With the increased proliferation of earth observing satellites, earth system scientists and forecasters require more advanced methods to gather and analyze the variety of information types and content that are available. This data spans across many observing characteristics such as observing geometry from geostationary (e.g., the NOAA Geostationary Operational Environmental Satellite GOES series), sun-synchronous (e.g., the DoD Defense Meteorological Satellite Program DMSP series), and other low earth orbits (e.g., the NASA/JAXA Global Precipitation Measurement GPM mission) through different governmental, commercial, and international partners. Near real-time application of these observations are developed for a variety of environmental characterizations in space, the atmosphere and weather, ocean, land and soil, and ice as well as apply to timescales from seconds through decades.
This project seeks to better leverage satellite-based remote sensing of the earth environment by improving use of this data for operational needs. Current efforts include, 1) development of novel methods to improve analysis and forecasting of tropical cyclones (see the NRL TC webpage: https://www.nrlmry.navy.mil/TC.html), 2) understand surface and atmospheric contributions to passive microwave remote sensing, including the NRL based WindSat (https://www.nrl.navy.mil/WindSat/), 3) leverage visible through infrared wavelengths to characterize and investigate aerosol and chemistry relationships , 4) characterize satellite-based observational uncertainties and relate to improvements in initialization of numerical weather prediction, 5) contributing algorithms to a generic earth science data processing platform that fuses multiple data types (using the Geo-located Information Processing System GeoIPS: https://github.com/USNavalResearchLaboratory/GeoIPS), 6) using machine learning methods to uncover relationships in satellite-derived big data sets, refining routines for operational demonstration, and developing verification and validation metrics to define skill.
Research associates will be stationed and work with scientists in the Remote Sensing Division in Washington, DC. This effort will entail collaborations with partners across the Naval Research Laboratory – including with the Marine Meteorology Division in Monterey, CA and the Oceanography and Marine Geosciences Divisions in Stennis, MS – and the larger research community that encompass other labs and universities. A broad range of backgrounds are encouraged to inquire, including individuals with experience in atmospheric sciences, weather and climate, oceanography, earth science and geophysics, space weather, remote sensing, machine learning and big data methods, data processing and data fusion.
Meteorology; Oceanography; Land/Soil/Ice; Middle Atmosphere; Clouds; Tropical Cyclones; Nowcasting; Machine Learning; Data Assimilation; Data Fusion