Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter

Abstract

The next‐generation geostationary satellite Himawari‐8 has a much higher observation frequency of the aerosol field than polar‐orbiting satellites. Aerosol analyses with a geostationary satellite can advance our understanding of the rapid spatiotemporal evolution of aerosols, which is especially critical for studies of air pollution and its mechanisms. We present a one‐monthlong hourly aerosol analysis using an aerosol data assimilation based on the local ensemble Kalman filter (LETKF), Himawari‐8‐retrieved hourly aerosol optical thicknesses (AOTs), and a global model named Non‐hydrostatic Icosahedral Atmospheric Model coupled with an aerosol model named Spectral Radiation Transport Model for Aerosol Species (NICAM‐SPRINTARS). To assimilate asynchronous observations and avoid frequent switching between the assimilation and ensemble aerosol forecasts, the LETKF is also extended to the four‐dimensional LETKF (4D‐LETKF). The hourly aerosol analyses are evaluated with both the assimilated Himawari‐8 AOTs and independent Moderate Resolution Imaging Spectroradiometer (MODIS)‐ and AErosol RObotic NETwork (AERONET)‐retrieved AOTs. All evaluations show that the assimilations positively affect the model performances and produce simulated AOTs that are closer to the observations. The analyses correctly reduce the significantly positive biases and root‐mean‐square errors of the control experiment, especially over East China and Australia. Our results also show that hourly aerosol analyses with more frequent Himawari‐8 observations are superior to those using the polar satellite MODIS observations. The performances among the LETKF and 4D‐LETKF experiments are generally not so different, but the computational load of the 4D‐LETKF is much lighter than that of the LETKF.

Quantifying the Impact of Excess Moisture From Transpiration From Crops on an Extreme Heat Wave Event in the Midwestern U.S.: A Top‐Down Constraint From Moderate Resolution Imaging Spectroradiometer Water Vapor Retrieval

“…The randomness coefficients applied to each member is drawn from a lognormal distribution with a mean of 1 and a variance equal to our initial assumption on the flux uncertainty which is set to 50%. The coefficients are kept constant temporally and spatially in order to generate a reasonable spread in the ensemble by minimizing the canceling effect (Dai et al, 2019).…”

“…The LETKF technique requires both localization in time and space (Dai et al, 2019; Hunt, Kostelich, et al, 2007; Liu, Bowman, et al, 2016; Liu, Chen, et al, 2016), which means that the posteriori is independently estimated in each model grid cell at a given time within a defined radius. We determine the spatial localization ( E ) using the normalized cumulative semiovariogram of observations (Şen, 1997), which strongly fit ( R 2 = 0.97) the Cressman formula: .…”

Inverting the East Asian Dust Emission Fluxes Using the Ensemble Kalman Smoother and Himawari-8 AODs: A Case Study with WRF-Chem v3.5.1

“…The EnKS solves an analysis equation very similar to that of EnKF but with time lags between the analysis and innovations (observations minus forecast) [20]. To efficiently assimilate the hourly aerosol observations from the next-generation geostationary satellite Himawari-8 [21], we apply a fourdimensional local ensemble transform Kalman filter (4D-LETKF) to solve the Kalman equations [22]. With each assimilation cycle, the 4D-LETKF finds the maximum likelihood solution of dust emissions from the following Kalman equations:…”

Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART

“…They are usually in the form of the mixing ratio of particle mass (PM) with diameters of less than 10 μm (PM 10 ) (Jiang et al, 2013) and 2.5 μm (PM 2.5 ) (Werner et al, 2019) or the mass concentrations of specific aerosol species (Henze et al, 2009;Li et al, 2013), which mostly originate from surface monitoring sites and sometimes from aircrafts. The other category is aerosol optical properties (AOP), which usually include the aerosol optical depth (AOD) (Bao et al, 2019;Dai et al, 2014;Dai et al, 2019;Rubin et al, 2017;Saide et al, 2014), vertical profile of aerosol extinction coefficient (AEXT) (Uno et al, 2008;Zhang et al, 2011) and backscattering coefficient (Kahnert, 2008;Sekiyama et al, 2010). These observations can be obtained from spaceborne instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization or from surface-based instruments such as sun photometers from the Aerosol Robotic Network (AERONET) and ground-based lidar.…”