This PDF file contains the front matter associated with SPIE Proceedings Volume 7088, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and the Conference Committee listing.

Airspace system demand is expected to increase as much as 300 percent by the year 2025 and the Next Generation Air Transportation System (NextGen) is being developed to accommodate the super-density operations that this will entail. Concomitantly, significant improvements in observations and forecasting are being undertaken to support NextGen which will require greatly improved and more uniformly applied data for aviation weather hazards and constraints which typically comprise storm-scale and microscale observables. Various phenomena are associated with these hazards and constraints such as convective weather, in-flight icing, turbulence, and volcanic ash as well as more mundane aviation parameters such as cloud tops and bases and fuel-freeze temperatures at various flight levels. Emerging problems for aviation in space weather and the environmental impacts of aviation are also occurring at these scales. Until recently, the threshold and objective observational requirements for these observables had not been comprehensively documented in a single, authoritative source. Scientists at NASA and NOAA have recently completed this task and have established baseline observational requirements for the Next Generation Air Transportation System (NextGen) and expanded and updated the NOAA Consolidated Observations Requirements List (CORL) for Aviation (CT-AWX) to better inform National Weather Service investments for current and future observing systems. This paper describes the process and results of this effort. These comprehensive aviation observation requirements will now be used to conduct gap analyses for the aviation component of the Integrated Earth Observing System and to inform the investment strategies of the FAA, NASA, and NOAA that are needed to develop the observational architecture to support NextGen and other users of storm and microscale observations.

The main objective of this work is to describe a research project on fog and visibility, and to summarize the results. The Fog Remote Sensing and Modeling (FRAM) project was designed to focus on 1) development of microphysical parameterizations for model applications, 2) development of remote sensing methods for fog nowcasting/forecasting, 3) understanding of issues related to instrument capabilities and improvement of the analysis, and 4) integration of model data with observations. The FRAM was conducted over three regions of Canada and US. These locations were: 1) Center for Atmospheric Research Experiments (CARE), Egbert, Ontario 2005-2006, 2) Lunenburg, Nova Scotia June of 2006 and 2007, and 3) U.S. Department Of Energy (DOE) ARM Climate Research Facility at Barrow, Alaska, US during the Indirect and Semi-Direct Aerosol Campaign (ISDAC) field program April of 2008; FRAM C, FRAM-L, and ISDAC-FRAM-B, respectively. FRAM-C was undertaken in a continental fog environment while FRAM-L was in a marine environment. The FRAM-B was undertaken to study ice fog conditions. During the project, numerous in-situ measurements were obtained, including droplet and aerosol spectra, precipitation, and visibility. Analysis of satellite microphysical retrievals and visibility parameterizations suggested that improved scientific understanding of fog formation can lead to better forecasting/nowcasting skills benefiting both aviation and public forecasting applications.

This paper describes how operational radar, satellite and lightning data may be used in conjunction with numerical weather model data to provide remote detection and diagnosis of atmospheric turbulence in and around thunderstorms. In-cloud turbulence is measured with the NEXRAD Turbulence Detection Algorithm (NTDA) using extensively qualitycontrolled, ground-based Doppler radar data. A real-time demonstration of the NTDA includes generation of a 3-D turbulence mosaic covering the CONUS east of the Rocky Mountains, a web-based display, and experimental uplinks of turbulence maps to en-route commercial aircraft. Near-cloud turbulence is inferred from thunderstorm morphology, intensity, growth rate and environment data provided by (1) satellite radiance measurements, rates of change, winds, and other derived features, (2) lightning strike measurements, (3) radar reflectivity measurements and (4) weather model data. These are combined via a machine learning technique trained using a database of in situ turbulence measurements from commercial aircraft to create a predictive model. This new capability is being developed under FAA and NASA funding to enhance current U.S. and international turbulence decision support systems, allowing rapid-update, highresolution, comprehensive assessments of atmospheric turbulence hazards for use by pilots, dispatchers, and air traffic controllers. It will also contribute to the comprehensive 4-D weather information database for NextGen.

This paper describes the use of a machine learning data fusion methodology to support development of an automated short-term thunderstorm forecasting system for aviation users. Information on current environmental conditions is combined with observations of current storms and derived indications of the onset of rapid change. Predictor data include satellite radiances and rates of change, satellite-derived cloud type, ground weather station measurements, land surface and climatology data, numerical weather prediction model fields, and radar-derived storm intensity and morphology. The machine learning methodology creates an ensemble of decision trees that can serve as a forecast logic to provide both deterministic and probabilistic estimates of thunderstorm intensity. It also provides evaluation of predictor importance, facilitating selection of a minimal skillful set of predictor variables and providing a tool to help determine what weather regimes may require specialized forecast logic. This work is sponsored by the Federal Aviation Administration's Aviation Weather Research Program. Its aim is to contribute to the development of the Consolidated Storm Prediction for Aviation (CoSPA) system, which is being developed in collaboration with the MIT Lincoln Laboratory and the NOAA Earth System Research Laboratory's Global Systems Division. CoSPA is scheduled to become part of the NextGen Initial Operating Capability by 2012.

Satellite-based brightness temperature observations are used in a wide range of applications for monitoring weather systems over land and especially over water, including short-term prediction of the evolution of weather systems. Results are presented from an evaluation of three extrapolation-based nowcasting procedures to predict satellitebased brightness temperatures up to 3 hours into the future. Analyses are based on using METEOSAT-8 Spinning Enhanced Visible and Infrared Imager (SEVIRI) data as a proxy for the Advanced Baseline Imager (ABI) to be flown on the next-generation National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite (GOES)-R series.

An oceanic convection diagnosis and nowcasting system is described whose domain of interest is the region between the southern continental United States and the northern extent of South America. In this system, geostationary satellite imagery are used to define the locations of deep convective clouds through the weighted combination of three independent algorithms. The resultant output, called the Convective Diagnosis Oceanic (CDO) product, is independently validated against space-borne radar and lighting products from the Tropical Rainfall Measuring Mission (TRMM) satellite to ascertain the ability of the CDO to discriminate hazardous convection. The CDO performed well in this preliminary investigation with some limitations noted. Short-term, 1-hr and 2-hr nowcasts of convection location are performed within the Convective Nowcasting Oceanic (CNO) system using a storm tracker. The CNO was found to have good statistical performance at extrapolating existing storm positions. Current work includes the development and implementation of additional atmospheric features for nowcasting convection initiation and to improve nowcasting of mature convection evolution.

The Forward-Looking Interferometer (FLI) is a new instrument concept for obtaining the measurements required to alert flight crews to potential weather hazards to safe flight. To meet the needs of the commercial fleet, such a sensor should address multiple hazards to warrant the costs of development, certification, installation, training, and maintenance. The FLI concept is based on high-resolution Infrared Fourier Transform Spectrometry (FTS) technologies that have been developed for ground based, airborne, and satellite remote sensing. The FLI concept is being evaluated for its potential to address multiple hazards including clear air turbulence (CAT), volcanic ash, wake vortices, low slant range visibility, dry wind shear, and icing, during all phases of flight. This project has three major elements: further sensitivity studies and applications of EOF (Empirical Orthogonal Function) Regression; development of algorithms to estimate the hazard severity; and field measurements to provide an empirical demonstration of the FLI aviation hazard detection and display capability. These theoretical and experimental studies will lead to a specification for a prototype airborne FLI instrument for use in future in-flight validation. The research team includes the Georgia Tech Research Institute, Hampton University, the University Corporation for Atmospheric Research, the Air Force Institute of Technology, and the University of Wisconsin.

The aircraft mounted TAMDAR Sensor currently provides an ice/no ice signal. Through a contract with NASA, AirDat has compiled icing intensity data from both the Cox Wind Tunnel as well as the University of Wyoming's King Air aircraft. This data, in addition to data collected from experiments conducted at the AirDat lab, was used to provide icing intensity output from the TAMDAR sensor.

In-flight icing hazards from supercooled small drops, drizzle and freezing rain pose a threat to all aircraft. Several products have been developed to provide hazard warning of in-flight icing to the aviation community. NCAR's Current Icing Product¹ (CIP) was developed to provide a near-realtime assessment of the hazard presented by supercooled liquid water (SLW) aloft in an algorithm that combines data from satellites, the Rapid Update Cycle (RUC) model, the national 2-D composite of S-band NEXRAD radar reflectivity, surface observations and pilot reports (PIREPs). NIRSS² (Fig. 1) was developed by NASA to provide a ground-based, qualitative in-flight icing hazard assessment in the airport environment with commercially available instrumentation. The system utilizes a multichannel radiometer³, built by Radiometrics Corporation, to derive the temperature profile and integrated liquid water (ILW). NIRSS's radar is a modified airborne X-band model WU-870 made by Honeywell. The ceilometer used is a standard Vaisala CT25K Laser Ceilometer. The data from the vertically pointing ceilometer and X-band radar are only used to define the cloud bases and tops. The liquid water content (LWC) is then distributed within the cloud layers by the system software. A qualitative icing hazard profile is produced where the vertical temperature is between 0 and -20°C and there is measurable LWC.

An airborne radar sensing technology for detecting and monitoring of multiple types of external hazards is investigated. Antennas with spatial and polarimetry diversity are adopted in the radar sensor to support the comprehensive hazard monitoring requirements. A knowledge-aided joint space-time processing approach is developed for monitoring wind hazard as well as estimating target direction and Doppler spectrum simultaneously. The hazard microphysics information can be retrieved through polarimetric data processing. In addition to the intelligent processing algorithms, the system design and the tradeoffs are considered.

The detection and avoidance of external hazards is an important aspect of overall efforts to improve the safety of future aircraft. Advanced sensor concepts may enhance the detection and quantification of risk due to external hazards. Such sensors, when integrated into cockpit operations, may substantially improve vehicle safety. This paper will describe research efforts to develop a simulation environment to evaluated advanced microwave sensor concepts such as airborne bistatic radars utilizing multiple non-cooperative illuminators or emitters-of-opportunity to detect weather hazards, area traffic, runway incursions, or other potential aircraft hazards. We will present initial efforts to develop a flexible microwave sensor simulation and assessment tool. This tool will be developed to assess the feasibility of various sensor concepts. Existing and potential future capability of the simulation environment will be described. In addition, the results of the application of the simulation tool to a bistatic sensor concept will be presented.

NASA has teamed with the FAA, DoD, industry, and academia for research into the remote detection and measurement of atmospheric conditions leading to aircraft icing hazards. The ultimate goal of this effort is to provide pilots, controllers, and dispatchers sufficient information to allow aircraft to avoid or minimize their exposure to the hazards of in-flight icing. Since the hazard of in-flight icing is the outcome of aircraft flight through clouds containing supercooled liquid water and strongly influenced by the aircraft's speed and configuration and by the length of exposure, the hazard can't be directly detected, but must be inferred based upon the measurement of conducive atmospheric conditions. Therefore, icing hazard detection is accomplished through the detection and measurement of liquid water in regions of measured sub-freezing air temperatures. The icing environment is currently remotely measured from the ground with a system fusing radar, lidar, and multi-frequency microwave radiometer sensors. Based upon expected ice accretion severity for the measured environment, a resultant aircraft hazard is then calculated. Because of the power, size, weight, and view angle constraints of airborne platforms, the current ground-based solution is not applicable for flight. Two current airborne concepts are the use of either multi-frequency radiometers or multi-frequency radar. Both ground-based and airborne solutions are required for the future since ground-based systems can provide hazard detection for all aircraft in airport terminal regions while airborne systems will be needed to provide equipped aircraft with flight path coverage between terminal regions.

Remote sensing from space plays an important role to know the environment. Today a lot of human activities look at satellite data to understand the situation and take decisions. For example the knowledge of meteorological conditions allow to plane civil protection missions and military campaigns. We can retrieve from satellite data series of parameters that can be used for different applications building applications to manage derived information. Decision makers have to take resolutions based on available information asking to put in evidence only useful parameters. A possible tool could supply pictures to visualize the situation or messages to initialize a numerical model of decision. The present paper wants to describe a possible application, giving an efficient instrument to positively conditioning military decision process. We simulated some possible missions and have designed an instrument to select and present environment parameters. It is also illustrated the concept of our application and shows some examples of output. The application is very flexible because it manages parameters retrieved from other applications. We show the integration of different Satellite Application Facilities products, applications of Italian Meteorological Service and conventional observations. In particular we have resolved some problems as: ingesting of user's requirements (parameters and geographical area), the retrieving of parameters in the database and optimization of spatial resolution.

The multi-agency Flight in Icing Remote Sensing Team (FIRST), a consortium of the National Aeronautics and Space Administration (NASA), the Federal Aviation Administration (FAA), the National Center for Atmospheric Research (NCAR), the National Oceanographic and Atmospheric Administration (NOAA), and the Army Corps of Engineers (USACE), has developed technologies for remotely detecting hazardous inflight icing conditions. The USACE Cold Regions Research and Engineering Laboratory (CRREL) assessed the potential of onboard passive microwave radiometers for remotely detecting icing conditions ahead of aircraft. The dual wavelength system differences the brightness temperature of Space and clouds, with greater differences potentially indicating closer and higher magnitude Cloud Liquid Water Content (CLWC). The Air Force RADiative TRANsfer model (RADTRAN) was enhanced to assess the flight track sensing concept, and a "flying" RADTRAN was developed to simulate a radiometer system flying through simulated clouds. Neural network techniques were developed to invert brightness temperatures and obtain integrated cloud liquid water. In addition, a dual wavelength Direct-Detection Polarimeter Radiometer (DDPR) system was built for detecting hazardous drizzle drops. This paper reviews technology development to date and addresses initial polarimeter performance.

Focused on the cloud detection task using EOS/MODIS information, this paper introduced a new method of cloud detection by use of the Support Vector Machines (SVMs) algorithm. The performance of SVMs was compared with the prevailing method of BP neural network (BP-NN) method with different training set numbers. The two methods show similar detection accuracy when the training set number is larger (with a number larger than 1500), while SVMs perform better than BP-NN method when the sampling number is small (with a number of 250 or less). SVMs method was then used to detect cloud over both land and sea; it distinguished cloud from snow cover, water body, and other land surface objectives clearly. Therefore, the SVMs technique is proved effective as compared with traditional methods in remote sensing image classification and is worthwhile to be popularized in the society of remote sensing applications.

The Laboratoire Régional des Ponts et Chaussées d'Angers - LRPC of Angers is currently studying the feasability of applying an optical technique based on the principle of the laser optical feedback to long distance fog vision. Optical feedback set up allows the creation of images on roadsigns. To create artificial fog conditions we used a vibrating cell that produces a micro-spray of water according to the principle of acoustic cavitation. To scale the sensitivity of the system under duplicatible conditions we also used optical densities linked to first-sight visibility distances. The current system produces, in a few seconds, 200 × 200 pixel images of a roadsign seen through dense artificial fog.

Vision through diuse media (fog, rain, smoke, snow, etc) is a large problem due to interactions between light and particles composing the medium. In this paper, one focuses particulary on scattering media as fog. Therefore, an analytical model for the determination of backscattered luminance induced by fog is described here. The model is based on the Mie's theory and takes the optical device geometry into account. An analysis of its resulst leads to several conclusions. The most notable and obvious one is that eliminate backscattering light is necessary to have good-contrasted images. Starting from this premise, one presents here a range-gated active imaging system enable to perform a temporal selection between backscattering pollution and photons holding optical information.