Ralph M. Parsons Laboratory Seminar Series: A multi-sensor data assimilation approach to terrestrial carbon cycle monitoring, inventories, markets and projections

Ralph M. Parsons Laboratory Seminar Series: A multi-sensor data assimilation approach to terrestrial carbon cycle monitoring, inventories, markets and projections

Please join us for the Ralph M. Parsons Laboratory Seminar Series with Professor Michael Dietze from Boston University. This seminar is in person at 15 Vassar Street, 48-316. Abstract: Improving our ability to understand and predict the dynamics of the terrestrial carbon cycle remains a pressing challenge despite a rapidly growing volume and diversity of Earth Observation data. State data assimilation represents a path forward via an iterative cycle of making process-based forecasts and then statistically reconciling these forecasts against numerous ground-based and remotely-sensed data constraints into a “reanalysis” data product that provides full spatiotemporal carbon budgets with robust uncertainty accounting. Here we report on an >100x expansion of the PEcAn+SIPNET reanalysis from 500 sites CONUS, 25 ensemble members, and 2 data constraints to a ~1km product across North America with 100 ensemble members and 9 data constraints: GEDI and Landtrendr AGB, MODIS LAI, SoilGrids Soil C, SMAP soil moisture, USFS Forest Inventory biomass, biomass increment, and FLUXNET NEE and LE. Synergistically, we use similar ML models both emulate the data assimilation system and to analyze and bias-correct downscaled C and water fluxes. This product preserves spatial, temporal, and across-variable covariances, and we demonstrate the impacts of these covariances on uncertainty accounting in GHG inventories, Scope 3 reporting, and the voluntary C markets. In addition, we review a wide range of ongoing validation activities, comparing the outputs of the reanalysis against withheld data from: ICESat2 lidar; USFS BigMap biomass; NEON soil C, soil respiration, and fine roots; and the ILAMB benchmark suite. Finally, if time permits, we touch on emulator-based recalibration efforts, the Ecological Forecasting Initiative (EFI), and EFI’s NEON forecasting challenge.