The New Improved ZHD and Weighted Mean Temperature Models Based on GNSS and Radiosonde Data Using GPT3 and Fourier Function
Compared to the zenith hydrostatic delay (ZHD) obtained from the Saastamonien model based on in-situ measured meteorological (IMM) data and radiosonde-derived weighted mean temperature (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline">...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/13/10/1648 |
| _version_ | 1827651741504503808 |
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| author | Li Li Ying Gao Siyi Xu Houxian Lu Qimin He Hang Yu |
| author_facet | Li Li Ying Gao Siyi Xu Houxian Lu Qimin He Hang Yu |
| author_sort | Li Li |
| collection | DOAJ |
| description | Compared to the zenith hydrostatic delay (ZHD) obtained from the Saastamonien model based on in-situ measured meteorological (IMM) data and radiosonde-derived weighted mean temperature (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula>), the ZHD and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> deviations of the GPT3 model have shown obvious periodic trends. This article analyzed the seasonal variations of GPT3-ZHD and GPT3-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> during the 2016–2020 period in the Yangtze River Delta region, and the new improved ZHD and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> models were established by the multi-order Fourier function. The precision of the improved-ZHD model was verified using IMM-ZHD products from 7 GNSS stations during the 2016–2020 period. Furthermore, the precisions of improved <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> and precipitable water vapor (PWV) were verified by radiosonde-derived <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> and PWV in the 2016–2019 period. Compared with the IMM-ZHD and GNSS-PWV products, the mean Bias and RMS of GPT3-ZHD are −0.5 mm and 2.1 mm, while those of GPT3-PWV are 2.7 mm and 11.1 mm. Compared to the radiosonde-derived <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula>, the mean Bias and RMS of GPT3-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> are −0.8 K and 3.2 K. The mean Bias and RMS of the improved-ZHD model from 2019 to 2020 are −0.1 mm and 0.5 mm, respectively, decreasing by 0.4 mm and 1.6 mm compared to the GPT3-ZHD, while those of the improved-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> are −0.6 K and 2.7 K, respectively, decreasing by 0.2 K and 0.5 K compared to GPT3-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula>. The mean Bias and RMS of PWV calculated by GNSS-ZTD, improved-ZHD, and improved-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> are 0.5 mm and 0.6 mm, respectively, compared to the GNSS-PWV, decreasing by 2.2 mm and 10.5 mm compared to the GPT3-PWV. It indicates that the improved ZHD and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> models can be used to obtain the high-precision PWV. It can be applied effectively in the retrieval of high-precision PWV in real-time in the Yangtze River Delta region. |
| first_indexed | 2024-03-09T20:43:50Z |
| format | Article |
| id | doaj.art-89215417d51e44e692019dd92d4bea38 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-09T20:43:50Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-89215417d51e44e692019dd92d4bea382023-11-23T22:51:32ZengMDPI AGAtmosphere2073-44332022-10-011310164810.3390/atmos13101648The New Improved ZHD and Weighted Mean Temperature Models Based on GNSS and Radiosonde Data Using GPT3 and Fourier FunctionLi Li0Ying Gao1Siyi Xu2Houxian Lu3Qimin He4Hang Yu5Research Center of Beidou Navigation and Remote Sensing, Suzhou University of Science and Technology, Suzhou 215009, ChinaSchool of Earth Sciences and Engineering, Hohai University, Nanjing 211100, ChinaResearch Center of Beidou Navigation and Remote Sensing, Suzhou University of Science and Technology, Suzhou 215009, ChinaResearch Center of Beidou Navigation and Remote Sensing, Suzhou University of Science and Technology, Suzhou 215009, ChinaResearch Center of Beidou Navigation and Remote Sensing, Suzhou University of Science and Technology, Suzhou 215009, ChinaResearch Center of Beidou Navigation and Remote Sensing, Suzhou University of Science and Technology, Suzhou 215009, ChinaCompared to the zenith hydrostatic delay (ZHD) obtained from the Saastamonien model based on in-situ measured meteorological (IMM) data and radiosonde-derived weighted mean temperature (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula>), the ZHD and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> deviations of the GPT3 model have shown obvious periodic trends. This article analyzed the seasonal variations of GPT3-ZHD and GPT3-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> during the 2016–2020 period in the Yangtze River Delta region, and the new improved ZHD and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> models were established by the multi-order Fourier function. The precision of the improved-ZHD model was verified using IMM-ZHD products from 7 GNSS stations during the 2016–2020 period. Furthermore, the precisions of improved <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> and precipitable water vapor (PWV) were verified by radiosonde-derived <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> and PWV in the 2016–2019 period. Compared with the IMM-ZHD and GNSS-PWV products, the mean Bias and RMS of GPT3-ZHD are −0.5 mm and 2.1 mm, while those of GPT3-PWV are 2.7 mm and 11.1 mm. Compared to the radiosonde-derived <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula>, the mean Bias and RMS of GPT3-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> are −0.8 K and 3.2 K. The mean Bias and RMS of the improved-ZHD model from 2019 to 2020 are −0.1 mm and 0.5 mm, respectively, decreasing by 0.4 mm and 1.6 mm compared to the GPT3-ZHD, while those of the improved-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> are −0.6 K and 2.7 K, respectively, decreasing by 0.2 K and 0.5 K compared to GPT3-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula>. The mean Bias and RMS of PWV calculated by GNSS-ZTD, improved-ZHD, and improved-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> are 0.5 mm and 0.6 mm, respectively, compared to the GNSS-PWV, decreasing by 2.2 mm and 10.5 mm compared to the GPT3-PWV. It indicates that the improved ZHD and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>T</mi><mi>m</mi></msub></mrow></semantics></math></inline-formula> models can be used to obtain the high-precision PWV. It can be applied effectively in the retrieval of high-precision PWV in real-time in the Yangtze River Delta region.https://www.mdpi.com/2073-4433/13/10/1648GPT3Fourier functionzenith hydrostatic delay (ZHD)weighted mean temperature (<i>T<sub>m</sub></i>)precipitable water vapor (PWV) |
| spellingShingle | Li Li Ying Gao Siyi Xu Houxian Lu Qimin He Hang Yu The New Improved ZHD and Weighted Mean Temperature Models Based on GNSS and Radiosonde Data Using GPT3 and Fourier Function Atmosphere GPT3 Fourier function zenith hydrostatic delay (ZHD) weighted mean temperature (<i>T<sub>m</sub></i>) precipitable water vapor (PWV) |
| title | The New Improved ZHD and Weighted Mean Temperature Models Based on GNSS and Radiosonde Data Using GPT3 and Fourier Function |
| title_full | The New Improved ZHD and Weighted Mean Temperature Models Based on GNSS and Radiosonde Data Using GPT3 and Fourier Function |
| title_fullStr | The New Improved ZHD and Weighted Mean Temperature Models Based on GNSS and Radiosonde Data Using GPT3 and Fourier Function |
| title_full_unstemmed | The New Improved ZHD and Weighted Mean Temperature Models Based on GNSS and Radiosonde Data Using GPT3 and Fourier Function |
| title_short | The New Improved ZHD and Weighted Mean Temperature Models Based on GNSS and Radiosonde Data Using GPT3 and Fourier Function |
| title_sort | new improved zhd and weighted mean temperature models based on gnss and radiosonde data using gpt3 and fourier function |
| topic | GPT3 Fourier function zenith hydrostatic delay (ZHD) weighted mean temperature (<i>T<sub>m</sub></i>) precipitable water vapor (PWV) |
| url | https://www.mdpi.com/2073-4433/13/10/1648 |
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