A three-dimensional variational data assimilation system for multiple  aerosol species with WRF/Chem and an application to PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; prediction

Li, Z.; Zang, Zengliang; Li, Q. B.; Chao, Yi; Chen, D.; Ye, Z.; Liu, Y.; Liou, Kuo‐Nan

doi:10.5194/acpd-12-13515-2012

Cited by 21 publications

(42 citation statements)

References 43 publications

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“…Data assimilation of ground‐based observational networks has been tested for aerosol prediction. For example, several studies have tested the assimilation of ground‐based particulate matter (PM) observations with most using optimal interpolation or variational methods [ Kahnert , ; Tombette et al , ; Pagowski et al , ; Schwartz et al , ; Jiang et al , ; Li et al , ; Lee et al , ]. Pagowski and Grell [] and Schwartz et al [] used ground‐based PM 2.5 measurements from the Environmental Protection Agency's AIRnow network for regional fine aerosol predictions in the United States using several different DA methods.…”

Section: Introductionmentioning

confidence: 99%

Assimilation of AERONET and MODIS AOT observations using variational and ensemble data assimilation methods and its impact on aerosol forecasting skill

Rubin

Reid²,

Hansen³

et al. 2017

JGR Atmospheres

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Data assimilation of Aerosol Robotic Network (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical thickness (AOT) for aerosol forecasting was tested within the Navy Aerosol Analysis Prediction System (NAAPS) framework, using variational and ensemble data assimilation methods. Navy aerosol forecasting currently makes use of a deterministic NAAPS simulation coupled to Navy Variational Data Assimilation System for aerosol optical depth, a two‐dimensional variational data assimilation system, for MODIS AOT assimilation. An ensemble version of NAAPS (ENAAPS) coupled to an ensemble adjustment Kalman filter (EAKF) from the Data Assimilation Research Testbed was recently developed, allowing for a range of data assimilation and forecasting experiments to be run with deterministic NAAPS and ENAAPS. The main findings are that the EAKF, with its flow‐dependent error covariances, makes better use of sparse observations such as AERONET AOT. Assimilating individual AERONET observations in the two‐dimensional variational system can increase the analysis errors when observations are located in high AOT gradient regions. By including AERONET with MODIS AOT assimilation, the magnitudes of peak aerosol events (AOT > 1) were better captured with improved temporal variability, especially in India and Asia where aerosol prediction is a challenge. Assimilating AERONET AOT with MODIS had little impact on the 24 h forecast skill compared to MODIS assimilation only, but differences were found downwind of AERONET sites. The 24 h forecast skill was approximately the same for forecasts initialized with analyses from AERONET AOT assimilation alone compared to MODIS assimilation, particularly in regions where the AERONET network is dense; including the United States and Europe, indicating that AERONET could serve as a backup observation network for over‐land synoptic‐scale aerosol events.

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Section: Introductionmentioning

confidence: 99%

Assimilation of AERONET and MODIS AOT observations using variational and ensemble data assimilation methods and its impact on aerosol forecasting skill

Rubin

Reid²,

Hansen³

et al. 2017

JGR Atmospheres

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“…Common DA methods are the optimal interpolation (OI)/3-dimensional variational (3D-Var) method (Daley, 1991), the ensemble Kalman filter (EnKF) (Evensen, 2009) and the 4-dimensional variational (4D-Var) method (Le Dimet and Talagrand, 1986). Following efforts in DA for trace gas modelling (Austin, 1992;Fisher and Lary, 1995;Elbern and Schmidt, 1999), in recent years, DA has been increasingly applied to aerosol forecasts (Collins et al, 2001;Benedetti et al, 2009;Tombette et al, 2009;Pagowski et al, 2010;Li et al, 2013;.…”

Section: Introductionmentioning

confidence: 99%

“…They showed a potentially powerful impact of the future lidar networks for PM 10 forecasts. Li et al (2013) used the OI for multiple aerosol species and for prediction of PM 2.5 in the Los Angeles basin. The OI method was also employed in a mesoscale numerical weather prediction system (GRAPES/CUACE_Dust) to study dust aerosol assimilation in eastern Asian (Wang and Niu, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…surface mass concentrations (Niu et al, 2008;Tombette et al, 2009;Pagowski et al, 2010;Li et al, 2013;Wang and Niu, 2013;Jiang et al, 2013), aerosol particle number size distributions (Viskari et al, 2012), AOD data from satellites or the AERONET network (Huneeus and Boucher, 2007;Adhikary et al, 2008;Benedetti et al, 2009;Schutgens et al, 2010a, b;Liu et al, 2011;Huneeus et al, 2012;Schwartz et al, 2012), lidar backscatter coefficients (Huneeus and Boucher, 2007;Kahnert, 2009;Sekiyama et al, 2010) and lidar extinction coefficients (Campbell et al, 2010;Zhang et al, 2011). Most studies showed fast-fading DA impact on aerosol forecasting, especially in the early forecast hours (Tombette et al, 2009;Jiang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin

et al. 2014

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Abstract. This paper presents a new application of assimilating lidar signals to aerosol forecasting. It aims at investigating the impact of a ground-based lidar network on the analysis and short-term forecasts of aerosols through a case study in the Mediterranean basin. To do so, we employ a data assimilation (DA) algorithm based on the optimal interpolation method developed in the POLAIR3D chemistry transport model (CTM) of the POLYPHEMUS air quality modelling platform. We assimilate hourly averaged normalised range-corrected lidar signals (PR 2 ) retrieved from a 72 h period of intensive and continuous measurements gases Research InfraStructure (ACTRIS) network and an additional system in Corsica deployed in the framework of the pre-ChArMEx (Chemistry-Aerosol Mediterranean Experiment)/TRAQA (TRAnsport à longue distance et Qualité de l'Air) campaign. This lidar campaign was dedicated to demonstrating the potential operationality of a research network like EARLINET and the potential usefulness of assimilation of lidar signals to aerosol forecasts. Particles with an aerodynamic diameter lower than 2.5 µm (PM 2.5 ) and those with an aerodynamic diameter higher than 2.5 µm but lower than 10 µm (PM 10−2.5 ) are analysed separately using the lidar observations at each DA step. First, we study the spatial and temporal influences of the assimilation of lidar signals on aerosol forecasting. We conduct sensitivity studies on algorithmic parameters, e.g. the horizontal correlation length (L h ) used in the background error covariance matrix (50 km, 100 km or 200 km), the altitudes at which DA is performed (0.75-3.5 km, 1.0-3.5 km or 1.5-3.5 km a.g.l.) and the assimilation period length (12 h or 24 h). We find that DA with L h = 100 km and assimilation from 1.0 to 3.5 km a.g.l. during a 12 h assimilation period length leads to the best scores for PM 10 and PM 2.5 during the forecast period with reference to available measurements from surface networks. Secondly, the aerosol simulation results without and with lidar DA using the optimal parameters (L h = 100 km, an assimilation altitude range from 1.0 to 3.5 km a.g.l. and a 12 h DA period) are evaluated using the level 2.0 (cloud-screened and quality-assured) aerosol optical depth (AOD) data from AERONET, and mass concentration measurements (PM 10 or PM 2.5 ) from the French air quality (BDQA) network and the EMEP-Spain/Portugal network. The results show that the simulation with DA leads to better scores than the one without DA for PM 2.5 , PM 10 and AOD. Additionally, the comparison of model results to evaluation data indicates that the temporal impact of assimilating lidar signals is longer than 36 h after the assimilation period.

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“…The 4D‐LETKF was also implemented to assimilate MODIS Dark Target and Deep Blue AOTs to improve dust analyses and forecasts with assimilations at four time slots (every 6 hr) for a 24‐hr assimilation window (Di Tomaso et al, ). In addition to the assimilation of routine satellite‐based and ground‐based AOTs, sparse surface or aircraft observations of aerosol mass concentrations, such as PM 2.5 and PM 10 , were also successfully assimilated to correct model simulations with variational or ensemble‐based methods (Li et al, ; Pagowski et al, ; Pagowski & Grell, ; Peng et al, ; Zang et al, ).…”

Section: Introductionmentioning

confidence: 99%

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

Dai

Cheng

Suzuki

et al. 2019

J Adv Model Earth Syst

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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.

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A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM<sub>2.5</sub> prediction

Cited by 21 publications

References 43 publications

Assimilation of AERONET and MODIS AOT observations using variational and ensemble data assimilation methods and its impact on aerosol forecasting skill

Assimilation of AERONET and MODIS AOT observations using variational and ensemble data assimilation methods and its impact on aerosol forecasting skill

Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin

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

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