An Evaluation of Dvorak Technique–Based Tropical Cyclone Intensity Estimates

2010 ◽  
Vol 25 (5) ◽  
pp. 1362-1379 ◽  
Author(s):  
John A. Knaff ◽  
Daniel P. Brown ◽  
Joe Courtney ◽  
Gregory M. Gallina ◽  
John L. Beven
Keyword(s):  
Tropical Cyclone ◽  
Affect Intensity ◽  
Translation Speed ◽  
Atlantic Basin ◽  
Cyclone Intensity ◽  
Wind Speeds ◽  
Uniform Manner ◽  
Mean Square Errors ◽  
Practical Accuracy

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.

scholarly journals pyPI (v1.3): Tropical Cyclone Potential Intensity Calculations in Python

2020 ◽  
Author(s):  
Daniel M. Gilford
Keyword(s):  
Tropical Cyclone ◽  
Heat Engine ◽  
Maximum Speed ◽  
Speed Limit ◽  
Tropical Cyclone Intensity ◽  
Cyclone Intensity ◽  
Wind Speeds ◽  
Intensity Calculations ◽  
First Time ◽  
Oceanographic Conditions

Abstract. Potential intensity (PI) is the maximum speed limit of a tropical cyclone found by modeling the storm as a thermal heat engine. Because there are significant correlations between PI and actual storm wind speeds, PI is a useful diagnostic for evaluating or predicting tropical cyclone intensity climatology and variability. Previous studies have calculated PI given a set of atmospheric and oceanographic conditions, but although a PI algorithm – originally developed by Kerry Emanuel – is in widespread use, it remains under-documented. The Tropical Cyclone Potential Intensity Calculations in Python (pyPI, v1.3) package develops the PI algorithm in Python, and for the first time details the full background and algorithm (line-by-line) used to compute tropical cyclone potential intensity constrained by thermodynamics. The pyPI package (1) provides a freely available, flexible, validated Python PI algorithm, (2) carefully documents the PI algorithm and its Python implementation, and (3) demonstrates and encourages the use of PI theory in tropical cyclone analyses. Validation shows pyPI output is nearly identical to the previous potential intensity computation, but is an improvement on the algorithm's consistency and handling of missing data. Example calculations with reanalyses data demonstrate pyPI's usefulness in climatological and meteorological research. Planned future improvements will improve on pyPI's assumptions, flexibility, and range of applications and tropical cyclone thermodynamic calculations.

Observational Evidence for Oceanic Controls on Hurricane Intensity

2011 ◽  
Vol 24 (4) ◽  
pp. 1138-1153 ◽  
Author(s):  
Ian D. Lloyd ◽  
Gabriel A. Vecchi
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
North Atlantic ◽  
Translation Speed ◽  
Ocean Stratification ◽  
Cyclone Intensity ◽  
The North ◽  
Sst Cooling ◽  
The North Atlantic ◽  
The Impact

Abstract The influence of oceanic changes on tropical cyclone activity is investigated using observational estimates of sea surface temperature (SST), air–sea fluxes, and ocean subsurface thermal structure during the period 1998–2007. SST conditions are examined before, during, and after the passage of tropical cyclones, through Lagrangian composites along cyclone tracks across all ocean basins, with particular focus on the North Atlantic. The influence of translation speed is explored by separating tropical cyclones according to the translation speed divided by the Coriolis parameter. On average for tropical cyclones up to category 2, SST cooling becomes larger as cyclone intensity increases, peaking at 1.8 K in the North Atlantic. Beyond category 2 hurricanes, however, the cooling no longer follows an increasing monotonic relationship with intensity. In the North Atlantic, the cooling for stronger hurricanes decreases, while in other ocean basins the cyclone-induced cooling does not significantly differ from category 2 to category 5 tropical cyclones, with the exception of the South Pacific. Since the SST response is nonmonotonic, with stronger cyclones producing more cooling up to category 2, but producing less or approximately equal cooling for categories 3–5, the observations indicate that oceanic feedbacks can inhibit intensification of cyclones. This result implies that large-scale oceanic conditions are a control on tropical cyclone intensity, since they control oceanic sensitivity to atmospheric forcing. Ocean subsurface thermal data provide additional support for this dependence, showing weaker upper-ocean stratification for stronger tropical cyclones. Intensification is suppressed by strong ocean stratification since it favors large SST cooling, but the ability of tropical cyclones to intensify is less inhibited when stratification is weak and cyclone-induced SST cooling is small. Thus, after accounting for tropical cyclone translation speeds and latitudes, it is argued that reduced cooling under extreme tropical cyclones is the manifestation of the impact of oceanic conditions on the ability of tropical cyclones to intensify.

scholarly journals Recent Progress in Tropical Cyclone Intensity Forecasting at the National Hurricane Center

2020 ◽  
Vol 35 (5) ◽  
pp. 1913-1922 ◽  
Author(s):  
John P. Cangialosi ◽  
Eric Blake ◽  
Mark DeMaria ◽  
Andrew Penny ◽  
Andrew Latto ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Forecast Error ◽  
Forecast Errors ◽  
Atlantic Basin ◽  
Cyclone Intensity ◽  
The Past ◽  
Intensity Forecast ◽  
Past Half Century ◽  
National Hurricane Center ◽  
Past Half

AbstractIt has been well documented that the National Hurricane Center (NHC) has made significant improvements in Atlantic basin tropical cyclone (TC) track forecasting during the past half century. In contrast, NHC’s TC intensity forecast errors changed little from the 1970s to the early 2000s. Recently, however, there has been a notable decrease in TC intensity forecast error and an increase in intensity forecast skill. This study documents these trends and discusses the advancements in TC intensity guidance that have led to the improvements in NHC’s intensity forecasts in the Atlantic basin. We conclude with a brief projection of future capabilities.

Filed observations of spray production function during Tropical Cyclones Olwyn and Veronica

2020 ◽  
Author(s):  
Alexander Babanin ◽  
Hongyu Ma ◽  
Xingkun Xu ◽  
Fangli Qiao
Keyword(s):  
Tropical Cyclones ◽  
Heat Fluxes ◽  
Sea Spray ◽  
Laser Array ◽  
Cyclone Intensity ◽  
Wind Speeds ◽  
The Indian Ocean ◽  
The Waves ◽  
Wave Properties

<p>Spray produced in Tropical Cyclones affects the dynamic and heat fluxes between the atmosphere and ocean, and thus can influence the Cyclone intensity in a number of ways. Measurements of the Sea Spray Generation Function (SSGF) in situ, however, are extremely challenging and correspondingly rare, and uncertainties in quantifying SSGF reach 1000 times.</p><p>In the presentation, measurements of the total volume of spray by means of a laser array in Tropical Cyclones Olwyn (2015) and Veronica (2019) in the Indian Ocean will be reported. They are used to develop a parameterisation of SSGF at wind speeds ranging from light to extreme. It is argued that the spray is produced by wind-over-the-waves, and therefore wave properties are also accounted for in the parameterisation.</p>

scholarly journals pyPI (v1.3): Tropical Cyclone Potential Intensity Calculations in Python

2021 ◽  
Vol 14 (5) ◽  
pp. 2351-2369
Author(s):  
Daniel M. Gilford
Keyword(s):  
Tropical Cyclone ◽  
Heat Engine ◽  
Maximum Speed ◽  
Speed Limit ◽  
Tropical Cyclone Intensity ◽  
Cyclone Intensity ◽  
Wind Speeds ◽  
Intensity Calculations ◽  
First Time ◽  
Oceanographic Conditions

Abstract. Potential intensity (PI) is the maximum speed limit of a tropical cyclone found by modeling the storm as a thermal heat engine. Because there are significant correlations between PI and actual storm wind speeds, PI is a useful diagnostic for evaluating or predicting tropical cyclone intensity climatology and variability. Previous studies have calculated PI given a set of atmospheric and oceanographic conditions, but although a PI algorithm – originally developed by Kerry Emanuel – is in widespread use, it remains under-documented. The Tropical Cyclone Potential Intensity Calculations in Python (pyPI, v1.3) package develops the PI algorithm in Python and for the first time details the full background and algorithm (line by line) used to compute tropical cyclone potential intensity constrained by thermodynamics. The pyPI package (1) provides a freely available, flexible, validated Python PI algorithm, (2) carefully documents the PI algorithm and its Python implementation, and (3) demonstrates and encourages the use of PI theory in tropical cyclone analyses. Validation shows pyPI output is nearly identical to the previous potential intensity computation but is an improvement on the algorithm's consistency and handling of missing data. Example calculations with reanalyses data demonstrate pyPI's usefulness in climatological and meteorological research. Planned future improvements will improve on pyPI's assumptions, flexibility, and range of applications and tropical cyclone thermodynamic calculations.

scholarly journals Using High-Resolution Simulations to Quantify Underestimates of Tornado Intensity from In Situ Observations

2017 ◽  
Vol 145 (5) ◽  
pp. 1963-1982 ◽  
Author(s):  
Nathan A. Dahl ◽  
David S. Nolan ◽  
George H. Bryan ◽  
Richard Rotunno
Keyword(s):  
High Resolution ◽  
Global Maximum ◽  
Translation Speed ◽  
Near Surface ◽  
Wind Speeds ◽  
Large Eddy ◽  
Damage Indicators ◽  
Simplifying Assumptions ◽  
In Situ Observations

Abstract Large-eddy simulations are used to produce realistic, high-resolution depictions of near-surface winds in translating tornadoes. The translation speed, swirl ratio, and vertical forcing are varied to provide a range of vortex intensities and structural types. Observation experiments are then performed in which the tornadoes are passed over groups of simulated sensors. Some of the experiments use indestructible, error-free anemometers while others limit the range of observable wind speeds to mimic the characteristics of damage indicators specified in the enhanced Fujita (EF) scale. Also, in some of the experiments the sensors are randomly placed while in others they are positioned in regularly spaced columns perpendicular to the vortex tracks to mimic field project deployments. Statistical analysis of the results provides quantitative insight into the limitations of tornado intensity estimates based on damage surveys or in situ measurements in rural or semirural areas. The mean negative bias relative to the “true” global maximum 3-s gust at 10 m AGL (the standard for EF ratings) exceeds 10 m s−1 in all cases and 45 m s−1 in some cases. A small number of sensors are generally sufficient to provide a good approximation of the running time-mean maximum during the period of observation, although the required spatial resolution of the sensor group is still substantially higher than that previously attained by any field program. Because of model limitations and simplifying assumptions, these results are regarded as a lower bound for tornado intensity underestimates in rural and semirural areas and provide a baseline for further inquiry.

scholarly journals The Advanced Dvorak Technique: Continued Development of an Objective Scheme to Estimate Tropical Cyclone Intensity Using Geostationary Infrared Satellite Imagery

2007 ◽  
Vol 22 (2) ◽  
pp. 287-298 ◽  
Author(s):  
Timothy L. Olander ◽  
Christopher S. Velden
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclone Intensity ◽  
Cyclone Intensity ◽  
University Of Wisconsin ◽  
Satellite Sensors ◽  
Computer Based ◽  
And Performance ◽  
The University ◽  
Forecast Time

Abstract Tropical cyclones are becoming an increasing menace to society as populations grow in coastal regions. Forecasting the intensity of these often-temperamental weather systems can be a real challenge, especially if the true intensity at the forecast time is not well known. To address this issue, techniques to accurately estimate tropical cyclone intensity from satellites are a natural goal because in situ observations over the vast oceanic basins are scarce. The most widely utilized satellite-based method to estimate tropical cyclone intensity is the Dvorak technique, a partially subjective scheme that has been employed operationally at tropical forecast centers around the world for over 30 yr. With the recent advent of improved satellite sensors, the rapid advances in computing capacity, and accumulated experience with the behavioral characteristics of the Dvorak technique, the development of a fully automated, computer-based objective scheme to derive tropical cyclone intensity has become possible. In this paper the advanced Dvorak technique is introduced, which, as its name implies, is a derivative of the original Dvorak technique. The advanced Dvorak technique builds on the basic conceptual model and empirically derived rules of the original Dvorak technique, but advances the science and applicability in an automated environment that does not require human intervention. The algorithm is the culmination of a body of research that includes the objective Dvorak technique (ODT) and advanced objective Dvorak technique (AODT) developed at the University of Wisconsin—Madison’s Cooperative Institute for Meteorological Satellite Studies. The ODT could only be applied to storms that possessed a minimum intensity of hurricane/typhoon strength. In addition, the ODT still required a storm center location to be manually selected by an analyst prior to algorithm execution. These issues were the primary motivations for the continued advancement of the algorithm (AODT). While these two objective schemes had as their primary goal to simply achieve the basic functionality and performance of the Dvorak technique in a computer-driven environment, the advanced Dvorak technique exceeds the boundaries of the original Dvorak technique through modifications based on rigorous statistical and empirical analysis. It is shown that the accuracy of the advanced Dvorak technique is statistically competitive with the original Dvorak technique, and can provide objective tropical cyclone intensity guidance for systems in all global basins.

Sign in / Sign up

Export Citation Format

Share Document