Possibilities for future improvements and studies:

• EOS investigators will soon be able to combine altimetry, scatterometry, ocean color, and SST measurements into ocean forecasting models. Global assimilation will provide operation products that are crucial for prediction of interannual oceanic fluctuations and will lead to a better understanding interaction of ocean/atmosphere.  This will lead to better predictions for El Nino. (EOS p. 28)
• Later this year the JASON-1 satellite will be launched as a joint mission between France and US and is a follow on to the highly successful TOPEX/Poseidon mission.  It will further investigate the interaction between the ocean and atmosphere and El Nino conditions. (http://topex-www.jpl.nasa.gov/jason1/)
• The GFDL model could be used to study the effect of remotely sensed data on hurricane intensity forecasts.

In closing, remote sensing has drastically improved hurricane understanding and forecasting.  As we have discussed, looking at both the oceanic conditions and atmospheric conditions such as sea surface temperature, hurricane heat potential, El Nino/La Nina, cloud top temperature, rainfall intensity, environmental steering flow, upper level divergence, and wind shear.  Satellite data can provide an abundant amount of information to determine if a tropical cyclone will develop and intensify.  Through further advancements in remote sensing and with new future satellite technology, improvements look promising.  In the future when more remotely sensed data is assimilated into hurricane forecasting models, more accurate forecasts will be produced, and hopefully fatalities and costs will continue to decline.
 
 
 

Download a hurricane model dependent on SST and other factors from:
MIT's Kerry Emmanuel Hurricane Model