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  • 1
    Online Resource
    Online Resource
    ProxyVis—A Proxy for Nighttime Visible Imagery Applicable to Geostationary Satellite Observations
    American Meteorological Society ; 2023
    In:  Weather and Forecasting Vol. 38, No. 12 ( 2023-12), p. 2527-2550
    Details
    In: Weather and Forecasting, American Meteorological Society, Vol. 38, No. 12 ( 2023-12), p. 2527-2550
    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.
    Type of Medium: Online Resource
    ISSN: 0882-8156 , 1520-0434
    URL: Article
    URL: Article
    DOI: 10.1175/WAF-D-23-0038.1
    DOI: 10.1175/WAF-D-23-0038.s1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2023
    detail.hit.zdb_id: 2025194-4
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  • 2
    Online Resource
    Online Resource
    Using Routinely Available Information to Estimate Tropical Cyclone Wind Structure
    American Meteorological Society ; 2016
    In:  Monthly Weather Review Vol. 144, No. 4 ( 2016-04-01), p. 1233-1247
    Details
    In: Monthly Weather Review, American Meteorological Society, Vol. 144, No. 4 ( 2016-04-01), p. 1233-1247
    Abstract: A relatively simple method to estimate tropical cyclone (TC) wind radii from routinely available information including storm data (location, motion, and intensity) and TC size is introduced. The method is based on a combination of techniques presented in previous works and makes an assumption that TCs are largely symmetric and that asymmetries are based solely on storm motion and location. The method was applied to TC size estimates from two sources: infrared satellite imagery and global model analyses. The validation shows that the methodology is comparable with other objective methods based on the error statistics. The technique has a variety of practical research and operational applications, some of which are also discussed.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/MWR-D-15-0267.1
    RVK:
    RA 1000
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2016
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
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  • 3
    Online Resource
    Online Resource
    Lightning-Based Tropical Cyclone Rapid Intensification Guidance
    American Meteorological Society ; 2023
    In:  Weather and Forecasting Vol. 38, No. 7 ( 2023-07), p. 1209-1227
    Details
    In: Weather and Forecasting, American Meteorological Society, Vol. 38, No. 7 ( 2023-07), p. 1209-1227
    Abstract: With several seasons of Geostationary Lightning Mapper (GLM) data, this work revisits incorporating lightning observations into operational tropical cyclone rapid intensification guidance. GLM provides freely available, real-time lightning data over the central and eastern North Pacific and North Atlantic Oceans. A long-term lightning dataset is needed to use GLM in a statistical–dynamical operational application to capture the relationship between lightning and the rare occurrence of rapid intensification. This work uses the World Wide Lightning Location Network (WWLLN) dataset from 2005 to 2017 to develop lightning-based predictors for rapid intensification guidance models. The models mimic the operational Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index and Rapid Intensification Prediction Aid frameworks. The frameworks are averaged to form a consensus as a means to isolate the impact of the lightning predictors. Two configurations for lightning predictors are assessed: a spatial configuration with 0–100-km inner core and 200–300-km rainband area for the preceding 6-h predictors and a temporal configuration with an inner core only for the preceding 0–1, 0–6, and 6–12 h. When tested on the 2018–21 seasons, the temporal configuration adds skill primarily to the 12–48-h forecasts when compared to the no-lightning version and rapid intensification operational consensus. When WWLLN is replaced with GLM, minor changes to the prediction are observed suggesting that this approach is suitable for operational applications and provides a new baseline for tropical cyclone lightning-based rapid intensification aids. Significance Statement The forecasting of rare, yet critical, tropical cyclone rapid intensification events continues to be challenging. The current operational tools to anticipate rapid intensity changes use a combination of numerical weather prediction–derived environmental conditions and satellite-based cloud top temperature variations of deep convection. Here, we use freely available Geostationary Lightning Mapper data, which provide independent information about convection, in similar intensity guidance frameworks using temporal and spatial aspects of lightning variability. Our results show an improvement in short-term (12–48 h) rapid intensification forecasts by using temporal lightning information, and our investigation highlights that users of Geostationary Lightning Mapper lightning information should be cognizant of the influence and impact of land on these observations.
    Type of Medium: Online Resource
    ISSN: 0882-8156 , 1520-0434
    URL: Article
    URL: Article
    DOI: 10.1175/WAF-D-22-0157.1
    DOI: 10.1175/WAF-D-22-0157.s1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2023
    detail.hit.zdb_id: 2025194-4
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  • 4
    Online Resource
    Online Resource
    The Quagmire of Arrested Development in Tropical Cyclones
    American Meteorological Society ; 2023
    In:  Weather and Forecasting Vol. 38, No. 9 ( 2023-09), p. 1661-1671
    Details
    In: Weather and Forecasting, American Meteorological Society, Vol. 38, No. 9 ( 2023-09), p. 1661-1671
    Abstract: The 48-h intensity forecasts for Hurricane Pamela (2021) from numerical weather prediction models, statistical–dynamical aids, and forecasters were a major forecast bust with Pamela making landfall as a minor rather than major hurricane. From the satellite presentation, Pamela exhibited a symmetric pattern referred to as central cold cover (CCC) in the subjective Dvorak intensity technique. Per the technique, the CCC pattern is accompanied by arrested development in intensity despite the seemingly favorable convective signature. To understand forecast uncertainty during occurrences, central cold cover frequency from 2011 to 2021 is documented. From these cases, composites of longwave infrared brightness temperatures from geostationary satellites for CCC cases are presented, and the surrounding tropical cyclone large-scale environment is quantified and compared with other tropical cyclones at similar latitudes and intensities. These composites show that central cold cover has a consistent presentation, but varies in the preceding hours for storms that eventually intensify or weaken. And, the synoptic-scale environment surrounding the tropical cyclone thermodynamically supports the vigorous deep convection associated with CCC. Finally, intensity forecast errors from numerical weather prediction models and statistical–dynamical aids are examined in comparison to similar tropical cyclones. This work shows that guidance struggles during CCC cases with intensity errors from these models being in the lowest percentiles of performance, particularly for 24- and 36-h forecasts. Significance Statement The appearance of symmetric cold clouds near the center of developing tropical cyclones is most often associated with future intensification. This simple relationship is widely used by statistical tropical cyclone intensity forecast models. Here, we reexamine and confirm that one subjectively determined nighttime cold cyclone cloud pattern termed the “central cold cover” pattern in Vern Dvorak’s seminal technique for estimating tropical cyclone intensity from infrared satellite images is indeed related to slow or arrested development, and represents a failure mode for these simple forecast models.
    Type of Medium: Online Resource
    ISSN: 0882-8156 , 1520-0434
    URL: Article
    DOI: 10.1175/WAF-D-22-0194.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2023
    detail.hit.zdb_id: 2025194-4
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  • 5
    Online Resource
    Online Resource
    Does ERA5 Mark a New Era for Resolving the Tropical Cyclone Environment?
    American Meteorological Society ; 2022
    In:  Journal of Climate Vol. 35, No. 21 ( 2022-11-01), p. 7147-7164
    Details
    In: Journal of Climate, American Meteorological Society, Vol. 35, No. 21 ( 2022-11-01), p. 7147-7164
    Abstract: The synoptic environment around tropical cyclones plays a significant role in vortex evolution. To capture the environment, the operational and research communities calculate diagnostic quantities. To aid with applications and research, the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED) combines disparate data sources. A key part of TC PRIMED is the environmental context. Often, environmental diagnostics come from multiple sources. However, TC PRIMED uses the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ERA5) product to provide a more complete representation of the storm environment from a single source. Reanalysis products usually poorly resolve tropical cyclones and their surrounding environment. To understand the uncertainty of large-scale diagnostics, ERA5 is compared to the Statistical Hurricane Intensity Prediction Scheme developmental dataset and the National Oceanic and Atmospheric Administration Gulfstream IV-SP dropwindsondes. This analysis highlights biases in the ERA5 environmental diagnostic quantities. Thermodynamic fields show the largest biases. The boundary layer exhibits a cold temperature bias that limits the amount of convective instability; also, the upper troposphere contains temperature biases and shows a high relative humidity bias. However, the upper-troposphere large-scale kinematic fields and derived metrics are low biased. In the lower troposphere, the temperature gradient and advection calculated from the thermal wind suggest that the low-level wind field is not representative of the observed distribution. These diagnostics comparisons provide uncertainty so that users of TC PRIMED can assess the implications for specific research and operational applications.
    Type of Medium: Online Resource
    ISSN: 0894-8755 , 1520-0442
    URL: Article
    URL: Article
    DOI: 10.1175/JCLI-D-22-0127.1
    DOI: 10.1175/JCLI-D-22-0127.s1
    RVK:
    UA 4548
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2022
    detail.hit.zdb_id: 246750-1
    detail.hit.zdb_id: 2021723-7
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  • 6
    Online Resource
    Online Resource
    Estimating tropical cyclone surface winds: Current status, emerging technologies, historical evolution, and a look to the future
    Elsevier BV ; 2021
    In:  Tropical Cyclone Research and Review Vol. 10, No. 3 ( 2021-09), p. 125-150
    Details
    In: Tropical Cyclone Research and Review, Elsevier BV, Vol. 10, No. 3 ( 2021-09), p. 125-150
    Type of Medium: Online Resource
    ISSN: 2225-6032
    URL: Article
    DOI: 10.1016/j.tcrr.2021.09.002
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 2970469-8
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  • 7
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    Online Resource
    An Automated Method to Analyze Tropical Cyclone Surface Winds from Real-Time Aircraft Reconnaissance Observations
    American Meteorological Society ; 2024
    In:  Weather and Forecasting Vol. 39, No. 2 ( 2024-02), p. 333-349
    Details
    In: Weather and Forecasting, American Meteorological Society, Vol. 39, No. 2 ( 2024-02), p. 333-349
    Abstract: This study describes an automated analysis of real-time tropical cyclone (TC) aircraft reconnaissance observations to estimate TC surface winds. The wind analysis uses an iterative, objective, data-weighted analysis approach with different smoothing constraints in the radial and azimuthal directions. Smoothing constraints penalize the data misfit when the solutions deviate from smoothed analyses and extend the aircraft information into areas not directly observed. The analysis composites observations following storm motion taken within 5 h prior and 3 h after analysis time and makes use of prescribed methods to move observations to a common flight level (CFL; 700 hPa) for analysis and to reduce reconnaissance observations to the surface. Comparing analyses to several observed and simulated wind fields shows that analyses fit the observations while extending observational information to poorly observed regions. However, resulting analyses tend toward greater symmetry as observational coverage decreases, and show sensitivity to the first guess information in unobserved radii. Analyses produce reasonable and useful estimates of operationally important characteristics of the wind field. But, due to the radial and azimuthal smoothing and the undersampling of typical aircraft reconnaissance flights, wind maxima are underestimated, and the radii of maximum wind are slightly overestimated. Varying observational coverage using model-based synthetic aircraft observations, these analyses improve as observational coverage increases, and for a typical observational pattern (two transects through the storm) the root-mean-square error deviation is 〈 10 kt ( 〈 5 m s −1 ). Significance Statement Many applications need estimates of 2D surface winds in tropical cyclones in real time. While real-time aircraft-based observations of the winds inside tropical cyclones have been available for several decades, there have been few automated and objective methods to analyze this information to provide estimates of the strength and distribution of the surface winds. Here, we provide details of one method that fuses these unique observations to provide useful 2D analyses of the winds in and around tropical cyclones.
    Type of Medium: Online Resource
    ISSN: 0882-8156 , 1520-0434
    URL: Article
    URL: Article
    DOI: 10.1175/WAF-D-23-0077.1
    DOI: 10.1175/WAF-D-23-0077.s1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2024
    detail.hit.zdb_id: 2025194-4
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  • 8
    Online Resource
    Online Resource
    Quantification and Exploration of Diurnal Oscillations in Tropical Cyclones
    American Meteorological Society ; 2019
    In:  Monthly Weather Review Vol. 147, No. 6 ( 2019-06), p. 2105-2121
    Details
    In: Monthly Weather Review, American Meteorological Society, Vol. 147, No. 6 ( 2019-06), p. 2105-2121
    Abstract: Diurnal oscillations of infrared cloud-top brightness temperatures (Tbs) in tropical cyclones (TCs) as inferred from storm-centered, direction-relative longwave infrared (~11 μ m) imagery are quantified for Northern Hemisphere TCs (2005–15) using statistical methods. These methods show that 45%, 54%, and 61% of at least tropical storm-, hurricane-, and major hurricane-strength TC cases have moderate or strong diurnal signals. Principal component analysis–based average behavior of all TCs with intensities of 34 kt (17.5 m s −1 ) or greater is shown to have a nearly symmetric diurnal signal where Tbs oscillate from warm to cold and cold to warm within and outside of a radius of approximately 220 km, with maximum central cooling occurring in the early morning (0300–0800 local standard time), and a nearly simultaneous maximum warming occurring near the 500-km radius—a radial standing wave with a node near 220-km radius. Amplitude and phase of these diurnal oscillations are quantified for individual 24-h periods (or cases) relative to the mean oscillation. Details of the diurnal behavior of TCs are used to examine preferred storm and environmental characteristics using a combination of spatial, composite, and regression analyses. Results suggest that diurnal, cloud-top Tb oscillations in TCs are strongest and most regular when storm characteristics (e.g., intensity and motion) and environmental conditions (e.g., vertical wind shear and low-level temperature advection) support azimuthally symmetric storm structures and when surrounding mid- and upper-level relative humidity values are greater. Finally, it is hypothesized that larger mid- and upper-level relative humidity values are necessary ingredients for robust, large-amplitude, and regular diurnal oscillations of Tbs in TCs.
    Type of Medium: Online Resource
    ISSN: 0027-0644 , 1520-0493
    URL: Article
    DOI: 10.1175/MWR-D-18-0379.1
    RVK:
    RA 1000
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2019
    detail.hit.zdb_id: 2033056-X
    detail.hit.zdb_id: 202616-8
    SSG: 14
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  • 9
    Online Resource
    Online Resource
    A Rapid Intensification Deterministic Ensemble (RIDE) for the Joint Typhoon Warning Center’s Area of Responsibility
    American Meteorological Society ; 2023
    In:  Weather and Forecasting Vol. 38, No. 7 ( 2023-07), p. 1229-1238
    Details
    In: Weather and Forecasting, American Meteorological Society, Vol. 38, No. 7 ( 2023-07), p. 1229-1238
    Abstract: The Rapid Intensification Deterministic Ensemble (RIDE) is an operational method used to estimate the probability of tropical cyclone rapid intensification in the Joint Typhoon Warning Center’s area of responsibility. Inputs to RIDE are current intensity, storm latitude, intensity change forecasts from seven routinely available operational deterministic models of intensity change, and the number of those models exceeding their individual 90th percentile of intensity change. Deterministic model inputs come from four numerical weather prediction models, two statistical–dynamical models, and one purely statistical model. In RIDE, logistic regression combines the deterministic inputs to form a probabilistic rapid intensification forecast model. RIDE then also generates deterministic intensity forecasts from these probabilistic forecasts that serve as forecaster guidance and as input to intensity consensus aids. Results based on a year of independent verification suggest good reliability and discrimination with a general tendency to underpredict rapid intensification events, but with few false alarms. Significance Statement An operational tropical cyclone forecaster makes a forecast with deterministic and probabilistic intensity guidance tools at their disposal. These models have a varying degree of abilities for predicting both intensity change and rapid intensification. The forecaster faces a dilemma in how to combine this disparate guidance to anticipate rapid intensification events. Here, the RIDE model provides probability forecasts associated with rapid intensification at 12-, 24-, 36-, 48-, and 72-h lead times and associated deterministic forecasts. RIDE provides skillful rapid intensification forecasts and helps rectify this forecast dilemma.
    Type of Medium: Online Resource
    ISSN: 0882-8156 , 1520-0434
    URL: Article
    DOI: 10.1175/WAF-D-23-0012.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2023
    detail.hit.zdb_id: 2025194-4
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  • 10
    Online Resource
    Online Resource
    Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED)
    American Meteorological Society ; 2022
    In:  Bulletin of the American Meteorological Society ( 2022-08-17)
    Details
    In: Bulletin of the American Meteorological Society, American Meteorological Society, ( 2022-08-17)
    Abstract: To study tropical cyclones and generate forecast applications using satellite observations, researchers often consolidate disparate sources of raw and ancillary data. Data consolidation involves obtaining, co-locating and inter-calibrating data from different sensors and derived products; calculating environmental diagnostics from a homogeneous source; and standardizing these various products for a straightforward analysis. To alleviate pre-processing issues and provide a long-term, global digital dataset of tropical cyclone satellite observations, we construct the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED). TC PRIMED contains tropical-cyclone-centric 1) inter-calibrated, multi-channel, multi-sensor microwave brightness temperatures, 2) retrieved rainfall from NASA’s Goddard Profiling Algorithm (GPROF), 3) nearly coincident geostationary satellite infrared brightness temperatures and derived metrics, 4) tropical cyclone position and intensity information, 5) ECMWF fifth-generation reanalysis fields and derived environmental diagnostics, and 6) precipitation radar observations from the TRMM and GPM Core Observatory satellites. TC PRIMED consists of over 176,000 overpasses of 2,101 storms from 1998 to 2019, providing researchers with an analysis-ready dataset to promote and support research into improving our understanding of the relationship between tropical cyclone convective and precipitation structure, intensity, and environment. Here, we briefly describe data sources and processing steps to create TC PRIMED. To demonstrate TC PRIMED’s potential utility for studying important tropical cyclone processes and for application development, we present a shear-relative composite analysis of several multi-sensor satellite variables relative to the tropical cyclone lifetime maximum intensity. The composite analysis provides a simple example of how TC PRIMED can benefit future studies to advance our understanding of tropical cyclones and improve forecasts.
    Type of Medium: Online Resource
    ISSN: 0003-0007 , 1520-0477
    URL: Article
    DOI: 10.1175/BAMS-D-21-0052.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2022
    detail.hit.zdb_id: 2029396-3
    detail.hit.zdb_id: 419957-1
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