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OCO-3 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, Forward Processing V11 (OCO3_L2_Fwd_FP) at GES DISC

Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11.The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.

Monthly Gridded Level 4 bias-corrected XCO2 and other select fields aggregated from ACOS as Level 4 monthly files V3 (ACOSMonthlyGriddedXCO2)

Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Greenhouse Gases Observing Satellite (GOSAT) bias corrected data.This is the latest version of this collection.

OCO-2 Level 2 bias-corrected solar-induced fluorescence and other select fields from the IMAP-DOAS algorithm aggregated as daily files, Retrospective processing V10r (OCO2_L2_Lite_SIF) at GES DISC

Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r. The OCO-2 SIF Lite files contain bias-corrected solar induced chlorophyll fluorescence along with other select fields aggregated as daily files. In early 2021, the OCO Team identified an issue with OCO-2 level 2 products processed since January 28, 2020. The Ancillary Geometric Product (AGAP) file, a static file used in OCO-2 Geolocation processing, was inadvertently replaced with an obsolete version. This AGAP file included a ~300 m pointing error. As a result, all OCO-2 Level 2, version 10r, data files for the period January 28 - December 31, 2020, were corrected and replaced. The replacement process was completed by the end of June, 2021. The significance of this error has been described in Kiel et al. (2019; doi:10.5194/amt-12-2241-2019).The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass the output from the IMAP-DOAS preprocessor, which is used for both screening of the official XCO2 product as well as for the retrieval of Solar-Induced Fluorescence from the 0.76 micrometer O2 A-band. The IMAP-DOAS preprocessor, just as the ABO2 cloud screen, is implemented in the operational OCO-2 processing pipeline.

Multi-Instrument Fused bias-corrected XCO2 and other select fields aggregated as Level 3 daily files V4 (MultiInstrumentFusedXCO2)

Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Orbiting Carbon Observatory (OCO-2) and Greenhouse Gases Observing Satellite (GOSAT) bias corrected data.This is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page.

Multi-Instrument Fused bias-corrected XCO2 and other select fields aggregated as Level 4 daily files V3 (MultiInstrumentFusedXCO2)

OCO-2 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, Retrospective processing V11.2r (OCO2_L2_Lite_FP) at GES DISC

Version 11.2r is the current version of the data set. The OCO-2 Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers.

opencc-by-4.0Apr 2020View details →

Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 in the Qinghai-Tibet Plateau——Part Ⅳ

A bias-corrected dataset containing daily meteorological data of the Qinghai-Tibet Plateau has been generated, by using a trend-preserving bias-correction, the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) approach together with a high-quality gridded meteorological dataset based on ground observation (CN05.1). The data set contains daily bias-corrected values of maximum/minimum near-surface air temperature, precipitation and mean near-surface wind speed from 15 models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) and their downscaled high-resolution dataset (NEX-GDDP) in the Qinghai-Tibet Plateau (QTP) during 1986-2095. This dataset can provide important reference for the study on future climate change and its impacts in the Qinghai-Tibet Plateau region. Note: For Wind in historical periods, the value "2333" refers to no data. Set them to NaN before using, for example (Matlab): Wind(Wind==2333)=nan; More details about this dataset can be found in the article: S. Chen, T. Ye, W. Liu, A. Wang and P. Shi. Evaluation and bias correction of the historical and future near-surface climate forcing in NEX-GDDP and CMIP5 over the Qinghai-Tibet plateau[J], Plateau Meteorology (in Chinese), 2020, DOI: 10.7522/j.issn.1000-0534. 2020. 00019.

opencc-by-4.0Apr 2020View details →

Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 in the Qinghai-Tibet Plateau——Part Ⅲ

A bias-corrected dataset containing daily meteorological data of the Qinghai-Tibet Plateau has been generated, by using a trend-preserving bias-correction, the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) approach together with a high-quality gridded meteorological dataset based on ground observation (CN05.1). The data set contains daily bias-corrected values of maximum/minimum near-surface air temperature, precipitation and mean near-surface wind speed from 15 models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) and their downscaled high-resolution dataset (NEX-GDDP) in the Qinghai-Tibet Plateau (QTP) during 1986-2095. This dataset can provide important reference for the study on future climate change and its impacts in the Qinghai-Tibet Plateau region. Note: For precipitation in historical periods, the value "2333" refers to no data. Set them to NaN before using, for example (in Matlab): Pr(Pr==2333)=nan; More details about this dataset can be found in the article: S. Chen, T. Ye, W. Liu, A. Wang and P. Shi. Evaluation and bias correction of the historical and future near-surface climate forcing in NEX-GDDP and CMIP5 over the Qinghai-Tibet plateau[J], Plateau Meteorology (in Chinese), 2020, DOI: 10.7522/j.issn.1000-0534. 2020. 00019.

opencc-by-4.0Oct 2023View details →

Data used in generation of results in 'Bias Correction of Climate Models using a Bayesian Hierarchical Model' J.Carter et. al.

The data used in generation of results in 'Bias Correction of Climate Models using a Bayesian Hierarchical Model' J.Carter et. al. The datasets are dictionaries and are saved with .npy extensions. The datasets can be loaded in Python with expressions like: 'scenario_base = np.load(f"{filepath}scenario_base_hierarchical.npy",allow_pickle="TRUE").item()'.

embargoedcc-by-sa-4.0Apr 2024View details →

Skillful bias correction of offshore near-surface wind speed and wind direction forecasting based on a multi-task machine learning model

<h3>Dataset</h3> 1. observation data over 14 weather stations Variables: hourly near-surface 2-min average wind speed, wind direction  2. ECMWF-IFS forecast data over 14 weather stations Variables: hourly predictors at surface level and upper level in next 48 hours (shown in Table 1. and Table 2.) Table 1. ECMWF-IFS forecast data at surface level <div> <table> <tbody> <tr> <td> Predictors </td> <td> Abbreviation </td> <td> Unit </td> </tr> <tr> <td> Temperature at 2 m </td> <td> 2t </td> <td> ℃ </td> </tr> <tr> <td> Sea surface temperature </td> <td> sst </td> <td> ℃ </td> </tr> <tr> <td> Dewpoint temperature at 2 m </td> <td> 2d </td> <td> ℃ </td> </tr> <tr> <td> Convective precipitation in the past hour </td> <td> cp </td> <td> mm </td> </tr> <tr> <td> Mean sea level pressure </td> <td> msl </td> <td> hPa </td> </tr> <tr> <td> Zonal component of wind speed at 10 m </td> <td> 10u </td> <td> m s-1 </td> </tr> <tr> <td> Meridional component of wind speed at 10 m </td> <td> 10v </td> <td> m s-1 </td> </tr> <tr> <td> Wind speed at 10 m </td> <td> 10ws </td> <td> m s-1 </td> </tr> <tr> <td> Wind direction at 10 m </td> <td> 10wd </td> <td> ° </td> </tr> <tr> <td> Zonal component of wind speed at 100 m </td> <td> 100u </td> <td> m s-1 </td> </tr> <tr> <td> Meridional component of wind speed at 100 m </td> <td> 100v </td> <td> m s-1 </td> </tr> <tr> <td> Wind speed at 100 m </td> <td> 100ws </td> <td> m s-1 </td> </tr> <tr> <td> Wind direction at 100 m </td> <td> 100wd </td> <td> ° </td> </tr> </tbody> </table> </div> <div> </div> Table 2. ECMWF-IFS forecast data at upper level <table> <tbody> <tr> <td> Predictors </td> <td> Abbreviation </td> <td> Unit </td> </tr> <tr> <td> Relative humidity at xxx hPa </td> <td> r_Lxxx </td> <td> % </td> </tr> <tr> <td> Temperature at xxx hPa </td> <td> t_Lxxx </td> <td> ℃ </td> </tr> <tr> <td> Vertical velocity of wind at xxx hPa </td> <td> w_Lxxx </td> <td> Pa s-1 </td> </tr> <tr> <td> Zonal component of wind at xxx hPa </td> <td> u_Lxxx </td> <td> m s-1 </td> </tr> <tr> <td> Meridional component of wind at xxx hPa </td> <td> v_Lxxx </td> <td> m s-1 </td> </tr> <tr> <td> Wind speed at xxx hPa </td> <td> ws_Lxxx </td> <td> m s-1 </td> </tr> <tr> <td> Wind direction at xxx hPa </td> <td> wd_Lxxx </td> <td> ° </td> </tr> </tbody> </table> <div> </div> 3. key variables constructed by feature engineering (1) sort-term statistics, including maximum, minimum, mean and variance of key variables (2t, 10u, 10v and 10ws) from ECMWF-IFS model during the next 48 hours,  (2) long-term statistics, including mean and deviation of key variables (2t, 10u, 10v and 10ws) from ECMWF-IFS model during history 3-yr period (January 2020–December 2022),  (3) thermodynamic factors,  including the low-level wind shear between 10ws and 100ws, vertical wind shear between 200 hPa and 850 hPa, the differences between sst and 2t. <h3>Scripts</h3> 1. Random Forest model training code 2. LightGBM model training code 3. XGBoost model training code 4. TabNet-MTL model training code

dryad24/100

Data from: Definition and estimation of vital rates from repeated censuses: choices, comparisons and bias corrections focusing on trees

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publicOct 2018View details →

dryad24/100

Data from: Bias correction of bounded location errors in presence-only data

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publicApr 2018View details →

geo20/100

Correcting open chromatin bias in bulk and single-cell CUT&Tag data with PATTY [CUT&Tag]

GEO Series GSE298565. Homo sapiens. 1 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.

openGEO-OpenFeb 2026View details →

geo20/100

Universal correction of enzymatic sequence bias

GEO Series GSE92674. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.

openGEO-OpenDec 2016View details →

geo20/100

Systematic bias in high-throughput sequencing data and its correction by BEADS

GEO Series GSE29427. Caenorhabditis elegans. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.

openGEO-OpenMay 2011View details →

zenodo20/100

Bias correction for convective mass flux

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opencc-by-4.0Oct 2024View details →

nasa20/100

Estimation and Bias Correction of Aerosol Abundance using Data-driven Machine Learning and Remote Sensing

Abstract—Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive analysis exploring possible factors which may be contributing to the inter-instrumental bias between MODIS and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology studies.

restrictednotspecifiedMar 2025View details →

geo12/100

Identification and Correction of Amplification Protocol Bias in Microarray Studies

GEO Series GSE42764. Homo sapiens. 34 samples. Type: Expression profiling by array.

openGEO-OpenJan 2014View details →

zenodo8/100

The role of surface shortwave flux correction in reducing climatological temperature biases

Coupled ocean-atmosphere climate models suffer from a number of climatological biases in sea surface temperature (SST) such as the cold tongue bias in the tropical Pacific, and warm biases along eastern subtropical ocean boundaries (e.g. Zheng et al. 2011; Erfani and Burls, 2019).  Here, we investigate the possibility of reducing these biases within both low and high-resolution versions of a fully coupled climate model (CESM) by globally (and regionally) correcting the net shortwave (SW()*) flux at the surface towards values derived from Clouds and Earth's Radiant Energy Systems (CERES)-Energy Balanced And Filled (EBAF) climatological estimates

restrictedNov 2019View details →

nasa0/100

ACOS GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files V9r (ACOS_L2_Lite_FP) at GES DISC

Version 9r is the current version of the data set. Older versions will no longer be available and are superseded by Version 9r.The ACOS Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files. Orbital granules of the ACOS Level 2 standard product (ACOS_L2S) are used as input. The "ACOS" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances.The GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the "ACOS" Level 2 production process.