Multi-variate factorisation of numerical simulations

Lunt, Daniel; Chandan, Deepak; Haywood, Alan M.; Lunt, G; Rougier, Jonathan; Salzmann, Ulrich; Schmidt, Gavin A.; Valdes, Paul J.

doi:10.5194/gmd-14-4307-2021

Cited by 11 publications

(17 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To do so, we employ a factorization approach used, for instance, in climate modeling. 10 To the best of our knowledge, this is the first time such an approach is adapted to a quantitative power sector model analysis.…”

Section: Discussionmentioning

confidence: 99%

“…There are several factorization methods, and our analysis builds on the factorization method by “Stein and Alpert” 9 and its extension, the “shared-interactions factorization”. 10 The basic principle of factorization relies on comparing the results of various counterfactual scenarios to separate the influence of different factors on a specific outcome variable. For a broader introduction to factor separation, we refer to the Supplemental information and to a recent paper 10 providing an excellent introduction and overview.…”

Section: Methodsmentioning

confidence: 99%

“…To determine the importance of each factor for storage capacity, we employ a factor separation method, 9 , 10 which attributes model outcomes to different model inputs. This can be achieved by systematically varying only specific model inputs and comparing the outcomes of selected model runs.…”

Section: Introductionmentioning

confidence: 99%

“…To identify the importance of these different factors, we use an adapted “factor separation” method. 9 , 10 As there is so far no established method to attribute outcomes of power market models to changing model inputs, we propose a modified procedure that builds on counterfactual scenarios and factor separation, which could also be used in other energy modeling applications. We are the first to employ factor separation in the context of energy modeling, using it to quantify the importance of which factors drive down storage needs in an interconnected central European energy system.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Geographical balancing of wind power decreases storage needs in a 100% renewable European power sector

Roth

Schill

2023

iScience

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Geographical balancing of wind power decreases storage needs in a 100% renewable European power sector

Roth

Schill

2023

iScience

View full text Add to dashboard Cite

“…( 14) can also be shown visually (Fig. 1), in which the processes of emissions, meteorology and chemistry are denoted by the three circles and the interactions among the different processes are denoted by the overlapping areas (Lunt et al, 2021).…”

Section: Algorithms Of the Qda Methods And Its Implementation In The ...mentioning

confidence: 99%

A quantitative decoupling analysis (QDA v1.0) method for assessing the contributions of meteorology, emissions, and chemistry to fine particulate pollution

Wang

Chen

et al. 2023

Preprint

View full text Add to dashboard Cite

Abstract. A comprehensive understanding of the effects of meteorology, emissions, and chemistry on severe haze is critical in the mitigation of air pollution. However, such an understanding is greatly hindered by the nonlinearity of atmospheric systems. In this study, we developed the quantitative decoupling analysis (QDA) method to quantify the effects of emissions, meteorology, chemical reactions, and their nonlinear interactions on fine particulate matter (PM2.5) pollution by running built-in scenario simulations in each model step. Different from previous methods, the QDA method achieves a fully decomposed analysis of hourly changes in the PM2.5 concentration during pollution events into seven parts, including the pure meteorological contribution (M), the pure emissions contribution (E), the pure chemistry contribution (C), and the interactions among these processes (i.e., ME, MC, EC, and MCE). Via embedding the QDA method into the Weather Research and Forecasting–Nested Air Quality Prediction Modeling System, we employed this method and combined it with the Integrated Process Rate method to study a typical heavy haze episode in Beijing. We evaluate the model performance against in situ meteorological and air quality observations and describe the QDA analytical factors of this case. Results showed that M varied most significantly at different stages of the episode, from 0.21 µg⋅m−3⋅h−1 during the accumulation stage to −11.82 µg⋅m−3⋅h−1 during the removal stage, indicating that the pure meteorological contribution dominated the hourly fluctuation amplitude of the PM2.5 concentration. M acted as the most important cleaner for PM2.5 in non-polluting periods but stopped being effective at this and instead became a contributor in the accumulation stage such that PM2.5 tended to grow rapidly under the superimposed influence of emissions and chemical processes, which would probably mark the beginning of a heavy pollution event. The contribution of E ranged from 0.63 to 0.88 µg⋅m−3⋅h−1 owing to the diurnal variation of emissions. The pure chemical contribution was shown to increase with the level of haze, becoming the largest (0.37 µg⋅m−3⋅h−1) in the maintenance period, which was 25 % higher than during the pre-contamination period. And C+CE made a significant contribution in the accumulation and maintenance stages, indicating that chemical reactions are more important in the polluted period than in other periods. Nonnegligible nonlinear effects exist among the processes of meteorology, emissions, and chemistry on PM2.5 concentrations (−1.83 to 2.44 µg⋅m−3⋅h−1) – something that has generally been ignored in previous studies and during the development of heavy-pollution control strategies. The nonlinear effects are helpful in eliminating the interference of other processes and obtaining a more purified result of the target process and have important indicative significances. The ratio of CE to C is positively correlated with the chemical speed. For precursors like NH3, the smaller value of CE in the most polluted period indicated that NH3 was more deficient, and thus reducing emissions of it in that period would have had the most efficient controlling effect on the PM2.5. This study highlights that the QDA method can be used to realize an in-depth understanding of the effects of adverse meteorological conditions in haze and to judge whether the precursors are excessive or not. Not only can the QDA method provide researchers and policymakers with valuable information for understanding the key factors behind heavy pollution, but it can also help modelers to identify the sources of uncertainties in numerical models.

show abstract

Contrasting roles of clouds as a sink and source of aerosols: A quantitative assessment using WRF-Chem over East Asia

Ryu

Min

Knote³

2022

Atmospheric Environment

View full text Add to dashboard Cite

Multi-variate factorisation of numerical simulations

Cited by 11 publications

References 17 publications

Geographical balancing of wind power decreases storage needs in a 100% renewable European power sector

Geographical balancing of wind power decreases storage needs in a 100% renewable European power sector

A quantitative decoupling analysis (QDA v1.0) method for assessing the contributions of meteorology, emissions, and chemistry to fine particulate pollution

Contrasting roles of clouds as a sink and source of aerosols: A quantitative assessment using WRF-Chem over East Asia

Contact Info

Product

Resources

About