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Indonesia S2S and Seasonal Forecasts using PyCPT

PyCPT is a Python interface and enhancement for the command line version of the International Research Institute for Climate and Society's Climate Predictability Tool (CPT), for seasonal and sub-seasonal skill assessment and forecast experiments.

This notes is describing on how to use PyCPT s2sv1.92 and seav1.92 for Subseasonal and Seasonal Forecasting in Indonesia

1 Installation

This section will explain on how to install the PyCPT inside Windows Subsystem for Linux (WSL) 2. This step-by-step guide was tested using Windows 11 with WSL2 - Ubuntu 22 enabled running on Thinkpad T480 2019, i7-8650U 1.9GHz, 64 GB 2400 MHz DDR4.

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# -*- coding: utf-8 -*-
"""
NAME
    03_mxd13q1_fillnullwithstats.py
    Filling null on MXD13Q1 data with long-term mean
DESCRIPTION
    Input data for this script will use MXD13Q1 16-days data generate from GEE or downloaded from NASA
    This script can do filling null on MXD13Q1 data with long-term mean
REQUIREMENT
    ArcGIS must installed before using this script, as it required arcpy module.

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#!/usr/bin/python
"""
NAME
    csv_concat.py
    Concatenate csv files by column, and use the date on filename as column name
DESCRIPTION
    Input data for this script will use any csv, which must have lon, lat and value (no header)
    All files required to follow the naming convention "_yyyymmdd.csv"
USAGE
    python csv_concat.py <csv_dir>

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Install the WRF Model in WSL2

This section will explain on how to install the Weather Research and Forecasting (WRF) Model inside Windows Subsystem for Linux (WSL) 2. This step-by-step guide was tested using Windows 11 with WSL2 - Debian 12 enabled running on ThinkStation P720.

--
Benny Istanto, Climate Geographer
GOST/DECSC/DECDG, The World Bank

Index

• 1. Working Directory

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# -*- coding: utf-8 -*-
"""
NAME
    spatiotemporal_regression.py
DESCRIPTION
    Perform a focal linear regression between two sets of timeseries raster
REQUIREMENT
    It required os, numpy, scipy, rasterio and sklearn module. 
    So it will work on any machine environment.
HOW-TO USE

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# -*- coding: utf-8 -*-
"""
NAME
    spatiotemporal_regression_arcpy.py
DESCRIPTION
    Perform a focal linear regression between two sets of timeseries raster
REQUIREMENT
    ArcGIS must installed before using this script, as it required arcpy module.
EXAMPLES
    \\arcgispro-py3\\python spatiotemporal_regression_arcpy.py

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# -*- coding: utf-8 -*-
"""
NAME
    modis_viproducts.py
    Generate derivative product from Vegetation Indices
DESCRIPTION
    Input data for this script will use MXD13Q1 8-days data generate from GEE or downloaded 
    from NASA. This script can do calculation for ratio, difference, standardize anomaly 
    and vegetation condition index.
    The calculation required timeseries VI and the long-term statistics (min, mean, max, std)

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# -*- coding: utf-8 -*-
"""
NAME
    spei_monthly_blend.py
    Global TerraClimate's SPEI-based dry/wet indicators blend.
DESCRIPTION
    Input data for this script will use TerraClimate SPEI monthly data in GeoTIFF format
    This experimental SPEI blends integrate several SPEI scales into a single product.
    The combines 3-, 6-, 9-, 12-, and 24-month SPEI to estimate the overall dry/wet conditions.
METHOD