Abstract:
Infrared sensors onboard geostationary satellites provide detailed
information about cloud top properties and the water vapor distribution
with high spatial and temporal resolutions that make them very useful
as a numerical weather prediction model validation tool. To
promote the routine use of these observations for this purpose, we
developed a near real-time GOES-based verification system for the High
Resolution Rapid Refresh (HRRR) model that provides operational
forecasters objective tools to determine the accuracy of current and
prior HRRR model forecasts when they are creating or updating
short-range forecasts. This capability has become increasingly
more important in recent years due to the implementation of rapidly
updating numerical models with many overlapping forecast cycles.
Besides serving as a useful forecaster model evaluation tool, long-term
statistics accumulated using this system also provide an excellent
means to assess the accuracy of the cloud and water vapor fields in the
HRRR model forecasts.
For this presentation, we will describe the capabilities of the near
real-time verification system and present results from several ongoing
model validation projects. Synthetic GOES 10.7 m infrared
brightness temperatures are generated for each HRRR forecast cycle
using the Community Radiative Transfer Model (CRTM) and are then
compared to real GOES observations using various statistical methods to
assess the model accuracy at each model forecast time. These
methods include dimensioned metrics such as root mean square error and
bias, neighborhood-based metrics such as the Fractions Skill Score, and
object-based verification tools using the Method for Object-Based
Diagnostic Evaluation (MODE) system. The model accuracy was
assessed for two one-month periods during August 2015 and January
2016. Overall, the results show that the simulated brightness
temperatures are often too warm during the first hour of the forecast,
indicating that the HRRR model initialization is deficient in
upper-level clouds. This warm bias, however, is quickly replaced
by a large cold bias due to the rapid generation of upper level clouds,
with the negative bias often lasting for many hours into the forecast
before the excessive cloud cover dissipates. Detailed analysis of
the MODE results showed that the HRRR initialization contains too many
small cloud objects, especially during August; however, the number of
cloud objects becomes too low by forecast hour 2. This behavior
is consistent with the changes in the brightness temperature bias and
indicates that the simulated cloud objects become too large after a few
hours..