Christopher C. Hennon

Globally Gridded Satellite observations for climate studies

Geostationary satellites have provided routine, high temporal resolution Earth observations since the 1970s. Despite the long period of record, use of these data in climate studies has been limited for numerous reasons, among them that no central archive of geostationary data for all international satellites exists, full temporal and spatial resolution data are voluminous, and diverse calibration and navigation formats encumber the uniform processing needed for multisatellite climate studies. The International Satellite Cloud Climatology Project (ISCCP) set the stage for overcoming these issues by archiving a subset of the full-resolution geostationary data at ~10-km resolution at 3-hourly intervals since 1983. Recent efforts at NOAAs National Climatic Data Center to provide convenient access to these data include remapping the data to a standard map projection, recalibrating the data to optimize temporal homogeneity, extending the record of observations back to 1980, and reformatting the data for broad ...

The Operational Use of QuikSCAT Ocean Surface Vector Winds at the National Hurricane Center

Abstract The utility and shortcomings of near-real-time ocean surface vector wind retrievals from the NASA Quick Scatterometer (QuikSCAT) in operational forecast and analysis activities at the National Hurricane Center (NHC) are described. The use of QuikSCAT data in tropical cyclone (TC) analysis and forecasting for center location/identification, intensity (maximum sustained wind) estimation, and analysis of outer wind radii is presented, along with shortcomings of the data due to the effects of rain contamination and wind direction uncertainties. Automated QuikSCAT solutions in TCs often fail to show a closed circulation, and those that do are often biased to the southwest of the NHC best-track position. QuikSCAT winds show the greatest skill in TC intensity estimation in moderate to strong tropical storms. In tropical depressions, a positive bias in QuikSCAT winds is seen due to enhanced backscatter by rain, while in major hurricanes rain attenuation, resolution, and signal saturation result in a larg...

Forecasting Tropical Cyclogenesis over the Atlantic Basin Using Large-Scale Data

Abstract A new dataset of tropical cloud clusters, which formed or propagated over the Atlantic basin during the 1998–2000 hurricane seasons, is used to develop a probabilistic prediction system for tropical cyclogenesis (TCG). Using data from the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis (NNR), eight large-scale predictors are calculated at every 6-h interval of a clusters life cycle. Discriminant analysis is then used to find a linear combination of the predictors that best separates the developing cloud clusters (those that became tropical depressions) and nondeveloping systems. Classification results are analyzed via composite and case study points of view. Despite the linear nature of the classification technique, the forecast system yields useful probabilistic forecasts for the vast majority of the hurricane season. The daily genesis potential (DGP) and latitude predictors are found to be the most significant at nearly all foreca...

An Objective Algorithm for Detecting and Tracking Tropical Cloud Clusters: Implications for Tropical Cyclogenesis Prediction

An algorithm to detect and track global tropical cloud clusters (TCCs) is presented. TCCs are organized large areas of convection that form over warm tropical waters. TCCs are important because they are the ‘‘seedlings’’ that can evolve into tropical cyclones. A TCC satisfies the necessary condition of a ‘‘preexisting disturbance,’’ which provides the required latent heat release to drive the development of tropical cyclone circulations. The operational prediction of tropical cyclogenesis is poor because of weaknesses in the observationalnetworkand numericalmodels;thus,past studies havefocused on identifying differencesbetween ‘‘developing’’ (evolving into a tropical cyclone) and ‘‘nondeveloping’’ (failing to do so) TCCs in the global analysis fields to produce statistical forecasts of these events. The algorithm presented here has been used to create a global dataset of all TCCs that formed from 1980 to 2008. Capitalizing on a global, Gridded Satellite (GridSat) infrared (IR) dataset, areas of persistent, intense convection are identified by analyzing characteristics of the IR brightness temperature (Tb) fields. Identified TCCs are tracked as they move around their ocean basin (or cross into others); variables such as TCC size, location, convective intensity, cloud-top height, development status (i.e., developing or nondeveloping), and a movement vector are recorded in Network Common Data Form (NetCDF). The algorithm can be adapted to near-real-time tracking of TCCs, which could be of great benefit to the tropical cyclone forecast community.

Improving Tropical Cyclogenesis Statistical Model Forecasts through the Application of a Neural Network Classifier

A binary neural network classifier is evaluated against linear discriminant analysis within the framework of a statistical model for forecasting tropical cyclogenesis (TCG). A dataset consisting of potential developing cloud clusters that formed during the 1998–2001 Atlantic hurricane seasons is used in conjunction with eight large-scale predictors of TCG. Each predictor value is calculated at analysis time. The model yields 6–48-h probability forecasts for genesis at 6-h intervals. Results consistently show that the neural network classifier performs comparably to or better than linear discriminant analysis on all performance measures examined, including probability of detection, Heidke skill score, and forecast reliability. Two case studies are presented to investigate model performance and the feasibility of adapting the model to operational forecast use.

Cyclone Center: Can Citizen Scientists Improve Tropical Cyclone Intensity Records?

AbstractThe global tropical cyclone (TC) intensity record, even in modern times, is uncertain because the vast majority of storms are only observed remotely. Forecasters determine the maximum wind speed using a patchwork of sporadic observations and remotely sensed data. A popular tool that aids forecasters is the Dvorak technique—a procedural system that estimates the maximum wind based on cloud features in IR and/or visible satellite imagery. Inherently, the application of the Dvorak procedure is open to subjectivity. Heterogeneities are also introduced into the historical record with the evolution of operational procedures, personnel, and observing platforms. These uncertainties impede our ability to identify the relationship between tropical cyclone intensities and, for example, recent climate change.A global reanalysis of TC intensity using experts is difficult because of the large number of storms. We will show that it is possible to effectively reanalyze the global record using crowdsourcing. Throu...

Tropical Cloud Cluster Climatology, Variability, and Genesis Productivity

Tropical cloud clusters (TCCs) are traditionally defined as synoptic-scale areas of deep convection and associatedcirrusoutflow.Theyplaya criticalrolein the energybalanceof thetropics,releasinglargeamounts of latent heat high in the troposphere. If conditions are favorable, TCCs can develop into tropical cyclones (TCs), which put coastal populations at risk. Previous work, usually connected with large field campaigns, has investigated TCC characteristics over small areas and time periods. Recently, developments in satellite reanalysis and global best track assimilation have allowed for the creation of a much more extensive database of TCC activity. The authors use the TCC database to produce an extensive global analysis of TCCs, focusing on TCC climatology, variability, and genesis productivity (GP) over a 28-yr period (1982‐2009). While global TCC frequency was fairly consistent over the time period, with relatively small interannual variability and no noticeable trend, regional analyses show a high degree of interannual variability with clear trends in some regions. Approximately 1600 TCCs develop around the globe each year; about 6.4% of those develop into TCs. The eastern North Pacific Ocean (EPAC) basin produces the highest number of TCCs (per unit area) in a given year, but the western North Pacific Ocean (WPAC) basin has the highest GP (;12%). Annual TCC frequency in some basins exhibits a strong correlation to sea surface temperatures (SSTs), particularly in the EPAC, North Atlantic Ocean, and WPAC. However, GP is not as sensitive to SST, supporting the hypothesis that the tropical cyclogenesis process is most sensitive to atmospheric dynamical considerations such as vertical wind shear and large-scale vorticity.

Improved Hurricane Ocean Vector Winds Using SeaWinds Active/Passive Retrievals

The SeaWinds scatterometer, onboard the QuikSCAT satellite, infers global ocean vector winds (OVWs); however, for a number of reasons, these measurements in hurricanes are significantly degraded. This paper presents an improved hurricane OVW retrieval approach, known as Q-Winds, which is derived from combined SeaWinds active and passive measurements. In this technique, the effects of rain are implicitly included in a new geophysical model function, which relates oceanic brightness temperature and radar backscatter measurements (at the top of the atmosphere) to the surface wind vector under both clear sky and in the presence of light to moderate rain. This approach extends the useful wind speed measurement range for tropical cyclones beyond that exhibited by the standard SeaWinds Project Level-2B (L2B) 12.5-km wind vector algorithm. A description of the Q-Winds algorithm is given, and examples of OVW retrievals are presented for the Q-Winds and L2B 12.5-km algorithms for ten hurricane overpasses in 2003-2008. These data are also compared to independent surface wind vector estimates from the National Oceanic and Atmospheric Administration Hurricane Research Divisions objective hurricane surface wind analysis technique known as H*Wind. These comparisons suggest that the Q-Winds OVW product agrees better with independently derived H^ Wind analysis winds than does the conventional L2B OVW product.

Identification of Tropical Cyclone Storm Types Using Crowdsourcing

AbstractThe Cyclone Center project maintains a website that allows visitors to answer questions based on tropical cyclone satellite imagery. The goal is to provide a reanalysis of satellite-derived tropical cyclone characteristics from a homogeneous historical database composed of satellite imagery with a common spatial resolution for use in long-term, global analyses. The determination of the cyclone “type” (curved band, eye, shear, etc.) is a starting point for this process. This analysis shows how multiple classifications of a single image are combined to provide probabilities of a particular image’s type using an expectation–maximization (EM) algorithm. Analysis suggests that the project needs about 10 classifications of an image to adequately determine the storm type. The algorithm is capable of characterizing classifiers with varying levels of expertise, though the project needs about 200 classifications to quantify an individual’s precision. The EM classifications are compared with an objective alg...

Citizen scientists analyzing tropical cyclone intensities

A new crowd sourcing project called CycloneCenter enables the public to analyze historical global tropical cyclone (TC) intensities. The primary goal of CycloneCenter, which launched in mid-September, is to resolve discrepancies in the recent global TC record arising principally from inconsistent development of tropical cyclone intensity data. The historical TC record is composed of data sets called “best tracks,” which contain a forecast agencys best assessment of TC tracks and intensities. Best track data have improved in quality since the beginning of the geostationary satellite era in the 1960s (because TCs could no longer disappear from sight). However, a global compilation of best track data (International Best Track Archive for Climate Stewardship (IBTrACS)) has brought to light large interagency differences between some TC best track intensities, even in the recent past [Knapp et al., 2010Knapp et al., 2010]. For example, maximum wind speed estimates for Tropical Cyclone Gay (1989) differed by as much as 70 knots as it was tracked by three different agencies.