Summary
This first POES training module has served as an introduction to the POES characteristics, data, and products useful to operational forecasting. POES satellites provide global coverage of meteorological phenomenon utilizing multi-spectral observations in the visible, infrared, and microwave portions of the electromagnetic spectrum. This allows POES to gather information about atmospheric and surface features and processes, which is complementary and in some cases unique to that provided by GOES and other conventional observing systems. By gaining a better understanding of what meteorological POES has to offer, operational forecasters can begin to improve and expand their use of POES data and products. The major components of this module include
  • Comparison of POES and GOES characteristics and capabilities
  • Outline of the instrument configurations of NOAA and DMSP polar orbiters
  • Comparison of POES and GOES imagery with an emphasis on the strengths of POES
  • Introduction to NOAA and DMSP products useful in operational forecasting
  • Module summary
  • Milestones in the history of POES development

(A POES history section has been included as an appendix item for those interested in reviewing the major milestones in the development of meteorological POES.)

Subsequent modules will expand on individual POES data and products, their availability, and specifically how they can be integrated with model and other in situ datasets to improve the accuracy and timeliness of forecasts and warnings of severe weather events.

Module two will begin with an examination of the microwave imaging capabilities of NOAA and DMSP POES. This is followed by a review of derived products, their strengths, weaknesses, and suggestions for potential uses.

Module three will examine the traditional as well as some of the newer applications of POES visible and infrared imagery and how these impact the forecast process.

Module four will tie together concepts and products presented in modules two and three to show how POES data can be integrated with other data sets to form a more complete depiction of the meteorological environment and facilitate improvements in the forecast process.