Assimilation of planetary boundary layer height measurements with multi-physics using an ensemble Kalman filter during the PECAN field campaign
Keming Pan1, Andrew Tangborn2, Jeffrey Anderson3, Joseph A. Santanello4, Brian J. Carroll5,6, Belay Demoz7, Yafang Guo1, and Avelino F. Arellano1
1Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ
2Environmental Modeling Center, NCEP/NOAA, College Park, MD
3Computational and Information System Lab, NSF NCAR, Boulder, CO
4Goddard Space Flight Center, NASA, Greenbelt, MD
5Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO
6NOAA Chemical Sciences Laboratory, Boulder, CO
7Department of Physics, The University of Maryland, Baltimore, MD
The Planetary Boundary Layer (PBL) is the lowest part of the troposphere, directly influenced by Earth’s surface and characterized by rapid exchanges of heat, moisture, and momentum with a timescale of an hour or less. The PBL Height (PBLH) is one of the most critical parameters controlled among others by the surface fluxes of buoyancy and moisture that influence the interactions between the surface and the atmosphere. While advancements in research, computing, and large-eddy simulations have improved our understanding of the PBL, significant challenges remain in accurately characterizing its structure and dynamics. Our studies consist of following tasks: (1) evaluate the estimates of PBLH and key PBL-related variables from a set of convective-permitting simulations conducted using the Weather Research and Forecast (WRF) model with a multi-physics configuration during the Plain Elevated Convection at Night (PECAN) campaign (June to mid-July 2015). (2) explore various strategies for integrating PBL data into WRF model, using the ensemble-based Data Assimilation Research Testbed (DART). Our analysis aims to quantify the variability in PBLH, along with its associated surface variables and atmospheric profiles, to gauge the influence of model physics on the accuracy of simulated PBL properties. Our results emphasize the need for further refinement of PBL DA, particularly in retaining information content by updating the associated state vector, and key variables related to land-atmosphere exchange. This work provides insights into future studies on PBL DA and on assessing the potential of current and future PBLH data in improving forecast accuracy.