Cloud-base and cloud-top heights determined from a ground-based cloud radar in Beijing, China

Zhou, Qing; Zhang, Yong; Bai, Li; Li, Liangxu; Feng, Jinming; Jia, Shuze; Lv, Shanshan; Fa, Tao; Guo, Jianping

doi:10.1016/j.atmosenv.2019.01.012

Cited by 25 publications

(31 citation statements)

References 48 publications

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“…It is considered to be cloudy if the reflectivity profile contains more than three bins of radar reflectivity data higher than −45 dBZ. Zhou et al (2019) used a threshold of −40 dBZ for cloud determination because their Ka radar was equipped with an all-solid transmitter that was different to our Ka radar. A higher threshold might miss some clouds with weak returns.…”

Section: Ka-band Radarmentioning

confidence: 99%

“…The Advanced Himawari Imager (AHI) on board the Himawari-8 (HW8) satellite, a geostationary meteorological satellite, has provided CTHs since July 2015 (Bessho et al, 2016). Zhou et al (2019) reported that the CTHs derived from surface Ka-band radar from December 2016 to November 2017 were 0.82 km higher than the retrieved CTH from the AHI radiance data using a Fengyun Geostationary Algorithm Testbed-Imager (FYGAT-I) science product algorithm. Mouri et al (2016) reported that the mean AHI CTH was lower than the MODIS and CALIOP CTH over 2 weeks of measurements.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar

et al. 2020

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Abstract. To better understand the accuracy of cloud top heights (CTHs) derived from passive satellite data, ground-based Ka-band radar measurements from 2016 and 2017 in Beijing are compared with CTH data inferred from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Himawari Imager (AHI). Relative to the radar CTHs, the MODIS CTHs are found to be underestimated by−1.10 ± 2.53 km on average and 49 % of CTH differences are within 1.0 km. The AHI CTHs are underestimated by −1.10 ± 2.27 km and 42 % are within 1.0 km. Both the MODIS and AHI CTH retrieval accuracy depends strongly on the cloud depth (CD). Large differences are mainly due to the retrieval of thin clouds of CD <1 km, especially when the cloud base height is higher than 4 km. For clouds with CD >1 km, the mean CTH difference decreases to -0.48±1.70 km for MODIS and to -0.76±1.63 km for AHI. It is found that MODIS CTHs with higher values (i.e. >6 km) show smaller discrepancy with radar CTH than those MODIS CTHs with lower values (i.e. <4 km). Statistical analysis illustrate that the CTH difference between the two satellite instruments is lower than the difference between the satellite instrument and the ground-based Ka-band radar. The monthly accuracy of both CTH retrieval algorithms is investigated and it is found that summer has the smallest retrieval difference.

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Section: Ka-band Radarmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar

et al. 2020

View full text Add to dashboard Cite

show abstract

“…It is considered to be cloudy if the reflectivity profile contains more than three bins of radar reflectivity data higher than −45 dBZ. Zhou et al (2019) used a threshold of −40 dBZ for cloud determination for their Ka radar, the transmitter of which was a different type compared with the IAP radar. A lower threshold might neglect some clouds with weak returns.…”

Section: Ka-band Radarmentioning

confidence: 99%

“…The Advanced Himawari Imager (AHI) onboard the Himawari-8 (HW8) satellite, a geostationary meteorological satellite, has provided CTHs since July 2015 (Bessho et al 2016). Zhou et al (2019) reported that the CTHs derived from surface Ka-band radar, from December 2016 to November 2017, are 0.82 km higher than those retrieved from the AHI radiance data based on a Fengyun Geostationary Algorithm Testbed-Imager (FYGAT-I) science product algorithm (Min et al 2017). Mouri et al (2016) found that the CTH was underestimated compared with the MODIS and CALIOP data over two weeks of measurements.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar

Huo¹,

Lü²,

Duan³

et al. 2019

Preprint

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Abstract. To better understand the accuracy of cloud top heights (CTHs) derived from passive satellite data, ground-based Ka-band radar measurements from 2016 and 2017 in Beijing were compared with CTH data inferred from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Himawari Imager (AHI). Relative to the radar CTHs, the MODIS CTHs were found to be underestimated by −1.10 ± 2.53 km and 49 % of CTH differences were within 1.0 km. Like the MODIS results, the AHI CTHs were underestimated by −1.10 ± 2.27 km and 42 % were within 1.0 km. Both the MODIS and AHI retrieval accuracy depended strongly on the cloud depth (CD). Large differences were mainly occurring for the retrieval of thin clouds of CD 1 km, the CTH difference decreased to −0.48 ± 1.70 km for MODIS and to −0.76 ± 1.63 km for AHI. MODIS CTHs greater than 6 km showed better agreement with the radar data than those less than 4 km. Statistical analysis showed that the average AHI CTHs were lower than the average MODIS CTHs by −0.64 ± 2.36 km. The monthly accuracy of both retrieval algorithms was studied and it was found that the AHI retrieval algorithm had the largest bias in winter while the MODIS retrieval algorithm had the lowest accuracy in spring.

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“…Cloud height, cloud coverage, and cloud type are three major aspects of cloud observation and have been extensively studied (Davies, ; Fu et al, ; Liu et al, ; Zhang et al, ; Zhou et al, ). However, due to the variability and diversity of cloud appearances, cloud type classification is extremely challenging and still under development.…”

Section: Introductionmentioning

confidence: 99%

Ground‐Based Cloud Classification Using Task‐Based Graph Convolutional Network

Liu

Zhong

et al. 2020

Geophysical Research Letters

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Clouds play a significant role in weather forecasts, water cycle, and climate system. However, existing methods ignore the relations of ground‐based cloud images. In this letter, we propose a novel method named task‐based graph convolutional network (TGCN) for ground‐based cloud classification, which takes image relations into consideration. To this end, we construct the graph using convolutional neural network‐based features of ground‐based cloud images which are learned in a supervised manner, and incorporate the graph computation into TGCN. Given that existing ground‐based cloud databases are with limited labeled training images and categorized according to different classification criteria, we release the largest ground‐based remote sensing cloud database (GRSCD) to provide a comparative study for different methods and to further improve the study of regional sky conditions. The experimental results on GRSCD manifest the effectiveness of TGCN for ground‐based cloud classification.

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Cloud-base and cloud-top heights determined from a ground-based cloud radar in Beijing, China

Cited by 25 publications

References 48 publications

Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar

Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar

Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar

Ground‐Based Cloud Classification Using Task‐Based Graph Convolutional Network

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