A functional programming solution to manage time-space wind data.

This is a functional programming solution to joint two wind datasets, which come from 2 different resources.

Achieved features:

• Auto-data-cleaning: Imputed on missing values in both 2D space and time.
• Auto-data-joint: Jointed two wind data sets from two different resources with three implemented methodologies.
• Scientific-data-I/O: output jointed wind data in standard scientific format, Netcdf4.

Dependencies:

This program requires Python2.7 and several packages installed:

• Nio
• xarray
• re
• datatime
• scipy
• NetCDF4
• geopy

Running recipes:

Modify stdinput.py to your needs, and simply run by:

    python main.py

Test results:

Retrieved two different wind data sets from GFS database and Hwind filesystem, respectively. See Grab.f90.

Methodology 1: replace GFS with Hwind data set.

Here are how Hwind data and GFS data looks like:

Methodology 2: add Gaussian Filtering at interface of two data sets during replacing.

Here are how Hwind data and GFS data looks like:

Methodology 3: use weight function at interface of two data sets during replacing.

Here are how Hwind data and GFS data looks like:

Results: sequence wind data visualization.

Here is the sequence wind velocity profile during hurricane Hermine in 2016.