Shooting Light: Hunt Clock vs iRain UCI Usage & Stats

First light and last light are terms that every hunter is familiar with. First light and last light are based on sunrise and sunset, and those times change from day-to-day. Shooting Light simply shows you when those times are. It doesn't show weather, and it's not part of another app that requires a yearly subscription and offers you things you don't need. It's a simple tool to show you first and last light, and optionally sunrise and sunset. * Keep track of available light Did work let out early? Catch an unexpected break from the family? Finish the chores in time for an evening hunt? Open Shooting Light to see how many hours of hunting you have left for the day. It might just be the thing that helps you decide to get out and sends you home with that next big buck. * Notifications You can easily enable notifications for the day by tapping the bell in the top left corner. This will schedule notifications for first light and last light so you can leave your phone in your pocket and let it alert you when you're able to start your hunt or when it's time to end it. * Scheduler Shooting light also comes with a tool that many of us have been waiting for. Doing time math in your head can be tricky – the scheduler lets you set a start time for your hunt and add time for things like packing up, driving, and walking to the spot. It'll do the math for you and let you know exactly when you should wake up or start getting ready. * Customize times for your area Every state and often hunting areas within your state have different regulations for when you can start or when you must end your hunt. You can customize the offset for first and last light to set it up for your specific hunt area. Keep in mind, it's your responsibility to make sure you're following your state's rules and regulations. Regardless of if you're hunting deer, elk, duck, or anything else, you can use Shooting Light to track when you should start/stop your hunt. Good luck and good huntin!

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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