GNSS水汽层析技术可以反演对流层水汽三维时空变化情况,但该技术比较复杂、运算量大,需要消耗一定的时间.故本文提出了一种利用地基GNSS反演的大气可降水量(precipitable water vapor,PWV)结合水汽在垂直方向上的指数分布特性来计算大...GNSS水汽层析技术可以反演对流层水汽三维时空变化情况,但该技术比较复杂、运算量大,需要消耗一定的时间.故本文提出了一种利用地基GNSS反演的大气可降水量(precipitable water vapor,PWV)结合水汽在垂直方向上的指数分布特性来计算大气水汽三维分布的快速层析方法.该方法利用香港地区2022年8月的GNSS数据开展试验,与传统GNSS水汽层析方法进行对比.试验结果表明:两种方法的层析解算结果与探空数据均具有良好的一致性.虽然快速层析方法的解算结果在底层区域缺少一些水汽变化的细节信息,精度略逊于传统层析方法,但是在中、高层时精度会有所提升,层析解算结果良好.而且本文提出的快速层析方法无需构建和解算复杂的层析方程组,可以在大量GNSS测站参与水汽层析时减少计算复杂度,提升运算能力,同时可以更快地得到任意高度层的水汽密度,是一种简便、高效的层析方法.展开更多
针对已有天顶湿延迟(zenith wet delay,ZWD)模型的建模数据未能顾及精细的日周期变化的问题,为充分探究顾及日周期变化对建模的精度影响,根据2015—2017年ECMWF提供的第5代再分析资料(ERA5)建立未顾及日变化的CZWD_1模型和顾及日变化的C...针对已有天顶湿延迟(zenith wet delay,ZWD)模型的建模数据未能顾及精细的日周期变化的问题,为充分探究顾及日周期变化对建模的精度影响,根据2015—2017年ECMWF提供的第5代再分析资料(ERA5)建立未顾及日变化的CZWD_1模型和顾及日变化的CZWD_2模型,利用未参与建模的2018年ERA5再分析资料和无线电探空数据进行精度验证,并与广泛使用的GPT3模型进行精度对比。结果表明:以2018年ERA5再分析资料为参考值,CZWD_2模型表现出最优的精度,年均均方根(root mean square,RMS)值相较于GPT3和CZWD_1模型分别提高了0.90 cm (18.7%)和0.32 cm (7.6%);以2018年无线电探空数据为参考值,CZWD_2模型的年均均方根(root mean square,RMS)值相较于GPT3和CZWD_1模型分别提高了1.24 cm (21.2%)和0.47 cm (9.3%)。此外,将所构建的ZWD模型应用于全球导航卫星系统(global navigation satellite systems,GNSS)水汽(precipitable water vapor,PWV)反演,CZWD_2模型表现出最优的反演精度,其RMS值相较于GPT3和CZWD_1模型分别提高了1.52 mm (27.7%)和0.38 mm (8.8%)。因此,CZWD_2模型更适用于中国东部地区的GNSS水汽探测及气象研究。展开更多
The potential of monitoring the movement of typhoons using the precipitable water vapor(PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movem...The potential of monitoring the movement of typhoons using the precipitable water vapor(PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movement of a typhoon in detail minutely and resulting in insufficient accuracy. Hence,based on PWV and meteorological data, we propose an improved typhoon monitoring mode. First, the European Centre for Medium-Range Weather Forecasts Reanalysis 5-derived PWV(ERA5-PWV) and the Global Navigation Satellite System-derived PWV(GNSS-PWV) were compared with the reference radiosonde PWV(RS-PWV). Then, using the PWV and atmospheric parameters derived from ERA5, we discussed the anomalous variations of PWV, pressure(P), precipitation, and wind speed during different typhoons. Finally, we compiled a list of critical factors related to typhoon movement, PWV and P. We developed an improved multi-factor typhoon monitoring mode(IMTM) with different models(i.e.,IMTM-I and IMTM-II) in different cases with a higher density of GNSS observation or only Numerical Weather Prediction(NWP) data. The IMTM was evaluated through the reference movement speeds of HATO and Mangkhut from the China Meteorological Observatory Typhoon Network(CMOTN). The results show that the root mean square(RMS) of the IMTM-I is 1.26 km/h based on ERA5-P and ERA5-PWV,and the absolute bias values are mostly within 2 km/h. Compared with the models considering the single factor ERA5-P/ERA5-PWV, the RMS of the IMTM-I is improved by 26.3% and 38.5%, respectively. The IMTM-II model manifests a residual of only 0.35 km/h. Compared with the single-factor model based on GNSS-PWV/P, the residual of the IMTM-II model is reduced by 90.8% and 84.1%, respectively. These results propose that the typhoon movement monitoring approach combining PWV and P has evident advantages over the single-factor model and is expected to supplement traditional typhoon monitoring.展开更多
文摘GNSS水汽层析技术可以反演对流层水汽三维时空变化情况,但该技术比较复杂、运算量大,需要消耗一定的时间.故本文提出了一种利用地基GNSS反演的大气可降水量(precipitable water vapor,PWV)结合水汽在垂直方向上的指数分布特性来计算大气水汽三维分布的快速层析方法.该方法利用香港地区2022年8月的GNSS数据开展试验,与传统GNSS水汽层析方法进行对比.试验结果表明:两种方法的层析解算结果与探空数据均具有良好的一致性.虽然快速层析方法的解算结果在底层区域缺少一些水汽变化的细节信息,精度略逊于传统层析方法,但是在中、高层时精度会有所提升,层析解算结果良好.而且本文提出的快速层析方法无需构建和解算复杂的层析方程组,可以在大量GNSS测站参与水汽层析时减少计算复杂度,提升运算能力,同时可以更快地得到任意高度层的水汽密度,是一种简便、高效的层析方法.
文摘针对已有天顶湿延迟(zenith wet delay,ZWD)模型的建模数据未能顾及精细的日周期变化的问题,为充分探究顾及日周期变化对建模的精度影响,根据2015—2017年ECMWF提供的第5代再分析资料(ERA5)建立未顾及日变化的CZWD_1模型和顾及日变化的CZWD_2模型,利用未参与建模的2018年ERA5再分析资料和无线电探空数据进行精度验证,并与广泛使用的GPT3模型进行精度对比。结果表明:以2018年ERA5再分析资料为参考值,CZWD_2模型表现出最优的精度,年均均方根(root mean square,RMS)值相较于GPT3和CZWD_1模型分别提高了0.90 cm (18.7%)和0.32 cm (7.6%);以2018年无线电探空数据为参考值,CZWD_2模型的年均均方根(root mean square,RMS)值相较于GPT3和CZWD_1模型分别提高了1.24 cm (21.2%)和0.47 cm (9.3%)。此外,将所构建的ZWD模型应用于全球导航卫星系统(global navigation satellite systems,GNSS)水汽(precipitable water vapor,PWV)反演,CZWD_2模型表现出最优的反演精度,其RMS值相较于GPT3和CZWD_1模型分别提高了1.52 mm (27.7%)和0.38 mm (8.8%)。因此,CZWD_2模型更适用于中国东部地区的GNSS水汽探测及气象研究。
基金supported by the Guangxi Natural Science Foundation of China (2020GXNSFBA297145,Guike AD23026177)the Foundation of Guilin University of Technology(GUTQDJJ6616032)+3 种基金Guangxi Key Laboratory of Spatial Information and Geomatics (21-238-21-05)the National Natural Science Foundation of China (42064002,42004025,42074035,42204006)the Innovative Training Program Foundation (202210596015,202210596402)the Open Fund of Hubei Luojia Laboratory(gran 230100020,230100019)。
文摘The potential of monitoring the movement of typhoons using the precipitable water vapor(PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movement of a typhoon in detail minutely and resulting in insufficient accuracy. Hence,based on PWV and meteorological data, we propose an improved typhoon monitoring mode. First, the European Centre for Medium-Range Weather Forecasts Reanalysis 5-derived PWV(ERA5-PWV) and the Global Navigation Satellite System-derived PWV(GNSS-PWV) were compared with the reference radiosonde PWV(RS-PWV). Then, using the PWV and atmospheric parameters derived from ERA5, we discussed the anomalous variations of PWV, pressure(P), precipitation, and wind speed during different typhoons. Finally, we compiled a list of critical factors related to typhoon movement, PWV and P. We developed an improved multi-factor typhoon monitoring mode(IMTM) with different models(i.e.,IMTM-I and IMTM-II) in different cases with a higher density of GNSS observation or only Numerical Weather Prediction(NWP) data. The IMTM was evaluated through the reference movement speeds of HATO and Mangkhut from the China Meteorological Observatory Typhoon Network(CMOTN). The results show that the root mean square(RMS) of the IMTM-I is 1.26 km/h based on ERA5-P and ERA5-PWV,and the absolute bias values are mostly within 2 km/h. Compared with the models considering the single factor ERA5-P/ERA5-PWV, the RMS of the IMTM-I is improved by 26.3% and 38.5%, respectively. The IMTM-II model manifests a residual of only 0.35 km/h. Compared with the single-factor model based on GNSS-PWV/P, the residual of the IMTM-II model is reduced by 90.8% and 84.1%, respectively. These results propose that the typhoon movement monitoring approach combining PWV and P has evident advantages over the single-factor model and is expected to supplement traditional typhoon monitoring.
