Aerosol data assimilation using data from Himawari‐8, a next‐generation geostationary meteorological satellite

Himawari-8, a next-generation geostationary meteorological satellite, was launched on 7 October 2014 and became operational on 7 July 2015. The advanced imager on board Himawari-8 is equipped with 16 observational bands (including three visible and three near-infrared bands) that enable retrieval of full-disk aerosol optical properties at 10 min intervals from geostationary (GEO) orbit. Here we show the first application of aerosol optical properties (AOPs) derived from Himawari-8 data to aerosol data assimilation. Validation of the assimilation experiment by comparison with independent observations demonstrated successful modeling of continental pollution that was not predicted by simulation without assimilation and reduced overestimates of dust front concentrations. These promising results suggest that AOPs derived from Himawari-8/9 and other planned GEO satellites will considerably improve forecasts of air quality, inverse modeling of emissions, and aerosol reanalysis through assimilation techniques.

[1]  Paul Ingmann,et al.  Requirements for the GMES Atmosphere Service and ESA's implementation concept: Sentinels-4/-5 and -5p , 2012 .

[2]  Mio Matsueda,et al.  Climate Simulations Using MRI-AGCM3.2 with 20-km Grid , 2012 .

[3]  M. Razinger,et al.  Aerosol analysis and forecast in the European Centre for Medium‐Range Weather Forecasts Integrated Forecast System: 2. Data assimilation , 2009 .

[4]  Young Sung Ghim,et al.  GIST-PM-Asia v1: development of a numerical system to improve particulate matter forecasts in South Korea using geostationary satellite-retrieved aerosol optical data over Northeast Asia , 2015 .

[5]  Nancy Collins,et al.  Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting , 2016 .

[6]  Greet Janssens-Maenhout,et al.  Emissions of air pollutants and greenhouse gases over Asian regions during 2000–2008: Regional Emission inventory in ASia (REAS) version 2 , 2013 .

[7]  Jassim A. Al-Saadi,et al.  Tropospheric Emissions: Monitoring of Pollution (TEMPO) , 2014 .

[8]  Yafang Cheng,et al.  Assimilation of next generation geostationary aerosol optical depth retrievals to improve air quality simulations , 2014 .

[9]  Makiko Nakata,et al.  Estimating Aerosol Emissions by Assimilating Remote Sensing Observations into a Global Transport Model , 2012, Remote. Sens..

[10]  Takemasa Miyoshi,et al.  Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model , 2009 .

[11]  F. Joseph Turk,et al.  An 11-year global gridded aerosol optical thickness reanalysis (v1.0) for atmospheric and climate sciences , 2016 .

[12]  Keywan Riahi,et al.  Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980–2010 period , 2011 .

[13]  E. Vermote,et al.  The MODIS Aerosol Algorithm, Products, and Validation , 2005 .

[14]  Thomas M. Hamill,et al.  Ensemble Data Assimilation with the NCEP Global Forecast System , 2008 .

[15]  Kelly Chance,et al.  Monitoring high-ozone events in the US Intermountain West using TEMPO geostationary satellite observations , 2013 .

[16]  A. Okuyama,et al.  An Introduction to Himawari-8/9— Japan’s New-Generation Geostationary Meteorological Satellites , 2016 .

[17]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[18]  Raupach,et al.  A model for predicting aeolian sand drift and dust entrainment on scales from paddock to region , 1996 .

[19]  Hiroshi Murakami,et al.  Data Assimilation of Himawari-8 Aerosol Observations: Asian Dust Forecast in June 2015 , 2016 .

[20]  Jeffrey S. Reid,et al.  A system for operational aerosol optical depth data assimilation over global oceans , 2008 .

[21]  Keiya Yumimoto,et al.  Long‐term inverse modeling of Asian dust: Interannual variations of its emission, transport, deposition, and radiative forcing , 2015 .

[22]  Satoru Fukuda,et al.  New approaches to removing cloud shadows and evaluating the 380 nm surface reflectance for improved aerosol optical thickness retrievals from the GOSAT/TANSO‐Cloud and Aerosol Imager , 2013 .

[23]  H. Murakami,et al.  Forecasting of Asian dust storm that occurred on May 10–13, 2011, using an ensemble-based data assimilation system☆ , 2016 .

[24]  Jianping Huang,et al.  Top‐down estimate of dust emissions through integration of MODIS and MISR aerosol retrievals with the GEOS‐Chem adjoint model , 2012 .

[25]  Henk Eskes,et al.  Simultaneous assimilation of satellite NO 2 , O 3 , CO, and HNO 3 data for the analysis of tropospheric chemical composition and emissions , 2012 .

[26]  Keiya Yumimoto,et al.  Impacts of geostationary satellite measurements on CO forecasting: An observing system simulation experiment with GEOS-Chem/LETKF data assimilation system , 2013 .

[27]  Emil M. Constantinescu,et al.  Predicting air quality: Improvements through advanced methods to integrate models and measurements , 2008, J. Comput. Phys..

[28]  T. Nakajima,et al.  Improvement of aerosol optical properties modeling over Eastern Asia with MODIS AOD assimilation in a global non-hydrostatic icosahedral aerosol transport model. , 2014, Environmental pollution.

[29]  G. Carmichael,et al.  Aerosol optical depth assimilation for a size-resolved sectional model: impacts of observationally constrained, multi-wavelength and fine mode retrievals on regional scale analyses and forecasts , 2013 .

[30]  H. Tsujino,et al.  A New Global Climate Model of the Meteorological Research Institute: MRI-CGCM3 —Model Description and Basic Performance— , 2012 .

[31]  Johannes Orphal,et al.  monitoring air quality from space : the case for the geostationary platform. , 2012 .

[32]  Istvan Szunyogh,et al.  Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter , 2005, physics/0511236.

[33]  S. Schubert,et al.  MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .

[34]  Teruyuki Nakajima,et al.  Development of a Two-Channel Aerosol Retrieval Algorithm on a Global Scale Using NOAA AVHRR , 1999 .

[35]  Sundar A. Christopher,et al.  GOES 8 aerosol optical thickness assimilation in a mesoscale model: Online integration of aerosol radiative effects , 2004 .

[36]  R. Koster,et al.  The Quick Fire Emissions Dataset (QFED): Documentation of Versions 2.1, 2.2 and 2.4. Volume 38; Technical Report Series on Global Modeling and Data Assimilation , 2015 .

[37]  Chenbo Xie,et al.  Lidar network observations of tropospheric aerosols , 2008, Asia-Pacific Remote Sensing.

[38]  Jhoon Kim,et al.  Plan of Korean Geostationary Environment Satellite over Asia-Pacific region , 2010 .

[39]  A. Smirnov,et al.  AERONET-a federated instrument network and data archive for aerosol Characterization , 1998 .

[40]  E. Vermote,et al.  Second‐generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance , 2007 .