scholarly journals Corrigendum: An attempt to retrieve continuous water vapor profiles in marine lower troposphere using shipboard Raman/Mie lidar system

SOLA ◽  
2021 ◽  
Author(s):  
Masaki Katsumata ◽  
Kyoko Taniguchi ◽  
Tomoaki Nishizawa
Keyword(s):  
Water Vapor ◽  
Lower Troposphere ◽  
Lidar System ◽  
Mie Lidar

scholarly journals An Attempt to Retrieve Continuous Water Vapor Profiles in Marine Lower Troposphere Using Shipboard Raman/Mie Lidar System

SOLA ◽  
2020 ◽  
Vol 16A (Special_Edition) ◽  
pp. 6-11
Author(s):  
Masaki Katsumata ◽  
Kyoko Taniguchi ◽  
Tomoaki Nishizawa
Keyword(s):  
Water Vapor ◽  
Lower Troposphere ◽  
Lidar System ◽  
Mie Lidar

scholarly journals Study and mitigation of calibration factor instabilities in a water vapor Raman lidar

2017 ◽  
Vol 10 (7) ◽  
pp. 2745-2758 ◽  
Author(s):  
Leslie David ◽  
Olivier Bock ◽  
Christian Thom ◽  
Pierre Bosser ◽  
Jacques Pelon
Keyword(s):  
Water Vapor ◽  
Lower Troposphere ◽  
Satellite System ◽  
Calibration Factor ◽  
Raman Lidar ◽  
Lidar System ◽  
Short Term ◽  
Term Stability ◽  
Water Vapor Mixing Ratio

Abstract. We have investigated calibration variations in the Rameau water vapor Raman lidar. This lidar system was developed by the Institut National de l'Information Géographique et Forestière (IGN) together with the Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS). It aims at calibrating Global Navigation Satellite System (GNSS) measurements for tropospheric wet delays and sounding the water vapor variability in the lower troposphere. The Rameau system demonstrated good capacity in retrieving water vapor mixing ratio (WVMR) profiles accurately in several campaigns. However, systematic short-term and long-term variations in the lidar calibration factor pointed to persistent instabilities. A careful testing of each subsystem independently revealed that these instabilities are mainly induced by mode fluctuations in the optic fiber used to couple the telescope to the detection subsystem and by the spatial nonuniformity of the photomultiplier photocathodes. Laboratory tests that replicate and quantify these instability sources are presented. A redesign of the detection subsystem is presented, which, combined with careful alignment procedures, is shown to significantly reduce the instabilities. Outdoor measurements were performed over a period of 5 months to check the stability of the modified lidar system. The calibration changes in the detection subsystem were monitored with lidar profile measurements using a common nitrogen filter in both Raman channels. A short-term stability of 2–3 % and a long-term drift of 2–3 % per month are demonstrated. Compared to the earlier Development of Methodologies for Water Vapour Measurement (DEMEVAP) campaign, this is a 3-fold improvement in the long-term stability of the detection subsystem. The overall water vapor calibration factors were determined and monitored with capacitive humidity sensor measurements and with GPS zenith wet delay (ZWD) data. The changes in the water vapor calibration factors are shown to be fairly consistent with the changes in the nitrogen calibration factors. The nitrogen calibration results can be used to correct the overall calibration factors without the need for additional water vapor measurements to within 1 % per month.

Development of a mobile Raman–Mie lidar system for all time water vapor and aerosol detection

2011 ◽  
Vol 112 (2) ◽  
pp. 230-235 ◽  
Author(s):  
Bo Liu ◽  
Decheng Wu ◽  
Aiyuan Fan ◽  
Bangxin Wang ◽  
Lin Yuan ◽  
...  
Keyword(s):  
Water Vapor ◽  
Lidar System ◽  
Mie Lidar

Development of Raman-Mie lidar system for aerosol and water vapor profiling

2018 ◽  
Author(s):  
Min Tan ◽  
JiWei Xu ◽  
DeCheng Wu ◽  
ChenBo Xie ◽  
YingJian Wang ◽  
...  
Keyword(s):  
Water Vapor ◽  
Lidar System ◽  
Mie Lidar

935 nm Differential Absorption Lidar System and Water Vapor Profiles in Convective Boundary Layer

2017 ◽  
Vol 37 (2) ◽  
pp. 0201003
Author(s):  
洪光烈 Hong Guanglie ◽  
李嘉唐 Li Jiatang ◽  
孔 伟 Kong Wei ◽  
葛 烨 Ge Ye ◽  
舒 嵘 Shu Rong
Keyword(s):  
Boundary Layer ◽  
Water Vapor ◽  
Convective Boundary Layer ◽  
Differential Absorption ◽  
Lidar System ◽  
Differential Absorption Lidar ◽  
Convective Boundary

scholarly journals A comparison between the GNSS tomography technique and the WRF model in retrieving 3D wet refractivity field in Hong Kong

2018 ◽  
Author(s):  
Zhaohui Xiong ◽  
Bao Zhang ◽  
Yibin Yao
Keyword(s):  
Hong Kong ◽  
Water Vapor ◽  
Weather Prediction ◽  
Wrf Model ◽  
Vertical Direction ◽  
Lower Troposphere ◽  
Reanalysis Data ◽  
Radiosonde Data ◽  
Rainy Period ◽  
Lower Accuracy

Abstract. Water vapor plays an important role in various scales of weather processes. However, there are limited means to monitor its 3-dimensional (3D) dynamical changes. The Numerical Weather Prediction (NWP) model and the Global Navigation Satellite System (GNSS) tomography technique are two of the limited means. Here, we conduct an interesting comparison between the GNSS tomography technique and the Weather Research and Forecasting (WRF) model (a representative of the NWP models) in retrieving Wet Refractivity (WR) in Hong Kong area during a rainy period and a rainless period. The GNSS tomography technique is used to retrieve WR from the GNSS slant wet delay. The WRF Data Assimilation (WRFDA) model is used to assimilate GNSS Zenith Tropospheric Delay (ZTD) to improve the background data. The WRF model is used to generate reanalysis data using the WRFDA output as the initial values. The radiosonde data are used to validate the WR derived from the GNSS tomography and the reanalysis data. The Root Mean Square (RMS) of the tomographic WR, the reanalysis WR that assimilate GNSS ZTD, and the reanalysis WR that without assimilating GNSS ZTD are 6.50 mm/km, 4.31 mm/km and 4.15 mm/km in the rainy period. The RMS becomes 7.02 mm/km, 7.26 mm/km and 6.35 mm/km in the rainless period. The lower accuracy in the rainless period is mainy due to the sharp variation of WR in the vertical direction. The results also show that assimilating GNSS ZTD into the WRFDA model only slightly improves the accuracy of the reanalysis WR and that the reanalysis WR is better than the tomographic WR in most cases. However, in a special experimental period when the water vapor is highly concentrated in the lower troposphere, the tomographic WR outperforms the reanalysis WR in the lower troposphere. When we assimilate the tomographic WR in the lower troposphere into the WRFDA model, the reanalysis WR is improved.

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