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bennyistanto/README.md

Hi! I am Benny Istanto 👋

An agricultural meteorologist by training and a certified GIS Professional (GISP). With over 15 years of experience working with the United Nations and various international organizations, I specialize in integrating advanced GIS modeling with climate technology to drive international development initiatives. My expertise encompasses agricultural climatology, satellite meteorology, simulation modeling, and the application of geospatial information management for natural resource management.

Currently, I serve as a Climate Geographer at the Geospatial Operations Support Team (GOST) of the Development Economic Data Group (DECDG) at The World Bank, Washington, DC. Collaborating with teams across the bank, I offer expertise in geospatial and climate analytics on a global scale.

I write a blog related to climate, GIS, remote sensing and application to humanitarian activities at https://benny.istan.to/blog, I run a “casual weekend project” called Climate Social Responsibility (CSR – https://benny.istan.to/csr), to provide satellite-based climate and vegetation products for free, i.e. rainfall and anomaly, dry and wet spell, standardized precipitation index, and crop phenology.

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  1. gee gee Public

    GEE script collection from my works

    JavaScript 12 1

  2. drought-propagation drought-propagation Public

    Drought Propagation from Meteorological to Hydrological using Cross-Correlation Analysis

    Jupyter Notebook 8

  3. Install the Weather Research and For... Install the Weather Research and Forecasting Model on Windows Subsytem for Linux
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    # Install the WRF Model in WSL2
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    This section will explain on how to install the Weather Research and Forecasting ([WRF](https://www.mmm.ucar.edu/models/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.
    
              
    
              
    
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        Fourier regression model to generate...  Fourier regression model to generate daily time series from monthly temperature data      
    

          
    
              
    
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        SPI or SPEI based Drought Characteri...  SPI or SPEI based Drought Characteristics      
    

          
    
              
    
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        S2S and Seasonal (un)calibrated Fore...  S2S and Seasonal (un)calibrated Forecasts using PyCPT for Indonesia      
    

          
    
              
    
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    # Indonesia S2S and Seasonal Forecasts using PyCPT
    
              
    
              
    
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    PyCPT is a Python interface and enhancement for the command line version of the [International Research Institute for Climate and Society](https://iri.columbia.edu/)'s Climate Predictability Tool ([CPT](https://iri.columbia.edu/cpt)), for seasonal and sub-seasonal skill assessment and forecast experiments.
    
              
    
              
    
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    This notes is describing on how to use PyCPT s2sv1.92 and seav1.92 for Subseasonal and Seasonal Forecasting in Indonesia
    
              
    
          
    
    
    
  
    

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