Description:

Satellite observations remain a critical tool for monitoring and forecasting Tropical Cyclones (TC).  These observations are beneficial to the operational community as they provide information about the near storm environment and storm parameters, such as exact location of the center and the maximum wind, intensity and size during the entire lifetime of each storm. This information is extremely valuable in remote locations, when in-situ (e.g, dropsondes, drones or airborne sensors) measurements are not available.  In recent years important advancements have been made in satellite monitoring of TC, among them: frequent acquisitions from high resolution Synthetic Aperture Radars (SAR) over all ocean basins, microwave Low Earth Orbiting (LEO) sensors with low frequency channels capable of measurements in the presence of rainfall, low-cost small satellites with high frequency channels and in some cases frequent revisit rates for water vapor, temperature and cloud monitoring, and rapid scanning with Geostationary sensors.  These satellite-based advancements have resulted in the development of new methods for TC analysis and for extracting useful information that can be easily integrated into the TC forecast process. This session aims at summarizing recent TC analysis methods, such as near-real-time determination of storm parameters directly from satellite observations or inferred using new AI/ML methods, or with proxy methods, such as advanced Dvorak techniques, multi-satellite consensus data, and statistical methods. These new methods include significant improvements in the quantification of not only variables such as intensity but the entire TC wind field.  An additional challenge in TC forecasting is the ability to predict Rapid Intensification (RI) and storm structure changes that impact the TC forecast.  New satellite-based methods have emerged to address these specific challenges.  This session will summarize the latest advancements in satellite-based tools for TC monitoring and provide a look at potential future innovations in both sensors and techniques.