Northwestern Pacific typhoon intensity controlled by changes in ocean temperatures.

Dominant climatic factors controlling the lifetime peak intensity of typhoons are determined from six decades of Pacific typhoon data. We find that upper ocean temperatures in the low-latitude northwestern Pacific (LLNWP) and sea surface temperatures in the central equatorial Pacific control the seasonal average lifetime peak intensity by setting the rate and duration of typhoon intensification, respectively. An anomalously strong LLNWP upper ocean warming has favored increased intensification rates and led to unprecedentedly high average typhoon intensity during the recent global warming hiatus period, despite a reduction in intensification duration tied to the central equatorial Pacific surface cooling. Continued LLNWP upper ocean warming as predicted under a moderate [that is, Representative Concentration Pathway (RCP) 4.5] climate change scenario is expected to further increase the average typhoon intensity by an additional 14% by 2100

ON THE RAPID INTENSIFICATION OF HURRICANE WILMA (2005)

Previous studies have focused mostly on the roles of environmental factors in the rapid intensification (RI) of tropical cyclones (TCs) due to the lack of high-resolution data in the inner-core regions. In this study, we examine the RI issue by analyzing 72-h cloud-permitting model predictions of Hurricane Wilma (2005) with the Weather and Research Forecast (WRF) model at the finest grid sizes of 1-2 km. The 72-h predictions cover Hurricane Wilma¡¦s initial 18-h spin up, an 18-h RI and the subsequent 36-h weakening stage. The model prediction uses the initial and lateral boundary conditions, including a bogus vortex, that are identical to the Geophysical Fluid Dynamics Laboratory's then-operational data, except for the time-independent sea surface temperature (SST) field. The model predicts an RI rate of more than 4 hPa h-1 for an 18-h period, with the minimum central pressure of less than 889 hPa. It was found that an upper-level warm core forms in the same layer as the upper outflow, in coincidence with the onset of RI. The warm core results from the subsidence of stratospheric air associated with the detrainment of convective bursts (CBs). The upper divergent outflow appears to play an important role in protecting the warm core from ventilation by environmental flows. Results also show the development of more CBs preceding RI, but most subsidence warming radiates away by internal gravity waves and storm-relative flows. In contrast, many fewer CBs occur during RI, but more subsidence warming contributes to the balanced upper-level cyclonic circulation in the warm core (as intense as 20,,aC) region. Furthermore, considerable CB activity can still take place in the outer eyewall as the storm weakens during its eyewall replacement. Sensitivity simulations reveal that the upper-level warm core and CB activity depend critically on warm SST. We conclude that significant CB activity in the inner-core regions is an important ingredient in generating an upper-level warm core that is hydrostatically more efficient to the RI of TCs, given all the other favorable environmental conditions. The formation of a divergent upper-level outflow that prevents the warm core from ventilation is examined through asymmetric contraction processes associated with new rainbands forming inside the eyewall. The relative vorticity, generated in the downshear region and then advected cyclonically downstream, can induce convergence in the boundary layer. With the aid of high moisture content, the convergence can trigger deep convection and contribute to the formation of the new rainbands. Finally, the importance of a small eye size is demonstrated using three widely accepted approximations: angular momentum conservation, solid body rotation and gradient wind balance. Results show that the storm intensifies much faster for a given contraction speed if the eye size is small

Suggested hurricane operational scenario for GOES I-M

Improvements in tropical cyclone forecasts require optimum use of remote sensing capabilities, because conventional data sources cannot provide the necessary spatial and temporal data density over tropical and subtropical oceanic regions. In 1989, the first of a series of geostationary weather satellites, GOES 1-M, will be launched with the capability for simultaneous imaging and sounding. Careful scheduling of the GOES 1-M will enable measurements of both the wind and mass fields over the entire tropical cyclone activity area. The document briefly describes the GOES 1-M imager and sounder, surveys the data needs for hurricane forecasting, discusses how geostationary satellite observations help to meet them, and proposes a GOES 1-M schedule of observations and hurricane relevant derived products

Global view of the upper level outflow patterns associated with tropical cyclone intensity changes during FGGE

October, 1985.Includes bibliographical references.Sponsored by NASA NAG 5-299

Dynamics and Predictability of Hurricane Humberto (2007) Revealed from Ensemble Analysis and Forecasting

This study uses short-range ensemble forecasts initialized with an Ensemble-Kalman filter to study the dynamics and predictability of Hurricane Humberto, which made landfall along the Texas coast in 2007. Statistical correlation is used to determine why some ensemble members strengthen the incipient low into a hurricane and others do not. It is found that deep moisture and high convective available potential energy (CAPE) are two of the most important factors for the genesis of Humberto. Variations in CAPE result in as much difference (ensemble spread) in the final hurricane intensity as do variations in deep moisture. CAPE differences here are related to the interaction between the cyclone and a nearby front, which tends to stabilize the lower troposphere in the vicinity of the circulation center. This subsequently weakens convection and slows genesis. Eventually the wind-induced surface heat exchange mechanism and differences in landfall time result in even larger ensemble spread.

CNN Profiler on Polar Coordinate Images for Tropical Cyclone Structure Analysis

Convolutional neural networks (CNN) have achieved great success in analyzing tropical cyclones (TC) with satellite images in several tasks, such as TC intensity estimation. In contrast, TC structure, which is conventionally described by a few parameters estimated subjectively by meteorology specialists, is still hard to be profiled objectively and routinely. This study applies CNN on satellite images to create the entire TC structure profiles, covering all the structural parameters. By utilizing the meteorological domain knowledge to construct TC wind profiles based on historical structure parameters, we provide valuable labels for training in our newly released benchmark dataset. With such a dataset, we hope to attract more attention to this crucial issue among data scientists. Meanwhile, a baseline is established with a specialized convolutional model operating on polar-coordinates. We discovered that it is more feasible and physically reasonable to extract structural information on polar-coordinates, instead of Cartesian coordinates, according to a TC's rotational and spiral natures. Experimental results on the released benchmark dataset verified the robustness of the proposed model and demonstrated the potential for applying deep learning techniques for this barely developed yet important topic.Comment: Submitted to AAAI202