Investigating the assimilation of CALIPSO global aerosol vertical observations using a four-dimensional ensemble Kalman filter

Yueming Cheng,Nick A J Schutgens,Daisuke Goto,Guangyu Shi,Teruyuki Nakajima,Tie Dai

doi:10.5194/acp-19-13445-2019

Abstract

Abstract. Aerosol vertical information is critical to quantify the influences of aerosol on the climate and environment; however, large uncertainties still persist in model simulations. In this study, the vertical aerosol extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are assimilated to optimize the hourly aerosol fields of the Non-hydrostatic ICosahedral Atmospheric Model (NICAM) online coupled with the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) using a four-dimensional local ensemble transform Kalman filter (4-D LETKF). A parallel assimilation experiment using bias-corrected aerosol optical thicknesses (AOTs) from the Moderate Resolution Imaging Spectroradiometer (MODIS) is conducted to investigate the effects of assimilating the observations (and whether to include vertical information) on the model performances. Additionally, an experiment simultaneously assimilating both CALIOP and MODIS observations is conducted. The assimilation experiments are successfully performed for 1 month, making it possible to evaluate the results in a statistical sense. The hourly analyses are validated via both the CALIOP-observed aerosol vertical extinction coefficients and the AOT observations from MODIS and the AErosol RObotic NETwork (AERONET). Our results reveal that both the CALIOP and MODIS assimilations can improve the model simulations. The CALIOP assimilation is superior to the MODIS assimilation in modifying the incorrect aerosol vertical distributions and reproducing the real magnitudes and variations, and the joint CALIOP and MODIS assimilation can further improve the simulated aerosol vertical distribution. However, the MODIS assimilation can better reproduce the AOT distributions than the CALIOP assimilation, and the inclusion of the CALIOP observations has an insignificant impact on the AOT analysis. This is probably due to the nadir-viewing CALIOP having much sparser coverage than MODIS. The assimilation efficiencies of CALIOP decrease with increasing distances of the overpass time, indicating that more aerosol vertical observation platforms are required to fill the sensor-specific observation gaps and hence improve the aerosol vertical data assimilation.

Highlights

Aerosols have significant impacts on air quality, climate change, radiation balance, and the hydrological cycle (Charlson et al, 1992; Huang et al, 2014; Liu et al, 2011, 2014; Nakajima et al, 2001; Ramanathan et al, 2001)
The results in the FR, LETKF-Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), 4D-LETKF24H-CALIPSO, and 4D-LETKF-24H-Naval Research Laboratory (NRL) experiments are firstly compared with the assimilated Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical extinctions and the NRL Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical thicknesses (AOTs) as self-verification
Based on the model performance evaluation statistical metrics (i.e., mean fractional bias (MFB), mean fractional error (MFE), root mean square error (RMSE), CORR, and index of agreement (IOA)), the two CALIOP assimilation experiments are better than the FR experiment, especially over the ocean regions

Summary

Introduction

Aerosols have significant impacts on air quality, climate change, radiation balance, and the hydrological cycle (Charlson et al, 1992; Huang et al, 2014; Liu et al, 2011, 2014; Nakajima et al, 2001; Ramanathan et al, 2001). Aerosols may contribute to regional differences in historical warming rates (Huang et al, 2017). Y. Cheng et al.: CALIPSO global aerosol vertical observations on the Earth system (Huneeus et al, 2011; Liu et al, 2015; Jia et al, 2015; Myhre et al, 2013; Sato et al, 2018; Sato and Suzuki, 2019; Textor et al, 2006). Aerosol data assimilation, which makes optimal use of both observations and numerical simulations to obtain the best possible estimates of aerosol behaviors, is an emerging way to obtain accurate predictions and characterizations of atmospheric aerosol and its influence

Results

Discussion

Conclusion