Abstract

A CIMSS vertical wind shear (VWS-C) dataset based on reprocessed GOES-East atmospheric motion vectors (AMVs) at 15-min intervals has a −0.36 correlation with the CIMSS Satellite Consensus (SATCON) intensity changes at 30-min intervals over the life cycle of Hurricane Joaquin (2015). Correlations are then calculated for four intensity change events including two rapid intensifications (RIs) and two decays, and four intensity change segments immediately before or after these events. During the first RI, the peak intensity increase of 16 kt (6 h)⁻¹ (1 kt ≈ 0.51 m s⁻¹) follows a small VWS-C decrease to a moderate 8 m s⁻¹ value (negative correlation). A 30-h period of continued RI following the first peak RI occurred under moderate magnitude VWS-C (negative correlation), but with a rotation of the VWS-C direction to become more aligned with the southwestward heading of Joaquin. During the second RI, the peak intensity increase of 15 kt (6 h)⁻¹ leads the rapid VWS-C increase (positive correlation), which the horizontal plots of VWS-C vectors demonstrate is related to an upper-tropospheric cyclone to the northeast of Joaquin. A conceptual model of ocean cooling within the anticyclonic track loop is proposed to explain a counterintuitive decreasing intensity when the VWS-C was also decreasing (positive correlation) during the Joaquin track reversal. These alternating negative and positive correlations during the four events and four segments of intensity change demonstrate the nonlinear relationships between the VWS-C and intensity changes during the life cycle of Joaquin that must be understood, analyzed, and modeled to improve tropical cyclone intensity forecasts, and especially RI events.

Abstract

The Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin—Madison recently (1997 season) began providing real-time Geostationary Operational Environmental Satellite (GOES) low-level cloud-drift winds in the vicinity of tropical cyclones on an experimental basis to the National Oceanic and Atmospheric Administration's (NOAA) Hurricane Research Division (HRD). The cloud-drift winds are derived from sequential high-resolution GOES visible channel imagery. These data were included in many of HRD's real-time tropical cyclone surface wind objective analyses, which were sent to NOAA's National Hurricane Center and the Central Pacific Hurricane Center on an experimental basis during the 1997–2001 hurricane seasons. These wind analyses were used to support the forecasters' tropical cyclone advisories and warnings. The satellite wind observations provide essential low-level coverage in the periphery of the tropical cyclone circulation where conventional in situ observations (e.g., ships, buoys, and Coastal-Marine Automated Network stations) are often widely spaced or nonexistent and reconnaissance aircraft do not normally fly. Though winds derived from microwave channels on polar orbiting satellites provide valuable surface wind data for HRD surface wind analyses, their swath coverage and orbital passes are limited spatially and temporally. GOES low-level visible (GLLV) winds offer nearly continuous spatial and temporal coverage in the western Atlantic and eastern Pacific basins. The GLLV winds were extrapolated to the surface using a planetary boundary layer model developed at HRD. These surface-adjusted satellite data were used in real-time surface wind analyses of 1998 Hurricane Georges, as well as in poststorm analyses of 1996 Hurricane Lili and 1997 Tropical Storm Claudette. The satellite observations often helped to define the spatial extent of the 17.5 m s⁻¹ (34 kt) surface wind radii and also redefined the 25.7 m s⁻¹ (50 kt) wind radius for one case. Examples of the impact of these data on real-time hurricane surface wind fields provided to the NHC will be discussed.

Abstract

Hurricane Opal intensified rapidly and unexpectedly over the Gulf of Mexico between 1800 UTC 3 October and 1000 UTC 4 October 1995. During this period the storm central pressure decreased from 963 to 916 hPa and sustained winds reached 68 m s⁻¹. Analyses that include high-resolution GOES-8 water vapor winds and European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction (NCEP) gridded datasets are employed to examine the rapid intensification phase of Opal.

Opal first reached tropical storm strength on 29–30 September 1995 as it interacted with a trough while situated over the Yucatan Peninsula. Opal deepened moderately (∼20 hPa) in the 24 h ending 1200 UTC 2 October as it achieved minimal hurricane strength and as it turned northeastward. The deepening occurred in conjunction with an environmental flow interaction as determined by an Eliassen balanced vortex outflow calculation.

As Opal accelerated toward the Gulf coast by 1200 UTC 3 October, it approached the equatorward jet-entrance region of a progressive synoptic-scale trough. The trough tail extended southwestward toward the lower Texas coast. As the poleward portion of the trough moved eastward, the equatorward end of the trough lagged behind, stretched meridionally, and partially fractured as it encountered a deformation region over the northwest Gulf. Enhanced outflow and increased divergence in the upper troposphere poleward of Opal was associated with the deformation zone and the partially fractured trough tail.

An analysis of the 300–200-hPa layer-averaged divergence and 6-h divergence change based on an analysis of the water vapor winds shows a significant increase in the magnitude and equatorward extension of the divergence core toward Opal that begins at 1200 UTC 3 October and is most apparent by 1800 UTC 3 October and 0000 UTC 4 October. This divergence increase is shown to precede convective growth in the eyewall and the onset of rapid intensification and is attributed to a jet–trough–hurricane interaction in a low-shear environment. Calculations of balanced vortex outflow based on the ECMWF and NCEP gridded datasets confirms this interpretation.

A crucial finding of this work is that the jet–trough–hurricane interaction and explosive intensification of Opal begins near 0000 UTC 4 October when the storm is far from its maximum potential intensity (MPI), and the 850–200-hPa shear within 500 km of the center is weak (2–3 m s⁻¹). In this first stage of rapid intensification the winds increase by almost 15 m s⁻¹ to 52 m s⁻¹ prior to the storm reaching an oceanic warm-core eddy. The second stage of rapid intensification occurs between 0600 and 1000 UTC 4 October when Opal is over the warm-core eddy and sustained winds increase to 68 m s⁻¹. During this second stage conditions are still favorable for a jet–trough–hurricane interaction as demonstrated by the balanced vortex outflow calculation. Opal weakens rapidly after 1200 UTC 4 October when the storm is near its MPI, the shear is increasing, and the eye is leaving the warm-core eddy. This weakening occurs as Opal moves closer to the trough. It is suggested that an important factor in determining whether a storm–trough interaction is favorable or unfavorable for intensification is how far a storm is from its MPI. The results suggest that a favorable storm–trough interaction (“good trough”) can occur when a storm is far from its MPI.

It is suggested that although the ECMWF (and to lesser extent NCEP) analyses reveal the trough–jet–hurricane interaction through the balanced vortex outflow calculation, that the failure of the same models to predict the rapid intensification of Opal can be attributed to the inability of the model to resolve the eye and internal strorm structure and the associated influence of the trough–jet–hurricane interaction on the diabatically driven storm secondary circulation. The analyses also indicate that the high spatial and temporal resolution of the GOES-8 water vapor winds reveal important mesoscale details of the trough–jet–hurricane interaction that would otherwise be hidden.

Abstract

Multiple observation and analysis datasets are used to demonstrate two key features of the atypical rapid intensification (ARI) process that occurred in Atlantic Hurricane Dorian (2019): 1) precession and nutations of the vortex tilt and 2) blocking of the impinging upper-level environmental flow by the outflow. As Dorian came under the influence of an upper-level anticyclone, traditional methods of estimating vertical wind shear all indicated relatively low values were acting on the storm; however, high-spatiotemporal-resolution atmospheric motion vectors (AMVs) indicated that the environmental flow at upper levels was actually impinging on the vortex core, resulting in a vertical tilt. We employ a novel ensemble of centers of individual swaths of dual-Doppler radar data from WP-3D aircraft to characterize the precession and wobble of the vortex tilt. This tilting and wobbling preceded a sequence of outflow surges that acted to repel the impinging environmental flow, thereby reducing the shear and permitting ARI. We then apply prior methodology on satellite imagery for distinguishing ARI features. Finally, we use the AMV dataset to experiment with different shear calculations and show that the upper-level cross-vortex flow approaches zero. We discuss the implication of these results with regard to prior works on ARI and intensification in shear.

Abstract

Geostationary satellite-derived atmospheric motion vectors (AMVs) have been used over several decades in a wide variety of meteorological applications. The ever-increasing horizontal and vertical resolution of numerical weather prediction models puts a greater demand on satellite-derived wind products to monitor flow accurately at smaller scales and higher temporal resolution. The focus of this paper is to evaluate the accuracy and potential applications of a newly developed experimental mesoscale AMV product derived from Geostationary Operational Environmental Satellite (GOES) imagery. The mesoscale AMV product is derived through a variant on processing methods used within the University of Wisconsin—Madison Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) AMV algorithm and features a significant increase in vector density throughout the troposphere and lower stratosphere over current NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) processing methods for GOES-12 Imager data. The primary objectives of this paper are to 1) highlight applications of experimental GOES mesoscale AMVs toward weather diagnosis and forecasting, 2) compare the coverage and accuracy of mesoscale AMVs with the NOAA/NESDIS operational AMV product, and 3) demonstrate the utility of 6-min NOAA Wind Profiler Network observations for satellite-derived AMV validation. Although the more conservative NOAA/NESDIS AMV product exhibits closer statistical agreement to rawinsonde and wind profiler observations than do the experimental mesoscale AMVs, a comparison of these two products for selected events shows that the mesoscale product better depicts the circulation center of a midlatitude cyclone, boundary layer confluence patterns, and a narrow low-level jet that is well correlated with subsequent severe thunderstorm development. Thus, while the individual experimental mesoscale AMVs may sacrifice some absolute accuracy, they show promise in providing greater temporal and spatial flow detail that can benefit diagnosis of upper-air flow patterns in near–real time. The results also show good agreement between 6-min wind profiler and rawinsonde observations within the 700–200-hPa layer, with larger differences in the stratosphere, near the mean top of the planetary boundary layer, and just above the earth’s surface. Despite these larger differences within select layers, the stability of the difference profile with height builds confidence in the use of 6-min, ∼404-MHz NOAA Wind Profiler Network observations to evaluate and better understand satellite AMV error characteristics.

Abstract

Enhanced atmospheric motion vectors (AMVs) produced from the geostationary Multifunctional Transport Satellite (MTSAT) are assimilated into the U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) to evaluate the impact of these observations on tropical cyclone track forecasts during the simultaneous western North Pacific Ocean Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (TPARC) and the Tropical Cyclone Structure—2008 (TCS-08) field experiments. Four-dimensional data assimilation is employed to take advantage of experimental high-resolution (space and time) AMVs produced for the field campaigns by the Cooperative Institute for Meteorological Satellite Studies. Two enhanced AMV datasets are considered: 1) extended periods produced at hourly intervals over a large western North Pacific domain using routinely available MTSAT imagery and 2) limited periods over a smaller storm-centered domain produced using special MTSAT rapid-scan imagery. Most of the locally impacted forecast cases involve Typhoons Sinlaku and Hagupit, although other storms are also examined. On average, the continuous assimilation of the hourly AMVs reduces the NOGAPS tropical cyclone track forecast errors—in particular, for forecasts longer than 72 h. It is shown that the AMVs can improve the environmental flow analyses that may be influencing the tropical cyclone tracks. Adding rapid-scan AMV observations further reduces the NOGAPS forecast errors. In addition to their benefit in traditional data assimilation, the enhanced AMVs show promise as a potential resource for advanced objective data-targeting methods.

Abstract

The influence of assimilating enhanced atmospheric motion vectors (AMVs) on mesoscale analyses and forecasts of tropical cyclones (TC) is investigated. AMVs from the geostationary Multifunctional Transport Satellite (MTSAT) are processed by the Cooperative Institute for Meteorological Satellite Studies (CIMSS, University of Wisconsin–Madison) for the duration of Typhoon Sinlaku (2008), which included a rapid intensification phase and a slow, meandering track. The ensemble Kalman filter and the Weather Research and Forecasting Model are utilized within the Data Assimilation Research Testbed. In addition to conventional observations, three different groups of AMVs are assimilated in parallel experiments: CTL, the same dataset assimilated in the NCEP operational analysis; CIMSS(h), hourly datasets processed by CIMSS; and CIMSS(h+RS), the dataset including AMVs from the rapid-scan mode. With an order of magnitude more AMV data assimilated, the CIMSS(h) analyses exhibit a superior track, intensity, and structure to CTL analyses. The corresponding 3-day ensemble forecasts initialized with CIMSS(h) yield smaller track and intensity errors than those initialized with CTL. During the period when rapid-scan AMVs are available, the CIMSS(h+RS) analyses offer additional modifications to the TC and its environment. In contrast to many members in the ensemble forecasts initialized from the CTL and CIMSS(h) analyses that predict an erroneous landfall in China, the CIMSS(h+RS) members capture recurvature, albeit prematurely. The results demonstrate the promise of assimilating enhanced AMV data into regional TC models. Further studies to identify optimal strategies for assimilating integrated full-resolution multivariate data from satellites are under way.

Abstract

Recent studies have shown that assimilating enhanced satellite-derived atmospheric motion vectors (AMVs) has improved mesoscale forecast of tropical cyclones (TC) track and intensity. The authors conduct data-denial experiments to understand where the TC analyses and forecasts benefit the most from the enhanced AMV information using an ensemble Kalman filter and the Weather Research and Forecasting Model. The Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin provides enhanced AMV datasets with higher density and temporal resolution using shorter-interval image triplets for the duration of Typhoon Sinlaku and Hurricane Ike (both 2008). These AMV datasets are then spatially and vertically subsetted to create six parallel cycled assimilation-forecast experiments for each TC: all AMVs; AMVs withheld between 100 and 350 hPa (upper layer), between 350 and 700 hPa (middle layer), and between 700 and 999 hPa (lower layer); and only AMVs within (interior) and outside (exterior) 1000-km radius of the TC center. All AMV subsets are found to be useful in some capacity. The interior and upper-layer AMVs are particularly crucial for improving initial TC position, intensity, and the three-dimensional wind structure along with their forecasts. Compared with denying interior or exterior AMVs, withholding AMVs in different tropospheric layers had less impact on TC intensity and size forecasts. The ensemble forecast is less certain (larger spread) in providing accurate TC track, intensity, and size when upper-layer AMVs or interior AMVs are withheld. This information could be useful to potential targeting scenarios, such as activating and focusing satellite rapid-scan operations, and decisions regarding observing system assessments and deployments.

Abstract

Dropwindsonde, Geostationary Operational Environmental Satellite-11 (GOES-11) rapid-scan atmospheric motion vectors, and NASA Quick Scatterometer (QuikSCAT) near-surface wind data collected during NASA’s Tropical Cloud Systems and Processes (TCSP) field experiment in July 2005 were assimilated into an advanced research version of the Weather Research and Forecasting (WRF) model using its three-dimensional variational data assimilation (3DVAR) system. The impacts of the mesoscale data assimilation on WRF numerical simulation of Tropical Storms Cindy and Gert (2005) near landfall are examined. Sensitivity of the forecasts to the assimilation of each single data type is investigated. Specifically, different 3DVAR strategies with different analysis update cycles and resolutions are compared in order to identify the better methodology for assimilating the data from research aircraft and satellite for tropical cyclone study.

The results presented herein indicate the following. 1) Assimilation of dropwindsonde and satellite wind data into the WRF model improves the forecasts of the two tropical storms up to the landfall time. The QuikSCAT wind information is very important for improving the storm track forecast, whereas the dropwindsonde and GOES-11 wind data are also necessary for improved forecasts of intensity and precipitation. 2) Data assimilation also improves the quantitative precipitation forecasts (QPFs) near landfall of the tropical storms. 3) A 1-h rapid-update analysis cycle at high resolution (9 km) provides more accurate tropical cyclone forecasts than a regular 6-h analysis cycle at coarse (27 km) resolution. The high-resolution rapidly updated 3DVAR analysis cycle might be a practical way to assimilate the data collected from tropical cyclone field experiments.

Abstract

It is known that both Dvorak technique and advanced Dvorak technique–derived intensity estimates for tropical cyclones during extratropical transition are less reliable because the empirical relationships between cloud patterns and cyclone intensity underlying each technique are primarily tropical in nature and thus less robust during extratropical transition. However, as direct observations of cyclone intensity during extratropical transition are rare, the precise extent to which such remotely sensed intensity estimates are in error is uncertain. To address this uncertainty and provide insight into how advanced Dvorak technique–derived intensity estimates during extratropical transition may be improved, the advanced Dvorak technique is applied to synthetic satellite imagery derived from 25 numerical simulations of Atlantic basin tropical cyclones—five cases, five microphysical parameterizations—during extratropical transition. From this, an internally consistent evaluation between model-derived and advanced Dvorak technique–derived cyclone intensity estimates is conducted. Intensity estimate error and bias peak at the beginning of extratropical transition and decline thereafter for maximum sustained surface wind. On average, synthetic advanced Dvorak technique–derived estimates of maximum sustained surface wind asymptote toward or remain near their weakest-possible values after extratropical transition begins. Minimum sea level pressure estimates exhibit minimal bias, although this result is sensitive to microphysical parameterization. Such sensitivity to microphysical parameterization, particularly with respect to cloud radiative properties, suggests that only qualitative insight regarding advanced Dvorak technique–derived intensity estimate error during extratropical transition may be obtained utilizing synthetic satellite imagery. Implications toward developing improved intensity estimates during extratropical transition are discussed.