A Non-MLE Approach for Satellite Scatterometer Wind Vector Retrievals in Tropical Cyclones

Satellite microwave scatterometers are the principal source of global synoptic-scale ocean vector wind (OVW) measurements for a number of scientific and operational oceanic wind applications. However, for extreme wind events such as tropical cyclones, their performance is significantly degraded. This paper presents a novel OVW retrieval algorithm for tropical cyclones which improves the accuracy of scatterometer based ocean surface winds when compared to low-flying aircraft with in-situ and remotely sensed observations. Unlike the traditional maximum likelihood estimation (MLE) wind vector retrieval technique, this new approach sequentially estimates scalar wind directions and wind speeds. A detailed description of the algorithm is provided along with results for ten QuikSCAT hurricane overpasses (from 2003-2008) to evaluate the performance of the new algorithm. Results are compared with independent surface wind analyses from the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division\u27s H*Wind surface analyses and with the corresponding SeaWinds Project\u27s L2B-12.5 km OVW products. They demonstrate that the proposed algorithm extends the SeaWinds capability to retrieve wind speeds beyond the current range of approximately 35 m/s (minimal hurricane category-1) with improved wind direction accuracy, making this new approach a potential candidate for current and future conically scanning scatterometer wind retrieval algorithms

Globally Gridded Satellite (GridSat) 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: there is no central archive of geostationary data for all international satellites, full temporal and spatial resolution data are voluminous, and diverse calibration and navigation formats encumber the uniform processing needed for multi-satellite climate studies. The International Satellite Cloud Climatology Project set the stage for overcoming these issues by archiving a subset of the full resolution geostationary data at approx.10 km resolution at 3 hourly intervals since 1983. Recent efforts at NOAA s 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 public distribution. The Gridded Satellite (GridSat) dataset includes observations from the visible, infrared window, and infrared water vapor channels. Data are stored in the netCDF format using standards that permit a wide variety of tools and libraries to quickly and easily process the data. A novel data layering approach, together with appropriate satellite and file metadata, allows users to access GridSat data at varying levels of complexity based on their needs. The result is a climate data record already in use by the meteorological community. Examples include reanalysis of tropical cyclones, studies of global precipitation, and detection and tracking of the intertropical convergence zone

Observing convective aggregation

Convective self-aggregation, the spontaneous organization of initially scattered convection into isolated convective clusters despite spatially homogeneous boundary conditions and forcing, was first recognized and studied in idealized numerical simulations. While there is a rich history of observational work on convective clustering and organization, there have been only a few studies that have analyzed observations to look specifically for processes related to self-aggregation in models. Here we review observational work in both of these categories and motivate the need for more of this work. We acknowledge that self-aggregation may appear to be far-removed from observed convective organization in terms of time scales, initial conditions, initiation processes, and mean state extremes, but we argue that these differences vary greatly across the diverse range of model simulations in the literature and that these comparisons are already offering important insights into real tropical phenomena. Some preliminary new findings are presented, including results showing that a self-aggregation simulation with square geometry has too broad a distribution of humidity and is too dry in the driest regions when compared with radiosonde records from Nauru, while an elongated channel simulation has realistic representations of atmospheric humidity and its variability. We discuss recent work increasing our understanding of how organized convection and climate change may interact, and how model discrepancies related to this question are prompting interest in observational comparisons. We also propose possible future directions for observational work related to convective aggregation, including novel satellite approaches and a ground-based observational network

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 which 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 a probability forecast for genesis at 6 hour intervals out to 48 hours prior to the event. Results consistently show that the neural network classifier outperforms linear discriminant analysis on all performance measures examined, including probability of detection, false alarm rate, Heidke Skill Score, and forecast reliability. 2 1

SeaWinds Hurricane Wind Retrievals and Comparisons with H *

This paper describes recent developments of an improved geophysical ocean wind vector retrieval algorithm that uses both active and passive measurements from QuikSCAT. This algorithm results in significant improvements in wind vector measurements in hurricanes and better rain-flagging of severely rain contaminated areas than does NASA\u27s standard wind vector product (L2B). By using a combined active/passive approach, we are able to infer wind estimates in the presence of light to moderate rain using the SeaWinds scatterometer. Rain effects (attenuation and volume scattering) are determined passively and then used to correct the measured ocean sigma-0 at 12.5 km wind vector cell resolution. Wind retrievals are performed using an improved geophysical model function (GMF) tuned for extreme wind events These ocean vector wind retrievals, known as Q-Winds, are compared with surface winds products from the NOAA Hurricane Research Division\u27s H*Wind Analysis System, which assimilates near-simultaneous measurements from insitu and remote sensors, such as, the Stepped Frequency Microwave Radiometer (SFMR), GPS dropsondes, and flight-level inertial navigation winds. Comparisons to H*Wind are presented for Q-Winds and the SeaWinds Project\u27s new L2B-12.5km ocean vector winds products. © 2008 IEEE

Seawinds Hurricane Wind Retrievals And Comparisons With H*Wind Surface Winds Analyses

This paper describes recent developments of an improved geophysical ocean wind vector retrieval algorithm that uses both active and passive measurements from QuikSCAT. This algorithm results in significant improvements in wind vector measurements in hurricanes and better rain-flagging of severely rain contaminated areas than does NASA\u27s standard wind vector product (L2B). By using a combined active/passive approach, we are able to infer wind estimates in the presence of light to moderate rain using the SeaWinds scatterometer. Rain effects (attenuation and volume scattering) are determined passively and then used to correct the measured ocean sigma-0 at 12.5 km wind vector cell resolution. Wind retrievals are performed using an improved geophysical model function (GMF) tuned for extreme wind events These ocean vector wind retrievals, known as Q-Winds, are compared with surface winds products from the NOAA Hurricane Research Division\u27s H*Wind Analysis System, which assimilates near-simultaneous measurements from insitu and remote sensors, such as, the Stepped Frequency Microwave Radiometer (SFMR), GPS dropsondes, and flight-level inertial navigation winds. Comparisons to H*Wind are presented for Q-Winds and the SeaWinds Project\u27s new L2B-12.5km ocean vector winds products. © 2008 IEEE

Evaluation Of Hurricane Ocean Vector Winds From Windsat

The ability to accurately measure ocean surface wind vectors from space in all weather conditions is important in many scientific and operational usages. One highly desirable application of satellite-based wind vector retrievals is to provide realistic estimates of tropical cyclone intensity for hurricane monitoring. Historically, the extreme environmental conditions in tropical cyclones (TCs) have been a challenge to traditional space-based wind vector sensing provided by microwave scatterometers. With the advent of passive microwave polarimetry, an alternate tool for estimating surface wind conditions in the TC has become available. This paper evaluates the WindSat polarimetric radiometer\u27s ability to accurately sense winds within TCs. Three anecdotal cases studies are presented from the 2003 Atlantic Hurricane season. Independent surface wind estimates from aircraft flights and other platforms are used to provide surface wind fields for comparison to WindSat retrievals. Results of a subjective comparison of wind flow patterns are presented as well as quantitative statistics for point location comparisons of wind speed and direction. © 2006 IEEE

Improved Hurricane Ocean Vector Winds Using Sea Winds 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 20032008. These data are also compared to independent surface wind vector estimates from the National Oceanic and Atmospheric Administration Hurricane Research Division\u27s 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. © 2006 IEEE

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

The 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. Through modifying the Dvorak technique into a series of simple questions that amateurs (“citizen scientists”) can answer on a website, we are working toward developing a new TC dataset that resolves intensity discrepancies in several recent TCs. Preliminary results suggest that the performance of human classifiers in some cases exceeds that of an automated Dvorak technique applied to the same data for times when the storm is transitioning into a hurricane