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Work with Numpy array, Pandas, to manage NASA's historical world rainfall data.

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World rainfall exercise

Here I present a basic workflow on handling rainfall data provided by NASA' Global Precipitation Climatology Project(GPCP).

GPCP 1-Degree Daily(1DD, version 1.2) is the dataset in use. 1DD contains world-wide precipitation estimates binned in 1 square degree (longitude,latitude) for each day since October,1996, until October,2015. The whole dataset is splited in 19x12+1=229 files, for each month of the period. Each data file is a 3-dimensional array of shape (lines,columns,fields): 180,360; lines represent latitude, columns represent longitude and fields are the days of the respective month.

From the dataset description page:

The data set archive consists of unformatted REAL*4 binary files with ASCII headers. Each file holds 28-31 daily fields. Each file occupies about 8 MB. The grid on which each field of values is presented is a 1°x1° latitude--longitude (Cylindrical Equal Distance) global array of points. It is size 360x180, with X (longitude) incrementing most rapidly West to East from the Prime Meridian, and then Y (latitude) incrementing North to South. Whole- and half-degree values are at grid edges:
 
First point center = (89.5°N,0.5°E)
Second point center = (89.5°N,1.5°E)
Last point center = (89.5°S,0.5°W)
Missing values are denoted by the value -99999., and the units are mm/day.
The standard reference is:
 
Huffman, G.J., R.F. Adler, M. Morrissey, D.T. Bolvin, S. Curtis, R. Joyce, B McGavock, J. Susskind, 2001: Global Precipitation at One-Degree Daily Resolution from Multi-Satellite Observations. J. Hydrometeor., 2, 36-50.

Goals

First of all, we have to read the GPCP-1DD dataset -- distributed in binary files (numpy arrays). Then, to visualize the data, we want to have world-map plot with data shown per country according to the epoch (month/year) or during a period.

The fundamental -- or main -- product here is a table of precipitations, per country per month.

The data model:

name lons lats ...
Brazil [0,1,0] [0,0,1] ...
  • World-Rainfall: monthly table with values for precipitation at each position of interest (for instance, the whole world)
longitude latitude precipitation
0.5 0.5 13
  • Countries-Rainfall: table with values for precipitation for each country on each month
period Country-A Country-B ...
Month-1/Year-1 value-A value-B ...

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Work with Numpy array, Pandas, to manage NASA's historical world rainfall data.

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