Using Satellite Derived Motion Winds to Improve Monitoring and Forecasting of Tropical Cyclones

Many tropical cyclone (TC) forecasting and monitoring activities rely heavily on global model output to assess environmental conditions and assess storm structure. In real-time, global model-based analyses are not available, and forecasters and applications, by necessity, make use of 6-hourly forecasts in place of the initial analyses and valid at that time. Using the 6-hour forecast as initial conditions provides a means to use the global model information to provide forecasts and environmental assessments. Despite the success of this approach, errors in global model forecasts, albeit a rare occurrence, will at times produce errant assessments of the environmental conditions, and these errors can produce suboptimal forecasts and situational assessments. And, this approach, updated every 6-hourly model cycle, lacks the fidelity to monitor short-term variability. In this work, we examine the following question. Can satellite-based derived motion winds provide improved initial conditions for statistical-dynamic models and/or provide higher-frequency environmental monitoring that increases situational awareness of fairly rapid environmental changes?

With the advent of the most recent generation of geostationary weather satellites, derived motion winds (DMVs) are being produced at both higher spatial and temporal scales. In the horizontal DMVs are created in sufficient numbers between 100 hPa and 350 hPa to create upper-layer analyses, in most cases, out to at least 1000 km. The temporal coverage of the DMVs used here ranges from hourly to 10 minutes. DMV from the USA’s GOES and Japan’s Himawari satellites are provided by the NOAA Center for Satellite Applications and Research and have a latency of around 2-h. The talk will discuss the creation of hourly analyses of winds in this upper-level and the construction of four basic upper-layer metrics, the results of using DMV-based metrics for initializing at least one statistical-dynamical model, and how DMVs can be used for additional derived metrics, including the monitoring of the instantaneous radius of maximum winds.

Disclaimer: The scientific results and conclusions, as well as any views or opinions expressed herein, are those of the author(s) and do not necessarily reflect those of NOAA or the Department of Commerce.