The use of remotely sensed data and innovative modeling to improve hurricane prediction

Robert Atlas; O. Reale; B.-W. Shen; S.-J. Lin

doi:10.1117/12.673221

4 May 2006 The use of remotely sensed data and innovative modeling to improve hurricane prediction

Robert Atlas, O. Reale, B.-W. Shen, S.-J. Lin

Proceedings Volume 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII; 62330U (2006) https://doi.org/10.1117/12.673221
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

The assimilation of remotely sensed data from aircraft and satellites has contributed substantially to the current accuracy of operational hurricane forecasting. In the 1960's, satellite imagery revolutionized hurricane detection and forecasting. Since that time, quantitative remotely sensed data (eg. atmospheric motion winds, passive infrared and microwave radiances or retrievals of temperature, moisture, surface wind and rain rate, active microwave measurements of surface wind and rain rate) and significant advances in modeling and data assimilation have increased the accuracy of hurricane track forecasts very significantly. The development of advanced next-generation models in combination new types of remotely sensed observations (eg. space-based lidar winds) should yield significant further improvements in the timing and location of landfall and in the predicted intensification of hurricanes.

Citation Download Citation

Robert Atlas, O. Reale, B.-W. Shen, and S.-J. Lin "The use of remotely sensed data and innovative modeling to improve hurricane prediction", Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 62330U (4 May 2006); https://doi.org/10.1117/12.673221

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available