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John A. Knaff
,
Mark DeMaria
,
Charles R. Sampson
,
James E. Peak
,
James Cummings
, and
Wayne H. Schubert

Abstract

The upper oceanic temporal response to tropical cyclone (TC) passage is investigated using a 6-yr daily record of data-driven analyses of two measures of upper ocean energy content based on the U.S. Navy’s Coupled Ocean Data Assimilation System and TC best-track records. Composite analyses of these data at points along the TC track are used to investigate the type, magnitude, and persistence of upper ocean response to TC passage, and to infer relationships between routinely available TC information and the upper ocean response. Upper oceanic energy decreases in these metrics are shown to persist for at least 30 days—long enough to possibly affect future TCs. Results also indicate that TC kinetic energy (KE) should be considered when assessing TC impacts on the upper ocean, and that existing TC best-track structure information, which is used here to estimate KE, is sufficient for such endeavors. Analyses also lead to recommendations concerning metrics of upper ocean energy. Finally, parameterizations for the lagged, along-track, upper ocean response to TC passage are developed. These show that the sea surface temperature (SST) is best related to the KE and the latitude whereas the upper ocean energy is a function of KE, initial upper ocean energy conditions, and translation speed. These parameterizations imply that the 10-day lagged SST cooling is approximately 0.7°C for a “typical” TC at 30° latitude, whereas the same storm results in 10-day (30-day) lagged decreases of upper oceanic energy by about 12 (7) kJ cm−2 and a 0.5°C (0.3°C) cooling of the top 100 m of ocean.

Full access
Stanley Q. Kidder
,
John A. Knaff
,
Sheldon J. Kusselson
,
Michael Turk
,
Ralph R. Ferraro
, and
Robert J. Kuligowski

Abstract

Inland flooding caused by heavy rainfall from landfalling tropical cyclones is a significant threat to life and property. The tropical rainfall potential (TRaP) technique, which couples satellite estimates of rain rate in tropical cyclones with track forecasts to produce a forecast of 24-h rainfall from a storm, was developed to better estimate the magnitude of this threat. This paper outlines the history of the TRaP technique, details its current algorithms, and offers examples of its use in forecasting. Part II of this paper covers verification of the technique.

Full access
John A. Knaff
,
Charles R. Sampson
,
Mark DeMaria
,
Timothy P. Marchok
,
James M. Gross
, and
Colin J. McAdie

Abstract

An operational model used to predict tropical cyclone wind structure in terms of significant wind radii (i.e., 34-, 50-, and 64-kt wind radii, where 1 kt = 0.52 m s−1) at the National Oceanic and Atmospheric Administration/National Hurricane Center (NHC) and the Department of Defense/Joint Typhoon Warning Center (JTWC) is described. The statistical-parametric model employs aspects of climatology and persistence to forecast tropical cyclone wind radii through 5 days. Separate versions of the model are created for the Atlantic, east Pacific, and western North Pacific by statistically fitting a modified Rankine vortex, which is generalized to allow wavenumber-1 asymmetries, to observed values of tropical cyclone wind radii as reported by NHC and JTWC. Descriptions of the developmental data and methods used to formulate the model are given. A 2-yr verification and comparison with operational forecasts and an independently developed wind radii forecast method that also employs climatology and persistence suggests that the statistical-parametric model does a good job of forecasting wind radii. The statistical-parametric model also provides reliable operational forecasts that serve as a baseline for evaluating the skill of operational forecasts and other wind radii forecast methods in these tropical cyclone basins.

Full access
John Kaplan
,
Christopher M. Rozoff
,
Mark DeMaria
,
Charles R. Sampson
,
James P. Kossin
,
Christopher S. Velden
,
Joseph J. Cione
,
Jason P. Dunion
,
John A. Knaff
,
Jun A. Zhang
,
John F. Dostalek
,
Jeffrey D. Hawkins
,
Thomas F. Lee
, and
Jeremy E. Solbrig

Abstract

New multi-lead-time versions of three statistical probabilistic tropical cyclone rapid intensification (RI) prediction models are developed for the Atlantic and eastern North Pacific basins. These are the linear-discriminant analysis–based Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index (SHIPS-RII), logistic regression, and Bayesian statistical RI models. Consensus RI models derived by averaging the three individual RI model probability forecasts are also generated. A verification of the cross-validated forecasts of the above RI models conducted for the 12-, 24-, 36-, and 48-h lead times indicates that these models generally exhibit skill relative to climatological forecasts, with the eastern Pacific models providing somewhat more skill than the Atlantic ones and the consensus versions providing more skill than the individual models. A verification of the deterministic RI model forecasts indicates that the operational intensity guidance exhibits some limited RI predictive skill, with the National Hurricane Center (NHC) official forecasts possessing the most skill within the first 24 h and the numerical models providing somewhat more skill at longer lead times. The Hurricane Weather Research and Forecasting Model (HWRF) generally provides the most skillful RI forecasts of any of the conventional intensity models while the new consensus RI model shows potential for providing increased skill over the existing operational intensity guidance. Finally, newly developed versions of the deterministic rapid intensification aid guidance that employ the new probabilistic consensus RI model forecasts along with the existing operational intensity model consensus produce lower mean errors and biases than the intensity consensus model alone.

Full access
Mark DeMaria
,
John A. Knaff
,
Michael J. Brennan
,
Daniel Brown
,
Richard D. Knabb
,
Robert T. DeMaria
,
Andrea Schumacher
,
Christopher A. Lauer
,
David P. Roberts
,
Charles R. Sampson
,
Pablo Santos
,
David Sharp
, and
Katherine A. Winters

Abstract

The National Hurricane Center Hurricane Probability Program, which estimated the probability of a tropical cyclone passing within a specific distance of a selected set of coastal stations, was replaced by the more general Tropical Cyclone Surface Wind Speed Probabilities in 2006. A Monte Carlo (MC) method is used to estimate the probabilities of 34-, 50-, and 64-kt (1 kt = 0.51 m s−1) winds at multiple time periods through 120 h. Versions of the MC model are available for the Atlantic, the combined eastern and central North Pacific, and the western North Pacific. This paper presents a verification of the operational runs of the MC model for the period 2008–11 and describes model improvements since 2007. The most significant change occurred in 2010 with the inclusion of a method to take into account the uncertainty of the track forecasts on a case-by-case basis, which is estimated from the spread of a dynamical model ensemble and other parameters. The previous version represented the track uncertainty from the error distributions from the previous 5 yr of forecasts from the operational centers, with no case-to-case variability. Results show the MC model provides robust estimates of the wind speed probabilities using a number of standard verification metrics, and that the inclusion of the case-by-case measure of track uncertainty improved the probability estimates. Beginning in 2008, an older operational wind speed probability table product was modified to include information from the MC model. This development and a verification of the new version of the table are described.

Full access
Galina Chirokova
,
John A. Knaff
,
Michael J. Brennan
,
Robert T. DeMaria
,
Monica Bozeman
,
Stephanie N. Stevenson
,
John L. Beven
,
Eric S. Blake
,
Alan Brammer
,
James W. Darlow
,
Mark DeMaria
,
Steven D. Miller
,
Christopher J. Slocum
,
Debra Molenar
, and
Donald W. Hillger

Abstract

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.

Open access
Stephen Baxter
,
Gerald D Bell
,
Eric S Blake
,
Francis G Bringas
,
Suzana J Camargo
,
Lin Chen
,
Caio A. S Coelho
,
Ricardo Domingues
,
Stanley B Goldenberg
,
Gustavo Goni
,
Nicolas Fauchereau
,
Michael S Halpert
,
Qiong He
,
Philip J Klotzbach
,
John A Knaff
,
Michelle L'Heureux
,
Chris W Landsea
,
I.-I Lin
,
Andrew M Lorrey
,
Jing-Jia Luo
,
Andrew D Magee
,
Richard J Pasch
,
Petra R Pearce
,
Alexandre B Pezza
,
Matthew Rosencrans
,
Blair C Trewin
,
Ryan E Truchelut
,
Bin Wang
,
H Wang
,
Kimberly M Wood
, and
John-Mark Woolley
Free access
Howard J. Diamond
,
Carl J. Schreck III
,
Adam Allgood
,
Emily J. Becker
,
Eric S. Blake
,
Francis G. Bringas
,
Suzana J. Camargo
,
Lin Chen
,
Caio A. S. Coelho
,
Nicolas Fauchereau
,
Stanley B. Goldenberg
,
Gustavo Goni
,
Michael S. Halpert
,
Qiong He
,
Zeng-Zhen Hu
,
Philip J. Klotzbach
,
John A. Knaff
,
Arun Kumar
,
Chris W. Landsea
,
Michelle L’Heureux
,
I.-I. Lin
,
Andrew M. Lorrey
,
Jing-Jia Luo
,
Andrew D. Magee
,
Richard J. Pasch
,
Alexandre B. Pezza
,
Matthew Rosencrans
,
Blair C. Trewin
,
Ryan E. Truchelut
,
Bin Wang
,
Hui Wang
,
Kimberly M. Wood
, and
John-Mark Woolley
Free access
Howard J. Diamond
,
Carl J. Schreck
,
Adam Allgood
,
Emily J. Becker
,
Eric S. Blake
,
Francis G. Bringas
,
Suzana J. Camargo
,
Lin Chen
,
Caio A.S. Coelho
,
Nicolas Fauchereau
,
Chris Fogarty
,
Stanley B. Goldenberg
,
Gustavo Goni
,
Daniel S. Harnos
,
Qiong He
,
Zeng-Zhen Hu
,
Philip J. Klotzbach
,
John A. Knaff
,
Arun Kumar
,
Michelle L’Heureux
,
Chris W. Landsea
,
I-I. Lin
,
Andrew M. Lorrey
,
Jing-Jia Luo
,
Andrew D. Magee
,
Richard J. Pasch
,
Alexandre B. Pezza
,
Matthew Rosencrans
,
Jozef Rozkošný
,
Blair C. Trewin
,
Ryan E. Truchelut
,
Bin Wang
,
Hui Wang
, and
Kimberly M. Wood
Open access
Howard J. Diamond
,
Carl J. Schreck III
,
Emily J. Becker
,
Gerald D. Bell
,
Eric S. Blake
,
Stephanie Bond
,
Francis G. Bringas
,
Suzana J. Camargo
,
Lin Chen
,
Caio A. S. Coelho
,
Ricardo Domingues
,
Stanley B. Goldenberg
,
Gustavo Goni
,
Nicolas Fauchereau
,
Michael S. Halpert
,
Qiong He
,
Philip J. Klotzbach
,
John A. Knaff
,
Michelle L'Heureux
,
Chris W. Landsea
,
I.-I. Lin
,
Andrew M. Lorrey
,
Jing-Jia Luo
,
Kyle MacRitchie
,
Andrew D. Magee
,
Ben Noll
,
Richard J. Pasch
,
Alexandre B. Pezza
,
Matthew Rosencrans
,
Michael K. Tippet
,
Blair C. Trewin
,
Ryan E. Truchelut
,
Bin Wang
,
Hui Wang
,
Kimberly M. Wood
,
John-Mark Woolley
, and
Steven H. Young
Free access