Assessing Overlapping Cloud Top Heights: An Extrapolation Method and Its Performance

Tan, Zhonghui; Ma, Shuo; Liu, Chao; Teng, Shiwen; Xu, Na; Hu, Xiuqing; Zhang, Peng; Yan, Wei

doi:10.1109/tgrs.2022.3170054

Cited by 8 publications

(4 citation statements)

References 36 publications

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“…One can see that the CBH retrievals are mostly close to those determined by 2B-CLDCLASS-LIDAR. There are significant CBH biases caused by multilayer clouds in regions near 53 • S. This is because the AHI cloud property products are produced on the single-layer cloud assumption and may be largely biased in multilayer cloud cases [31][32][33]. Clearly, the errors inherited from upstream cloud property products would affect the CBH retrievals.…”

Section: Data Quality Controlmentioning

confidence: 99%

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: Data Quality Controlmentioning

confidence: 99%

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

Tan

Zhao

et al. 2023

Remote Sensing

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“…Continuous monitoring of the atmosphere in a geostationary orbit is extremely important for enhancing the prediction accuracy of HIW events. Geostationary hyperspectral infrared sounders (GeoHIS) boast superior temporal resolutions (Li, Paul Menzel, et al., 2022), enabling the observation of both thermodynamic and dynamical information (Di et al., 2018; Ma et al., 2021; Yan et al., 2023; Yin et al., 2020), specifically the atmospheric temperature, moisture, and wind information at different pressure levels. Carrying a GeoHIS called Geostationary Interferometric Infrared Sounder (GIIRS), China’s FY‐4A represents the first attempt at a new generation in geostationary meteorological satellite technology (Yang et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Added Value of Three‐Dimensional Horizontal Winds From Geostationary Interferometric Infrared Sounder for Typhoon Forecast in a Regional NWP Model

Meng,

Tan,

et al. 2024

JGR Atmospheres

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High temporal resolution geostationary hyperspectral infrared sounders can simultaneously profile atmospheric temperature and moisture, and track moisture features to provide 3D horizontal winds in clear and partially cloudy scenarios. The thermodynamic information obtained has been integral to enhancing tropical cyclone (TC) forecasts. However, the potential benefits derived from the dynamic information provided by geostationary hyperspectral infrared sounders (GeoHIS) are yet to be fully comprehended for numerical weather prediction (NWP). With the re‐estimated observation error and multivariate hydrometeor background error covariance, the 3D horizontal winds from the Geostationary Interferometric Infrared Sounder (GIIRS) are synergistically assimilated with the hydrometeor information. The impact of the GIIRS‐derived 3D horizontal wind assimilation on Typhoon Maria (2018) and Lekima (2019) analysis and forecast are evaluated. The results show that the assimilating GIIRS‐derived 3D horizontal wind notably reduces the RMSEs of analysis and forecast, particularly in the U and V components. The improvement in accuracy of large‐scale fields substantially enhances the forecasts of typhoons' track and maximum wind speed, as well as the central sea‐level pressure. Moreover, the prediction of the spatial distribution and intensity for landfall precipitation is also improved. The detailed diagnoses of the Maria show that a more southerly subtropical high generated by the additional horizontal wind assimilation improves the rainfall spatial distribution, and the reasonable water vapor transportation caused by the improved dynamic conditions corrects the precipitation intensity. This work highlights the importance of dynamic information in TC forecasting and emphasizes the need for efficient and high‐precision 3D wind measurements to improve NWP.

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“…In recent decades, numerous efforts have been made to retrieve cloud properties using passive satellite instruments (Lai et al, 2019;Li et al,2023;Min et al, 2020;Minnis et al, 2011;Poulsen et al, 2012;Tan et al, 2022;Zhao et al, 2012). A common method involves combining data from visible (VIS) 40 and near-infrared (NIR) channels to construct lookup tables (LUT) for daytime cloud microphysical properties, such as cloud optical thickness (COT) and cloud effective radius (CER) (Painemal and Zuidema, 2011;Twomey et al, 1980;Nakajima et al, 1990).…”

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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Assessing Overlapping Cloud Top Heights: An Extrapolation Method and Its Performance

Cited by 8 publications

References 36 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

Added Value of Three‐Dimensional Horizontal Winds From Geostationary Interferometric Infrared Sounder for Typhoon Forecast in a Regional NWP Model

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

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