Latest Progress of the Chinese Meteorological Satellite Program and Core Data Processing Technologies

Zhang, Peng; Lu, Qifeng; Hu, Xiuqing; Gu, Shanqiang; Yang, Ling; Min, Min; Lin, Chunye; Xu, Na; Sun, Lin; Bai, Wenguang; Ma, Gang; Xian, Di

doi:10.1007/s00376-019-8215-x

Cited by 107 publications

(35 citation statements)

References 92 publications

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“…The retrieval algorithm used in Sect. 2.4 is available at https://github.com/ zxdawn/BEHR-LNOx (last access: 20 March 2020; https://doi.org/10.5281/zenodo.3553426, Zhang and Laughner, 2019). The WRF-Chem model output and LNO x product are available upon request to Xin Zhang (xinzhang1215@gmail.com).…”

mentioning

confidence: 99%

Estimates of lightning NOx production based on high-resolution OMI NO2 retrievals over the continental US

Zhang

Yin

et al. 2020

Atmos. Meas. Tech.

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Abstract. Lightning serves as the dominant source of nitrogen oxides (NOx=NO+NO2) in the upper troposphere (UT), with a strong impact on ozone chemistry and the hydroxyl radical production. However, the production efficiency (PE) of lightning nitrogen oxides (LNOx) is still quite uncertain (32–1100 mol NO per flash). Satellite measurements are a powerful tool to estimate LNOx directly compared to conventional platforms. To apply satellite data in both clean and polluted regions, a new algorithm for calculating LNOx has been developed that uses the Berkeley High-Resolution (BEHR) v3.0B NO2 retrieval algorithm and the Weather Research and Forecasting model coupled with chemistry (WRF-Chem). LNOx PE over the continental US is estimated using the NO2 product of the Ozone Monitoring Instrument (OMI) data and the Earth Networks Total Lightning Network (ENTLN) data. Focusing on the summer season during 2014, we find that the lightning NO2 (LNO2) PE is 32±15 mol NO2 per flash and 6±3 mol NO2 per stroke while LNOx PE is 90±50 mol NOx per flash and 17±10 mol NOx per stroke. Results reveal that our method reduces the sensitivity to the background NO2 and includes much of the below-cloud LNO2. As the LNOx parameterization varies in studies, the sensitivity of our calculations to the setting of the amount of lightning NO (LNO) is evaluated. Careful consideration of the ratio of LNO2 to NO2 is also needed, given its large influence on the estimation of LNO2 PE.

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mentioning

confidence: 99%

Estimates of lightning NOx production based on high-resolution OMI NO2 retrievals over the continental US

Zhang

Yin

et al. 2020

Atmos. Meas. Tech.

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“…Therefore, Earth observation data becomes a natural "testing ground" for big data. As shown in Table 1, since 1998, China has launched FY, ZiYuan (ZY), HJ, and GF series of EO satellites [28], which generate a lot of remote sensing data that are applied to support the development of the national economy. With increasing spatial resolutions of sensors and shorter revisiting times, a call for 'bring the user to the data and not the data to the user' has started in the EO community [29].…”

Section: Big Data and Beodmentioning

confidence: 99%

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Yao

Xia

et al. 2019

Remote Sensing

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In the era of big data, the explosive growth of Earth observation data and the rapid advancement in cloud computing technology make the global-oriented spatiotemporal data simulation possible. These dual developments also provide advantageous conditions for discrete global grid systems (DGGS). DGGS are designed to portray real-world phenomena by providing a spatiotemporal unified framework on a standard discrete geospatial data structure and theoretical support to address the challenges from big data storage, processing, and analysis to visualization and data sharing. In this paper, the trinity of big Earth observation data (BEOD), cloud computing, and DGGS is proposed, and based on this trinity theory, we explore the opportunities and challenges to handle BEOD from two aspects, namely, information technology and unified data framework. Our focus is on how cloud computing and DGGS can provide an excellent solution to enable big Earth observation data. Firstly, we describe the current status and data characteristics of Earth observation data, which indicate the arrival of the era of big data in the Earth observation domain. Subsequently, we review the cloud computing technology and DGGS framework, especially the works and contributions made in the field of BEOD, including spatial cloud computing, mainstream big data platform, DGGS standards, data models, and applications. From the aforementioned views of the general introduction, the research opportunities and challenges are enumerated and discussed, including EO data management, data fusion, and grid encoding, which are concerned with analysis models and processing performance of big Earth observation data with discrete global grid systems in the cloud environment.Remote Sens. 2020, 12, 62 2 of 15 (CEOS), over 500 EO satellites have been launched in the last half-century, and more than 150 satellites will be launched in the next 12 years [2,3]. China has launched more than 60 satellites since 1970 for comprehensive observation of the Earth's systems, including HuanJing (HJ), FengYun (FY), China-brazil earth resource satellite (CBERS), and GaoFen (GF) series. From the European Space Agency (ESA), in terms of Sentinel family, a total of 50,964,670 products had been downloaded by users since the start of data access operations, with a total data volume of 41. 35 PB [4]. The big Earth observation data (BEOD) has gradually promoted the development of global industries, research institutions, and application sectors, which has had a profound impact on the study of the Earth system [5,6], contributing to human activities, environmental monitoring, and climate changes, and also provided abundant data resource for the construction of digital Earth [7][8][9].BEOD also poses challenges to uses in terms of problem complexity, automatic analysis, and processing efficiency [10][11][12][13]. Fortunately, the rapid advancement of cloud computing technology in recent years provides strong computing power, especially for the efficiency of big geospatial data management and process...

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“…The Advanced Geosynchronous Radiation Imager (AGRI) mounted onboard the FY-4A, the latest Chinese FengYun (FY) geostationary orbit meteorological satellite, installs 14 spectral bands with a spectral coverage from 0.47 micron (visible band) to 13.5 micron (infrared band) and a full-disk observation frequency of 15 min (Zhang et al, 2019). Compared with the FY-2 satellites, the FY-4A satellite further strengthens the ability to monitor clouds by providing continuous measurements on optical thickness, liquid water content, phase state, cloud top pressure.…”

Section: Satellite Datamentioning

confidence: 99%

Fy-4a Satellite Based Cloud Microphysical Variation Analysis of Airborne Cloud Seeding Operations in Sichuan Basin

Lin

Wang²,

Liu

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Based on a satellite retrieval methodology, the cloud microphysical properties of two airborne cloud seeding operations in Sichuan Basin of Southwest China are analyzed in this paper. The methodology for the retrieval of the cloud particle effective radius (Re), cloud temperature (T) and microphysical structure is based on the data from the AGRI onboard the Chinese FY-4A satellite. A microphysical red–green–blue (RGB) composite visualization has been devised to qualitatively highlight the cloud composition. This RGB scheme is displayed by compositing visible 0.65 micron channel, near infrared 2.2 micron channel and infrared 11.0 micron channel. And the vertical structure and development status of the clouds are demonstrated by the T-Re profiles. The results show that after the cloud seeding operation on June 11, 2018, the cloud particle effective radius increases, and the ground observed PM10 and PM2.5 pollutants decrease as well. As for the cloud seeding operation on November, 17, 2018, the satellite inversion shows that the medium and low level clouds are rich in super-cooled water with small cloud particle effective radius. After the cloud seeding operation, the effective radius of the cloud droplets increases significantly. Both of these two cloud seeding operation in Sichuan Basin demonstrate obvious precipitation enhancement results in the seeding impact area.

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Latest Progress of the Chinese Meteorological Satellite Program and Core Data Processing Technologies

Cited by 107 publications

References 92 publications

Estimates of lightning NO<sub><i>x</i></sub> production based on high-resolution OMI NO<sub>2</sub> retrievals over the continental US

Estimates of lightning NO<sub><i>x</i></sub> production based on high-resolution OMI NO<sub>2</sub> retrievals over the continental US

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Fy-4a Satellite Based Cloud Microphysical Variation Analysis of Airborne Cloud Seeding Operations in Sichuan Basin

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Latest Progress of the Chinese Meteorological Satellite Program and Core Data Processing Technologies

Cited by 107 publications

References 92 publications

Estimates of lightning NO&lt;sub&gt;&lt;i&gt;x&lt;/i&gt;&lt;/sub&gt; production based on high-resolution OMI NO&lt;sub&gt;2&lt;/sub&gt; retrievals over the continental US

Estimates of lightning NO&lt;sub&gt;&lt;i&gt;x&lt;/i&gt;&lt;/sub&gt; production based on high-resolution OMI NO&lt;sub&gt;2&lt;/sub&gt; retrievals over the continental US

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Fy-4a Satellite Based Cloud Microphysical Variation Analysis of Airborne Cloud Seeding Operations in Sichuan Basin

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Estimates of lightning NO<sub><i>x</i></sub> production based on high-resolution OMI NO<sub>2</sub> retrievals over the continental US

Estimates of lightning NO<sub><i>x</i></sub> production based on high-resolution OMI NO<sub>2</sub> retrievals over the continental US