Northeast Pacific annual accumulated cyclonic energy rank-profile

The ranking of events is a powerful way to study the complexity of rare catastrophic events as earthquakes and hurricanes. Hurricane activity can be quantified by the annual accumulated cyclone energy index (ACE), which contains the information of the maximum sustained wind speed, duration and frequency of the tropical cyclone season. Here, the ranking of the Northeast Pacific annual ACE is obtained and fitted using nonlinear regression with several two- and three-parameter ranking laws that fit the tail and head of the data, where lives the information of relevant events for human society. The logarithmic like function [Formula: see text] overperforms all other fits. A sliding window analysis of the parameters [Formula: see text] and [Formula: see text] of such a function shows that forcing and dissipation processes are anticorrelated.

Progress toward Cloud-Cleared Infrared radiance assimilation in a global modeling framework: Application to the 2017 Atlantic Tropical Cyclone Season.

<p>Previous work by this team has demonstrated that assimilation of IR radiances in partially cloudy regions is beneficial to numerical weather predictions (NWPs), improving the representation of tropical cyclones (TCs) in global analyses and forecasts. The specific technique used by this team is based on the “cloud-clearing CC” methodology. Cloud-cleared hyperspectral IR radiances (CCRs), if thinned more aggressively than clear-sky radiances, have shown a strong impact on the analyzed representation and structure of TCs. However, the use of CCRs in an operational context is limited by 1) latency; and 2) external dependencies present in the original cloud-clearing algorithm. In this study, the Atmospheric InfraRed Sounder (AIRS) CC algorithm was (a) ported to NASA high end computing resources (HEC), (b) deprived of external dependencies, and (c) parallelized improving the processing by a factor of 70. The revised AIRS CC algorithm is now customizable, allowing user’s choice of channel selection, user’s model's fields as first guess, and could perform in real time. This study examines the benefits achieved when assimilating CCRs using the NASA’s Goddard Earth Observing System (GEOS) hybrid 4DEnVar system. The focus is on the 2017 Atlantic hurricane season with three infamous hurricanes (Harvey, Irma, and Maria) investigated in depth.  The impact of assimilating customized CCRs on the analyzed representation of tropical cyclone horizontal and vertical structure and on forecast skill is discussed.</p>

Tropical Cyclone Potential Intensity during the Record-Breaking 2020 Hurricane Season

<p>The 2020 Atlantic hurricane season broke records, including becoming the most active tropical cyclone season (30 named storms), having the latest-named category five hurricane (Iota), and recording the most hurricane landfalls (twelve) in US history. This extraordinary activity yields an unusually large set of observed tropical cyclone (TC) intensities for a single season, which may be studied with theoretical and statistical analyses---something which is typically untenable for an average season. A tool to analyze these 2020 hurricane intensities is potential intensity (PI), which is the theoretical maximum speed limit of a tropical cyclone found by treating the storm as a thermal heat engine. From this thermodynamic perspective, was the 2020 hurricane season unprecedented? We explore this question using pyPI: a new python package which rapidly and transparently calculates potential intensity given a set of environmental conditions (https://github.com/dgilford/tcpyPI). Using reanalyses data, we rank 2020 potential intensity among all previous hurricane seasons (in the satellite era) and consider what environmental conditions made 2020 unique. The high number of observed storms allows us to build on previous work and perform a statistical analysis, which assesses the viability and value of potential intensity theory during the 2020 hurricane season. In particular, we calculate the normalized wind along the track of each storm (observed maximum intensity divided by potential intensity), which generally shows a uniform probability distribution function. The uniform shape of this distribution suggests that potential intensity theory is viable for seasonal intensity forecasting as long as storm counts are sufficiently high. In seasons with at least 25 storms one may expect that ~10% of the most intense observed hurricanes storms will have observed maximum intensities within 10% of their along-track potential intensity. Finally, we discuss how this approach and software could be improved/adapted for operational applications, and ask for feedback from the broader tropical cyclone community.</p>

Variation of the Tropical Cyclone Season Start in the Western North Pacific

The variation of the tropical cyclone (TC) season in the western North Pacific (WNP) was analyzed based on the percentiles of annual TC formation dates. The results show that the length of the TC season is highly modulated by the TC season’s start rather than its end. The start of the TC season in the WNP has large interannual variation that is closely associated with the variation of the sea surface temperature (SST) in the Indian Ocean (IO) and the central-eastern Pacific (CEP). When the SSTs of the IO and CEP are warm (cold) in the preceding winter, anomalous high (low) pressure and anticyclonic (cyclonic) circulation are induced around the WNP TC basin the following spring, resulting in a late (early) start of the TC season. These results suggest that a strong El Niño in the preceding winter significantly delays the TC season start in the following year.