Regional and Mesoscale Meteorology Branch

Synthetic Imagery in Forecasting Orographic Cirrus

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: http://rammb.cira.colostate.edu/training/visit/training_sessions/synthetic_imagery_in_forecasting_orographic_cirrus/synthetic_imagery_in_forecasting_orographic_cirrus_audio.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.