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John F. Weaver
,
John A. Knaff
,
Dan Bikos
,
Gary S. Wade
, and
Jaime M. Daniels

Abstract

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.

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Mark DeMaria
,
Michelle Mainelli
,
Lynn K. Shay
,
John A. Knaff
, and
John Kaplan

Abstract

Modifications to the Atlantic and east Pacific versions of the operational Statistical Hurricane Intensity Prediction Scheme (SHIPS) for each year from 1997 to 2003 are described. Major changes include the addition of a method to account for the storm decay over land in 2000, the extension of the forecasts from 3 to 5 days in 2001, and the use of an operational global model for the evaluation of the atmospheric predictors instead of a simple dry-adiabatic model beginning in 2001.

A verification of the SHIPS operational intensity forecasts is presented. Results show that the 1997–2003 SHIPS forecasts had statistically significant skill (relative to climatology and persistence) out to 72 h in the Atlantic, and at 48 and 72 h in the east Pacific. The inclusion of the land effects reduced the intensity errors by up to 15% in the Atlantic, and up to 3% in the east Pacific, primarily for the shorter-range forecasts. The inclusion of land effects did not significantly degrade the forecasts at any time period. Results also showed that the 4–5-day forecasts that began in 2001 did not have skill in the Atlantic, but had some skill in the east Pacific.

An experimental version of SHIPS that included satellite observations was tested during the 2002 and 2003 seasons. New predictors included brightness temperature information from Geostationary Operational Environmental Satellite (GOES) channel 4 (10.7 μm) imagery, and oceanic heat content (OHC) estimates inferred from satellite altimetry observations. The OHC estimates were only available for the Atlantic basin. The GOES data significantly improved the east Pacific forecasts by up to 7% at 12–72 h. The combination of GOES and satellite altimetry improved the Atlantic forecasts by up to 3.5% through 72 h for those storms west of 50°W.

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John A. Knaff
,
Daniel P. Brown
,
Joe Courtney
,
Gregory M. Gallina
, and
John L. Beven II

Abstract

The satellite-based Dvorak technique (DVKT) is the most widely available and readily used tool for operationally estimating the maximum wind speeds associated with tropical cyclones. The DVKT itself produces internally consistent results, is reproducible, and has shown practical accuracy given the high cost of in situ or airborne observations. For these reasons, the DVKT has been used in a reasonably uniform manner globally for approximately 20 years. Despite the nearly universal use of this technique, relatively few systematic verifications of the DVKT have been conducted. This study, which makes use of 20 yr of subjectively determined DVKT-based intensity estimates and best-track intensity estimates influenced by aircraft observations (i.e., ±2 h) in the Atlantic basin, seeks to 1) identify the factors (intensity, intensity trends, radius of outer closed isobar, storm speed, and latitude) that bias the DVKT-based intensity estimates, 2) quantify those biases as well as the general error characteristics associated with this technique, and 3) provide guidance for better use of the operational DVKT intensity estimates. Results show that the biases associated with the DVKT-based intensity estimates are a function of intensity (i.e., maximum sustained wind speed), 12-h intensity trend, latitude, and translation speed and size measured by the radius of the outer closed isobar. Root-mean-square errors (RMSE), however, are shown to be primarily a function of intensity, with the best signal-to-noise (intensity to RMSE) ratio occurring in an intensity range of 90–125 kt (46–64 m s−1). The knowledge of how these factors affect intensity estimates, which is quantified in this paper, can be used to better calibrate Dvorak intensity estimates for tropical cyclone forecast operations, postseason best-track analysis, and climatological reanalysis efforts. As a demonstration of this capability, the bias corrections developed in the Atlantic basin are also tested using a limited east Pacific basin sample, showing that biases and errors can be significantly reduced.

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John F. Weaver
,
John A. Knaff
,
Dan Bikos
,
Gary S. Wade
, and
Jaime M. Daniels
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John A. Knaff
,
Stacey A. Seseske
,
Mark DeMaria
, and
Julie L. Demuth

Abstract

Axisymmetric temperatures and gradient-balanced winds associated with tropical cyclones derived from the Advanced Microwave Sounding Unit are stratified by the 24-h averaged vector difference of the horizontal wind between 200 and 850 hPa (or vertical wind shear). Using 186 total cases that are limited to tropical cyclones with intensities greater than 33 m s−1 (or mature) and are located over sea surface temperatures greater than 26.4°C, vertical wind shear–based composites are created. Results show that as the vertical wind shear increased, the upper-level warm-core structure associated with the tropical cyclone descended, resulting in a shallower balanced vortex. These observationally based results are presented in the context of recent mesoscale modeling results of the effect of shear on tropical cyclone structure.

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Morris A. Bender
,
Timothy P. Marchok
,
Charles R. Sampson
,
John A. Knaff
, and
Matthew J. Morin

Abstract

The impact of storm size on the forecast of tropical cyclone storm track and intensity is investigated using the 2016 version of the operational GFDL hurricane model. Evaluation was made for 1529 forecasts in the Atlantic, eastern Pacific, and western North Pacific basins, during the 2014 and 2015 seasons. The track and intensity errors were computed from forecasts in which the 34-kt (where 1 kt = 0.514 m s−1) wind radii obtained from the operational TC vitals that are used to initialize TCs in the GFDL model were replaced with wind radii estimates derived using an equally weighted average of six objective estimates. It was found that modifying the radius of 34-kt winds had a significant positive impact on the intensity forecasts in the 1–2 day lead times. For example, at 48 h, the intensity error was reduced 10%, 5%, and 4% in the Atlantic, eastern Pacific, and western North Pacific, respectively. The largest improvements in intensity forecasts were for those tropical cyclones undergoing rapid intensification, with a maximum error reduction in the 1–2 day forecast lead time of 14% and 17% in the eastern and western North Pacific, respectively. The large negative intensity biases in the eastern and western North Pacific were also reduced 25% and 75% in the 12–72-h forecast lead times. Although the overall impact on the average track error was neutral, forecasts of recurving storms were improved and tracks of nonrecurving storms degraded. Results also suggest that objective specification of storm size may impact intensity forecasts in other high-resolution numerical models, particularly for tropical cyclones entering a rapid intensification phase.

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John A. Knaff
,
Scott P. Longmore
,
Robert T. DeMaria
, and
Debra A. Molenar

Abstract

A new and improved method for estimating tropical-cyclone (TC) flight-level winds using globally and routinely available TC information and infrared (IR) satellite imagery is presented. The developmental dataset is composed of aircraft reconnaissance (1995–2012) that has been analyzed to a 1 km × 10° polar grid that extends outward 165 km from the TC center. The additional use of an azimuthally average tangential wind at 500 km, based on global model analyses, allows the estimation of winds at larger radii. Analyses are rotated to a direction-relative framework, normalized by dividing the wind field by the observed maximum, and then decomposed into azimuthal wavenumbers in terms of amplitudes and phases. Using a single-field principal component method, the amplitudes and phases of the wind field are then statistically related to principal components of motion-relative IR images and factors related to the climatological radius of maximum winds. The IR principal components allow the wind field to be related to the radial and azimuthal variability of the wind field. Results show that this method, when provided with the storm location, the estimated TC intensity, the TC motion vector, and a single IR image, is able to estimate the azimuthal wavenumber 0 and 1 components of the wind field. The resulting wind field reconstruction significantly improves on the method currently used for satellite-based operational TC wind field estimates. This application has several potential uses that are discussed within.

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John A. Knaff
,
Mark DeMaria
,
Debra A. Molenar
,
Charles R. Sampson
, and
Matthew G. Seybold

Abstract

A method to estimate objectively the surface wind fields associated with tropical cyclones using only data from multiple satellite platforms and satellite-based wind retrieval techniques is described. The analyses are computed on a polar grid using a variational data-fitting method that allows for the application of variable data weights to input data. The combination of gross quality control and the weighted variational analysis also produces wind estimates that have generally smaller errors than do the raw input data. The resulting surface winds compare well to the NOAA Hurricane Research Division H*Wind aircraft reconnaissance–based surface wind analyses, and operationally important wind radii estimated from these wind fields are shown to be generally more accurate than those based on climatological data. Most important, the analysis system produces global tropical cyclone surface wind analyses and related products every 6 h—without aircraft reconnaissance data. Also, the analysis and products are available in time for consideration by forecasters at the Joint Typhoon Warning Center, the Central Pacific Hurricane Center, and the National Hurricane Center in preparing their forecasts and advisories. This Multiplatform Tropical Cyclone Surface Wind Analysis (MTCSWA) product is slated to become an operationally supported product at the National Environmental Satellite Data and Information Service (NESDIS). The input data, analysis method, products, and verification statistics associated with the MTCSWA are discussed within.

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Charles R. Sampson
,
Efren A. Serra
,
John A. Knaff
, and
Joshua H. Cossuth

Abstract

The U.S. Navy is keenly interested in analyses and predictions of waves at sea due to their effects on important tasks such as shipping, base preparedness, and disaster relief. U.S. Tropical Cyclone (TC) Forecast Centers routinely disseminate wind probabilities consistent with official TC forecasts worldwide, but do not do the same for wave forecasts. These probabilities are especially important at longer leads where TC forecast accuracy diminishes. This work describes global wave probabilities consistent with both the official TC forecasts and their wind probabilities. Real-time runs for 84 TCs between May 2018 and March 2019, with probabilities generated for 12- and 18-ft significant wave heights are used to calculate verification statistics. This results in 347, 319, 261, 214, 155, and 112 verification cases at lead times of 1, 2, 3, 4, and 5 days where each verification case consists of a 20° × 20° latitude–longitude grid around the verifying TC position. When compared with wave probabilities generated solely by a global numerical weather prediction model, the wind probability–based algorithm demonstrates improved consistency with official forecasts and provides additional benefits. Those benefits include an improved capability to discriminate between 12- and 18-ft significant wave events and nonevents. The verification statistics also shows that the wind probability–based algorithm has a consistent high bias. How these biases can be reduced in future efforts is also discussed.

Open access
John A. Knaff
,
Thomas A. Cram
,
Andrea B. Schumacher
,
James P. Kossin
, and
Mark DeMaria

Abstract

Annular hurricanes are a subset of intense tropical cyclones that have been shown in previous work to be significantly stronger, to maintain their peak intensities longer, and to weaken more slowly than average tropical cyclones. Because of these characteristics, they represent a significant forecasting challenge. This paper updates the list of annular hurricanes to encompass the years 1995–2006 in both the North Atlantic and eastern–central North Pacific tropical cyclone basins. Because annular hurricanes have a unique appearance in infrared satellite imagery, and form in a specific set of environmental conditions, an objective real-time method of identifying these hurricanes is developed. However, since the occurrence of annular hurricanes is rare (∼4% of all hurricanes), a special algorithm to detect annular hurricanes is developed that employs two steps to identify the candidates: 1) prescreening the data and 2) applying a linear discriminant analysis. This algorithm is trained using a dependent dataset (1995–2003) that includes 11 annular hurricanes. The resulting algorithm is then independently tested using datasets from the years 2004–06, which contained an additional three annular hurricanes. Results indicate that the algorithm is able to discriminate annular hurricanes from tropical cyclones with intensities greater than 84 kt (43.2 m s−1). The probability of detection or hit rate produced by this scheme is shown to be ∼96% with a false alarm rate of ∼6%, based on 1363 six-hour time periods with a tropical cyclone with an intensity greater than 84 kt (1995–2006).

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