Regional and Mesoscale Meteorology Branch

Introduction:

Synthetic imagery analysis in forecasting orographic cirrus (lee wave clouds) has advantages:

• Orographic cirrus is more readily identified compared to looking at model output fields, such as relative humidity over a layer.
• Increased temporal resolution – hourly rather than model output times (NAM – 3 hourly ; GFS – 6 hourly)
• Best approach is to blend synthetic imagery (WRF-ARW, CRAS, etc.) with model output fields from multiple operational models. This method combines a visual way of identifying orographic cirrus with an ensemble approach of looking at multiple models.

This is an experimental GOES-R Proving Ground Product designed to foster GOES-R readiness.

Training Session Options:

NOAA/NWS students – to begin the training, use the web-based video, YouTube video, or audio playback options below (if present for this session). Certificates of completion for NOAA/NWS employees can be obtained by accessing the session via the Commerce Learn Center

1. Audio playback (recommended for low-bandwidth users) – This is an audio playback version in the form of a downloadable VISITview and can be taken at anytime. Create a directory to download the audio playback file (195 MB) from the following link: Download Exe After extracting the files into that directory click on either the visitplay.bat or visitauto.bat file to start the lesson. If both files are present, use visitauto.bat
2. YouTube video:

References / Additional Links:

• Talking points are available for this lesson and may be printed out to easily review the session in detail at any time.
• GOES-R product suite
• FAQ page on NSSL 4-km WRF-ARW Synthetic Imagery
• CIMSS WRF-ARW synthetic imagery
• CRAS synthetic imagery from CIMSS via AWIPS
• NSSL 4-km WRF-ARW model output
• Bikos, D., Lindsey, D.T., Otkin, J., Sieglaff, J., Grasso, L., Siewert, C., Correia Jr., J., Coniglio, M., Rabin, R., Kain, J.S., and S. Dembek, 2012: Synthetic Satellite Imagery For Real-Time High Resolution Model Evaluation. Wea. Forecasting,27,784-795. http://dx.doi.org/10.1175/WAF-D-11-00130.1
• Grasso, L.D., M. Sengupta, J.F., Dostalek, R.L. Brummer, and M. DeMaria, 2008: Synthetic Satellite Imagery for Current and Future Environmental Satellites. International Journal of Remote Sensing. 29:15, 4373.
• Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang and J. G. Powers, 2005: A Description of the Advanced Research WRF Version 2, NCAR technical note.