scholarly journals A low-order coupled chemistry meteorology model for testing online and offline data assimilation schemes

2015 ◽  
Vol 8 (8) ◽  
pp. 7347-7394
Author(s):  
J.-M. Haussaire ◽  
M. Bocquet
Keyword(s):  
Data Assimilation ◽  
Wind Field ◽  
Chemical Species ◽  
Highly Nonlinear ◽  
Volatile Organic ◽  
Testing Data ◽  
Latitude Circle ◽  
Physical And Chemical ◽  
The Impact ◽  
Low Order

Abstract. Bocquet and Sakov (2013) have introduced a low-order model based on the coupling of the chaotic Lorenz-95 model which simulates winds along a mid-latitude circle, with the transport of a tracer species advected by this zonal wind field. This model, named L95-T, can serve as a playground for testing data assimilation schemes with an online model. Here, the tracer part of the model is extended to a reduced photochemistry module. This coupled chemistry meteorology model (CCMM), the L95-GRS model, mimics continental and transcontinental transport and the photochemistry of ozone, volatile organic compounds and nitrogen oxides. Its numerical implementation is described. The model is shown to reproduce the major physical and chemical processes being considered. L95-T and L95-GRS are specifically designed and useful for testing advanced data assimilation schemes, such as the iterative ensemble Kalman smoother (IEnKS) which combines the best of ensemble and variational methods. These models provide useful insights prior to the implementation of data assimilation methods on larger models. We illustrate their use with data assimilation schemes on preliminary, yet instructive numerical experiments. In particular, online and offline data assimilation strategies can be conveniently tested and discussed with this low-order CCMM. The impact of observed chemical species concentrations on the wind field can be quantitatively estimated. The impacts of the wind chaotic dynamics and of the chemical species non-chaotic but highly nonlinear dynamics on the data assimilation strategies are illustrated.

scholarly journals A low-order coupled chemistry meteorology model for testing online and offline data assimilation schemes: L95-GRS (v1.0)

2016 ◽  
Vol 9 (1) ◽  
pp. 393-412 ◽  
Author(s):  
J.-M. Haussaire ◽  
M. Bocquet
Keyword(s):  
Data Assimilation ◽  
Wind Field ◽  
Chemical Species ◽  
Highly Nonlinear ◽  
Testing Data ◽  
Latitude Circle ◽  
Physical And Chemical ◽  
The Impact ◽  
Field Estimate ◽  
Low Order

Abstract. Bocquet and Sakov (2013) introduced a low-order model based on the coupling of the chaotic Lorenz-95 (L95) model, which simulates winds along a mid-latitude circle, with the transport of a tracer species advected by this zonal wind field. This model, named L95-T, can serve as a playground for testing data assimilation schemes with an online model. Here, the tracer part of the model is extended to a reduced photochemistry module. This coupled chemistry meteorology model (CCMM), the L95-GRS (generic reaction set) model, mimics continental and transcontinental transport and the photochemistry of ozone, volatile organic compounds and nitrogen oxides. Its numerical implementation is described. The model is shown to reproduce the major physical and chemical processes being considered. L95-T and L95-GRS are specifically designed and useful for testing advanced data assimilation schemes, such as the iterative ensemble Kalman smoother (IEnKS), which combines the best of ensemble and variational methods. These models provide useful insights prior to the implementation of data assimilation methods into larger models. We illustrate their use with data assimilation schemes on preliminary yet instructive numerical experiments. In particular, online and offline data assimilation strategies can be conveniently tested and discussed with this low-order CCMM. The impact of observed chemical species concentrations on the wind field estimate can be quantitatively assessed. The impacts of the wind chaotic dynamics and of the chemical species non-chaotic but highly nonlinear dynamics on the data assimilation strategies are illustrated.

scholarly journals Generalized background error covariance matrix model (GEN_BE v2.0)

2015 ◽  
Vol 8 (3) ◽  
pp. 669-696 ◽  
Author(s):  
G. Descombes ◽  
T. Auligné ◽  
F. Vandenberghe ◽  
D. M. Barker ◽  
J. Barré
Keyword(s):  
Data Assimilation ◽  
Covariance Matrix ◽  
Chemical Species ◽  
Variational Data Assimilation ◽  
Control Variables ◽  
Background Error ◽  
Error Covariance Matrix ◽  
Background Error Covariance ◽  
Error Covariance ◽  
The Impact

Abstract. The specification of state background error statistics is a key component of data assimilation since it affects the impact observations will have on the analysis. In the variational data assimilation approach, applied in geophysical sciences, the dimensions of the background error covariance matrix (B) are usually too large to be explicitly determined and B needs to be modeled. Recent efforts to include new variables in the analysis such as cloud parameters and chemical species have required the development of the code to GENerate the Background Errors (GEN_BE) version 2.0 for the Weather Research and Forecasting (WRF) community model. GEN_BE allows for a simpler, flexible, robust, and community-oriented framework that gathers methods used by some meteorological operational centers and researchers. We present the advantages of this new design for the data assimilation community by performing benchmarks of different modeling of B and showing some of the new features in data assimilation test cases. As data assimilation for clouds remains a challenge, we present a multivariate approach that includes hydrometeors in the control variables and new correlated errors. In addition, the GEN_BE v2.0 code is employed to diagnose error parameter statistics for chemical species, which shows that it is a tool flexible enough to implement new control variables. While the generation of the background errors statistics code was first developed for atmospheric research, the new version (GEN_BE v2.0) can be easily applied to other domains of science and chosen to diagnose and model B. Initially developed for variational data assimilation, the model of the B matrix may be useful for variational ensemble hybrid methods as well.

scholarly journals Influences of Organic Volatile Compounds on the Secondary Organic Carbon of Fine Particulate Matter in the Fruit Tree Area

2021 ◽  
Vol 11 (17) ◽  
pp. 8193
Author(s):  
Myoungki Song ◽  
Minwook Kim ◽  
Sea-Ho Oh ◽  
Chaehyeong Park ◽  
Moonsu Kim ◽  
...  
Keyword(s):  
Fine Particulate Matter ◽  
Chemical Properties ◽  
Photochemical Reactions ◽  
Secondary Products ◽  
Agricultural Chemicals ◽  
Volatile Organic ◽  
Secondary Organic Carbon ◽  
Agricultural Areas ◽  
Physical And Chemical ◽  
The Impact

Three combined investigations were conducted to examine the sources of PM2.5 in agricultural areas. The first was the measurement of PM2.5 and gaseous compounds in the greenhouse, which is a relatively closed system, while the second was the analysis of pesticide components used in agricultural areas. Finally, the physical and chemical properties of PM2.5 were analyzed in an orchard area and compared with the results of the greenhouse and agricultural chemical analyses. As a result, this research was able to confirm the source of emission and characteristics of PM2.5 originating from the agricultural area. Volatile organic compounds (VOCs) in agricultural areas are emitted by agricultural chemicals, and the discharged agricultural chemicals are first absorbed into the soil, and then released into the air by evaporation. Finally, the secondary products of PM2.5 in agricultural areas were estimated to have positive relationships with the VOCs from agricultural chemicals, and NH3 from fertilizers. The photochemical reactions of VOCs and NH3 were responsible for the impact on secondary products.

scholarly journals Generalized Background Error covariance matrix model (GEN_BE v2.0)

2014 ◽  
Vol 7 (4) ◽  
pp. 4291-4352
Author(s):  
G. Descombes ◽  
T. Auligné ◽  
F. Vandenberghe ◽  
D. M. Barker
Keyword(s):  
Data Assimilation ◽  
Covariance Matrix ◽  
Chemical Species ◽  
Variational Data Assimilation ◽  
Control Variables ◽  
Background Error ◽  
Error Covariance Matrix ◽  
Background Error Covariance ◽  
Error Covariance ◽  
The Impact

Abstract. The specification of state background error statistics is a key component of data assimilation since it affects the impact observations will have on the analysis. In the variational data assimilation approach, applied in geophysical sciences, the dimensions of the background error covariance matrix (B) are usually too large to be explicitly determined and B needs to be modeled. Recent efforts to include new variables in the analysis such as cloud parameters and chemical species have required the development of the code to GENerate the Background Errors (GEN_BE) version 2.0 for the Weather Research and Forecasting (WRF) community model to allow for a simpler, flexible, robust, and community-oriented framework that gathers methods used by meteorological operational centers and researchers. We present the advantages of this new design for the data assimilation community by performing benchmarks and showing some of the new features on data assimilation test cases. As data assimilation for clouds remains a challenge, we present a multivariate approach that includes hydrometeors in the control variables and new correlated errors. In addition, the GEN_BE v2.0 code is employed to diagnose error parameter statistics for chemical species, which shows that it is a tool flexible enough to involve new control variables. While the generation of the background errors statistics code has been first developed for atmospheric research, the new version (GEN_BE v2.0) can be easily extended to other domains of science and be chosen as a testbed for diagnostic and new modeling of B. Initially developed for variational data assimilation, the model of the B matrix may be useful for variational ensemble hybrid methods as well.

Recent Advances in Speciation Analyses of Tobacco and Other Important Economic Crops

2020 ◽  
Vol 17 ◽  
Author(s):  
Zhiping Jiang ◽  
Zhizhang Tian ◽  
Chuntao Zhang ◽  
Dengke Li ◽  
Ruoxin Wu ◽  
...  
Keyword(s):  
Method Comparison ◽  
Chemical Species ◽  
Complete Information ◽  
Speciation Analysis ◽  
Elemental Content ◽  
Specific Element ◽  
Balance Accuracy ◽  
Comprehensive Knowledge ◽  
Total Elemental Content ◽  
The Impact

Background: Speciation analysis is defined as the analytical activities of identifying and/or measuring the quantities of one or more individual chemical species in a sample. The knowledge of elemental species provides more complete information about mobility, bioavailability and the impact of elements on ecological systems or biological organisms. It is no longer sufficient to quantitate the total elemental content of samples to define toxicity or essentiality. Thus speciation analysis is of vital importance and generally offers a better understanding of a specific element. Discussion: Thorough speciation scheme consisting of sampling, sample preparation, species analysis and evaluation were described. Special emphasis is placed on recent speciation analysis approaches including both direct and coupling methods. A current summary of advantages and limitations of the various methods as well as an illustrative method comparison are presented. Certain elements and species of interest are briefly mentioned and practical examples of speciation applications in tobacco and other important economic crops are also discussed. Aim/Conclusion: This review aims to offer comprehensive knowledge about elemental speciation and provide readers with valuable information. Many strategies have been developed for the determination of multiple elemental species in tobacco and other important economic crops. Nevertheless, it is an eternal pursuit to establish speciation methods which can balance accuracy, agility as well as universality.

THE IMPACT OF PHYSICAL AND CHEMICAL WEATHERING ON IRON SLAG FROM STANDISH, NY

2019 ◽  
Author(s):  
Maria L. Leonard ◽  
◽  
Rachel M. Kelk ◽  
Dori J. Farthing
Keyword(s):  
Chemical Weathering ◽  
Iron Slag ◽  
Physical And Chemical ◽  
The Impact

scholarly journals Shock interactions in inviscid air and $$\hbox {CO}_2$$–$$\hbox {N}_2$$ flows in thermochemical non-equilibrium

Shock Waves ◽  
2021 ◽  
Author(s):  
C. Garbacz ◽  
W. T. Maier ◽  
J. B. Scoggins ◽  
T. D. Economon ◽  
T. Magin ◽  
...  
Keyword(s):  
Chemical Species ◽  
High Energy ◽  
Ideal Gas ◽  
Shock Interaction ◽  
Interaction Patterns ◽  
Shock Interactions ◽  
Wave Interference ◽  
Type V ◽  
The Impact ◽  
Non Equilibrium

AbstractThe present study aims at providing insights into shock wave interference patterns in gas flows when a mixture different than air is considered. High-energy non-equilibrium flows of air and $$\hbox {CO}_2$$ CO 2 –$$\hbox {N}_2$$ N 2 over a double-wedge geometry are studied numerically. The impact of freestream temperature on the non-equilibrium shock interaction patterns is investigated by simulating two different sets of freestream conditions. To this purpose, the SU2 solver has been extended to account for the conservation of chemical species as well as multiple energies and coupled to the Mutation++ library (Multicomponent Thermodynamic And Transport properties for IONized gases in C++) that provides all the necessary thermochemical properties of the mixture and chemical species. An analysis of the shock interference patterns is presented with respect to the existing taxonomy of interactions. A comparison between calorically perfect ideal gas and non-equilibrium simulations confirms that non-equilibrium effects greatly influence the shock interaction patterns. When thermochemical relaxation is considered, a type VI interaction is obtained for the $$\hbox {CO}_2$$ CO 2 -dominated flow, for both freestream temperatures of 300 K and 1000 K; for air, a type V six-shock interaction and a type VI interaction are obtained, respectively. We conclude that the increase in freestream temperature has a large impact on the shock interaction pattern of the air flow, whereas for the $$\hbox {CO}_2$$ CO 2 –$$\hbox {N}_2$$ N 2 flow the pattern does not change.

scholarly journals Effect of the Assimilation Frequency of Radar Reflectivity on Rain Storm Prediction by Using WRF-3DVAR

2021 ◽  
Vol 13 (11) ◽  
pp. 2103
Author(s):  
Yuchen Liu ◽  
Jia Liu ◽  
Chuanzhe Li ◽  
Fuliang Yu ◽  
Wei Wang
Keyword(s):  
Data Assimilation ◽  
Northern China ◽  
Horizontal Resolution ◽  
Radar Reflectivity ◽  
Small Scale ◽  
Time Interval ◽  
Outer Domain ◽  
Storm Prediction ◽  
The Impact ◽  
Assimilation Time

An attempt was made to evaluate the impact of assimilating Doppler Weather Radar (DWR) reflectivity together with Global Telecommunication System (GTS) data in the three-dimensional variational data assimilation (3DVAR) system of the Weather Research Forecast (WRF) model on rain storm prediction in Daqinghe basin of northern China. The aim of this study was to explore the potential effects of data assimilation frequency and to evaluate the outputs from different domain resolutions in improving the meso-scale NWP rainfall products. In this study, four numerical experiments (no assimilation, 1 and 6 h assimilation time interval with DWR and GTS at 1 km horizontal resolution, 6 h assimilation time interval with radar reflectivity, and GTS data at 3 km horizontal resolution) are carried out to evaluate the impact of data assimilation on prediction of convective rain storms. The results show that the assimilation of radar reflectivity and GTS data collectively enhanced the performance of the WRF-3DVAR system over the Beijing-Tianjin-Hebei region of northern China. It is indicated by the experimental results that the rapid update assimilation has a positive impact on the prediction of the location, tendency, and development of rain storms associated with the study area. In order to explore the influence of data assimilation in the outer domain on the output of the inner domain, the rainfall outputs of 3 and 1 km resolution are compared. The results show that the data assimilation in the outer domain has a positive effect on the output of the inner domain. Since the 3DVAR system is able to analyze certain small-scale and convective-scale features through the incorporation of radar observations, hourly assimilation time interval does not always significantly improve precipitation forecasts because of the inaccurate radar reflectivity observations. Therefore, before data assimilation, the validity of assimilation data should be judged as far as possible in advance, which can not only improve the prediction accuracy, but also improve the assimilation efficiency.

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