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.
Abstract: The NOAA STAR SOCD OceanView(OV) is a web-based visualization application delivering an integrated display of remote sensing, in situ, and model output data over oceans, coastal waterways, and inland bodies of water. OV's objective is to assist both expert and general public users in understanding the diverse water bodies in space and over time, both from a synoptic and an event-scale perspective. The OV incorporates data and products primarily from NOAA and some non-NOAA partner sources, spanning satellites, airborne and field platforms, and environmental modeling output. Various datasets produced by the SOCD science teams and distributed publicly either via the SOCD CoastWatch program or other online media comprise the bulk of the information presented in OV. Other data include products, images, or information from ESA CCI, NASA WorldView, EU CMEMS, USGS, NOAA NCEI, and NASA EONET. OceanView 1.0 was publicly released on 19-May-2021 at https://www.star.nesdis.noaa.gov/socd/ov/. This release was the culmination of nearly two years of work, from vision to design and implementation. An enhanced version 1.1 with an improved timeline widget and bell icon for changelog notification was released on 19-Jun-2021. Besides serving the satellite remote sensing community and ocean enthusiasts, the OceanView will contribute to global earth observing activities led by SOCD, including the Committee on Earth Observation Satellites (CEOS) Coastal Observations, Applications, Services and Tools (COAST) Ad Hoc Team, as well as the Group on Earth Observations (GEO) Blue Planet and AquaWatch initiatives. These efforts are directed to the UN Decade of Ocean Science for Sustainable Development. The presentation will brief on modern integrated mapping approaches and provide a live demo and outlook.
Bio(s): Prasanjit Dash is a remote sensing scientist with over 20 years of experience in terrestrial infrared satellite applications. Since 2006, he has been with NOAA NESDIS STAR SOCD and the Cooperative Institute for Research in the Atmosphere of Colorado State University (CSU CIRA), excluding mid-2016 to 2017 when he contributed to the Copernicus Sentinel-3 mission at EUMETSAT. Prasanjit received a Ph.D. in Physics from the Karlsruhe Institute of Technology, Germany, in 2004 and an MBA from College of Business, Colorado State University, USA, in 2017.
Presenter(s): Hal Bloom, Science and Technology Corporation (STC)
Sponsor(s): STAR Science Seminar Series
Abstract: U.S. Space Force (USSF) Space and Missile Systems Center (SMC) project to develop and demonstrate an Electro-Optical / Infrared (EO/IR) LEO-based cloud characterization solution that supports U.S. warfighter operations. During the first phase, the team (ASTRA, STC, and LM) are building a prototype system that includes an 8-channel IR camera system for cloud measurements, the spacecraft, and the ground system which includes mission operations, mission data management, product generation and quality validation, and distribution. The Rapid Revisit Optical Cloud Imager (RROCI) will utilize commercial off-the-shelf systems to produce cloud characterization products, provide theater weather, and comparison of payload outputs to existing satellite data from a 12U satellite that meets USSF mission requirements. The objective is to create a constellation of imagers to replace the cloud characterization capabilities of DMSP. Launch is currently scheduled for Jan 2022.
Bio(s): Mr. Hal Bloom is the Group Vice President for Science and Engineering for the Science and Technology Corporation (STC) where he has responsibility for over 200 FTE on government contracts at NASA and NOAA involved in Earth / Space Science, engineering and hardware fabrication. He is currently the Deputy Mission manager for the DoD Space Force Earth Weather Satellite (EWS) ASTRA team Rapid Revisit Optical Cloud Imager (RROCI), with responsibilities in mission systems engineering, science algorithm development, and build of the ground segment. Hal is a former NOAA employee. He was the GOES-R and NPOESS satellite Deputy Program Manager with the responsibility in both programs of managing a multiple agency (e.g. NASA, DOD, NOAA) workforce in meeting the technical, schedule and cost requirements of mission space and ground system development for these two large systems. Earlier, Hal was the Payload Manager for NOAA/NASA of the NPOESS/NPP instrument development and engineering activities. Hal also led design and development as Instrument Manager of the NPP Cross-Track Infrared Sounder (CrIS). Before his NOAA experience Hal led a team in the development of the initial CRIS and VIIRS algorithms for the exploratory NPOESS and NPP mission products. Also, in private industry Hal led a team of researchers to improve weather forecasting models by insertion of improved algorithms and products derived from the NOAA operational GOES and POES satellite systems.
Presenter(s): Vijay Natraj and Derek Posselt (NASA/JPL)
Sponsor(s): STAR Science Seminar Series
Abstract: In response to the 2020 NOAA Geostationary Orbit Concept Exploration Broad Agency Announcement, JPL's Panchromatic imaging Fourier Transform Spectrometer (PanFTS) team conducted an infrared sounder study, GEO IR Sounder. By integrating retrievals for thermal emission and reflected solar bands in a single instrument, GEO IR Sounder improves the sensitivity to the lower troposphere and near surface. Consequently, the additional retrieval degrees of freedom improve storm intensity, structure and track forecast accuracy, as well as air quality and climate research. We will present our multi-band retrieval simulation and forecast OSSE results.
Bio(s): Vijay Natraj is a Research Scientist in the Aerosols and Clouds group at JPL. He has 15 years of experience in radiative transfer modeling with application to remote sensing of Earth and (exo)planetary atmospheres. He leads several projects on using innovative radiative transfer techniques to retrieve surface reflectance, understand aerosol vertical distribution, profile temperature and water vapor in the planetary boundary layer and improve diagnosis of clouds in climate models. These efforts are directly related to important missions recommended by the 2017 Earth Science Decadal Survey. In addition, he leads a project to utilize measurements of Earth's sunlit disk from Lagrangian orbit to model the Earth as a proxy exoplanet. -------------------------------------------------------------------------------------------------------Derek Posselt is a research scientist with the Atmospheric Physics and Weather group in the Earth Science Section at NASA Jet Propulsion Laboratory (JPL), California Institute of Technology (Caltech). He is also a visiting Associate Researcher at the Joint Institute for Regional Earth System Science and Engineering (JIFRESSE) at the University of California, Los Angeles (UCLA).Dr. Posselt has 18 years of experience working on satellite data applications and the development of satellite missions, and 19 years of experience confronting numerical models with remote sensing and in-situ observations. He served as CYGNSS Deputy Principal Investigator from 2012 - 2016, and currently coordinates extended science team activities for the mission. He is actively involved in the quantitative analysis of satellite information, including the use of uncertainty quantification (UQ) algorithms and observing system simulation experiments (OSSEs). He is also actively engaged in the development of new data assimilation and retrieval algorithms, particularly in a Bayesian probabilistic context.His research interests include: remote sensing of cloud and precipitation properties, numerical modeling of cloud systems, and the use of Bayesian algorithms in the development of new data assimilation methodologies and remote sensing techniques. He has experience as a user and developer of the NASA Goddard Earth Observing System (GEOS) model, the NASA Goddard Cumulus Ensemble (GCE) model, the NCAR Cloud Model (CM1), and the Weather Research and Forecasting (WRF) model.Dr. Posselt is currently a member of the science teams for the Cyclone Global Navigation Satellite System (CYGNSS), Aerosols Clouds Ecosystems (ACE), and CloudSat missions.
Presenter(s): David Trossman (NOAA STAR/NESDIS) and Robert Tyler (NASA GSFC Geodesy and Geophysics Laboratory and UMBC JCEST)
Sponsor(s): STAR Science Seminar Series
Abstract: A new remote sensing-based approach to monitor ocean heat content (OHC) anomalies is proposed to overcome challenges with observing OHC over the entire ocean. The output of an ocean state estimate - using the Estimating the Circulation & Climate of the Ocean (ECCO) framework - is assumed to be perfect observational data and used to identify prospective variables that could be calculated from remotely monitored characteristics of the ocean. The depth-integrated electrical conductivity (potentially derived from magnetometry) is shown to be highly predictive of OHC in poorly observed regions - such as those covered by sea ice - so it is used together with sea surface heights (from altimetry) and ocean bottom pressures (from gravimetry) to estimate OHC. The seafloor depth, sea surface height anomalies, ocean bottom pressure, and depth-integrated electrical conductivity explain virtually all of the variance in OHC. To demonstrate the feasibility of a method that uses these ocean characteristics - inferable from global satellite coverage - to monitor OHC, the output of ECCO is sampled along historical hydrographic transects, a machine learning algorithm - called a Generalized Additive Model or GAM - is trained on these samples, and OHC is estimated everywhere. This remote monitoring method can estimate global OHC within 0.15% spatial root-mean-square error (RMSE) on a bi-decadal time scale. This RMSE is sensitive to the spatial variance in OHC that gets sampled by hydrographic transects, the variables included in the GAM, and their measurement errors when inferred from satellite data - in particular the noise levels of depth-integrated electrical conductivity and ocean bottom pressure. OHC could be remotely monitored over sufficiently long time scales when enough spatial variance in OHC is explained in the training data over those time scales. This method could potentially supplement existing methods to monitor OHC.
Bio(s):
David Trossman is a physical oceanographer, by training. He received his PhD at the University of Washington in Seattle, did a postdoc at the University of Michigan in Ann Arbor, did another postdoc at McGill University, was a researcher jointly at NASA Goddard Space Flight Center and Johns Hopkins University through the GESTAR cooperative agreement, was a researcher at the University of Texas in Austin's Oden Institute for Computational Engineering and Sciences, and is currently a senior scientist at NOAA STAR/NESDIS through Global Science & Technology. In general, his research has taken two trajectories. 1) He has studied the physical and biogeochemical consequences of ocean circulation and mixing as well as the interactions between the ocean and other components of the Earth system in order to understand and improve the realism of Earth system models. 2) He has also probed the information content of physical and biogeochemical observational data sources to advance the reconstruction of the ocean's historical conditions through statistical techniques.
