An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate

Abstract Global pressure and temperature 2 wet (GPT2w) is an empirical model providing the mean values plus annual and semiannual amplitudes of weighted mean temperature (Tm), which makes it a widely used tool in converting zenith wet delay (ZWD) to precipitable water vapor (PWV) in GNSS meteorology. The model meets the needs of real-time Tm anywhere in the world without relying on any other meteorological observations compared with traditional Tm calculation methods. It outperforms the other empirical Tm models released in recent years. Due to the lack of the Tm vertical adjustment in the model, the accuracy of Tm estimated by the model is subject to certain constraints, especially at sites which have large altitude differences compared with the GPT2w grid points. We explored the Tm lapse rate for the vertical adjustment using 10 years of 37 monthly mean pressure level data from the European Center for Medium-Range Weather Forecasts (ECMWF) and extended the GPT2w model to a new one called the GPT2wh model. Three schemes with different height ranges were established to fit the Tm lapse rate, and the most appropriate scheme was selected by adopting the goodness of fit measures, including the coefficient of determination (R-squared) and the root mean square error (RMSE). In addition to the mean value, annual and semiannual amplitudes for Tm lapse rate on a regular 1° grid were determined and stored in the GPT2wh model. The performance of the new model was assessed against the GPT2w model using different data sources in 2011, i.e., the ECMWF data and globally distributed radiosonde data. The numerical results show that the GPT2wh model outperforms the GPT2w model with an improved RMSE of 7.36/5.00/2.45/1.37/0.51/0.03 K at different height levels in the ECMWF comparison. In comparison with the radiosonde data, the mean RMSE of the GPT2wh model improves by 0.33 K from 4.16 to 3.83 K, i.e., an approximately 8% improvement against the GPT2w model. The impact of Tm on GNSS-PWV was analyzed, showing that the GPT2wh model can effectively improve the accuracy of the converted PWV. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yang, Fei [verfasserIn] Guo, Jiming [verfasserIn] Meng, Xiaolin [verfasserIn] Shi, Junbo [verfasserIn] Zhang, Di [verfasserIn] Zhao, Yinzhi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	GNSS meteorology Weighted mean temperature GPT2w model ECMWF data Radiosonde

Übergeordnetes Werk:	Enthalten in: GPS solutions - Berlin : Springer, 1995, 24(2020), 2 vom: 15. Feb.
Übergeordnetes Werk:	volume:24 ; year:2020 ; number:2 ; day:15 ; month:02

Links:	Volltext

DOI / URN:	10.1007/s10291-020-0953-9

Katalog-ID:	SPR009801642

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245	1	3	\|a An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate
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520			\|a Abstract Global pressure and temperature 2 wet (GPT2w) is an empirical model providing the mean values plus annual and semiannual amplitudes of weighted mean temperature (Tm), which makes it a widely used tool in converting zenith wet delay (ZWD) to precipitable water vapor (PWV) in GNSS meteorology. The model meets the needs of real-time Tm anywhere in the world without relying on any other meteorological observations compared with traditional Tm calculation methods. It outperforms the other empirical Tm models released in recent years. Due to the lack of the Tm vertical adjustment in the model, the accuracy of Tm estimated by the model is subject to certain constraints, especially at sites which have large altitude differences compared with the GPT2w grid points. We explored the Tm lapse rate for the vertical adjustment using 10 years of 37 monthly mean pressure level data from the European Center for Medium-Range Weather Forecasts (ECMWF) and extended the GPT2w model to a new one called the GPT2wh model. Three schemes with different height ranges were established to fit the Tm lapse rate, and the most appropriate scheme was selected by adopting the goodness of fit measures, including the coefficient of determination (R-squared) and the root mean square error (RMSE). In addition to the mean value, annual and semiannual amplitudes for Tm lapse rate on a regular 1° grid were determined and stored in the GPT2wh model. The performance of the new model was assessed against the GPT2w model using different data sources in 2011, i.e., the ECMWF data and globally distributed radiosonde data. The numerical results show that the GPT2wh model outperforms the GPT2w model with an improved RMSE of 7.36/5.00/2.45/1.37/0.51/0.03 K at different height levels in the ECMWF comparison. In comparison with the radiosonde data, the mean RMSE of the GPT2wh model improves by 0.33 K from 4.16 to 3.83 K, i.e., an approximately 8% improvement against the GPT2w model. The impact of Tm on GNSS-PWV was analyzed, showing that the GPT2wh model can effectively improve the accuracy of the converted PWV.
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700	1		\|a Zhao, Yinzhi \|e verfasserin \|4 aut
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matchkey_str	article:15211886:2020----::nmrvdegtdeneprtrtmdlaeog
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publishDate	2020
allfields	10.1007/s10291-020-0953-9 doi (DE-627)SPR009801642 (SPR)s10291-020-0953-9-e DE-627 ger DE-627 rakwb eng 520 ASE 550 ASE 53.84 bkl Yang, Fei verfasserin aut An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Global pressure and temperature 2 wet (GPT2w) is an empirical model providing the mean values plus annual and semiannual amplitudes of weighted mean temperature (Tm), which makes it a widely used tool in converting zenith wet delay (ZWD) to precipitable water vapor (PWV) in GNSS meteorology. The model meets the needs of real-time Tm anywhere in the world without relying on any other meteorological observations compared with traditional Tm calculation methods. It outperforms the other empirical Tm models released in recent years. Due to the lack of the Tm vertical adjustment in the model, the accuracy of Tm estimated by the model is subject to certain constraints, especially at sites which have large altitude differences compared with the GPT2w grid points. We explored the Tm lapse rate for the vertical adjustment using 10 years of 37 monthly mean pressure level data from the European Center for Medium-Range Weather Forecasts (ECMWF) and extended the GPT2w model to a new one called the GPT2wh model. Three schemes with different height ranges were established to fit the Tm lapse rate, and the most appropriate scheme was selected by adopting the goodness of fit measures, including the coefficient of determination (R-squared) and the root mean square error (RMSE). In addition to the mean value, annual and semiannual amplitudes for Tm lapse rate on a regular 1° grid were determined and stored in the GPT2wh model. The performance of the new model was assessed against the GPT2w model using different data sources in 2011, i.e., the ECMWF data and globally distributed radiosonde data. The numerical results show that the GPT2wh model outperforms the GPT2w model with an improved RMSE of 7.36/5.00/2.45/1.37/0.51/0.03 K at different height levels in the ECMWF comparison. In comparison with the radiosonde data, the mean RMSE of the GPT2wh model improves by 0.33 K from 4.16 to 3.83 K, i.e., an approximately 8% improvement against the GPT2w model. The impact of Tm on GNSS-PWV was analyzed, showing that the GPT2wh model can effectively improve the accuracy of the converted PWV. GNSS meteorology (dpeaa)DE-He213 Weighted mean temperature (dpeaa)DE-He213 GPT2w model (dpeaa)DE-He213 ECMWF data (dpeaa)DE-He213 Radiosonde (dpeaa)DE-He213 Guo, Jiming verfasserin aut Meng, Xiaolin verfasserin aut Shi, Junbo verfasserin aut Zhang, Di verfasserin aut Zhao, Yinzhi verfasserin aut Enthalten in GPS solutions Berlin : Springer, 1995 24(2020), 2 vom: 15. Feb. (DE-627)357170016 (DE-600)2094351-9 1521-1886 nnns volume:24 year:2020 number:2 day:15 month:02 https://dx.doi.org/10.1007/s10291-020-0953-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.84 ASE AR 24 2020 2 15 02
spelling	10.1007/s10291-020-0953-9 doi (DE-627)SPR009801642 (SPR)s10291-020-0953-9-e DE-627 ger DE-627 rakwb eng 520 ASE 550 ASE 53.84 bkl Yang, Fei verfasserin aut An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Global pressure and temperature 2 wet (GPT2w) is an empirical model providing the mean values plus annual and semiannual amplitudes of weighted mean temperature (Tm), which makes it a widely used tool in converting zenith wet delay (ZWD) to precipitable water vapor (PWV) in GNSS meteorology. The model meets the needs of real-time Tm anywhere in the world without relying on any other meteorological observations compared with traditional Tm calculation methods. It outperforms the other empirical Tm models released in recent years. Due to the lack of the Tm vertical adjustment in the model, the accuracy of Tm estimated by the model is subject to certain constraints, especially at sites which have large altitude differences compared with the GPT2w grid points. We explored the Tm lapse rate for the vertical adjustment using 10 years of 37 monthly mean pressure level data from the European Center for Medium-Range Weather Forecasts (ECMWF) and extended the GPT2w model to a new one called the GPT2wh model. Three schemes with different height ranges were established to fit the Tm lapse rate, and the most appropriate scheme was selected by adopting the goodness of fit measures, including the coefficient of determination (R-squared) and the root mean square error (RMSE). In addition to the mean value, annual and semiannual amplitudes for Tm lapse rate on a regular 1° grid were determined and stored in the GPT2wh model. The performance of the new model was assessed against the GPT2w model using different data sources in 2011, i.e., the ECMWF data and globally distributed radiosonde data. The numerical results show that the GPT2wh model outperforms the GPT2w model with an improved RMSE of 7.36/5.00/2.45/1.37/0.51/0.03 K at different height levels in the ECMWF comparison. In comparison with the radiosonde data, the mean RMSE of the GPT2wh model improves by 0.33 K from 4.16 to 3.83 K, i.e., an approximately 8% improvement against the GPT2w model. The impact of Tm on GNSS-PWV was analyzed, showing that the GPT2wh model can effectively improve the accuracy of the converted PWV. GNSS meteorology (dpeaa)DE-He213 Weighted mean temperature (dpeaa)DE-He213 GPT2w model (dpeaa)DE-He213 ECMWF data (dpeaa)DE-He213 Radiosonde (dpeaa)DE-He213 Guo, Jiming verfasserin aut Meng, Xiaolin verfasserin aut Shi, Junbo verfasserin aut Zhang, Di verfasserin aut Zhao, Yinzhi verfasserin aut Enthalten in GPS solutions Berlin : Springer, 1995 24(2020), 2 vom: 15. Feb. (DE-627)357170016 (DE-600)2094351-9 1521-1886 nnns volume:24 year:2020 number:2 day:15 month:02 https://dx.doi.org/10.1007/s10291-020-0953-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.84 ASE AR 24 2020 2 15 02
allfields_unstemmed	10.1007/s10291-020-0953-9 doi (DE-627)SPR009801642 (SPR)s10291-020-0953-9-e DE-627 ger DE-627 rakwb eng 520 ASE 550 ASE 53.84 bkl Yang, Fei verfasserin aut An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Global pressure and temperature 2 wet (GPT2w) is an empirical model providing the mean values plus annual and semiannual amplitudes of weighted mean temperature (Tm), which makes it a widely used tool in converting zenith wet delay (ZWD) to precipitable water vapor (PWV) in GNSS meteorology. The model meets the needs of real-time Tm anywhere in the world without relying on any other meteorological observations compared with traditional Tm calculation methods. It outperforms the other empirical Tm models released in recent years. Due to the lack of the Tm vertical adjustment in the model, the accuracy of Tm estimated by the model is subject to certain constraints, especially at sites which have large altitude differences compared with the GPT2w grid points. We explored the Tm lapse rate for the vertical adjustment using 10 years of 37 monthly mean pressure level data from the European Center for Medium-Range Weather Forecasts (ECMWF) and extended the GPT2w model to a new one called the GPT2wh model. Three schemes with different height ranges were established to fit the Tm lapse rate, and the most appropriate scheme was selected by adopting the goodness of fit measures, including the coefficient of determination (R-squared) and the root mean square error (RMSE). In addition to the mean value, annual and semiannual amplitudes for Tm lapse rate on a regular 1° grid were determined and stored in the GPT2wh model. The performance of the new model was assessed against the GPT2w model using different data sources in 2011, i.e., the ECMWF data and globally distributed radiosonde data. The numerical results show that the GPT2wh model outperforms the GPT2w model with an improved RMSE of 7.36/5.00/2.45/1.37/0.51/0.03 K at different height levels in the ECMWF comparison. In comparison with the radiosonde data, the mean RMSE of the GPT2wh model improves by 0.33 K from 4.16 to 3.83 K, i.e., an approximately 8% improvement against the GPT2w model. The impact of Tm on GNSS-PWV was analyzed, showing that the GPT2wh model can effectively improve the accuracy of the converted PWV. GNSS meteorology (dpeaa)DE-He213 Weighted mean temperature (dpeaa)DE-He213 GPT2w model (dpeaa)DE-He213 ECMWF data (dpeaa)DE-He213 Radiosonde (dpeaa)DE-He213 Guo, Jiming verfasserin aut Meng, Xiaolin verfasserin aut Shi, Junbo verfasserin aut Zhang, Di verfasserin aut Zhao, Yinzhi verfasserin aut Enthalten in GPS solutions Berlin : Springer, 1995 24(2020), 2 vom: 15. Feb. (DE-627)357170016 (DE-600)2094351-9 1521-1886 nnns volume:24 year:2020 number:2 day:15 month:02 https://dx.doi.org/10.1007/s10291-020-0953-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.84 ASE AR 24 2020 2 15 02
allfieldsGer	10.1007/s10291-020-0953-9 doi (DE-627)SPR009801642 (SPR)s10291-020-0953-9-e DE-627 ger DE-627 rakwb eng 520 ASE 550 ASE 53.84 bkl Yang, Fei verfasserin aut An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Global pressure and temperature 2 wet (GPT2w) is an empirical model providing the mean values plus annual and semiannual amplitudes of weighted mean temperature (Tm), which makes it a widely used tool in converting zenith wet delay (ZWD) to precipitable water vapor (PWV) in GNSS meteorology. The model meets the needs of real-time Tm anywhere in the world without relying on any other meteorological observations compared with traditional Tm calculation methods. It outperforms the other empirical Tm models released in recent years. Due to the lack of the Tm vertical adjustment in the model, the accuracy of Tm estimated by the model is subject to certain constraints, especially at sites which have large altitude differences compared with the GPT2w grid points. We explored the Tm lapse rate for the vertical adjustment using 10 years of 37 monthly mean pressure level data from the European Center for Medium-Range Weather Forecasts (ECMWF) and extended the GPT2w model to a new one called the GPT2wh model. Three schemes with different height ranges were established to fit the Tm lapse rate, and the most appropriate scheme was selected by adopting the goodness of fit measures, including the coefficient of determination (R-squared) and the root mean square error (RMSE). In addition to the mean value, annual and semiannual amplitudes for Tm lapse rate on a regular 1° grid were determined and stored in the GPT2wh model. The performance of the new model was assessed against the GPT2w model using different data sources in 2011, i.e., the ECMWF data and globally distributed radiosonde data. The numerical results show that the GPT2wh model outperforms the GPT2w model with an improved RMSE of 7.36/5.00/2.45/1.37/0.51/0.03 K at different height levels in the ECMWF comparison. In comparison with the radiosonde data, the mean RMSE of the GPT2wh model improves by 0.33 K from 4.16 to 3.83 K, i.e., an approximately 8% improvement against the GPT2w model. The impact of Tm on GNSS-PWV was analyzed, showing that the GPT2wh model can effectively improve the accuracy of the converted PWV. GNSS meteorology (dpeaa)DE-He213 Weighted mean temperature (dpeaa)DE-He213 GPT2w model (dpeaa)DE-He213 ECMWF data (dpeaa)DE-He213 Radiosonde (dpeaa)DE-He213 Guo, Jiming verfasserin aut Meng, Xiaolin verfasserin aut Shi, Junbo verfasserin aut Zhang, Di verfasserin aut Zhao, Yinzhi verfasserin aut Enthalten in GPS solutions Berlin : Springer, 1995 24(2020), 2 vom: 15. Feb. (DE-627)357170016 (DE-600)2094351-9 1521-1886 nnns volume:24 year:2020 number:2 day:15 month:02 https://dx.doi.org/10.1007/s10291-020-0953-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.84 ASE AR 24 2020 2 15 02
allfieldsSound	10.1007/s10291-020-0953-9 doi (DE-627)SPR009801642 (SPR)s10291-020-0953-9-e DE-627 ger DE-627 rakwb eng 520 ASE 550 ASE 53.84 bkl Yang, Fei verfasserin aut An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Global pressure and temperature 2 wet (GPT2w) is an empirical model providing the mean values plus annual and semiannual amplitudes of weighted mean temperature (Tm), which makes it a widely used tool in converting zenith wet delay (ZWD) to precipitable water vapor (PWV) in GNSS meteorology. The model meets the needs of real-time Tm anywhere in the world without relying on any other meteorological observations compared with traditional Tm calculation methods. It outperforms the other empirical Tm models released in recent years. Due to the lack of the Tm vertical adjustment in the model, the accuracy of Tm estimated by the model is subject to certain constraints, especially at sites which have large altitude differences compared with the GPT2w grid points. We explored the Tm lapse rate for the vertical adjustment using 10 years of 37 monthly mean pressure level data from the European Center for Medium-Range Weather Forecasts (ECMWF) and extended the GPT2w model to a new one called the GPT2wh model. Three schemes with different height ranges were established to fit the Tm lapse rate, and the most appropriate scheme was selected by adopting the goodness of fit measures, including the coefficient of determination (R-squared) and the root mean square error (RMSE). In addition to the mean value, annual and semiannual amplitudes for Tm lapse rate on a regular 1° grid were determined and stored in the GPT2wh model. The performance of the new model was assessed against the GPT2w model using different data sources in 2011, i.e., the ECMWF data and globally distributed radiosonde data. The numerical results show that the GPT2wh model outperforms the GPT2w model with an improved RMSE of 7.36/5.00/2.45/1.37/0.51/0.03 K at different height levels in the ECMWF comparison. In comparison with the radiosonde data, the mean RMSE of the GPT2wh model improves by 0.33 K from 4.16 to 3.83 K, i.e., an approximately 8% improvement against the GPT2w model. The impact of Tm on GNSS-PWV was analyzed, showing that the GPT2wh model can effectively improve the accuracy of the converted PWV. GNSS meteorology (dpeaa)DE-He213 Weighted mean temperature (dpeaa)DE-He213 GPT2w model (dpeaa)DE-He213 ECMWF data (dpeaa)DE-He213 Radiosonde (dpeaa)DE-He213 Guo, Jiming verfasserin aut Meng, Xiaolin verfasserin aut Shi, Junbo verfasserin aut Zhang, Di verfasserin aut Zhao, Yinzhi verfasserin aut Enthalten in GPS solutions Berlin : Springer, 1995 24(2020), 2 vom: 15. Feb. (DE-627)357170016 (DE-600)2094351-9 1521-1886 nnns volume:24 year:2020 number:2 day:15 month:02 https://dx.doi.org/10.1007/s10291-020-0953-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.84 ASE AR 24 2020 2 15 02
language	English
source	Enthalten in GPS solutions 24(2020), 2 vom: 15. Feb. volume:24 year:2020 number:2 day:15 month:02
sourceStr	Enthalten in GPS solutions 24(2020), 2 vom: 15. Feb. volume:24 year:2020 number:2 day:15 month:02
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	GNSS meteorology Weighted mean temperature GPT2w model ECMWF data Radiosonde
dewey-raw	520
isfreeaccess_bool	false
container_title	GPS solutions
authorswithroles_txt_mv	Yang, Fei @@aut@@ Guo, Jiming @@aut@@ Meng, Xiaolin @@aut@@ Shi, Junbo @@aut@@ Zhang, Di @@aut@@ Zhao, Yinzhi @@aut@@
publishDateDaySort_date	2020-02-15T00:00:00Z
hierarchy_top_id	357170016
dewey-sort	3520
id	SPR009801642
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR009801642</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220110213835.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10291-020-0953-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR009801642</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10291-020-0953-9-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">520</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.84</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yang, Fei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Global pressure and temperature 2 wet (GPT2w) is an empirical model providing the mean values plus annual and semiannual amplitudes of weighted mean temperature (Tm), which makes it a widely used tool in converting zenith wet delay (ZWD) to precipitable water vapor (PWV) in GNSS meteorology. The model meets the needs of real-time Tm anywhere in the world without relying on any other meteorological observations compared with traditional Tm calculation methods. It outperforms the other empirical Tm models released in recent years. Due to the lack of the Tm vertical adjustment in the model, the accuracy of Tm estimated by the model is subject to certain constraints, especially at sites which have large altitude differences compared with the GPT2w grid points. We explored the Tm lapse rate for the vertical adjustment using 10 years of 37 monthly mean pressure level data from the European Center for Medium-Range Weather Forecasts (ECMWF) and extended the GPT2w model to a new one called the GPT2wh model. Three schemes with different height ranges were established to fit the Tm lapse rate, and the most appropriate scheme was selected by adopting the goodness of fit measures, including the coefficient of determination (R-squared) and the root mean square error (RMSE). In addition to the mean value, annual and semiannual amplitudes for Tm lapse rate on a regular 1° grid were determined and stored in the GPT2wh model. The performance of the new model was assessed against the GPT2w model using different data sources in 2011, i.e., the ECMWF data and globally distributed radiosonde data. The numerical results show that the GPT2wh model outperforms the GPT2w model with an improved RMSE of 7.36/5.00/2.45/1.37/0.51/0.03 K at different height levels in the ECMWF comparison. In comparison with the radiosonde data, the mean RMSE of the GPT2wh model improves by 0.33 K from 4.16 to 3.83 K, i.e., an approximately 8% improvement against the GPT2w model. 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author	Yang, Fei
spellingShingle	Yang, Fei ddc 520 ddc 550 bkl 53.84 misc GNSS meteorology misc Weighted mean temperature misc GPT2w model misc ECMWF data misc Radiosonde An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate
authorStr	Yang, Fei
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topic_title	520 ASE 550 ASE 53.84 bkl An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate GNSS meteorology (dpeaa)DE-He213 Weighted mean temperature (dpeaa)DE-He213 GPT2w model (dpeaa)DE-He213 ECMWF data (dpeaa)DE-He213 Radiosonde (dpeaa)DE-He213
topic	ddc 520 ddc 550 bkl 53.84 misc GNSS meteorology misc Weighted mean temperature misc GPT2w model misc ECMWF data misc Radiosonde
topic_unstemmed	ddc 520 ddc 550 bkl 53.84 misc GNSS meteorology misc Weighted mean temperature misc GPT2w model misc ECMWF data misc Radiosonde
topic_browse	ddc 520 ddc 550 bkl 53.84 misc GNSS meteorology misc Weighted mean temperature misc GPT2w model misc ECMWF data misc Radiosonde
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title	An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate
ctrlnum	(DE-627)SPR009801642 (SPR)s10291-020-0953-9-e
title_full	An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate
author_sort	Yang, Fei
journal	GPS solutions
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author_browse	Yang, Fei Guo, Jiming Meng, Xiaolin Shi, Junbo Zhang, Di Zhao, Yinzhi
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title_sort	improved weighted mean temperature (tm) model based on gpt2w with tm lapse rate
title_auth	An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate
abstract	Abstract Global pressure and temperature 2 wet (GPT2w) is an empirical model providing the mean values plus annual and semiannual amplitudes of weighted mean temperature (Tm), which makes it a widely used tool in converting zenith wet delay (ZWD) to precipitable water vapor (PWV) in GNSS meteorology. The model meets the needs of real-time Tm anywhere in the world without relying on any other meteorological observations compared with traditional Tm calculation methods. It outperforms the other empirical Tm models released in recent years. Due to the lack of the Tm vertical adjustment in the model, the accuracy of Tm estimated by the model is subject to certain constraints, especially at sites which have large altitude differences compared with the GPT2w grid points. We explored the Tm lapse rate for the vertical adjustment using 10 years of 37 monthly mean pressure level data from the European Center for Medium-Range Weather Forecasts (ECMWF) and extended the GPT2w model to a new one called the GPT2wh model. Three schemes with different height ranges were established to fit the Tm lapse rate, and the most appropriate scheme was selected by adopting the goodness of fit measures, including the coefficient of determination (R-squared) and the root mean square error (RMSE). In addition to the mean value, annual and semiannual amplitudes for Tm lapse rate on a regular 1° grid were determined and stored in the GPT2wh model. The performance of the new model was assessed against the GPT2w model using different data sources in 2011, i.e., the ECMWF data and globally distributed radiosonde data. The numerical results show that the GPT2wh model outperforms the GPT2w model with an improved RMSE of 7.36/5.00/2.45/1.37/0.51/0.03 K at different height levels in the ECMWF comparison. In comparison with the radiosonde data, the mean RMSE of the GPT2wh model improves by 0.33 K from 4.16 to 3.83 K, i.e., an approximately 8% improvement against the GPT2w model. The impact of Tm on GNSS-PWV was analyzed, showing that the GPT2wh model can effectively improve the accuracy of the converted PWV.
abstractGer	Abstract Global pressure and temperature 2 wet (GPT2w) is an empirical model providing the mean values plus annual and semiannual amplitudes of weighted mean temperature (Tm), which makes it a widely used tool in converting zenith wet delay (ZWD) to precipitable water vapor (PWV) in GNSS meteorology. The model meets the needs of real-time Tm anywhere in the world without relying on any other meteorological observations compared with traditional Tm calculation methods. It outperforms the other empirical Tm models released in recent years. Due to the lack of the Tm vertical adjustment in the model, the accuracy of Tm estimated by the model is subject to certain constraints, especially at sites which have large altitude differences compared with the GPT2w grid points. We explored the Tm lapse rate for the vertical adjustment using 10 years of 37 monthly mean pressure level data from the European Center for Medium-Range Weather Forecasts (ECMWF) and extended the GPT2w model to a new one called the GPT2wh model. Three schemes with different height ranges were established to fit the Tm lapse rate, and the most appropriate scheme was selected by adopting the goodness of fit measures, including the coefficient of determination (R-squared) and the root mean square error (RMSE). In addition to the mean value, annual and semiannual amplitudes for Tm lapse rate on a regular 1° grid were determined and stored in the GPT2wh model. The performance of the new model was assessed against the GPT2w model using different data sources in 2011, i.e., the ECMWF data and globally distributed radiosonde data. The numerical results show that the GPT2wh model outperforms the GPT2w model with an improved RMSE of 7.36/5.00/2.45/1.37/0.51/0.03 K at different height levels in the ECMWF comparison. In comparison with the radiosonde data, the mean RMSE of the GPT2wh model improves by 0.33 K from 4.16 to 3.83 K, i.e., an approximately 8% improvement against the GPT2w model. The impact of Tm on GNSS-PWV was analyzed, showing that the GPT2wh model can effectively improve the accuracy of the converted PWV.
abstract_unstemmed	Abstract Global pressure and temperature 2 wet (GPT2w) is an empirical model providing the mean values plus annual and semiannual amplitudes of weighted mean temperature (Tm), which makes it a widely used tool in converting zenith wet delay (ZWD) to precipitable water vapor (PWV) in GNSS meteorology. The model meets the needs of real-time Tm anywhere in the world without relying on any other meteorological observations compared with traditional Tm calculation methods. It outperforms the other empirical Tm models released in recent years. Due to the lack of the Tm vertical adjustment in the model, the accuracy of Tm estimated by the model is subject to certain constraints, especially at sites which have large altitude differences compared with the GPT2w grid points. We explored the Tm lapse rate for the vertical adjustment using 10 years of 37 monthly mean pressure level data from the European Center for Medium-Range Weather Forecasts (ECMWF) and extended the GPT2w model to a new one called the GPT2wh model. Three schemes with different height ranges were established to fit the Tm lapse rate, and the most appropriate scheme was selected by adopting the goodness of fit measures, including the coefficient of determination (R-squared) and the root mean square error (RMSE). In addition to the mean value, annual and semiannual amplitudes for Tm lapse rate on a regular 1° grid were determined and stored in the GPT2wh model. The performance of the new model was assessed against the GPT2w model using different data sources in 2011, i.e., the ECMWF data and globally distributed radiosonde data. The numerical results show that the GPT2wh model outperforms the GPT2w model with an improved RMSE of 7.36/5.00/2.45/1.37/0.51/0.03 K at different height levels in the ECMWF comparison. In comparison with the radiosonde data, the mean RMSE of the GPT2wh model improves by 0.33 K from 4.16 to 3.83 K, i.e., an approximately 8% improvement against the GPT2w model. The impact of Tm on GNSS-PWV was analyzed, showing that the GPT2wh model can effectively improve the accuracy of the converted PWV.
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container_issue	2
title_short	An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate
url	https://dx.doi.org/10.1007/s10291-020-0953-9
remote_bool	true
author2	Guo, Jiming Meng, Xiaolin Shi, Junbo Zhang, Di Zhao, Yinzhi
author2Str	Guo, Jiming Meng, Xiaolin Shi, Junbo Zhang, Di Zhao, Yinzhi
ppnlink	357170016
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isOA_txt	false
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doi_str	10.1007/s10291-020-0953-9
up_date	2024-07-04T03:06:41.206Z
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An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate

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