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Kenneth Holmlund, Christopher S. Velden, and Michael Rohn

Abstract

The coverage and quality of atmospheric motion vectors (AMVs) derived from geostationary satellite imagery have improved considerably over the past few years. This is due not only to the deployment of the new generation of satellites, but is also a result of improved data processing and automated quality control (AQC) schemes. The postprocessing of the Geostationary Operational Environmental Satellite (GOES) derived displacement vectors at the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service (NOAA/NESDIS) has been fully automated since early 1996. At the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) AQC was used as support to the manual quality control (MQC) for Meteosat vector fields until September 1998 when the MQC was discontinued and fully replaced with the automated procedure. The AQC schemes at the two organizations are quite diverse. Based on a method developed at the University of Wisconsin–Cooperative Institute for Meteorological Satellite Studies, the NOAA/NESDIS AQC involves an objective three-dimensional recursive filter analysis of the derived wind fields. The fit of each vector to that analysis yields a recursive filter flag (RFF). The AQC scheme employed at EUMETSAT derives a quality indicator (QI) for each individual vector based on the properties of the vector itself and its consistency with other AMVs in close proximity. Mainly relying on satellite data, this QI-based scheme has been proven to provide a good estimate of the reliability of the derived displacements, but it fails to identify fleets of winds that are consistently assigned to a wrong height. The RFF-based scheme is capable of readjusting the heights attributed to the wind vectors, which yields a better fit to the analysis and ancillary data. These quality estimates can be employed by the user community to select the part of the vector field that best suits their application, as well as in data assimilation schemes for optimizing the data selection procedures. Even though both schemes have already been successfully implemented into operational environments, the possibility exists to exploit the advantages of both approaches to create a superior combined methodology.

In order to substantiate the differences of the two methodologies, the two schemes were applied to the high-density AMV fields derived during the 1998 North Pacific Experiment from GOES and the Geostationary Meteorological Satellite (controlled by the Japan Meteorological Agency) multispectral imagery data. The performance of the two schemes was evaluated by examining departures from the analysis fields of the European Centre for Medium-Range Weather Forecasts (ECMWF) assimilation scheme, and a new combined methodology was further evaluated by looking at the impact on medium-range forecasts verified against the operational forecasts. It will be shown that the new combined AQC approach yields superior ECMWF forecast results.

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Christopher S. Velden and Graham A. Mills

Abstract

On 1 December 1987, an unusual midlatitude cyclone affected much of southeastern Australia. The storm was characterized by unforced rapid deepening to a near record low (locally) mean sea-level pressure, high winds, anomalously cold surface temperatures, and near-record rainfall in some areas. The storm resulted in extensive damage, including a massive livestock kill. Comparison with storm tracks over southern Australia from the past 20 years shows that the path of this storm was quite unusual for this time of year.

Utilizing a series of analyses prepared from an incremental limited area data assimilation system, it is shown that: 1) an amplifying upper-tropospheric wave influenced the initial development and path of the cyclone as it crossed the southern coast of Australia, 2) transverse circulations associated with two juxtaposed upper-level jet streaks embedded in the wave focussed upper-level divergence and midlevel ascent over the low during its rapid intensification phase, and 3) a distinct upper-tropospheric isentropic potential vorticity maximum was identified well upstream of the developing low, but with no evidence of an extrusion of this air penetrating and enhancing the low-level circulation as has been found in other cases of rapid cyclogenesis.

Given that inadequate operational numerical weather prediction (NWP) guidance was partially to blame for the underforecast of this event, the operational limited area NWP forecasts are presented and compared with forecasts based on the research analyses from the assimilation system. 11 is shown that improved forecasts of cyclone intensification and of precipitation result when the model is initialized with the assimilation analyses. Further improvements are obtained when the grid resolution of the forecast model is increased. With the operational implementation of the assimilation system into the Australian Bureau of Meteorology (BOM) in 1989, the improved guidance resulting from the assimilated analyses is currently available to forecasters in Australia.

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Anthony J. Wimmers and Christopher S. Velden

Satellite-based passive microwave imagery of tropical cyclones (TCs) is an invaluable resource for assessing the organization and evolution of convective structures in TCs when often no other comparable observations exist. However, the current constellation of low-Earth-orbiting environmental satellites that can effectively image TCs in the microwave range make only semirandom passes over TC targets, roughly every 3 - 6 h, but vary from less than 30 min to more than 25 h between passes. These irregular time gaps hamper the ability of analysts/forecasters to easily incorporate these data into a diagnosis of the state of the TC. To address this issue, we have developed a family of algorithms called Morphed Integrated Microwave Imagery at the Cooperative Institute for Meteorological Satellite Studies (MIMIC) to create synthetic “morphed” images that utilize the observed imagery to fill in the time gaps and present time-continuous animations of tropical cyclones and their environment. MIMIC-TC is a product that presents a storm-centered 15-min-resolution animation of microwave imagery in the ice-scattering range (85–92 GHz), which can be interpreted very much like a ground-based radar animation. A second product, MIMIC-IR, animates a tropical cyclone-retrieved precipitation field layered over geostationary infrared imagery. These tools allow forecasters and analysts to use microwave imagery to follow trends in a tropical cyclone's structure more efficiently and effectively, which can result in higher-confidence short-term intensity forecasts.

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Christopher S. Velden and William L. Smith

Abstract

NOAA satellite microwave soundings, which penetrate high clouds, delineate the development and dissipation of the upper tropospheric warm core associated with a tropical cyclone. The storm's “core” may be detected from microwave imagery. Vertical cross sections reveal the intensification of the upper tropospheric warm core as the storm developes, and the downward propagation of the warm core as the storm dissipates. Excellent correlation is found between the horizontal Laplacian of an upper tropospheric temperature field and the intensity of the storm, as categorized by its surface central pressure and maximum sustained wind speed at the eye wall. The microwave monitoring of tropical cyclones is achieved in real time at the University of Wisconsin's Space Science and Engineering Center through high-speed teleconnections to direct readout receiving systems at Wallops Island, Virginia and Redwood City, California.

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Timothy L. Olander and Christopher S. Velden

Abstract

A technique to identify and quantify intense convection in tropical cyclones (TCs) using bispectral, geostationary satellite imagery is explored. This technique involves differencing the water vapor (WV) and infrared window (IRW) channel brightness temperature values, which are available on all current operational geostationary weather satellites. Both the derived IRW minus WV (IRWV) imagery and the raw data values can be used in a variety of methods to provide TC forecasters with important information about current and future intensity trends, a component within the operational TC forecasting arena that has shown little improvement during the past few decades.

In this paper several possible uses for this bispectral technique, both qualitative and quantitative, are explored and outlined. Qualitative monitoring of intense convection can be used as a proxy for passive microwave (MW) imager data obtained from polar-orbiting satellite platforms when not available. In addition, the derived imagery may aid in the TC storm center identification process, both manually and objectively, especially in difficult situations where the IRW imagery alone cannot be used such as when the storm circulation center and/or eye features are obscured by a cirrus canopy. Quantitative methods discussed involve the predictive quality of the IRWV data in terms of TC intensity changes, primarily during TC intensification. Strong correlations exist between storm intensity changes and IRWV values at varying 6-h forecast interval periods, peaking between the 12- and 24-h time periods. Implications for the use of the IRWV data on such objective satellite intensity estimate algorithms as the University of Wisconsin—Madison (UW) Cooperative Institute for Meteorological Satellite Studies (CIMSS) advanced Dvorak technique (ADT) are also discussed.

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Shixuan Zhang, Zhaoxia Pu, and Christopher Velden

Abstract

The impacts of enhanced satellite-derived atmospheric motion vectors (AMVs) on the numerical prediction of intensity changes during Hurricanes Gonzalo (2014) and Joaquin (2015) are examined. Enhanced AMVs benefit from special data-processing strategies and are examined for impact on model forecasts via assimilation experiments by employing the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting (HWRF) Model using a Gridpoint Statistical Interpolation analysis system (GSI)-based ensemble–variational hybrid system. Two different data assimilation (DA) configurations, one with and one without the use of vortex initialization (VI), are compared. It is found that the assimilation of enhanced AMVs can improve the HWRF track and intensity forecasts of Gonzalo and Joaquin during their intensity change phases. The degree of data impact depends on the DA configuration used. Overall, assimilation of enhanced AMVs in the innermost domain (e.g., storm inner-core region and its immediate vicinity) outperforms other DA configurations, both with and without VI, as it results in better track and intensity forecasts. Compared to the experiment with VI, assimilation of enhanced AMVs without VI reveals more notable data impact on the forecasts of Hurricane Gonzalo, as the VI before DA alters the first guess and reduces the actual number of AMV observations assimilated into the DA system. Even with VI, assimilation of enhanced AMVs in the inner-core region can at least partially mitigate the negative effect of VI on the intensity forecast of Hurricane Gonzalo and alleviate the unrealistic vortex weakening in the simulation by removing unrealistic outflow structure and unfavorable thermodynamic conditions, thus leading to improved intensity forecasts.

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Timothy L. Olander and Christopher S. Velden

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.

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Anthony Wimmers, Christopher Velden, and Joshua H. Cossuth

Abstract

A deep learning convolutional neural network model is used to explore the possibilities of estimating tropical cyclone (TC) intensity from satellite images in the 37- and 85–92-GHz bands. The model, called “DeepMicroNet,” has unique properties such as a probabilistic output, the ability to operate from partial scans, and resiliency to imprecise TC center fixes. The 85–92-GHz band is the more influential data source in the model, with 37 GHz adding a marginal benefit. Training the model on global best track intensities produces model estimates precise enough to replicate known best track intensity biases when compared to aircraft reconnaissance observations. Model root-mean-square error (RMSE) is 14.3 kt (1 kt ≈ 0.5144 m s−1) compared to two years of independent best track records, but this improves to an RMSE of 10.6 kt when compared to the higher-standard aircraft reconnaissance-aided best track dataset, and to 9.6 kt compared to the reconnaissance-aided best track when using the higher-resolution TRMM TMI and Aqua AMSR-E microwave observations only. A shortage of training and independent testing data for category 5 TCs leaves the results at this intensity range inconclusive. Based on this initial study, the application of deep learning to TC intensity analysis holds tremendous promise for further development with more advanced methodologies and expanded training datasets.

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Jason P. Dunion and Christopher S. Velden

Abstract

Beginning with the 1997 hurricane season, the Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin—Madison began demonstrating the derivation of real-time Geostationary Operational Environmental Satellite (GOES) low-level cloud-drift winds in the vicinity of Atlantic tropical cyclones. The winds are derived from tracking low-level clouds in sequential, high-resolution GOES visible channel imagery. Since then, these data have been provided to the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division (HRD) for evaluation in their real-time tropical cyclone surface wind objective analyses (H*Wind) that are disseminated to forecasters at the NOAA National Hurricane Center on an experimental basis. These wind analyses are proving useful as guidance to support forecasters's tropical cyclone advisories and warnings. The GOES satellite wind observations often provide essential near-surface coverage in the outer radii of the tropical cyclone circulation where conventional in situ observations (e.g., ships and buoys) are frequently widely spaced or nonexistent and reconnaissance aircraft do not normally fly. The GOES low-level cloud-tracked winds are extrapolated to the surface using a planetary boundary layer model developed at HRD for hurricane environments.

In this study, the unadjusted GOES winds are validated against wind profiles from the newly deployed global positioning system dropwindsondes, and the surface-adjusted winds are compared with collocated in situ surface measurements. The results show the ability of the GOES winds to provide valuable quantitative data in the periphery of tropical cyclones. It is also shown that the current scheme employed to extrapolate the winds to the surface results in small biases in both speed and direction. Nonlinear adjustments to account for these biases are presented.

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Christopher S. Velden and Lance M. Leslie

Abstract

A simple barotropic model is employed to investigate relative impacts on tropical cyclone motion forecasts in the Australian region when wind analyses from different tropospheric levels or layers are used as the input to the model. The model is initialized with selected horizontal wind analyses from individual pressure levels, and vertical averages of several pressure levels (layer-means).

The 48-h mean forecast errors (MFE) from this model are analyzed for 300 tropical cyclone cases that cover a wide range of intensities. A significant reduction in the track forecast errors results when the depth of the vertically-averaged initial wind analysis depends upon the initial storm intensity. Mean forecast errors show that the traditionally-utilized 1000-100-hPa deep layer-mean (DLM) analysis is a good approximation of future motion only in cases of very intense tropical cyclones. Shallower, lower-tropospheric layer-means consistently outperform single-level analyses, and are best correlated with future motion in weak and moderate intensity cases.

These results suggest that barotropic track forecasting in the Australian region can be significantly improved if the depth of the vertically-averaged initial wind analysis is based upon the tropical cyclone intensity.

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