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Kenneth R. Knapp, Christopher S. Velden, and Anthony J. Wimmers

Abstract

Intense tropical cyclones (TCs) generally produce a cloud-free center with calm winds, called the eye. The Automated Rotational Center Hurricane Eye Retrieval (ARCHER) algorithm is used to analyze Hurricane Satellite (HURSAT) B1 infrared satellite imagery data for storms occurring globally from 1982 to 2015. HURSAT B1 data provide 3-hourly observations of TCs. The result is a 34-yr climatology of eye location and size. During that time period, eyes are identified in about 13% of all infrared images and slightly more than half of all storms produced an eye. Those that produce an eye have (on average) 30 h of eye scenes. Hurricane Ioke (1992) had the most eye images (98, which is 12 complete days with an eye). The median wind speed of a system with an eye is 97 kt (50 m s−1) [cf. 35 kt (18 m s−1) for those without an eye]. Eyes are much more frequent in the Northern Hemisphere (particularly in the western Pacific) but eyes are larger in the Southern Hemisphere. The regions where eyes occur are expanding poleward, thus expanding the area at risk of TC-related damage. Also, eye scene occurrence can provide an objective measure of TC activity in place of those based on maximum wind speeds, which can be affected by available observations and forecast agency practices.

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Brian J. Soden, Christopher S. Velden, and Robert E. Tuleya

Abstract

A series of experimental forecasts are performed to evaluate the impact of enhanced satellite-derived winds on numerical hurricane track predictions. The winds are derived from Geostationary Operational Environmental Satellite-8 (GOES-8) multispectral radiance observations by tracking cloud and water vapor patterns from successive satellite images. A three-dimensional optimum interpolation method is developed to assimilate the satellite winds directly into the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane prediction system. A series of parallel forecasts are then performed, both with and without the assimilation of GOES winds. Except for the assimilation of the satellite winds, the model integrations are identical in all other respects. A strength of this study is the large number of experiments performed. Over 100 cases are examined from 11 different storms covering three seasons (1996–98), enabling the authors to account for and examine the case-to-case variability in the forecast results when performing the assessment. On average, assimilation of the GOES winds leads to statistically significant improvements for all forecast periods, with the relative reductions in track error ranging from ∼5% at 12 h to ∼12% at 36 h. The percentage of improved forecasts increases following the assimilation of the satellite winds, with roughly three improved forecasts for every two degraded ones. Inclusion of the satellite winds also dramatically reduces the westward bias that has been a persistent feature of the GFDL model forecasts, implying that much of this bias may be related to errors in the initial conditions rather than a deficiency in the model itself. Finally, a composite analysis of the deep-layer flow fields suggests that the reduction in track error may be associated with the ability of the GOES winds to more accurately depict the strength of vorticity gyres in the environmental flow. These results offer compelling evidence that the assimilation of satellite winds can significantly improve the accuracy of hurricane track forecasts.

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Rolf H. Langland, Christopher Velden, Patricia M. Pauley, and Howard Berger

Abstract

The impacts of special Geostationary Operational Environmental Satellite (GOES) rapid-scan (RS) wind observations on numerical model 24–120-h track forecasts of Hurricane Katrina are examined in a series of data assimilation and forecast experiments. The RS wind vectors are derived from geostationary satellites by tracking cloud motions through successive 5-min images. In these experiments, RS wind observations are added over the area 15°–60°N, 60°–110°W, and they supplement the observations used in operational forecasts. The inclusion of RS wind observations reduces errors in numerical forecasts of the Katrina landfall position at 1200 UTC 29 August 2005 by an average of 12% compared to control cases that include “targeted” dropsonde observations in the Katrina environment. The largest average improvements are made to the 84- to 120-h Katrina track forecasts, rather than to the short-range track forecasts. These results suggest that RS wind observations can potentially be used in future cases to improve track forecasts of tropical cyclones.

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Christopher S. Velden, Brian M. Goodman, and Robert T. Merrill

Abstract

A method is examined for estimating the intensity of western North Pacific tropical cyclones from satellite passive microwave observations. Vertical profiles of atmospheric temperature derived from radiances remotely sensed by the Microwave Sounding Unit (MSU) onboard the current NOAA series of polar orbiting satellites are used to depict upper-tiopospheric warm anomalies associated with these storms. Data from a large sample of western North Pacific tropical cyclones are used to develop a nonlinear statistical relationship between the satellite-depicted warm core anomalies and the surface intensifies as measured by reconnaissance aircraft. Results based on an 82-case dependent sample indicate standard errors of 13 mb and 15 kt for estimates of the surface pressure anomalies and maximum wind speeds. These errors are reduced considerably when a bias in the sample intensity distribution is taken into account. Comparisons of results and method accuracy are made with a previous study of North Atlantic tropical cyclones.

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James S. Goerss, Christopher S. Velden, and Jeffrey D. Hawkins

Abstract

Experimental wind datasets were derived for two time periods (13–20 July and 24 August–10 September 1995) from GOES-8 observations processed at the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies (UW CIMSS). The first dataset was focused on Tropical Storm Chantal, and the second dataset was focused on the multiple-storm environment that included Hurricanes Humberto, Iris, and Luis. Both datasets feature a processing and quality control strategy designed to optimize the quantity and content of geostationary satellite-derived winds in the vicinity of tropical cyclones. Specifically, the winds were extracted from high-density targets obtained from multispectral imagery, which included three water vapor bands (6.7, 7.0, and 7.3 μm), infrared, and visible. The Navy Operational Global Atmospheric Prediction System (NOGAPS) was used as the vehicle to determine the impact of these winds upon tropical cyclone track forecasts. During the 1995 Atlantic hurricane season the NOGAPS forecasts were found to be quite skillful, displaying relative improvement of tropical cyclone position error with respect to CLIPER (climate and persistence) of 20% at 24 h, 35% at 48 h, and 33% at 72 h. The NOGAPS data assimilation system was run with and without the high-density GOES-8 winds for the two aforementioned time periods. The assimilation of these winds resulted in significant improvements in the NOGAPS forecasts for Tropical Storm Chantal and Hurricane Iris and mixed results for Hurricanes Humberto and Luis. Overall, for all four cyclones, the NOGAPS forecasts made with the use of the UW CIMSS winds displayed relative improvement of forecast position error with respect to those made without the use of the UW CIMSS winds of 14% at 24 h, and 12% at both 48 and 72 h.

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Christopher Velden, Timothy Olander, Derrick Herndon, and James P. Kossin

Abstract

In recent years, a number of extremely powerful tropical cyclones have revived community debate on methodologies used to estimate the lifetime maximum intensity (LMI) of these events. And how do these storms rank historically? In this study, the most updated version of an objective satellite-based intensity estimation algorithm [advanced Dvorak technique (ADT)] is employed and applied to the highest-resolution (spatial and temporal) geostationary satellite data available for extreme-intensity tropical cyclones that occurred during the era of these satellites (1979–present). Cases with reconnaissance aircraft observations are examined and used to calibrate the ADT at extreme intensities. Bias corrections for observing properties such as satellite viewing angle and image spatiotemporal resolution, and storm characteristics such as small eye size are also considered.

The results of these intensity estimates (maximum sustained 1-min wind) show that eastern North Pacific Hurricane Patricia (2015) ranks as the strongest storm in any basin (182 kt), followed by western North Pacific Typhoons Haiyan (2013), Tip (1979), and Gay (1992). The following are the strongest classifications in other basins—Atlantic: Gilbert (1988), north Indian Ocean basin: Paradip (1999), south Indian Ocean: Gafilo (2004), Australian region: Monica (2006), and southeast Pacific basin: Pam (2015). In addition, ADT LMI estimates for four storms exceed the maximum allowable limit imposed by the operational Dvorak technique. This upper bound on intensity may be an unnatural constraint, especially if tropical cyclones get stronger in a warmer biosphere as some theorize. This argues for the need of an extension to the Dvorak scale to allow higher intensity estimates.

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P. Anil Rao, Christopher S. Velden, and Scott A. Braun

Abstract

Errors in the height assignment of some satellite-derived winds exist because the satellites sense radiation emitted from a finite layer of the atmosphere rather than a specific level. Problems in data assimilation may arise because the motion of a measured layer is often represented by a single-level value. In this research, Geostationary Operational Environmental Satellite (GOES)–derived cloud and water-vapor motion winds are compared with collocated rawinsonde observations (raobs). The satellite winds are compared with the entire profile of the collocated raob data to determine the vertical error characteristics of the satellite winds. These results are then tested in numerical weather prediction. Comparisons with the entire profile of the collocated raobs indicate that clear-air water-vapor winds represent deeper layers than do either infrared or water-vapor cloud-tracked winds. In addition, it is found that if the vertical gradient of moisture is smooth and uniform from near the height assignment upward, the clear-air water-vapor wind tends to represent a deeper layer than if the moisture gradient contains a sharp peak. The information from the comparisons is then used in numerical model simulations of two separate events to test the results. In the first case, the use of the satellite data results in improved storm tracks during the initial ∼24-h forecast period. Mean statistics indicate that the use of satellite winds generally improves the simulation with time. The simulation results suggest that it is beneficial to spread the satellite wind information over multiple levels, particularly when the moisture profile is used to define the vertical influence.

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Christopher S. Velden, Timothy L. Olander, and Steve Wanzong

Abstract

Satellite-based remote sensing has long been recognized as an important method to reconnoiter oceanic tropical cyclones due to the scarcity of in situ observations. Beyond the standard qualitative applications offered by imagery, algorithms are being developed to process the information-wealthy imagery into quantitative parameters necessary to positively impact objective analyses on which numerical track predictions are initialized. Techniques developed at the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies enable the automated extraction of displacement vectors from animated imagery featuring sequential geostationary satellite multispectral observations of clouds and water vapor. Recent upgrades to these algorithms and a focused processing strategy directed toward optimizing the retrieved wind vector coverage are discussed. In combination with advanced sensing technology afforded by the National Oceanic and Atmospheric Administration’s latest generation of geostationary meteorological satellites, GOES-8, superior vector yield and quality are being realized.

In this set of two papers, datasets produced during the 1995 Atlantic hurricane season are examined for their impact on tropical cyclone analyses and numerical track forecasts. In Part I, the wind retrieval methodology and data characteristics are described, along with a brief discussion of the tropical cyclones selected for study. Part II addresses the input of the GOES-8 wind information into a global data assimilation system, and the resultant impact on numerical track predictions.

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Christopher S. Velden, Timothy L. Olander, and Raymond M. Zehr

Abstract

The standard method for estimating the intensity of tropical cyclones is based on satellite observations (Dvorak technique) and is utilized operationally by tropical analysis centers around the world. The technique relies on image pattern recognition along with analyst interpretation of empirically based rules regarding the vigor and organization of convection surrounding the storm center. While this method performs well enough in most cases to be employed operationally, there are situations when analyst judgment can lead to discrepancies between different analysis centers estimating the same storm.

In an attempt to eliminate this subjectivity, a computer-based algorithm that operates objectively on digital infrared information has been developed. An original version of this algorithm (engineered primarily by the third author) has been significantly modified and advanced to include selected “Dvorak rules,” additional constraints, and a time-averaging scheme. This modified version, the Objective Dvorak Technique (ODT), is applicable to tropical cyclones that have attained tropical storm or hurricane strength.

The performance of the ODT is evaluated on cases from the 1995 and 1996 Atlantic hurricane seasons. Reconnaissance aircraft measurements of minimum surface pressure are used to validate the satellite-based estimates. Statistical analysis indicates the technique to be competitive with, and in some cases superior to, the Dvorak-based intensity estimates produced operationally by satellite analysts from tropical analysis centers. Further analysis reveals situations where the algorithm needs improvement, and directions for future research and modifications are suggested.

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Sarah M. Griffin, Kristopher M. Bedka, and Christopher S. Velden

Abstract

Assigning accurate heights to convective cloud tops that penetrate into the upper troposphere–lower stratosphere (UTLS) region using infrared (IR) satellite imagery has been an unresolved issue for the satellite research community. The height assignment for the tops of optically thick clouds is typically accomplished by matching the observed IR brightness temperature (BT) with a collocated rawinsonde or numerical weather prediction (NWP) profile. However, “overshooting tops” (OTs) are typically colder (in BT) than any vertical level in the associated profile, leaving the height of these tops undetermined using this standard approach. A new method is described here for calculating the heights of convectively driven OTs using the characteristic temperature lapse rate of the cloud top as it ascends into the UTLS region. Using 108 MODIS-identified OT events that are directly observed by the CloudSat Cloud Profiling Radar (CPR), the MODIS-derived brightness temperature difference (BTD) between the OT and anvil regions can be defined. This BTD is combined with the CPR- and NWP-derived height difference between these two regions to determine the mean lapse rate, −7.34 K km−1, for the 108 events. The anvil height is typically well known, and an automated OT detection algorithm is used to derive BTD, so the lapse rate allows a height to be calculated for any detected OT. An empirical fit between MODIS and geostationary imager IR BT for OTs and anvil regions was performed to enable application of this method to coarser-spatial-resolution geostationary data. Validation indicates that ~75% (65%) of MODIS (geostationary) OT heights are within ±500 m of the coincident CPR-estimated heights.

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