Titanlib

Titanlib is a library of automatic quality control routines for weather observations. It emphases spatial checks and is suitable for use with dense observation networks, such as citizen weather observations. It is written in C++ and has bindings for python and R. The library consists of a set of functions that perform tests on data.

Titanlib is currently under active development and the current version is a prototype for testing. Feedback is welcome, either by using the issue tracker in Github, by contacting Thomas Nipen ([email protected]), or by joining the slack channel (see below).

Documentation

For more information on how to use Titanlib, check out the wiki at https://github.com/metno/titanlib/wiki.

Features

• A wide variety of spatial checks, such as spatial consistency test, buddy check, isolation check.
• Plausability tests such as range check and climatology check.
• Fast C++ implementation for efficient processing of large observation datasets

Join the Titanlib community on slack

Titanlib has a slack channel for users to share experiences with Titanlib (https://titanlib.slack.com/). Here you can get help with installation, choosing appropriate settings for QC tests, and more. Contact Louise Oram ([email protected]) if you want to get added.

Quick-start in python

The easiest is to install the latest release of the package using pip. Provided you have installed the dependencies listed above, you can install the most recent release of the python package as follows:

pip install titanlib

Installation

For full installation instructions, see the following wiki page.

Python example

Here is an example using the buddy check, which has the following function signature:

buddy_check(points, values, radius, num_min, threshold, max_elev_diff, elev_gradient, min_std, num_iterations)

The test reveals that the last observation (-111) is likely faulty:

import titanlib

# Set up some fake data
lats = [60,60.1,60.2]
lons = [10,10,10]
elevs = [0,0,0]
obs = [0, 1, -111]
points = titanlib.Points(lats, lons, elevs)

# Run the buddy check on the data
flags = titanlib.buddy_check(points,
   obs,
   [50000], # radius
   [2],     # num_min
   2,       # threshold
   200,     # max_elev_diff
   0,       # elev_gradient
   1,       # min_std
   2,       # num_iterations
)

print(flags)

>>> [0 0 1]

R example

Run the following code in R from the build directory, or if you want to run from any other directory, just put in the proper paths for rtitanlib and titanlib.R

dyn.load(paste("extras/SWIG/R/titanlib", .Platform$dynlib.ext, sep=""))
source("extras/SWIG/R/titanlib.R")
cacheMetaData(1)

sct(c(60,60.1,60.2), c(10,10,10), c(0,0,0), c(0,1,-111),50000,2,2,100,0,1,2)

See also the Installation tips and tricks on the wiki .

Copyright and license

Copyright © 2019-2022 Norwegian Meteorological Institute. Titanlib is licensed under The GNU Lesser General Public License (LGPL). See LICENSE file.