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The Atlantic major hurricanes during the period of 1995–2005 are examined using best-track data, aircraft-based observations of central pressure, and infrared (IR) satellite images. There were 45 Atlantic major hurricanes (Saffir–Simpson category 3 or higher) during this 11-yr period, which is well above the long-term average. Descriptive statistics (e.g., average, variability, and range) of various characteristics are presented, including intensity, intensification rate, major hurricane duration, location, storm motion, size, and landfall observations. IR images are shown along with IR-derived quantities such as the digital Dvorak technique intensity and IR-defined cold cloud areas. In addition to the satellite intensity estimates, the associated component IR temperatures are documented. A pressure–wind relationship is evaluated, and the deviations of maximum intensity measurements from the pressure–wind relationship are discussed. The Atlantic major hurricane activity of the 1995–2005 period distinctly exceeds the long-term average; however, the average location where major hurricanes reach maximum intensity has not changed. The maximum intensity for each 1995–2005 Atlantic major hurricane is given both as the highest maximum surface wind (Vmax) and the lowest minimum sea level pressure (MSLP). Comparisons are made to other Atlantic major hurricanes with low MSLP back to 1950. Maximum 24-h intensification rates average 21.1 m s-1 day-1 and range up to 48.8 m s-1 day-1 in terms of Vmax. The largest 24-h MSLP decreases average 34.2 hPa and range from 15 to 97 hPa. Major hurricane duration averages 2.7 days with a maximum of 10 days. Hurricane size, as given by the average radius of gale force wind at maximum intensity, averages 250.8 km and has an extremely large range from 92.5 to 427.4 km. [ABSTRACT FROM AUTHOR]

Previous work, in which Advanced Microwave Sounding Unit (AMSU) data from the Atlantic Ocean and east Pacific Ocean basins during 1999–2001 were used to provide objective estimates of 1-min maximum sustained surface winds, minimum sea level pressure, and the radii of 34-, 50-, and 64-kt (1 kt ≡ 0.5144 m s-1) winds in the northeast, southeast, southwest, and northwest quadrants of tropical cyclones, is updated to reflect larger datasets, improved statistical analysis techniques, and improved estimation through dependent variable transforms. A multiple regression approach, which utilizes best-subset predictor selection and cross validation, is employed to develop the estimation models, where the dependent data (i.e., maximum sustained winds, minimum pressure, wind radii) are from the extended best track and the independent data consist of AMSU-derived parameters that give information about retrieved pressure, winds, temperature, moisture, and satellite resolution. The developmental regression models result in mean absolute errors (MAE) of 10.8 kt and 7.8 hPa for estimating maximum winds and minimum pressure, respectively. The MAE for the 34-, 50-, and 64-kt azimuthally averaged wind radii are 16.9, 13.3, and 6.8 n mi (1 n mi ≡ 1852 m), respectively. [ABSTRACT FROM AUTHOR]

A method is developed to adjust the Kaplan and DeMaria tropical cyclone inland wind decay model for storms that move over narrow landmasses. The basic assumption that the wind speed decay rate after landfall is proportional to the wind speed is modified to include a factor equal to the fraction of the storm circulation that is over land. The storm circulation is defined as a circular area with a fixed radius. Application of the modified model to Atlantic Ocean cases from 1967 to 2003 showed that a circulation radius of 110 km minimizes the bias in the total sample of landfalling cases and reduces the mean absolute error of the predicted maximum winds by about 12%. This radius is about 2 times the radius of maximum wind of a typical Atlantic tropical cyclone. The modified decay model was applied to the Statistical Hurricane Intensity Prediction Scheme (SHIPS), which uses the Kaplan and DeMaria decay model to adjust the intensity for the portion of the predicted track that is over land. The modified decay model reduced the intensity forecast errors by up to 8% relative to the original decay model for cases from 2001 to 2004 in which the storm was within 500 km from land. [ABSTRACT FROM AUTHOR]

Horizontal winds at 850 hPa from tropical cyclones retrieved using the nonlinear balance equation, where the mass field was determined from Advanced Microwave Sounding Unit (AMSU) temperature soundings, are compared with the surface wind fields derived from NASA's Quick Scatterometer (QuikSCAT) and Hurricane Research Division H*Wind analyses. It was found that the AMSU-derived wind speeds at 850 hPa have linear relations with the surface wind speeds from QuikSCAT or H*Wind. There are also characteristic biases of wind direction between AMSU and QuikSCAT or H*Wind. Using this information to adjust the speed and correct for the directional bias, a new algorithm was developed for estimation of the tropical cyclone surface wind field from the AMSU-derived 850-hPa winds. The algorithm was evaluated in two independent cases from Hurricanes Floyd (1999) and Michelle (2001), which were observed simultaneously by AMSU, QuikSCAT, and H*Wind. In this evaluation the AMSU adjustment algorithm for wind speed worked well. Results also showed that the bias correction algorithm for wind direction has room for improvement. [ABSTRACT FROM AUTHOR]

Advanced Microwave Sounding Unit (AMSU) data are used to provide objective estimates of 1-min maximum sustained surface winds, minimum sea level pressure, and the radii of 34-, 50-, and 64-kt (1 kt ≡ 0.5144 m s[sup -1] ) winds in the northeast, southeast, southwest, and northwest quadrants of tropical cyclones. The algorithms are derived from AMSU temperature, pressure, and wind retrievals from all tropical cyclones in the Atlantic and east Pacific basins during 1999–2001. National Hurricane Center best-track intensity and operational radii estimates are used as dependent variables in a multiple-regression approach. The intensity algorithms are evaluated for the developmental sample using a jackknife procedure and independent cases from the 2002 hurricane season. Jackknife results for the maximum winds and minimum sea level pressure estimates are mean absolute errors (MAE) of 11.0 kt and 6.7 hPa, respectively, and rmse of 14.1 kt and 9.3 hPa, respectively. For cases with corresponding reconnaissance data, the MAE are 10.7 kt and 6.1 hPa, and the rmse are 14.9 kt and 9.2 hPa. The independent cases for 2002 have errors that are only slightly larger than those from the developmental sample. Results from the jackknife evaluation of the 34-, 50-, and 64-kt radii show mean errors of 30, 24, and 14 n mi, respectively. The results for the independent sample from 2002 are generally comparable to the developmental sample, except for the 64-kt wind radii, which have larger errors. The radii errors for the 2002 sample with aircraft reconnaissance data available are all comparable to the errors from the jackknife sample, including the 64-kt radii. [ABSTRACT FROM AUTHOR]

This paper utilizes a severe thunderstorm case from 24 July 2000 to demonstrate the relevance of Geostationary Operational Environmental Satellite (GOES) rapid-scan imagery and sounder data in the short-range forecasting and nowcasting time frames. Results show how these data can be employed quickly and effectively during the warning decision-making process. Various aspects of the severe storm environment are identified that could only be diagnosed in this case using satellite data. The data used in this study are unique in that the imager and sounder input both come from one of the newest of the geostationary satellites, GOES-11. The datasets were collected as a part of the satellite's 6-week science test. During this test period, continuous 1-min imagery and 30-min sounder data were available. The new satellite has now been placed on standby and will be put in service when either GOES-East or GOES-West fails. Two new high-resolution satellite products are presented that are currently in the developmental phase. These will be field tested and implemented within the next couple of years. [ABSTRACT FROM AUTHOR]

Discusses the use of the El Nino-Southern Oscillation Climatology and Persistence (ENSO-CLIPER) model as a baseline for determination of the skill in forecasting the event. Aspects to be considered in evaluating the ENSO prediction models; Replacement of the use of anomalies as a skill threshold by output from ENSO-CLIPER.

The synoptic environment around tropical cyclones plays a significant role in vortex evolution. To capture the environment, the operational and research communities calculate diagnostic quantities. To aid with applications and research, the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED) combines disparate data sources. A key part of TC PRIMED is the environmental context. Often, environmental diagnostics come from multiple sources. However, TC PRIMED uses the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ERA5) product to provide a more complete representation of the storm environment from a single source. Reanalysis products usually poorly resolve tropical cyclones and their surrounding environment. To understand the uncertainty of large-scale diagnostics, ERA5 is compared to the Statistical Hurricane Intensity Prediction Scheme developmental dataset and the National Oceanic and Atmospheric Administration Gulfstream IV-SP dropwindsondes. This analysis highlights biases in the ERA5 environmental diagnostic quantities. Thermodynamic fields show the largest biases. The boundary layer exhibits a cold temperature bias that limits the amount of convective instability; also, the upper troposphere contains temperature biases and shows a high relative humidity bias. However, the upper-troposphere large-scale kinematic fields and derived metrics are low biased. In the lower troposphere, the temperature gradient and advection calculated from the thermal wind suggest that the low-level wind field is not representative of the observed distribution. These diagnostics comparisons provide uncertainty so that users of TC PRIMED can assess the implications for specific research and operational applications. [ABSTRACT FROM AUTHOR]

A ubiquitous cold signal near the tropopause, here called “tropopause layer cooling” (TLC), has been documented in deep convective regions such as tropical cyclones (TCs). Temperature retrievals from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) reveal cooling of order 0.1–1 K day−1 on spatial scales of order 1000 km above TCs. Data from the Cloud Profiling Radar (onboard CloudSat) and from the Cloud–Aerosol Lidar with Orthogonal Polarization [onboard the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)] are used to analyze cloud distributions associated with TCs. Evidence is found that convective clouds within TCs reach the upper part of the tropical tropopause layer (TTL) more frequently than do convective clouds outside TCs, raising the possibility that convective clouds within TCs and associated cirrus clouds modulate TLC. The contribution of clouds to radiative heating rates is then quantified using the CloudSat and CALIPSO datasets: in the lower TTL (below the tropopause), clouds produce longwave cooling of order 0.1–1 K day−1 inside the TC main convective region, and longwave warming of order 0.01–0.1 K day−1 outside; in the upper TTL (near and above the tropopause), clouds produce longwave cooling of the same order as TLC inside the TC main convective region, and one order of magnitude smaller outside. Considering that clouds also produce shortwave warming, cloud radiative effects are suggested to explain only modest amounts of TLC while other processes must provide the remaining cooling. [ABSTRACT FROM AUTHOR]

While tropical cyclone (TC) risk is a global concern, high regional differences exist in the quality of available data. This paper introduces InCyc, a globally consistent database of high-resolution full-physics simulations of historical cyclones. InCyc is designed to facilitate analysis of TC wind risk across basins and is made available to research institutions. We illustrate the value of this database with a case study focused on key wind risk parameters, namely, the location and intensity of peak winds for the North Atlantic and western North Pacific basins. A novel approach based on random forest algorithms is introduced to predict the full distribution of these TC wind risk parameters. Based on a leave-one-storm-out evaluation, the analysis of the predictions shows how this innovative approach compares to other parametric models commonly used for wind risk assessment. We finally discuss why capturing the full distribution of variability is crucial as well as the broader use in the context of TC risk assessment systems (i.e., "catastrophe models"). [ABSTRACT FROM AUTHOR]

Here we explore the latest four years (2019-2022) of using satellite data to objectively analyze tropical cyclones (TC) and issue recommendations for improved analysis. We first discuss new methods of direct retrieval from SAR and geostationary imagers. Next, we survey some of the most prominent new techniques in AI and discuss their major capabilities (especially accuracy in nonlinear TC behavior, characterization of model uncertainty and creation of synthetic satellite imagery) and limitations (especially lack of transparency and limited amount of training data). We also identify concerns with biases and unlabeled uncertainties in the Best Track records as being a first-order limitation for further progress in objective methods. The article concludes with recommendations to improve future objective methods, especially in the area of more accurate and reliable training data sets. [ABSTRACT FROM AUTHOR]

The radius of maximum wind Rmax, an important parameter in tropical cyclone (TC) ocean surface wind structure, is currently resolved by only a few sensors so that, in most cases, it is estimated subjectively or via crude statistical models. Recently, a semiempirical model relying on an outer wind radius, intensity, and latitude was fit to best-track data. In this study we revise this semiempirical model and discuss its physical basis. While intensity and latitude are taken from best-track data, Rmax observations from high-resolution (3 km) spaceborne synthetic aperture radar (SAR) and wind radii from an intercalibrated dataset of medium-resolution radiometers and scatterometers are considered to revise the model coefficients. The new version of the model is then applied to the period 2010–20 and yields Rmax reanalyses and trends that are more accurate than best-track data. SAR measurements corroborate that fundamental conservation principles constrain the radial wind structure on average, endorsing the physical basis of the model. Observations highlight that departures from the average conservation situation are mainly explained by wind profile shape variations, confirming the model's physical basis, which further shows that radial inflow, boundary layer depth, and drag coefficient also play roles. Physical understanding will benefit from improved observations of the near-core region from accumulated SAR observations and future missions. In the meantime, the revised model offers an efficient tool to provide guidance on Rmax when a radiometer or scatterometer observation is available, for either operations or reanalysis purposes. [ABSTRACT FROM AUTHOR]

This review paper reports on the state-of-the-art concerning observations of surface winds, waves, and currents from space and their use for scientific research and subsequent applications. The development of observations of sea state parameters from space dates back to the 1970s, with a significant increase in the number and diversity of space missions since the 1990s. Sensors used to monitor the sea-state parameters from space are mainly based on microwave techniques. They are either specifically designed to monitor surface parameters or are used for their abilities to provide opportunistic measurements complementary to their primary purpose. The principles on which is based on the estimation of the sea surface parameters are first described, including the performance and limitations of each method. Numerous examples and references on the use of these observations for scientific and operational applications are then given. The richness and diversity of these applications are linked to the importance of knowledge of the sea state in many fields. Firstly, surface wind, waves, and currents are significant factors influencing exchanges at the air/sea interface, impacting oceanic and atmospheric boundary layers, contributing to sea level rise at the coasts, and interacting with the sea-ice formation or destruction in the polar zones. Secondly, ocean surface currents combined with wind- and wave- induced drift contribute to the transport of heat, salt, and pollutants. Waves and surface currents also impact sediment transport and erosion in coastal areas. For operational applications, observations of surface parameters are necessary on the one hand to constrain the numerical solutions of predictive models (numerical wave, oceanic, or atmospheric models), and on the other hand to validate their results. In turn, these predictive models are used to guarantee safe, efficient, and successful offshore operations, including the commercial shipping and energy sector, as well as tourism and coastal activities. Long-time series of global sea-state observations are also becoming increasingly important to analyze the impact of climate change on our environment. All these aspects are recalled in the article, relating to both historical and contemporary activities in these fields. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This contribution summarizes the significant progress in a variety of topic areas related to internal tropical cyclone (TC) intensity change processes over 2018-2022 from the WMO Tenth International Workshop on Tropical Cyclones (IWTC-10). These topic areas include surface and boundary layer processes; TC internal structure and microphysical processes; and, radiation interactions with TCs. Recent studies better frame the uncertainty in the surface drag and enthalpy coefficients at high wind speeds. These parameters greatly impact TC intensity and it is therefore important that more direct measurements of these boundary layer parameters are made. Particularly significant scientific strides have been made in TC boundary layers. These advancements have been achieved through improved coupled models, large-eddy simulations, theoretical advancements, and detailed observations. It is now clear that the research field needs to better represent the eddy viscosity throughout the depth of the boundary layer. Furthermore, detailed study of coherent structures in TC boundary layers will likely be a propitious direction for the research community. Meanwhile, in-depth observational field campaigns and assiduous data analysis have made significant headway into verifying theory and modeling studies of intensification processes related to TC vortex alignment, efficient latent heating distributions, and overall 3D structure. Substantial efforts have also been made to better understand the intricate roles radiative processes play in TC evolution and intensity change. Finally, some promising progress has been made in the development of time-dependent theories of TC intensification and the predictability of internal TC intensity change. Overall, there have been well-earned gains in the understanding of intensity change processes intrinsic to the TC system, but the journey is not complete. This paper highlights some of the most relevant and important research areas that are still shedding new light into internal factors governing TC intensity change. [ABSTRACT FROM AUTHOR]

The National Hurricane Center (NHC) uses a variety of guidance models for its operational tropical cyclone track, intensity, and wind structure forecasts, and as baselines for the evaluation of forecast skill. A set of the simpler models, collectively known as the NHC guidance suite, is maintained by NHC. The models comprising the guidance suite are briefly described and evaluated, with details provided for those that have not been documented previously. Decay-SHIFOR is a modified version of the Statistical Hurricane Intensity Forecast (SHIFOR) model that includes decay over land; this modification improves the SHIFOR forecasts through about 96 h. T-CLIPER, a climatology and persistence model that predicts track and intensity using a trajectory approach, has error characteristics similar to those of CLIPER and D-SHIFOR but can be run to any forecast length. The Trajectory and Beta model (TAB), another trajectory track model, applies a gridpoint spatial filter to smooth winds from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model. TAB model errors were 10%–15% lower than those of the Beta and Advection model (BAM), the model it replaced in 2017. Optimizing TAB's vertical weights shows that the lower troposphere's environmental flow provides a better match to observed tropical cyclone motion than does the upper troposphere's, and that the optimal steering layer is shallower for higher-latitude and weaker tropical cyclones. The advantages and disadvantages of the D-SHIFOR, T-CLIPER, and TAB models relative to their earlier counterparts are discussed. Significance Statement: This paper provides a comprehensive summary and evaluation of a set of simpler forecast models used as guidance for NHC's operational tropical cyclone forecasts, and as baselines for the evaluation of forecast skill; these include newer techniques that extend forecasts to 7 days and beyond. [ABSTRACT FROM AUTHOR]

The mean absolute error of the official tropical cyclone (TC) intensity forecasts from the National Hurricane Center (NHC) and the Joint Typhoon Warning Center (JTWC) shows limited evidence of improvement over the past two decades. This result has sometimes erroneously been used to conclude that little or no progress has been made in the TC intensity guidance models. This article documents statistically significant improvements in operational TC intensity guidance over the past 24 years (1989-2012) in four tropical cyclone basins (Atlantic, eastern North Pacific, western North Pacific, and Southern Hemisphere). Errors from the best available model have decreased at 1%-2% yr−1 at 24-72 h, with faster improvement rates at 96 and 120 h. Although these rates are only about one-third to one-half of the rates of reduction of the track forecast models, most are statistically significant at the 95% level. These error reductions resulted from improvements in statistical-dynamical intensity models and consensus techniques that combine information from statistical-dynamical and dynamical models. The reason that the official NHC and JTWC intensity forecast errors have decreased slower than the guidance errors is because in the first half of the analyzed period, their subjective forecasts were more accurate than any of the available guidance. It is only in the last decade that the objective intensity guidance has become accurate enough to influence the NHC and JTWC forecast errors. [ABSTRACT FROM AUTHOR]

B Correction to: Surveys in Geophysics b https://doi.org/10.1007/s10712-023-09771-2 In the affiliations section at the end of the article, the affiliation of Hayley Evers-King was incorrectly indicated as "Met Office". The correct affiliation is: EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites), Eumetsat Allee 1, 64295 Darmstadt, Germany. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. [Extracted from the article]

Tropical cyclone (TC) size, usually measured with the radius of gale force wind (34 kt or 17 m s−1), is an important parameter for estimating TC risks such as wind damage, rainfall distribution, and storm surge. Previous studies have reported that there is a very weak relationship between TC size and TC intensity. A close examination presented here using satellite-based wind analyses suggests that the relationship between TC size and intensity is nonlinear. TC size generally increases with increasing TC maximum sustained wind before a maximum of 2.50° latitude at an intensity of 103 kt or 53.0 m s−1 and then slowly decreases as the TC intensity further increases. The observed relationship between TC size and intensity is compared to the relationships produced by an 11-yr seasonal numerical simulation of TC activity. The numerical simulations were able to produce neither the observed maximum sustained winds nor the observed nonlinear relationship between TC size and intensity. This finding suggests that TC size cannot reasonably be simulated with 9-km horizontal resolution and increased resolution is needed to study TC size variations using numerical simulations. [ABSTRACT FROM AUTHOR]

The article offers information on a workshop conducted by the Bermuda Institute of Ocean Sciences' Risk Prediction Initiative (RPI) which was held in Asheville, North Carolina from May 22, 2017 to May 23, 2017. Topics discussed include focus of the workshop on global tropical cyclone (TC) and catastrophic risk; advantage of higher-resolution sensors in tropical cyclone; and information on application of numerical weather prediction (NWP) techniques.

The article offers information concerning the reliability of global tropical cyclone databases in detecting the trends of tropical cyclone intensity, particularly the frequency of extreme tropical cyclones. It has been stated that recent studies discover a large, sudden increased in the tropical cyclone intensities which are linked to warming sea surface temperatures, possibly associated with global warming. Tropical cyclone intensity is formed through the maximum sustained surface wind that occurs in the eyewall of a tropical cyclone over an area of a just a few dozen square kilometers. Moreover, Dvorak technique is a satellite-based pattern recognition scheme used to estimate globally the tropical cyclone intensity.