| dataFormat | netCDF |
| featureType | Grid |
| dataSize | 20939359 |
| id | fmc_alaska3000m/composite_lfmc_alaska_2023328.nc |
Dataset: composite_lfmc_alaska_2023328.nc
Catalog: /thredds/catalog/wsap/fmc/alaska3000m/catalog.html
Access:
| Service | Type | Description |
|---|---|---|
| OpenDAP | Data Access | Access dataset through OPeNDAP using the DAP2 protcol. |
| DAP4 | Data Access | Access dataset through OPeNDAP using the DAP4 protocol. |
| HTTPServer | Data Access | HTTP file download. |
| WCS | Data Access | Supports access to geospatial data as 'coverages'. |
| WMS | Data Access | Supports access to georegistered map images from geoscience datasets. |
| NetcdfSubset | Data Access | A web service for subsetting CDM scientific datasets. |
Viewers:
| Viewer | Type | Description |
|---|---|---|
| Godiva3 | Browser | |
| default_viewer.ipynb | Jupyter Notebook | The TDS default viewer attempts to plot any Variable contained in the Dataset. |
Description:
- summary: Dead and live fuel moisture content retrievals over the Contiguous U.S. and Alaska Dead and live fuel moisture content (FMC) are essential for, among other aspects, effectively estimating fire danger and for initializing models used tactically to manage wildland fires and understand their behavior. Unfortunately, FMC observations are sparse for dead FMC and even sparser, and infrequent, for live FMC. To overcome these limitations, in previous work we developed a regression model that uses machine learning (ML) to estimate the dead and live FMC based on data from Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. The main limitations of these MODIS-based FMC estimations are 1) the inability to represent the diurnal cycle of FMC; 2) the lack of local details due to limitations in horizontal resolution; 3) coverage that is restricted to the contiguous U.S. (CONUS); and 4) the ages of the MODIS instruments, which are well beyond their life expectancy. To overcome the previous limitations, we are combining reflectance data from the Advanced Baseline Imager (ABI) aboard GOES-16 and 17, and the Visible-Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi National Polar-orbiting Partnership (S-NPP). VIIRS data provides high spatial resolution, ABI data high temporal resolution. The two datasets are being blended to estimate FMC at a 375-m granularity over CONUS with 1-h frequency. VIIRS-based estimations of FMC also will be provided for Alaska with intra-diurnal updates. To illustrate the value of the FMC retrievals we start a two-year real-time demonstration of the products in February 2023. During the first year, the FMC retrievals over CONUS and Alaska will be based on VIIRS. During the second year of the demonstration, the CONUS retrievals will be upgraded to provide hourly updates. The FMC retrievals are publicly available and can be downloaded from the demonstration tab of this website. During the demonstration we will also use the Weather Research and Forecasting model with fire behavior extensions (WRF-Fire) to quantify the value of the FMC products for predicting fire spread and smoke transport and dispersion. In addition, we will use the FMC retrievals to quantify its value for a fire weather index.
- Fuel Moisture Content tool
Keywords:
- : TDS
- : NetCDF
Dates:
- modified : 2023-11-25T11:14:11.160Z
Creators:
-
Dr. Pedro A. Jimenez
- email: jimenez@ucar.edu
- https://staff.ucar.edu/users/jimenez
Publishers:
-
National Center for Atmospheric Research
- email: jimenez@ucar.edu
- https://ral.ucar.edu/tool/fuel-moisture-content-retrievals
GeospatialCoverage:
- Longitude: -134.1 to -60.78 Resolution=9.0 km
- Latitude: 21.2 to 52.5 Resolution=9.0 km
TimeCoverage:
- Start: 2020-02-01T00:00:00Z
- End: 2021-01-31T23:00:00Z
- Duration: 12.023 months
Properties:
- institution = NCAR