Models to compute global horizontal irradiance (GHI) and direct normal irradiance (DNI) have been in
development over the last three decades. These models can be classified as empirical or physical based on
the approach. Empirical models relate ground-based observations with satellite measurements and use these
relations to compute surface radiation. Physical models consider the physics behind the radiation received
at the satellite and create retrievals to estimate surface radiation. While empirical methods have been
traditionally used for computing surface radiation for the solar energy industry, the advent of faster
computing has made operational physical models viable. The Global Solar Insolation Project (GSIP) is a
physical model that computes DNI and GHI using the visible and infrared channel measurements from a
weather satellite. GSIP uses a two-stage scheme that first retrieves cloud properties and uses those
properties in a radiative transfer model to calculate GHI and DNI. Developed for polar orbiting satellites,
GSIP has been adapted to NOAA's Geostationary Operation Environmental Satellite series and can run
operationally at high spatial resolutions. This method holds the possibility of creating high quality datasets
of GHI and DNI for use by the solar energy industry. We present an outline of the methodology and results
from running the model as well as a validation study using ground-based instruments.
Realistic simulated satellite imagery for GOES-R ABI using state of the art mesoscale modeling and accurate radiative
transfer is being produced at the Cooperative Institute for Research in the Atmosphere (CIRA) and used in developing
and testing new products.
Products which have been produced in support of the GOES-R Algorithm Working Group (AWG) include 6-hour
imagery at 5 minute intervals for 4 GOES-R ABI bands (2.25 μm, 3.9 μm, 10.35 μm, and 11.2 μm) that include fire
hotspots. The imagery was initially produced at 400 m resolution and a point-spread function applied on the data to
create ABI resolution imagery. Also created was corresponding imagery for current GOES at 2 bands (3.9 μm and 10.7
μm). These fire hotspots were simulated for 4 different cases over Kansas, Central America, and California.
Additionally, high quality imagery for 10 GOES-R ABI bands (3.9 μm and higher) were produced for 4 extreme weather
events. These simulations include a lake effect snow case, a severe weather case, Hurricane Wilma, and Hurricane Lili.
All simulations for extreme weather events were also performed for current GOES and compared with available imagery
for quality control purposes.
Future work focuses on the creation of additional fire proxy datasets including true-color imagery for 3 ABI visible
bands. This project also supports the GOES-R AWG Aviation Team in their effort to test their convective initiation
algorithm by providing simulated ABI datasets for bands between 2.25 μm and 13.3 μm for a severe weather case. In
addition, simulated ABI was generated from MSG infrared (IR) window band imagery and corresponding simulated ABI
for the 7 tropical cyclones from 2006-2008 that became hurricanes in the east Atlantic for evaluation of the GOES-R
ADT algorithm conducted by the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies
In preparation for future satellites, we have developed a method to simulate satellite observations using a cloud-scale numerical model and radiative transfer models. In short, a numerical cloud model was used to simulate mesoscale weather events, including severe storms and tropical cyclones. A second model was used to calculate the brightness temperatures of the clear and cloud sky scenes from the model simulations. This procedure allows for advanced product development for severe weather (precipitation estimation, updraft diagnosis products) and tropical cyclones (intensity estimation). The development of products in advance of the satellite launch extends the useful life of the satellite system. Examples of this method for
a severe storm case will be presented.