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#!/usr/bin/python
# Rename variable, dimension and unit in a netcdf file for climate-indices calculation
# Source: https://github.com/monocongo/climate_indices/issues/404#issuecomment-735474320
# https://stackoverflow.com/a/55686749

import pandas as pd
import xarray as xr

ds = xr.open_dataset("/Users/bennyistanto/Temp/CHIRTS/nc/rbb_cli_chirts_monthly_tmax_1983_2016_d1.nc",
        decode_times=False)

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Calculate SPI using dekad rainfall data in netCDF format

Below is another way to acquired different data (CHIRPS) using different tool (CDO - Climate Data Operator) and NCO - netCDF Operator.

Why CHIRPS? Because I want to get higher resolution, more frequent monitoring (updated every dekad ~ 10days), and long-term historical data from 1981 – now.

Lets try!

This step-by-step guide was tested using Macbook Pro, 2.9 GHz 6-Core Intel Core i9, 32 GB 2400 MHz DDR4, running on macOS Catalina 10.15.7

bennyistanto

# Linear regression between every 5×5 pixels group of cells of two rasters

library(raster)
library(gstat)
library(sp)
library(orcutt)
library(lmtest)
library(zoo)

# Set working directory

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#!/usr/bin/python
# Source: https://gis.stackexchange.com/a/354798/97103

import geopandas
import rioxarray
import xarray
from shapely.geometry import mapping


CHIRPS_daily = xarray.open_dataarray('Z:\Temp\CHIRPS\NC\chirps-v2.0.1981.days_p05.nc')

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#!/usr/bin/env python
"""
-------------------------------------------------------------------------------------------------------------
Convert raster GeoTIFF in a folder to single NetCDF file with time dimension enabled that is CF-Compliant
http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html
 
Based on Rich Signell's answer on StackExchange: https://gis.stackexchange.com/a/70487
This script was tested using TerraClimate monthly data. Adjustment is needed if using other timesteps data 
NCO (http://nco.sourceforge.net), GDAL, NetCDF4, numpy must be installed before using this script
 

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#!/bin/bash
# Arrange IMERG data based on number of days within month
# Source: https://github.com/andyprasetya

# -- Start script: change directory ke target directory, misal: 'imerg'
cd imerg
# -- Set shell option ke 'nullglob' - optional, bisa di-comment/disabled
shopt -s nullglob
# Mulai loop ke seluruh file dalam target directory.
# Biasanya menggunakan 'i', tapi ini pakai 'file' untuk memperjelas penulisan variable

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#!/usr/bin/python
# -*- coding: utf8 -*-
import arcpy
import os

#Overwrite the output if exist
arcpy.env.overwriteOutput = True

# Set local variables
ws_in = r"Z:\Temp\CHIRPS\SPI\Month1D1"

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#This script required daily CHIRPS data and previous wetspell data, if previous data not available
#then it will start with 0.
import os
import arcpy
from arcpy.sa import *
from datetime import date, timedelta

# to execute first DSLD data
def execute_first_DSLD(_tiffolder, _DSLDFolder, threshold):
    sr = arcpy.SpatialReference(4326)

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#This script required daily CHIRPS data and previous drspell data, if previous data not available
#then it will start with 0.
import os
import arcpy
from arcpy.sa import *
from datetime import date, timedelta

# to execute first DSLR data
def execute_first_DSLR(_tiffolder, _DSLRFolder, threshold):
    sr = arcpy.SpatialReference(4326)

bennyistanto

import arcpy
import os
from datetime import date
from datetime import timedelta
import multiprocessing

#Overwrite the output if exist
arcpy.env.overwriteOutput = True

#Working directory