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Notebooks for post-processing the retrieved National Water Model Retrospective run results and inputs at SNOTEL sites
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This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (help@cuahsi.org) for information on this resource. |
Type: | Resource | |
Storage: | The size of this resource is 26.6 GB | |
Created: | Feb 06, 2021 at 1:13 a.m. | |
Last updated: | Feb 11, 2022 at 2:32 a.m. (Metadata update) | |
Published date: | Feb 11, 2022 at 2:32 a.m. | |
DOI: | 10.4211/hs.1b66a752b0cc467eb0f46bda5fdc4b34 | |
Citation: | See how to cite this resource |
Sharing Status: | Published |
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Views: | 1891 |
Downloads: | 56 |
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Abstract
This resource contains Jupyter Notebooks that are used for post-processing the retrieved National Water Model retrospective simulations (NWM-R2), which are geospatial gridded results with a spatial resolution of 1 km and temporal resolution of 3 h. The NWM-R2 grid cells used were from https://doi.org/10.4211/hs.7839e3f3b4f54940bd3591b24803cacf and snow water equivalent and snow-covered area fraction at these grid cells from https://doi.org/10.4211/hs.3d4976bf6eb84dfbbe11446ab0e31a0a that retrieved this information from the NOAA Google Cloud.
Subject Keywords
Content
Readme.md
This resource has three folders: input, code, and output.
Input:
The input folder contains two files.
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SNOTEL_indices_at_NWM.csv: This file includes the SNOTEL site information from Garousi-Nejad and Tarboton (2022).
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geo_em.d01_1km: This NetCDF file is the NWM physiographic input information obtained from https://www.nco.ncep.noaa.gov/pmb/codes/nwprod/nwm.v2.0.4/parm/domain/ on Nov 2020 and is saved in this HydroShare resource.
Code:
The code folder contains three Jupyter Notebooks.
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NWMv2_GeoDomain.ipynb: This notebook retrieves the NWM elevation input data at the SNOTEL location. This script requires two inputs, both available in the input folder. The output of this notebook is available in the ./output folder.
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NWM_RRv2_Post-processing-INPUTS.ipynb: This notebook computes daily precipitation and the daily average temperature from the hourly precipitation and air temperature data, prepared by WRF-Hydro scientists and saved into the ./output/downloads folder of this resource. The outputs of this notebook are available in the ./output/preprocessed folder.
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NWM_RRv2_Post-processing-RESULTS.ipynb: This notebook averages 3-hour NWM‐R2 results of snow water equivalent and snow-covered area fraction to daily values to have a similar temporal resolution when comparing the NWM‐R2 results with SNOTEL and MODIS observations because both these datasets produce daily data.
Output:
The output folder contains two folders and one CSV file.
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NWM_HGT_M.csv: This is the output of the NWMv2_GeoDomain.ipynb jupyter notebook. It includes the NWM elevation data for the grid cells containing SNOTEL sites.
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downloads folder: This folder contains four CSV files.
- NWM_P.csv: The NWM hourly precipitation input data at grid cells containing SNOTEL sites, prepared by the WRF-Hydro team.
- NWM_T.csv: The NWM hourly temperature input data at grid cells containing SNOTEL sites, prepared by the WRF-Hydro team.
- NWM_SNEQV.csv: The 3-hour NWM-R2 results of snow water equivalent at grid cells containing SNOTEL sites, downloaded from https://doi.org/10.4211/hs.3d4976bf6eb84dfbbe11446ab0e31a0a and merged into one CSV file.
- NWM_FSNO.csv: The 3-hour NWM-R2 results of snow covered area fraction at grid cells containing SNOTEL sites, downloaded from https://doi.org/10.4211/hs.3d4976bf6eb84dfbbe11446ab0e31a0a and merged into one CSV file.
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preprocessed folder: This folder contains four CSV files.
- NWM_P_Filter.csv: The output of the NWM_RRv2_Post-processing-INPUTS.ipynb jupyter notebook.
- NWM_T_Filter.csv: The output of the NWM_RRv2_Post-processing-INPUTS.ipynb jupyter notebook.
- NWM_SNEQV_Filter.csv: The output of the NWM_RRv2_Post-processing-RESULTS.ipynb jupyter notebook.
- NWM_FSNO_Filter.csv: The output of the NWM_RRv2_Post-processing-RESULTS.ipynb jupyter notebook.
Related Resources
This resource is referenced by | Garousi-Nejad, I. and Tarboton, D. (2022), "A comparison of National Water Model retrospective analysis snow outputs at snow telemetry sites across the Western United States", Hydrological Processes, https://doi.org/10.1002/hyp.14469 |
The content of this resource is derived from | Garousi-Nejad, I., D. Tarboton (2022). Notebook to get the indices of National Water Model grid cells containing SNOTEL sites, HydroShare, https://doi.org/10.4211/hs.7839e3f3b4f54940bd3591b24803cacf |
The content of this resource is derived from | Tarboton, D., I. Garousi-Nejad (2022). Notebook for retrieval of National Water Model Retrospective run results at SNOTEL sites, HydroShare, https://www.hydroshare.org/resource/1b66a752b0cc467eb0f46bda5fdc4b34 |
The content of this resource is derived from | National Center for Environmental Prediction, NOAA (2022). National Water Model v2 physiographic input, retrieved from https://www.nco.ncep.noaa.gov/pmb/codes/nwprod/nwm.v2.0.4/parm/domain/, accessed Nov 30, 2020, saved in https://www.hydroshare.org/resource/1b66a752b0cc467eb0f46bda5fdc4b34/ |
Title | Owners | Sharing Status | My Permission |
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Data for a comparison of national water model retrospective analysis snow outputs at SNOTEL sites across the Western U.S. | Irene Garousi-Nejad · David Tarboton | Published | Open Access |
Credits
Funding Agencies
This resource was created using funding from the following sources:
Agency Name | Award Title | Award Number |
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Utah Water Research Laboratory | Graduate Student Research Assistantship for I Garousi-Nejad | |
National Science Foundation | Collaborative Research: SI2-SSI: Cyberinfrastructure for Advancing Hydrologic Knowledge through Collaborative Integration of Data Science, Modeling and Analysis | OAC-1664061 |
National Science Foundation | Collaborative Research: SI2-SSI: Cyberinfrastructure for Advancing Hydrologic Knowledge through Collaborative Integration of Data Science, Modeling and Analysis | OAC-1664018 |
National Science Foundation | Collaborative Research: SI2-SSI: Cyberinfrastructure for Advancing Hydrologic Knowledge through Collaborative Integration of Data Science, Modeling and Analysis | OAC-1664119 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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