PCSSR‐DNNWA: A Physical Constraints Based Surface Snowfall Rate Retrieval Algorithm Using Deep Neural Networks With Attention Module

Yan, Songkun; Ma, Zhanshan; Li, Xiaoqing; Hu, Hao; Xu, Jintao; Ji, Qingwen; Weng, Fuzhong

doi:10.1029/2023gl103923

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…Therefore, frequencies of approximately 90 and 160 GHz are suitable for retrieving information about snowfall. Therefore, higher‐frequency window channels, such as 166 GHz, as well as profile information captured at 118 GHz, are often incorporated to assist in snowfall retrieval (Bauer & Mugnai, 2003; Levizzani et al., 2011; Yan et al., 2023).…”

Section: Retrieval Algorithmmentioning

confidence: 99%

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A Machine Learning Method to Retrieve Global Rainfall and Snowfall Rates From the Passive Microwave Observations of FY‐3E

Zhao,

Wang,

2024

JGR Atmospheres

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Passive microwave radiometers onboard satellites rely on the received upwelling radiation to retrieve precipitation, which is a mixed signal from the surface, atmosphere and precipitation hydrometeors. Liquid precipitation droplets increase the upwelling radiation from the surface at lower frequencies, while ice particles cause a decrease in upwelling radiation at higher frequencies. The task of the retrieval algorithm is to identify the precipitation phase and to isolate the signal of precipitation from that of the surface. This study develops a machine learning method to retrieve rainfall and snowfall rates based on observations from the Microwave Hydrometer Sounder and Microwave Temperature Sounder onboard FY‐3E. Self‐organized mapping (SOM) is selected to classify the precipitation and underlying surface types, and an artificial neural network (ANN) is subsequently used to relate the brightness temperature to the precipitation rate for the clusters derived from the SOM. The half‐hour product of the Integrated Multi‐Satellite Retrieval for Global Precipitation Measurement (IMERG) is used to train the ANN. To address the issue that number of heavy precipitation samples are not enough for training, the simulation of radiative transfer for TOVS is used as a supplement to heavy rain samples. The SOM‐ANN algorithm outperforms the IMERG and Goddard profiling algorithm (GPROF) retrieval products in both rainfall and snowfall retrieval. Compared with the hourly observations at ∼4,400 stations during a 2‐year period, the root mean square errors of SOM‐ANN proposed here are 1.06 and 0.34 mm/hr for the rainfall and snowfall rates, which are better than those of IMERG (1.23 and 0.42 mm/hr) and GPROF (1.22 and 0.44 mm/hr).

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Section: Retrieval Algorithmmentioning

confidence: 99%

“…However, in machine learning, these channels can be maximally utilized, exploiting large sample sizes and nonlinear fitting. This is the advantage of using machine learning to retrieve precipitation (Orescanin et al., 2022; Yan et al., 2023). Therefore, in our training process, we also include the MWTS channels as inputs.…”

Section: Retrieval Algorithmmentioning

confidence: 99%

A Machine Learning Method to Retrieve Global Rainfall and Snowfall Rates From the Passive Microwave Observations of FY‐3E

Zhao,

Wang,

2024

JGR Atmospheres

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Gaussian mutation-alpine skiing optimization algorithm-recurrent attention unit-gated recurrent unit-extreme learning machine model: an advanced predictive model for predicting evaporation

Ehteram,

Barzegari Banadkooki,

Afshari Nia

2024

Stoch Environ Res Risk Assess

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A Machine Learning Approach for Deriving Atmospheric Temperatures and Typhoon Warm Cores From FY‐3E MWTS‐3 Observations

Niu,

Zou

2024

Journal of Geophysical Research: Machine Learning and Computati

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Machine learning has gained an increasing popularity in the fields of satellite retrieval and numerical weather modeling. In this study, machine‐learning (ML) neural‐network (NN) models are utilized to retrieve atmospheric temperatures from observations of brightness temperature from the Microwave Temperature Sounder‐3 (MWTS‐3) onboard the China's first dawn‐dusk polar‐orbiting satellite Fengyun (FY)‐3E, with the ERA5 reanalysis serving as training data sets. The root mean square errors of the ML‐retrieved temperatures at all pressure levels are smaller than those obtained by a previously used traditional linear regression method compared to the ERA5 reanalysis over global oceans as well as radiosonde observations over land. Less than a 1‐week period of training data is usually sufficient for an ML NN model to converge in less than 50–100 iterations. The shortest time period of training data is 3‐days right before the testing data period. While the horizontal patterns and temporal evolutions of the ML‐retrieved warm cores of Typhoon Malakas (2022) and Typhoon Haikui (2023) in the upper troposphere compared favorably with those obtained by traditional regression methods as well as the ERA5 reanalysis in the testing periods. The vertical structures of ML‐retrieved warm cores extend further down to the middle and lower troposphere while those from the traditional regression method are confined in the upper troposphere. A comparison among ML results with additional training data sets across different seasons confirms the above conclusion.

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PCSSR‐DNNWA: A Physical Constraints Based Surface Snowfall Rate Retrieval Algorithm Using Deep Neural Networks With Attention Module

Cited by 3 publications

References 27 publications

A Machine Learning Method to Retrieve Global Rainfall and Snowfall Rates From the Passive Microwave Observations of FY‐3E

A Machine Learning Method to Retrieve Global Rainfall and Snowfall Rates From the Passive Microwave Observations of FY‐3E

Gaussian mutation-alpine skiing optimization algorithm-recurrent attention unit-gated recurrent unit-extreme learning machine model: an advanced predictive model for predicting evaporation

A Machine Learning Approach for Deriving Atmospheric Temperatures and Typhoon Warm Cores From FY‐3E MWTS‐3 Observations

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