R Tools for Observations, Receptors and Footprints (rtorf)

NOAA Obspack is a collection of greenhouse gases observations

rtorf only depends on data.table and ncdf4, which is basically parallel C, so it can be installed in any machine.

Installation

Using git bash/powershell

git clone https://github.com/noaa-gml/rtorf
R CMD INSTALL rtorf

Using remotes R package

remotes::install_github("noaa-gml/rtorf")

library(rtorf)
library(data.table)
utils::packageVersion("rtorf")

## [1] '3.1.0'

ObsPack summary

The first step consists in constructing a summary for ObsPack (CH4, CO2 or other). This is required to read the data, but also, identify agl, which is present in some of the file names. This function returns a data.frame. Optionally, the user can indicate a path to store the data.frame. obs_summary also prints a summary of the data. The second argument is the categories, and by default includes the categories shown below, to account for all the files. Then the summary data.frame contains the columns id as the full path to each file, name which is the name or relative path of the file, n just an id, sector such as tower, and the column agl which indicates the agl indicated in the name of the file if available. To read the documentation of this function, the user must run ?obs_summary.

We first define the categories

cate = c(
  "aircraft-pfp",
  "aircraft-insitu",
  "aircraft-flask",
  "surface-insitu",
  "surface-flask",
  "surface-pfp",
  "tower-insitu",
  "aircore",
  "shipboard-insitu",
  "shipboard-flask"
)

obs <- "Z:/obspack/obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08/data/nc/"
index <- obs_summary(obs = obs, categories = cate)

## Number of files of index: 0
##           sector     N
##           <char> <int>
## 1: Total sectors     0
## Detected 0 files with agl
## Detected 0 files without agl

For each one of these dataset ids, check the articles in the documentation

https://noaa-gml.github.io/rtorf/

e.g. for the aircraft-pfp we can read $CO_2$ and $CH_4$here

Implementation in python:

I’m currently implementing a version in python pytorf:

Special thanks to all the

contributors

and

Stargazers

Note about legacy code.

I received the task of reformat legacy code used to read PARTICLE.DAT and generate NetCDF. This code is really old and since STILT and other have permissive licenses, it is here, however, some code is not exported to NAMESPACE. A future version of rtorf, will have a new design for the legacy code, a modern and efficient approach, keeping the essential tasks.