BAMM Research Articles

Sensitivity of HAFS-B Tropical Cyclone Forecasts to Planetary Boundary Layer and Microphysics Parameterizations

Abstract Understanding how model physics impact tropical cyclone (TC) structure, motion, and evolution is critical for the development of TC forecast models. This study examines the impacts of microphysics and planetary boundary layer (PBL) physics on forecasts using the Hurricane Analysis and Forecast System (HAFS), which is newly operational in 2023. The “HAFS-B” version is specifically evaluated, and three sensitivity tests (for over 400 cases in 15 Atlantic TCs) are compared with retrospective HAFS-B runs. Sensitivity tests are generated by 1) changing the microphysics in HAFS-B from Thompson to GFDL, 2) turning off the TC-specific PBL modifications that have been implemented in operational HAFS-B, and 3) combining the PBL and microphysics modifications. The forecasts are compared through standard verification metrics, and also examination of composite structure. Verification results show that Thompson microphysics slightly degrades the days 3–4 forecast track in HAFS-B, but improves forecasts of long-term intensity. The TC-specific PBL changes lead to a reduction in a negative intensity bias and improvement in RI skill, but cause some degradation in prediction of 34-kt (1 kt ≈ 0.51 m s−1) wind radii. Composites illustrate slightly deeper vortices in runs with the Thompson microphysics, and stronger PBL inflow with the TC-specific PBL modifications. These combined results demonstrate the critical role of model physics in regulating TC structure and intensity, and point to the need to continue to develop improvements to HAFS physics. The study also shows that the combination of both PBL and microphysics modifications (which are both included in one of the two versions of HAFS in the first operational implementation) leads to the best overall results. Significance Statement A new hurricane model, the Hurricane Analysis and Forecast System (HAFS), is being introduced for operational prediction during the 2023 hurricane season. One of the most important parts of any forecast model are the “physics parameterizations,” or approximations of physical processes that govern things like turbulence, cloud formation, etc. In this study, we tested these approximations in one configuration of HAFS, HAFS-B. Specifically, we looked at two different versions of the microphysics (modeling the growth of water and ice in clouds) and boundary layer physics (the approximations for turbulence in the lowest level of the atmosphere). We found that both of these sets of model physics had important effects on the forecasts from HAFS. The microphysics had notable impacts on the track forecasts, and also changed the vertical depth of the model hurricanes. The boundary layer physics, including some of our changes based on observed hurricanes and turbulence-resolving models, helped the model better predict rapid intensification (periods where the wind speed increases quickly). Work is ongoing to improve the model physics for better forecasts of rapid intensification and overall storm structure, including storm size. The study also shows the combination of both PBL and microphysics modifications overall leads to the best results and thus was used as one of the two first operational implementations of HAFS.

Ocean Temperature Profiling Lidar: Analysis of Technology and Potential for Rapid Ocean Observations

Development of ocean measurement technologies can improve monitoring of the global Ocean Heat Content (OHC) and Heat Storage Rate (HSR) that serve as early-warning indices for climate-critical circulation processes such as the Atlantic Meridional Overturning Circulation and provide real-time OHC assessments for tropical cyclone forecast models. This paper examines the potential of remotely measuring ocean temperature profiles using a simulated Brillouin lidar for calculating ocean HSR. A series of data analysis (‘Nature’) and Observational Systems Simulation Experiments (OSSEs) were carried out using 26 years (1992–2017) of daily mean temperature and salinity outputs from the ECCOv4r4 ocean circulation model. The focus of this study is to compare various OSSEs carried out to measure the HSR using a simulated Brillouin lidar against the HSR calculated from the ECCOv4r4 model results. Brillouin lidar simulations are used to predict the probability of detecting a return lidar signal under varying sampling strategies. Correlations were calculated for the difference between sampling strategies. These comparisons ignore the measurement errors inherent in a Brillouin lidar. Brillouin lidar technology and instruments are known to contain numerous, instrument-dependent errors and remain an engineering challenge. A significant decrease in the ability to measuring global ocean HSRs is a consequence of measuring ocean temperature from nadir-pointing instruments that can only take measurements along-track. Other sources of errors include the inability to fully profile ocean regions with deep mixed layers, such as the Southern Ocean and North Atlantic, and ocean regions with high light attenuation levels.

MGTCF: Multi-Generator Tropical Cyclone Forecasting with Heterogeneous Meteorological Data

Accurate forecasting of tropical cyclone (TC) plays a critical role in the prevention and defense of TC disasters. We must explore a more accurate method for TC prediction. Deep learning methods are increasingly being implemented to make TC prediction more accurate. However, most existing methods lack a generic framework for adapting heterogeneous meteorological data and do not focus on the importance of the environment. Therefore, we propose a Multi-Generator Tropical Cyclone Forecasting model (MGTCF), a generic, extensible, multi-modal TC prediction model with the key modules of Generator Chooser Network (GC-Net) and Environment Net (Env-Net). The proposed method can utilize heterogeneous meteorologic data efficiently and mine environmental factors. In addition, the Multi-generator with Generator Chooser Net is proposed to tackle the drawbacks of single-generator TC prediction methods: the prediction of undesired out-of-distribution samples and the problems stemming from insufficient learning ability. To prove the effectiveness of MGTCF, we conduct extensive experiments on the China Meteorological Administration Tropical Cyclone Best Track Dataset. MGTCF obtains better performance compared with other deep learning methods and outperforms the official prediction method of the China Central Meteorological Observatory in most indexes.

Examining the sensitivity of ocean response to oceanic grid resolution in COAMPS-TC during Hurricane Irma (2017)

Using the Navy's Coupled Ocean-Atmospheric Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC), we investigate the sensitivity of upper ocean momentum processes to varying ocean horizontal and vertical resolutions during Hurricane Irma (2017). This study aims to examine the impact of ocean resolution sensitivity on the representation of the ocean mixed layer (OML) and thermocline heat and salt budget when the tropical cyclone (TC) is at maximum intensity east of the Lesser Antilles. Results indicate that changes in ocean resolution over the forecast period do not greatly affect TC track but have variable influence on TC intensity. OML budget analyses reveal that at coarser horizontal resolutions, the overall OML response is sensitive to vertical ocean resolution. Additionally, the dominant OML processes change as horizontal resolution increases with the dependence shifting from horizontal advection to vertical mixing. Examining budget tendencies with respect to TC quadrant reveals that the right-rear quadrant ocean response is least sensitive to grid spacing. However, the OML response within the front quadrants is most sensitive to resolution changes. Moreover, thermocline downwelling is apparent in both front quadrants and the left-rear quadrant. This downwelling response is caused by a mass convergence within the OML, pumping surface water into the ocean interior. Ocean grid resolution sensitivities in coupled TC forecast models are vital to understand since OML response is indicative of air-sea response and, thus, may contribute to forecasted TC intensity. • Modeled TC intensity varies up to 12 kt at 24 h by only changing ocean grid resolution. • Dominant oceanic processes are sensitive to ocean grid resolution and change with respect to TC quadrant. • Consider increasing upper ocean vertical resolution for horizontal resolutions ≥ 10 km.

University of Minnesota Medical School.

University of Minnesota Medical School.

Northwestern University Feinberg School of Medicine.

Northwestern University Feinberg School of Medicine.

Strategies for Assimilating High-Density Atmospheric Motion Vectors into a Regional Tropical Cyclone Forecast Model (HWRF)

In recent years, atmospheric numerical modeling frameworks and satellite observing systems have both undergone significant advances. While these developments offer considerable potential for improving forecasts of high-impact weather events such as tropical cyclones (TC), much work remains to be done regarding the targeted processing and optimal use of observations now becoming available with high spatiotemporal resolution. Using the 2019 version of NCEP’s HWRF model, we explore several different strategies for the assimilation of TC-scale, high-density atmospheric motion vectors (AMVs) derived from the new-generation GOES-R series of geostationary satellites. Using 2017’s Atlantic Hurricane Irma as a case study, we examine the HWRF forecast impacts of observation pre-processing, including thinning and adjustments to observation errors. It is demonstrated that enhanced vortex-scale GOES-16 AMVs contribute to notable improvements in HWRF track forecast error compared to a baseline control experiment that does not incorporate the high-density AMVs. Impacts on TC intensity and structure (i.e., wind radii) forecast errors are less robust, but results from the optimization experiments suggest that further work (both with regard to data assimilation strategies and advancements in the methods themselves) should lead to improvements in these forecast variables as well.

Near-Future Prediction of Tropical Cyclone Activity over the North Atlantic

Prediction of tropical cyclone (TC) activity is essential to better prepare for and mitigate TC-induced disasters. Although many studies have attempted to predict TC activity on various time scales, very few have focused on near-future predictions. Here a decrease in seasonal TC activity over the North Atlantic (NA) for 2016–30 is shown using a track-pattern-based TC prediction model. The TC model is forced by long-term coupled simulations initialized using reanalysis data. Unfavorable conditions for TC development including strengthened vertical wind shear, enhanced low-level anticyclonic flow, and cooled sea surface temperature (SST) over the tropical NA are found in the simulations. Most of the environmental changes are attributable to cooling of the NA basinwide SST (NASST) and more frequent El Niño episodes in the near future. The consistent NASST warming trend in the projections from phase 5 of the Coupled Model Intercomparison Project (CMIP5) suggests that natural variability is more dominant than anthropogenic forcing over the NA in the near-future period.

Numerical Simulations of Typhoon Morakot (2009) Using a Multiply Nested Tropical Cyclone Prediction Model

Abstract High-impact Typhoon Morakot (2009) was investigated using a multiply nested regional tropical cyclone prediction model. In the numerical simulations, the horizontal grid spacing, cumulus parameterizations, and microphysical parameterizations were varied, and the sensitivity of the track, intensity, and quantitative precipitation forecasts (QPFs) was examined. With regard to horizontal grid spacing, it is found that convective-permitting (5 km) resolution is necessary for a reasonably accurate QPF, while little benefit is gained through the use of a fourth domain at 1.67-km horizontal resolution. Significant sensitivity of the track forecast was found to the cumulus parameterization, which impacted the model QPFs. In particular, the simplified Arakawa–Schubert parameterization tended to erroneously regenerate the remnants of Tropical Storm Goni to the southwest of Morakot, affecting the large-scale steering flow and the track of Morakot. Strong sensitivity of the QPFs to the microphysical parameterization was found, with the track and intensity showing little sensitivity. It is also found that Morakot’s accumulated precipitation was reasonably predictable, with the control simulation producing an equitable threat score of 0.56 for the 3-day accumulated precipitation using a threshold of 500 mm. This high predictability of precipitation is due in part to more predictable large-scale and topographic forcing.

A Track Pattern–Based Seasonal Prediction of Tropical Cyclone Activity over the North Atlantic

Abstract A seasonal prediction model of tropical cyclone (TC) activities for the period August–October over the North Atlantic (NA) has been developed on the basis of TC track patterns. Using the fuzzy c-means method, a total of 432 TCs in the period 1965–2012 are categorized into the following four groups: 1) TCs off the U.S. East Coast, 2) TCs over the Gulf of Mexico, 3) TCs that recurve into the open ocean of the central NA, and 4) TCs that move westward in the southern NA. The model is applied to predict the four TC groups separately in conjunction with global climate forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2). By adding the distributions of the four TC tracks with precalculated weighting factors, this seasonal TC forecast model provides the spatial distribution of TC activities over the entire NA basin. Multiple forecasts initialized in six consecutive months from February to July are generated at monthly intervals to examine the applicability of this model in operational TC forecasting. Cross validations of individual forecasts show that the model can reasonably predict the observed TC frequencies over NA at the 99% confidence level. The model shows a stable spatial prediction skill, proving its advantage for forecasting regional TC activities several months in advance. In particular, the model can generate reliable information on regional TC counts in the near-coastal regions as well as in the entire NA basin.

The 2012 Triply Nested, High-Resolution Operational Version of the Hurricane Weather Research and Forecasting Model (HWRF): Track and Intensity Forecast Verifications

Abstract The Hurricane Weather Research and Forecasting Model (HWRF) was operationally implemented with a 27-km outer domain and a 9-km moving nest in 2007 (H007) as a tropical cyclone forecast model for the North Atlantic and eastern Pacific hurricane basins. During the 2012 hurricane season, a modified version of HWRF (H212), which increased horizontal resolution by adding a third (3 km) nest within the 9-km nest, replaced H007. H212 thus became the first operational model running at convection-permitting resolution. In addition, there were modifications to the initialization, model physics, tracking algorithm, etc. This paper compares H212 hindcast forecasts for the 2010–11 Atlantic hurricane seasons with forecasts from H007 and H3GP, a triply nested research version of HWRF. H212 reduced track forecast errors for almost all forecast times versus H007 and H3GP. H3GP was superior for intensity forecasts, although H212 showed some improvement over H007. Stratifying the cases by initial vertical wind shear revealed that the main weakness for H212 intensity forecasts was for cases with initially high shear. In these cases, H212 over- and under-intensified storms that were initially stronger and weaker, respectively. These results suggest the primary deficiency negatively impacting H212 intensity forecasts, especially in cases of rapid intensification, was that physics calls were too infrequent for the 3-km inner mesh. Correcting this deficiency along with additional modifications in the 2013 operational version yielded improved track and intensity forecasts. These intensity forecasts were comparable to statistical–dynamical models, showing that dynamical models can contribute to a decrease in operational forecast errors.

Applicability of the superensemble to the tropical cyclone track forecasts in the western North Pacific

In this study a superensemble was constructed and assessed to examine its applicability to the tropical cyclone track forecasts in the western North Pacific. The data used for this study were outputs of 20 tropical cyclone forecast models and analyzed tropical cyclone tracks by the Korea Meteorological Administration from 2011 to 2013. The annual mean track errors were analyzed at the 24-, 48-, 72-, 96- and 120-h periods for 2012 and 2013, and the superensemble forecasts showed lower annual errors than the simple mean consensus (using 20 numerical models), ECMWF_TIGG, and GFS. The superensemble track errors for individual tropical cyclone cases were lower than the simple mean consensus over 60% of the total cases, and lower than the best-performing model over 50% of the total cases for the 24-, 48-, and 72-h forecast periods. In the track error distribution, the superensemble had lower density for relatively large errors than the simple mean consensus, and higher density for smaller errors than single models. When the results are combined, the probability of the superensemble yielding lower errors than the simple mean consensus and single models becomes high, which indicates that the superensemble can serve as an objective reference for the tropical cyclone track forecasts.

The air-sea interface and surface stress under tropical cyclones.

Tropical cyclone track prediction is steadily improving, while storm intensity prediction has seen little progress in the last quarter century. Important physics are not yet well understood and implemented in tropical cyclone forecast models. Missing and unresolved physics, especially at the air-sea interface, are among the factors limiting storm predictions. In a laboratory experiment and coordinated numerical simulation, conducted in this work, the microstructure of the air-water interface under hurricane force wind resembled Kelvin-Helmholtz shear instability between fluids with a large density difference. Supported by these observations, we bring forth the concept that the resulting two-phase environment suppresses short gravity-capillary waves and alters the aerodynamic properties of the sea surface. The unified wave-form and two-phase parameterization model shows the well-known increase of the drag coefficient (Cd) with wind speed, up to ~30 ms−1. Around 60 ms−1, the new parameterization predicts a local peak of Ck/Cd, under constant enthalpy exchange coefficient Ck. This peak may explain rapid intensification of some storms to major tropical cyclones and the previously reported local peak of lifetime maximum intensity (bimodal distribution) in the best-track records. The bimodal distribution of maximum lifetime intensity, however, can also be explained by environmental parameters of tropical cyclones alone.

Real-Time Upper-Ocean Temperature Observations from Aircraft during Operational Hurricane Reconnaissance Missions: AXBT Demonstration Project Year One Results

Abstract Thousands of aircraft observations of upper-ocean thermal structures have been obtained during hurricane and typhoon research field experiments in recent decades. The results from these experiments suggest a strong correlation between upper-ocean thermal variability and tropical cyclone (TC) intensity change. In response to these results, during the Office of the Federal Coordinator of Meteorology (OFCM) 2011 Interdepartmental Hurricane Conference (IHC), the Working Group for Hurricane and Winter Storms Operations and Research (WG/HWSOR) approved a 3-yr project to demonstrate the usefulness of airborne expendable bathythermographs (AXBTs) in an operational setting. The goal of this project was to initialize and validate coupled TC forecast models and was extended to improve input to statistical intensity forecast models. During the first season of the demonstration project, 109 AXBTs were deployed between 28 July and 28 August 2011. Successes included AXBT deployment from WC-130J aircraft during operational reconnaissance missions tasked by the National Hurricane Center (NHC), real-time onboard and postflight data processing, real-time data transmission to U.S. Navy and NOAA hurricane numerical prediction centers, and near-real-time assimilation of upper-ocean temperature observations into the Naval Research Laboratory Coupled Ocean–Atmosphere Mesoscale Prediction System-Tropical Cyclones (COAMPS-TC) forecast model. Initial results showed 1) increased model accuracy in upper-ocean temperatures, 2) minor improvements in TC track forecasts, and 3) minor improvements in TC intensity forecasts in both coupled dynamical and statistical models [COAMPS-TC and the Statistical Hurricane Intensity Prediction Scheme (SHIPS), respectively].

Evaluation of Multiple Dynamic Initialization Schemes for Tropical Cyclone Prediction

Abstract Three different dynamic initialization schemes for tropical cyclone (TC) prediction in numerical prediction systems are described and evaluated. The first scheme involves the removal of the analyzed vortex, followed by the insertion of a dynamically initialized vortex into the model analyses. This scheme is referred to as the tropical cyclone dynamic initialization scheme (TCDI) because the TC component is nudged to the observed surface pressure in an independent three-dimensional primitive equation model prior to insertion. The second scheme is a 12-h relaxation to the analyses' horizontal momentum before the forecast integration begins, and is called the dynamic initialization (DI) scheme. The third scheme is a combination of the previous two schemes, and is called the two-stage dynamic initialization scheme (TCDI/DI). In the first stage, TCDI is implemented in order to improve the representation of the TC vortex. In the second stage, DI is invoked in order to improve the balance between the inserted TC vortex and its environment. All three dynamic initialization schemes are compared with a control (CNTL) scheme, which creates the initial vortex using synthetic TC observations that match the observed intensity and structure in a three-dimensional variational data assimilation (3DVAR) system. The four schemes are tested on 120 cases in the North Atlantic and western North Pacific basins during 2010 and 2011 using the Naval Research Laboratory's TC prediction model: Coupled Ocean–Atmosphere Mesoscale Prediction System-Tropical Cyclones (COAMPS-TC). It is demonstrated that TCDI/DI performed the best overall with regard to intensity forecasts, reducing the average minimum central pressure error for all lead times by 24.4% compared to the CNTL scheme.

Assimilation of GBVTD‐retrieved winds from single‐Doppler radar for short‐term forecasting of super typhoon Saomai (0608) at landfall

AbstractA single‐Doppler wind retrieval method called the Ground‐Based Velocity‐Track Display technique (GBVTD) has been developed in recent years to retrieve horizontal circulations of tropical cyclones. The technique is able to retrieve axisymmetric tangential and radial winds, asymmetric tangential winds for wave numbers 1–3, and along‐beam mean winds in tropical cyclones. It has been successfully applied to tropical cyclone monitoring and warning. This study explores, for the first time, the assimilation of GBVTD‐retrieved winds into a tropical cyclone prediction model, and examines its impact relative to that of directly assimilated radial velocity data. super typhoon Saomai (2006), the most intense landfalling typhoon ever recorded in China, is chosen as the test case, and data from the coastal operational radar at Wenzhou, China, are used. The ARPS 3DVAR system is used to assimilate either the radial velocity data directly or the GBVTD‐retrieved winds, at 30 min intervals for 2 hours.The assimilation of the GBVTD‐retrieved winds results in much improved structure and intensity analyses of Saomai compared to those in the Japan Meteorological Agency mesoscale reanalysis and compared to the analysis assimilating radial velocity (Vr) data directly. The ability of the GBVTD method in providing wind information covering the full circle of the inner‐core circulation is the primary reason for its superior performance over direct assimilation of Vr data; for the latter, the azimuthal data coverage is often incomplete. With the improved initial conditions, the subsequent forecasts of typhoon intensity, track and precipitation are also improved. The improvements to both track and intensity predictions persist over a 12‐hour forecast period, which is mostly after landfall. Subjective and quantitative evaluations of the precipitation and circulation patterns show consistent results. A further sensitivity experiment shows that the axisymmetric wind component in the GBVTD retrieval has the dominant impact on the prediction. Copyright © 2011 Royal Meteorological Society

Corticosteroid-binding globulin in ursids: binding affinities and nutritional regulation

Event Abstract Back to Event Corticosteroid-binding globulin in ursids: binding affinities and nutritional regulation Brian A. Chow1, Seth W. Donahue2, Marc R. Cattet3, Gordon B. Stenhouse4 and Mathilakath M. Vijayan1* 1 University of Waterloo, Department of Biology, Canada 2 Michigan Technological University, United States 3 University of Saskatchewan, Canadian Wildlife Service, Canada 4 Foothills Research Institute, Canada The majority of corticosteroid in circulation in mammals is bound reversibly and with high affinity to a transport protein, corticosteroid-binding globulin (CBG). Cortisol bound to CBG is biologically unavailable to tissues, and consequently, CBG plays a key role in reducing free cortisol concentration and regulating the cellular actions of this hormone, including protecting tissues from potentially deleterious actions. However, little is known about either serum levels of CBG or its role in serum cortisol regulation in bears. Our lab previously reported differential expression of CBG between sexes in grizzly and polar bears, and between feeding and fasting polar bears by western immunoblotting. In this study, we report the development of a direct ELISA specific for grizzly bear CBG that accurately and reliably quantifies concentrations of this protein in grizzly, polar, and black bear sera. We have also calculated the equilibrium dissociation constants (Kd) of CBG in these species by Scatchard analysis. The black bear CBG showed the highest affinity for cortisol, followed by grizzly and polar bear CBG. The differences in Kd between these species may reflect differences in hormonal action in these species. With these data and total cortisol concentrations, which were measured with a commercial RIA, we calculated the free cortisol concentrations in the serum of these ursid species. We compared free serum cortisol concentrations between feeding and fasting animals to help establish the physiological role of cortisol in the seasonal metabolic adjustments of these animals. Acknowledgements Research funding provided by Natural Sciences and Engineering Research Council of Canada CRD grant and Alberta Innovation and Science grant. We would like to thank Dr. Rudy Boonstra and Brendan Delehanty (University of Toronto Scarborough) for their help with the saturation binding assay. Keywords: black bear, CBG, cortisol, ELISA, equilibrium dissociation constant, grizzly bear, polar bear Conference: NASCE 2011: The inaugural meeting of the North American Society for Comparative Endocrinology, Ann Arbor, United States, 13 Jul - 16 Jul, 2011. Presentation Type: Poster Topic: Stress hormones Citation: Chow BA, Donahue SW, Cattet MR, Stenhouse GB and Vijayan MM (2011). Corticosteroid-binding globulin in ursids: binding affinities and nutritional regulation. Front. Endocrinol. Conference Abstract: NASCE 2011: The inaugural meeting of the North American Society for Comparative Endocrinology. doi: 10.3389/conf.fendo.2011.04.00117 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Jul 2011; Published Online: 09 Aug 2011. * Correspondence: Dr. Mathilakath M Vijayan, University of Waterloo, Department of Biology, Waterloo, Ontario, N2L3G1, Canada, mmvijaya@ucalgary.ca Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Brian A Chow Seth W Donahue Marc R Cattet Gordon B Stenhouse Mathilakath M Vijayan Google Brian A Chow Seth W Donahue Marc R Cattet Gordon B Stenhouse Mathilakath M Vijayan Google Scholar Brian A Chow Seth W Donahue Marc R Cattet Gordon B Stenhouse Mathilakath M Vijayan PubMed Brian A Chow Seth W Donahue Marc R Cattet Gordon B Stenhouse Mathilakath M Vijayan Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

BAMM in the balance

BAMM in the balance

Improving Ocean Model Initialization for Coupled Tropical Cyclone Forecast Models Using GODAE Nowcasts

Abstract To simulate tropical cyclone (TC) intensification, coupled ocean–atmosphere prediction models must realistically reproduce the magnitude and pattern of storm-forced sea surface temperature (SST) cooling. The potential for the ocean to support intensification depends on the thermal energy available to the storm, which in turn depends on both the temperature and thickness of the upper-ocean warm layer. The ocean heat content (OHC) is used as an index of this potential. Large differences in available thermal energy associated with energetic boundary currents and ocean eddies require their accurate initialization in ocean models. Two generations of the experimental U.S. Navy ocean nowcast–forecast system based on the Hybrid Coordinate Ocean Model (HYCOM) are evaluated for this purpose in the NW Caribbean Sea and Gulf of Mexico prior to Hurricanes Isidore and Lili (2002), Ivan (2004), and Katrina (2005). Evaluations are conducted by comparison to in situ measurements, the navy’s three-dimensional Modular Ocean Data Assimilation System (MODAS) temperature and salinity analyses, microwave satellite SST, and fields of OHC and 26°C isotherm depth derived from satellite altimetry. Both nowcast–forecast systems represent the position of important oceanographic features with reasonable accuracy. Initial fields provided by the first-generation product had a large upper-ocean cold bias because the nowcast was initialized from a biased older-model run. SST response in a free-running Isidore simulation is improved by using initial and boundary fields with reduced cold bias generated from a HYCOM nowcast that relaxed model fields to MODAS analyses. A new climatological initialization procedure used for the second-generation nowcast system tended to reduce the cold bias, but the nowcast still could not adequately reproduce anomalously warm conditions present before all storms within the first few months following nowcast initialization. The initial cold biases in both nowcast products tended to decrease with time. A realistic free-running HYCOM simulation of the ocean response to Ivan illustrates the critical importance of correctly initializing both warm-core rings and cold-core eddies to correctly simulate the magnitude and pattern of SST cooling.

Tropical cyclone identification and tracking system using integrated neural oscillatory elastic graph matching and hybrid RBF network track mining techniques

In this paper, we present an automatic and integrated neural network-based tropical cyclone (TC) identification and track mining system. The proposed system consists of two main modules: 1) TC pattern identification system using neural oscillatory elastic graph matching model (NOEGM); and 2) TC track mining system using hybrid radial basis function (HRBF) network with time difference and structural learning (TDSL) algorithm.For system evaluation, 120 TC cases appeared in the period between 1985 and 1998 provided by National Oceanic and Atmospheric Administration (NOAA) are being used. In TC pattern recognition from satellite pictures, an overall 98% of correct TC pattern segmentation rate and over 97% of correct classification rate are attained. Moreover, for TC track and intensity mining test, promising result of over 86% is achieved with the application of the hybrid RBF network. Comparing with the bureau numerical TC prediction model (OTCM) used by Guam and the enhanced model (TFS) proposed by Jeng et al., the proposed hybrid RBF has attained an over 30% and 18% improvement in forecast errors.