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Anthony J. Wimmers and Christopher S. Velden

Abstract

Conventional methods of viewing and combining retrieved geophysical fields from polar-orbiting satellites often complicate the work of end users because of the erratic time differences between overpasses, the significant time gaps between elements of a composite image, or simply the different requirements for interpretation between contributing instruments. However, it is possible to mitigate these issues for any number of retrieved quantities in which the tracer lifetime exceeds the sampling time. This paper presents a method that uses “advective blending” to create high-fidelity composites of data from polar-orbiting satellites at high temporal resolution, including a characterization of error as a function of time gap between satellite overpasses. The method is especially effective for tracers with lifetimes of longer than 7 h. Examples are presented using microwave-based retrievals of total precipitable water (TPW) over the ocean, from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) Morphed Integrated Microwave Imagery at CIMSS TPW product (MIMIC-TPW). The mean average error of a global 0.25° × 0.25° product at 1-h resolution is 0.5–2 mm, which is very reasonable for most applications.

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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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Robert T. Merrill and Christopher S. Velden

Abstract

Isentropic coordinate analyses of rawinsondes and cloud motion wind vectors derived from geostationary satellite imagery are employed to describe the three-dimensional upper-tropospheric and lower-stratospheric circulation attending western North Pacific Supertyphoon Flo during September 1990. Outflow from the storm is concentrated in several evolving channels in the horizontal. In terms of vertical structure, net outflow evaluated at 6° latitude (666 km) radius is found to occur at higher levels and over an increasing range of potential temperature θ as the tropical cyclone intensifies. Outflow on the equatorward side of the tropical cyclone tends to occur at greater θ values (higher altitudes) than poleward outflow. Potential vorticity also decreases within the corresponding isentropic layers associated with the outflow. The implications of the vertical variability of outflow structure in terms of the interactions between storm and environment, and effects on storm structural changes, are considered briefly.

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Christopher S. Velden and John A. Young

Abstract

The 1992/93 Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (COARE) was specifically designed to monitor multiscale interactions between the atmosphere and ocean over the western Pacific warm pool. To help meet this objective, satellite observations were used to augment the enhanced COARE conventional data array in both space and time.

In this paper the authors present a descriptive overview of convective cloud variability and sea surface temperature during the four-month intensive observational period (IOP) as revealed by satellite. Time series of Geostationary Meteorological Satellite infrared brightness temperatures are evaluated at selected equatorial locations in the western Pacific and eastern Indian Oceans. Intraseasonal modes of transient convection/cloudiness are revealed, with two eastward-propagating Madden-Julian oscillations identified. Spectral analysis on the time series data indicates that higher-frequency variations in regional convective activity are also found to occur.

Several satellite cloud signatures and patterns were detected during a strong west wind burst event in late December (1992), and these are described in detail. Time-composited sea surface temperature (SST) fields derived from satellite radiances indicate that significant regional variations in SST occurred during the passage of the west wind event. The satellite-derived SST fields compiled during the IOP are validated against in situ observations in the COARE domain, with a 0.25°C warm bias noted in the composited satellite data.

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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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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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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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Jason P. Dunion, Christopher D. Thorncroft, and Christopher S. Velden

Abstract

The diurnal cycle of tropical convection and the tropical cyclone (TC) cirrus canopy has been described extensively in previous studies. However, a complete understanding of the TC diurnal cycle remains elusive and is an area of ongoing research. This work describes a new technique that uses infrared satellite image differencing to examine the evolution of the TC diurnal cycle for all North Atlantic major hurricanes from 2001 to 2010. The imagery reveals cyclical pulses in the infrared cloud field that regularly propagate radially outward from the storm. These diurnal pulses begin forming in the storm’s inner core near the time of sunset each day and continue to move away from the storm overnight, reaching areas several hundreds of kilometers from the circulation center by the following afternoon. A marked warming of the cloud tops occurs behind this propagating feature and there can be pronounced structural changes to a storm as it moves away from the inner core. This suggests that the TC diurnal cycle may be an important element of TC dynamics and may have relevance to TC structure and intensity change. Evidence is also presented showing the existence of statistically significant diurnal signals in TC wind radii and objective Dvorak satellite-based intensity estimates for the 10-yr hurricane dataset that was examined. Findings indicate that TC diurnal pulses are a distinguishing characteristic of the TC diurnal cycle and the repeatability of TC diurnal pulsing in time and space suggests that it may be an unrealized, yet fundamental TC process.

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Carolyn A. Reynolds, Rolf Langland, Patricia M. Pauley, and Christopher Velden

Abstract

The impacts of assimilating dropwindsonde data and enhanced atmospheric motion vectors (AMVs) on tropical cyclone track forecasts are examined using the Navy global data assimilation and forecasting systems. Enhanced AMVs have the largest impact on eastern Pacific storms, while dropwindsonde data have the largest impact on Atlantic storms. Results in the western Pacific are mixed. Two western Pacific storms, Nuri and Jangmi, are examined in detail. For Nuri, dropwindsonde data and enhanced AMVs are at least as likely to degrade as to improve forecasts. For Jangmi, additional data improve track forecasts in most cases. An erroneous weakening of the forecasted subtropical high appears to contribute to the track errors for Nuri and Jangmi. Assimilation of enhanced AMVs systematically increases the analyzed heights in this region, counteracting this model bias. However, the impact of enhanced AMVs decreases rapidly as the model biases saturate at similar levels for experiments with and without the enhanced AMVs after the first few forecast days. Experiments are also conducted in which the errors assigned to synthetic tropical cyclone observations are increased. Moderate increases in the assigned errors improve track forecasts on average, but larger increases in the assigned errors produce mixed results. Both experiments allow for reductions in innovations and residuals when compared to dropwindsonde observations. These experiments suggest that a reformulation of the synthetic tropical cyclone observation scheme may lead to improved forecasts as more in situ and remote observations become available.

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