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The sea surface temperature (SST) beneath a tropical cyclone (TC) is of great importance to its dynamics; therefore, understanding and accurately estimating the magnitude of SST cooling is of vital importance. Existing studies have explored important influences on SST of TC translation speed, maximum surface winds, ocean thermal condition, and ocean stratification. But the influence of the TC wind radii (or collectively called the TC size) on SST has been largely overlooked. In this study, we assess the influence of wind radii uncertainty on SST cooling by a total of 15,983 numerical simulations for the western North Pacific during the 2014–2018 seasons. Results show a 6%–20% SST cooling error induced using wind radii from the Joint Typhoon Warning Center official forecast and a 35%–40% SST cooling error using wind radii from the operational runs of the Hurricane Weather Research and Forecasting (HWRF) model. Our results indicate that SST cooling is most sensitive to the radius of 64 kt winds (R64) due to its effects on the integrated kinetic energy of the TC and subsequent mixing of the ocean surface layer. It is also found that the correlation between SST cooling induced by the TC and its size is 0.49, which is the highest among all the parameters tested. This suggests that it is extremely important to get TC size correct in order to predict the SST cooling response, which then impacts TC evolution in numerical weather prediction models. Key Points: TC wind radii analyses and forecasts from operational Joint Typhoon Warning Center forecast and Hurricane Weather Research and Forecasting model contain various degrees of uncertaintyUncertainty of wind radii can lead to a significant error in sea surface temperature (SST) cooling estimationThe correlation between SST cooling and tropical cyclone size is highest among all the parameters tested in this study [ABSTRACT FROM AUTHOR]

C‐band high‐resolution radar (synthetic aperture radar [SAR]) is the only spaceborne instrument able to probe at very high resolution and over all ocean basins the sea surface under extreme weather conditions. When coanalyzed with Stepped Frequency Microwave Radiometer wind estimates, the radar backscatter signals acquired in major hurricanes from Sentinel‐1 and Radarsat‐2 SAR reveal high sensitivity in the cross‐polarized channel for wind speeds up to 75 m/s. The combination of the two copolarized and cross‐polarized channels can then be used to derive high‐resolution surface wind estimates. The retrieval methods and impacts of intense rainfall are discussed in the context of a Hurricane Irma (2017) case study. On 7 September 2017, Sentinel‐1 measurements intercepted Hurricane Irma when it was at category 5 intensity. When compared to Stepped Frequency Microwave Radiometer, SAR‐derived wind speeds yield bias and root‐mean‐square of about 1.5 and 5.0 m/s, respectively. The retrieved wind structure parameters for the outer core are found to be in agreement with the Best‐Track and combined satellite‐ and aircraft‐based analyses. SAR measurements uniquely describe the inner core and provide independent measurements of the maximum wind speed and the radius of maximum wind. Near the radius of maximum wind a 65‐m/s increase in wind speed in less than 10 km is detected, corresponding to an instantaneous absolute vorticity of order 210 times the Coriolis parameter. Using a parametric Holland model and the environmental surface pressure (1,011 hPa), SAR‐derived wind speeds correspond to a central surface pressure of 918 hPa (921 hPa from the Best‐Track) in Irma's eye. Key Points: No saturation of the cross‐polarized C‐band ocean backscattered signal for surface wind speeds up to 75 m/s is observedCombined copolarization and cross‐polarization are used for ocean surface wind retrieval from Sentinel‐1 and Radarsat‐2 SAR over major hurricanesIrma category 5 hurricane wind structure is described at high resolution O(1 km) and compared with other existing and independent analysis [ABSTRACT FROM AUTHOR]

Hurricane forecasting skills may be improved by utilizing increased precipitation observations available from the Global Precipitation Measurement mission ( GPM). This study adds to the Gridpoint Statistical Interpolation ( GSI) capability to assimilate satellite-retrieved hydrometeor profile data in the operational Hurricane Weather Research and Forecasting ( HWRF) system. The newly developed Hurricane Goddard Profiling ( GPROF) algorithm produces Tropical Rainfall Measuring Mission ( TRMM)/ GPM hydrometeor retrievals specifically for hurricanes. Two new observation operators are developed and implemented in GSI to assimilate Hurricane GPROF retrieved hydrometeors in HWRF. They are based on the assumption that all water vapour in excess of saturation with respect to ice or liquid is immediately condensed out. Two sets of single observation experiments that include assimilation of solid or liquid hydrometeor from Hurricane GPROF are performed. Results suggest that assimilating single retrieved solid or liquid hydrometeor information impacts the current set of control variables of GSI by adjusting the environment that includes temperature, pressure and moisture fields toward saturation with respect to ice or liquid. These results are explained in a physically consistent manner, implying satisfactory observation operators and meaningful structure of background error covariance employed by GSI. Applied to two real hurricane cases, Leslie (2012) and Gonzalo (2014), the assimilation of the Hurricane GPROF data in the innermost domain of HWRF shows a physically reasonable adjustment and an improvement of the analysis compared to observations. However, the impact of assimilating the Hurricane GPROF retrieved hydrometeors on the subsequent HWRF forecasts, measured by hurricane tracks, intensities, sizes, satellite-retrieved rain rates, and corresponding infrared images, is inconclusive. Possible causes are discussed. [ABSTRACT FROM AUTHOR]

A soft-computing cyclone intensity prediction scheme ( SCIPS) is introduced using an artificial neural network ( ANN) approach and adding ocean heat content, as an additional predictor to the normally used atmospheric parameters, to predict tropical cyclone intensity change in the western north Pacific Ocean. We used 1997-2004 data to develop and validate this scheme. The ANN-based estimations have been compared with observations and estimations using the multiple linear regression ( MLR). SCIPS performance improves upon MLR as the lead hour increases from 12 to 120 h and also for high intensifying cyclones. [ABSTRACT FROM AUTHOR]

The article reports on the study which examines the timing of lifelong maximum intensity associated with recurving tropical cyclones in the western North Pacific, North Atlantic and Southern Hemisphere. It reveals that tropical cyclones are less likely to experience peak intensity within more than or less than 12-24 hours of recurvature. It is stated that most intense tropical cyclones have a greater tendency to reach peak intensity before recurvature. It cites that the cumulative distributions of maximum intensity relative to the time of recurvature can be different for other intensity ranges.