I'm working on a script for calculating the Rainfall Anomaly Index1 (RAI) for an area using satellite imagery to estimate precipitation. The context I have seen the RAI used in before is for weather station estimates, which essentially corresponds to a single pixel in my imagery. What I would like to do is calculate droughts using the imagery, but the RAI formula changes depending on if the difference in rainfall in the given time period is negative (drought) or positive (flood).
So my scenario is that within the area I would like to calculate the RAI for I have some pixels that experience a drought, and some that technically experience flooding. My ideas to deal with this is to a) take an average for the entire area and based on that either use the positive or the negative formula or b) calculate the index for each pixel individually, then averaging the index at the end.
Any suggestions as to how I should approach this problem?
1The definition of RAI that I'm using is this from Space-Time Distribution of Rainfall Anomaly Index (RAI) for the Salgado Basin, Ceará State - Brazil , which in turn cites
- ROOY MP. van. A Rainfall Anomaly Index Independent of Time and Space. Notos. 1965; 14, 43p.
- FREITAS MAS. Um sistema de suporte à decisão para o monitoramento de secas meteorológicas em regiões semiáridas. Rev. Tecnol. 2005; (suppl 19): p. 84-95.
For the time being I've decided to use a fairly simple index that only considers precipitation as an input to get my code to work.