Evaluation of Cloud Mask and Cloud Top Height from Fengyun-4A with MODIS Cloud Retrievals over the Tibetan Plateau

Xu, Wenduo; Lyu, Daren

doi:10.3390/rs13081418

Cited by 8 publications

(2 citation statements)

References 64 publications

(123 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quality of the data is comparable to the accuracy of similar cloud products from Himawari-8 [12]. These FY-4A products have been used to study the microphysical evolution of clouds [13][14][15][16].…”

Section: Introductionmentioning

confidence: 81%

FY-4A Measurement of Cloud-Seeding Effect and Validation of a Catalyst T&D Algorithm

Yan,

Zhou,

et al. 2024

Atmosphere

View full text Add to dashboard Cite

The transport and dispersion (T&D) of catalyst particles seeded by weather modification aircraft is crucial for assessing their weather modification effects. This study investigates the capabilities of the Chinese geostationary weather satellite FY-4A for identifying the physical response of cloud seeding with AgI-based catalysts and continuously monitoring its evolution for a weather event that occurred on 15 December 2019 in Henan Province, China. Satellite measurements are also used to verify an operational catalyst T&D algorithm. The results show that FY-4A exhibits a remarkable capability of identifying the cloud-seeding tracks and continuously tracing their evolution for a period of over 3 h. About 60 min after the cloud seeding, the cloud crystallization track became clear in the FY-4A tri-channel composite cloud image and lasted for about 218 min. During this time period, the cloud track moved with the cloud system about 153 km downstream (northeast of the operation area). An operational catalyst T&D model was run to simulate the cloud track, and the outputs were extensively compared with the satellite observations. It was found that the forecast cloud track closely agreed with the satellite observations in terms of the track widths, morphology, and movement. Finally, the FY-4A measurements show that there were significant differences in the microphysical properties across the cloud track. The effective cloud radius inside the cloud track was up to 15 μm larger than that of the surrounding clouds; the cloud optical thickness was about 30 μm smaller; and the cloud-top heights inside the cloud track were up to 1 km lower. These features indicate that the cloud-seeding catalysts led to the development of ice-phase processes within the supercooled cloud, with the formation of large ice particles and some precipitation sedimentation.

show abstract

Section: Introductionmentioning

confidence: 81%

FY-4A Measurement of Cloud-Seeding Effect and Validation of a Catalyst T&D Algorithm

Yan,

Zhou,

et al. 2024

Atmosphere

View full text Add to dashboard Cite

show abstract

“…To quantitatively compare the new method with MODIS cloud products, we introduce three statistical scores: the probability of detection (POD), false-alarm ratio (FAR), and hit rate (HR). The definitions of the POD, FAR, and HR of binary events (i.e., clear sky/cloudy) are as follows [38]:…”

Section: Accuracy Of the New Cloud Detection Methodsmentioning

confidence: 99%

Development and Evaluation of a New Method for AMSU-A Cloud Detection over Land

Qin

2021

Remote Sensing

View full text Add to dashboard Cite

Satellite data are the main source of information for operational data assimilation systems, and Advanced Microwave Sounding Unit-A (AMSU-A) data are one of the types of satellite data that contribute most to the reduction of numerical forecast errors. However, the assimilation of AMSU-A data over land lags behind that over the ocean. In this respect, the accuracy of cloud detection over land is one of the factors affecting the assimilation of AMSU-A data, especially for the window and low-peaking channel (23–53.59 GHz and 89 GHz) data. Strong surface emissivity and high spatial and temporal variability make it difficult to distinguish between the radiative contributions of clouds and the atmosphere. Based on the differences in the response characteristics of different channels to clouds, five AMSU-A window and low-peaking channels (channels 1–4 and 15) were selected to develop a new index for cloud detection over land. Case studies showed that the AMSU-A cloud index can detect most of the convective clouds; additionally, by further matching the MHS (Microwave Humidity Sounder) cloud detection index, we can effectively distinguish between cloudy and clear-sky observations. Batch test results also verified the accuracy and stability of the new cloud detection method. By referring to the MODIS (Moderate Resolution Imaging Spectroradiometer) cloud product, the POD (probability of detection) of the cloud fields of view with the new method was nearly 84%. By using the new cloud detection method to remove the cloudy data, the bias and standard deviation of the observation-minus-simulated brightness temperature (O−B) were significantly reduced, with the bias of O−B for channels 2–4 being below 1.0 K and the standard deviation of channels 5 and 6 being nearly 1.0 K.

show abstract

An integrated framework for estimating the hourly all-time cloudy-sky surface long-wave downward radiation for Fengyun-4A/AGRI

Zeng,

Cheng,

Sun

et al. 2024

Remote Sensing of Environment

View full text Add to dashboard Cite

Evaluation of Cloud Mask and Cloud Top Height from Fengyun-4A with MODIS Cloud Retrievals over the Tibetan Plateau

Cited by 8 publications

References 64 publications

FY-4A Measurement of Cloud-Seeding Effect and Validation of a Catalyst T&D Algorithm

FY-4A Measurement of Cloud-Seeding Effect and Validation of a Catalyst T&D Algorithm

Development and Evaluation of a New Method for AMSU-A Cloud Detection over Land

An integrated framework for estimating the hourly all-time cloudy-sky surface long-wave downward radiation for Fengyun-4A/AGRI

Contact Info

Product

Resources

About