Uncertainty-function-based continuation framework in data assimilation algorithms for atmospheric chemistry models

2021 ◽  
Author(s):  
Alexey V. Penenko ◽  
Viktoriia S. Konopleva ◽  
Peter M. Golenko ◽  
Vladimir V. Penenko
Keyword(s):  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Uncertainty Function

scholarly journals Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

2014 ◽  
Vol 14 (23) ◽  
pp. 32233-32323 ◽  
Author(s):  
M. Bocquet ◽  
H. Elbern ◽  
H. Eskes ◽  
M. Hirtl ◽  
R. Žabkar ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Hybrid Methods ◽  
Model Performance ◽  
Chemical Data ◽  
Current Status ◽  
Model Parameters ◽  
Chemical Transformations ◽  
Future Prospects ◽  
Main Challenge

Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM.

scholarly journals Data assimilation of dust aerosol observations for CUACE/Dust forecasting system

2007 ◽  
Vol 7 (3) ◽  
pp. 8309-8332 ◽  
Author(s):  
T. Niu ◽  
S. L. Gong ◽  
G. F. Zhu ◽  
H. L. Liu ◽  
X. Q. Hu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Three Dimensional ◽  
System A ◽  
Source Regions ◽  
Operational Forecasts ◽  
Forecasting System ◽  
Dust Loading ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A data assimilation system (DAS) was developed for the Chinese Unified Atmospheric Chemistry Environment – Dust (CUACE/Dust) forecast system and applied in the operational forecasts of sand and dust storm (SDS) in spring 2006. The system is based on a three dimensional variational method (3D-Var) and uses extensively the measurements of surface visibility and dust loading retrieval from the Chinese geostationary satellite FY-2C. The results show that a major improvement to the capability of CUACE/Dust in forecasting the short-term variability in the spatial distribution and intensity of dust concentrations has been achieved, especially in those areas far from the source regions. The seasonal mean Threat Score (TS) over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the data assimilation system, a 41% enhancement. The assimilation results usually agree with the dust loading retrieved from FY-2C and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful for the unification of observation and numerical modeling results.

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

2016 ◽  
Vol 16 (2) ◽  
pp. 989-1002 ◽  
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 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.

A NLS-4Dvar Assimilation System of Surface PM2.5 with WRF-CMAQ Model : Observing System Simulation Experiments

2020 ◽  
Author(s):  
Shan Zhang ◽  
Xiangjun Tian ◽  
Hongqin Zhang ◽  
Xiao Han ◽  
Meigen Zhang
Keyword(s):  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Initial Conditions ◽  
System Simulation ◽  
Fine Particulate Matter ◽  
Wrf Model ◽  
Nonlinear Least Squares ◽  
Simulation Experiments ◽  
Improve Model ◽  
Assimilation System

<p>        While complete atmospheric chemical transport models have been developed to understanding the complex interactions of atmospheric chemistry and physics, there are large uncertainties in numerical approaches. Data assimilation is an efficient method to improve model forecast of aerosols with optimized initial conditions. We have developed a new framework for assimilating surface fine particulate matter (PM<sub>2.5</sub>) observations in coupled Weather Research and Forecasting (WRF) model and Community Multiscale Air Quality (CMAQ) model, based on nonlinear least squares four-dimensional variational (NLS-4DVar) data assimilation method. The NLS-4DVar approach, which does not require the tangent and adjoint models, has been extensive used in meteorological and environmental areas due to the low computational complexity. Two parallel experiments were designed in the observing system simulation experiments (OSSEs) to evaluate the effectiveness of this system. Hourly PM2.5 observations over China be assimilated in WRF-CMAQ model with 6-h assimilation window, while the background state without data assimilation is conducted as control experiment. The results show that the assimilation significantly reduced the uncertainties of initial conditions (ICs) for WRF-CMAQ model and leads to better forecast. The newly developed PM<sub>2.5</sub> data assimilation system can improve PM<sub>2.5</sub> prediction effectively and easily. In the future, we expect emission to be optimized together with concentrations, and integrate meteorological assimilation into aerosol assimilation system.</p>

scholarly journals Copernicus stratospheric ozone service, 2009–2012: validation, system intercomparison and roles of input data sets

2015 ◽  
Vol 15 (5) ◽  
pp. 2269-2293 ◽  
Author(s):  
K. Lefever ◽  
R. van der A ◽  
F. Baier ◽  
Y. Christophe ◽  
Q. Errera ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Ozone Depletion ◽  
Total Ozone ◽  
Chemical Constituents ◽  
Stratospheric Ozone ◽  
Atmospheric Composition ◽  
The Arctic ◽  
Data Sets ◽  
The Tropics

Abstract. This paper evaluates and discusses the quality of the stratospheric ozone analyses delivered in near real time by the MACC (Monitoring Atmospheric Composition and Climate) project during the 3-year period between September 2009 and September 2012. Ozone analyses produced by four different chemical data assimilation (CDA) systems are examined and compared: the Integrated Forecast System coupled to the Model for OZone And Related chemical Tracers (IFS-MOZART); the Belgian Assimilation System for Chemical ObsErvations (BASCOE); the Synoptic Analysis of Chemical Constituents by Advanced Data Assimilation (SACADA); and the Data Assimilation Model based on Transport Model version 3 (TM3DAM). The assimilated satellite ozone retrievals differed for each system; SACADA and TM3DAM assimilated only total ozone observations, BASCOE assimilated profiles for ozone and some related species, while IFS-MOZART assimilated both types of ozone observations. All analyses deliver total column values that agree well with ground-based observations (biases < 5%) and have a realistic seasonal cycle, except for BASCOE analyses, which underestimate total ozone in the tropics all year long by 7 to 10%, and SACADA analyses, which overestimate total ozone in polar night regions by up to 30%. The validation of the vertical distribution is based on independent observations from ozonesondes and the ACE-FTS (Atmospheric Chemistry Experiment – Fourier Transform Spectrometer) satellite instrument. It cannot be performed with TM3DAM, which is designed only to deliver analyses of total ozone columns. Vertically alternating positive and negative biases are found in the IFS-MOZART analyses as well as an overestimation of 30 to 60% in the polar lower stratosphere during polar ozone depletion events. SACADA underestimates lower stratospheric ozone by up to 50% during these events above the South Pole and overestimates it by approximately the same amount in the tropics. The three-dimensional (3-D) analyses delivered by BASCOE are found to have the best quality among the three systems resolving the vertical dimension, with biases not exceeding 10% all year long, at all stratospheric levels and in all latitude bands, except in the tropical lowermost stratosphere. The northern spring 2011 period is studied in more detail to evaluate the ability of the analyses to represent the exceptional ozone depletion event, which happened above the Arctic in March 2011. Offline sensitivity tests are performed during this month and indicate that the differences between the forward models or the assimilation algorithms are much less important than the characteristics of the assimilated data sets. They also show that IFS-MOZART is able to deliver realistic analyses of ozone both in the troposphere and in the stratosphere, but this requires the assimilation of observations from nadir-looking instruments as well as the assimilation of profiles, which are well resolved vertically and extend into the lowermost stratosphere.

scholarly journals Data assimilation of dust aerosol observations for the CUACE/dust forecasting system

2008 ◽  
Vol 8 (13) ◽  
pp. 3473-3482 ◽  
Author(s):  
T. Niu ◽  
S. L. Gong ◽  
G. F. Zhu ◽  
H. L. Liu ◽  
X. Q. Hu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Three Dimensional ◽  
Forecast Model ◽  
System A ◽  
Operational Forecasts ◽  
Forecasting System ◽  
Dust Loading ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A data assimilation system (DAS) was developed for the Chinese Unified Atmospheric Chemistry Environment – Dust (CUACE/Dust) forecast system and applied in the operational forecasts of sand and dust storm (SDS) in spring 2006. The system is based on a three dimensional variational method (3D-Var) and uses extensively the measurements of surface visibility (phenomena) and dust loading retrieval from the Chinese geostationary satellite FY-2C. By a number of case studies, the DAS was found to provide corrections to both under- and over-estimates of SDS, presenting a major improvement to the forecasting capability of CUACE/Dust in the short-term variability in the spatial distribution and intensity of dust concentrations in both source regions and downwind areas. The seasonal mean Threat Score (TS) over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the data assimilation system, a 41% enhancement. The forecast results with DAS usually agree with the dust loading retrieved from FY-2C and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful by the unification of observation and numerical model to improve the performance of forecast model.

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