Ensemble
data assimilation and model validation studies using cloud and water
vapor sensitive infrared brightness temperatures
Jason
A. Otkin
University of Wisconsin-Madison
Noon March 10 in Room 2155
Abstract:
Infrared brightness temperatures provide valuable information about
atmospheric water vapor, cloud cover, and surface properties, and thus
are very useful for a wide variety of research purposes. In this
presentation, results will be shown for several prior and ongoing
studies that have used satellite observations to examine the accuracy
of high-resolution numerical model simulations and to improve the
analysis and forecast accuracy of regional forecast models through use
of ensemble data assimilation systems.
The impact of assimilating both clear and cloud-affected infrared
brightness temperatures was assessed using regional-scale Observing
System Simulation Experiments (OSSEs) with simulated GOES-R Advanced
Baseline Imager (ABI) observations and through real data experiments
using observations from the SEVIRI sensor onboard the Meteosat Second
Generation satellite. Overall, the OSSE results showed that the
assimilation of cloud-affected brightness temperatures had a large
positive impact on the cloud and moisture analyses, with lesser or no
impact on the wind and temperature analyses. Short range precipitation
forecasts for a high-impact weather event were greatly improved when
cloudy observations were assimilated. For the real data experiments,
observations from the SEVIRI 6.2 μm band that is sensitive to clouds
and water vapor in the upper troposphere were assimilated. A bias
correction scheme based on the observed and simulated cloud top
pressure was also developed. The results showed that the biases were
greatly reduced when the SEVIRI infrared observations were assimilated
using the cloud-dependent bias correction scheme. Similar to the OSSE
results, the largest improvements at the end of the assimilation period
were observed in the relative humidity field, with lesser impact on the
temperature and wind fields.
Preliminary results will also be shown from two new model validation
studies that will be used to examine the accuracy of the simulated
cloud and water vapor fields in the operational High Resolution Rapid
Refresh (HRRR) and Hurricane WRF (HWRF) models. A real-time GOES-based
verification system is being developed for the HRRR model that will be
used to provide forecasters objective guidance concerning which of the
many overlapping forecasts at a given time is the most accurate based
on quantitative comparison of the observed and simulated satellite
imagery. Similar retrospective studies are being performed for
different configurations of the HWRF model to assess the accuracy of
the cloud and moisture fields.