README.md

In raffscallion/goesfire: Tools for the GOES-ABI Fire (Hot Spot Characterization) Product

goesfire

Utilities for processing GOES-ABI FDC data in R

Installation

To install the latest development builds from GitHub:

if (!require("devtools"))
  install.packages("devtools")
devtools::install_github("raffscallion/goesfire")

Processing netCDF files

To turn a single FDCC or FDCF netCDF file into a data frame of hot spots, run:

fires <- extract_fires(input_file, maskvals = c(10, 11, 30, 31))

Note that the mask values selected here are only those most likely to be fire. Running extract_fires() without specifying maskvals will return a record for each grid cell (much larger than when only fire mask values are specified). This can be used to get information on the mask values.

CLASS subscription processing

To facilitate routine processing of data from a CLASS subscription, something like this will work:

library(goesfire)

url <- "ftp://ftp.avl.class.noaa.gov/sub/[username]/[subscription_number]/"
download_path <- "local path for downloading netcdf files"
output_path <- "local path for processed csv files"
outname <- "GOES_FDC" # this will get prepended to the output filenames
fires <- batch_process(url, download_path, output_path, outname)

This will download all FDCC netCDF files available in the subscription folder that do not already exist in the download path, process them into csv format, and write a single concatenated csv file to the output path.

Viewing results

Once some fires have been processed to csv, you can use the viewer to look at them.

goesfire::run_viewer()

To use the viewer, select a list of processed csv files and they will be displayed on the map and time series.

Emissions processing

An estimate of total particulate matter emissions can be calculated by using the emission coefficients available from NASA FEER. The code has been tested with the FEERv1.0_Ce.csv file acquired from the FEER website. In this example, a folder of FDC csv files produced with extract_fires() is concatenated into a single output csv with calculated emissions. The emissions files can also be explored with the viewer.

library(goesfire)
library(fs)
library(tidyverse)

input_path <- "C:/my_csv_path/"
output_path <- "C:/my_emissions_path/"
outname <- "GOES_Emissions.csv"

# The first 6 lines are descriptive header and -9999 is the fill value
feer <- read_csv("/path_to_feer/FEERv1.0_Ce.csv", skip = 6, na = "-9999")

files <- dir_ls(input_path, glob = "*.csv")

emissions <- map_dfr(files, read_csv, col_types = cols()) %>%
  goesfire::feer_emissions(feer) %>%
  aggregate_hourly()

# Write to csv
write_csv(emissions, paste0(output_path, outname))

Hourly aggregation

To aggregate the sub-hourly data, use the aggregate_hourly() function.

hourly <- extract_fires(infile, vars = vars, maskvals = maskvals) %>%
  aggregate_hourly()

raffscallion/goesfire documentation built on June 9, 2025, 2:46 a.m.