scholarly journals 2015 and 2016 winter-time air pollution in China: SO<sub>2</sub> emission changes derived from a WRF/Chem-EnKF coupled data assimilation system

2019 ◽  
Author(s):  
Dan Chen ◽  
Zhiquan Liu ◽  
Junmei Ban ◽  
Min Chen
Keyword(s):  
Data Assimilation ◽  
Emission Reduction ◽  
Emission Inventory ◽  
Southern China ◽  
Control Strategies ◽  
River Delta ◽  
Central China ◽  
Western China ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. Ambient pollutants in China changes significantly in recent years due to strict control strategies implemented by the government. The control strategies also bring uncertainties to both the bottom-up emission inventory and the model-ready gridded emission inputs especially in winter season. In this study, we updated the WRF/Chem-EnKF Data Assimilation system to quantitatively estimate the gridded hourly SO2 emissions using hourly surface observations as constraints. Different from our previous study, in which meteorology and emission were both perturbed to obtain larger spread aiming to improve forecast skills; in this study, only emission was perturbed to ensure analyzed emission purely reflect necessary adjustments due to the emission uncertainties. In addition, direct emissions instead of emission scaling factors were used as analysis variable, which allowed for the detection of new emission sources. 2010 MEIC emission inventory (for January) was used as priori to generate 2015 and 2016 January analyzed emissions. The SO2 emission changing trends for northern, western and southern China from 2010 to 2015 and that from 2015 to 2016 (for the month of January) were investigated. The January 2010–2015 differences showed inhomogeneous change patterns in different regions: (1) significant emission reduction in southern China, (2) significant emission reduction in larger cities but widely increase in surrounding suburban and rural regions for northern China which may indicate the missing raw coal combustion for winter heating that not taken into account in the priori emission inventory; (3) significantly large emission increase in western China due to the energy expansion strategy. This not only reflected the changes during the five years, but also combined the uncertainties in the priori emissions. The January 2015–2016 differences showed widely emission reduction from 2015 to 2016, indicating the stricter control strategy fully executed nationwide. These changes were corresponded to facts in reality, indicating that the updated DA system was capable to detect the emission deficiencies and optimize the emission. By generating the hourly analyzed emissions, the diurnal pattern of emissions (in terms of hourly factors) were also obtained. Forecast experiments showed the improvements by using analyzed emissions were much larger in southern China than that in northern and western China. For Sichuan Basin, Central China, Yangzi River Delta, and Pearl River Delta, BIAS and RMSE decreased by 61.8 %–78.2 % and 27.9 %–52.2 %, respectively, and correlation coefficients increased by 12.5 %–47.1 %. However, the improvement in northern and western China were limited due to small spread. Another limitation of the study is that the analyzed emissions are still model dependent, as the ensembles are conducted through WRF/Chem model and thus the performances of ensembles are model dependent.

scholarly journals The 2015 and 2016 wintertime air pollution in China: SO<sub>2</sub> emission changes derived from a WRF-Chem/EnKF coupled data assimilation system

2019 ◽  
Vol 19 (13) ◽  
pp. 8619-8650 ◽  
Author(s):  
Dan Chen ◽  
Zhiquan Liu ◽  
Junmei Ban ◽  
Min Chen
Keyword(s):  
Data Assimilation ◽  
Emission Inventory ◽  
Southern China ◽  
Control Strategies ◽  
A Priori ◽  
River Delta ◽  
Western China ◽  
So2 Emissions ◽  
Emission Reductions ◽  
Emission Changes

Abstract. Ambient pollutants and emissions in China have changed significantly in recent years due to strict control strategies implemented by the government. It is of great interest to evaluate the reduction of emissions and the air quality response using a data assimilation (DA) approach. In this study, we updated the WRF-Chem/EnKF (Weather Research and Forecasting – WRF, model coupled with the chemistry/ensemble Kalman filter – Chem/EnKF) system to directly analyze SO2 emissions instead of using emission scaling factors, as in our previous study. Our purpose is to investigate whether the WRF-Chem/EnKF system is capable of detecting the emission deficiencies in the bottom-up emission inventory (2010-MEIC, Multi-resolution Emission Inventory for China), dynamically updating the spatial–temporal emission changes (2010 to 2015/2016) and, most importantly, locating the “new” (emerging) emission sources that are not considered in the a priori emission inventory. The 2010 January MEIC emission inventory was used as the a priori inventory (to generate background emission fields). The 2015 and 2016 January emissions were obtained by assimilating the hourly surface SO2 concentration observations for January 2015 and 2016. The SO2 emission changes for northern, western, and southern China from 2010 to 2015 and from 2015 to 2016 (for the month of January) from the EnSRF (ensemble square root filter) approach were investigated, and the emission control strategies during the corresponding period were discussed. The January 2010–2015 differences showed inhomogeneous change patterns in different regions, including (1) significant emission reductions in southern China; (2) significant emission reductions in larger cities with a wide increase in the surrounding suburban and rural regions in northern China, which may indicate missing raw coal combustion for winter heating that was not taken into account in the a priori emission inventory; and (3) significantly large emission increases in western China due to the energy expansion strategy. The January 2015–2016 differences showed wide emission reductions from 2015 to 2016, indicating stricter control strategies having been fully executed nationwide. These derived emission changes coincided with the period of the energy development national strategy in northwestern China and the regulations for the reduction of SO2 emissions, indicating that the updated DA system was possibly capable of detecting emission deficiencies, dynamically updating the spatial–temporal emission changes (2010 to 2015/2016), and locating newly added sources. Forecast experiments using the a priori and updated emissions were conducted. Comparisons showed improvements from using updated emissions. The improvements in southern China were much larger than those in northern and western China. For the Sichuan Basin, central China, the Yangtze River Delta, and the Pearl River Delta, the BIAS (bias, equal to the difference between the modeled value and the observational value, representing the overall model tendency) decreased by 61.8 %–78.2 % (for different regions), the RMSE decreased by 27.9 %–52.2 %, and CORR values (correlation coefficient, equal to the linear relationship between the modeled values and the observational values) increased by 12.5 %–47.1 %. The limitation of the study is that the analyzed emissions are still model-dependent, as the ensembles are conducted using the WRF-Chem model; therefore, the performances of the ensembles are model-dependent. Our study indicated that the WRF-Chem/EnSRF system is not only capable of improving the emissions and forecasts in the model but can also evaluate realistic emission changes. Thus, it is possible to apply the system for the evaluation of emission changes in the future.

scholarly journals Mapping urban CO2 emissions using DMSP/OLS ‘city lights’ satellite data in China

2016 ◽  
Vol 49 (2) ◽  
pp. 248-251 ◽  
Author(s):  
Yan Wang ◽  
Guangdong Li
Keyword(s):  
Satellite Data ◽  
Emission Reduction ◽  
Yangtze River Delta ◽  
River Delta ◽  
Central China ◽  
Western China ◽  
Eastern Coast ◽  
Level Energy ◽  
National Bureau ◽  
Administrative Units

China, the world’s top CO2 emitter, is faced with pressure of energy-saving emission reduction. In the 2015 Paris Climate Conference (COP21), China announced its plan, aiming to cut down CO2 emissions by 60%–65% per unit of GDP in comparison to 2005’s level by 2030. To achieve this ambitious goal, reliable national, provincial, and city-level statistics are fundamental for multi-scale mitigation policy-makings as well as for the allocation of responsibilities among different administrative units. However, China implemented a top-down energy statistical system. The National Bureau of Statistics only publishes annually both national and provincial energy statistics. Only part of cities released their statistics, which results in missing data in city-level energy statistics. This also affects data transparency and accuracy of energy and CO2 emission statistics, and as a result, increases difficulty in allocation of CO2 emission reduction responsibilities. In order to fill this lacuna, we employed a standardized remote sensing inversion approach for estimating China’s city-level CO2 emissions from energy consumptions by integrating DMSP/OLS ‘city lights’ satellite data and statistical data. The end product is a map of city-level CO2 emissions in China. The most topping CO2 emitters are located in the major urban agglomerations along the more economically developed eastern coast (e.g. Yangtze River Delta, Beijing–Tianjin–Hebei, Shandong Peninsula, and Pearl River Delta). Other regions with high CO2 emissions are Shanxi and Henan in Central China, as well as the Chengdu–Chongqing and Shaanxi in West China. Regions with low CO2 emissions are western China, and most of Central China and South China.

scholarly journals Retrospective analysis of 2015–2017 wintertime PM<sub>2.5</sub> in China: response to emission regulations and the role of meteorology

2019 ◽  
Vol 19 (11) ◽  
pp. 7409-7427 ◽  
Author(s):  
Dan Chen ◽  
Zhiquan Liu ◽  
Junmei Ban ◽  
Pusheng Zhao ◽  
Min Chen
Keyword(s):  
Data Assimilation ◽  
Control Strategies ◽  
Dominant Role ◽  
Fine Particulate Matter ◽  
Emission Control ◽  
River Delta ◽  
Control Measures ◽  
Central China ◽  
Assimilation Experiment

Abstract. To better characterize anthropogenic emission-relevant aerosol species, the Gridpoint Statistical Interpolation (GSI) and Weather Research and Forecasting with Chemistry (WRF/Chem) data assimilation system was updated from the GOCART aerosol scheme to the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) 4-bin (MOSAIC-4BIN) aerosol scheme. Three years (2015–2017) of wintertime (January) surface PM2.5 (fine particulate matter with an aerodynamic diameter smaller than 2.5 µm) observations from more than 1600 sites were assimilated hourly using the updated three-dimensional variational (3DVAR) system. In the control experiment (without assimilation) using Multi-resolution Emission Inventory for China 2010 (MEIC_2010) emissions, the modeled January averaged PM2.5 concentrations were severely overestimated in the Sichuan Basin, central China, the Yangtze River Delta and the Pearl River Delta by 98–134, 46–101, 32–59 and 19–60 µg m−3, respectively, indicating that the emissions for 2010 are not appropriate for 2015–2017, as strict emission control strategies were implemented in recent years. Meanwhile, underestimations of 11–12, 53–96 and 22–40 µg m−3 were observed in northeastern China, Xinjiang and the Energy Golden Triangle, respectively. The assimilation experiment significantly reduced both high and low biases to within ±5 µg m−3. The observations and the reanalysis data from the assimilation experiment were used to investigate the year-to-year changes and the driving factors. The role of emissions was obtained by subtracting the meteorological impacts (by control experiments) from the total combined differences (by assimilation experiments). The results show a reduction in PM2.5 of approximately 15 µg m−3 for the month of January from 2015 to 2016 in the North China Plain (NCP), but meteorology played the dominant role (contributing a reduction of approximately 12 µg m−3). The change (for January) from 2016 to 2017 in NCP was different; meteorology caused an increase in PM2.5 of approximately 23 µg m−3, while emission control measures caused a decrease of 8 µg m−3, and the combined effects still showed a PM2.5 increase for that region. The analysis confirmed that emission control strategies were indeed implemented and emissions were reduced in both years. Using a data assimilation approach, this study helps identify the reasons why emission control strategies may or may not have an immediately visible impact. There are still large uncertainties in this approach, especially the inaccurate emission inputs, and neglecting aerosol–meteorology feedbacks in the model can generate large uncertainties in the analysis as well.

scholarly journals DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations

2021 ◽  
pp. 1-6
Author(s):  
Hao Luo ◽  
Qinghua Yang ◽  
Longjiang Mu ◽  
Xiangshan Tian-Kunze ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Southern Ocean ◽  
Sea Ice ◽  
Coupled Model ◽  
Sea Ice Concentration ◽  
Model Free ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Data Assimilation System ◽  
Assimilation System

Abstract To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

scholarly journals Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

2021 ◽  
Author(s):  
Magnus Lindskog ◽  
Adam Dybbroe ◽  
Roger Randriamampianina
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Implementation Process ◽  
Satellite System ◽  
Limited Area ◽  
Global Navigation Satellite ◽  
Microwave Radiances ◽  
Data Assimilation System ◽  
Assimilation System

AbstractMetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.

Sign in / Sign up

Export Citation Format

Share Document