All-sky infrared radiance data assimilation of FY-4A AGRI with different physical parameterizations for the prediction of an extremely heavy rainfall event

Xu, Dongmei; Zhang, Xuewei; Liu, Zhiquan; Shen, Feifei

doi:10.1016/j.atmosres.2023.106898

Cited by 19 publications

(16 citation statements)

References 66 publications

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“…The FY-4 satellite marks a significant advancement in meteorological research and technology [ 21 ]. Compared to similar international satellites, FY-4A is equipped with 14 imaging bands in its AGRI, on par with international standards.…”

Section: Methodsmentioning

confidence: 99%

Error Model for the Assimilation of All-Sky FY-4A/AGRI Infrared Radiance Observations

Pu,

2024

Sensors

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The Advanced Geostationary Radiation Imager (AGRI) carried by the FengYun-4A (FY-4A) satellite enables the continuous observation of local weather. However, FY-4A/AGRI infrared satellite observations are strongly influenced by clouds, which complicates their use in all-sky data assimilation. The presence of clouds leads to increased uncertainty, and the observation-minus-background (O−B) differences can significantly deviate from the Gaussian distribution assumed in the variational data assimilation theory. In this study, we introduce two cloud-affected (Ca) indices to quantify the impact of cloud amount and establish dynamic observation error models to address biases between O−B and Gaussian distributions when assimilating all-sky data from FY-4A/AGRI observations. For each Ca index, we evaluate two dynamic observation error models: a two-segment and a three-segment linear model. Our findings indicate that the three-segment linear model we propose better conforms to the statistical characteristics of FY-4A/AGRI observations and improves the Gaussianity of the O−B probability density function. Dynamic observation error models developed in this study are capable of handling cloud-free or cloud-affected FY-4A/AGRI observations in a uniform manner without cloud detection.

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Section: Methodsmentioning

confidence: 99%

Error Model for the Assimilation of All-Sky FY-4A/AGRI Infrared Radiance Observations

Pu,

2024

Sensors

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“…The vertical layers in the WRF model are set to 41, and the model top is at 50 hPa. The parameterization scheme configuration of the WRF model refers to multiple classic schemes (Liu et al, 2019;Shen et al, 2021;Shen et al, 2022;Wang et al, 2023;Xu et al, 2023), i.e., the Thompson microphysics scheme (Thompson et al, 2008), the Rapid Radiative Transfer Model longwave radiation scheme (Mlawer et al, 1997), the Dudhia shortwave radiation scheme (Dudhia, 1989), the Mesoscale Model 5 Monin-Obukhov near-surface layer scheme (Chen and Dudhia, 2001), the Noah Land Surface Model scheme (Ek et al, 2003) and the Yonsei University boundary layer scheme (Hong et al, 2006).…”

Section: Numerical Model and Data Assimilation Methodsmentioning

confidence: 99%

Prediction model for transmission line icing based on data assimilation and model integration

Wang,

Shen,

Jin

et al. 2024

Front. Environ. Sci.

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With the increase of extreme weather events such as cold waves, power transmission line icing has become more and more severe, affecting the safe and stable operation of power systems. Thus, icing prediction has become crucial for power grids. In this study, we collect multi-source data including the historical observations of meteorological elements and transmission line icing in Sichuan during 2017–2019, and develop an artificial intelligence (artificial intelligence)-based integrated model to achieve icing thickness prediction according to meteorological elements. Using the Weather Research and Forecasting model and the three-dimensional variational data assimilation method, we analyze the weather conditions in Sichuan of China during the 2020 winter, and obtain the high-precision meteorological element fields that are related to icing prediction. The forecasted meteorological elements are then combined with the AI-based integrated model to predict icing conditions, assisting in the warning of transmission line icing. The results indicate that the AI-based integrated model displays superior performance on the accurate prediction of icing thickness in the test set, with only two samples having prediction errors of more than 3 mm. Data assimilation can effectively improve the forecast accuracy of meteorological elements near icing observation stations and thus enhance the accuracy of icing thickness prediction. In particular, icing thickness prediction is remarkably improved at Gaoqiao, Laolinkou and Erlangshan stations.

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“…We have extended this work to include multiple WV channels available in advanced sensors like the FY-4A Advanced Geostationary Radiation Imager (AGRI). Additionally, our study, conducted over a month-long period, contrasts with the latest case studies by other researchers (Shi et al, 2023;Xu, Zhang, et al, 2023). Furthermore, the key to this work is the use of the Community Radiative Transfer Model (CRTM) observation operator, which allows for the inclusion of five different types of hydrometeors (cloud water, ice, rain, snow, and graupel) as independent control variables.…”

Section: Introductionmentioning

confidence: 99%

All‐sky assimilation of FY‐4A AGRI water vapor channels: An observing system experiment study for south Asian monsoon prediction

Niu,

Tang,

Wang

2024

Quart J Royal Meteoro Soc

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In this study, a month‐long parallel experiment is conducted to assess the assimilation effects of all‐sky radiance (ASR) versus clear‐sky radiance (CSR) in the single and multiple water vapor (WV) channels provided by the Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun‐4A (FY‐4A) satellite. The Weather Research and Forecasting model data assimilation system (WRFDA) is utilized for assimilating the FY‐4A AGRI WV observations. Compared with the CSR, the assimilated data of the ASR increases by ~1.7 times in WV channel 9 and around 2.4 times in the multiple WV channels (channels 9 and 10). Both the CSR and ASR assimilation results demonstrate that the simulated brightness temperature (TB) from the WRF analyses is closer to the TB observations from Sondeur Atmospherique du Profil d'Humidite Intertropicale par Radiometrie (SAPHIR) channels 1–2 and Microwave Humidity Sounder (MHS) channel 3, as compared with the control experiment where only conventional data are assimilated. Furthermore, a larger improvement is noted in assimilation of multiple WV channel against assimilation of a single WV channel for both CSR and ASR runs. Overall, the analyses and forecasts generated from the ASR run exhibit superior performance compared with the CSR run when verified against SAPHIR and MHS measurements, as well as the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) analyses. The verification of the rainfall predicted by the WRF against the Global Satellite Mapping of Precipitation (GSMaP) products over India indicates that the assimilation of the ASR in multiple WV channels has the largest impact on rainfall prediction. Moreover, the ASR run demonstrates an accurate prediction of heavy rainfall events that are missed in the control experiment and underestimated in the CSR run.

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All-sky infrared radiance data assimilation of FY-4A AGRI with different physical parameterizations for the prediction of an extremely heavy rainfall event

Cited by 19 publications

References 66 publications

Error Model for the Assimilation of All-Sky FY-4A/AGRI Infrared Radiance Observations

Error Model for the Assimilation of All-Sky FY-4A/AGRI Infrared Radiance Observations

Prediction model for transmission line icing based on data assimilation and model integration

All‐sky assimilation of FY‐4A AGRI water vapor channels: An observing system experiment study for south Asian monsoon prediction

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