The 2023 Atlantic hurricane season was above normal, producing 20 named storms, 7 hurricanes, 3 major hurricanes and seasonal Accumulated Cyclone Energy that exceeded the 1991–2020 average. Hurricane Idalia was the most damaging hurricane of the year, making landfall as a Category 3 hurricane in Florida, resulting in eight direct fatalities and $3.6 billion USD in damage. The above-normal 2023 hurricane season occurred during a strong El Niño event. El Niño events tend to be associated with increased vertical wind shear across the Caribbean and tropical Atlantic, yet vertical wind shear during the peak hurricane season months of August–October was well below normal. The primary driver of the above-normal season was likely record warm tropical Atlantic sea surface temperatures (SSTs), which effectively counteracted some of the canonical impacts of El Niño. The extremely warm tropical Atlantic and Caribbean were associated with weaker-than-normal trade winds driven by an anomalously weak subtropical ridge, resulting in a positive wind-evaporation-SST feedback. We tested atmospheric circulation sensitivity to SSTs in both the tropical and subtropical Pacific and the Atlantic using the atmospheric component of the Community Earth System Model version 2.3. We found that the extremely warm Atlantic was the primary driver of the reduced vertical wind shear relative to other moderate/strong El Niño events. The concentrated warmth in the eastern tropical Pacific in August–October may have contributed to increased levels of vertical wind shear than if the warming had been more evenly spread across the eastern and central tropical Pacific.

Tropical cyclone (TC) genesis prediction is a major scientific challenge to the TC operation and research community. This report surveys the current status of TC genesis forecasts by a number of major operational centers covering the key ocean basins across both hemispheres. Since IWTC-9, we see an emergence of probabilistic TC genesis forecast products by operational centers, typically supported by the statistical processing of a combination of ensemble prediction and satellite analysis, covering time periods of couple of days to weeks ahead. The prevalence of multi-center grand ensemble approach highlights the uncertainties involved and the forecast challenges in quantitative genesis prediction. While operational practice might differ across agencies, verification efforts generally report a steady or slightly improving skill level in terms of reliability, which likely results from the continual improvement in global numerical weather prediction capability.

Eyewall replacement cycles (ERCs) in tropical cyclones (TCs) are generally associated with rapid changes in TC wind intensity and broadening of the TC wind-field, both of which can create unique forecasting challenges. As part of the NOAA Joint Hurricane Testbed Project, a new model was developed to provide operational probabilistic guidance on ERC onset. The model is based on the time evolution of TC wind-intensity and passive satellite microwave imagery, and is named “M-PERC” for Microwave-based Probability of Eyewall Replacement Cycle. The model was initially developed in the Atlantic basin, but is found to be globally applicable and skillful. The development of M-PERC and its performance characteristics are described here, as well as a new intensity prediction model that extends previous work. Application of these models is expected to contribute to a reduction of TC intensity forecast error.

Visible satellite imagery is widely used by operational weather forecast centers for tropical and extratropical cyclone analysis and marine forecasting. The absence of visible imagery at night can significantly degrade forecast capabilities, such as determining tropical cyclone center locations or tracking warm-topped convective clusters. This paper documents ProxyVis imagery, an infrared-based proxy for daytime visible imagery developed to address the lack of visible satellite imagery at night and the limitations of existing nighttime visible options. ProxyVis was trained on the VIIRS day/night band imagery at times close to the full moon using VIIRS IR channels with closely matching GOES - 16 / 17 / 18 , Himawari - 8 / 9 , and Meteosat - 9 / 10 / 11 channels. The final operational product applies the ProxyVis algorithms to geostationary satellite data and combines daytime visible and nighttime ProxyVis data to create full-disk animated GeoProxyVis imagery. The simple versions of the ProxyVis algorithm enable its generation from earlier GOES and Meteosat satellite imagery. ProxyVis offers significant improvement over existing operational products for tracking nighttime oceanic low-level clouds. Further, it is qualitatively similar to visible imagery for a wide range of backgrounds and synoptic conditions and phenomena, enabling forecasters to use it without special training. ProxyVis was first introduced to National Hurricane Center (NHC) operations in 2018 and was found to be extremely useful by forecasters becoming part of their standard operational satellite product suite in 2019. Currently, ProxyVis implemented for GOES - 16 / 18 , Himawari - 9 , and Meteosat - 9 / 10 / 11 is being used in operational settings and evaluated for transition to operations at multiple NWS offices and the Joint Typhoon Warning Center. Significance Statement This paper describes ProxyVis imagery, a new method for combining infrared channels to qualitatively mimic daytime visible imagery at nighttime. ProxyVis demonstrates that a simple linear regression can combine just a few commonly available infrared channels to develop a nighttime proxy for visible imagery that significantly improves a forecaster’s ability to track low-level oceanic clouds and circulation features at night, works for all current geostationary satellites, and is useful across a wide range of backgrounds and meteorological scenarios. Animated ProxyVis geostationary imagery has been operational at the National Hurricane Center since 2019 and is also currently being transitioned to operations at other NWS offices and the Joint Typhoon Warning Center.

This review summarizes the rapporteur report on tropical cyclone (TC) intensity change from the operational perspective, as presented to the 10th International Workshop on TCs (IWTC-10) held in Bali, Indonesia, from Dec. 5–9, 2022. The accuracy of TC intensity forecasts issued by operational forecast centers depends on three aspects: real-time observations, TC dynamical model forecast guidance, and techniques and methods used by forecasters. The rapporteur report covers the progress made over the past four years (2018–2021) in all three aspects. This review focuses on the progress of dynamical model forecast guidance. The companion paper (Part II) summarizes the advance from operational centers. The dynamical model forecast guidance continues to be the main factor leading to the improvement of operational TC intensity forecasts. Here, we describe recent advances and developments of major operational regional dynamical TC models and their intensity forecast performance, including HWRF, HMON, COAMPS-TC, Met Office Regional Model, CMA-TYM, and newly developed HAFS. The performance of global dynamical models, including NOAA's GFS, Met Office Global Model (MOGM), JMA's GSM, and IFS (ECMWF), has also been improved in recent years due to their increased horizontal and vertical resolution as well as improved data assimilation systems. Recent challenging cases of rapid intensification are presented and discussed.

Seasonal tropical cyclone (TC) forecasting has evolved substantially since its commencement in the early 1980s. However, present operational seasonal TC forecasting services still do not meet the requirements of society and stakeholders: current operational products are mainly basin-scale information, while more detailed sub-basin scale information such as potential risks of TC landfall is anticipated for decision making. To fill this gap and make the TC science and services move forward, this paper reviews recent research and development in seasonal tropical cyclone (TC) forecasting. In particular, this paper features new research topics on seasonal TC predictability in neutral conditions of El Niño–Southern Oscillation (ENSO), emerging forecasting techniques of seasonal TC activity including Machine Learning/Artificial Intelligence, and multi-annual TC predictions. We also review the skill of forecast systems at predicting landfalling statistics for certain regions of the North Atlantic, Western North Pacific and South Indian oceans and discuss the gap that remains between current products and potential user's expectations. New knowledge and advanced forecasting techniques are expected to further enhance the capability of seasonal TC forecasting and lead to more actionable and fit-for-purpose products.

This paper summarizes the progress and activities of tropical cyclone (TC) operational forecast centers during the last four years (2018–2021). It is part II of the review on TC intensity change from the operational perspective in the rapporteur report presented to the 10th International Workshop on TCs (IWTC) held in Bali, Indonesia, from Dec. 5–9, 2022. Part I of the review has focused on the progress of dynamical model forecast guidance. This part discusses the performance of TC intensity and rapid intensification forecasts from several operational centers. It is shown that the TC intensity forecast errors have continued to decrease since the 9th IWTC held in 2018. In particular, the improvement of rapid intensification forecasts has accelerated, compared with years before 2018. Consensus models, operational procedures, tools and techniques, as well as recent challenging cases from 2018 to 2021 identified by operational forecast centers are described. Research needs and recommendations are also discussed.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Corresponding author: Howard J. Diamond / howard.diamond@noaa.gov

Click to increase image sizeClick to decrease image size AcknowledgementsThe cyclone summaries are based on Tropical Cyclone Reports prepared by NHC hurricane specialists Robbie Berg, Jack Beven, Eric Blake, Daniel Brown, Lisa Bucci, John Cangialosi, Andrew Latto, Philippe Papin, Richard Pasch, and Brad Reinhart. These reports are available on the Internet at: https://www.nhc.noaa.gov/data/tcr/index.php?season=2022&basin=epac.Additional informationNotes on contributorsEric BlakeERIC S. BLAKE is a senior hurricane specialist at NOAA's National Hurricane Center (NHC) in Miami, Florida

Cerebral atrophy rate is increasingly used as an outcome measure for Alzheimer's disease (AD) trials. We used the Alzheimer's disease Neuroimaging initiative (ADNI) dataset to assess if adjusting for baseline characteristics can reduce sample sizes. Controls (n = 199), patients with mild cognitive impairment (MCI) (n = 334) and AD (n = 144) had two MRI scans, 1-year apart; approximately 55% had baseline CSF tau, p-tau, and Abeta1-42. Whole brain (KN-BSI) and hippocampal (HMAPS-HBSI) atrophy rate, and ventricular expansion (VBSI) were calculated for each group; numbers required to power a placebo-controlled trial were estimated. Sample sizes per arm (80% power, 25% absolute rate reduction) for AD were (95% CI): brain atrophy = 81 (64,109), hippocampal atrophy = 88 (68,119), ventricular expansion = 118 (92,157); and for MCI: brain atrophy = 149 (122,188), hippocampal atrophy = 201 (160,262), ventricular expansion = 234 (191,295). To detect a 25% reduction relative to normal aging required increased sample sizes approximately 3-fold (AD), and approximately 5-fold (MCI). Disease severity and Abeta1-42 contributed significantly to atrophy rate variability. Adjusting for 11 predefined covariates reduced sample sizes by up to 30%. Treatment trials in AD should consider the effects of normal aging; adjusting for baseline characteristics can significantly reduce required sample sizes.