An analysis of GPT2/GPT2w+Saastamoinen models for estimating zenith tropospheric delay over Asian area
The tropospheric delay is a systematic error source in the Global Navigation Satellite System (GNSS) positioning. However, without accuracy meteorological information, the quality of the Zenith Tropospheric Delay (ZTD) derived from empirical tropospheric models like Saastamoinen model will degrade,...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Jiye [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Schlagwörter: |
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| Umfang: |
9 |
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| Übergeordnetes Werk: |
Enthalten in: Posttranscriptional actions of triiodothyronine on - Bargi-Souza, Paula ELSEVIER, 2018, including COSPAR information bulletin : the official journal of the Committee on Space Research (COSPAR), a scientific committee of the International Council of Scientific Unions (ICSU), Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:59 ; year:2017 ; number:3 ; day:1 ; month:02 ; pages:824-832 ; extent:9 |
| Links: |
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| DOI / URN: |
10.1016/j.asr.2016.09.019 |
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| Katalog-ID: |
ELV014994216 |
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| 245 | 1 | 0 | |a An analysis of GPT2/GPT2w+Saastamoinen models for estimating zenith tropospheric delay over Asian area |
| 264 | 1 | |c 2017transfer abstract | |
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| 520 | |a The tropospheric delay is a systematic error source in the Global Navigation Satellite System (GNSS) positioning. However, without accuracy meteorological information, the quality of the Zenith Tropospheric Delay (ZTD) derived from empirical tropospheric models like Saastamoinen model will degrade, leading to inaccurate estimates of positions. To solve the above problem, on the basis of Global Pressure and Temperature 2/2w (GPT2/GPT2w) model, this paper conducted GPT2/GPT2w+Saastamoinen models for estimating ZTD over Asian area. As GPT2w model has two resolutions of 1 and 5 degrees, the effects of two models (GPT2_5w+S refers to GPT2w+Saastamoinen model with the resolution of 5 degree; GPT2_1w+S refers to GPT2w+Saastamoinen model with the resolution of 1 degree) were analyzed respectively. The model’s validation was carried out using the International GNSS Service (IGS) ZTD values derived from the observed data in the year 2012 at 27 IGS stations. The results show that the GPT2_1w+S model can provide tropospheric delay corrections with bias of 0.66cm and Root Mean Square (RMS) of 4.93cm, which is superior to GPT2+S model. The annual bias and RMS for the GPT2_5w+S model are slightly worse than that for the GPT2_1w+S model. For most stations, the bias and RMS show seasonal characteristics. The relation between the annual bias and RMS with latitude for the models is not obvious, and a latitude dependency between the models could not be detected. | ||
| 520 | |a The tropospheric delay is a systematic error source in the Global Navigation Satellite System (GNSS) positioning. However, without accuracy meteorological information, the quality of the Zenith Tropospheric Delay (ZTD) derived from empirical tropospheric models like Saastamoinen model will degrade, leading to inaccurate estimates of positions. To solve the above problem, on the basis of Global Pressure and Temperature 2/2w (GPT2/GPT2w) model, this paper conducted GPT2/GPT2w+Saastamoinen models for estimating ZTD over Asian area. As GPT2w model has two resolutions of 1 and 5 degrees, the effects of two models (GPT2_5w+S refers to GPT2w+Saastamoinen model with the resolution of 5 degree; GPT2_1w+S refers to GPT2w+Saastamoinen model with the resolution of 1 degree) were analyzed respectively. The model’s validation was carried out using the International GNSS Service (IGS) ZTD values derived from the observed data in the year 2012 at 27 IGS stations. The results show that the GPT2_1w+S model can provide tropospheric delay corrections with bias of 0.66cm and Root Mean Square (RMS) of 4.93cm, which is superior to GPT2+S model. The annual bias and RMS for the GPT2_5w+S model are slightly worse than that for the GPT2_1w+S model. For most stations, the bias and RMS show seasonal characteristics. The relation between the annual bias and RMS with latitude for the models is not obvious, and a latitude dependency between the models could not be detected. | ||
| 650 | 7 | |a Zenith tropospheric delay |2 Elsevier | |
| 650 | 7 | |a GPT2 model |2 Elsevier | |
| 650 | 7 | |a GPT2w model |2 Elsevier | |
| 650 | 7 | |a Saastamoinen model |2 Elsevier | |
| 700 | 1 | |a Chen, Xihong |4 oth | |
| 700 | 1 | |a Sun, Jizhe |4 oth | |
| 700 | 1 | |a Liu, Qiang |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Bargi-Souza, Paula ELSEVIER |t Posttranscriptional actions of triiodothyronine on |d 2018 |d including COSPAR information bulletin : the official journal of the Committee on Space Research (COSPAR), a scientific committee of the International Council of Scientific Unions (ICSU) |g Amsterdam [u.a.] |w (DE-627)ELV000905844 |
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10.1016/j.asr.2016.09.019 doi GBV00000000000110A.pica (DE-627)ELV014994216 (ELSEVIER)S0273-1177(16)30532-4 DE-627 ger DE-627 rakwb eng 520 620 520 DE-600 620 DE-600 610 570 VZ 44.89 bkl Liu, Jiye verfasserin aut An analysis of GPT2/GPT2w+Saastamoinen models for estimating zenith tropospheric delay over Asian area 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The tropospheric delay is a systematic error source in the Global Navigation Satellite System (GNSS) positioning. However, without accuracy meteorological information, the quality of the Zenith Tropospheric Delay (ZTD) derived from empirical tropospheric models like Saastamoinen model will degrade, leading to inaccurate estimates of positions. To solve the above problem, on the basis of Global Pressure and Temperature 2/2w (GPT2/GPT2w) model, this paper conducted GPT2/GPT2w+Saastamoinen models for estimating ZTD over Asian area. As GPT2w model has two resolutions of 1 and 5 degrees, the effects of two models (GPT2_5w+S refers to GPT2w+Saastamoinen model with the resolution of 5 degree; GPT2_1w+S refers to GPT2w+Saastamoinen model with the resolution of 1 degree) were analyzed respectively. The model’s validation was carried out using the International GNSS Service (IGS) ZTD values derived from the observed data in the year 2012 at 27 IGS stations. The results show that the GPT2_1w+S model can provide tropospheric delay corrections with bias of 0.66cm and Root Mean Square (RMS) of 4.93cm, which is superior to GPT2+S model. The annual bias and RMS for the GPT2_5w+S model are slightly worse than that for the GPT2_1w+S model. For most stations, the bias and RMS show seasonal characteristics. The relation between the annual bias and RMS with latitude for the models is not obvious, and a latitude dependency between the models could not be detected. The tropospheric delay is a systematic error source in the Global Navigation Satellite System (GNSS) positioning. However, without accuracy meteorological information, the quality of the Zenith Tropospheric Delay (ZTD) derived from empirical tropospheric models like Saastamoinen model will degrade, leading to inaccurate estimates of positions. To solve the above problem, on the basis of Global Pressure and Temperature 2/2w (GPT2/GPT2w) model, this paper conducted GPT2/GPT2w+Saastamoinen models for estimating ZTD over Asian area. As GPT2w model has two resolutions of 1 and 5 degrees, the effects of two models (GPT2_5w+S refers to GPT2w+Saastamoinen model with the resolution of 5 degree; GPT2_1w+S refers to GPT2w+Saastamoinen model with the resolution of 1 degree) were analyzed respectively. The model’s validation was carried out using the International GNSS Service (IGS) ZTD values derived from the observed data in the year 2012 at 27 IGS stations. The results show that the GPT2_1w+S model can provide tropospheric delay corrections with bias of 0.66cm and Root Mean Square (RMS) of 4.93cm, which is superior to GPT2+S model. The annual bias and RMS for the GPT2_5w+S model are slightly worse than that for the GPT2_1w+S model. For most stations, the bias and RMS show seasonal characteristics. The relation between the annual bias and RMS with latitude for the models is not obvious, and a latitude dependency between the models could not be detected. Zenith tropospheric delay Elsevier GPT2 model Elsevier GPT2w model Elsevier Saastamoinen model Elsevier Chen, Xihong oth Sun, Jizhe oth Liu, Qiang oth Enthalten in Elsevier Science Bargi-Souza, Paula ELSEVIER Posttranscriptional actions of triiodothyronine on 2018 including COSPAR information bulletin : the official journal of the Committee on Space Research (COSPAR), a scientific committee of the International Council of Scientific Unions (ICSU) Amsterdam [u.a.] (DE-627)ELV000905844 volume:59 year:2017 number:3 day:1 month:02 pages:824-832 extent:9 https://doi.org/10.1016/j.asr.2016.09.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.89 Endokrinologie VZ AR 59 2017 3 1 0201 824-832 9 045F 520 |
| spelling |
10.1016/j.asr.2016.09.019 doi GBV00000000000110A.pica (DE-627)ELV014994216 (ELSEVIER)S0273-1177(16)30532-4 DE-627 ger DE-627 rakwb eng 520 620 520 DE-600 620 DE-600 610 570 VZ 44.89 bkl Liu, Jiye verfasserin aut An analysis of GPT2/GPT2w+Saastamoinen models for estimating zenith tropospheric delay over Asian area 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The tropospheric delay is a systematic error source in the Global Navigation Satellite System (GNSS) positioning. However, without accuracy meteorological information, the quality of the Zenith Tropospheric Delay (ZTD) derived from empirical tropospheric models like Saastamoinen model will degrade, leading to inaccurate estimates of positions. To solve the above problem, on the basis of Global Pressure and Temperature 2/2w (GPT2/GPT2w) model, this paper conducted GPT2/GPT2w+Saastamoinen models for estimating ZTD over Asian area. As GPT2w model has two resolutions of 1 and 5 degrees, the effects of two models (GPT2_5w+S refers to GPT2w+Saastamoinen model with the resolution of 5 degree; GPT2_1w+S refers to GPT2w+Saastamoinen model with the resolution of 1 degree) were analyzed respectively. The model’s validation was carried out using the International GNSS Service (IGS) ZTD values derived from the observed data in the year 2012 at 27 IGS stations. The results show that the GPT2_1w+S model can provide tropospheric delay corrections with bias of 0.66cm and Root Mean Square (RMS) of 4.93cm, which is superior to GPT2+S model. The annual bias and RMS for the GPT2_5w+S model are slightly worse than that for the GPT2_1w+S model. For most stations, the bias and RMS show seasonal characteristics. The relation between the annual bias and RMS with latitude for the models is not obvious, and a latitude dependency between the models could not be detected. The tropospheric delay is a systematic error source in the Global Navigation Satellite System (GNSS) positioning. However, without accuracy meteorological information, the quality of the Zenith Tropospheric Delay (ZTD) derived from empirical tropospheric models like Saastamoinen model will degrade, leading to inaccurate estimates of positions. To solve the above problem, on the basis of Global Pressure and Temperature 2/2w (GPT2/GPT2w) model, this paper conducted GPT2/GPT2w+Saastamoinen models for estimating ZTD over Asian area. As GPT2w model has two resolutions of 1 and 5 degrees, the effects of two models (GPT2_5w+S refers to GPT2w+Saastamoinen model with the resolution of 5 degree; GPT2_1w+S refers to GPT2w+Saastamoinen model with the resolution of 1 degree) were analyzed respectively. The model’s validation was carried out using the International GNSS Service (IGS) ZTD values derived from the observed data in the year 2012 at 27 IGS stations. The results show that the GPT2_1w+S model can provide tropospheric delay corrections with bias of 0.66cm and Root Mean Square (RMS) of 4.93cm, which is superior to GPT2+S model. The annual bias and RMS for the GPT2_5w+S model are slightly worse than that for the GPT2_1w+S model. For most stations, the bias and RMS show seasonal characteristics. The relation between the annual bias and RMS with latitude for the models is not obvious, and a latitude dependency between the models could not be detected. Zenith tropospheric delay Elsevier GPT2 model Elsevier GPT2w model Elsevier Saastamoinen model Elsevier Chen, Xihong oth Sun, Jizhe oth Liu, Qiang oth Enthalten in Elsevier Science Bargi-Souza, Paula ELSEVIER Posttranscriptional actions of triiodothyronine on 2018 including COSPAR information bulletin : the official journal of the Committee on Space Research (COSPAR), a scientific committee of the International Council of Scientific Unions (ICSU) Amsterdam [u.a.] (DE-627)ELV000905844 volume:59 year:2017 number:3 day:1 month:02 pages:824-832 extent:9 https://doi.org/10.1016/j.asr.2016.09.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.89 Endokrinologie VZ AR 59 2017 3 1 0201 824-832 9 045F 520 |
| allfields_unstemmed |
10.1016/j.asr.2016.09.019 doi GBV00000000000110A.pica (DE-627)ELV014994216 (ELSEVIER)S0273-1177(16)30532-4 DE-627 ger DE-627 rakwb eng 520 620 520 DE-600 620 DE-600 610 570 VZ 44.89 bkl Liu, Jiye verfasserin aut An analysis of GPT2/GPT2w+Saastamoinen models for estimating zenith tropospheric delay over Asian area 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The tropospheric delay is a systematic error source in the Global Navigation Satellite System (GNSS) positioning. However, without accuracy meteorological information, the quality of the Zenith Tropospheric Delay (ZTD) derived from empirical tropospheric models like Saastamoinen model will degrade, leading to inaccurate estimates of positions. To solve the above problem, on the basis of Global Pressure and Temperature 2/2w (GPT2/GPT2w) model, this paper conducted GPT2/GPT2w+Saastamoinen models for estimating ZTD over Asian area. As GPT2w model has two resolutions of 1 and 5 degrees, the effects of two models (GPT2_5w+S refers to GPT2w+Saastamoinen model with the resolution of 5 degree; GPT2_1w+S refers to GPT2w+Saastamoinen model with the resolution of 1 degree) were analyzed respectively. The model’s validation was carried out using the International GNSS Service (IGS) ZTD values derived from the observed data in the year 2012 at 27 IGS stations. The results show that the GPT2_1w+S model can provide tropospheric delay corrections with bias of 0.66cm and Root Mean Square (RMS) of 4.93cm, which is superior to GPT2+S model. The annual bias and RMS for the GPT2_5w+S model are slightly worse than that for the GPT2_1w+S model. For most stations, the bias and RMS show seasonal characteristics. The relation between the annual bias and RMS with latitude for the models is not obvious, and a latitude dependency between the models could not be detected. The tropospheric delay is a systematic error source in the Global Navigation Satellite System (GNSS) positioning. However, without accuracy meteorological information, the quality of the Zenith Tropospheric Delay (ZTD) derived from empirical tropospheric models like Saastamoinen model will degrade, leading to inaccurate estimates of positions. To solve the above problem, on the basis of Global Pressure and Temperature 2/2w (GPT2/GPT2w) model, this paper conducted GPT2/GPT2w+Saastamoinen models for estimating ZTD over Asian area. As GPT2w model has two resolutions of 1 and 5 degrees, the effects of two models (GPT2_5w+S refers to GPT2w+Saastamoinen model with the resolution of 5 degree; GPT2_1w+S refers to GPT2w+Saastamoinen model with the resolution of 1 degree) were analyzed respectively. The model’s validation was carried out using the International GNSS Service (IGS) ZTD values derived from the observed data in the year 2012 at 27 IGS stations. The results show that the GPT2_1w+S model can provide tropospheric delay corrections with bias of 0.66cm and Root Mean Square (RMS) of 4.93cm, which is superior to GPT2+S model. The annual bias and RMS for the GPT2_5w+S model are slightly worse than that for the GPT2_1w+S model. For most stations, the bias and RMS show seasonal characteristics. The relation between the annual bias and RMS with latitude for the models is not obvious, and a latitude dependency between the models could not be detected. Zenith tropospheric delay Elsevier GPT2 model Elsevier GPT2w model Elsevier Saastamoinen model Elsevier Chen, Xihong oth Sun, Jizhe oth Liu, Qiang oth Enthalten in Elsevier Science Bargi-Souza, Paula ELSEVIER Posttranscriptional actions of triiodothyronine on 2018 including COSPAR information bulletin : the official journal of the Committee on Space Research (COSPAR), a scientific committee of the International Council of Scientific Unions (ICSU) Amsterdam [u.a.] (DE-627)ELV000905844 volume:59 year:2017 number:3 day:1 month:02 pages:824-832 extent:9 https://doi.org/10.1016/j.asr.2016.09.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.89 Endokrinologie VZ AR 59 2017 3 1 0201 824-832 9 045F 520 |
| allfieldsGer |
10.1016/j.asr.2016.09.019 doi GBV00000000000110A.pica (DE-627)ELV014994216 (ELSEVIER)S0273-1177(16)30532-4 DE-627 ger DE-627 rakwb eng 520 620 520 DE-600 620 DE-600 610 570 VZ 44.89 bkl Liu, Jiye verfasserin aut An analysis of GPT2/GPT2w+Saastamoinen models for estimating zenith tropospheric delay over Asian area 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The tropospheric delay is a systematic error source in the Global Navigation Satellite System (GNSS) positioning. However, without accuracy meteorological information, the quality of the Zenith Tropospheric Delay (ZTD) derived from empirical tropospheric models like Saastamoinen model will degrade, leading to inaccurate estimates of positions. To solve the above problem, on the basis of Global Pressure and Temperature 2/2w (GPT2/GPT2w) model, this paper conducted GPT2/GPT2w+Saastamoinen models for estimating ZTD over Asian area. As GPT2w model has two resolutions of 1 and 5 degrees, the effects of two models (GPT2_5w+S refers to GPT2w+Saastamoinen model with the resolution of 5 degree; GPT2_1w+S refers to GPT2w+Saastamoinen model with the resolution of 1 degree) were analyzed respectively. The model’s validation was carried out using the International GNSS Service (IGS) ZTD values derived from the observed data in the year 2012 at 27 IGS stations. The results show that the GPT2_1w+S model can provide tropospheric delay corrections with bias of 0.66cm and Root Mean Square (RMS) of 4.93cm, which is superior to GPT2+S model. The annual bias and RMS for the GPT2_5w+S model are slightly worse than that for the GPT2_1w+S model. For most stations, the bias and RMS show seasonal characteristics. The relation between the annual bias and RMS with latitude for the models is not obvious, and a latitude dependency between the models could not be detected. The tropospheric delay is a systematic error source in the Global Navigation Satellite System (GNSS) positioning. However, without accuracy meteorological information, the quality of the Zenith Tropospheric Delay (ZTD) derived from empirical tropospheric models like Saastamoinen model will degrade, leading to inaccurate estimates of positions. To solve the above problem, on the basis of Global Pressure and Temperature 2/2w (GPT2/GPT2w) model, this paper conducted GPT2/GPT2w+Saastamoinen models for estimating ZTD over Asian area. As GPT2w model has two resolutions of 1 and 5 degrees, the effects of two models (GPT2_5w+S refers to GPT2w+Saastamoinen model with the resolution of 5 degree; GPT2_1w+S refers to GPT2w+Saastamoinen model with the resolution of 1 degree) were analyzed respectively. The model’s validation was carried out using the International GNSS Service (IGS) ZTD values derived from the observed data in the year 2012 at 27 IGS stations. The results show that the GPT2_1w+S model can provide tropospheric delay corrections with bias of 0.66cm and Root Mean Square (RMS) of 4.93cm, which is superior to GPT2+S model. The annual bias and RMS for the GPT2_5w+S model are slightly worse than that for the GPT2_1w+S model. For most stations, the bias and RMS show seasonal characteristics. The relation between the annual bias and RMS with latitude for the models is not obvious, and a latitude dependency between the models could not be detected. Zenith tropospheric delay Elsevier GPT2 model Elsevier GPT2w model Elsevier Saastamoinen model Elsevier Chen, Xihong oth Sun, Jizhe oth Liu, Qiang oth Enthalten in Elsevier Science Bargi-Souza, Paula ELSEVIER Posttranscriptional actions of triiodothyronine on 2018 including COSPAR information bulletin : the official journal of the Committee on Space Research (COSPAR), a scientific committee of the International Council of Scientific Unions (ICSU) Amsterdam [u.a.] (DE-627)ELV000905844 volume:59 year:2017 number:3 day:1 month:02 pages:824-832 extent:9 https://doi.org/10.1016/j.asr.2016.09.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.89 Endokrinologie VZ AR 59 2017 3 1 0201 824-832 9 045F 520 |
| allfieldsSound |
10.1016/j.asr.2016.09.019 doi GBV00000000000110A.pica (DE-627)ELV014994216 (ELSEVIER)S0273-1177(16)30532-4 DE-627 ger DE-627 rakwb eng 520 620 520 DE-600 620 DE-600 610 570 VZ 44.89 bkl Liu, Jiye verfasserin aut An analysis of GPT2/GPT2w+Saastamoinen models for estimating zenith tropospheric delay over Asian area 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The tropospheric delay is a systematic error source in the Global Navigation Satellite System (GNSS) positioning. However, without accuracy meteorological information, the quality of the Zenith Tropospheric Delay (ZTD) derived from empirical tropospheric models like Saastamoinen model will degrade, leading to inaccurate estimates of positions. To solve the above problem, on the basis of Global Pressure and Temperature 2/2w (GPT2/GPT2w) model, this paper conducted GPT2/GPT2w+Saastamoinen models for estimating ZTD over Asian area. As GPT2w model has two resolutions of 1 and 5 degrees, the effects of two models (GPT2_5w+S refers to GPT2w+Saastamoinen model with the resolution of 5 degree; GPT2_1w+S refers to GPT2w+Saastamoinen model with the resolution of 1 degree) were analyzed respectively. The model’s validation was carried out using the International GNSS Service (IGS) ZTD values derived from the observed data in the year 2012 at 27 IGS stations. The results show that the GPT2_1w+S model can provide tropospheric delay corrections with bias of 0.66cm and Root Mean Square (RMS) of 4.93cm, which is superior to GPT2+S model. The annual bias and RMS for the GPT2_5w+S model are slightly worse than that for the GPT2_1w+S model. For most stations, the bias and RMS show seasonal characteristics. The relation between the annual bias and RMS with latitude for the models is not obvious, and a latitude dependency between the models could not be detected. The tropospheric delay is a systematic error source in the Global Navigation Satellite System (GNSS) positioning. However, without accuracy meteorological information, the quality of the Zenith Tropospheric Delay (ZTD) derived from empirical tropospheric models like Saastamoinen model will degrade, leading to inaccurate estimates of positions. To solve the above problem, on the basis of Global Pressure and Temperature 2/2w (GPT2/GPT2w) model, this paper conducted GPT2/GPT2w+Saastamoinen models for estimating ZTD over Asian area. As GPT2w model has two resolutions of 1 and 5 degrees, the effects of two models (GPT2_5w+S refers to GPT2w+Saastamoinen model with the resolution of 5 degree; GPT2_1w+S refers to GPT2w+Saastamoinen model with the resolution of 1 degree) were analyzed respectively. The model’s validation was carried out using the International GNSS Service (IGS) ZTD values derived from the observed data in the year 2012 at 27 IGS stations. The results show that the GPT2_1w+S model can provide tropospheric delay corrections with bias of 0.66cm and Root Mean Square (RMS) of 4.93cm, which is superior to GPT2+S model. The annual bias and RMS for the GPT2_5w+S model are slightly worse than that for the GPT2_1w+S model. For most stations, the bias and RMS show seasonal characteristics. The relation between the annual bias and RMS with latitude for the models is not obvious, and a latitude dependency between the models could not be detected. Zenith tropospheric delay Elsevier GPT2 model Elsevier GPT2w model Elsevier Saastamoinen model Elsevier Chen, Xihong oth Sun, Jizhe oth Liu, Qiang oth Enthalten in Elsevier Science Bargi-Souza, Paula ELSEVIER Posttranscriptional actions of triiodothyronine on 2018 including COSPAR information bulletin : the official journal of the Committee on Space Research (COSPAR), a scientific committee of the International Council of Scientific Unions (ICSU) Amsterdam [u.a.] (DE-627)ELV000905844 volume:59 year:2017 number:3 day:1 month:02 pages:824-832 extent:9 https://doi.org/10.1016/j.asr.2016.09.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.89 Endokrinologie VZ AR 59 2017 3 1 0201 824-832 9 045F 520 |
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An analysis of GPT2/GPT2w+Saastamoinen models for estimating zenith tropospheric delay over Asian area |
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The tropospheric delay is a systematic error source in the Global Navigation Satellite System (GNSS) positioning. However, without accuracy meteorological information, the quality of the Zenith Tropospheric Delay (ZTD) derived from empirical tropospheric models like Saastamoinen model will degrade, leading to inaccurate estimates of positions. To solve the above problem, on the basis of Global Pressure and Temperature 2/2w (GPT2/GPT2w) model, this paper conducted GPT2/GPT2w+Saastamoinen models for estimating ZTD over Asian area. As GPT2w model has two resolutions of 1 and 5 degrees, the effects of two models (GPT2_5w+S refers to GPT2w+Saastamoinen model with the resolution of 5 degree; GPT2_1w+S refers to GPT2w+Saastamoinen model with the resolution of 1 degree) were analyzed respectively. The model’s validation was carried out using the International GNSS Service (IGS) ZTD values derived from the observed data in the year 2012 at 27 IGS stations. The results show that the GPT2_1w+S model can provide tropospheric delay corrections with bias of 0.66cm and Root Mean Square (RMS) of 4.93cm, which is superior to GPT2+S model. The annual bias and RMS for the GPT2_5w+S model are slightly worse than that for the GPT2_1w+S model. For most stations, the bias and RMS show seasonal characteristics. The relation between the annual bias and RMS with latitude for the models is not obvious, and a latitude dependency between the models could not be detected. |
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The tropospheric delay is a systematic error source in the Global Navigation Satellite System (GNSS) positioning. However, without accuracy meteorological information, the quality of the Zenith Tropospheric Delay (ZTD) derived from empirical tropospheric models like Saastamoinen model will degrade, leading to inaccurate estimates of positions. To solve the above problem, on the basis of Global Pressure and Temperature 2/2w (GPT2/GPT2w) model, this paper conducted GPT2/GPT2w+Saastamoinen models for estimating ZTD over Asian area. As GPT2w model has two resolutions of 1 and 5 degrees, the effects of two models (GPT2_5w+S refers to GPT2w+Saastamoinen model with the resolution of 5 degree; GPT2_1w+S refers to GPT2w+Saastamoinen model with the resolution of 1 degree) were analyzed respectively. The model’s validation was carried out using the International GNSS Service (IGS) ZTD values derived from the observed data in the year 2012 at 27 IGS stations. The results show that the GPT2_1w+S model can provide tropospheric delay corrections with bias of 0.66cm and Root Mean Square (RMS) of 4.93cm, which is superior to GPT2+S model. The annual bias and RMS for the GPT2_5w+S model are slightly worse than that for the GPT2_1w+S model. For most stations, the bias and RMS show seasonal characteristics. The relation between the annual bias and RMS with latitude for the models is not obvious, and a latitude dependency between the models could not be detected. |
| abstract_unstemmed |
The tropospheric delay is a systematic error source in the Global Navigation Satellite System (GNSS) positioning. However, without accuracy meteorological information, the quality of the Zenith Tropospheric Delay (ZTD) derived from empirical tropospheric models like Saastamoinen model will degrade, leading to inaccurate estimates of positions. To solve the above problem, on the basis of Global Pressure and Temperature 2/2w (GPT2/GPT2w) model, this paper conducted GPT2/GPT2w+Saastamoinen models for estimating ZTD over Asian area. As GPT2w model has two resolutions of 1 and 5 degrees, the effects of two models (GPT2_5w+S refers to GPT2w+Saastamoinen model with the resolution of 5 degree; GPT2_1w+S refers to GPT2w+Saastamoinen model with the resolution of 1 degree) were analyzed respectively. The model’s validation was carried out using the International GNSS Service (IGS) ZTD values derived from the observed data in the year 2012 at 27 IGS stations. The results show that the GPT2_1w+S model can provide tropospheric delay corrections with bias of 0.66cm and Root Mean Square (RMS) of 4.93cm, which is superior to GPT2+S model. The annual bias and RMS for the GPT2_5w+S model are slightly worse than that for the GPT2_1w+S model. For most stations, the bias and RMS show seasonal characteristics. The relation between the annual bias and RMS with latitude for the models is not obvious, and a latitude dependency between the models could not be detected. |
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The results show that the GPT2_1w+S model can provide tropospheric delay corrections with bias of 0.66cm and Root Mean Square (RMS) of 4.93cm, which is superior to GPT2+S model. The annual bias and RMS for the GPT2_5w+S model are slightly worse than that for the GPT2_1w+S model. For most stations, the bias and RMS show seasonal characteristics. The relation between the annual bias and RMS with latitude for the models is not obvious, and a latitude dependency between the models could not be detected.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The tropospheric delay is a systematic error source in the Global Navigation Satellite System (GNSS) positioning. However, without accuracy meteorological information, the quality of the Zenith Tropospheric Delay (ZTD) derived from empirical tropospheric models like Saastamoinen model will degrade, leading to inaccurate estimates of positions. To solve the above problem, on the basis of Global Pressure and Temperature 2/2w (GPT2/GPT2w) model, this paper conducted GPT2/GPT2w+Saastamoinen models for estimating ZTD over Asian area. As GPT2w model has two resolutions of 1 and 5 degrees, the effects of two models (GPT2_5w+S refers to GPT2w+Saastamoinen model with the resolution of 5 degree; GPT2_1w+S refers to GPT2w+Saastamoinen model with the resolution of 1 degree) were analyzed respectively. The model’s validation was carried out using the International GNSS Service (IGS) ZTD values derived from the observed data in the year 2012 at 27 IGS stations. The results show that the GPT2_1w+S model can provide tropospheric delay corrections with bias of 0.66cm and Root Mean Square (RMS) of 4.93cm, which is superior to GPT2+S model. The annual bias and RMS for the GPT2_5w+S model are slightly worse than that for the GPT2_1w+S model. For most stations, the bias and RMS show seasonal characteristics. The relation between the annual bias and RMS with latitude for the models is not obvious, and a latitude dependency between the models could not be detected.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Zenith tropospheric delay</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">GPT2 model</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">GPT2w model</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Saastamoinen model</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Xihong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Jizhe</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Qiang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Bargi-Souza, Paula ELSEVIER</subfield><subfield code="t">Posttranscriptional actions of triiodothyronine on</subfield><subfield code="d">2018</subfield><subfield code="d">including COSPAR information bulletin : the official journal of the Committee on Space Research (COSPAR), a scientific committee of the International Council of Scientific Unions (ICSU)</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV000905844</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:59</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:3</subfield><subfield code="g">day:1</subfield><subfield code="g">month:02</subfield><subfield code="g">pages:824-832</subfield><subfield code="g">extent:9</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.asr.2016.09.019</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.89</subfield><subfield code="j">Endokrinologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">59</subfield><subfield code="j">2017</subfield><subfield code="e">3</subfield><subfield code="b">1</subfield><subfield code="c">0201</subfield><subfield code="h">824-832</subfield><subfield code="g">9</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">520</subfield></datafield></record></collection>
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