Dead Fuel Moisture Content Reanalysis Dataset of California (2000-2020)

Publication Date

2024

Degree Type

Dataset

Department

Meteorology and Climate Science

Associated Publication

https://doi.org/10.3390/fire7100358

Abstract

A novel reanalysis dataset of dead fuel moisture content (DFMC) across California from 2000 to 2020 at a 2 km resolution. Utilizing a data assimilation system that integrates a simplified time-lag fuel moisture model with 10-hour fuel moisture observations from Remote Automated Weather Stations (RAWS) allowed predictions of 10-hour fuel moisture content by our method with a mean absolute error of 0.03 g/g compared to the widely used Nelson model whose predictions have a mean absolute error of 0.05 g/g. The presented product provides gridded hourly moisture estimates for 1-hour, 10-hour, 100-hour, and 1000-hour fuels, essential for analyzing historical fire activity and understanding climatological trends. The dataset consists of 253 monthly Network Common Data Form (NetCDF) files of 1 GB each. The dataset also includes an extra NetCDF file with the grid coordinates.

Comments

The dataset was published by SJSU Wildfire Interdisciplinary Research Center.

http://www.met.sjsu.edu/~012762230/datasets/fmrp

DOI

10.31979/data.fg9p-zqbx

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