一種用于中國地區(qū)的對流層天頂延遲實時修正模型
doi: 10.11999/JEIT180353 cstr: 32379.14.JEIT180353
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信息工程大學(xué)信息系統(tǒng)工程學(xué)院 ??鄭州 ??450001
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鄭州大學(xué)信息工程學(xué)院 ??鄭州 ??450001
Real-time Correction Model for Zenith Tropospheric Delay Applied to the Chinese Region
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Institute of Information Systems Engineering, Information Engineering University, Zhengzhou 450001, China
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School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
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摘要:
針對目前對流層延遲修正受限于探空數(shù)據(jù)不足導(dǎo)致修正效率低的問題，該文結(jié)合Saastamoinen和GPT2w模型構(gòu)建形成組合模型Sa+GPT2w模型，通過利用GPT2w模型提供的高精度氣象數(shù)據(jù)，實現(xiàn)中國地區(qū)對流層天頂延遲(ZTD)的實時修正，克服對探空數(shù)據(jù)的依賴，并用實測數(shù)據(jù)對計算結(jié)果進行驗證。以IGS提供的中國地區(qū)2015至2017年ZTD時間序列為評估標準時，Sa+GPT2w模型(bias: 1.661 cm, RMS: 4.711 cm)的精度較同等條件下的Sa+EGNOS, Sa+UNB3m和Hop+GPT2w模型分別提升50.5%, 41.9%和37.1%；以GGOS Atmosphere 2017年ZTD數(shù)據(jù)為標準時，Sa+GPT2w模型(bias: 1.551 cm, RMS: 4.859 cm)的精度相對同等條件下的另3種模型分別提升49.5%, 38.5%和46.8%；最后對Sa+EGNOS, Sa+UNB3m和Sa+GPT2w模型在ZTD修正中誤差結(jié)果的時空分布特征進行分析。研究結(jié)果可為在中國地區(qū)的導(dǎo)航定位、大氣折射研究中，應(yīng)用不同氣象參數(shù)模型進行ZTD修正的有效性和可能達到的精度提供參考。
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關(guān)鍵詞:
- 對流層天頂延遲 /
- Saastamoinen模型 /
- GPT2w模型 /
- EGNOS模型 /
- UNB3m模型
Abstract:In view of the correction for tropospheric delay is limited by the shortage of sounding data, which leads to the problem that the low correction efficiency, this paper proposes a model named Sa+GPT2w, combining Saastamoinen model with GPT2w model. In this paper, the real-time correction for Zenith Tropospheric Delay (ZTD) over China is realized by using the high-precision meteorological values provided by the GPT2w model, and the results are verified by the measured data. Taking the ZTD in 2015-2017 of International GNSS Service(IGS) as a reference, the accuracy of the Sa+GPT2w model (bias: 1.661 cm, RMS: 4.711 cm) rises by 50.5%, 41.9% and 37.1%, respectively, relative to the Sa+EGNOS, Sa+UNB3m and the Hop+GPT2w models. Moreover, using the ZTD from Global Geodetic Observing System (GGOS) in 2017 as a standard, the Sa+GPT2w model (bias: 1.551 cm, RMS: 4.859 cm) improves the accuracy by 49.5%, 38.5% and 46.8% relative to other three models, respectively. Finally, this paper analyzes the temporal and spatial distribution characteristics of the bias and RMS of the above three models. The results provide a significant reference for the effectiveness of correction for ZTD by using different meteorological models in the research of navigation and atmospheric refraction over China.
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Key words:
- Zenith Tropospheric Delay (ZTD) /
- Saastamoinen model /
- GPT2w model /
- EGNOS model /
- UNB3m model
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表 1 中國地區(qū)IGS測站信息(按緯度升序排列)
ID 測站 緯度(°N) 經(jīng)度(°E) 高程(m) A TCMS 24.80 120.99 77.3 B TWTF 24.95 121.16 184.0 C KUNM 25.03 102.80 2019.1 D LHAZ 29.66 91.10 3622.0 E WUHN 30.53 114.36 42.6 F SHAO 31.10 121.20 22.1 G XIAN 34.37 109.22 498.5 H BJFS 39.61 115.89 98.3 I GUAO 43.47 87.17 2049.2 J URUM 43.59 87.63 917.9 K CHAN 43.79 125.44 253.7 
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表 2 相對IGS測站數(shù)據(jù)的誤差統(tǒng)計結(jié)果(cm)
ID 年 Sa+EGNOS Sa+UNB3m Sa+GPT2w Hop+GPT2w bias RMS bias RMS bias RMS bias RMS A 2015 1.048 7.969 2.509 8.328 1.615 5.883 0.911 5.641 2016 2.159 8.585 3.621 9.126 2.729 5.969 2.025 5.700 2017 1.312 8.385 2.773 8.777 1.879 5.811 1.175 5.644 B 2015 0.214 7.736 2.622 8.148 1.497 5.768 1.951 5.906 2016 1.100 8.216 3.508 8.854 2.386 5.743 2.839 5.956 2017 0.316 8.102 2.724 8.519 1.600 5.649 2.053 5.804 C 2015 –6.939 9.817 3.631 7.251 0.530 3.595 10.546 11.207 2016 –6.728 9.753 3.839 7.502 0.742 3.813 10.771 11.466 2017 –6.756 9.977 3.814 7.634 0.713 3.732 10.735 11.417 D 2015 –10.197 11.839 1.494 4.427 0.355 1.667 12.816 12.938 2016 –9.778 11.908 1.899 5.330 0.765 2.039 13.221 13.347 2017 –10.007 12.117 1.626 5.224 0.486 1.906 12.95 13.055 E 2015 –2.216 10.376 –0.799 10.115 3.739 7.087 3.057 6.748 2016 –1.250 11.535 0.160 11.381 4.710 8.061 4.028 7.673 2017 –1.468 11.776 –0.060 11.577 4.485 7.843 3.803 7.465 F 2015 –2.810 11.278 –1.492 11.002 2.263 7.479 1.285 7.237 2016 –1.591 11.837 –0.271 11.675 3.479 7.839 2.502 7.432 2017 –2.531 12.166 –1.201 11.896 2.545 6.999 1.567 6.692 G 2015 –4.477 9.843 –0.053 8.354 1.763 5.257 3.966 6.345 2016 –3.738 10.055 0.678 8.896 2.493 5.313 4.696 6.648 2017 –4.066 10.106 0.358 8.816 2.172 5.314 4.375 6.526 H 2015 –3.988 9.464 –1.637 8.703 1.136 4.207 0.875 4.152 2016 –3.576 10.227 –1.228 9.639 1.546 4.929 1.286 4.822 2017 –4.088 10.626 –1.736 9.861 1.038 4.811 0.777 4.743 I 2015 –4.156 7.327 2.122 4.873 0.656 2.259 10.206 10.429 2016 –3.727 7.334 2.541 5.408 1.073 2.727 10.615 10.906 2017 –4.202 7.415 2.100 4.927 0.634 2.184 10.177 10.385 J 2015 –3.051 6.980 1.290 5.517 1.115 3.320 5.437 6.261 2016 –2.440 7.308 1.890 6.324 1.711 4.069 6.033 7.064 2017 –3.071 7.117 1.269 5.619 1.095 3.228 5.417 6.202 K 2015 –3.780 8.453 –1.401 7.474 0.640 3.447 1.282 3.654 2016 –3.531 8.846 –1.155 8.008 0.882 3.712 1.524 3.907 2017 –4.092 9.337 –1.716 8.323 0.327 3.815 0.969 3.922 平均 –3.397 9.509 1.021 8.106 1.661 4.711 5.026 7.494
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表 3 相對GGOS格網(wǎng)數(shù)據(jù)的誤差統(tǒng)計結(jié)果(cm)
統(tǒng)計類型 Sa+EGNOS Sa+UNB3m Sa+GPT2w Hop+GPT2w bias Min –6.961 –0.812 –0.086 2.716 Max 1.932 3.461 3.445 9.473 Mean –3.605 1.393 1.551 6.581 RMS Min 7.768 5.480 2.585 6.928 Max 11.428 10.010 7.284 11.786 Mean 9.631 7.899 4.859 9.135
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表 4 相對IGS數(shù)據(jù)的誤差統(tǒng)計結(jié)果(cm)
ID 年份 Sa+EGNOS Sa+UNB3m Sa+GPT2w bias RMS bias RMS bias RMS B 2012 0.240 7.435 2.648 7.900 1.775 5.965 2018 –7.218 9.611 –4.839 7.940 –0.058 5.082 G 2012 –4.849 10.676 –0.419 9.080 1.375 5.153 2018 –11.801 13.128 –7.368 8.823 –0.069 2.302 I 2012 –4.717 7.863 1.564 4.861 0.086 2.005 2018 –8.477 9.787 –1.877 3.303 –0.303 1.104
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