A Refined Regional Model for Estimating Pressure, Temperature, and Water Vapor Pressure for Geodetic Applications in China

Li, Junyu; Bao, Zhejing; Yao, Yibin; Liu, Lilong; Sun, Zhangyu; Yan, Xiao‐Hai

doi:10.3390/rs12111713

Cited by 18 publications

(13 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The height information of the MODIS PWV is obtained from the Shuttle Radar Topography Mission Digital Elevation Model (STRM DEM) Version 4.1 data with a spatial resolution of 250 m [25,26]. The height system used in this study is the orthometric height, and the method of converting the ellipsoid height of the GNSS PWV to the orthometric height can be found in Li et al [27].…”

Section: Pwv Datamentioning

confidence: 99%

Modify the Accuracy of MODIS PWV in China: A Performance Comparison Using Random Forest, Generalized Regression Neural Network and Back-Propagation Neural Network

et al. 2021

View full text Add to dashboard Cite

Water vapor plays an important role in climate change and water cycling, but there are few water vapor products with both high spatial resolution and high accuracy that effectively monitor the change of water vapor. The high precision Global Navigation Satellite System (GNSS) Precipitable Water Vapor (PWV) is often used to calibrate the high spatial resolution Moderate−resolution Imaging Spectroradiometer (MODIS) PWV to produce new PWV product with high accuracy and high spatial resolution. In addition, the machine learning method has a good performance in modifying the accuracy of MODIS PWV. However, the accuracy improvement of different machine learning methods and different modeling timescale is different. In this article, we use three machine learning methods, namely, the Random Forest (RF), Generalized Regression Neural Network (GRNN), and Back−propagation Neural Network (BPNN) to calibrate MODIS PWV in 2019, at annual and monthly timescales. We also use the Multiple Linear Regression (MLR) method for comparison. The root mean squares (RMSs) at the annual timescale with the three machine learning methods are 4.1 mm (BPNN), 3.3 mm (RF), and 3.9 mm (GRNN), and the average RMSs become 2.9 mm (BPNN), 2.8 mm (RF), and 2.5 mm (GRNN) at the monthly timescale. Those results are all better than the MLR method (5.0 mm at the annual timescale and 4.6 mm at the monthly timescale). When there is an obvious variation pattern in the training sample, the RF method can capture the pattern to achieve the best results since the RF achieves the best performance at the annual timescale. Dividing such samples into several sub−samples each having higher internal consistency could further improve the performance of machine learning methods, especially for the GRNN, since GRNN achieves the best performance at the monthly timescale, and the performance of those three machine learning methods at the monthly timescale is better than that of annual timescale. The spatial and temporal variation patterns of the RMS values are significantly weakened after the modeling by machine learning methods for both three methods.

show abstract

Section: Pwv Datamentioning

confidence: 99%

Modify the Accuracy of MODIS PWV in China: A Performance Comparison Using Random Forest, Generalized Regression Neural Network and Back-Propagation Neural Network

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Since the GNSS PWV is based on the ellipsoidal height while the MODIS and ERA5 PWV are based on the orthometric height, we need to unify the height system. We use the method in Li et al [35] to make height corrections to the GNSS PWV so that all the PWV data are based on the orthometric height.…”

Section: Modis Pwvmentioning

confidence: 99%

Fusing Precipitable Water Vapor Data in CHINA at Different Timescales Using an Artificial Neural Network

et al. 2021

Self Cite

View full text Add to dashboard Cite

Global climate change has noticeable influences on the water vapor redistribution in China, which is embodied by the fact that both wetting and drying tendencies were observed across China. This poses the necessity to monitor and understand the water vapor evolution in China. However, observations of water vapor from different techniques are subjected to systematic biases, different spatiotemporal resolutions and coverages, and different accuracy, which would hamper their joint use, potentially leading to contradictory conclusions when using different techniques. Data fusion is a promising way to address this problem. Some scholars have proposed several methods to fuse multi-source PWV data in China region, such as the enhanced spatial and temporal adaptive reflectance fusion model, the hybrid PWV fusion model, and the linear calibration model. Although these models can produce PWV products with improved accuracy, they still have some shortcomings, such as no consideration for spatial or temporal variations in bias or inevitably impose some biases inaccurate information since assumptions made for interpolations are imperfect. In this study, we use the high-quality Global Navigation Satellite System (GNSS) precipitable water vapor (PWV) to calibrate and optimize the Moderate-resolution Imaging Spectroradiometer (MODIS) and the European Centre for Medium-Range Weather Forecasts ReAnalyses 5 (ERA5) PWV in 2018–2019 through a Generalized Regression Neural Network (GRNN) at annual, quarterly, and monthly timescales. Validation results demonstrate that modifying the MODIS and ERA5 PWV at the monthly timescale results in the best accuracy. In the monthly experiment, the average bias, standard deviation (STD), and root mean square (RMS) error of modified MODIS PWV are 0.0 mm, 2.6 mm, and 2.6 mm, respectively. The percentage improvement is as high as 50% in terms of RMS compared to the original MODIS PWV. It becomes 0.0 mm, 1.7 mm, and 1.7 mm for the modified ERA5 PWV and the percentage improvement is 40%. Since the biases among different products are well-calibrated and the accuracy of MODIS and ERA5 PWV is improved to the same level of GNSS PWV, we can fuse them by simply merging them. Finally, we generate a new product of PWV in China with a temporal resolution of 1 day, a spatial resolution better than 31 km, and an accuracy better than 2.7 mm, which will serve as a high-quality product for investigating the water vapor redistribution under a changing climate.

show abstract

“…This kind model including the UNB3 model [13], the GPT models [14][15][16][17], the TropGrid models [17][18][19], etc. [20,21]. The latter directly model the tropospheric delay according to its temporal characteristics, including the IGGtrop models [5,22], the GZTD models [23,24], the CTrop model [25], etc.…”

Section: Introductionmentioning

confidence: 99%

A Regional Model for Predicting Tropospheric Delay and Weighted Mean Temperature in China Based on GRAPES_MESO Forecasting Products

Cao

Zhang

et al. 2021

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Accurate tropospheric delay (TD) and weighted mean temperature (Tm) are important for Global Navigation Satellite System (GNSS) positioning and GNSS meteorology. For this purpose, plenty of empirical models have been built to provide estimates of TD and Tm. However, these models cannot resolve TD and Tm variations at synoptic timescales since they only model the average annual, semi-annual, and/or daily variations. As a result, the existed empirical models cannot perform well under extreme weather conditions. To address this limitation, we propose to estimate Zenith Hydrostatic Delay (ZHD), Zenith Wet Delay (ZWD), and Tm directly from the stratified numerical weather forecasting products of the mesoscale version of the Global/Regional Assimilation and PrEdiction System (GRAPES_MESO) of China. The GRAPES_MESO forecasting data has a temporal resolution of 3 h, which provides the opportunity to resolve the synoptic variation. However, it is found that the estimated ZWD and Tm exhibit apparent systematic deviation from in situ observation-based estimates, which is due to the inherent biases in the GRAPES_MESO data. To solve this problem, we propose to correct these biases using a linear model and a spherical cap harmonic model. The estimates after correction are termed as the “CTropGrid” products. When validated by the radiosonde data, the CTropGrid product has biases of 1.5 mm, −0.7 mm, and −0.1 K, and Root Mean Square (RMS) error of 8.9 mm, 20.2 mm, and 1.5 K for ZHD, ZWD, and Tm. Compared to the widely used GPT2w model, the CTropGrid products have improved the accuracies of ZHD, ZWD, and Tm by 11.9%, 55.6%, and 60.5% in terms of RMS. When validating the Zenith Tropospheric Delay (ZTD) products (the sum of ZHD and ZWD) using the IGS ZTD data, the CTropGrid ZTD has a bias of −0.7 mm and an RMS of 35.8 mm, which is 22.7% better than the GPT2w model in terms of RMS. Besides the accuracy improvements, the CTropGrid products well model the synoptic-scale variations of ZHD, ZWD, and Tm. Compared to the existing empirical models that only capture the tidal (seasonal and/or diurnal) variations, the CTropGrid products capture well the non-tidal variations of ZHD, ZWD, and Tm, which enhances the tropospheric delay corrections and GNSS water vapor monitoring at synoptic timescales. Therefore, the CTropGrid product is an important progress in GNSS positioning and GNSS meteorology.

show abstract

A Refined Regional Model for Estimating Pressure, Temperature, and Water Vapor Pressure for Geodetic Applications in China

Cited by 18 publications

References 37 publications

Modify the Accuracy of MODIS PWV in China: A Performance Comparison Using Random Forest, Generalized Regression Neural Network and Back-Propagation Neural Network

Modify the Accuracy of MODIS PWV in China: A Performance Comparison Using Random Forest, Generalized Regression Neural Network and Back-Propagation Neural Network

Fusing Precipitable Water Vapor Data in CHINA at Different Timescales Using an Artificial Neural Network

A Regional Model for Predicting Tropospheric Delay and Weighted Mean Temperature in China Based on GRAPES_MESO Forecasting Products

Contact Info

Product

Resources

About