Wide-Area Retrieval of Water Vapor Field Using an Improved Node Parameterization Tomography

Chen, Biyan; Liu, Jin; Wang, Jinyong; Wu, Jin; Wang, Wei

doi:10.1109/lgrs.2023.3293824

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…This dataset can be downloaded from https://cds.climate.copernicus.eu/ (Albergel et al, 2018). ERA5 is renowned for its superior accuracy compared to its predecessor, ERA-Interim, and has gained widespread usage in meteorological research (Hersbach et al, 2020;Lu et al, 2023;Chen et al, 2023). Moreover, the monthly averaged dataset, in terms of accuracy, rivals the daily dataset while demonstrating greater stability (Dogan and Erdogan, 2022).…”

Section: Era5 Pwvmentioning

confidence: 99%

A Grid Model for Vertical Correction of Precipitable Water Vapor over the Chinese Mainland and Surrounding Areas Using Random Forest

Li,

Wang,

Liu

et al. 2023

Preprint

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Abstract. Various ground-based observing techniques provide precipitable water vapor (PWV) products with different spatial resolutions. To effectively integrate these products, especially in terms of vertical orientation, spatial interpolation is essential. In this context, we have developed a model to characterize PWV variation with altitude in the study area. Our model, known as RF-PWV (a PWV vertical correction grid model with a 1° x 1° resolution), is constructed using random forest based on the relationship between PWV differences from the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) monthly average hourly data and height differences and time. When validated against 1-h ERA5 PWV profiles, RF-PWV exhibits a 99.84 % reduction in Bias and a 63.41 % decrease in RMSE compared to the most recent model, C-PWVC1. Furthermore, when validated against radiosonde data, RF-PWV shows a 96.36 % reduction in Bias and a 5 % decrease in RMSE compared to C-PWVC1. Additionally, RF-PWV outperforms C-PWVC1 in terms of resistance to seasonal and height differences interference. The model eliminates the need for meteorological parameters, allowing for high-precision PWV vertical correction by inputting only time and height differences. Consequently, RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.

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Section: Era5 Pwvmentioning

confidence: 99%

A Grid Model for Vertical Correction of Precipitable Water Vapor over the Chinese Mainland and Surrounding Areas Using Random Forest

Li,

Wang,

Liu

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Time-Dependent Ionospheric Tomography Based on Two-Step Reconstruction and Node Parameterization Algorithm

Chen,

Wang,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A grid model for vertical correction of precipitable water vapor over the Chinese mainland and surrounding areas using random forest

Li,

Wang,

Liu

et al. 2024

Geosci. Model Dev.

View full text Add to dashboard Cite

Abstract. Various ground-based observing techniques provide precipitable water vapor (PWV) products with different spatial resolutions. To effectively integrate these products, especially in terms of vertical orientation, spatial interpolation is essential. In this context, we have developed a model to characterize PWV variation with altitude over our study area. Our model, known as RF-PWV (a PWV vertical correction grid model with a 1° × 1° resolution), is constructed using random forest based on the relationship between the differences in different pressure level PWV data from the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) monthly average hourly data and corresponding differences in their height differences over time. When validated against 1 h ERA5 PWV profiles, RF-PWV exhibits a 99.84 % reduction in bias and a 63.41 % decrease in the RMSE compared with the most recent model, C-PWVC1. Furthermore, when validated against radiosonde data, RF-PWV shows a 96.36 % reduction in bias and a 5 % decrease in the RMSE compared with C-PWVC1. Additionally, RF-PWV outperforms C-PWVC1 in terms of resistance to seasonal and height difference interference. The model eliminates the need for meteorological parameters, allowing for high-precision PWV vertical correction by inputting only time and height differences. Consequently, RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.

show abstract

Wide-Area Retrieval of Water Vapor Field Using an Improved Node Parameterization Tomography

Cited by 3 publications

References 17 publications

A Grid Model for Vertical Correction of Precipitable Water Vapor over the Chinese Mainland and Surrounding Areas Using Random Forest

A Grid Model for Vertical Correction of Precipitable Water Vapor over the Chinese Mainland and Surrounding Areas Using Random Forest

Time-Dependent Ionospheric Tomography Based on Two-Step Reconstruction and Node Parameterization Algorithm

A grid model for vertical correction of precipitable water vapor over the Chinese mainland and surrounding areas using random forest

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