- A33I: Next-Generation PBL Observations: Synergistic Approaches and Technological Breakthroughs from NASA's WH²yMSIE-APEX Campaigns Poster
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NOLA CC
Primary Convener:Generic 'disconnected' Message
Antonia Gambacorta, NASA Goddard Space Flight Center
Convener:
Amin Nehrir, NASA Langley Research Center
Alexander Kotsakis, Earth System Science Interdisciplinary Center
Shawn Serbin, NASA Goddard Space Flight Center
Chair:
Alexander Kotsakis, Earth System Science Interdisciplinary Center
Andrew Feldman, University of Maryland College Park
Stephen Nicholls, SSAI
Narges Shahroudi, NASA , Goddard Space Flight Center and University of Maryland
Carol Clayson, Woods Hole Oceanographic Institution
The 2017 Decadal Survey identified Earth's Planetary Boundary Layer (PBL) as an Incubation-Targeted Observable, acknowledging limitations in current observation capabilities. To address these challenges, the 2024 Westcoast & Heartland Hyperspectral Microwave Sensor Intensive Experiment (WH²yMSIE) was deployed combining multiple observational platforms—space, airborne, and ground-based—utilizing passive and active sensors with participation from over twenty institutions across government agencies, academia, and industry. WH²yMSIE captured diverse PBL regimes, with particular focus on demonstrating the CoSMIR-H instrument for potential space deployment of hyperspectral microwave technology. The complementary APEX experiment provided validation through active sensors plus dropsonde measurements from the under-flying G-III aircraft. Together, these campaigns generated unprecedented datasets enabling advanced multi-sensor approaches to PBL sounding. This session welcomes presentations on improved PBL retrievals utilizing WH²yMSIE and APEX datasets, with a focus on synergistic multi-sensor approaches informing PBL mission architecture decisions and advancing remote sensing technology transition to operational satellite missions.
Index Terms
3307 Boundary layer processes
3322 Land|atmosphere interactions
3360 Remote sensing
3394 Instruments and techniques
Suggested Itineraries:
Disasters‚ Calamities and Extreme Events
Space Weather
Machine Learning and AI
Open Science and Open Data
Neighborhoods:
3. Earth Covering
Scientific DisciplineSuggested ItinerariesNeighborhoodType
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