Inverse modeling of black carbon emissions over China using ensemble data  assimilation

Abstract. Emissions inventories of black carbon (BC), which are traditionally constructed using a bottom-up approach based on activity data and emissions factors, are considered to contain a large level of uncertainty. In this paper, an ensemble optimal interpolation (EnOI) data assimilation technique is used to investigate the possibility of optimally recovering the spatially resolved emissions bias of BC. An inverse modeling system for emissions is established for an atmospheric chemistry aerosol model and two key problems related to ensemble data assimilation in the top-down emissions estimation are discussed: (1) how to obtain reasonable ensembles of prior emissions and (2) establishing a scheme to localize the background-error matrix. An experiment involving 1-year-long simulation cycle with EnOI inversion of BC emissions is performed for 2008. The bias of the BC emissions intensity in China at each grid point is corrected by this inverse system. The inverse emission over China in January is 240.1 Gg, and annual emission is about 2539.3 Gg, which is about 1.8 times of bottom-up emission inventory. The results show that, even though only monthly mean BC measurements are employed to inverse the emissions, the accuracy of the daily model simulation improves. Using top-down emissions, the average root mean square error of simulated daily BC is decreased by nearly 30 %. These results are valuable and promising for a better understanding of aerosol emissions and distributions, as well as aerosol forecasting.

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Inverse modeling of black carbon emissions over China using ensemble data assimilation

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-20851-2015 ◽

2015 ◽

Vol 15 (15) ◽

pp. 20851-20879

Author(s):

P. Wang ◽

H. Wang ◽

Y. Q. Wang ◽

X. Y. Zhang ◽

S. L. Gong ◽

...

Keyword(s):

Data Assimilation ◽

Black Carbon ◽

Atmospheric Chemistry ◽

Inverse Modeling ◽

Optimal Interpolation ◽

Top Down ◽

Bottom Up ◽

Error Matrix ◽

Ensemble Data Assimilation ◽

Ensemble Data

Abstract. Emissions inventories of black carbon (BC), which are traditionally constructed using a "bottom-up" approach based on activity data and emissions factors, are considered to contain a large level of uncertainty. In this paper, an ensemble optimal interpolation (EnOI) data assimilation technique is used to investigate the possibility of optimally recovering the spatially resolved emissions bias of BC. An inverse modeling system for emissions is established for an atmospheric chemistry aerosol model and two key problems related to ensemble data assimilation in the top-down emissions estimation are discussed: (1) how to obtain reasonable ensembles of prior emissions; and (2) establishing a scheme to localize the background-error matrix. An experiment involving a one month simulation cycle with EnOI inversion of BC emissions is performed for January 2008. The bias of the BC emissions intensity in China at each grid point is corrected by this inverse system. The inversed emission over China in January is 240.1 Gg, and annual emission is about 2750 Gg, which is over 1.8 times of bottom-up emission inventory. The results show that, even though only monthly mean BC measurements are employed to inverse the emissions, the accuracy of the daily model simulation improves. Using top-down emissions, the average root-mean-square error of simulated daily BC is decreased by nearly 30 %. These results are valuable and promising for a better understanding of aerosol emissions and distributions, as well as aerosol forecasting.

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The Data Assimilation Research Testbed: Nonlinear Algorithms and Novel Applications for Community Ensemble Data Assimilation

10.5194/egusphere-egu2020-3128 ◽

2020 ◽

Author(s):

Jeffrey Anderson ◽

Nancy Collins ◽

Moha El Gharamti ◽

Timothy Hoar ◽

Kevin Raeder ◽

...

Keyword(s):

Data Assimilation ◽

General Circulation ◽

Prediction Models ◽

Circulation Model ◽

Global Ocean ◽

Optimal Interpolation ◽

Correlated Noise ◽

Ensemble Data Assimilation ◽

Ensemble Data ◽

Novel Applications

The Data Assimilation Research Testbed (DART) is a community facility for ensemble data assimilation developed and maintained by the National Center for Atmospheric Research (NCAR). DART provides ensemble data assimilation capabilities for NCAR community earth system models and many other prediction models. It is straightforward to add interfaces for new models and new observations to DART.DART provides traditional ensemble data assimilation algorithms that implicitly assume Gaussianity and linearity. Traditional algorithms can still work when these assumptions are violated. However, it is possible to greatly improve results by extending ensemble algorithms to explicitly account for aspects of nonlinearity and non-Gaussianity. Two new algorithms have been added to DART. 1). Anamorphosis transforms variables to make the assimilation problem more linear and Gaussian before transforming posterior estimates back to the original model variables; 2). The marginal correction rank histogram filter (MCRHF) directly represents arbitrary non-Gaussian distributions. These methods are particularly valuable for data assimilation for bounded quantities like tracers or streamflow.DART is being applied to a number of novel applications. Examples in the poster include 1). An eddy-resolving global ocean ensemble reanalysis with the POP ocean model and an ensemble optimal interpolation; 2). The WRF-Hydro/DART system now includes a multi-parametric ensemble, anamorphosis, and spatially-correlated noise for the forcing fields.&#160;3). Results from the Carbon Monitoring System over Mountains using CLM5 to assimilate remotely-sensed observations (LAI, biomass, and SIF) for a field site in Colorado; 4). Assimilation of MODIS snow cover fraction and daily GRACE total water storage data and its impact on soil moisture using the DART/NOAH-MP system. 5). An ensemble atmospheric reanalysis using the CAM general circulation model.

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Initiation of ensemble data assimilation

Tellus A Dynamic Meteorology and Oceanography ◽

10.3402/tellusa.v58i2.14766 ◽

2006 ◽

Cited By ~ 1

Author(s):

M. Zupanski ◽

S. J. Fletcher ◽

I. M. Navon ◽

B. Uzunoglu ◽

R. P. Heikes ◽

...

Keyword(s):

Data Assimilation ◽

Ensemble Data Assimilation ◽

Ensemble Data

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Using a machine learning proxy for localization in ensemble data assimilation

Computational Geosciences ◽

10.1007/s10596-020-10031-0 ◽

2021 ◽

Vol 25 (3) ◽

pp. 931-944

Author(s):

Johann M. Lacerda ◽

Alexandre A. Emerick ◽

Adolfo P. Pires

Keyword(s):

Machine Learning ◽

Data Assimilation ◽

Ensemble Data Assimilation ◽

Ensemble Data

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Maximum Likelihood Ensemble Filter: Theoretical Aspects

Monthly Weather Review ◽

10.1175/mwr2946.1 ◽

2005 ◽

Vol 133 (6) ◽

pp. 1710-1726 ◽

Cited By ~ 222

Author(s):

Milija Zupanski

Keyword(s):

Maximum Likelihood ◽

Data Assimilation ◽

Cost Function ◽

Ensemble Data Assimilation ◽

Error Covariance ◽

Ensemble Data ◽

Analysis Error ◽

Nonlinear Observation ◽

The Cost ◽

Maximum Likelihood Ensemble Filter

Abstract A new ensemble-based data assimilation method, named the maximum likelihood ensemble filter (MLEF), is presented. The analysis solution maximizes the likelihood of the posterior probability distribution, obtained by minimization of a cost function that depends on a general nonlinear observation operator. The MLEF belongs to the class of deterministic ensemble filters, since no perturbed observations are employed. As in variational and ensemble data assimilation methods, the cost function is derived using a Gaussian probability density function framework. Like other ensemble data assimilation algorithms, the MLEF produces an estimate of the analysis uncertainty (e.g., analysis error covariance). In addition to the common use of ensembles in calculation of the forecast error covariance, the ensembles in MLEF are exploited to efficiently calculate the Hessian preconditioning and the gradient of the cost function. A sufficient number of iterative minimization steps is 2–3, because of superior Hessian preconditioning. The MLEF method is well suited for use with highly nonlinear observation operators, for a small additional computational cost of minimization. The consistent treatment of nonlinear observation operators through optimization is an advantage of the MLEF over other ensemble data assimilation algorithms. The cost of MLEF is comparable to the cost of existing ensemble Kalman filter algorithms. The method is directly applicable to most complex forecast models and observation operators. In this paper, the MLEF method is applied to data assimilation with the one-dimensional Korteweg–de Vries–Burgers equation. The tested observation operator is quadratic, in order to make the assimilation problem more challenging. The results illustrate the stability of the MLEF performance, as well as the benefit of the cost function minimization. The improvement is noted in terms of the rms error, as well as the analysis error covariance. The statistics of innovation vectors (observation minus forecast) also indicate a stable performance of the MLEF algorithm. Additional experiments suggest the amplified benefit of targeted observations in ensemble data assimilation.

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An adaptive covariance relaxation method for ensemble data assimilation

Quarterly Journal of the Royal Meteorological Society ◽

10.1002/qj.2576 ◽

2015 ◽

Vol 141 (692) ◽

pp. 2898-2906 ◽

Cited By ~ 24

Author(s):

Yue Ying ◽

Fuqing Zhang

Keyword(s):

Data Assimilation ◽

Relaxation Method ◽

Ensemble Data Assimilation ◽

Ensemble Data

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Convection-Permitting Forecasts Initialized with Continuously Cycling Limited-Area 3DVAR, Ensemble Kalman Filter, and “Hybrid” Variational–Ensemble Data Assimilation Systems

Monthly Weather Review ◽

10.1175/mwr-d-13-00100.1 ◽

2014 ◽

Vol 142 (2) ◽

pp. 716-738 ◽

Cited By ~ 49

Author(s):

Craig S. Schwartz ◽

Zhiquan Liu

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Three Dimensional ◽

Skill Score ◽

Limited Area ◽

Computational Domain ◽

Ensemble Data Assimilation ◽

Ensemble Data ◽

Precipitation Characteristics ◽

High Precipitation

Abstract Analyses with 20-km horizontal grid spacing were produced from parallel continuously cycling three-dimensional variational (3DVAR), ensemble square root Kalman filter (EnSRF), and “hybrid” variational–ensemble data assimilation (DA) systems between 0000 UTC 6 May and 0000 UTC 21 June 2011 over a domain spanning the contiguous United States. Beginning 9 May, the 0000 UTC analyses initialized 36-h Weather Research and Forecasting Model (WRF) forecasts containing a large convection-permitting 4-km nest. These 4-km 3DVAR-, EnSRF-, and hybrid-initialized forecasts were compared to benchmark WRF forecasts initialized by interpolating 0000 UTC Global Forecast System (GFS) analyses onto the computational domain. While important differences regarding mean state characteristics of the 20-km DA systems were noted, verification efforts focused on the 4-km precipitation forecasts. The 3DVAR-, hybrid-, and EnSRF-initialized 4-km precipitation forecasts performed similarly regarding general precipitation characteristics, such as timing of the diurnal cycle, and all three forecast sets had high precipitation biases at heavier rainfall rates. However, meaningful differences emerged regarding precipitation placement as quantified by the fractions skill score. For most forecast hours, the hybrid-initialized 4-km precipitation forecasts were better than the EnSRF-, 3DVAR-, and GFS-initialized forecasts, and the improvement was often statistically significant at the 95th percentile. These results demonstrate the potential of limited-area continuously cycling hybrid DA configurations and suggest additional hybrid development is warranted.

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Southern High-Latitude Ensemble Data Assimilation in the Antarctic Mesoscale Prediction System

Monthly Weather Review ◽

10.1175/mwr3042.1 ◽

2005 ◽

Vol 133 (12) ◽

pp. 3431-3449 ◽

Cited By ~ 43

Author(s):

D. M. Barker

Keyword(s):

Data Assimilation ◽

Sampling Error ◽

Forecast Error ◽

Weather Prediction ◽

Forecast Errors ◽

Prediction System ◽

Spatial Covariance ◽

Ensemble Data Assimilation ◽

Ensemble Data ◽

The Antarctic

Abstract Ensemble data assimilation systems incorporate observations into numerical models via solution of the Kalman filter update equations, and estimates of forecast error covariances derived from ensembles of model integrations. In this paper, a particular algorithm, the ensemble square root filter (EnSRF), is tested in a limited-area, polar numerical weather prediction (NWP) model: the Antarctic Mesoscale Prediction System (AMPS). For application in the real-time AMPS, the number of model integrations that can be run to provide forecast error covariances is limited, resulting in an ensemble sampling error that degrades the analysis fit to observations. In this work, multivariate, climatologically plausible forecast error covariances are specified via averaged forecast difference statistics. Ensemble representations of the “true” forecast errors, created using randomized control variables of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) three-dimensional variational (3DVAR) data assimilation system, are then used to assess the dependence of sampling error on ensemble size, data density, and localization of covariances using simulated observation networks. Results highlight the detrimental impact of ensemble sampling error on the analysis increment structure of correlated, but unobserved fields—an issue not addressed by the spatial covariance localization techniques used to date. A 12-hourly cycling EnSRF/AMPS assimilation/forecast system is tested for a two-week period in December 2002 using real, conventional (surface, rawinsonde, satellite retrieval) observations. The dependence of forecast scores on methods used to maintain ensemble spread and the inclusion of perturbations to lateral boundary conditions are studied.

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