data dype.</li> <li>'month': Month of data acquisition.</li> <li>'difference': The elevation difference between segment and subsegment at the midpoint ( 'h_te_best_fit_20m_2' minus 'h_te_best_fit'). If you want to use h_te_best_fit_20m_2 instead of h_te_best_fit as elevation from ICESat-2, you can do it by df_after_dtm1 - difference, snowdepth_dtm1 - difference.</li> </ol> <p>Columns on elevation difference and snow depth (in meters):</p> <ol> <li>'<strong>dh_after_dtm1</strong>': The elevation difference between the snow-free segment and DTM1 (ICESat-2 minus DTM1). This serves as an independent variable y in the bias correction model for DTM1. Here, 'after' means after co-registration.</li> <li>'<strong>snowdepth_dtm1</strong>': The elevation difference between the snow-on segment and DTM1 (ICESat-2 minus DTM1), representing the raw snow depth as measured against DTM1.</li> <li>'<strong>sd_correct_dtm1</strong>': Corrected snow depth using DTM1, adjusted by bias correction model.</li> <li>'<strong>df_dtm1_era5</strong>': Difference betwen 'sd_correct_dtm1' and 'sde_era'. (sd_correct_dtm1 minus sde_era), providing a comparison between corrected snow depth from DTM1 and snow depth from ERA5 Land reanalysis</li> <li><strong>'dh_after_dtm10'</strong>: The elevation difference between the snow-free segment and DTM10 (ICESat-2 minus DTM10), used in bias correction for DTM10.</li> <li><strong>'snowdepth_dtm10'</strong>: The elevation difference between the snow-on segment and DTM10 (ICESat-2 minus DTM10).</li> <li><strong>'sd_correct_dtm10'</strong>: Corrected snow depth using DTM10, adjusted by bias correction model.</li> <li><strong>'df_dtm10_era5'</strong>: Difference between 'sd_correct_dtm10' and 'sde_era'.</li> <li><strong>'dh_after_cop30'</strong>: The elevation difference between the snow-free segment and Copernicus GLO30 (ICESat-2 minus Copernicus GLO30).</li> <li><strong>'snowdepth_cop30'</strong>: The elevation difference between the snow-on segment and Copernicus GLO30.</li> <li><strong>'sd_correct_cop30'</strong>: The adjusted snow depth using Copernicus GLO30, adjusted by bias correction model.</li> <li><strong>'df_cop30_era5'</strong>: The discrepancy between 'sd_correct_cop30' and 'sde_era'.</li> <li><strong>'dh_after_fab'</strong>: The elevation difference between the snow-free segment and FABDEM (ICESat-2 minus FABDEM), used in bias correction for FABDEM.</li> <li><strong>'snowdepth_fab'</strong>: The elevation difference between the snow-on segment and FABDEM, representing the uncorrected snow depth.</li> <li><strong>'sd_correct_fab'</strong>: The corrected snow depth using FABDEM, adjusted by bias correction model.</li> <li><strong>'df_fab_era5'</strong>: The difference between 'sd_correct_fab' and 'sde_era'.</li> </ol> <p>More explanation (especially on how the parameters are calculated, such as wind aspect index) is available in related works and blog posts on<a href="https://zhihaol.eu.org/blog/2023/subgrid/"> snow depth</a>, and <a href="https://zhihaol.eu.org/blog/2023/dataset/">DEM bias correction</a>.</p> <p>This dataset includes a comprehensive collection of snow depth data and correlated environmental variables for Mainland Norway. Researchers can use this dataset to investigate the following:</p> <ul> <li>The difference between ICESat-2 and DEMs. For example, how 'df_after_dtm1'<strong> </strong>relates to terrain parameters.</li> <li>The residual bias of ICESat-2 derived snow depth, for example, snowdepth_dtm1 and bias-corrected sd_correct_dtm1. You can train a better bias correction to retrieve snow depth again. You can compare your model with my model by 'dh_reg_dtm1', 'dh_reg_dtm10', 'dh_reg_cop30', and 'dh_reg_fab', which are the elevation differences after bias correction for each DEM.</li> <li>The difference between ICESat-2-derived snow depth and snow depth from ERA5 Land, for example, 'df_dtm1_era5'.</li> <li>The spatial distribution of snow depth or subgrid variability.</li> </ul>
Retrieval practice facilitates memory updating by enhancing and differentiating medial prefrontal cortex representations
Open the record for dataset details and reuse information.
ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC
<p>This data set is used for the training, validation and evaluation of retrievals of temperature and specific humidity profiles, as well as integrated water vapour from simlulated or measured microwave brightness temperatures (TBs), which are described in <strong>[1]</strong>.</p> <p>The data set consists of yearly files (2001-2018, 6-hourly resolution) that include data from the European Centre for Medium-Range Weather Forecasts's ERA5 reanalysis <strong>[2]</strong> and simulated TBs in the microwave spectrum. TB simulations were performed with PAMTRA <strong>[3,4]</strong> on the native ERA5 model level resolution at frequencies of a low frequency Humidity and Temperature Profiler (HATPRO, 22-58 GHz) and of a Low Humidity Profiler (LHUMPRO-243-340, aka MiRAC-P, 175-340 GHz). Afterwards, the ERA5 model level data has been interpolated to a new height grid (dimension 'z'), of which the lowest 43 indices equal the height grid of the retrieval that is developed with this data set. The upper 11 indices are included for additional TB simulations needed for the information content estimation performed and are not used for the retrievals to avoid the tropopause.</p> <p>The trained retrieval is applied to observations from the HATPRO and MiRAC-P that were installed onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition.</p> <p><strong>[1]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p> <p><strong>[2]:</strong> Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.</p> <p><strong>[3]:</strong> Mech, M., Maahn, M., Kneifel, S., Ori, D., Orlandi, E., Kollias, P., Schemann, V., and Crewell, S.: PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere, Geoscientific Model Development, 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, 2020.</p> <p><strong>[4]:</strong> Mech, M., Maahn, M., Ori, D., Kneifel, S., and Orlandi, E.: PAMTRA Package – Passive and Active Microwave TRANsfer, available at: https://github.com/igmk/pamtra (last access: 6 September 2020), 2019c.</p>
RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 version 2.4.0 operated at Heidelberg University
<p>The data set contains bias-corrected column averaged dry air mole fractions (XCO2) retrieved with the RemoTeCv2.4.0 full-physics algorithm (Butz et al. 2011, Guerlet et al. 2013) applied on GOSAT TANSO-FTS Level 1B (L1B) data from 2009-04-18 to 2019-06-30. The GOSAT TANSO-FTS L1B data product is produced by JAXA/NOIES/MOE and provided by ESA. The XCO2 data together with related variables are aggregated as daily files, only good quality retrievals are included.</p> <p> </p> <p>If the data is used for publications, please contact andre.butz@uni-heidelberg.de to discuss potential co-authorship and technical details.</p> <p>To cite the data in publications:</p> <p>André Butz (2019), RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 version 2.4.0, Institute of Environmental Physics, Heidelberg University, Heidelberg, Germany, Accessed: [Date], 10.5281/zenodo.5886662</p> <p> </p> <p>Summary:</p> <p>Shortname: REMOTEC_L2_CO2_GOSAT</p> <p>Longname: RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 version 2.4.0</p> <p>DOI: 10.5281/zenodo.5886662</p> <p>Version: 2.4.0</p> <p>Format: netCDF</p> <p>Spatial Coverage: -180.0,-90.0,180.0,90.0</p> <p>Temporal Coverage: 2009-04-18 to 2019-06-30</p>
GLARE: Guided LexRank for Advanced Retrieval in Legal Analysis
<p>This page includes legal datasets used in the paper GLARE: Guided LexRank for Advanced Retrieval in Legal Analysis.</p> <p>The Guided Lexrank algorithm is applied to dataset <a href="../api/records/13696090/draft/files/special_appeal.csv/content" target="_blank" rel="noopener noreferrer">special_appeal.csv</a> to summarize the texts of legal documents. The obtained summary and the texts of the topics contained in dataset <a href="../api/records/13696090/draft/files/themes.csv/content" target="_blank" rel="noopener noreferrer">themes.csv</a> are submitted to the BM25 algorithm for similarity assessment. From a list of topics, the GLARE method produces a ranking with suggested topics for a given document.</p>
Dataset for manuscript "Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations"
<p>This is the long-term satellite retrieval dataset of dust aerosol optical depth at 10 μm (DAOD<sub>10μm</sub>) and dust coarse mode effective diameter (D<sub>eff</sub>) based on collocated MODIS and CALIOP observations from July 2006 to August 2018. The full description is in the manuscript "<strong>Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations" </strong>by Zheng, Jianyu, et al. The readme file for the data is in "readme_dust_aod_size_product.txt". The variable list of Level-2 data is in "variable_list_L2.txt". The variable list of Level-3 data is in "variable_list_L3.txt".</p>
The optimal period for oocyte retrieval after the administration of recombinant human chorionic gonadotropin in in vitro fertilization
<p>This is the dataset of the study called "The optimal period for oocyte retrieval after the administration of recombinant human chorionic gonadotropin in in vitro fertilization".</p> <p><strong>Abstract</strong></p> <p>Background</p> <p>Our objective was to investigate the existence of an optimal period for oocyte retrieval in regards to the clinical pregnancy occurrence after the administration of recombinant human chorionic gonadotropin (rhCG) (Ovitrelle®).</p> <p>Methods</p> <p>We studied the digital records of 3362 middle eastern couples who underwent in vitro fertilization (IVF) treatment between 2019 and 2021.</p> <p>Results</p> <p>Through statistical testing, we found that there is a significant positive correlation between the oocyte retrieval period and the clinical pregnancy occurrence up to the 37th hour, where retrieval at the 37th hour was found to provide the most optimal outcome, especially in the case of gonadotropin-releasing hormone agonist (GnRHa) long protocol.</p> <p>Conclusions</p> <p>This cohort study recommends retrieval at hour 37 after ovulation triggering under the described conditions.</p>
Hourly non-gridded volcanic ash properties retrieved from SEVIRI measurements for the Eyjafjallajökull 2010 eruption
<p>- Publishing date:<br> 14.05.2020</p> <p>- Title:<br> Hourly non-gridded volcanic ash properties retrieved from SEVIRI<br> measurements for the Eyjafjallajökull 2010 eruption </p> <p>- Authors of data set:<br> Arve Kylling (aky@nilu.no), NILU - Norwegian Institute for Air Research<br> Espen Sollum, NILU - Norwegian Institute for Air Research</p> <p>- Description:<br> Ash satellite detection and retrievals were made using infrared<br> measurements by SEVIRI on board the MSG-2 satellite. MSG-2 is<br> geostationary, centred at approximately 0N latitude, and has a 70<br> degree view coverage (Schmetz et al., 2002). Pixel resolution is 3 ×<br> 3 km at nadir, while at the edge of the coverage it increases to 10<br> × 10 km. Observations are available every 15 min. Pixels are<br> identified as containing ash if the brightness temperature<br> difference (BTD) between the SEVIRI 10.8 and 12.0 μm channels<br> (Prata, 1989) is below a certain threshold value, here −0.5 K. The<br> BTDs have been adjusted for water vapour absorption using the approach of<br> Yu et al. (2002). Ash clouds give negative BTDs, ice give positive<br> BTDs, and BTDs of water clouds are closer to zero. The ash mass<br> loading and effective ash particle radius are retrieved as described<br> in Kylling et al. (2015). The retrieval is based on a modification<br> of the Bayesian optimal estimation technique used by Francis et<br> al. (2012). We assume andesite ash with refractive index from Pollack<br> et al. (1973), spherical ash particles, and a lognormal size<br> distribution. The lognormal size distribution is described by the<br> geometric mean radius and the geometric standard deviation. The data<br> set includes retrievals for geometric standard deviation of 1.5,<br> 1.75, 2.0, and 2.25, which is a subset of the values used by Francis<br> et al. (2012). The data set has been used by Steensen et al. (2017).</p> <p> Data comes as hourly files broadly covering Iceland, Europe and the<br> surrounding oceans. The files are in bzip2 netcdf-format which<br> should be self-explanatory. </p> <p>- Version:<br> 1.0</p> <p>- Language:<br> English</p> <p>- Keywords<br> Volcanic ash, remote sensing, SEVIRI, Eyjafjallajökull 2010</p> <p>- Additional notes<br> None</p> <p>- Access right:<br> Open access</p> <p>- License:<br> CC BY-SA 4.0 </p> <p>- Funding:<br> Partly funded by the Norwegian ash project financed by the Norwegian<br> Ministry of Transport and Communications and Avinor. </p> <p>- References:<br> Francis, P. N., Cooke, M. C., and Saunders, R.W.: Retrieval of<br> physical properties of volcanic ash using Meteosat: A case study<br> from the 2010 Eyjafjallajokull eruption, J. Geophys. Res. Atmos.,<br> 117, D00U09, https://doi.org/10.1029/2011JD016788, 2012.</p> <p> Kylling, A., Kristiansen, N., Stohl, A., Buras-Schnell, R., Emde,<br> C., and Gasteiger, J.: A model sensitivity study of the impact of<br> clouds on satellite detection and retrieval of volcanic ash, Atmos. <br> Meas. Tech., 8, 1935-1949, https://doi.org/10.5194/amt-8-1935-<br> 2015, 2015.<br> <br> Pollack, J. B., Toon, O. B., and Khare, B. N.: Optical properties of<br> some terrestrial rocks and glasses, Icarus, 19, 372-389,<br> https://doi.org/10.1016/0019-1035(73)90115-2, 1973. </p> <p> Prata, A. J.: Observations of volcanic ash clouds in the 10-12 um<br> window using AVHRR/2 data, Int. J. Remote Sens., 10, 751-761,<br> 1989.</p> <p> Schmetz, J., Pili, P., Tjemkes, S., and Just, D.: An introduction to<br> Meteosat second generation (MSG), B. Am. Meteorol. Soc., 83,<br> 977-992, 2002.<br> <br> Steensen, B. M., Kylling, A., Kristiansen, N. I., and Schulz, M.:<br> Uncertainty assessment and applicability of an inversion method for<br> volcanic ash forecasting, Atmos. Chem. Phys., 17, 9205-9222,<br> https://doi.org/10.5194/acp-17-9205-2017, 2017. </p> <p> Yu, T., Rose, W. I., and Prata, A. J.: Atmospheric correction for<br> satellite-based volcanic ash mapping and retrievals using "split<br> window" IR data from GOES and AVHRR, J. Geophys. Res. Atmos., 107,<br> https://doi.org/10.1029/2001JD000706, 2002. </p>
Developing a deep Learning network to retrieve ocean hydrographic profiles in the North Atlantic from combined satellite and in situ measurements: test datasets.
<p>We provide here the datasets used for the test and assessment of a deep learning algorithm which is presently candidate for the development of a daily 3D ocean product covering the North Atlantic at 1/10° resolution, over the 2010-2018 period, as part of the European Space Agency World Ocean Circulation project (ESA-WOC). The method is based on a stacked Long Short-Term Memory neural network, coupled to a Monte-Carlo dropout approach, and allows to project satellite-derived sea surface temperature, sea surface salinity and absolute dynamic topography data at depth after training with sparse co-located in situ vertical hydrographic profiles (Buongiorno Nardelli, 2020, doi:<a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.3390%2Frs12193151?_sg%5B0%5D=0xE-347r7Hvb80klJcEo811AhUiXq-twG_E6l4yB-BfIKkVtW-lVLGcO02mTFkUczvozYYI0WCPyUBFR3kzWNGGZKg.ftvLheFrzHIJriO4qW2bdxalvR_TWt3MpwUfvto3EemhRgvDRGwJ9Mdy4Xr0IcGCfICivf4j-VqTgKxVvXRogA">10.3390/rs12193151</a>). </p> <p>The test dataset presented here includes different sets of co-located temperature and salinity vertical profiles: </p> <ul> <li>in situ observations extracted from the quality controlled Argo and CTD profiles produced by Copernicus Marine Environment Monitoring Service CORA 5.2 (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) and interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals);</li> <li>climatological profiles extracted from World Ocean Atlas 2013 optimally interpolated monthly fields (Locarnini et al., 2013; Zweng et al., 2013), interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals), upsized to a 1/10° horizontal grid through a cubic spline and linearly interpolated in time between the central day of each month;</li> <li>synthetic profiles obtained through three different techniques: multivariate EOF reconstruction, a 2 layer feed-forward network (with 1000 units in each hidden layer) and a stacked LSTM network (with 2 LSTM layers and 35 hidden units)</li> </ul> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Deep Learning network to retrieve ocean hydrographic profiles from combined satellite and in situ measurements, 2020, <em>submitted</em>.</p> <p>Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E., Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D. R., Hamilton, M. and Seidov, D.: World Ocean Atlas 2013. Vol. 1: Temperature., S. Levitus, Ed.; A. Mishonov, Tech. Ed.; NOAA Atlas NESDIS, 73(September), 40, doi:10.1182/blood-2011-06-357442, 2013.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p> <p>Zweng, M. M., Reagan, J. R., Antonov, J. I., Mishonov, A. V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., Seidov, D. and Bidlle, M. M.: World Ocean Atlas 2013, Volume 2: Salinity, NOAA Atlas NESDIS, 119(1), 227–237, doi:10.1182/blood-2011-06-357442, 2013.</p> <p> </p>
A global dataset of SST anomaly evolving processes retrieved from remote sensing products (GDSSTAEP V1.0)
<p> The GDSSTAEP includes three datasets and two relationship files with a time range from January 1982 to December 2009. Three datasets formatted in SHP are a dataset of process object-oriented SSTA, named DSPOSSTA, storing SSTA process objects, a dataset of sequence object-oriented SSTA, named DSSOSSTA, storing SSTA sequence objects, and a dataset of variation object-oriented SSTA, named DSVOSSTA, storing SSTA variation objects, respectively. And two relationship files formatted in CSV store the evolving behaviors among sequence objects of SSTA and variation objects of SSTA, respectively. </p> <p> </p>
ICESat-2 Water Depth Retrieval Comparisons for Four Supraglacial on Amery Ice Shelf, East Antarctica
<p>This archive contains the code used for analysis and producing figures for the following paper:</p> <p>Fricker, H.A., Arndt, P.S., Brunt, K.M., Datta, R.T., Fair, Z., Jasinski, M.F., Kingslake, J., Magruder, L.A., Moussavi, M., Pope, A. and Spergel, J.J., 2021. “ICESat-2 meltwater depth estimates: application to surface melt on Amery Ice Shelf, East Antarctica.” Geophysical Research Letters, 48(8), DOI: 10.1029/2020GL090550. URL: <a href="https://doi.org/10.1029/2020GL090550">https://doi.org/10.1029/2020GL090550</a><br><br>These materials are also on GitHub:<br><a href="github.com/fliphilipp/ameryMeltLakesICESat2">https://github.com/fliphilipp/ameryMeltLakesICESat2</a> </p> <p> </p> <p>The code for generating manually annotated baseline depth estimates from ICESat-2 ATL03 photon data is available here:<br><a href="https://github.com/fliphilipp/pondpicking">https://github.com/fliphilipp/pondpicking</a></p>
Supporting Data for: Information Retrieval Interfaces in Virtual Reality - A Scoping Review Focused on Current Generation Technology
<p>This is the full data set of all reviewed research items obtained from Google Scholar, Web of Science and Scopus for the Scoping Literature Review <em><a href="https://doi.org/10.1371/journal.pone.0246398">Information Retrieval Interfaces in Virtual Reality - A Scoping Review Focused on Current Generation VR technology</a>.</em></p>
Touché-25-Advertisement-in-Retrieval-Augmented-Generation
<p>Dataset for Sub-Task 1 (Generation) of the <a href="https://touche.webis.de/clef25/touche25-web/advertisement-detection.html">Touché 2025 Task 4</a>. The goal of this task is to research advertisements in retrieval augmented generation (RAG). Towards this goal, the dataset provides queries from the <a href="https://zenodo.org/records/10802427">Webis Generated Native Ads 2024</a> dataset and corresponding document segments from the segmented version of <a href="https://trec-rag.github.io/annoucements/2024-corpus-finalization/">MS MARCO V2.1</a>.</p>
A global gridded CO2 flux dataset inferred from OCO-2 retrievals using the GONGGA inversion system (v2025)
<p><strong>Data Description</strong></p> <p>Here we provide a global monthly CO2 flux dataset at 1° × 1° spatial resolution for the period 2014.9-2024.12. The dataset is generated using the GONGGA (Global ObservatioN-based system for monitoring Greenhouse GAs) inversion system by assimilating OCO-2 (Observing Carbon Observatory 2) v11.2r column CO2 retrievals that scaled to the WMO X2019 standard. The dataset contains fluxes from biosphere (Net Ecosystem Exchange, NEE) (both prior and posterior), ocean (both prior and posterior), biomass burning emissions and fossil fuel emissions.</p> <p>We also provide the posterior model simulated values corresponding to all measurements contained in the lastest release of NOAA’s ObsPack database (obspack_co2_1_GLOBALVIEWplus_v10.1_2024-11-13 and obspack_co2_1_NRT_v10.1_2025-02-07).</p> <p><strong>Change from v2024</strong></p> <ul> <li>Assimilation of OCO-2 v11.2r retrievals</li> <li>Update of prior fluxes</li> </ul> <p><strong>Data version specification</strong></p> <p>v202x.ori refers to original GONGGA flux data with 3-hourly time resolution and 2° latitude × 2.5° longitude spatial resolution, v202x refers to GONGGA flux data resampled to monthly time resolution and 1° latitude × 1° longitude spatial resolution for facilitating comparisons with other GCP inversion results.</p> <p><strong>Article citation</strong></p> <p>Jin, Z., Wang, T., Zhang, H., Wang, Y., Ding, J., Tian, X., Constraint of satellite CO2 retrieval on the global carbon cycle from a Chinese atmospheric inversion system. Science China Earth Sciences, 2023, 66: 609-618, doi: 10.1007/s11430-022-1036-7.</p> <p>Jin, Z., Tian, X., Wang, Y., Zhang, H., Zhao, M., Wang, T., Ding, J., and Piao, S.: A global surface CO2 flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system, Earth System Science Data, 2024, 16: 2857-2876, doi: 10.5194/essd-16-2857-2024.</p>
ForensicVM Windows 11 Virtualization Dataset: Cloud and Local Application Data Retrieval with Virtualization and Password Bypass Challenge
<p>A Windows 11 Pro dataset designed in VirtualBox, complete with local and cloud apps, is to be virtually analyzed for crucial evidence. Bypassing the straightforward 'Bart' password is essential for access, yet original passwords should remain unchanged for others to attempt the same challenge. The task involves determining the password's nature, Bart's motives, identifying involved cloud applications, and extracting data both offline and online. The viability of dead box forensics for complete data retrieval is questioned, alongside what additional information network access could unveil. The challenge includes identifying two financial applications, extracting their data, and gathering cryptocurrency-related information, presented as an engaging forensicVM showcase by Nuno Mourinho, Mario Candeias, and Rogerio Bravo (Escola Superior de Tecnologia e Gestão de Beja, Instituto Politécnico de Beja).</p>
CLaMS mean age of air tracers for 15/01/2011 interpolated on simulated CAIRT retrieval grid
<p>The dataset contains simulation results from the Chemical Lagrangian Model of the Stratosphere (CLaMS) for January 15, 2011. These results are interpolated onto the simulated retrieval grid of the Changing-Atmosphere Infrared Tomography Explorer (CAIRT). The data includes six trace gases (SF₆, N₂O, CFC-11 (F11), CFC-12 (F12), HCFC-22 (F22), and CH₄) and the "exact" model mean age of air (BA). The file is provided in NetCDF format.</p>
Proportions of Phytoplankton Functional Groups (PFT) retrieved using a Self-Organizing Map in the North Atlantic
<p>Concentrations of diagnostic pigments are retrieved from satellite data (Chl-a + Rrs at 4 wavelengths + SST) using a SOM trained on a global in-situ HPLC dataset (see El Hourany et al. 2019). Pigments are converted to PFTs using empirical coefficients. Three sets of coefficients are used and the results are averaged (see El Hourany et al. 2024). The PFT data is expressed as the proportion of each group in the total community abundance:<br><code>Proportion_i = (alpha_i * Pig_i) / sum_j(alpha_j * Pig_j)</code><br>There are seven PFT groups (diatoms, dinoflagellates, haptophytes, green algae, cryptophytes, pelagophytes, prokaryotes).</p> <p>The satellite input Chl-a data is included as well. It was retrieved from the Globcolour portal (<a href="https://hermes.acri.fr/">https://hermes.acri.fr/</a>) in 2022. The CHL-1 product for case 1 waters is used. It uses the AVW merging method: single-sensor level-2 Chl-a data is merged from multiple sensors (SeaWiFS, MERIS, MODIS-Aqua, VIIRS-NPP/JPSS1, OLCI-A/B).</p> <p>The data spans from 2002 to 2020, at a daily resolution. It is available on a regular latitude/longitude grid, at a resolution of 1/24° (approximately 4 km) in a window of bounds 15°N−55°N ; 82°W−40°W.</p> <h2><strong>Storage</strong></h2> <p>All variables are stored in the same daily file: <code>PFT_SOM-DAP_GLOB_4km_daily/[year]/[month]/PFT_[year][month][day]_GLOB_4.nc</code>. The PFT proportions are stored as percentages (ranging between 0 and 100). Files are NetCDF4, and the metadata follow CF conventions.</p> <p>The PFT variables are stored using linear packing (see <a href="https://nco.sourceforge.net/nco.html#Linear-Packing">https://nco.sourceforge.net/nco.html#Linear-Packing</a>) as 16-bits unsigned integers (<code>NC_USHORT</code>), with a scale factor of 1.54e-3. This means values are discretized between 0 and ~100.92 (<code>=(2**16 - 2) * 1.54e-3</code>), with a discretization step of 1.54e-3.<br>As for the PFT variables, these values are in percent points.</p> <h2><strong>References</strong></h2> <ul> <li>El Hourany, R., Abboud-Abi Saab, M., Faour, G., Aumont, O., Crépon, M., Thiria, S.<br>“Estimation of secondary phytoplankton pigments from satellite observations using Self-Organizing Maps (SOMs)”,<br><em>J. Geophys. Res. Oceans</em> 124, 1357–1378, <a href="https://doi.org/10.1029/2018jc014450">https://doi.org/10.1029/2018jc014450</a>, <strong>2019</strong></li> <li> <div>El Hourany, R, Pierella Karlusich J., Zinger L., Loisel H., Levy M., and Bowler C.<br>“Linking Satellites to Genes with Machine Learning to Estimate Phytoplankton Community Structure from Space”<em>,<br>Ocean Science</em> 20 no. 1, 217−39. <a href="https://doi.org/10.5194/os-20-217-2024">https://doi.org/10.5194/os-20-217-2024</a>, <strong>2024</strong></div> </li> </ul> <h2><strong>Changelog</strong></h2> <h3>v1.2</h3> <ul> <li>[2024-01-24] Finished re-arranging data and checked validity. 553 missing days / source files (about 6% of total data).</li> <li>[2023-12-01] Fix SST projection at PFT generation. Data fully re-generated.</li> </ul> <h3>v1.1</h3> <ul> <li>[2023-08-30] Store PFT data using linear packing. </li> </ul> <h3>v1.0</h3> <ul> <li>[2023-08-03] those data are reorganised, and metadata is added, using the script <a href="https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads">https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads</a></li> <li>[2023-07] data generated with the SOM were stored on `spirit:/data/lollier/PFT`</li> </ul>
Virtual RHI lidar scans retrieved in high-fidelity wake vortex simulations of landing aircraft under turbulent crosswind conditions - LES Lidar Simulator (LLS)
<p>This dataset contains two types of virtual measurements of multiple pulsed lidar integrated into high-fidelity hybrid RANS-LES wake vortex simulations of a landing Airbus A340 aircraft. Simulations have been performed for four different atmospheric conditions, varying in crosswind and therefore turbulence in the atmosphere.</p> <p>The two lidar simulator types are:</p> <ul> <li>LLS: LES Lidar Simulator (no noise, Signal to Noise Ratio >> 1). The analysis is based on the Range Gate Weighting function (RWF) [based on formulations in for example [1]]. Given the assumption of no instrument noise, no spectral analysis in the frequency domain is required.</li> <li>LLSn: LES Lidar Simulator with noise (realisitic Signal to Noise Ratio). Based on formulations in [2], where non-linear low-pass spatial filters accurately model real field measurements. Both background noise and aerosol noise contained in real lidar measurements are modeled. </li> </ul> <p>Provided are the raw lidar scans in RHI format, with the position and strength of the wake vortices within each lidar scan given by a pressure-vorticity tracking algorithm from the wake vortex simulation (simulation truth, ST). Misidentifications have been removed from the provided dataset. In addition to the labels for each wake vortex lidar scan, also wind background scans are provided for the crosswind simulation cases. These background scans give a better understanding of the prevailing atmospheric condition within which the aircraft lands. </p> <p>Furthermore the evaluation of the LLS scans using the Method of Radial Velocities (RV method) [3], a state-of-the-art wake vortex characterization method for lidar scans is provided for a subset of the LLS dataset. For analysing the impact of the RWF for LLS scans, selected lidar scans are also given in a 'naive' fashion (lidar scans assuming point measurements are possible).</p> <p><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></p> <h3><strong>Overview</strong></h3> <p><strong>In total each dataset (LLS and LLSn) contains 8 Aircraft landing simulation with associated </strong><strong>virtual lidar RHI scans:</strong></p> <ul> <li>2x no wind</li> <li>6x crosswind (specified at height b_0)</li> <li>0.5w_0 from port direction</li> <li>0.5w_0 from starboard direction</li> <li>1.0w_0 from port direction</li> <li>1.0w_0 from starboard direction</li> <li>2.0w_0 from port direction</li> <li>2.0w_0 from starboard direction</li> </ul> <p><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></p> <h3><strong>The dataset has 13 folders:</strong></h3> <div> <p><strong>POS1_POS2_POS8_scans = Individual scans of various simulations as well as background wind scans (no wake vortices). </strong></p> <p>POS1_POS2: 0_0, 0_5, 1_0, 2_0 corresponds to the wind, same as POS1_POS2 below</p> <p>POS8: LLS or LLSn, corresponding to the type of lidar simulator (without or with instrument noise, respectively)</p> </div> <div> </div> <div>Scan naming convention: POS1_POS2_POS3_POS4_POS5_POS6_POS7.csv</div> <div> </div> <div>Example: 0_5_D_248_8_161.2201878198302_168.4201878198237.csv</div> <div> </div> <div> <ul> <li>POS1_POS2: Together they form a factor which is multiplied with the initial descend speed of the wake vortex pair, w_0. The definition for w_0 can be found in [4]. It is common for the crosswind speed to be set according to the multiples of w_0. Due to the landing of the A340 aircraft, and the logarithmic nature of the wind simulation, we set the crosswind at the b_0 altitude of the simulation. For a definition of b_0, also see [4]. For the above example, 0.5w_0 is the crosswind speed. Note that every 26 lidar positions (POS4), the direction of the crosswind changes, if there is a crosswind.</li> </ul> </div> <div> - Crosswind approaches from port side: POS4: 0-25, 52-77, 104-129, 156-181, 208-233, 260-285</div> <div> - Crosswind approaches from starboard side: POS4: 26-51, 78-103, 130-155, 182-207, 234-259, 286-311</div> <div> <ul> <li>POS3: Label indicating which part of the numerical simulation this scan belongs to (a full landing simulation consists of A-D). A: Hybrid RANS-LES, B,C,D: temporal LES (in order). For the above example, D indicates that the lidar scan was recorded during the last part of the simulation. W: Prior to the wake vortex simulations with crosswind, a background wind scan for each lidar position is recorded.</li> </ul> </div> <div> <ul> <li>POS4: Specifies the number of the lidar, implications are the longitudinal position along the glide path of the aircraft. In the packs of lidar positions described in the description of POS1_POS2, the last lidar position is the closest to the touchdown point of the aircraft, smaller lidar positions within this pack are further away (in order) - also see the lid_plane_info directory. Note that the lidar position also adjusts the spectrum of the elevation angles used (and thus the size of the lidar scan). For that see the associated virtual lidar scan raw data. In the above example we have lidar position 248, thus crosswind approaching from starboard and rather mid-way of the longitudinal glide path direction.</li> </ul> </div> <div> <ul> <li>POS5: Specifies the number of the scan for this lidar position (POS4) and simulation (POS1__POS2). In the above example this is scan number 8.</li> </ul> </div> <div> <ul> <li>POS6_POS7: Specifies the simulation time within which the scan was measured during the aircraft landing simulation (POS1__POS2). In the above example this is 161.2201878198302 s to 168.4201878198237 s.</li> </ul> </div> <div>The scans purely with wind, an no wake vortices are stored in an additional directory within conv_scans. The case '0_0' (no wind) does not require wind scans. </div> <div> </div> <div>Within the scan files, we have a four (LLS) or five (LLSn) columns:</div> <div> <ul> <li>t: Simulation time</li> <li>ELE: Elevation angle [deg] of the lidar beam</li> <li>R: Range from lidar [m]</li> <li>v_r: LOS velocity (radial velocity) along lidar beam</li> <li>snr: Signal to Noise Ratio </li> </ul> <p> </p> <p><strong>wind_POS8_scans = Simulated lidar scans of the background wind for LLS and LLSn.</strong></p> <p>POS8: LLS or LLSn, corresponding to the type of lidar simulator (without or with instrument noise, respectively)</p> <p>Scans simulated here are in the same format as <em>POS1_POS2_POS8_scans</em>. Wind scans for point measurement scans are found in <em>naive_scan_subset </em>- the scans with POS4 = 0 should be used if more are available.</p> <p> </p> <p><strong>naive_scan_subset = Simulated lidar scans simulating velocity point measurements.</strong></p> </div> <div> <p>The format is the same as for <em>POS1_POS2_POS8_scans</em>, with the difference of being sorted first by wind subdirectories and then lidar number (LID).</p> <p> </p> <p><strong>labels = Labels of wake vortices for the above wake vortex scans. </strong></p> </div> <div>We have 4 main files, where each file represents the targets for one wind strength 0.0w_0, 0.5w_0, 1.0w_0, 2.0w_0.</div> <div>Within the labels files, we have a multitude of columns with different data:</div> <div> <ul> <li>#Time: Simulation (not scan) time.</li> <li>y_uw : lateral position in simulation domain of upwind (port) vortex.</li> <li>z_uw : height position in simulation domain of upwind (port) vortex.</li> <li>y_dw : lateral position in simulation domain of downwind (starboard) vortex.</li> <li>z_dw : height position in simulation domain of downwind (starboard) vortex.</li> <li>G_515_uw: Gamma 515 Circulation strength (see additional notes) of upwind (port) vortex [m^2/s].</li> <li>G_515_dw: Gamma 515 Circulation strength (see additional notes) of downwind (starboard) vortex [m^2/s].</li> <li>cr_uw: Core radius of upwind (port) vortex (in meters).</li> <li>cr_dw: Core radius of downwind (starboard) vortex (in meters).</li> <li>uw distance from lidar [m]: Horizontal distance of upwind (port) vortex from respective lidar.</li> <li>dw distance from lidar [m]: Horizontal distance of downwind (starboard) vortex from respective lidar.</li> <li>uw height [m]: Vertical distance of upwind (port) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m).</li> <li>dw height [m]: Vertical distance of downwind (starboard) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m).</li> <li>vortex age uw [s]: Age of upwind (port) vortex (after first generated by aircraft at respective measurement plane of lidar).</li> <li>vortex age dw [s]: Age of downwind (starboard) vortex (after first generated by aircraft at respective measurement plane of lidar).</li> <li>scan: Associated scan.</li> <li>LID: Associated lidar number.</li> </ul> <p>On top of the summarizing files for each simulation, in the subdirectory <em>individual</em>, the label for each scan is given in a separate file.</p> <p>The above gives information on the simulation truth, furthermore the file <em>labels_with_rv.csv</em> can be found here, where the following extra label columns are given for a subset of scans: [in the following Conv and Naive refer to LLS scans and point measurement scans, respectively]</p> <ul> <li>wind: Strength of crosswind, same as POS1_POS2 in <em>POS1_POS2_scans. </em>The sign corresponds to the wind direction. Negative is a crosswind from the port side of the aircraft, positive is a crosswind from the starboard side of the aircraft.</li> <li>Conv RV: 1 indicates the RV method has been evaluated for LLS scans, 0 if not.</li> <li>Naive RV: 1 indicates the RV method has been evaluated for point measurement scans, 0 if not.</li> <li>conv rv G_515_uw: Gamma 515 Circulation strength (see additional notes) of upwind (port) vortex computed using the RV method on LLS scans [m^2/s].</li> <li>conv rv G_515_dw: Gamma 515 Circulation strength (see additional notes) of downwind (starboard) vortex computed using the RV method on LLS scans [m^2/s].</li> <li>conv rv uw distance from lidar [m]: Horizontal distance of upwind (port) vortex from respective lidar using the RV method on LLS scans.</li> <li>conv rv dw distance from lidar [m]: Horizontal distance of downwind (starboard) vortex from respective lidar using the RV method on LLS scans.</li> <li>conv rv uw height [m]: Vertical distance of upwind (port) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m) using the RV method on LLS scans.</li> <li>conv rv dw height [m]: Vertical distance of downwind (starboard) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m) using the RV method on LLS scans.</li> <li>naive rv G_515_uw: Gamma 515 Circulation strength (see additional notes) of upwind (port) vortex computed using the RV method on point measurement scans [m^2/s].</li> <li>naive rv G_515_dw: Gamma 515 Circulation strength (see additional notes) of downwind (starboard) vortex computed using the RV method on point measurement scans [m^2/s].</li> <li>naive rv uw distance from lidar [m]: Horizontal distance of upwind (port) vortex from respective lidar using the RV method on point measurement scans.</li> <li>naive rv dw distance from lidar [m]: Horizontal distance of downwind (starboard) vortex from respective lidar using the RV method on point measurement scans.</li> <li>naive rv uw height [m]: Vertical distance of upwind (port) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m) using the RV method on point measurement scans.</li> <li>naive rv dw height [m]: Vertical distance of downwind (starboard) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m) using the RV method on point measurement scans.</li> <li>uw phi [deg]: Elevation angle to the center of the upwind (port) vortex from the simulation truth.</li> <li>uw range [m]: Range from the lidar to the center of the upwind (port) vortex from the simulation truth.</li> <li>dw phi [deg]: Elevation angle to the center of the downwind (starboard) vortex from the simulation truth.</li> <li>dw range [m]: Range from the lidar to the center of the downwind (starboard) vortex from the simulation truth.</li> <li>conv rv uw phi [deg]: Elevation angle to the center of the upwind (port) vortex using the RV method on LLS scans.</li> <li>conv rv uw range [m]: Range from the lidar to the center of the upwind (port) vortex using the RV method on LLS scans.</li> <li>conv rv dw phi [deg]: Elevation angle to the center of the downwind (starboard) vortex using the RV method on LLS scans.</li> <li>conv rv dw range [m]: Range from the lidar to the center of the downwind (starboard) vortex using the RV method on LLS scans.</li> <li>naive rv uw phi [deg]: Elevation angle to the center of the upwind (port) vortex using the RV method on point measurement scans.</li> <li>naive rv uw range [m]: Range from the lidar to the center of the upwind (port) vortex using the RV method on point measurement scans.</li> <li>naive rv dw phi [deg]: Elevation angle to the center of the downwind (starboard) vortex using the RV method on point measurement scans.</li> <li>naive rv dw range [m]: Range from the lidar to the center of the downwind (starboard) vortex using the RV method on point measurement scans.</li> <li>y err conv uw [m]: Cartesian localization error (RV minus ST) in y-direction (lateral) between simulation truth and RV method of upwind (port) vortex center on LLS scans.</li> <li>y err conv dw [m]: Cartesian localization error (RV minus ST) in y-direction (lateral) between simulation truth and RV method of downwind (starboard) vortex center on LLS scans.</li> <li>z err conv uw [m]: Cartesian localization error (RV minus ST) in z-direction (vertical) between simulation truth and RV method of upwind (port) vortex center on LLS scans.</li> <li>z err conv dw [m]: Cartesian localization error (RV minus ST) in z-direction (vertical) between simulation truth and RV method of downwind (starboard) vortex center on LLS scans.</li> <li>y err naive uw [m]: Cartesian localization error (RV minus ST) in y-direction (lateral) between simulation truth and RV method of upwind (port) vortex center on point measurement scans.</li> <li>y err naive dw [m]: Cartesian localization error (RV minus ST) in y-direction (lateral) between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans.</li> <li>z err naive uw [m]: Cartesian localization error (RV minus ST) in z-direction (vertical) between simulation truth and RV method of upwind (port) vortex center on point measurement scans.</li> <li>z err naive dw [m]: Cartesian localization error (RV minus ST) in z-direction (vertical) between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans.</li> <li>D err conv uw [m]: Euclidean distance error for the Cartesian coordinates to the vortex center between simulation truth and RV method of upwind (port) vortex center on LLS scans.</li> <li>D err conv dw [m]: Euclidean distance for the Cartesian coordinates to the vortex center between simulation truth and RV method of downwind (starboard) vortex center on LLS scans.</li> <li>D err naive uw [m]: Euclidean distance error for the Cartesian coordinates to the vortex center between simulation truth and RV method of upwind (port) vortex center on point measurement scans.</li> <li>D err naive dw [m]: Euclidean distance for the Cartesian coordinates to the vortex center between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans.</li> <li>plus_minus_phi: Indicates whether a RHI lidar scan features a positive lidar scanning rate (plus), or a negative one (negative).</li> <li>conv err G_515_uw: Circulation error (RV minus ST) between simulation truth and RV method of upwind (port) on LLS scans [m^2/s]. </li> <li>conv err G_515_dw: Circulation error (RV minus ST) between simulation truth and RV method of downwind (starboard) on LLS scans [m^2/s]. </li> <li>naive err G_515_uw: Circulation error (RV minus ST) between simulation truth and RV method of upwind (port) on point measurement scans [m^2/s]. </li> <li>naive err G_515_dw: Circulation error (RV minus ST) between simulation truth and RV method of downwind (starboard) on point measurement scans [m^2/s]. </li> <li>conv err uw phi: Elevation angle error (RV minus ST) between simulation truth and RV method of upwind (port) vortex center on LLS scans [deg].</li> <li>conv err dw phi: Elevation angle error (RV minus ST) between simulation truth and RV method of downwind (starboard) vortex center on LLS scans [deg].</li> <li>naive err uw phi: Elevation angle error (RV minus ST) between simulation truth and RV method of upwind (port) vortex center on LLS scans [deg].</li> <li>naive err dw phi: Elevation angle error (RV minus ST) between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans [deg].</li> <li>conv err uw range: Range from lidar error (RV minus ST) between simulation truth and RV method of upwind (port) vortex center on LLS scans [m].</li> <li>conv err dw range: Range from lidar error (RV minus ST) between simulation truth and RV method of downwind (starboard) vortex center on LLS scans [m].</li> <li>naive err uw range: Range from lidar error (RV minus ST) between simulation truth and RV method of upwind (port) vortex center on point measurement scans [m].</li> <li>naive err dw range: Range from lidar error (RV minus ST) between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans [m].</li> </ul> <p><em>0_0_movement.csv</em> gives insight on the vertical movement on selected vortices in selected lidar scans of the 0_0 wind case. </p> <p>The following data columns exist within the file (note csv file with ; separator):</p> <ul> <li>Str_dw: Vertical movement of the downwind (starboard) vortex, 0 is neutral, -1 is downward, and 1 is upward.</li> <li>Prt_uw: Vertical movement of the upwind (port) vortex, 0 is neutral, -1 is downward, and 1 is upward.</li> <li>scan_code: Identfies associated lidar scan with POS3_POS4_POS5 as in <em>POS1_POS2_scans.</em></li> </ul> <p> </p> <p><strong>lid_plane_info = Additional guidance on the simulated lidars.</strong></p> <p>Each simulation case has a separate file due to minimal ground speed changes of the aircraft. </p> <p>The following data columns exist within each file:</p> <ul> <li>Index: Gives the lidar number corresponding to POS4 and LID.</li> <li>Plane Pos [x]: Gives the longitudinal position of the lidar with respect to the glide path of the aircraft in meters.</li> <li>time plane passed [s]: Gives the simulation time when the aircraft first passes the measurement plane (related to the previous column.</li> <li>height plane passed [m]: Aircraft altitude at the respective lidar positon.</li> <li>lateral position lidar from GP [m]: Lateral position of the lidar from the glide path of the aircraft.</li> </ul> <p><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></p> </div> <h3>Additional Notes: </h3> <div> <ul> <li>Gamma 515 circulation [4]: The Gamma 515 circulation is the averaged circulation of a vortex evaluated at radii 5-15m from the vortex center. </li> </ul> </div> <div> <div> <ul> <li>Lidar scans may contain 2 or 1 vortex. In the case of 1 vortex, the other has G_515_?? set to nan. Note that the position may still available in the labels data sets, but only vortices with available G_515_?? values should be used.</li> <li>The upwind and downwind definitions should purely be taken as names, rather than physical meaning. The names are derived from the simulation, due to mirroring and other post-processing these can be misleading however.</li> </ul> </div> </div> <div><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></div> <div> <p><strong>Funding</strong>: This dataset was generated within the mFUND <a href="https://bmdv.bund.de/SharedDocs/DE/Artikel/DG/mfund-projekte/kiwi.html">KIWI project</a> funded by the Federal Ministry for Digital and Transportation Germany and the DLR undertaking "Wetter und Disruptive Ereignisse".</p> <p><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></p> <p><strong>Acknowledgements:</strong></p> <p>The necessary RANS simulations were performed as part of the EU-funded AWIATOR by DLR's Institute of Aerodynamics and Flow Technology.</p> <p>We acknowledge Airbus for the allowance to use the RANS data.</p> <p>The LES was performed with the incompressible Navier-Stokes code MGLET [5] and kindly provided by the Technical University of Munich, Hydromechanics.</p> <p>The wake vortex simulations were computed on the high performance computer SuperMUC-NG by Leibniz-Rechenzentrum (LRZ).</p> </div> <div><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></div> <div> </div> <div><strong>References</strong>: </div> <div> </div> <div>[1] Robey, Rachel, and Julie K. Lundquist. "Behavior and mechanisms of Doppler wind lidar error in varying stability regimes." <em>Atmospheric Measurement Techniques</em> 15.15 (2022): 4585-4622.</div> <div> </div> <div>[2] Stephan, Anton, Norman Wildmann, and Igor Smalikho. "Effectiveness of the MFAS Method for Determining the Wind Velocity Vector from Windcube 200s Lidar Measurements." <em>Atmospheric and Oceanic Optics</em> 32.5 (2019): 555-563.</div> <div> </div> <div>[3] Smalikho, Igor., et al. "Method of radial velocities for the estimation of aircraft wake vortex parameters from data measured by coherent Doppler lidar." <em>Optics Express</em> 23.19 (2015): A1194-A1207.</div> <div> </div> <div>[4] Gerz, Thomas, Frank Holzäpfel, and Denis Darracq. "Commercial aircraft wake vortices." <em>Progress in Aerospace Sciences</em> 38.3 (2002): 181-208. </div> <div> </div> <div>[5] Manhart, Michael. "A zonal grid algorithm for DNS of turbulent boundary layers." <em>Computers & fluids</em> 33.3 (2004): 435-461.</div>
Dataset for "Implementation of disequilibrium chemistry to spectral retrieval code ARCiS and application to 16 exoplanet transmission spectra. Indication of disequilibrium chemistry for HD 209458b and WASP-39b"
<p>This is the supplemental materials for the Astronomy & Astrophysics publication "Implementation of disequilibrium chemistry to spectral retrieval code ARCiS and application to 16 exoplanet transmission spectra. Indication of disequilibrium chemistry for HD 209458b and WASP-39b". Please refer to "README.md" for details.</p>
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