iRain UCI Downloads & RevenueiRain UCI · App Store
iRain UCI is #195 in Weather Free in India and charting in 1 of 30 countries we track.
Updated 8 Nov 2026 · rankings refresh through the day
DeveloperUniversity of California, Irvine
CategoryWeather
PriceFree
Released
Rating★ 0.0 (0)
Version2.3.6
Age rating4+
First seen by us
8 Nov 2026
8 Nov 2026Installs (est.)1k+ worldwide
Full timeline · 2 events in the last 12 months
- 🆕iRain UCI first spotted by AppInsider#96 in Top Free · Weather · India
- 🆕iRain UCI released on the App Store
Collected from the stores by AppInsider since we first spotted the app. Dates are when we saw each change.
Rating
0.0 ★
0 ratings
Measured
Best rank now
#195
Weather FreeMeasured
iRain UCI on the App Store
App details
App ID
982858283
Publisher
University of California, Irvine
On the store since
Apr 2015
11 years on the store
Minimum OS
iOS 13.0+
iPhone 6s and later
Devices
iPhone · iPad
Universal purchase
Content rating
4+
Languages
1
EN
Size
48 MB
Also listed in
Education
iRain UCI downloads
Worldwide, per day, and installs to date. The band is open; the exact number is free with an account.
How this is calculated
Apple publishes no download counts. App Store downloads come from chart positions and daily rating growth per country, calibrated on Google Play install counts; lifetime installs from ratings worldwide × installs per rating. Confidence is the chance the real number is within half to double ours.
Where iRain UCI's downloads come from Modelled
Last 30 days: organic search, browsing the store, features and paid ads.
Where it ranks now
Every top chart it appears in across 30 countries.
| Country | Charts | Rank |
|---|---|---|
| India | WeatherFree | #195 |
Apps near iRain UCI on the chart
Weather Free, around #195.
Ratings
App Store, worldwide.
Rating
0.0 ★
0 ratings
Measured
Ratings
0
Measured
About iRain UCI
Welcome to the iRain app. The app is licensed to the Center for Hydrometeorology & Remote Sensing (CHRS) at the University of California Irvine (UCI).
iRain provides access to real-time global high-resolution (~4km) satellite precipitation products from the PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using the Artificial Neural Networks - Cloud Classification System), which has been developed by the research team at CHRS. The building block of iRain rests on the satellite precipitation estimates generated by the PERSIANN algorithm which has been under development for over two decades. More detailed information about PERSIANN-CCS can be found at http://chrs.web.uci.edu.
iRain allows users to visualize real-time global satellite precipitation observations and track extreme precipitation events globally. Users can also use the crowdsourcing functionality of the app to report their local rainfall information to supplement our data.
The web version of iRain is hosted at:
http://irain.eng.uci.edu.
Acknowledgments:
The iRain system is the product developed by Dr. Phu Nguyen, Assistant Adjunct Professor in the Center for Hydrometeorology and Remote Sensing (CHRS) at UCI and his team, which includes a dedicated group of mostly undergraduate students, including Phat Huynh, Thanh N. Palacios, Hoang Tran, and April Huie.
The iRain mobile app was developed by David Furman.
Financial support of the following agencies is acknowledged:
- NASA funding resulted in the development of the PERSIANN algorithm and several of its variations.
- ICIWaRM of the US Army Corps of Engineers has provided a major part of support for Mr. Dan Braithwaite, Programmer Analyst III at CHRS.
- The US Army Research Office’s support has resulted in additional refinement of the PERSIANN algorithm
- Cooperation with UNESCO’s G-WADI program has been critical to the dissemination of data worldwide.
- NOAA funding through the Climate Data Record (CDR) program resulted in the PERSIANN-CDR dataset.
The following members of CHRS (former and current) are greatly acknowledged.
Kuolin Hsu, Xiaogang Gao, Bisher Imam, Dan Braithwaite, Hamed Ashouri, Andrea Thorstensen, Scott Sellars, Ali Behrangi.
Special thanks to Hien To, Ph.D. student at USC, for his contribution on the crowdsourcing technologies.
Soroosh Sorooshian, Ph.D. NAE
CHRS Director
iRain provides access to real-time global high-resolution (~4km) satellite precipitation products from the PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using the Artificial Neural Networks - Cloud Classification System), which has been developed by the research team at CHRS. The building block of iRain rests on the satellite precipitation estimates generated by the PERSIANN algorithm which has been under development for over two decades. More detailed information about PERSIANN-CCS can be found at http://chrs.web.uci.edu.
iRain allows users to visualize real-time global satellite precipitation observations and track extreme precipitation events globally. Users can also use the crowdsourcing functionality of the app to report their local rainfall information to supplement our data.
The web version of iRain is hosted at:
http://irain.eng.uci.edu.
Acknowledgments:
The iRain system is the product developed by Dr. Phu Nguyen, Assistant Adjunct Professor in the Center for Hydrometeorology and Remote Sensing (CHRS) at UCI and his team, which includes a dedicated group of mostly undergraduate students, including Phat Huynh, Thanh N. Palacios, Hoang Tran, and April Huie.
The iRain mobile app was developed by David Furman.
Financial support of the following agencies is acknowledged:
- NASA funding resulted in the development of the PERSIANN algorithm and several of its variations.
- ICIWaRM of the US Army Corps of Engineers has provided a major part of support for Mr. Dan Braithwaite, Programmer Analyst III at CHRS.
- The US Army Research Office’s support has resulted in additional refinement of the PERSIANN algorithm
- Cooperation with UNESCO’s G-WADI program has been critical to the dissemination of data worldwide.
- NOAA funding through the Climate Data Record (CDR) program resulted in the PERSIANN-CDR dataset.
The following members of CHRS (former and current) are greatly acknowledged.
Kuolin Hsu, Xiaogang Gao, Bisher Imam, Dan Braithwaite, Hamed Ashouri, Andrea Thorstensen, Scott Sellars, Ali Behrangi.
Special thanks to Hien To, Ph.D. student at USC, for his contribution on the crowdsourcing technologies.
Soroosh Sorooshian, Ph.D. NAE
CHRS Director
Latest updates
2.3.6
Bug fixes & stability improvements