Adjoint inversion modeling of Asian dust emission using lidar observations

A four-dimensional variational (4D-Var) data assimilation system for a regional dust model (RAMS/CFORS-4DVAR; RC4) is applied to an adjoint inversion of a heavy dust event over eastern Asia during 20 March–4 April 2007. The vertical profiles of the dust extinction coefficients derived from NIES Lidar network are directly assimilated, with validation using observation data. Two experiments assess impacts of observation site selection: Experiment A uses five Japanese observation sites located downwind of dust source regions; Experiment B uses these and two other sites near source regions. Assimilation improves the modeled dust extinction coefficients. Experiment A and Experiment B assimilation results are mutually consistent, indicating that observations of Experiment A distributed over Japan can provide comprehensive information related to dust emission inversion. Time series data of dust AOT calculated using modeled and Lidar dust extinction coefficients improve the model results. At Seoul, Matsue, and Toyama, assimilation reduces the root mean square differences of dust AOT by 35–40%. However, at Beijing and Tsukuba, the RMS differences degrade because of fewer observations during the heavy dust event. Vertical profiles of the dust layer observed by CALIPSO are compared with assimilation results. The dense dust layer was trapped at potential temperatures (θ) of 280–300 K and was higher toward the north; the model reproduces those characteristics well. Latitudinal distributions of modeled dust AOT along the CALIPSO orbit paths agree well with those of CALIPSO dust AOT, OMI AI, and MODIS coarse-mode AOT, capturing the latitude at which AOTs and AI have high values. Assimilation results show increased dust emissions over the Gobi Desert and Mongolia; especially for 29–30 March, emission flux is about 10 times greater. Strong dust uplift fluxes over the Gobi Desert and Mongolia cause the heavy dust event. Total optimized dust emissions are 57.9 Tg (Experiment A; 57.8% larger than before assimilation) and 56.3 Tg (Experiment B; 53.4% larger).

[1]  Nobuo Sugimoto,et al.  Dust model intercomparison (DMIP) study over Asia: Overview , 2006 .

[2]  Adrian Sandu,et al.  Chemical data assimilation of Transport and Chemical Evolution over the Pacific (TRACE-P) aircraft measurements , 2006 .

[3]  D. Winker,et al.  Initial performance assessment of CALIOP , 2007 .

[4]  Y. Q. Wang,et al.  Data assimilation of dust aerosol observations for the CUACE/dust forecasting system , 2007 .

[5]  Zifa Wang,et al.  Meteorological Characteristics and Dust Distribution of the Tarim Basin Simulated by the Nesting RAMS/CFORS Dust Model , 2005 .

[6]  Barry E. Schwartz,et al.  An Hourly Assimilation–Forecast Cycle: The RUC , 2004 .

[7]  Irina N. Sokolik,et al.  Direct radiative forcing by anthropogenic airborne mineral aerosols , 1996, Nature.

[8]  Jun Zhou,et al.  A high-resolution numerical study of the Asian dust storms of April 2001 : Characterization of Asian aerosols and their radiative impacts on climate , 2003 .

[9]  F. G. Fernald Analysis of atmospheric lidar observations: some comments. , 1984, Applied optics.

[10]  Peng Zhang,et al.  Operational retrieval of Asian sand and dust storm from FY-2C geostationary meteorological satellite and its application to real time forecast in Asia , 2008 .

[11]  J. Seinfeld,et al.  Development of the adjoint of GEOS-Chem , 2006 .

[12]  Dongfang Wang,et al.  Characterization of soil dust aerosol in China and its transport and distribution during 2001 ACE‐Asia: 1. Network observations , 2003 .

[13]  R. Draxler An Overview of the HYSPLIT_4 Modelling System for Trajectories, Dispersion, and Deposition , 1998 .

[14]  Yoram J. Kaufman,et al.  Retrieving global aerosol sources from satellites using inverse modeling , 2008 .

[15]  Hauke Schmidt,et al.  A four-dimensional variational chemistry data assimilation scheme for Eulerian chemistry transport modeling , 1999 .

[16]  Ian G. McKendry,et al.  Characterization of soil dust aerosol in China and its transport and distribution during 2001 ACE-Asia: 2. Model simulation and validation , 2003 .

[17]  Hendrik Elbern,et al.  Ozone episode analysis by four-dimensional variational chemistry data assimilation , 2001 .

[18]  Nobuo Sugimoto,et al.  A high-resolution numerical study of the Asian dust storms of April 2001 , 2003 .

[19]  Robert A Harley,et al.  Adjoint sensitivity analysis for a three-dimensional photochemical model: implementation and method comparison. , 2006, Environmental science & technology.

[20]  Scott C. Doney,et al.  Variational data assimilation for atmospheric CO2 , 2006 .

[21]  Zhaoyan Liu,et al.  Extinction-to-backscatter ratio of Asian dust observed with high-spectral-resolution lidar and Raman lidar. , 2002, Applied optics.

[22]  Nobuo Sugimoto,et al.  Continuous observations of Asian dust and other aerosols by polarization lidars in China and Japan during ACE-Asia , 2004 .

[23]  Adrian Sandu,et al.  Four-dimensional data assimilation experiments with International Consortium for Atmospheric Research on Transport and Transformation ozone measurements , 2007 .

[24]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[25]  Philippe Bousquet,et al.  Inferring CO2 sources and sinks from satellite observations: Method and application to TOVS data , 2005 .

[26]  Hajime Okamoto,et al.  NETWORK OBSERVATIONS OF ASIAN DUST AND AIR POLLUTION AEROSOLS USING TWO-WAVELENGTH POLARIZATION LIDARS , 2006 .

[27]  Henk Eskes,et al.  Methane Emissions from Sciamachy Observations Sensitivity Analysis of Methane Emissions Derived from Sciamachy Observations through Inverse Modelling Acpd Methane Emissions from Sciamachy Observations , 2022 .

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

[29]  Hajime Okamoto,et al.  Global three‐dimensional simulation of aerosol optical thickness distribution of various origins , 2000 .

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

[31]  Hendrik Elbern,et al.  Emission rate and chemical state estimation by 4-dimensional variational inversion , 2007 .

[32]  David M. Winker,et al.  Use of probability distribution functions for discriminating between cloud and aerosol in lidar backscatter data , 2004 .

[33]  Lance M. Leslie,et al.  Northeast Asian dust storms: Real‐time numerical prediction and validation , 2003 .

[34]  Nobuo Sugimoto,et al.  Adjoint inverse modeling of dust emission and transport over East Asia , 2007 .

[35]  P. Courtier,et al.  Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. I: Theory , 2007 .

[36]  Thomas A. Cahill,et al.  Numerical study of Asian dust transport during the springtime of 2001 simulated with the Chemical Weather Forecasting System (CFORS) model , 2004 .

[37]  Teruyuki Nakajima,et al.  Observation of dust and anthropogenic aerosol plumes in the Northwest Pacific with a two‐wavelength polarization lidar on board the research vessel Mirai , 2002 .

[38]  R. Pielke,et al.  A comprehensive meteorological modeling system—RAMS , 1992 .

[39]  J. Müller,et al.  Grid‐based versus big region approach for inverting CO emissions using Measurement of Pollution in the Troposphere (MOPITT) data , 2006 .

[40]  Adrian Sandu,et al.  Adjoint inverse modeling of black carbon during the Asian Pacific Regional Aerosol Characterization Experiment , 2005 .

[41]  Masaru Chiba,et al.  Global Simulation of Dust Aerosol with a Chemical Transport Model, MASINGAR( ADEC-Aeolian Dust Experiment on Climate Impact-) , 2005 .

[42]  J. Overpeck,et al.  Possible role of dust-induced regional warming in abrupt climate change during the last glacial period , 1996, Nature.

[43]  Hendrik Elbern,et al.  Variational data assimilation for tropospheric chemistry modeling , 1997 .

[44]  Trissevgeni Stavrakou,et al.  Inversion of CO and NO x emissions using the adjoint of the IMAGES model , 2004 .

[45]  Itsushi Uno,et al.  Adjoint inverse modeling of CO emissions over Eastern Asia using four-dimensional variational data assimilation , 2006 .

[46]  Y. Ishikawa,et al.  State Estimation of the North Pacific Ocean by a Four-Dimensional Variational Data Assimilation Experiment , 2003 .