Tropical Overshooting Cloud-Top Height Retrieval from Himawari-8 Imagery Based on Random Forest Model

Wang, Gaoyun; Wang, Hongqing; Zhuang, Yizhou; Wu, Qiong; Chen, Siyue; Kang, Haokai

doi:10.3390/atmos12020173

Cited by 6 publications

(3 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, neural networks have been widely applied in many fields and have demonstrated their effectiveness as a powerful tool for extracting cloud or other meteorological parameters [47][48][49][50][51][52][53][54][55]. Training neural network algorithms often requires large amounts of good-quality data to produce accurate results, such as in modeling nonlinear problems and automating tasks.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Retrieval of Cloud Top Height Using Neural Networks and Data from Cloud-Aerosol Lidar with Orthogonal Polarization

Cheng,

He,

Xue

et al. 2024

Sensors

View full text Add to dashboard Cite

In order to enhance the retrieval accuracy of cloud top height (CTH) from MODIS data, neural network models were employed based on Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Three types of methods were established using MODIS inputs: cloud parameters, calibrated radiance, and a combination of both. From a statistical standpoint, models with combination inputs demonstrated the best performance, followed by models with calibrated radiance inputs, while models relying solely on calibrated radiance had poorer applicability. This work found that cloud top pressure (CTP) and cloud top temperature played a crucial role in CTH retrieval from MODIS data. However, within the same type of models, there were slight differences in the retrieved results, and these differences were not dependent on the quantity of input parameters. Therefore, the model with fewer inputs using cloud parameters and calibrated radiance was recommended and employed for individual case studies. This model produced results closest to the actual cloud top structure of the typhoon and exhibited similar cloud distribution patterns when compared with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) CTHs from a climatic statistical perspective. This suggests that the recommended model has good applicability and credibility in CTH retrieval from MODIS images. This work provides a method to improve accurate CTHs from MODIS data for better utilization.

show abstract

Section: Introductionmentioning

confidence: 99%

Remote Sensing Retrieval of Cloud Top Height Using Neural Networks and Data from Cloud-Aerosol Lidar with Orthogonal Polarization

Cheng,

He,

Xue

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Different cloud types exert varying impacts on the radiative balance of Earth's surface‐atmosphere system, influencing microphysical parameter retrieval precision and the efficacy of parameterization schemes in numerical models (J. Li et al., 2003; Stephens, 2005; Y. Wang et al., 2023). Accurate cloud classification also plays a crucial role in enhancing the analysis of precipitation intensity and humidity fields, offering valuable insights into current weather conditions and future developments (Mahrooghy et al., 2012; Platt, 1983).…”

Section: Introductionmentioning

confidence: 99%

Advancing Cloud Classification Over the Tibetan Plateau: A New Algorithm Reveals Seasonal and Diurnal Variations

Bao,

Letu,

Shang

et al. 2024

Geophysical Research Letters

View full text Add to dashboard Cite

The cloud classification algorithm widely used in the International Satellite Cloud Climatology Project (ISCCP) tends to underestimate low clouds over the Tibetan Plateau (TP), often mistaking water clouds for high‐level clouds. To address this issue, we propose a new algorithm based on cloud‐top temperature and optical thickness, which we apply to TP using Advanced Himawari Imager (AHI) geostationary satellite data. Compared with Clouds and the Earth's Radiant Energy System cloud‐type products and ISCCP results obtained from AHI data, this new algorithm markedly improved low‐cloud detection accuracy and better aligned with cloud phase results. Validation with lidar cloud‐type products further confirmed the superiority of this new algorithm. Diurnal cloud variations over the TP show morning dominance shifting to afternoon high clouds and evening mid‐level clouds. Winter is dominated by high clouds, summer by mid‐level clouds, spring by daytime low clouds and nighttime high clouds, and autumn by low and mid‐level clouds.

show abstract

“…CTH also plays a pivotal role in weather processes (Ohring & Adler, 1978). An overshooting convective cloud top usually indicates an extreme weather system (Bedka et al, 2010; Lane et al, 2003; Wang et al, 2021). Therefore, CTH is usually used as an indicator for the development of extreme weather systems in research.…”

Section: Introductionmentioning

confidence: 99%

Nowcast for cloud top height from Himawari‐8 data based on deep learning algorithms

Tan

et al. 2023

Meteorological Applications

View full text Add to dashboard Cite

Cloud top height (CTH) indicates the vertical development of clouds. Intensely vertically developing clouds are usually accompanied by extreme weather systems and pose a threat to aviation safety. Therefore, nowcast for CTH is necessary and meaningful to guide aviation flights. In this study, we researched the nowcast for CTH (mainly within 0–2 h) based on deep learning algorithms. With Sichuan Province as the study area, we collected CTH data of Himawari‐8 satellite from 2018 to 2020. Convolutional‐long‐short‐term‐memory (ConvLSTM) and trajectory‐gated‐recurrent‐unit (TrajGRU) were used to build nowcast models in the encoder‐forecaster framework. The optical flow model and persistence were used as benchmarks. The results showed that the deep learning models did not have significant advantages over the benchmarks in the first 20 min. However, with increasing nowcast time, the nowcast skills of the deep learning models were gradually exhibited. For all four seasons, the TrajGRU‐based model showed superior performance over the ConvLSTM‐based model and the benchmarks. In spring, autumn and winter, the results yielded by the ConvLSTM‐based model were second only to those of the TrajGRU‐based model. However, in summer, the ConvLSTM‐based model did not outperform the persistence. The results of the optical flow model worsened significantly with increasing nowcast time. In contrast to the persistence, the optical flow model had almost no nowcast skills after 40 min.

show abstract

Tropical Overshooting Cloud-Top Height Retrieval from Himawari-8 Imagery Based on Random Forest Model

Cited by 6 publications

References 57 publications

Remote Sensing Retrieval of Cloud Top Height Using Neural Networks and Data from Cloud-Aerosol Lidar with Orthogonal Polarization

Remote Sensing Retrieval of Cloud Top Height Using Neural Networks and Data from Cloud-Aerosol Lidar with Orthogonal Polarization

Advancing Cloud Classification Over the Tibetan Plateau: A New Algorithm Reveals Seasonal and Diurnal Variations

Nowcast for cloud top height from Himawari‐8 data based on deep learning algorithms

Contact Info

Product

Resources

About