Cloud-Type-Dependent 1DVAR Algorithm for Retrieving Hydrometeors and Precipitation in Tropical Cyclone Nanmadol from GMI Data.
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| Title: | Cloud-Type-Dependent 1DVAR Algorithm for Retrieving Hydrometeors and Precipitation in Tropical Cyclone Nanmadol from GMI Data. |
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| Authors: | Han, Linjun1,2 (AUTHOR), Weng, Fuzhong3 (AUTHOR) wengfz@cma.gov.cn, Hu, Hao3 (AUTHOR), Hu, Xiuqing4 (AUTHOR) huxq@cma.gov.cn |
| Source: | Advances in Atmospheric Sciences. Mar2024, Vol. 41 Issue 3, p407-419. 13p. |
| Subject Terms: | *Tropical cyclones, *Drop size distribution, *Rainwater, *Rainfall, *Brightness temperature, *Algorithms |
| Abstract (English): | Understanding the structure of tropical cyclone (TC) hydrometeors is crucial for detecting the changes in the distribution and intensity of precipitation. In this study, the GMI brightness temperature and cloud-dependent 1DVAR algorithm were used to retrieve the hydrometeor profiles and surface rain rate of TC Nanmadol (2022). The Advanced Radiative Transfer Modeling System (ARMS) was used to calculate the Jacobian and degrees of freedom (ΔDOF) of cloud water, rainwater, and graupel for different channels of GMI in convective conditions. The retrieval results were compared with the Dual-frequency Precipitation Radar (DPR), GMI 2A, and IMERG products. It is shown that from all channels of GMI, rain water has the highest ΔDOF, at 1.72. According to the radiance Jacobian to atmospheric state variables, cloud water emission dominates its scattering. For rain water, the emission of channels 1–4 dominates scattering. Compared with the GMI 2A precipitation product, the 1DVAR precipitation rate has a higher correlation coefficient (0.713) with the IMERG product and can better reflect the location of TC precipitation. Near the TC eyewall, the highest radar echo top indicates strong convection. Near the melting layer where Ka-band attenuation is strong, the double frequency difference of DPR data reflects the location of the melting. The DPR drop size distribution (DSD) product shows that there is a significant increase in particle size below the melting layer in the spiral rain band. Thus, the particle size may be one of the main reasons for the smaller rain water below the melting layer retrieved from 1DVAR. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): | 摘 要: 台风水凝物结构对于降水的水平、 强度变化至关重要. 本研究使用GMI亮温数据和云依赖的一维变分(1DVAR)算法反演TC南玛都的水凝物结构和降雨率. ARMS用于计算GMI通道在对流条件下的云水、 雨水和霰的雅可比矩阵和自由度(∆DOF). 反演结果与双频降水雷达(DPR)、 GMI 2A和IMERG产品进行比较. 结果显示, 在GMI所有通道中, 雨水的∆DOF最高, 为1.72, 即在对流条件下, GMI通道含有雨水的信息量最高. 根据雅可比矩阵, 云水在对流条件下发射大于散射. 对于雨水, 通道1到4发射大于散射. 与GMI 2A降水产品相比, 1DVAR降水率与IMERG产品具有更高的相关系数(0.713), 能更好地反映TC降水位置. 在眼墙附近, 回波顶高最高标志着此时对流最强. DPR数据的双频差反映了融化层的位置且Ka波段在融化层附近衰减严重. DPR粒子谱(DSD)产品显示, 在融化层下的螺旋雨带中, 粒子尺寸显著增加. 由此, 粒子尺寸可能是融化层下1DVAR反演雨水量级较小的主要原因之一. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 174639082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Cloud-Type-Dependent 1DVAR Algorithm for Retrieving Hydrometeors and Precipitation in Tropical Cyclone Nanmadol from GMI Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Han%2C+Linjun%22">Han, Linjun</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Weng%2C+Fuzhong%22">Weng, Fuzhong</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> wengfz@cma.gov.cn</i><br /><searchLink fieldCode="AR" term="%22Hu%2C+Hao%22">Hu, Hao</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Xiuqing%22">Hu, Xiuqing</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> huxq@cma.gov.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Advances+in+Atmospheric+Sciences%22">Advances in Atmospheric Sciences</searchLink>. Mar2024, Vol. 41 Issue 3, p407-419. 13p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Tropical+cyclones%22">Tropical cyclones</searchLink><br />*<searchLink fieldCode="DE" term="%22Drop+size+distribution%22">Drop size distribution</searchLink><br />*<searchLink fieldCode="DE" term="%22Rainwater%22">Rainwater</searchLink><br />*<searchLink fieldCode="DE" term="%22Rainfall%22">Rainfall</searchLink><br />*<searchLink fieldCode="DE" term="%22Brightness+temperature%22">Brightness temperature</searchLink><br />*<searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: Understanding the structure of tropical cyclone (TC) hydrometeors is crucial for detecting the changes in the distribution and intensity of precipitation. In this study, the GMI brightness temperature and cloud-dependent 1DVAR algorithm were used to retrieve the hydrometeor profiles and surface rain rate of TC Nanmadol (2022). The Advanced Radiative Transfer Modeling System (ARMS) was used to calculate the Jacobian and degrees of freedom (ΔDOF) of cloud water, rainwater, and graupel for different channels of GMI in convective conditions. The retrieval results were compared with the Dual-frequency Precipitation Radar (DPR), GMI 2A, and IMERG products. It is shown that from all channels of GMI, rain water has the highest ΔDOF, at 1.72. According to the radiance Jacobian to atmospheric state variables, cloud water emission dominates its scattering. For rain water, the emission of channels 1–4 dominates scattering. Compared with the GMI 2A precipitation product, the 1DVAR precipitation rate has a higher correlation coefficient (0.713) with the IMERG product and can better reflect the location of TC precipitation. Near the TC eyewall, the highest radar echo top indicates strong convection. Near the melting layer where Ka-band attenuation is strong, the double frequency difference of DPR data reflects the location of the melting. The DPR drop size distribution (DSD) product shows that there is a significant increase in particle size below the melting layer in the spiral rain band. Thus, the particle size may be one of the main reasons for the smaller rain water below the melting layer retrieved from 1DVAR. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (Chinese) Group: Ab Data: 摘 要: 台风水凝物结构对于降水的水平、 强度变化至关重要. 本研究使用GMI亮温数据和云依赖的一维变分(1DVAR)算法反演TC南玛都的水凝物结构和降雨率. ARMS用于计算GMI通道在对流条件下的云水、 雨水和霰的雅可比矩阵和自由度(∆DOF). 反演结果与双频降水雷达(DPR)、 GMI 2A和IMERG产品进行比较. 结果显示, 在GMI所有通道中, 雨水的∆DOF最高, 为1.72, 即在对流条件下, GMI通道含有雨水的信息量最高. 根据雅可比矩阵, 云水在对流条件下发射大于散射. 对于雨水, 通道1到4发射大于散射. 与GMI 2A降水产品相比, 1DVAR降水率与IMERG产品具有更高的相关系数(0.713), 能更好地反映TC降水位置. 在眼墙附近, 回波顶高最高标志着此时对流最强. DPR数据的双频差反映了融化层的位置且Ka波段在融化层附近衰减严重. DPR粒子谱(DSD)产品显示, 在融化层下的螺旋雨带中, 粒子尺寸显著增加. 由此, 粒子尺寸可能是融化层下1DVAR反演雨水量级较小的主要原因之一. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00376-023-3084-8 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 407 Subjects: – SubjectFull: Tropical cyclones Type: general – SubjectFull: Drop size distribution Type: general – SubjectFull: Rainwater Type: general – SubjectFull: Rainfall Type: general – SubjectFull: Brightness temperature Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: Cloud-Type-Dependent 1DVAR Algorithm for Retrieving Hydrometeors and Precipitation in Tropical Cyclone Nanmadol from GMI Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Han, Linjun – PersonEntity: Name: NameFull: Weng, Fuzhong – PersonEntity: Name: NameFull: Hu, Hao – PersonEntity: Name: NameFull: Hu, Xiuqing IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 02561530 Numbering: – Type: volume Value: 41 – Type: issue Value: 3 Titles: – TitleFull: Advances in Atmospheric Sciences Type: main |
| ResultId | 1 |
