RainMapper: New Smartphone App Uses Remote Sensing to Provide Rainfall Totals Across Globe
In developed countries, precipitation forecasting generally involves integrating data from weather stations, radiosondes, Doppler radar and weather satellites, not to mention numerical forecasting using supercomputers. Needless to say, such systems are not available in all parts of the world.
However, often an estimate of how much rain has fallen recently in or around the area of interest is sufficient. For this, the University of California-Irvine’s Center for Hydrometeorology and Remote Sensing (CHRS; http://chrs.web.uci.edu/
), along with UNESCO-IHP’s arid regions program G-WADI (www.gwadi.org
), have developed solutions for both personal computers and mobile-devices.
The G-WADI PERSIANN-CCS GeoServer (http://hydis.eng.uci.edu/gwadi/
) is a tool that harnesses remotely sensed information to observe, monitor and analyze extreme weather events as they occur. At its heart is the PERSIANN-CCS algorithm, or Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Cloud Classification System. PERSIANN-CCS is a real-time, global (60°N to 60°S), ~4 km-resolution, satellite-based precipitation product. It uses the open-source MapServer software from the University of Minnesota to provide user-friendly, web-based mapping and visualization of satellite precipitation data. It displays this information even in remote areas and over oceans where observations are limited.
For scientists, engineers, or even backpackers out in the field with access to a mobile network, there is a version of G-WADI PERSIANN-CCS for iOS- and Android-based devices called RainMapper. RainMapper can be freely downloaded from the App Store
and Google Play
The app experiences only about a one-hour delay (or “latency”) from the actual satellite observation to its availability on RainMapper, and has an option of three-hour to 72-hour totals. Applications for its use range from flash-flood warning to when to plant rain-fed crops. It may be particularly useful for transboundary basins where data-sharing is limited.