Impact of assimilating atmospheric motion vectors from Himawari-8 and clear-sky radiance from FY-4A GIIRS on binary typhoons

Xie, Qian; Li, Deqin; Yang, Yi; Ma, Yuanyuan; Pan, Xiao; Chen, Min

doi:10.1016/j.atmosres.2022.106550

Cited by 6 publications

(3 citation statements)

References 57 publications

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“…The launch of China's Fengyun satellite series, particularly FY-4A with its advanced geosynchronous radiation imager (AGRI), has significantly enhanced the temporal and spatial resolution and increased the volume of AMV data compared with the FY-2 series [7][8][9]. Extensive research and applications have been performed based on this advancement.…”

Section: Introductionmentioning

confidence: 99%

A Deep-Learning-Based Error-Correction Method for Atmospheric Motion Vectors

Cao,

Leng,

Zhao

et al. 2024

Remote Sensing

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Atmospheric motion vectors, which can be used to infer wind speed and direction based on the trajectory of cloud movement, are instrumental in enhancing atmospheric wind-field insights, contributing notably to wind-field optimization and forecasting. However, a widespread problem with vector data is their inaccuracy, which, when coupled with the mediocre effectiveness of existing correction methods, limits their practical utility in forecasting, often falling short of expectations. Deep-learning techniques are used to refine atmospheric motion vector data from the FY-4A satellite, notably enhancing data quality. Post-training data undergoes a thorough analysis using a quality evaluation function, followed by its integration into a numerical weather prediction system in order to conduct forecasting experiments. Results indicate a marked improvement in data quality post-error correction by the model, characterized by a significant reduction in root mean square error and a notable increase in correlation coefficients. Furthermore, refined data demonstrate a considerable enhancement in the accuracy of meteorological element forecasts, particularly for Asian and Western Pacific regions.

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Section: Introductionmentioning

confidence: 99%

A Deep-Learning-Based Error-Correction Method for Atmospheric Motion Vectors

Cao,

Leng,

Zhao

et al. 2024

Remote Sensing

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“…Despite being an experimental mission, the instrument FY‐4A/GIIRS offers a rare early opportunity to get ready for the assimilation of GEO‐hyperspectral radiances in NWP models. Recent studies (Yin et al ., 2022; Xie et al ., 2023; Zhang et al ., 2023) demonstrate that assimilation of the clear‐sky FY‐4A GIIRS radiance is significant in regional NWP models. In terms of satellite data assimilation, it is assumed that the observation‐minus‐background (O − B) values, follow a Gaussian unbiased distribution (Dee, 2005).…”

Section: Introductionmentioning

confidence: 99%

Performances between the FY‐4A/GIIRS and FY‐4B/GIIRS Long‐Wave InfraRed (LWIR) channels under clear‐sky and all‐sky conditions

Niu

Zhang

Yang³

et al. 2023

Quart J Royal Meteoro Soc

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The observation‐minus‐background (O − B) bias characteristics are compared for the Long‐Wave InfraRed (LWIR) channels of the Geostationary Interferometric Infrared Sounder (GIIRS) on board Fengyun‐4A (FY‐4A) and Fengyun‐4B (FY‐4B) satellites under clear‐sky and all‐sky conditions. Under the clear‐sky condition, the data quality of the FY‐4B/GIIRS is better than that of the FY‐4A/GIIRS. Specifically, the standard deviations (<1 K) of the O − B values for FY‐4B/GIIRS LWIR channels are smaller than those (>1 K) of the FY‐4A/GIIRS, especially in carbon dioxide (CO2) and ozone (O3) absorption bands. The diurnal variations of the mean O − B biases for the O3 band of the FY‐4B/GIIRS are less than those of FY‐4A/GIIRS. To our surprise, the diurnal variations of O − B biases for the FY‐4B/GIIRS and FY‐4A/GIIRS channels show an obvious antiphase characteristic in the CO2 band, which could be caused by diurnal environmental field variations from the different geostationary (GEO) platforms. In addition, the FY‐4B/GIIRS channel 7 (703.75 cm−1) has smaller pixel‐dependent biases than those of FY‐4A/GIIRS. Under the all‐sky condition, the observation and cloudy simulation (Bcloudy$$ {\mathrm{B}}_{\mathrm{cloudy}} $$) for channels 7 (703.75 cm−1), 36 (721.875 cm−1), and 320 (900 cm−1) of the FY‐4B/GIIRS are more consistent than those of FY‐4A/GIIRS with smaller root‐mean‐square errors (RMSEs), which also indicates a better observation quality in the cloudy area.

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“…It enables the high temporal and spatial resolution detection of weather systems over China and its surrounding areas and helps to obtain a more detailed atmospheric vertical structure, such as retrieving atmospheric temperature profiles for 1 km layers and moisture profiles for 2 km layers [14,15]. Advancements such as the development of the fast radiative transfer model [16], the evaluation of bias characteristics [17], and the development of retrieval algorithms [18][19][20] have progressed the application of FY-4A/GIIRS data in NWP models [21][22][23]. Atmospheric temperature and humidity profile products retrieved from spaceborne sounders make noteworthy contributions to weather monitoring and forecasting [24,25].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Temperature and Humidity Profiles Retrieved from Fengyun-4B and Implications for Typhoon Assimilation and Forecasting

Yang,

Chen,

Bai

et al. 2023

Remote Sensing

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Fengyun-4B (FY-4B) is the first operational satellite from China’s latest generation of geostationary meteorological satellites. It is equipped with the Geostationary Interferometric Infrared Sounder (GIIRS), which is able to obtain highly accurate atmospheric temperature and humidity profiles through hyperspectral detection in long- and mid-wave infrared spectral bands. In this study, the accuracy of the FY-4B/GIIRS temperature and humidity profile retrievals over two months is evaluated using radiosonde observations and ERA5 reanalysis data. We go a step further to investigate the impact of the satellite retrievals on assimilation and forecasts for Typhoons Chaba and Ma-on in 2022. Results reveal that the root-mean-square difference (RMSD) for the FY-4B/GIIRS temperature and humidity profile retrievals were within 1 K and 1.5 g/kg, respectively, demonstrating high overall accuracy. Moreover, assimilating temperature and humidity profiles from FY-4B/GIIRS positively impacts model analysis and prediction, improving typhoon track and intensity forecasts. Additionally, improvements have been discovered in predicting precipitation, particularly with high-magnitude rainfall events.

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Impact of assimilating atmospheric motion vectors from Himawari-8 and clear-sky radiance from FY-4A GIIRS on binary typhoons

Cited by 6 publications

References 57 publications

A Deep-Learning-Based Error-Correction Method for Atmospheric Motion Vectors

A Deep-Learning-Based Error-Correction Method for Atmospheric Motion Vectors

Performances between the FY‐4A/GIIRS and FY‐4B/GIIRS Long‐Wave InfraRed (LWIR) channels under clear‐sky and all‐sky conditions

Evaluation of Temperature and Humidity Profiles Retrieved from Fengyun-4B and Implications for Typhoon Assimilation and Forecasting

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