CIRA » Home » Research » Project » JCSDA Utility of GOES-R Instruments for Hurricane Data Assimilation and Forecasting
Project Title: Utility of GOES-R Instruments for Hurricane Data Assimilation and Forecasting. This is a collaborative project with CIMSS/UW.
Principal Investigators: Dr Milija Zupanski (CSU) and Dr Jun Li (CIMSS/UW)
Co-PIs, Collaborators: Dr Louis Grasso (CSU), Dr John Knaff (NESDIS/STARR-RAMMB)
Postdoctoral Scientists: Man Zhang (CSU), Karina Apodaca (CSU)
Sponsor: NOAA
Project duration: 1 June 2010 - 31 May 2013
Funded amount (CSU): $449,698
Goals and objectives:
1. Using NCEP operational HWRF modeling system to demonstrate the utility of the GOES-R ABI imager and the lightning mapper using proxy data from SEVIRI and ground based lightning networks.
2. Conducting development of full spatial resolution advanced sounder product and error characterization at CIMSS using AIRS and IASI, also simulate ABI legacy type profiles from advanced sounder measurements,
3. Incorporating the CIMSS full spatial resolution sounding product into the MLEF-HWRF system and conduct control data assimilation experiments,
4. Perform experiments on using full spatial resolution advanced sounding product and simulated ABI legacy type profiles in order to demonstrate unique advantage of advanced sounding product in data assimilation for tropical cyclone forecast,
5. Use METEOSAT SEVIRI instrument and lightning ground based network for lightning data assimilation as proxy for the GOES-R ABI and GLM instruments. Rely on the lightning forward operator being developed under the GOES-R Risk Reduction program and other programs. Conduct hurricane data assimilation and forecasting experiments using lightning data, and compare to the control experiment (without lightning data), and
6. Make final evaluation of the value-added impact of lightning data for tropical cyclones. Make recommendations for future operational use of GOES-R GLM measurements in applications to tropical cyclones.
Summary:
New instruments on the GOES-R satellite, such as the Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM), have the capability to improve assimilation and prediction of clouds and tropical storms. The combined impact of these observations, and of the advanced infrared (IR) sounder planned for the follow-up GOES missions, can be of fundamental value for hurricane assimilation and prediction, as the otherwise available observations are scarce and of insufficient resolution. Examining the utility of these observations well in advance of the launch of the satellite gives an important head start that will results in better preparedness for future operational assimilation tasks.
However, current operational data assimilation is not well suited for assimilation of water vapor and cloud properties, mostly due to unknown background error covariance structure and dynamical balance relationships. In the proposed research we will examine the utility of the GOES-R ABI and GLM, and of the advanced IR sounder, for hurricane data assimilation and prediction, using ensemble data assimilation with the NCEP operational Hurricane WRF (HWRF) modeling system.
This proposed work will also include the evaluation of the impact of METEOSAT SEVIRI data as proxies for ABI, and of ground-based lightning data as proxies for GLM. The evaluation will be done in the NCEP’s computing environment, using operational observations and the HWRF model, thus the program codes and the results of this research will have a direct path to the potential operational application at the National Centers for Environmental Prediction (NCEP).
Selected Accomplishments:
