Introduction to the NJIAS Himawari-8/9 Cloud Feature Dataset for climate and typhoon research
<p>The use of remote sensing methods to accurately measure cloud properties and their spatiotemporal changes has been widely welcomed in many fields of atmospheric research. The Nanjing Joint Institute for Atmospheric Sciences (NJIAS) Himawari-8/9 Cloud Feature Dataset (HCFD) provides a compre...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2024-04-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/16/1747/2024/essd-16-1747-2024.pdf |
| _version_ | 1797218114165800960 |
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| author | X. Zhuge X. Zou L. Yu X. Li M. Zeng Y. Chen B. Zhang B. Yao F. Tang F. Chen W. Kan |
| author_facet | X. Zhuge X. Zou L. Yu X. Li M. Zeng Y. Chen B. Zhang B. Yao F. Tang F. Chen W. Kan |
| author_sort | X. Zhuge |
| collection | DOAJ |
| description | <p>The use of remote sensing methods to accurately measure cloud properties and their spatiotemporal changes has been widely welcomed in many fields of atmospheric research. The Nanjing Joint Institute for Atmospheric Sciences (NJIAS) Himawari-8/9 Cloud Feature Dataset (HCFD) provides a comprehensive description of cloud features over the East Asia and west North Pacific regions for the 7-year period from April 2016 to December 2022. Multiple cloud variables, such as cloud mask, phase/type, top height, optical thickness, and particle effective radius, as well as snow, dust, and haze masks, were generated from the visible and infrared measurements of the Advanced Himawari Imager (AHI) on board the Japanese geostationary satellites Himawari-8 and Himawari-9 using a series of recently developed cloud retrieval algorithms. Verifications with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) 1 <span class="inline-formula">km</span> cloud layer product and the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 cloud product (MYD06) demonstrate that the NJIAS HCFD gives higher skill scores than the Japanese Himawari-8/9 operational cloud product for all cloud variables except for cloud particle effective radius. The NJIAS HCFD even outperforms the MYD06 in nighttime cloud detection; cloud-top height, pressure, and temperature estimation; and infrared-only cloud-top phase determination. All evaluations are performed at the nominal 2 <span class="inline-formula">km</span> scale, not including the effects of sub-pixel cloudiness or very thin cirrus. Two examples are presented to demonstrate applications of the NJIAS HCFD for climate and typhoon research. The NJIAS HCFD has been published in the Science Data Bank (<span class="uri">https://doi.org/10.57760/sciencedb.09950</span>, Zhuge 2023a; <span class="uri">https://doi.org/10.57760/sciencedb.09953</span>, Zhuge 2023b; <span class="uri">https://doi.org/10.57760/sciencedb.09954</span>, Zhuge 2023c; <span class="uri">https://doi.org/10.57760/sciencedb.10158</span>, Zhuge 2023d; <span class="uri">https://doi.org/10.57760/sciencedb.09945</span>, Zhuge 2023e).</p> |
| first_indexed | 2024-04-24T12:12:36Z |
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| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-04-24T12:12:36Z |
| publishDate | 2024-04-01 |
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| spelling | doaj.art-86430f6f7e4f43b98d264aa307c8e7422024-04-08T11:44:46ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162024-04-01161747176910.5194/essd-16-1747-2024Introduction to the NJIAS Himawari-8/9 Cloud Feature Dataset for climate and typhoon researchX. Zhuge0X. Zou1L. Yu2X. Li3M. Zeng4Y. Chen5B. Zhang6B. Yao7F. Tang8F. Chen9W. Kan10Key Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, ChinaJoint Center of Data Assimilation for Research and Application, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaKey Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, ChinaKey Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, ChinaKey Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, ChinaSchool of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, ChinaKey Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, ChinaKey Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, ChinaKey Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, ChinaKey Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, ChinaKey Laboratory of Transportation Meteorology of CMA, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China<p>The use of remote sensing methods to accurately measure cloud properties and their spatiotemporal changes has been widely welcomed in many fields of atmospheric research. The Nanjing Joint Institute for Atmospheric Sciences (NJIAS) Himawari-8/9 Cloud Feature Dataset (HCFD) provides a comprehensive description of cloud features over the East Asia and west North Pacific regions for the 7-year period from April 2016 to December 2022. Multiple cloud variables, such as cloud mask, phase/type, top height, optical thickness, and particle effective radius, as well as snow, dust, and haze masks, were generated from the visible and infrared measurements of the Advanced Himawari Imager (AHI) on board the Japanese geostationary satellites Himawari-8 and Himawari-9 using a series of recently developed cloud retrieval algorithms. Verifications with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) 1 <span class="inline-formula">km</span> cloud layer product and the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 cloud product (MYD06) demonstrate that the NJIAS HCFD gives higher skill scores than the Japanese Himawari-8/9 operational cloud product for all cloud variables except for cloud particle effective radius. The NJIAS HCFD even outperforms the MYD06 in nighttime cloud detection; cloud-top height, pressure, and temperature estimation; and infrared-only cloud-top phase determination. All evaluations are performed at the nominal 2 <span class="inline-formula">km</span> scale, not including the effects of sub-pixel cloudiness or very thin cirrus. Two examples are presented to demonstrate applications of the NJIAS HCFD for climate and typhoon research. The NJIAS HCFD has been published in the Science Data Bank (<span class="uri">https://doi.org/10.57760/sciencedb.09950</span>, Zhuge 2023a; <span class="uri">https://doi.org/10.57760/sciencedb.09953</span>, Zhuge 2023b; <span class="uri">https://doi.org/10.57760/sciencedb.09954</span>, Zhuge 2023c; <span class="uri">https://doi.org/10.57760/sciencedb.10158</span>, Zhuge 2023d; <span class="uri">https://doi.org/10.57760/sciencedb.09945</span>, Zhuge 2023e).</p>https://essd.copernicus.org/articles/16/1747/2024/essd-16-1747-2024.pdf |
| spellingShingle | X. Zhuge X. Zou L. Yu X. Li M. Zeng Y. Chen B. Zhang B. Yao F. Tang F. Chen W. Kan Introduction to the NJIAS Himawari-8/9 Cloud Feature Dataset for climate and typhoon research Earth System Science Data |
| title | Introduction to the NJIAS Himawari-8/9 Cloud Feature Dataset for climate and typhoon research |
| title_full | Introduction to the NJIAS Himawari-8/9 Cloud Feature Dataset for climate and typhoon research |
| title_fullStr | Introduction to the NJIAS Himawari-8/9 Cloud Feature Dataset for climate and typhoon research |
| title_full_unstemmed | Introduction to the NJIAS Himawari-8/9 Cloud Feature Dataset for climate and typhoon research |
| title_short | Introduction to the NJIAS Himawari-8/9 Cloud Feature Dataset for climate and typhoon research |
| title_sort | introduction to the njias himawari 8 9 cloud feature dataset for climate and typhoon research |
| url | https://essd.copernicus.org/articles/16/1747/2024/essd-16-1747-2024.pdf |
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