Retrieving cloud base height from passive radiometer observations via a systematic effective cloud water content table

Tan, Zhonghui; Ma, Shuo; Liu, Chao; Teng, Shiwen; Letu, Husi; Zhang, Peng; Ai, Weihua

doi:10.1016/j.rse.2023.113633

Cited by 2 publications

(4 citation statements)

References 42 publications

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“…Hence, most existing methods first estimate CGT using conventional CWP retrievals, and then calculate CBH as the difference between the CGT retrievals and existing CTH retrievals. Recently, Tan et al [26] developed a method to retrieve CBH from passive radiometers, utilizing an improved characterization for mutable cloud water contents (CWCs). This was achieved by introducing a variable named effective CWC (ECWC) to represent the vertically varying CWC and subsequently constructing a systematic ECWC lookup table (LUT) based on collocated active and passive satellite observations.…”

Section: Cbh Estimation Methodsmentioning

confidence: 99%

“…An analysis based on long-term and large-scale CBH data derived from geostationary satellite observations would be useful for conducting these tasks. In particular, our previous study developed a novel CBH retrieval algorithm for passive radiometers [26], providing an opportunity to analyze the climatology of CBH.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we applied the CBH retrieval algorithm developed by [26] to the geostationary Himawari-8 satellite observations to produce a continuous and large-scale CBH dataset, which will be utilized to analyze the CBH climatology related to cloud properties, seasons, and geolocations. The remainder of this paper is organized as follows: Section 2 introduces the data and the CBH retrieval method.…”

Section: Introductionmentioning

confidence: 99%

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Climatology of Cloud Base Height Retrieved from Long-Term Geostationary Satellite Observations

Tan

Zhao

et al. 2023

Remote Sensing

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Cloud base height (CBH) is crucial for parameterizing the cloud vertical structure (CVS), but knowledge concerning the temporal and spatial distribution of CBH is still poor owing to the lack of large-scale and continuous CBH observations. Taking advantage of high temporal and spatial resolution observations from the Advanced Himawari Imager (AHI) on board the geostationary Himawari-8 satellite, this study investigated the climatology of CBH by applying a novel CBH retrieval algorithm to AHI observations. We first evaluated the accuracy of the AHI-derived CBH retrievals using the active measurements of CVS from the CloudSat and CALIPSO satellites, and the results indicated that our CBH retrievals for single-layer clouds perform well, with a mean bias of 0.3 ± 1.9 km. Therefore, the CBH climatology was compiled based on AHI-derived CBH retrievals for single-layer clouds for the time period between September 2015 and August 2018. Overall, the distribution of CBH is tightly associated with cloud phase, cloud type, and cloud top height and also exhibits significant geographical distribution and temporal variation. Clouds at low latitudes are generally higher than those at middle and high latitudes, with CBHs peaking in summer and lowest in winter. In addition, the surface type affects the distribution of CBH. The proportion of low clouds over the ocean is larger than that over the land, while high cloud occurs most frequently over the coastal area. Due to periodic changes in environmental conditions, cloud types also undergo significant diurnal changes, resulting in periodic changes in the vertical structure of clouds.

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Section: Cbh Estimation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Climatology of Cloud Base Height Retrieved from Long-Term Geostationary Satellite Observations

Tan

Zhao

et al. 2023

Remote Sensing

Self Cite

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“…Recently, machine learning techniques such as random forests, artificial neural network, and deep learning have gained significant traction in remote sensing (Bai et al, 2021;Guo et al 2022;Shi et al,2020;Tan et al,2023;Yuan et al, 2020;Zhao et al, 2023). Häkansson et al (2018) used a neural 70 network algorithm to retrieve cloud top properties from several passive polar orbit sensors, greatly improving CTH retrievals.…”

Section: Introductionmentioning

confidence: 99%

Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation

Huang,

Wang,

Liu

et al. 2024

Preprint

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Abstract. While traditional thermal infrared retrieval algorithms based on radiative transfer models (RTM) could not effectively retrieve the cloud optical thickness of thick clouds, machine learning based algorithms were found to be able to provide reasonable estimations for both daytime and nighttime. Nevertheless, stand-alone machine learning algorithms are occasionally criticized for the lack of explicit physical processes. In this study, RTM simulations and a machine learning algorithm are synergistically utilized using the optimal estimation (OE) method to retrieve cloud properties from thermal infrared radiometry measured by Moderate Resolution Imaging Spectroradiometer (MODIS). In the new algorithm, retrievals from a machine learning algorithm are used to provide priori states for the iterative process of OE method, and an RTM is used to create radiance lookup tables that are used in the iteration processes. Compared with stand-alone OE, the cloud properties retrieved by the new algorithm show an overall better performance by using the spatial statistic information obtained by machine learning algorithm. Compared with stand-alone machine-learning based algorithm, the radiances simulated based on retrievals from the new method align more closely with observations, and physical radiative processes are handled explicitly in the new algorithm. Therefore, the new method combines the advantages of RTM-based cloud retrieval methods and machine-learning models. These findings highlight the potential for machine-learning-based algorithms to enhance the efficacy of conventional remote sensing techniques.

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Retrieving cloud base height from passive radiometer observations via a systematic effective cloud water content table

Cited by 2 publications

References 42 publications

Climatology of Cloud Base Height Retrieved from Long-Term Geostationary Satellite Observations

Climatology of Cloud Base Height Retrieved from Long-Term Geostationary Satellite Observations

Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation

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