<p>I think Standardized Precipitation Index (SPI) is good fit with your objective. The SPI value is spanning from -3 to +3, positive SPI values indicate greater than median precipitation (Wet) and negative values indicate less than median precipitation (Dry).</p>
<ol>
<li><p>You can read the official guideline from WMO here: <a href="http://www.wamis.org/agm/pubs/SPI/WMO_1090_EN.pdf" rel="nofollow noreferrer">http://www.wamis.org/agm/pubs/SPI/WMO_1090_EN.pdf</a></p>
</li>
<li><p>To calculate SPI using satellite precipitation estimates, you can follow a training from NASA: <a href="https://appliedsciences.nasa.gov/join-mission/training/english/arset-applications-gpm-imerg-reanalysis-assessing-extreme-dry-and-wet" rel="nofollow noreferrer">https://appliedsciences.nasa.gov/join-mission/training/english/arset-applications-gpm-imerg-reanalysis-assessing-extreme-dry-and-wet</a></p>
</li>
<li><p>You can check this website <a href="https://livingatlas.arcgis.com/droughtaware/drought/index.html" rel="nofollow noreferrer">https://livingatlas.arcgis.com/droughtaware/drought/index.html</a> on how SPI used to monitor drought.</p>
</li>
</ol>

CC BY-SA 4.0
2021-10-25T11:28:55.077Z
That's fantastic, thanks for the help! Actually, I wanted to make an attempt at developing an app using the SPI as well, as it's much more useful than the RAI, but I figured I'd give the RAI a shot first as it's simpler. The resources you've linked are very helpful!
Mach
CC BY-SA 4.0
2021-10-22T15:46:20.950Z
2021-10-22T15:46:20.950Z
user97103
<p>I'm working on a script for calculating the Rainfall Anomaly Index<sup>1</sup> (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).</p>
<p>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.</p>
<p>Any suggestions as to how I should approach this problem?</p>
<hr />
<p><sup>1</sup>The definition of RAI that I'm using is this from <a href="https://www.semanticscholar.org/paper/Space-Time-Distribution-of-Rainfall-Anomaly-Index-Costa-Rodrigues/9ade87dfe6b27ab7afd33398303627cf7461e171" rel="nofollow noreferrer">Space-Time Distribution of Rainfall Anomaly Index (RAI) for the Salgado Basin, Ceará State - Brazil</a> , which in turn cites</p>
<ul>
<li>ROOY MP. van. A Rainfall Anomaly Index Independent of Time and Space. Notos. 1965; 14, 43p.</li>
<li>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.</li>
</ul>
<p>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.</p>

CC BY-SA 4.0
2021-07-18T03:41:09.520Z
Can you add a link or cite a source to any definition of RAI? I found two here, but there may be better ones out there, perhaps one you are using yourself? https://agrimetsoft.com/faq/What%20is%20RAI(Rainfall%20Anomaly%20Index) and also https://www.droughtmanagement.info/rainfall-anomaly-index-rai/
uhoh
CC BY-SA 4.0
2021-07-20T23:01:17.253Z
The definition I'm using is this one: https://www.semanticscholar.org/paper/Space-Time-Distribution-of-Rainfall-Anomaly-Index-Costa-Rodrigues/9ade87dfe6b27ab7afd33398303627cf7461e171 , which in turn cites "ROOY MP. van. A Rainfall Anomaly Index Independent of Time and Space. Notos. 1965; 14, 43p." and "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.
Mach
CC BY-SA 4.0
2021-07-21T00:35:21.430Z
Great, *thanks!* I've added that information back into your question. Some people don't read the comments.
uhoh
CC BY-SA 4.0
2021-07-26T12:14:35.160Z
Sweet, thanks for the help! Also I talked it over with a friend of mine, and after some back and forth I came to the conclusion that if the goal of the research question requires you to quantify a drought over an area, then it would make sense to take the average of that area. Given that the index is applied to data from a single weather station, I figured you could treat the precipitation from an entire area as that of a weather station. E.g. there was a drought in a certain region in 2018, so if I consider that entire region to be my rain gauge, it would make sense to take the average.
Mach
<hydrology><precipitation><weather-satellites><gis>
Rainfall Anomaly Index using satellite data
Mach
# Rainfall Anomaly Index using satellite data

(Asked by: username_0 on 2021-07-16)

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](https://www.semanticscholar.org/paper/Space-Time-Distribution-of-Rainfall-Anomaly-Index-Costa-Rodrigues/9ade87dfe6b27ab7afd33398303627cf7461e171) , 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.

**Tags:** hydrology, precipitation, weather-satellites, gis

> **Comments:**
> **username_1**: Can you add a link or cite a source to any definition of RAI? I found two here, but there may be better ones out there, perhaps one you are using yourself? https://agrimetsoft.com/faq/What%20is%20RAI(Rainfall%20Anomaly%20Index) and also https://www.droughtmanagement.info/rainfall-anomaly-index-rai/  
> **username_0**: The definition I'm using is this one: https://www.semanticscholar.org/paper/Space-Time-Distribution-of-Rainfall-Anomaly-Index-Costa-Rodrigues/9ade87dfe6b27ab7afd33398303627cf7461e171 , which in turn cites "ROOY MP. van. A Rainfall Anomaly Index Independent of Time and Space. Notos. 1965; 14, 43p." and "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.  
> **username_1**: Great, *thanks!* I've added that information back into your question. Some people don't read the comments.  
> **username_0**: Sweet, thanks for the help! Also I talked it over with a friend of mine, and after some back and forth I came to the conclusion that if the goal of the research question requires you to quantify a drought over an area, then it would make sense to take the average of that area. Given that the index is applied to data from a single weather station, I figured you could treat the precipitation from an entire area as that of a weather station. E.g. there was a drought in a certain region in 2018, so if I consider that entire region to be my rain gauge, it would make sense to take the average.  

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## 1 answers

### Answer by username_2 on 2021-10-22. Score: 1 | Accepted answer

I think Standardized Precipitation Index (SPI) is good fit with your objective. The SPI value is spanning from -3 to +3, positive SPI values indicate greater than median precipitation (Wet) and negative values indicate less than median precipitation (Dry).

1. You can read the official guideline from WMO here: <http://www.wamis.org/agm/pubs/SPI/WMO_1090_EN.pdf>
2. To calculate SPI using satellite precipitation estimates, you can follow a training from NASA: <https://appliedsciences.nasa.gov/join-mission/training/english/arset-applications-gpm-imerg-reanalysis-assessing-extreme-dry-and-wet>
3. You can check this website <https://livingatlas.arcgis.com/droughtaware/drought/index.html> on how SPI used to monitor drought.

> **Comments:**
> **username_0**: That's fantastic, thanks for the help! Actually, I wanted to make an attempt at developing an app using the SPI as well, as it's much more useful than the RAI, but I figured I'd give the RAI a shot first as it's simpler. The resources you've linked are very helpful!  

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