This page lists past seminars and presentations by STAR
scientists and visiting scientists. These seminars include the STAR
Science Forum and similar events. Presentation materials for
seminars will be provided when available.
Conference Room # 2552-2553, NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD, NCWCP - Large Conf Rm - 2552-2553
Abstract: The Community Radiative Transfer Model (CRTM) is a fast, 1-D radiative transfer model designed to simulate top-of-the-atmosphere radiances consistent with a wide variety of satellite based sensors. The CRTM was primarily developed by JCSDA-funded scientists with essential contributions from NOAA/STAR and NOAA/EMC scientists. The primary goal of CRTM is to provide fast, accurate satellite radiance simulations and associated Jacobian calculations under all weather and surface conditions. CRTM supports all current operational and many research passive sensors, covering wavelengths ranging from the visible through the microwave. The model has undergone substantial improvement and expansion, since the first version in 2004. The CRTM has been used in the NOAA/NCEP and U.S. Navy operational data assimilation systems and by many other JCSDA partners such as NOAA/NESDIS/STAR, NOAA/OAR, NASA/GMAO, Naval Research Laboratory, Air Force Weather, and within multiple university environments. Over the past 14 years, both external research groups and operational centers alike have made essential contributions to the continued development and growth of CRTM. A major goal of the CRTM core team is to ensure that CRTM becomes a true community radiative transfer model for all users. The CRTM official baseline code is developed and maintained based on internal and community-wide inputs, consisting of both improvements and externally contributed codes. This presentation will briefly review the scientific and technical basis of CRTM, including its many strengths and limitations. There will also be an overview of the current status of the recently released CRTM version 2.3.0; and the future planned release of CRTM version 3.0.0 - which will represent a major milestone in CRTM's development and capabilities.
Bio(s): Dr. Benjamin T. Johnson joined NOAA/NESDIS/STAR (via AER, Inc.) in support of JCSDA in July 2015. In January 2017, he was hired through UCAR as the JCSDA project lead for the Community Radiative Transfer Model (CRTM). Dr. Johnson's primary responsibilities are to ensure that the CRTM project continues to be proactively developed and managed to meet operational user requirements. This involves coordinating efforts and support for a large number of users and developers across a wide range of agencies and universities, both domestic and international. Dr. Johnson received a B.S. in Physics from Oklahoma State University, with an emphasis on hard-sphere sedimentation crystallization and photonics. Combining his interest in weather, computing, and physics, he studied Atmospheric Science at Purdue University, where he received a M.S. degree. The next stop was the University of Wisconsin, where he completed his Ph.D. in Atmospheric Science advised by Dr. Grant Petty. Before completing his Ph.D. in 2007, Dr. Johnson started working at NASA Goddard Space Flight Center in 2004 on the Global Precipitation Measurement mission, primarily focused on precipitation retrieval algorithm development and satellite observation simulations. During the intervening years, he has coordinated multiple NASA field campaigns as a mission scientist, and actively participates in the CGMS/WMO International Precipitation Working Group (IPWG), International TOVs Working Group (ITWC), and the International Workshop on Space-based Snowfall Measurement (IWSSM). He is a member of the American Geophysical Union (AGU), and the American Meteorological Society (AMS). Dr. Johnson's primary areas of expertise are measuring and simulating cloud microphysical processes, theoretical and applied atmospheric radiative transfer, satellite remote sensing of clouds and precipitation, and satellite-based radar simulations in cold-cloud precipitating scenes.
STAR Science Seminars with SOCD / NOAA Ocean Color Coordinating Group
This seminar was originally scheduled for 12/5/2018.
Presenter(s): Dr. Timothy S. Moore - Ocean Process Analysis Laboratory Institute for Earth, Oceans and Space University of New Hampshire
Sponsor(s):
SOCD / NOAA Ocean Color Coordinating Group The NOCCG is a NOAA organization founded in 2011 by Dr. Paul DiGiacomo, Chief of the Satellite Oceanography and Climatology Division at NOAA/NESDIS/STAR. The purpose of the NOCCG is to keep members up to date about developments in the field of satellite ocean color and connect ocean color science development with users and applications. We have representatives from all the NOAA line offices, including National Marine Fisheries Service, Office of Oceanic and Atmospheric Research, National Ocean Service, National Weather Service and from several levels of the National Environmental and Satellite Data and Information Service (where Paul is housed). Dr. Cara Wilson of South East Fisheries Science Center is our current chair. We meet bi-weekly on Wednesday afternoons, 3 PM Eastern Time in room 3555 at the National Center for Weather and Climate Prediction building in College Park, MD with teleconferencing and Webex for out of town members and guests. We host a guest speaker, usually about once a month.
Audio: USA participants: 866-564-7828 Passcode: 9942991
Abstract:
In the summer of 2016, a robotic sun photometer called the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Photometer Revision for Incident Surface Measurements (SeaPRISM), was deployed at a Coast Guard channel marker in western Lake Erie, measuring atmospheric properties and spectral water-leaving radiance. The instrument was deployed by the National Oceanic and Atmospheric Administration (NOAA) to support remote sensing validation and harmful algal bloom (HAB) satellite products. The Lake Erie SeaPRISM is also part of the international federated AERONET program maintained by the National Aeronautics and Space Administration (NASA), and more specifically is part of the AERONET Ocean Color (AERNOET-OC) network. The main purpose of this component of AERONET is specific to calibration/validation efforts for ocean color. In the summer of 2017, a new 12-channel version was deployed at the same site with additional channels in the red and near-infrared. This unit is the first lake' version of the SeaPRISM. In this webinar, the data collected by the SeaPRISM at this site over the last three years (2016-2018) will be examined in the context of HABs and remote sensing validation. The SeaPRISM observations in relation to remote sensing validation and on cyanobacteria blooms from hourly to weekly time scales will be highlighted in this presentation.
Bio(s):
Dr. Moore has been working with ocean color remote sensing for over 25 years. Throughout that time, he has been involved with bio-optical algorithm development, application, and satellite validation. He was worked with ocean color imagery in marine and freshwater systems. He was a member of the NASA MODIS Science Team and NASA PACE Science Team. For the past six years, he has been working extensively in the western Lake Erie system collaborating with NOAA GLERL and other regional entities. Under a collaborative project between NOAA NESDIS, NOAA GLERL and UNH, Dr. Moore led the effort to introduce an autonomous, robotic radiometer to Lake Erie with a unique band configuration, which will be the subject of his presentation.
Eric Wendoloski, Connor Sprague, and Ingrid Guch - The Aerospace Corporation
Date & Time:
17 December 2018
11:30 am - 12:30 pm ET
Location:
Conference Room # 2552-2553, NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD, NCWCP - Large Conf Rm - 2552-2553
Presenter(s): Eric Wendoloski, Connor Sprague, and Ingrid Guch of The Aerospace Corporation
Sponsor(s): STAR Science Seminar Series
Audio: USA participants: 866-832-9297 Passcode: 6070416
Abstract: The number of machine-learning (ML) applications has surged within the meteorological community over the last several years. This surge includes the development and application of numerous ML techniques to improve forecasting as well as physical models while reducing computational complexity and time. Given the vast trove of available satellite-based weather imagery and the gridded structure of many meteorological datasets, deep-learning (DL) methods for providing predictions and diagnostics for numerous subdomains are experiencing increased adoption. However, full adoption will require forecasters and decision makers to interpret why model output is produced given the input, especially if the output has implications for human well-being. Due to their complex architectures, interpreting DL models can be especially difficult, and models are often treated as black boxes. This work examines contemporary methods for assessing the interpretability of a convolutional neural network (CNN) trained to predict tropical cyclone (TC) intensity based on available satellite-weather data, primarily in the IR band. CNNs excel at distilling image data into the most important feature abstractions for developing functional associations between input images and required prediction output. The goal of this work is not necessarily to produce the most accurate TC intensity model, but to assess whether such a DL architecture is capable of learning physically relevant abstractions for the problem at hand. We will describe and apply interpretability methods to the TC intensity CNN model to assess the importance of physical concepts to final predictions. We will also assess the traceability of predictions across the learned network.
Bio(s):
Eric Wendoloski is a Senior Member of the Technical staff for The Aerospace Corporation in Chantilly, VA. Since joining Aerospace, Eric has supported projects ranging from the deployment of machine-learning-based applications to large scale systems engineering studies including the NOAA Satellite Observing System Architecture (NSOSA) study. Prior to joining Aerospace, Eric obtained his B.S. (2013) in meteorology from Millersville University and his M.S. (2015) in meteorology from Penn State University. Eric was also a recipient of the Ernest F. Hollings scholarship as an undergraduate. Connor Sprague is a graduate intern at The Aerospace Corporation. His research focuses on applicable machine learning and data science for hurricane prediction and tracking, data fusion, and predictive weather algorithms. He is finishing M.S in systems engineering at the Missouri University of Science and Technology. Ingrid Guch is a Systems Director at The Aerospace Corporation. She has over 20 years' experience working with NOAA/NESDIS including in product operations, systems development, applications and research. She is currently co-located with the NOAA/NESDIS Center for Satellite Applications and Research in College Park, MD. She obtained her B.S. in mathematical sciences from University of California Santa Barbara and her M.S. in atmospheric sciences from Colorado State University.
Conference Room # 2552-2553, NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD, NCWCP - Large Conf Rm - 2552-2553
Audio: USA participants: 866-832-9297 Passcode: 6070416
Abstract: Due to the stability of the lunar reflectance and the fact that it is an exo-atmospheric target with flux levels close to levels observed by Earth Remote Sensing instruments, many sensors routinely measure the lunar spectral irradiance. While many sources of uncertainly that arise when vicariously calibrating sensors using land targets are eliminated, lunar measurements are complicated - though predictable - because of the lunar irradiance is a function of the relative positions of the Sun, Moon, and Observer among other variables. The United States Geological Survey has developed a model, called the Robotic Lunar Observatory (ROLO) Model of lunar reflectance/irradiance that accounts for changes in lunar irradiance as a function of these variables; utilizing the ROLO Model, NASA has demonstrated the ability to track sensor responsivity changes at the 0.1 % level. The current uncertainties in the ROLO Model are estimated to be between 3 % and 6 % in the VNIR spectral region and are not traceable to the International System of Units (the SI). The objective of the airborne Lunar Spectral Irradiance (air-LUSI) project is to make highly accurate (sub-0.5 % uncertainty), SI-traceable measurements of the lunar spectral irradiance in the VNIR region using a laboratory instrument on an airplane at 70,000 feet. The measurements, corrected for residual atmospheric attenuation, will be compared with the ROLO model-predicted exo-atmospheric lunar irradiance and may be used to establish limits on the uncertainty in the ROLO Model as well as to possibly serve as tie-points to the Model over this spectral range. The first step was to integrate the air-LUSI instrument onto a NASA ER-2 research aircraft and have Engineering Flights to demonstrate that the instrument concept was valid and that the instrument could function properly at-altitude. Two Engineering Flights took place in August 2018 in Palmdale, CA at NASA's Armstrong Flight Research Center. The talk will focus on what happened during the deployment both from a technical and personal point of view; results of the radiometric measurements and the performance of the instrument lend insight into a path forward to lower uncertainty measurements during the next Flight Campaign and will be presented.
Bio(s): Mr. Thomas Larason is an Electronics Engineer in the Sensor Science Division at the National Institute of Standards and Technology (NIST). His career at NIST began in 1989 where his research has focused on the development, characterization and calibration of detectors that measure ultraviolet, visible, and near infrared light. Additional research areas include the measurement of photocurrent, aperture area, and the development of new transfer standards. He has collaborated with both university and industry researchers on various projects, for example, investigating UV light sensors used for the inactivation of pathogens for drinking water. He has twice received the Department of Commerce Bronze Medal Award.
Conference Room # 2552-2553, NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD, NCWCP - Large Conf Rm - 2552-2553
Abstract: A difference in current NESDIS STAR (post 2000) and pre-2000 Office of Research and Applications (ORA) infra-structure is the lack of an independent Research to Operation (R2O) sanctioning function for EDRs that is overseen by the government. At least for operational atmospheric soundings, the responsibility for assessments leading to R2O transition currently lies mainly with the developer. Enterprise assessment, in simplest form, means comparing different product suites for a given EDR to the same targets. This seminar focuses on what enterprise validation should entail and then cites examples using the NOAA Products Validation System (NPROVS) currently operated at STAR for EDR soundings (vertical temperature and moisture profiles). The seminar also explores enterprise validation scenarios for gas profiles and non-sounding products. The goal is to entice STAR and developers that it is in the mutual best interest to reconsider the original ORA structure. In general, the underlying framework of enterprise validation consists of routinely compiled datasets of collocated ground-truth, models and EDR products from multiple satellites, sensors and product suites. This is the carrot, STAR would inherit the mundane, time consuming dirty-work (when you consider monitoring, reprocessing missing data, etc) of creating and maintaining enterprise validation datasets. Once compiled however, these datasets are a source for reliable, routine assessment across multiple EDR suites and highly useful to identify problem areas and provide a stamp of approval for proposed upgrades including for new satellites (Small Satellites, COSMIC-2 ). This does not replace developer assessment, but shifts that assessment to focus on development and leaving the busy, mundane work of enterprise assessment to the government (STAR). Examples of enterprise validation and value to developers in product monitoring and identifying problem areas is demonstrated using NPROVS for atmospheric temperature and moisture soundings. Soundings inherit the relatively large and diverse global radiosonde network including subsets of reference observations providing multiple targets for assessment. This is not true for other products, so extending enterprise assessment to product suites such as of gases, surface temperature, clouds, aerosol, fires etc requires a separate consideration for each. As discussed, the key for each suite is defining/accessing the available sets of products (including test products) and then underscoring them common sets of ground-truth targets, models, intensive observations, etc. These steps clearly must leverage and integrate the existing capabilities and expertise from respective developers requiring extensive interaction. Questions concerning why developers should bother when they can do it themselves (can they? do they?) and perceptions that STAR managed enterprise assessments would undermine (not enhance) developers are addressed.
Bio(s): Anthony Reale received B.S. degrees in Meteorology and Physics from the State University of New York, College at Oswego in 1976. Following three years as a research fellow at the University of Nevada, Reno, he received his M.S. degree in Atmospheric Physics in 1979. He then spent three years in the field conducting remote sensing measurements programs to establish background air-quality and meteorological profiles at selected locations in the pristine eastern Mohave Desert. Mr. Reale was hired as a NOAA support contractor in 1983 where he began working on the problem of deriving atmospheric sounding products from remote satellite sensors onboard NOAA operational polar orbiting satellites. Mr. Reale was hired by NOAA in 1984 where he provided technical guidance and direction to government and support contractor staff focused on the development of scientific software and associated graphical evaluation tools to assess atmospheric sounding products from operational satellites. Beginning 2008, he became task leader for the development of the NOAA Products Validation System (NPROVS), designed to provide an enterprise approach for assessing atmospheric profiles from multiple satellites against in-situ (radiosonde) observations. This was expanded in 2013 to include reference radiosondes from the Global Climate Observing System (GCOS) Reference Upper Air network (GRUAN).
STAR Science Seminars with SOCD / NOAA Ocean Color Coordinating Group
Presenter(s): Dr. Steven G. Ackleson, Section Head Oceanographer at U.S. Naval Research Laboratory (presenting in person)
Sponsor(s):
SOCD / NOAA Ocean Color Coordinating Group The NOCCG is a NOAA organization founded in 2011 by Dr. Paul DiGiacomo, Chief of the Satellite Oceanography and Climatology Division at NOAA/NESDIS/STAR. The purpose of the NOCCG is to keep members up to date about developments in the field of satellite ocean color and connect ocean color science development with users and applications. We have representatives from all the NOAA line offices, including National Marine Fisheries Service, Office of Oceanic and Atmospheric Research, National Ocean Service, National Weather Service and from several levels of the National Environmental and Satellite Data and Information Service (where Paul is housed). Dr. Cara Wilson of South East Fisheries Science Center is our current chair. We meet bi-weekly on Wednesday afternoons, 3 PM Eastern Time in room 3555 at the National Center for Weather and Climate Prediction building in College Park, MD with teleconferencing and Webex for out of town members and guests. We host a guest speaker, usually about once a month.
Audio: USA participants: 866-564-7828 Passcode: 9942991
Abstract:
Satellite remote sensing systems designed for coastal aquatic applications strive to provide high quality data across the visible and near infrared portions of the electromagnetic spectrum. Data quality is driven by uncertainties related to sensor design and environmental variability. The work is focused on the impact of sensor signal to noise (SNR) on the retrieval of key aquatic ecological parameters; water column impurity concentration (chlorophyll, colored dissolved organic matter, and suspended sediment) water depth, and benthic cover. Uncertainty is defined as parameter variability producing a reflectance signal that is indistinguishable from the true condition. The impact of sensor SNR is investigated using modeling methods and remote sensing data analyses. The results quantify parameter retrieval uncertainty as a function of sensor design SNR and environmental noise attributed to surface glint. The results will be discussed within the context of future satellite systems designed for coastal applications, such as the NASA Surface Biology and Geology sensor.
Audio: USA participants: 866-832-9297 Passcode: 6070416
Abstract: This seminar summarizes the plan, execution, and results of the instrument calibration and radiance validation for the Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellites R-Series (GOES-R). GOES-R is the new generation of NOAA's GOES that provides constant surveillance of the United States and its surrounding for the next two decades, with GOES-16 operational since December 2017 and GOES-17 to be operational in December 2018. We begin with a review of transition to Imager, the instrument for NOAA's 2nd generation of GOES, with emphasis on the technological advancement at the time that brought much improved performance to meet users' demands and calibration difficulties that challenge the instrument scientists. Some of these difficulties were anticipated, some even overly prepared, but some not so much. The transition to ABI, the key payload of NOAA's 3rd generation of GOES, resembles many of the last transition, meanwhile some new tools have become available (e.g., Global Space-based Inter-Calibration System or GSICS) or applicable (e.g., Rayleigh scattering). The GOES-R Calibration Working Group (CWG) planned and executed the ABI calibration and validation with all these considerations. We end with a summary of ABI performance.
Bio(s): Xiangqian (Fred) Wu leads calibration support for NOAA's operations of Advanced Very High Resolution Radiometer (AVHRR) on POES (since 2002), Imager and Sounder on GOES (since 2004), Ozone Mapper Profiler Suite (OMPS) on S-NPP (2011-2014), and Advanced Baseline Imager (ABI) on GOES-R (since 2014). He has been a member of the WMO-sponsored Global Space-based Inter-Calibration System (GSICS) Research Working Group since its inception in 2006, and served as its first chair.
STAR Science Seminars with SOCD / NOAA Ocean Color Coordinating Group
Presenter(s): David Cotten, Research Scientist The University of Georgia Small Satellite Research Laboratory (presenting remotely)
Sponsor(s):
SOCD / NOAA Ocean Color Coordinating Group The NOCCG is a NOAA organization founded in 2011 by Dr. Paul DiGiacomo, Chief of the Satellite Oceanography and Climatology Division at NOAA/NESDIS/STAR. The purpose of the NOCCG is to keep members up to date about developments in the field of satellite ocean color and connect ocean color science development with users and applications. We have representatives from all the NOAA line offices, including National Marine Fisheries Service, Office of Oceanic and Atmospheric Research, National Ocean Service, National Weather Service and from several levels of the National Environmental and Satellite Data and Information Service (where Paul is housed). Dr. Cara Wilson of South East Fisheries Science Center is our current chair. We meet bi-weekly on Wednesday afternoons, 3 PM Eastern Time in room 3555 at the National Center for Weather and Climate Prediction building in College Park, MD with teleconferencing and Webex for out of town members and guests. We host a guest speaker, usually about once a month.
Abstract: This work introduces the mission concept, technologies, and development status for the Spectral Ocean Color (SPOC) small satellite mission, which will use an adjustable multispectral imager to map sensitive coastal regions and off coast water quality near the state of Georgia and beyond. SPOC is being developed by The University of Georgia's Small Satellite Research Laboratory (SSRL) with funds from NASA's Undergraduates Student Instrument Project (USIP). The project is led by undergraduates from a wide range of backgrounds and supervised by a multidisciplinary team of Principal Investigators. The mission will collect spectral data along a 300km swath using the grating spectrometer to diffract the incoming radiation into the 440-865 nm spectral range. The resulting images will be 75 km x 300 km in size, have a 120 m spatial resolution, and a spectral resolution of 20 nm, covering 16 adjustable spectral bands.
Abstract: This year, 5 Global TechnicalSymposium (GTS) sessions will be held focusing on topics such as Human Space Flight, Space Communication and Navigation, Small Satellite Missions and Citizen Science in Global Earth Observation. Of particular note to NOAA is the Earth Observation session, which will focus on citizen science. The session will feature 6 speakers from Europe, Africa, and North America (including our own Manoj Nair from NESDIS NCEI Boulder, Co Chair Harry Cikanek, NESDIS STAR, co-rapporteur, NESDIS OSAAP Kate Becker) discussing various citizen science projects and the policy implications of citizen science. The session will feature a demonstration of CrowdMag, an app providing real-time crowd-sourced magnetic data; the demonstration will include outputs using data gathered by those who downloaded the app at last year's IAC.
Abstract: We present a suite of hurricane products (wind, wave, rain, pressure, eye location) that can be generated from the Spaceborne synthetic Aperture Radar onboard Canadian RADARSAT and ESA's Sentinel-1 satellites.
Bio(s): Xiaofeng Li received his Ph.D. in physical oceanography from North Carolina State University in 1997. He has been supporting the National Environmental Satellite, Data, and Information Service (NESDIS) tasks ever since. He has authored more than 130 peer-reviewed publications and edited 3 books. He currently serves as an Associate Editor of IEEE Transactions on Geoscience and Remote Sensing and the Ocean Section Editor-in-Chief of Remote Sensing
Remote Access: Located outside Silver Spring? Please register for the webinar https://attendee.gotowebinar.com/register/1938566935465839874 After registering, you will receive a confirmation email containing information about joining the webinar. Participants can use their telephone OR computer mic & speakers (VoIP).
Accessibility: If you would like to request an ASL interpreter in person or via webcam for an upcoming webinar, please apply through NOAA Workplace Management Office's Sign Language Interpreting Services Program.
Huan Meng, NESDIS/Center for Satellite Applications and Research, and Kristopher White, NWS/Huntsville, AL Weather Forecast Office and NASA/MSFC/Short-term Prediction Research and Transition Center
Date & Time:
18 July 2018
12:00 pm - 1:00 pm ET
Location:
Room 2554-2555, NCWCP, 5830 University Research Ct, College Park, MD 20740, USA
STAR Seminar Series OneNOAA Science Seminar Series
Presenter(s): Huan Meng, NESDIS/Center for Satellite Applications and Research, and Kristopher White, NWS/Huntsville, AL Weather Forecast Office and NASA/MSFC/Short-term Prediction Research and Transition Center
Abstract: An over land snowfall rate (SFR) product has been produced operationally at NOAA/NESDIS since 2012. The product utilizes the passive microwave measurements from the ATMS sensor aboard S-NPP and NOAA-20, and from AMSU and MHS sensor pair aboard the Polar Operational Environmental Satellites (POES) operated by NOAA and EUMETSAT. Recently, SFR product has also been developed for SSMIS aboard the DMSP satellites and for GMI aboard NASA's GPM core satellite. The SFR algorithm consists of two components: snowfall detection and snowfall rate estimation. Both components mainly rely on the high frequencies at and above 88/89 GHz due to their sensitivity to solid precipitation. The snowfall detection component is a statistical algorithm that optimally combines snowfall probabilities derived from a satellite-based module and a numerical weather prediction model-based module. The snowfall rate component is a physical, 1DVAR-based algorithm. The SFR product has been validated extensively against gauge observations and radar snowfall rate estimates with satisfactory results. As part of a project supported by the JPSS Proving Ground and Risk Reduction program, the SFR product retrieved from eight satellites was also evaluated at some NWS Weather Forecast Offices in winter 2017-2018. NWS meteorologists evaluated and provided feedback regarding the SFR product suite via an online survey, emails and a webinar. Evaluation results affirmed operational utility of the SFR product, especially as it pertains to the analysis and forecast of snowfall rates in regions that lack necessary radar and in-situ observations. Some data issues were also discovered and addressed during the evaluation period, highlighting the positive aspects of the intensive assessment process, which fosters direct interaction between product developers and end-users. Conclusions and recommendations for future iterations of the SFR product will also be discussed.
Bio(s): Huan Meng: Huan Meng received her MS in Physical Oceanography from the Florida State University in 1993, and her PhD in Hydrology from the Colorado State University in 2004. She supported STAR as a contractor between 1999 and 2006 and then joined STAR as a federal employee. Her research interest is in the development of passive microwave products. She has received two NOAA group bronze medals. One of them was for developing NOAA's first operational snowfall rate product. Kristopher White: Mr. White received his B.S. in Meteorology at the University of Oklahoma in 2002. After graduation, Mr. White worked at the Reagan Missile Test Site on the Kwajalein Atoll, serving as lead mission meteorologist for several test missions, including introductory SpaceX launch tests. Mr. White entered the National Weather Service (NWS) in Duluth, Minnesota in 2006, and was later promoted to journey and then lead meteorologist at the NWS office in Huntsville, Alabama. In 2011, Mr. White was promoted to Applications Integration Meteorologist, serving both as a lead meteorologist and principal liaison between the NWS and the NASA SPoRT program.
9:00 - 9:15 AM EDT Opening Remarks - Harry Cikanek (Director, Center for Satellite Applications and Research - STAR)
9:15 - 9:45 AM EDT Introduction to the NWS Operations - Alek Krautmann - (NESDIS Program Coordination Officer)
9:45 - 10:00 AM EDT Break
10:00 - 10:45 AM EDT Satellite Data during Severe Weather Ops - Jennifer VogtMiller (General Forecaster/ Satellite Focal - Weather Forecast Office, (WFO) Albany, NWS)
10:45 - 11:15 AM EDT Discussion
11:15 - 12:00 PM EDT Satellite Data in Operations - Christopher Gitro (Lead Forecaster/Satellite Focal WFO Kansas City, NWS) / Michael Jurewicz (Lead Forecaster/Satellite Focal WFO Binghamton, NWS)
12:00 - 1:00 PM EDT Lunch
1:00 - 2:00 PM EDT Vision - Distance Learning Capabilities - Tim Schmit - Satellite Research Meteorologist (STAR & ASPB)
2:00 - 2:15 PM EDT Break
2:15 - 3:00 PM EDT AWIPS Access - SET/ABI - Deb Molenar - IT Specialist (STAR)
3:00 - 3:30 PM EDT Discussion
3:30 - 3:45 PM EDT Break
3:45 - 4:15 PM EDT Satellite Liaison - Bill Line (Meteorologist/WFO Pueblo, NWS)
4:15 - 5:00 PM EDT Path Forward/Discussion - Harry Cikanek (Director, Center for Satellite Applications and Research - STAR)----------------------------------------------------------------
