Atmosphere

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14 pages, 3569 KiB

Open AccessArticle

An Enhanced Atmospheric Pre-Corrected Differential Absorption (APDA) Algorithm by Extending LUTs Applied to Analyze ZY1-02D Hyperspectral Images

by Hongwei Zhang, Hao Zhang, Xiaobo Zhu, Shuning Zhang, Zhonghui Ma and Xuetao Hao

Atmosphere 2023, 14(10), 1560; https://doi.org/10.3390/atmos14101560 - 13 Oct 2023

Viewed by 1164

Abstract

Water vapor is a crucial component of the atmosphere. Its absorption significantly influences remote sensing by impacting radiation signals transmitted through the atmosphere. Determining columnar water vapor (CWV) from hyperspectral remote sensing data is essential during the imagery atmospheric correction process. Over the [...] Read more.

Water vapor is a crucial component of the atmosphere. Its absorption significantly influences remote sensing by impacting radiation signals transmitted through the atmosphere. Determining columnar water vapor (CWV) from hyperspectral remote sensing data is essential during the imagery atmospheric correction process. Over the past 40 years, numerous CWV inversion algorithms have been developed, with refinements to enhance retrieval accuracy and reliability. In this study, we proposed an enhanced atmospheric pre-corrected differential absorption (APDA) algorithm. This enhancement was achieved by thoroughly analyzing water vapor absorption in relation to elevation and aerosol optical depth and extending look up tables (LUTs). The enhanced method utilizes a pre-built MODTRAN lookup table and is applied to ZY1-02D hyperspectral data from a satellite launched in 2020. We compared the inversion results of 10 ZY1-02D scenes obtained using the improved method with AERONET measurements and inversion results from commonly used atmospheric correction software, namely, FLAASH and ATCOR. The updated algorithm demonstrated a lower average error (0.0568 g·cm⁻²) and relative average error (10.49%) compared to the ATCOR software (0.17 g·cm⁻² and 40.78%, respectively) and the FLAASH module (0.13 g·cm⁻² and 30.82%, respectively). Consequently, the enhanced method outperforms traditional CWV inversion algorithms, especially at high altitudes. Full article

(This article belongs to the Special Issue New Insights in Atmospheric Water Vapor Retrieval)

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28 pages, 2893 KiB

Open AccessArticle

Investigating the Inter-Relationships among Multiple Atmospheric Variables and Their Responses to Precipitation

by Haobo Li, Suelynn Choy, Safoora Zaminpardaz, Brett Carter, Chayn Sun, Smrati Purwar, Hong Liang, Linqi Li and Xiaoming Wang

Atmosphere 2023, 14(3), 571; https://doi.org/10.3390/atmos14030571 - 16 Mar 2023

Cited by 8 | Viewed by 2589

Abstract

In this study, a comprehensive investigation into the inter-relationships among twelve atmospheric variables and their responses to precipitation was conducted. These variables include two Global Navigation Satellite Systems (GNSS) tropospheric products, eight weather variables and two time-varying parameters. Their observations and corresponding precipitation [...] Read more.

In this study, a comprehensive investigation into the inter-relationships among twelve atmospheric variables and their responses to precipitation was conducted. These variables include two Global Navigation Satellite Systems (GNSS) tropospheric products, eight weather variables and two time-varying parameters. Their observations and corresponding precipitation record over the period 2008–2019 were obtained from a pair of GNSS/weather stations in Hong Kong. Firstly, based on the correlation and regression analyses, the cross-relationships among the variables were systematically analyzed. Typically, the variables of precipitable water vapor (PWV), zenith total delay (ZTD), temperature, pressure, wet-bulb temperature and dew-point temperature have closer cross-correlativity. Next, the responses of these variables to precipitation of different intensities were investigated and some precursory information of precipitation contained in these variables was revealed. The lead times of using ZTD and PWV to detect heavy precipitation are about 8 h. Finally, by using the principal component analysis, it is shown that heavy precipitation can be effectively detected using these variables, among which, ZTD, PWV and cloud coverage play more prominent roles. The research findings can not only increase the utilization and uptake of atmospheric variables in the detection of precipitation, but also provide clues in the development of more robust precipitation forecasting models. Full article

(This article belongs to the Special Issue New Insights in Atmospheric Water Vapor Retrieval)

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18 pages, 17849 KiB

Open AccessArticle

The Sensitivityof GPS Precipitable Water Vapor Jumps to Intense Precipitation Associated with Tropical Organized Convective Systems

by Thamiris B. Campos, Luiz F. Sapucci, Cristiano Eichholz, Luiz A. T. Machado and David K. Adams

Atmosphere 2023, 14(2), 262; https://doi.org/10.3390/atmos14020262 - 28 Jan 2023

Viewed by 1914

Abstract

The Global Positioning System (GPS) consists of a constellation of satellites that transmit radio frequency signals to many users with varied applications. For meteorological purposes, the based-ground GPS receivers can provide high-quality column or precipitable water vapor (PWV), as obtained by radiosondes, but [...] Read more.

The Global Positioning System (GPS) consists of a constellation of satellites that transmit radio frequency signals to many users with varied applications. For meteorological purposes, the based-ground GPS receivers can provide high-quality column or precipitable water vapor (PWV), as obtained by radiosondes, but with high temporal resolution and low cost. A dense GPS network containing 16 ground-based receivers was installed in Belém city, Brazil, during the period 2–29 June 2011. This network provides a unique opportunity to evaluate the sensitivityof rapid increases in GPS PWV (GPS PWV jumps to the intense precipitation often associated with tropical organized convective systems. Results reveal a characteristic timescale of water vapor convergence before GPS-PWV maximum, which can be used for indicating the occurrence of precipitation associated with organized convective systems. A PWV increase of 4 mm h

^{- 1}

in a period of an hour or 30 min before the maximum peak GPS-PWV (a peak of at least 57 mm) was observed during organized convection events. The contingency table obtained indicates a probability of detection of 84% and a false alarm ratio of 25% to forecast precipitation events. These results obtained suggest that GPS-PWV jumps can be employed to predict the events associated with organized convection. Full article

(This article belongs to the Special Issue New Insights in Atmospheric Water Vapor Retrieval)

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17 pages, 3994 KiB

Open AccessArticle

Improvement and Comparison of Multi-Reference Station Regional Tropospheric Delay Modeling Method Considering the Effect of Height Difference

by Yifan Wang, Yakun Pu, Yunbin Yuan, Hongxing Zhang and Min Song

Atmosphere 2023, 14(1), 83; https://doi.org/10.3390/atmos14010083 - 31 Dec 2022

Cited by 2 | Viewed by 1500

Abstract

Tropospheric delay information is particularly important for network RTK (Network Real-time Kinematic) positioning. Conventionally, tropospheric delay information at a virtual reference station (VRS) is obtained using the linear interpolation method (LIM). However, the conventional LIM cannot work well when there is a substantial [...] Read more.

Tropospheric delay information is particularly important for network RTK (Network Real-time Kinematic) positioning. Conventionally, tropospheric delay information at a virtual reference station (VRS) is obtained using the linear interpolation method (LIM). However, the conventional LIM cannot work well when there is a substantial height difference between the rover station and the reference station. Consequently, we propose a modified linear interpolation method (MLIM) by carefully handling the height difference between the rover station and the reference station. The new MLIM method first corrects the systematic error of the double-difference (DD) tropospheric delay in the elevation direction caused by the height difference, and then utilizes the linear interpolation algorithm to obtain the tropospheric delay of the VRS station. To determine the parameters of the low-order surface model (LSM), we also propose a modified LSM (MLSM) interpolation method in the triangular network and evaluate it in the positioning domains. The two new interpolation methods are evaluated using two regional GNSS networks with obvious height disparities. Results show that the DD tropospheric delay interpolation accuracy obtained by the new MLIM and MLSM is improved by 56.5% and 78.7% on average in the two experiments compared to the conventional method. The new MLIM and MLSM are more accurate than the traditional LIM (TLIM) in cases with low elevation satellites. Additionally, the positioning accuracies are improved by using the MLIM and MLSM methods. The MLIM and MLSM outperform TLIM in the up-component by an average of 72.8% and 80.7%, respectively. Full article

(This article belongs to the Special Issue New Insights in Atmospheric Water Vapor Retrieval)

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15 pages, 3865 KiB

Open AccessArticle

The New PWV Conversion Models Based on GNSS and Meteorological Elements in the China Region

by Li Li, Xun Wang, Yun Wei and Hao Wang

Atmosphere 2022, 13(11), 1810; https://doi.org/10.3390/atmos13111810 - 31 Oct 2022

Cited by 3 | Viewed by 1880

Abstract

To address the problems of cumbersome processes, large data, and error accumulation in the calculation of conventional GNSS precipitable water volume (PWV), the multi-factor PWV conversion models were established using the multiple linear regression fitting method. This paper analyzed the correlation between PWV [...] Read more.

To address the problems of cumbersome processes, large data, and error accumulation in the calculation of conventional GNSS precipitable water volume (PWV), the multi-factor PWV conversion models were established using the multiple linear regression fitting method. This paper analyzed the correlation between PWV and zenith tropospheric delay (ZTD), surface temperature (T), and atmospheric pressure (P) based on the data from 38 GNSS stations in the China region from 2017 to 2018. The research results showed that the mean deviation of the one-factor PWV conversion model based on the GNSS-ZTD was 12.16 mm, and its RMS was 14.30 mm. After adding surface temperature as an independent variable to form the two-factor PWV conversion model, the mean deviation and RMS decreased to 9.07 mm and 11.15 mm. The mean deviation of the two-factor PWV conversion model based on atmospheric pressure and GNSS-ZTD was 0.31 mm, and its RMS was 0.39 mm. The mean deviation of the three-factor PWV conversion model based on surface temperature, atmospheric pressure, and GNSS-ZTD was 0.33 mm, and its RMS was 0.38 mm. The accuracies of the two-factor and three-factor PWV conversion models were similar. The external precision assessment of PWV conversion models was verified by 12 GNSS stations unused for the modelling establishment. The mean deviation and RMS of the two multi-factor PWV conversion models were both less than 0.16 mm and 0.33 mm, which proves their widespread applicability in the China region. Full article

(This article belongs to the Special Issue New Insights in Atmospheric Water Vapor Retrieval)

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23 pages, 9111 KiB

Open AccessArticle

Comprehensive Validation and Calibration of MODIS PWV over Mainland China

by Yanyan Zhao, Hongwei Zhao, Junqiang Li and Gongwei Xiao

Atmosphere 2022, 13(11), 1763; https://doi.org/10.3390/atmos13111763 - 26 Oct 2022

Cited by 7 | Viewed by 1885

Abstract

Although ground-based precipitable water vapor (PWV) can be obtained with a high temporal resolution and spatial resolution of tens of kilometers in an urban area using Global Navigation Satellite System (GNSS) observation, it remains fairly sparse in the vast regions over Mainland China. [...] Read more.

Although ground-based precipitable water vapor (PWV) can be obtained with a high temporal resolution and spatial resolution of tens of kilometers in an urban area using Global Navigation Satellite System (GNSS) observation, it remains fairly sparse in the vast regions over Mainland China. Satellite-derived PWV has a high spatial resolution, thereby enabling the accurate investigation of regional climate change. However, understanding the quality of satellite-derived PWV products is a prerequisite before use, which has become the focus of this study. PWV products, namely, MOD05_L2 and MYD05_L2, over the entirety of Mainland China derived from MODerate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites are validated over the period 2000–2017 using multiple sources, including GNSS, radiosonde, AErosol RObotic NETwork (AERONET), and European Center for Medium-Range Weather Forecasts ERA-Interim (ECMWF). The accuracy of MODIS PWV products is less than 4.3 mm over the entirety of Mainland China; however, it varies in four areas of South China (SC), North China (NC), Northwest China (NWC), and Tibetan Plateau (TP), separately with values ranging from 3 mm to 6 mm. A linear fit model is applied to calibrate the MODIS PWV products, and the accuracies of the corrected PWV from MODIS infrared (IR) and near-infrared (NIR) products have been improved by approximately 7.5% and 50.6%, respectively. The MODIS PWV is compared and calibrated in the four areas, and the improved accuracies vary widely. The root mean square error (RMSE) of IR PWV is approximately less than 4 mm over China except for the SC area with a value of approximately 5.3 mm after calibration, whereas the values of NIR PWV are approximately 2 mm over the entirety of Mainland China, except for the TP area, with a value of approximately 2.6 mm. The MODIS NIR PWV performs better than that of IR PWV data in most areas of Mainland China regardless of whether with calibration. The validation and calibration of MODIS water vapor products over Mainland China also indicate their capability to investigate the seasonal and annual variations as well as long-term trend changes in water vapor in China. Full article

(This article belongs to the Special Issue New Insights in Atmospheric Water Vapor Retrieval)

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16 pages, 2701 KiB

Open AccessArticle

A Combined Linear–Nonlinear Short-Term Rainfall Forecast Method Using GNSS-Derived PWV

by Zengqi Ma, Guohe Guo, Min Cai, Xuewen Chen, Wenjie Li and Liang Zhang

Atmosphere 2022, 13(9), 1381; https://doi.org/10.3390/atmos13091381 - 28 Aug 2022

Cited by 3 | Viewed by 1663

Abstract

Short-term rainfall forecast using GNSS-derived tropospheric parameters has gradually become a research hotspot in GNSS meteorology. Nevertheless, the occurrence of rainfall can be attributed to the impact of various weather factors. With only using tropospheric parameters retrieved from GNSS (such as ZTD or [...] Read more.

Short-term rainfall forecast using GNSS-derived tropospheric parameters has gradually become a research hotspot in GNSS meteorology. Nevertheless, the occurrence of rainfall can be attributed to the impact of various weather factors. With only using tropospheric parameters retrieved from GNSS (such as ZTD or PWV) for linear forecast, it could be challenging to describe the process of rainfall occurrence accurately. Unlike traditional linear algorithms, machine learning can construct better the relationship between various meteorological parameters and rainfall. Therefore, a combined linear–nonlinear short-term rainfall forecast method is proposed in this paper. In this method, the PWV time series is first linearly fitted using least squares, and rainfall events are determined based on the PWV value, PWV variation, and PWV variation rate. Then, a support vector machine (SVM) is used to establish a nonlinear rainfall forecasting model using the PWV value, air temperature, air pressure, and rainfall. Finally, the previous two rainfall forecast methods are combined to obtain the final rainfall event. To evaluate the accuracy of the proposed method, experiments were conducted utilizing the temperature, pressure, and rainfall data from ERA5. The experimental results show that, compared to existing short-term rainfall forecast models, the proposed method could significantly lower the false alarm rate (FAR) of rainfall forecasts without compromising the true detection rate (TDR), which were 26.33% and 98.66%, respectively. In addition, the proposed method was verified using measured GNSS and meteorological data from Yunmao City, Guangdong, and the TDR and FAR of the verified results were 100% and 20.2%, respectively, which were proven to apply to actual rainfall forecasts. Full article

(This article belongs to the Special Issue New Insights in Atmospheric Water Vapor Retrieval)

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15 pages, 6695 KiB

Open AccessArticle

Random Forest-Based Model for Estimating Weighted Mean Temperature in Mainland China

by Haojie Li, Junyu Li, Lilong Liu, Liangke Huang, Qingzhi Zhao and Lv Zhou

Atmosphere 2022, 13(9), 1368; https://doi.org/10.3390/atmos13091368 - 26 Aug 2022

Cited by 2 | Viewed by 1532

Abstract

The weighted mean temperature (T_m) is a vital parameter for converting zenith wet delay (ZWD) into precipitation water vapor (PWV) and plays an essential part in the Global Navigation Satellite System (GNSS) inversion of PWV. To address the inability of [...] Read more.

The weighted mean temperature (T_m) is a vital parameter for converting zenith wet delay (ZWD) into precipitation water vapor (PWV) and plays an essential part in the Global Navigation Satellite System (GNSS) inversion of PWV. To address the inability of current mainstream models to fit the nonlinear relationship between T_m and meteorological and spatiotemporal factors, whose accuracy is limited, a weighted mean temperature model using the random forest (named RF_Tm) was proposed to enhance the accuracy of the T_m predictions in mainland China. The validation with the T_m from 84 radiosonde stations in 2018 showed that the root mean square (RMS) of the RF_Tm model was reduced by 38.8%, 44.7%, and 35.5% relative to the widely used Global Pressure and Temperature 3 (GPT3) with 1° × 1°/5° × 5° versions and Bevis, respectively. The Bias and RMS of the new model in different latitude bands, various height intervals, and different times were significantly better than those of the other three comparative models. The accuracy of the new model presented a more stable adaptability. Therefore, this study provides a new idea for estimating T_m and can provide a more accurate T_m for GNSS meteorology. Full article

(This article belongs to the Special Issue New Insights in Atmospheric Water Vapor Retrieval)

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