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Abstract. Detecting various anomalies using optical satellite data prior to strong earthquakes is key to understanding and forecasting earthquake activities because of its recognition of thermal-radiation-related phenomena in seismic preparation phases. Data from satellite observations serve as a powerful tool in monitoring earthquake preparation areas at a global scale and in a nearly real-time manner. Over the past several decades, many new different data sources have been utilized in this field, and progressive anomaly detection approaches have been developed. This paper reviews the progress and development of pre-seismic anomaly detection technology in this decade. First, precursor parameters, including parameters from the top of the atmosphere, in the atmosphere, and on the Earth's surface, are stated and discussed. Second, different anomaly detection methods, which are used to extract anomalous signals that probably indicate future seismic events, are presented. Finally, certain critical problems with the current research are highlighted, and new developing trends and perspectives for future work are discussed. The development of Earth observation satellites and anomaly detection algorithms can enrich available information sources, provide advanced tools for multilevel earthquake monitoring, and improve short- and medium-term forecasting, which play a large and growing role in pre-seismic anomaly detection research.

16 pags, 9 figs. -- Corrigendum to "Simultaneous satellite observations of IO and BrO over Antarctica" published in Atmos. Chem. Phys., 12, 6565–6580, 2012 ; This article reports on satellite observations of iodine monoxide (IO) and bromine monoxide (BrO). The region of interest is Antarctica in the time between spring and autumn. Both molecules, IO and BrO, are reactive halogen species and strongly influence tropospheric composition. As a result, a better understanding of their spatial distribution and temporal evolution is necessary to assess accurately their role in tropospheric chemistry. Especially in the case of IO, information on its present magnitude, spatial distribution patterns and source regions is still sparse. The present study is based on six years of SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) data recorded in nadir viewing geometry. Multi-year averages of monthly mean IO columns are presented and compared to the distributions of BrO. Influences of the IO air mass factor and the IO absorption cross section temperature dependence on the absolute vertical columns are discussed. The long-term observations of IO and BrO columns yield new insight into the temporal and spatial variation of IO above the Antarctic region. The occurrence of IO on Antarctic sea ice in late spring (November) is discovered and presented. In addition, the comparison between IO and BrO distributions show many differences, which argues for different mechanisms and individual nature of the release of the two halogen oxide precursors. The state of the ecosystem, in particular the changing condition of the sea ice in late spring, is used to explain the observations of the IO behaviour over Antarctica and the differences between IO and BrO distributions. © 2012 Author(s). ; This study has been financially supported by ESA through the TIBAGS project within the CESN framework. Further financial support was received from the State and University of Bremen, the German Aerospace (DLR), and the European Union ; Peer reviewed

This is the final version of the article. Available from European Geosciences Union via the DOI in this record. ; Many different interactions between aerosols and clouds have been postulated, based on correlations between satellite retrieved aerosol and cloud properties. Previous studies highlighted the importance of meteorological covariations to the observed correlations. In this work, we make use of multiple temporally-spaced satellite retrievals to observe the development of cloud regimes. The observation of cloud regime development allows us to account for the influences of cloud fraction (CF) and meteorological factors on the aerosol retrieval. By accounting for the aerosol index (AI)-CF relationship, we reduce the influence of meteorological correlations compared to "snapshot" studies, finding that simple correlations overestimate any aerosol effect on CF by at least a factor of two. We find an increased occurrence of transitions into the stratocumulus regime over ocean with increases in MODIS AI, consistent with the hypothesis that aerosols increase stratocumulus persistence. We also observe an increase in transitions into the deep convective regime over land, consistent with the aerosol invigoration hypothesis. We find changes in the transitions from the shallow cumulus regime in different aerosol environments. The strength of these changes is strongly dependent on Low Troposphere Static Stability and 10 m windspeed, but less so on other meteorological factors. Whilst we have reduced the error due to meteorological and CF effects on the aerosol retrieval, meteorological covariation with the cloud and aerosol properties is harder to remove, so these results likely represent an upper bound on the effect of aerosols on cloud development and CF. ; This work was supported by a UK Natural Environment Research Council (NERC) DPhil studentship and funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement no. FP7-280025.

Recent studies show that air pollution also affects Africa. Air quality is worsening in large cities with growing populations. Satellite observations over some Central African cities seem to confirm this pollution for species such as NO2, HCHO and aerosols. The sources of pollution are generally different from those found in Europe for example. In Central Africa, particularly in the Congo Basin, the main sources of NO2 and HCHO emissions are forest fires and the use of embers in cooking. Kinshasa, the capital of the Democratic Republic of Congo, a large megalopolis of about 11 million inhabitants, like several other large cities in Africa, lack ground-based atmospheric measurement systems. To improve this situation, the researchers of the University of Kinshasa (Unikin) in collaboration with the UV-Vis group of the Belgian Institute for Space Aeronomy (IASB) have set up a first installation of a simple atmospheric observation equipment. This equipment was installed on the roof of the Faculty of Sciences of Unikin ( -4.42°S, 15.31°E) in May 2017 and has operated until November 2019. The instrument is based on a compact AVANTES spectrometer covering the spectral range 290 - 450 nm with 0.7 nm resolution. The spectrometer is a Czerny-Turner type with an entry slit of 50 µm wide, and an array of 1200 l/mm. A 10 m long and 600 µm thick diameter optical fiber is connected to the spectrometer to receive the incident light beam from the sky. Measurements were mainly made by looking in a fixed direction. In November 2019, a Multi-Axis DOAS instrument (MAX-DOAS) has been installed to replace the first instrument. The measurements clearly show the signature of polluting species such as NO2 and HCHO in Kinshasa's atmosphere. In this study, we therefore show all the different steps of the algorithm we used to obtain the vertical columns from the observations of the instrument installed in Kinshasa. We present a first comparison of these ground-based observations of NO2 in Kinshasa with those from the OMI and TROPOMI satellites for clear days between May and November 2017.

We quantify methane emissions in China and the contributions from different sectors by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. The inversion uses as prior estimate the national sector-resolved anthropogenic emission inventory reported by the Chinese government to the United Nations Framework Convention on Climate Change (UNFCCC) and thus serves as a direct evaluation of that inventory. Emissions are optimized with a Gaussian mixture model (GMM) at up to 0.25° × 0.3125° resolution. The optimization is done analytically assuming lognormally distributed errors on prior emissions. Errors and information content on the optimal estimates are obtained directly from the analytical solution and also through a 36-member inversion ensemble. Our optimal estimate for total anthropogenic emissions in China is 65.0 (57.7–68.4) Tg a -1 , where parentheses indicate uncertainty range. Contributions from individual sectors include 16.6 (15.6–17.6) Tg a -1 for coal, 2.3 (1.8–2.5) for oil, 0.29 (0.23–0.32) for gas, 17.8 (15.1–21.0) for livestock, 9.3 (8.2–9.9) for waste, 11.9 (10.7–12.7) for rice paddies, and 6.7 (5.8–7.1) for other sources. Our estimate is 21 % higher than the Chinese inventory reported to the UNFCCC (53.6 Tg a -1 ), reflecting upward corrections to emissions from oil (+147 %), gas (+61 %), livestock (+37 %), waste (+41 %), and rice paddies (+34 %), but downward correction for coal (-15 %). It is also higher than previous inverse studies (43–62 Tg a -1 ) that used the much sparser GOSAT satellite observations and were conducted at coarser resolution. We are in particular better able to separate coal and rice emissions. Our higher livestock emissions are attributed largely to northern China where GOSAT has little sensitivity. Our higher waste emissions reflect at least in part a rapid growth in wastewater treatment in China. Underestimate of oil emissions in the UNFCCC report appears to reflect unaccounted super-emitting facilities. Gas emissions in China are mostly from distribution, in part because of low emission factors from production and in part because 42 % of the gas is imported. Our estimate of emissions per unit of domestic gas production indicates a low life-cycle loss rate of 1.7 (1.3–1.9) %, which would imply net climate benefits from the current coal-to-gas energy transition in China. However, this small loss rate is somewhat misleading considering China's high gas imports, including from Turkmenistan where emission per unit of gas production is very high.

Abstract. Ensemble forecasts at kilometre scale of two severe storms over the Mediterranean region are verified against satellite observations. In complement to assessing the forecasts against ground-based measurements, brightness temperature (BT) images are computed from forecast fields and directly compared to BTs observed from satellite. The so-called model-to-satellite approach is very effective in identifying systematic errors in the prediction of cloud cover for BTs in the infrared window and in verifying the forecasted convective activity with BTs in the microwave range. This approach is combined with the calculation of meteorological scores for an objective evaluation of ensemble forecasts. The application of the approach is shown in the context of two Mediterranean case studies, a tropical-like storm and a heavy precipitating event. Assessment of cloud cover and convective activity using satellite observations in the infrared (10.8 μm) and microwave regions (183–191 GHz) provides results consistent with other traditional methods using rainfall measurements. In addition, for the tropical-like storm, differences among forecasts occur much earlier in terms of cloud cover and deep convective activity than they do in terms of deepening and track. Further, the underdispersion of the ensemble forecasts of the two high-impact weather events is easily identified with satellite diagnostics. This suggests that such an approach could be a useful method for verifying ensemble forecasts, particularly in data-sparse regions.