Climate & Weather Forecasting

Forecast Modeling:

AzRISE is working with two partnering efforts for solar forecasting:


Mike Leuthold


Eric Betterton

Solar Forecasting by the Department of Atmospheric Sciences (Eric Betterton and Mike Leuthold) in partnership with TEP. Forecasting of wind and cloud motion at specific TEP solar fields, using numerical weather prediction (NWP) models and analysis of satellite images.

Funding TEP


Joe Simmons


B.G. Potter

Watt-Sun: A multi-scale, multi-model, machine-learning solar forecasting technology: AzRISE is part of a private-public collaboration led by IBM with Argonne National Laboratory, National Renewable Energy Laboratory, Northrup Grumman, Northeastern University, and National Oceanic and Atmospheric Administration. Watt-Sun collaborates with TEP, Green Mountain Power, Juwi Solar Inc., Petra Solar, CALISO, and NEISO to develop a national program to predict solar power generation wire with high temporal and spatial resolution, based on advanced NOAA models and AzRISE ground-based measurement.

Funding DOE

Sensor-based Forecasting:


Alex Cronin


Vincent Lonij

Research focused on developing methods to forecast the arrival of clouds at specific locations. Techniques include interconnecting geographically dispersed photovoltaic (PV) systems, using dispatchable spinning reserves, energy storage, and smart grids. Sensor-based forecasting uses highly dispersed sensors surrounding each solar field. These sensors can provide a measure of cloud velocity and direction.

Funding UA and TEP


Team Members:

4a. Forecast modeling:
Mike Leuthold
Eric Betterton
4b. Watt-Sun Project:
Joe Simmons
BG Potter
Alex Cronin
Russell Beal
Christian Bokrand (FGCU)
IBM team
4c. Sensor-based forecasting:
Alex Cronin
Vincent Lonij
Adria Brooks

Funding from: TEP

Funding from: DOE, State of Florida

Funding from: TEP and DOE