Modeling Regional Pollution Transport Events During KORUS‐AQ: Progress and Challenges in Improving Representation of Land‐Atmosphere Feedbacks

Abstract

This study evaluates the impact of assimilating soil moisture data from National Aeronautics and Space Administration (NASA)'s Soil Moisture Active Passive (SMAP) on short‐term regional weather and air quality modeling in East Asia during the Korea‐U.S. Air Quality Study (KORUS‐AQ) airborne campaign. SMAP data are assimilated into the Noah land surface model using an ensemble Kalman filter approach in the Land Information System framework, which is semicoupled with the NASA‐Unified Weather Research and Forecasting model with online chemistry (NUWRF‐Chem). With SMAP assimilation included, water vapor and carbon monoxide (CO) transport from northern central China transitional climate zones to South Korea is better represented in NUWRF‐Chem during two studied pollution events. Influenced by different synoptic conditions and emission patterns, impact of SMAP assimilation on modeled CO in South Korea is intense (>30 ppbv) during one event and less significant (<8 ppbv) during the other. SMAP assimilation impact on air quality modeling skill is complicated by other error sources such as the chemical initial and boundary conditions (IC/LBC) and emission inputs of NUWRF‐Chem. Using a satellite‐observation‐constrained chemical IC/LBC instead of a free‐running, coarser‐resolution chemical IC/LBC reduces modeled CO by up to 80 ppbv over South Korea. Consequently, CO performance is improved in the middle‐upper troposphere whereas degraded in the lower troposphere. Remaining negative CO biases result largely from the emissions inputs. The advancements in land surface modeling and chemical IC/LBC presented here are expected to benefit future investigations on constraining emissions using observations, which can in turn enable more accurate assessments of SMAP assimilation and chemical IC/LBC impacts.

Satellite soil moisture data assimilation impacts on modeling weather and ozone in the southeastern US – part I: an overview

“…The inclusion of the SMAP DA did not dominantly improve or degrade the overall T2, RH and WS performance of WRF-Chem (e.g., Figures 2g;3d;3h, based on the root-mean-square error (RMSE) metric): i.e., improvements on T2, RH, and WS occurred in 47%, 51% and 52% of the model grids where observations are available, and the domain-wide mean RMSE changes for T2, RH, and WS are ~0 °K, -0.024%, and -0.005 ms -1 , respectively. This is qualitatively consistent with the findings in Huang et al (2018) and Yin and Zhan (2018) for dense vegetation regions (i.e., green vegetation fraction >0.6), based on RMSE and other evaluation metrics. Additionally, as discussed in Huang et al (2018), unrealistic model representations of terrain height can pose challenges for evaluating the modeled surface weather fields with ground-based observations.…”

“…This is qualitatively consistent with the findings in Huang et al (2018) and Yin and Zhan (2018) for dense vegetation regions (i.e., green vegetation fraction >0.6), based on RMSE and other evaluation metrics. Additionally, as discussed in Huang et al (2018), unrealistic model representations of terrain height can pose challenges for evaluating the modeled surface weather fields with ground-based observations. The 12 km model grid used in this work well represents terrain height (i.e., |model-actual|<15 m) at over 70% of the model grids that have collocated observations, but at some locations the discrepancies between the model and actual terrain height exceed 100 m. Furthermore, human activities such as irrigation can significantly modify water budget and land-atmosphere coupling strength over agricultural regions (e.g., Lu et al, 2017), but these were unmodeled (i.e., not accounted for) in the used modeling system.…”

“…The changes in WRF-Chem temperature gradients due to the SMAP DA led to slight WS enhancements over many of the model grids (Figure 3f). On the 13-day timescale, the SMAP DA had less discernable impacts on rainfall, consistent with findings from Koster et al (2010Koster et al ( , 2011 and Huang et al (2018). The SMAP DA impacts on mean rainfall rate and diurnal cycles show noisy patterns (Figure 4d;g;h), and positive and negative SM-precipitation relationships are both found.…”

“…According to SMAP Product Specification Document (https://nsidc.org/data/smap/technicalreferences), only successful retrievals that are no smaller than the valid minimum value of 0.02 m 3 m -3 and of recommended quality (i.e., the retrieval quality flag has "0" values for bits 0, 1, 2) were used. Evaluation of SMAP data over North America with in-situ and LSM output suggests better data quality over flat and less forested regions (Pan et al, 2016), and previous studies have demonstrated that the SMAP DA improvements on weather variables are more distinguishable over regions with sparse vegetation (e.g., Huang et al, 2018;Yin and Zhan, 2018). Before the DA, SMAP data were re-projected to the model grid and bias correction was applied via matching the means and standard deviations of the Noah LSM and SMAP data for each grid (de Rosnay et al, 2013;Huang et al, 2018;Yin and Zhan, 2018) during August of 2015-2019.…”

“…Version 3.6 of the widely-used, four-soil-layer Noah LSM (Chen and Dudhia, 2001) within LIS (Kumar et al, 2006) version 7.1rp8 served as the land component of the used modeling/DA system. This version of Noah is similar to Noah version 3.3 (Huang et al, 2017b(Huang et al, , 2018, except that snow physics is based on the University of Arizona scheme (Wang et al, 2009). An offline Noah simulation was performed within LIS prior to all WRF-Chem simulations for equilibrated land conditions, covering the period of 1999-2019.…”