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12 results for “Satellite Data Assimilation”
PEATCLSM(Tb): A land surface data assimilation product for peatlands using PEATCLSM and brightness temperature (Tb) satellite observations (Northern Hemisphere output)
<p>The datasets archived here include simulation results shown in the paper, “Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework”, published in Remote Sensing of Environment Journal (Bechtold et al., 2020). The output was produced by combining peatland-specific land surface modeling (Bechtold et al., 2019b) embedded in the NASA Catchment Land Surface Model (CLSM) with L-band brightness temperature (Tb) observations (SMOS), applying the data assimilation framework of the SMAP Level‐4 Soil Moisture product (Reichle et al., 2019). We provide netcdf files (9-km resolution EASEv2 grid, period Jan 2010 – Nov 2019, and between 45°N and 70°N, NE Asia excluded) of the four experiments of the manuscript: model-only (open-loop, OL) and data assimilation (DA) for each land model version, that is CLSM without and with the use of the PEATCLSM modules. The highest accuracy is provided by the DA product using PEATCLSM and Tb observations. When referring to the latter product use the name ‘PEATCLSM(Tb)’. We provide three types of netcdf files:<br> • daily_images_*.nc: Daily land states and fluxes (Table 1), provided as netCDF image-chunked image stack<br> • ObsFcstAna_images_*.nc: Brightness temperature observations, forecasts and analysis (Table 2), provided as netCDF image-chunked image stack<br> • incr_timeseries_*.nc: Data assimilation increments (Table 3), provided as netCDF timeseries-chunked image stack</p> <p>The file content is described in the file PEATCLSM_Tb_Documentation_20200505.pdf</p> <p>Please contact Michel Bechtold (michel.bechtold@kuleuven.be) for any questions.</p> <p>Data usage statement:<br> This work is licensed under a Creative Commons Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0/<br> If you decide to work with this data, we kindly ask to be informed at the outset of the nature of this work. If the data are essential to the work, or if an important result or conclusion depends on the PEATCLSM(Tb) data product, we would appreciate that you discuss these findings with us to ensure correct use and interpretation of the PEATCLSM(Tb) product. Furthermore, we are continuously improving the data assimilation product, a discussion of your work at an early stage may (i) help us to improve our product, and (ii) allow us to provide you with a newer version. Thanks!</p> <p>References:</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., & Koster, R. D. (2019a). PEAT-CLSM simulation output (Northern Peatlands) version 1. https://doi.org/10.17605/OSF.IO/E58YM</p> <p>Bechtold, M. et al. (2019b). PEAT‐CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(7), 2130–2162. https://doi.org/10.1029/2018MS001574</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., Roose, D., Balliston, N., Burdun, I., Devito, K., Kurbatova, J., Strack, M., & Zarov, E. A. (2020). Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework. <em>Remote Sensing of Environment</em>. https://doi.org/10.1016/j.rse.2020.111805</p> <p>Reichle, R. H., Liu, Q., Koster, R. D., Crow, W. T., De Lannoy, G. J. M., Kimball, J. S., Ardizzone, J. V., Bosch, D., Colliander, A., Cosh, M., Kolassa, J., Mahanama, S. P., Prueger, J., Starks, P., & Walker, J. P. (2019). Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(10), 3106–3130. https://doi.org/10.1029/2019MS001729</p>
Data associated with the publication "Interannual variability in the Australian carbon cycle over 2015-2019, based on assimilation of OCO-2 satellite data".
<p>This dataset refers to the publication "Interannual variability in the Australian carbon cycle over 2015-2019, based on assimilation of OCO-2 satellite data". https://doi.org/10.5194/acp-2022-15.</p> <p> </p>
Data associated with the publication "Was Australia a sink or source of CO2 in 2015? Data assimilation using OCO-2 satellite measurements"
<p>This dataset refers to the publication "Was Australia a sink or source of CO2 in 2015? Data assimilation using OCO-2 satellite measurements". https://doi.org/10.5194/acp-2021-16. </p>
The prediction data analyzed in "Seasonal Arctic sea ice prediction using a newly developed fully coupled regional model with the assimilation of satellite sea ice observations"
<p>The outputs of seasonal predictions with the new modeling system analyzed in the article including:</p> <p>Sea ice concentration (SIC)</p> <p>Sea ice thickness (SIT)</p> <p>Sea surface temperature (SST)</p> <p>Near surface air temperature (T2) </p>
Data for: Ensemble-based data assimilation of significant wave height from Sofar Spotters and satellite altimeters with a global operational wave model
<p>An ensemble-based method for wave data assimilation is implemented using significant wave height observations from the globally distributed network of Sofar Spotter buoys and satellite altimeters. The Local Ensemble Transform Kalman Filter (LETKF) method generates skillful analysis fields resulting in reduced forecast errors out to 2.5 days when used as initial conditions in a cycled wave data assimilation system. The LETKF method provides more physically realistic model state updates that better reflect the underlying sea state dynamics and uncertainty compared to methods such as optimal interpolation. Skill assessment far from any included observations and inspection of specific storm events highlights the advantages of LETKF over an optimal interpolation method for data assimilation. This advancement has immediate value in improving predictions of the sea state and, more broadly, enabling future coupled data assimilation and utilization of global surface observations across domains (atmosphere-wave-ocean).</p>
Data for: Ensemble-based data assimilation of significant wave height from Sofar Spotters and satellite altimeters with a global operational wave model
Open the record for dataset details and reuse information.
Air Quality Forecasts Improved by Combining Data Assimilation and Machine Learning with Satellite AOD
<p>Input data for random forest model. </p> <p> </p> <p>1) UM_RDAPS.egg file: It provides analysis and forecast products four times a day (00, 06, 12, 18 UTC) in 12 km x 12 km spatial resolution. In this study, analysis products were only considered as the input variables (i.e., 2m temperature and dew-point temperature, relative humidity (RH), maximum wind speed, visibility at height above the ground, planetary boundary layer height (PBLH), and surface pressure). The accumulated maximum wind speed during 1, 3, 5, 7 days were also used in this study.</p> <p>2) data_1.zip file: GOCI Aerosol product, MODIS Land cover, MODIS NDVI, Population density, Road density, SRTM_DEM. </p> <p> </p> <p>The detailed information of input variables is written in the supporting information of the paper.</p> <p> </p> <p> </p> <p> </p>
PEATCLSM(Tb): A land surface data assimilation product for peatlands using PEATCLSM and brightness temperature (Tb) satellite observations (Northern Hemisphere output, Jan 2010 through Sep 2021)
<p>The dataset archived here includes an extended version of the analysis output shown in the paper, “Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework”, published in Remote Sensing of Environment Journal (Bechtold et al., 2020). The output was produced by combining peatland-specific land surface modeling (Bechtold et al., 2019b) embedded in the NASA Catchment Land Surface Model (CLSM) with L-band brightness temperature (Tb) observations (SMOS), applying the data assimilation framework of the SMAP Level‐4 Soil Moisture product (Reichle et al., 2019). We provide a single NetCDF file of the analysis output (9-km resolution EASEv2 grid, period Jan 2010 through Sep 2021, and between 45°N and 70°N, NE Asia excluded):<br> • daily_images.nc: Daily land states and fluxes (Table 1), provided as netCDF image-chunked image stack</p> <p>The file content is described in the file PEATCLSM_Tb_Documentation_20230830.pdf</p> <p>Please contact Michel Bechtold (michel.bechtold@kuleuven.be) for any questions.</p> <p>Data usage statement:<br> This work is licensed under a Creative Commons Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0/<br> If you decide to work with this data, we kindly ask to be informed at the outset of the nature of this work. If the data are essential to the work, or if an important result or conclusion depends on the PEATCLSM(Tb) data product, we would appreciate that you discuss these findings with us to ensure correct use and interpretation of the PEATCLSM(Tb) product. Furthermore, we are continuously improving the data assimilation product, a discussion of your work at an early stage may (i) help us to improve our product, and (ii) allow us to provide you with a newer version. Thanks!</p> <p>References:</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., & Koster, R. D. (2019a). PEAT-CLSM simulation output (Northern Peatlands) version 1. https://doi.org/10.17605/OSF.IO/E58YM</p> <p>Bechtold, M. et al. (2019b). PEAT‐CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(7), 2130–2162. https://doi.org/10.1029/2018MS001574</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., Roose, D., Balliston, N., Burdun, I., Devito, K., Kurbatova, J., Strack, M., & Zarov, E. A. (2020). Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework. <em>Remote Sensing of Environment</em>. https://doi.org/10.1016/j.rse.2020.111805</p> <p>Reichle, R. H., Liu, Q., Koster, R. D., Crow, W. T., De Lannoy, G. J. M., Kimball, J. S., Ardizzone, J. V., Bosch, D., Colliander, A., Cosh, M., Kolassa, J., Mahanama, S. P., Prueger, J., Starks, P., & Walker, J. P. (2019). Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(10), 3106–3130. https://doi.org/10.1029/2019MS001729</p>
Assimilation of Satellite Radiance Data with the POD-4DEnVar Method for the Forecasting of Binary Typhoons
<p>The data and codes for ploting the figures in our manuscript are provided here.</p>
NASA Ocean Biogeochemical Model assimilating satellite chlorophyll data global daily VR2017 (NOBM_DAY) at GES DISC
This is the assimilated daily data from NASA Ocean Biogeochemical Model (NOBM). The NOBM is a comprehensive, interactive ocean biogeochemical model coupled with a circulation and radiative model in the global oceans (Gregg and Casey, 2007). It spans the domain from -84 to 72 degree latitude in increments of 1.25 degree longitude by 2/3 degree latitude, including only open ocean areas where bottom depth > 200m. NOBM contains 4 phytoplankton groups, 4 nutrient groups, a single herbivore group, and 3 detrital pools, and the major ocean carbon components, dissolved organic and inorganic carbon (DOC and DIC).
NASA Ocean Biogeochemical Model assimilating satellite chlorophyll data global monthly VR2017 (NOBM_MON) at GES DISC
This is the assimilated monthly data from NASA Ocean Biogeochemical Model (NOBM). The NOBM is a comprehensive, interactive ocean biogeochemical model coupled with a circulation and radiative model in the global oceans (Gregg and Casey, 2007). It spans the domain from -84 to 72 degree latitude in increments of 1.25 degree longitude by 2/3 degree latitude, including only open ocean areas where bottom depth >200m. NOBM contains 4 phytoplankton groups, 4 nutrient groups, a single herbivore group, and 3 detrital pools, and the major ocean carbon components, dissolved organic and inorganic carbon (DOC and DIC).
Satellite-derived melt assimilation MAR simulations over the Antarctic Peninsula daily data
<p>The Modèle Atmosphérique Régionale (MAR), is a regional climate model designed to simulate poles' climate. here is provided a data set of MAR simulations in which microwave sensor date have been assimilated. The files contain snow and athmosphere related variables over the Antarctic Peninsula for the 2019-2020 period.</p> <p>MAR is a polar-oriented regional climate model mostly used to study both the Greenland and Antarctic ice sheet. Its atmospheric dynamics are based on hydrostatic approximation of primitive equations originally described in Gallée and Schayes (1994) and on a radiative transfer scheme adapted from Morcrette (2002). The model has been parameterized to resolve the topmost 20 meters of the snowpack, divided into 30 layers of time varying thickness. Layers maximum water content holding capacity is fixed at 5%. Beyond it, the water freely percolates through the snowpack or runoffs above impermeable layers.</p> <p>For this work, MARv3.12 was run at a 7.5 km resolution over the Antarctic Peninsula March 2017 and May 2021. Snowpack was initialized in 2017 with a previous MAR simulation (Kittel et al., 2021). Finally, the simulations with assimilation were started in January 2019, restarting from the simulation without assimilation. </p> <p> </p> <p>Simulation for the 2020-2021 season are available on demand. Contact tdethinne@uliege.be</p>
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