Standard Precipitation Index

Standardized index of the degree to which rainfall has been above or below seasonal trends for a given region

  • Source: CHG UCSB

Overview

The dataset shown on Resource Watch shows global precipitation rates for 2006 to 2019. It utilizes the Standard Precipitation Index (SPI) as a form of measurement, which shows how precipitation during a time period varies to historical averages for the region. SPI is based on how many standard deviations the precipitation value is away from the mean value. The dataset was created using the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) model, which analyzes average monthly historical precipitation rates, satellite imagery, and local weather stations to create new data points that are released every 5 days. The final dataset is presented at a spatial resolution of 0.05 degrees (around 5 kilometers at the equator).

This dataset was created in collaboration between the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center and University of California, Santa Barbara Climate Hazards Group. It was created to help understand how precipitation varies in space and time to assist in predicting droughts. Additionally, it helps put seasonal drought conditions in perspective with historical conditions to provide governments with an understanding of their severity. An early warning system and understanding of seasonal variation are integral for governments creating water use and sustainability policies.

Methodology

The final Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset was created in three parts. First researchers created the Climate Hazards Precipitation Climatology (CHPClim) dataset for historical monthly precipitation from 1980-2009. The CHPClim dataset was formed with a combination of satellite observations, data from weather stations, and rainfall predictors, like elevation, latitude, and longitude. Second, to create near-present rainfall data researchers reviewed satellite imagery to determine how long cold cloud tops (<235° K) were present in an area. Cold cloud tops are representative of rain. This information was used to estimate rainfall for the region in millimeters over a 5 day period and mapped using a grid system. The historical averages from CHPClim were compared with near-present rainfall estimates to determine how the rainfall compares to historical rates and help produce an unbiased estimate. Lastly, observations were taken from the five closest weather stations for each 0.05 degree grid cell. Values from each weather station were weighted based on their proximity to the grid cell. The results were presented using the Standard Precipitation Index (SPI).

SPI is a way to compare recent precipitation to historical averages for a region. It is based on the number of standard deviations rainfall is away from the historical values. In this case, standard deviation is used to express how far recent precipitation data is from historical mean precipitation rates compared to all of the prior rainfall data for the area. For example, if a region had fairly constant precipitation rates it can have a higher standard deviation with a smaller difference in precipitation compared to a region with a lot of variability. In the final dataset the number of standard deviations (SD) away from historical mean precipitation rates were given descriptive titles as follows:

  • SPI ≤ -2 SD = Extremely Dry;
  • -2 SD < SPI ≤ -1.5 SD = Severely Dry;
  • -1.5 SD < SPI ≤ -1 SD = Moderately Dry;
  • -1 SD < SPI ≤ 1 SD = Near Normal;
  • 1 SD < SPI ≤ 1.5 SD = Moderately Wet;
  • 1.5 SD < SPI ≤ 2 SD = Severely Wet,
  • SPI ≥ 2 SD = Extremely Wet.

The dataset was created by Climate Hazards Group at the University of California, Santa Barbara. Satellite imagery used to determine precipitation and cloud cover are thermal infrared (IR) satellite observations from two NOAA sources, the Climate Prediction Center (CPC) IR and the National Climatic Data Center (NCDC) B1 IR, the Tropical Rainfall Measuring Mission (TRMM) 3B42 product from NASA, and atmospheric model rainfall fields from the NOAA Climate Forecast System, version 2 (CFSv2). Weather station data was collected from the Agromet Group of the Food and Agriculture Organization of the United Nations (FAO) and the Global Historical Climate Network (GHCN).

The final results were mapped globally between the latitudes of 50°S–50°N and longitudes 180°E–180°W at a spatial resolution of 0.05 degrees (around 5 km).

Data shown on Resource Watch Map

  • 2006 - 2019 Standard Precipitation Index (SPI): A comparison of yearly rainfall to historical averages for the region using the Standardized Precipitation Index.

Additional data for every year between 1980 to present are available from the data provider. Please click on the “Learn more” button to find this data on the source website.

See the documentation on how Resource Watch retrieved and pre-processed the data on Github.

Disclaimer

Excerpts of this description page were taken from the source metadata. Resource Watch shows only a subset of the dataset. For access to the full dataset and additional information, click on the “Learn more” button.

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Formal name

Standard Precipitation Index (SPI)

Cautions

  • There are data gaps in the 1980s. These gaps were filled using current rainfall estimates projected back in time. They were corrected for bias, but creating estimates back in time is not what the database was created for and it is possible that errors exist.
  • There are inherently a number of challenges trying to integrate weather station data from so many sources. There is the risk that reports have been duplicated or there are incomplete observations. Both issues were addressed, but still represent possible sources of bias in the final dataset.

Suggested citation

Funk, C.C., P.J. Peterson, M.F. Landsfeld, D.H. Pedreros, J.P. Verdin, J.D. Rowland, B.E. Romero, G.J. Husak, J.C. Michaelsen, and A.P. Verdin. 2014. "A Quasi-global Precipitation Time Series for Drought Monitoring." U.S. Geological Survey Data Series 832. 4 pp. http://dx.doi.org/10.3133/ds832. ftp://ftp.chg.ucsb.edu/pub/org/chg/products/CHIRPS-2.0. Accessed through Resource Watch, (date). www.resourcewatch.org.

Sources

U.S. Geological Survey (USGS) Earth Resources Observation and Science Center and University of California, Santa Barbara Climate Hazards Group

Geographic coverage

Global

Spatial resolution

0.05 degrees (around 5 km at the equator)

Date of content

1980-2019

Frequency of updates

5 days

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