Cloud Base Height Estimation from ISCCP Cloud-Type Classification Applied to A-Train Data
Cloud base height (CBH) is an important cloud macro parameter that plays a key role in global radiation balance and aviation flight. Building on a previous algorithm, CBH is estimated by combining measurements from CloudSat/CALIPSO and MODIS based on the International Satellite Cloud Climatology Pro...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2017/3231719 |
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| _version_ | 1849407933636411392 |
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| author | Yao Liang Xuejin Sun Steven D. Miller Haoran Li Yongbo Zhou Riwei Zhang Shaohui Li |
| author_facet | Yao Liang Xuejin Sun Steven D. Miller Haoran Li Yongbo Zhou Riwei Zhang Shaohui Li |
| author_sort | Yao Liang |
| collection | DOAJ |
| description | Cloud base height (CBH) is an important cloud macro parameter that plays a key role in global radiation balance and aviation flight. Building on a previous algorithm, CBH is estimated by combining measurements from CloudSat/CALIPSO and MODIS based on the International Satellite Cloud Climatology Project (ISCCP) cloud-type classification and a weighted distance algorithm. Additional constraints on cloud water path (CWP) and cloud top height (CTH) are introduced. The combined algorithm takes advantage of active and passive remote sensing to effectively estimate CBH in a wide-swath imagery where the cloud vertical structure details are known only along the curtain slice of the nonscanning active sensors. Comparisons between the estimated and observed CBHs show high correlation. The coefficient of association (R2) is 0.8602 with separation distance between donor and recipient points in the range of 0 to 100 km and falls off to 0.5856 when the separation distance increases to the range of 401 to 600 km. Also, differences are mainly within 1 km when separation distance ranges from 0 km to 600 km. The CBH estimation method was applied to the 3D cloud structure of Tropical Cyclone Bill, and the method is further assessed by comparing CTH estimated by the algorithm with the MODIS CTH product. |
| format | Article |
| id | doaj-art-42f75bd69eed4a22b9c09b6a0ae96334 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-42f75bd69eed4a22b9c09b6a0ae963342025-08-20T03:35:54ZengWileyAdvances in Meteorology1687-93091687-93172017-01-01201710.1155/2017/32317193231719Cloud Base Height Estimation from ISCCP Cloud-Type Classification Applied to A-Train DataYao Liang0Xuejin Sun1Steven D. Miller2Haoran Li3Yongbo Zhou4Riwei Zhang5Shaohui Li6College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, ChinaCooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USACollege of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, ChinaState Key Laboratory of Aerospace Dynamics, Xi’an 710043, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, ChinaCloud base height (CBH) is an important cloud macro parameter that plays a key role in global radiation balance and aviation flight. Building on a previous algorithm, CBH is estimated by combining measurements from CloudSat/CALIPSO and MODIS based on the International Satellite Cloud Climatology Project (ISCCP) cloud-type classification and a weighted distance algorithm. Additional constraints on cloud water path (CWP) and cloud top height (CTH) are introduced. The combined algorithm takes advantage of active and passive remote sensing to effectively estimate CBH in a wide-swath imagery where the cloud vertical structure details are known only along the curtain slice of the nonscanning active sensors. Comparisons between the estimated and observed CBHs show high correlation. The coefficient of association (R2) is 0.8602 with separation distance between donor and recipient points in the range of 0 to 100 km and falls off to 0.5856 when the separation distance increases to the range of 401 to 600 km. Also, differences are mainly within 1 km when separation distance ranges from 0 km to 600 km. The CBH estimation method was applied to the 3D cloud structure of Tropical Cyclone Bill, and the method is further assessed by comparing CTH estimated by the algorithm with the MODIS CTH product.http://dx.doi.org/10.1155/2017/3231719 |
| spellingShingle | Yao Liang Xuejin Sun Steven D. Miller Haoran Li Yongbo Zhou Riwei Zhang Shaohui Li Cloud Base Height Estimation from ISCCP Cloud-Type Classification Applied to A-Train Data Advances in Meteorology |
| title | Cloud Base Height Estimation from ISCCP Cloud-Type Classification Applied to A-Train Data |
| title_full | Cloud Base Height Estimation from ISCCP Cloud-Type Classification Applied to A-Train Data |
| title_fullStr | Cloud Base Height Estimation from ISCCP Cloud-Type Classification Applied to A-Train Data |
| title_full_unstemmed | Cloud Base Height Estimation from ISCCP Cloud-Type Classification Applied to A-Train Data |
| title_short | Cloud Base Height Estimation from ISCCP Cloud-Type Classification Applied to A-Train Data |
| title_sort | cloud base height estimation from isccp cloud type classification applied to a train data |
| url | http://dx.doi.org/10.1155/2017/3231719 |
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