Search Results

You are looking at 41 - 50 of 78 items for

  • Author or Editor: Christopher Velden x
  • Refine by Access: All Content x
Clear All Modify Search
Christopher S. Velden, Christopher M. Hayden, W. Paul Menzel, James L. Franklin, and James S. Lynch

Abstract

While qualitative information from meteorological satellites has long been recognized as critical for monitoring tropical cyclone activity, quantitative data are required to improve the objective analysis and numerical weather prediction of these events. In this paper, results are presented that show that the inclusion of high-density, multispectral, satellite-derived information into the analysis of tropical cyclone environmental wind fields can effectively reduce the error of objective track forecasts. Two independent analysis and barotropic track-forecast systems are utilized in order to examine the consistency of the results. Both systems yield a 10%–23% reduction in middle- to long-range track-forecast errors with the inclusion of the satellite wind observations.

Full access
James L. Franklin, John Kaplan, Christopher S. Velden, and Christopher M. Hayden

Abstract

Omega dropwindsonde and other in situ (INS) data collected during the NOAA/Hurricane Research Division's (HRD) Hurricane Field Program are used as a ground truth dataset for the evaluation of VISSR Atmospheric Sounder (VAS) soundings over the subtropical Atlantic. The experiments were coordinated with the Cooperative Institute for Meteorological Satellite Services at the University of Wisconsin. The focus of this study is to determine whether soundings derived from VAS radiances are an improvement over the first-guess data used as a starting point in the sounding retrieval process. First guess inputs for this study are provided by NMCs Regional Analysis and Forecast System (RAFS) nested grid model (NGM).

In a case study, an objective algorithm is used to analyze the INS, VAS, and first-guess data at and below 500 mb from an HRD experiment on 1–2 September 1988. The case study is supplemented by a statistical investigation of data composited from other HRD experiments. In particular, we examine VAS estimates of horizontal temperature and moisture gradients to see if they represent improvements over the first guess.

The temperature and moisture descriptions in the vicinity of a 500 mb cold low were improved by the VAS in the case study; however, VAS temperature gradients were found to be generally less accurate than those of the first guess. Temperature gradients from the VAS were also consistently stronger than INS or first-guess gradients. The composite study found that large-scale VAS moisture gradients were better than those of the first guess. Other results indicate a preferred mode for VAS modifications to the guess: the primary impact of the VAS radiances on the first guess was to improve the description of the phasing of horizontal features. The VAS representation of the amplitude of features, however, was not consistently an improvement. This suggests that in tropical applications, VAS data may be most suitable for subjective forecasting uses; if VAS data are to be used in numerical weather prediction, strongest weight should be given to the representation of the location of weather features (troughs, ridges, etc.), and relatively weak weight should be given to the representation of the strength of these features.

Full access
Christopher M. Rozoff, Christopher S. Velden, John Kaplan, James P. Kossin, and Anthony J. Wimmers

Abstract

The probabilistic prediction of tropical cyclone (TC) rapid intensification (RI) in the Atlantic and eastern Pacific Ocean basins is examined here using a series of logistic regression models trained on environmental and infrared satellite-derived features. The environmental predictors are based on averaged values over a 24-h period following the forecast time. These models are compared against equivalent models enhanced with additional TC predictors created from passive satellite microwave imagery (MI). Leave-one-year-out cross validation on the developmental dataset shows that the inclusion of MI-based predictors yields more skillful RI models for a variety of RI and intensity thresholds. Compared with the baseline forecast skill of the non-MI-based RI models, the relative skill improvements from including MI-based predictors range from 10.6% to 44.9%. Using archived real-time data during the period 2004–13, evaluation of simulated real-time models is also carried out. Unlike in the model development stage, the simulated real-time setting involves using Global Forecast System forecasts for the non-satellite-based predictors instead of “perfect” observational-based predictors in the developmental data. In this case, the MI-based RI models still generate superior skill to the baseline RI models lacking MI-based predictors. The relative improvements gained in adding MI-based predictors are most notable in the Atlantic, where the non-MI versions of the models suffer acutely from the use of imperfect real-time data. In the Atlantic, relative skill improvements provided from the inclusion of MI-based predictors range from 53.5% to 103.0%. The eastern Pacific relative improvements are less impressive but are still uniformly positive.

Full access
Sarah A. Monette, Christopher S. Velden, Kyle S. Griffin, and Christopher M. Rozoff

Abstract

A geostationary satellite–derived cloud product that is based on a tropical-overshooting-top (TOT) detection algorithm is described for applications over tropical oceans. TOTs are identified using a modified version of a midlatitude overshooting-top detection algorithm developed for severe-weather applications. The algorithm is applied to identify TOT activity associated with Atlantic Ocean tropical cyclones (TCs). The detected TOTs can serve as a proxy for “hot towers,” which represent intense convection with possible links to TC rapid intensification (RI). The purpose of this study is to describe the adaptation of the midlatitude overshooting-top detection algorithm to the tropics and to provide an initial exploration of possible correlations between TOT trends in developing TCs and subsequent RI. This is followed by a cursory examination of the TOT parameter’s potential as a predictor of RI both on its own and in multiparameter RI forecast schemes. RI forecast skill potential is investigated by examining empirical thresholds of TOT activity and trends within prescribed radii of a large sample of developing North Atlantic TC centers. An independent test on Atlantic TCs in 2006–07 reveals that an empirically based TOT scheme has potential as a predictor for RI occurring in the subsequent 24 h, especially for RI maximum wind thresholds of 25 and 30 kt (24 h)−1 (1 kt ≈ 0.5 m s−1). As expected, the stand-alone TOT-based RI scheme is comparatively less accurate than existing objective multiparameter RI prediction methods. A preliminary experiment that adds TOT-based predictors to an objective logistic regression-based scheme is shown to improve slightly the forecast skill of RI, however.

Full access
Steven J. Nieman, W. Paul Menzei, Christopher M. Hayden, Donald Gray, Steven T. Wanzong, Christopher S. Velden, and Jaime Daniels

Cloud-drift winds have been produced from geostationary satellite data in the Western Hemisphere since the early 1970s. During the early years, winds were used as an aid for the short-term forecaster in an era when numerical forecasts were often of questionable quality, especially over oceanic regions. Increased computing resources over the last two decades have led to significant advances in the performance of numerical forecast models. As a result, continental forecasts now stand to gain little from the inspection or assimilation of cloud-drift wind fields. However, the oceanic data void remains, and although numerical forecasts in such areas have improved, they still suffer from a lack of in situ observations. During the same two decades, the quality of geostationary satellite data has improved considerably, and the cloud-drift wind production process has also benefited from increased computing power. As a result, fully automated wind production is now possible, yielding cloud-drift winds whose quality and quantity is sufficient to add useful information to numerical model forecasts in oceanic and coastal regions. This article will detail the automated cloud-drift wind production process, as operated by the National Environmental Satellite Data and Information Service within the National Oceanic and Atmospheric Administration.

Full access
Christopher S. Velden, Christopher M. Hayden, Steven J W. Nieman, W. Paul Menzel, Steven Wanzong, and James S. Goerss

The coverage and quality of remotely sensed upper-tropospheric moisture parameters have improved considerably with the deployment of a new generation of operational geostationary meteorological satellites: GOES-8/9 and GMS-5. The GOES-8/9 water vapor imaging capabilities have increased as a result of improved radiometric sensitivity and higher spatial resolution. The addition of a water vapor sensing channel on the latest GMS permits nearly global viewing of upper-tropospheric water vapor (when joined with GOES and Meteosat) and enhances the commonality of geostationary meteorological satellite observing capabilities. Upper-tropospheric motions derived from sequential water vapor imagery provided by these satellites can be objectively extracted by automated techniques. Wind fields can be deduced in both cloudy and cloud-free environments. In addition to the spatially coherent nature of these vector fields, the GOES-8/9 multispectral water vapor sensing capabilities allow for determination of wind fields over multiple tropospheric layers in cloud-free environments. This article provides an update on the latest efforts to extract water vapor motion displacements over meteorological scales ranging from subsynoptic to global. The potential applications of these data to impact operations, numerical assimilation and prediction, and research studies are discussed.

Full access
Christopher S. Velden, William L. Smith, and Max Mayfield

Initial results are presented on research designed to evaluate the usefulness of Visible Infrared Spin Scan Radiometer Atmospheric Sounder (VAS) data in tropical cyclone applications. It is part of the National Aeronautics and Space Administration funded VAS demonstration, and the A/ational Oceanic and Atmospheric Administration (NOAA) Operational FAS Assessment (NOVA) program. The University of Wisconsin (UW) Space Science and Engineering Center (SSEC) and the National Environmental Satellite, Data, and Information Service (NESDIS) Development Laboratory at the SSEC have been working with the National Hurricane Center (NHC), and the NOAA/Environmental Research Laboratories Atlantic Oceanographic and Meteorological Laboratory—Hurricane Research Division (HRD) to explore the different uses of geostationary satellite VAS data in tropical cyclone analysis and forecasting. Because of the cloud-penetrating capability of the microwave component of the TIROS Operational Vertical Sounder (TOVS), polar orbiting satellite TOVS soundings in cloudy regions are used in some cases to enhance the VAS products along with cloud drift and water vapor motion winds derived from VAS imagery. This report describes some of the VAS/TOVS products being generated and evaluated on the Man-computer Interactive Data Access System (McIDAS) at the UW-SSEC and the NHC.

Full access
Ting-Chi Wu, Christopher S. Velden, Sharanya J. Majumdar, Hui Liu, and Jeffrey L. Anderson

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.

Full access
Jason P. Dunion, Samuel H. Houston, Christopher S. Velden, and Mark D. Powell

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.

Full access
Kristopher M. Bedka, Christopher S. Velden, Ralph A. Petersen, Wayne F. Feltz, and John R. Mecikalski

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.

Full access