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ARS Home » Pacific West Area » Riverside, California » Agricultural Water Efficiency and Salinity Research Unit » Research » Publications at this Location » Publication #417841
Research Project: Protection of Food and Water Supplies from Pathogens and Human Induced Chemicals of Emerging Concern

Location: Agricultural Water Efficiency and Salinity Research Unit

Title: Improving surface flux and boundary layer simulations using NASA land surface remote sensing

Author
item WU, FAN - Pennsylvania State University
item DAVIS, KENNETH - Pennsylvania State University
item ZHANG, LI - California Air Resources Board
item JIANG, YUEGI - Pennsylvania State University
item Anderson, Raymond
item CAI, CHENXIA - California Air Resources Board
item CUI, YU YAN - California Air Resources Board
item ZHAO, ZHAN - California Air Resources Board

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 11/15/2024
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Atmospheric boundary layer (ABL) simulation is tightly coupled with land surface energy fluxes. In the Weather Research and Forecasting (WRF) model, accurately representing surface energy dynamics remains a challenge due to land complexity and limited observational constraints. Space-based observations can enhance model simulations through real-time data assimilation. This study uses the NASA Land Information System (LIS) and Unified WRF (NU-WRF) model to investigate how high-resolution satellite measurements improve surface flux and ABL simulations in WRF. We focus on the San Joaquin Valley (SJV) of California, an irrigated agricultural region with a semi-arid climate. We apply parameterized irrigation in NU-WRF/LIS alongside land surface remote sensing. Model performance is evaluated using ground observations from eddy-covariance flux towers and LiDAR Ceilometers. We compare four model simulations, each with different irrigation and remote sensing setups, but all running with the Noah-MP Land Surface Model: WRF without irrigation, LIS without irrigation, LIS with irrigation, and NU-WRF/LIS with irrigation. During the growing season in the SJV, we find: 1) WRF significantly overestimates sensible heat and underestimates latent heat fluxes due to the lack of irrigation parameterizations, leading to higher ABL heights (ABLHs); 2) Both LIS with and without irrigation show smaller heat flux biases. LIS irrigation shows even smaller biases over irrigated cropland; 3) LIS simulates higher soil moisture, leaf area index, and vegetation fraction than WRF, with more pronounced soil moisture changes; 4) NU-WRF/LIS with irrigation systematically predicts lower surface wind, lower temperature, higher humidity, and lower ABLH than WRF. Overall, the SJV is too dry in WRF during the growing season when irrigation occurs. LIS improves the surface heat flux simulations, suggesting soil moisture as a crucial variable in the data assimilation. NU-WRF/LIS with irrigation reduces ABLH model biases during the growing season, but fails to improve winter or nighttime ABLH. Further analysis of WRF with irrigation and LIS open-loop without remote sensing can help separate the effects of irrigation scheme and satellite data assimilation, identifying which is more important for improving surface flux simulations.