scholarly journals Supplementary material to "Decadal changes in global surface NO<sub><i>x</i></sub> emissions from multi-constituent satellite data assimilation"

2016 ◽  
Author(s):  
Kazuyuki Miyazaki ◽  
Henk Eskes ◽  
Kengo Sudo ◽  
K. Forkert Boersma ◽  
Kevin Bowman ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Satellite Data ◽  
Supplementary Material ◽  
Global Surface

scholarly journals Decadal changes in global surface NO<sub><i>x</i></sub> emissions from multi-constituent satellite data assimilation

2017 ◽  
Vol 17 (2) ◽  
pp. 807-837 ◽  
Author(s):  
Kazuyuki Miyazaki ◽  
Henk Eskes ◽  
Kengo Sudo ◽  
K. Folkert Boersma ◽  
Kevin Bowman ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Satellite Data ◽  
Western Europe ◽  
Tropospheric Chemistry ◽  
Positive Trend ◽  
Ozone Monitoring Instrument ◽  
The Usa ◽  
Ozone Monitoring ◽  
Scanning Imaging ◽  
Global Surface

Abstract. Global surface emissions of nitrogen oxides (NOx) over a 10-year period (2005–2014) are estimated from an assimilation of multiple satellite data sets: tropospheric NO2 columns from Ozone Monitoring Instrument (OMI), Global Ozone Monitoring Experiment-2 (GOME-2), and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), O3 profiles from Tropospheric Emission Spectrometer (TES), CO profiles from Measurement of Pollution in the Troposphere (MOPITT), and O3 and HNO3 profiles from Microwave Limb Sounder (MLS) using an ensemble Kalman filter technique. Chemical concentrations of various species and emission sources of several precursors are simultaneously optimized. This is expected to improve the emission inversion because the emission estimates are influenced by biases in the modelled tropospheric chemistry, which can be partly corrected by also optimizing the concentrations. We present detailed distributions of the estimated emission distributions for all major regions, the diurnal and seasonal variability, and the evolution of these emissions over the 10-year period. The estimated regional total emissions show a strong positive trend over India (+29 % decade−1), China (+26 % decade−1), and the Middle East (+20 % decade−1), and a negative trend over the USA (−38 % decade−1), southern Africa (−8.2 % decade−1), and western Europe (−8.8 % decade−1). The negative trends in the USA and western Europe are larger during 2005–2010 relative to 2011–2014, whereas the trend in China becomes negative after 2011. The data assimilation also suggests a large uncertainty in anthropogenic and fire-related emission factors and an important underestimation of soil NOx sources in the emission inventories. Despite the large trends observed for individual regions, the global total emission is almost constant between 2005 (47.9 Tg N yr−1) and 2014 (47.5 Tg N yr−1).

scholarly journals Gain Form of the Ensemble Transform Kalman Filter and Its Relevance to Satellite Data Assimilation with Model Space Ensemble Covariance Localization

2017 ◽  
Vol 145 (11) ◽  
pp. 4575-4592 ◽  
Author(s):  
Craig H. Bishop ◽  
Jeffrey S. Whitaker ◽  
Lili Lei
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Satellite Data ◽  
Localization Length ◽  
Model Space ◽  
Ensemble Size ◽  
Length Scales ◽  
Ensemble Transform Kalman Filter ◽  
Ensemble Transform ◽  
Expanded Ensemble

To ameliorate suboptimality in ensemble data assimilation, methods have been introduced that involve expanding the ensemble size. Such expansions can incorporate model space covariance localization and/or estimates of climatological or model error covariances. Model space covariance localization in the vertical overcomes problematic aspects of ensemble-based satellite data assimilation. In the case of the ensemble transform Kalman filter (ETKF), the expanded ensemble size associated with vertical covariance localization would also enable the simultaneous update of entire vertical columns of model variables from hyperspectral and multispectral satellite sounders. However, if the original formulation of the ETKF were applied to an expanded ensemble, it would produce an analysis ensemble that was the same size as the expanded forecast ensemble. This article describes a variation on the ETKF called the gain ETKF (GETKF) that takes advantage of covariances from the expanded ensemble, while producing an analysis ensemble that has the required size of the unexpanded forecast ensemble. The approach also yields an inflation factor that depends on the localization length scale that causes the GETKF to perform differently to an ensemble square root filter (EnSRF) using the same expanded ensemble. Experimentation described herein shows that the GETKF outperforms a range of alternative ETKF-based solutions to the aforementioned problems. In cycling data assimilation experiments with a newly developed storm-track version of the Lorenz-96 model, the GETKF analysis root-mean-square error (RMSE) matches the EnSRF RMSE at shorter than optimal localization length scales but is superior in that it yields smaller RMSEs for longer localization length scales.

scholarly journals Supplementary material to "Satellite Data Reveals a Common Combustion Emission Pathway for Major Cities in China"

2018 ◽  
Author(s):  
Wenfu Tang ◽  
Avelino F. Arellano ◽  
Benjamin Gaubert ◽  
Kazuyuki Miyazaki ◽  
Helen M. Worden
Keyword(s):  
Satellite Data ◽  
Emission Pathway ◽  
Combustion Emission ◽  
Supplementary Material
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