Effective use of Himawari-8 data in Thunderstorm detection, monitoring and forecasting

Spatial Resolution

• Improved resolution of local mesoscale triggers (seabreeze fronts, local convergence lines)

• Stormtop and overshooting top Brightness Temperatures (BT) can be more accurately determined with less "pixel averaging" in the high resolution satellite data.
• The satellite data will provide a clearer picture of storm top signatures such as overshooting tops, the "warm wake" or "enhanced V" (thermal couplet) that are often associated with severe storms.

• More effective implementation of Derived Products such as the Cloud Top Cooling Product and the Automatic Overshooting Top Detection Algorithm.

Temporal Resolution

• Permits better determination of the areas where convection may develop (eg. moist low level regions)
• Better detection and monitoring of Synoptic / Mesoscale triggers to convection (dry lines / seabreeze fronts, local convergence lines). It is possible to detect these features in the 10 minute satellite data before they are apparent in the radar signal.
• Better identification and monitoring of cumulus development that may transition into Cb (ie. clumping of cumulus and development of towering cumulus). It is possible to detect these features in the 10 minute satellite data before they are apparent in the radar signal.
• Rapid infrared-based cloud top cooling rates corresponding to developing Cb can be better monitored.
  

• Easier and earlier discrimination between persisting and dissipating storms (pulse convection versus organised convection).
• Better monitoring of the movement and organisation of storms (eg. near-continuous monitoring of overshooting stormtops, splitting of supercells, organisation of storms into squall lines)
• Permits very short term forecasting of rapidly moving and potentially short lived convection (eg. monsoon squall lines).
• Better monitoring of steering flow by examining the movement of the storms in the high resolution imagery.
• Better monitoring of shear and its effect on the convective development.
• More readily able to detect rotation in Cb clouds.
• Better able to monitor storms associated with potentially intense rain rates (slow moving storms with persisting overshooting tops, train effect convection etc.)

• Permits monitoring of the evolution of secondary features such as storm outflow boundaries and convection that may be generated by this. NWP cannot predict this yet.
  

• More effective implementation of Derived Products such as the Cloud Top Cooling Product and the Automatic Overshooting Top Detection Algorithm and other Cb-alerting Algorithms.
  

Additional Channels

• Low level moisture boundaries may be monitored using the Dust RGB product.

• The Day Convection RGB product can highlight storm top ice particle size and hence areas of potentially severe convection.Easier and earlier discrimination between persisting and dissipating storms (pulse convection versus organised convection).

• Developing appropriate Derived Products utilising the extra channels and implementing these within the forecasting routine.