scholarly journals Comparisons between the WRF data assimilation and the GNSS tomography technique in retrieving 3-D wet refractivity fields in Hong Kong

2019 ◽  
Vol 37 (1) ◽  
pp. 25-36 ◽  
Author(s):  
Zhaohui Xiong ◽  
Bao Zhang ◽  
Yibin Yao
Keyword(s):  
Hong Kong ◽  
Water Vapor ◽  
Data Assimilation ◽  
Vertical Direction ◽  
Lower Troposphere ◽  
Radiosonde Data ◽  
Tropospheric Delay ◽  
Dry Period ◽  
Lower Accuracy ◽  
Background Data

Abstract. Water vapor plays an important role in various scales of weather processes. However, there are limited means to accurately describe its three-dimensional (3-D) dynamical changes. The data assimilation technique 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 Data Assimilation (WRFDA) model (a representative of the data assimilation models) in retrieving wet refractivity (WR) in the Hong Kong area during a wet period and a dry period. The GNSS tomography technique is used to retrieve WR from the GNSS slant wet delays. The WRFDA is used to assimilate the zenith tropospheric delay to improve the background data. The radiosonde data are used to validate the WR derived from the GNSS tomography, the WRFDA output, and the background data. The root mean square (rms) of the WR derived from the tomography results, the WRFDA output, and the background data are 6.50, 4.31, and 4.15 mm km−1 in the wet period. The rms becomes 7.02, 7.26, and 6.35 mm km−1 in the dry period. The lower accuracy in the dry period is mainly due to the sharp variation of WR in the vertical direction. The results also show that assimilating GNSS ZTD into the WRFDA only slightly improves the accuracy of the WR and that the WRFDA 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 WRFDA WR in the lower troposphere. When we assimilate the tomographic WR in the lower troposphere into the WRFDA, the retrieved WR is improved.

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.

scholarly journals Application of GNSS Methodologies to Obtain Precipitable Water Vapor (PWV) and Its Comparison with Radiosonde Data

Proceedings ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 24 ◽  
Author(s):  
Raquel Perdiguer-López ◽  
José Luis Berné-Valero ◽  
Natalia Garrido-Villén
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Weather Conditions ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Tropospheric Delay ◽  
The North ◽  
Radiosonde Station ◽  
Observation Method ◽  
North Of Spain

A processing methodology with GNSS observations to obtain Zenith Tropospheric Delay using Bernese GNSS Software version 5.2 is revised in order to obtain Precipitable Water Vapor (PWV). The most traditional PWV observation method is the radiosonde and it is often used as a standard to validate those derived from GNSS. For this reason, a location in the north of Spain, in A Coruña, which has a GNSS station with available data and also a radiosonde station, was chosen. Two GPS weeks, in different weather conditions were calculated. The result of the comparison between the GNSS- retrieved PWV and Radiosonde-PWV is explained in the last section of this paper.

scholarly journals Detecting Water Vapor Variability during Heavy Precipitation Events in Hong Kong Using the GPS Tomographic Technique

2017 ◽  
Vol 34 (5) ◽  
pp. 1001-1019 ◽  
Author(s):  
Biyan Chen ◽  
Zhizhao Liu ◽  
Wai-Kin Wong ◽  
Wang-Chun Woo
Keyword(s):  
Hong Kong ◽  
Water Vapor ◽  
Heavy Rain ◽  
Heavy Precipitation ◽  
Radiosonde Data ◽  
Weather Forecasts ◽  
Moisture Variation ◽  
Tomographic Technique ◽  
Rain Events ◽  
Precipitation Events

AbstractWater vapor has a strong influence on the evolution of heavy precipitation events due to the huge latent heat associated with the phase change process of water. Accurate monitoring of atmospheric water vapor distribution is thus essential in predicting the severity and life cycle of heavy rain. This paper presents a systematic study on the application of tomographic solutions to investigate water vapor variations during heavy precipitation events. Using global positioning system (GPS) observations, the wet refractivity field was constructed at a temporal resolution of 30 min for three heavy precipitation events occurring in Hong Kong, China, in 2010–14. The zenith wet delay (ZWD) is shown to be a good indicator in observing the water vapor evolution in heavy rain events. The variabilities of water vapor at five altitude layers (<1000, 1000–2000, 2000–3000, 3000–5000, and >5000 m) were examined. It revealed that water vapor above 3000 m has larger fluctuation than that under 3000 m, though it accounts for only 10%–25% of the total amount of water vapor. The relative humidity fields derived from tomographic results revealed moisture variation, accumulation, saturation, and condensation during the heavy rain events. The water vapor variabilities observed by tomography have been validated using European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis and radiosonde data. The results positively demonstrated the potential of using water vapor tomographic technique for detecting and monitoring the evolution of heavy rain events.

scholarly journals Evaluation of the ZWD/ZTD Values Derived from MERRA-2 Global Reanalysis Products Using GNSS Observations and Radiosonde Data

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6440
Author(s):  
Liangke Huang ◽  
Lijie Guo ◽  
Lilong Liu ◽  
Hua Chen ◽  
Jun Chen ◽  
...  
Keyword(s):  
Satellite System ◽  
Reanalysis Data ◽  
Radiosonde Data ◽  
Tropospheric Delay ◽  
International Gnss Service ◽  
Vapor Detection ◽  
Lower Accuracy ◽  
Temporal And Spatial Characteristics ◽  
Global Navigation Satellite ◽  
Better Than

Tropospheric delay is one of the main errors affecting high-precision positioning and navigation and is a key parameter of water vapor detection in the Global Navigation Satellite System (GNSS). The second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) is the latest generation of reanalysis data collected by the National Aeronautics and Space Administration (NASA), which can be used to calculate tropospheric delay products with high spatial and temporal resolution. However, there is no report analyzing the accuracy of the zenith tropospheric delay (ZTD) and zenith wet delay (ZWD) calculated from MERRA-2 data. This paper evaluates the performance of the ZTD and ZWD values derived from global MERRA-2 data using global radiosonde data and International GNSS Service (IGS) precise ZTD products. The results are as follows: (1) Taking the precision ZTD products of 316 IGS stations from around the world from 2015 to 2017 as the reference, the average root mean square (RMS) of the ZTD values calculated from the MERRA-2 data is better than 1.35 cm, and the accuracy difference between different years is small. The bias and RMS of the ZTD values show certain seasonal variations, with a higher accuracy in winter and a lower accuracy in summer, and the RMS decreases from the equator to the poles. However, those of the ZTD values do not show obvious variations according to elevation. (2) Relative to the radiosonde data, the RMS of the ZWD and ZTD values calculated from the MERRA-2 data are better than 1.37 cm and 1.45 cm, respectively. Furthermore, the bias and RMS of the ZWD and ZTD values also show some temporal and spatial characteristics, which are similar to the test results of the IGS stations. It is suggested that MERRA-2 data can be used for global tropospheric vertical profile model construction because of their high accuracy and good stability in the global calculation of the ZWD and ZTD.

scholarly journals Near real-time water vapor tomography using ground-based GPS and meteorological data: long-term experiment in Hong Kong

2014 ◽  
Vol 32 (8) ◽  
pp. 911-923 ◽  
Author(s):  
P. Jiang ◽  
S. R. Ye ◽  
Y. Y. Liu ◽  
J. J. Zhang ◽  
P. F. Xia
Keyword(s):  
Hong Kong ◽  
Water Vapor ◽  
Real Time ◽  
Time Window ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Radiosonde Data ◽  
Double Difference ◽  
Statistical Measures ◽  
Term Experiment

Abstract. Water vapor tomography is a promising technique for reconstructing the 4-D moisture field, which is important to the weather forecasting and nowcasting as well as to the numerical weather prediction. A near real-time 4-D water vapor tomographic system is developed in this study. GPS slant water vapor (SWV) observations are derived by a sliding time window strategy using double-difference model and predicted orbits. Besides GPS SWV, surface water vapor measurements are also assimilated as real time observations into the tomographic system in order to improve the distribution of observations in the lowest layers of tomographic grid. A 1-year term experiment in Hong Kong was carried out. The feasibility of the GPS data processing strategy is demonstrated by the good agreement between the time series of GPS-derived Precipitable Water Vapor (PWV) and radio-sounding-derived PWV with a bias of 0.04 mm and a root-mean-square error (RMSE) of 1.75 mm. Using surface humidity observations in the tomographic system, the bias and RMSE between tomography and radiosonde data are decreased by half in the ground level, but such improved effects weaken gradually with the rise of altitude until becoming adverse above 4000 m. The overall bias is decreased from 0.17 to 0.13 g m−3 and RMSE is reduced from 1.43 to 1.28 g m−3. By taking the correlation coefficient and RMSE between tomography and radiosonde individual profile as the statistical measures, quality of individual profile is also improved as the success rate of tomographic solution is increased from 44.44 to 63.82% while the failure rate is reduced from 55.56 to 36.18%.

scholarly journals Improved Zenith Tropospheric Delay Modeling Using the Piecewise Model of Atmospheric Refractivity

2020 ◽  
Vol 12 (23) ◽  
pp. 3876
Author(s):  
Liu Yang ◽  
Jingxiang Gao ◽  
Dantong Zhu ◽  
Nanshan Zheng ◽  
Zengke Li
Keyword(s):  
Weather Forecasting ◽  
Vertical Direction ◽  
Satellite System ◽  
Exponential Model ◽  
Reanalysis Data ◽  
Radiosonde Data ◽  
Tropospheric Delay ◽  
Zenith Tropospheric Delay ◽  
Piecewise Model ◽  
Surrounding Areas

As one of the atmosphere propagation delays, the tropospheric delay is a significant error source that should be properly handled in high-precision global navigation satellite system (GNSS) applications. We propose an improved zenith tropospheric delay (ZTD) modeling method whereby the piecewise model of the atmospheric refractivity is introduced. Compared with using the exponential model to fit ZTD in vertical direction, the ZTD piecewise model has a better performance. Based on ERA5 2.5° × 2.5° reanalysis data produced by the European Centre for Medium-Range Weather Forecasting (ECMWF) from 2013 to 2017, we establish the regional gridded ZTD model (RGZTD) using a trigonometric function for China and the surrounding areas, which ranges from 70° E to 135° E in longitude and from 15° N to 55° N in latitude. To verify the performance of RGZTD model, the ERA5 ZTD data in 2017–2018, the radiosonde ZTD data from 86 radiosonde stations over China in 2017–2018, and the tropospheric delay products on 251 GNSS stations from Crustal Movement Observation Network of China (CMONOC) in 2016–2017 are used as external compliance check data. The results show that the overall accuracy of RGZTD model is better than that of exponential model, UNB3m model, and GPT3 model. Moreover, the accuracy can be improved by about 13.4%, 7.1%, and 6.2% when ERA5 reanalysis data, radiosonde data, and CMONOC data are used as reference values, respectively. High-accuracy ZTD data can be provided because the RGZTD model takes into account the vertical variation of ZTD through the new piecewise model.

scholarly journals High-Precision GNSS PWV and Its Variation Characteristics in China Based on Individual Station Meteorological Data

2021 ◽  
Vol 13 (7) ◽  
pp. 1296
Author(s):  
Mingliang Wu ◽  
Shuanggen Jin ◽  
Zhicai Li ◽  
Yunchang Cao ◽  
Fan Ping ◽  
...  
Keyword(s):  
Water Vapor ◽  
High Precision ◽  
North China ◽  
Southern China ◽  
Meteorological Data ◽  
Water Vapor Pressure ◽  
Eastern China ◽  
Radiosonde Data ◽  
Lower Accuracy ◽  
Weighted Mean Temperature

The Global Navigation Satellite System (GNSS) plays an important role in retrieving high temporal–spatial resolution precipitable water vapor (PWV) and its applications. The weighted mean temperature (Tm) is a key parameter for the GNSS PWV estimation, which acts as the conversion factor from the zenith wet delay (ZWD) to the PWV. The Tm is determined by the air pressure and water vapor pressure, while it is not available nearby most GNSS stations. The empirical formular is often applied for the GNSS station surface temperature (Ts) but has a lower accuracy. In this paper, the temporal and spatial distribution characteristics of the coefficients of the linear Tm-Ts model are analyzed, and then a piecewise-linear Tm-Ts relationship is established for each GPS station using radiosonde data collected from 2011 to 2019. The Tm accuracy was increased by more than 10% and 20% for 86 and 52 radiosonde stations, respectively. The PWV time series at 377 GNSS stations from the infrastructure construction of national geodetic datum modernization and Crustal Movement Observation Network of China (CMONC) were further obtained from the GPS observations and meteorological data from 2011 to 2019. The PWV accuracy was improved when compared with the Bevis model. Furthermore, the daily and monthly average values, long-term trend, and its change characteristics of the PWV were analyzed using the high-precision inversion model. The results showed that the averaged PWV was higher in Central-Eastern China and Southern China and lower in Northwest China, Northeast China, and North China. The PWV is increasing in most parts of China, while the some PWVs in North China show a downward trend.

scholarly journals Compact Operational Tropospheric Water Vapor and Temperature Raman Lidar with Turbulence Resolution

2020 ◽  
Author(s):  
Diego Lange Vega ◽  
Andreas Behrendt ◽  
Volker Wulfmeyer
Keyword(s):  
Boundary Layer ◽  
Water Vapor ◽  
Data Assimilation ◽  
Inversion Layer ◽  
Lower Troposphere ◽  
Weather Conditions ◽  
Statistical Uncertainty ◽  
Temperature Profiles ◽  
Free Troposphere ◽  
Earth System

&lt;p&gt;Here we present the new Atmospheric Raman Temperature and Humidity Sounder (ARTHUS), an exceptional tool for observations in the atmospheric boundary layer during daytime and nighttime with a very short latency. ARTHUS measurements resolve the strength of the inversion layer at the planetary boundary layer top, elevated lids in the free troposphere during daytime and nighttime, and turbulent fluctuations in water vapor and temperature, simultaneously, also during daytime.&lt;/p&gt;&lt;p&gt;The observation of atmospheric moisture and temperature profiles is essential for the understanding and prediction of earth system processes. These are fundamental components of the global and regional energy and water cycles, they determine the radiative transfer through the atmosphere, and are critical for the clouds formation and precipitation. Also, it is expected that the assimilation of high-quality, lower tropospheric WV and T profiles will result in a considerable improvement of the skill of weather forecast models particularly with respect to extreme events.&lt;/p&gt;&lt;p&gt;Very stable and reliable performance was demonstrably achieved during more than 2500 hours of operations time experiencing a huge variety of weather conditions. ARTHUS provides temperature profiles with resolutions of 10-60 s and 7.5-100 m vertically in the lower free troposphere. During daytime, the statistical uncertainty of the WV mixing ratio is &lt;2 % in the lower troposphere for resolutions of 5 minutes and 100 m. Temperature statistical uncertainty is &lt;0.5 K even up to the middle troposphere. ARTHUS fulfills the stringent WMO breakthrough requirements on nowcasting and very short-range forecasting.&lt;/p&gt;&lt;p&gt;This performance serves very well the next generation of very fast rapid-update-cycle data assimilation systems. Ground-based stations and networks can be set up or extended for climate monitoring, verification of weather, climate and earth system models, data assimilation for improving weather forecasts.&lt;/p&gt;

scholarly journals Compact Operational Tropospheric Water Vapor and Temperature Raman Lidar  with Turbulence Resolution

2021 ◽  
Author(s):  
Diego Lange ◽  
Andreas Behrendt ◽  
Volker Wulfmeyer
Keyword(s):  
Boundary Layer ◽  
Water Vapor ◽  
Data Assimilation ◽  
Inversion Layer ◽  
Lower Troposphere ◽  
Statistical Uncertainty ◽  
Temperature Profiles ◽  
Free Troposphere ◽  
Earth System ◽  
Raman Lidar

&lt;p&gt;We present the Atmospheric Raman Temperature and Humidity Sounder (ARTHUS), a new tool for observations in the atmospheric boundary layer and lower free troposphere during daytime and nighttime with very high resolution up to the turbulence scale, high accuracy and precision, and very short latency and illustrate its performance with new measurements examples. ARTHUS measurements resolve the strength of the inversion layer at the planetary boundary layer top, elevated lids in the free troposphere, and turbulent fluctuations in water vapor and temperature, simultaneously (Lange et al., 2019). In addition to thermodynamic variables, ARTHUS provides also independent profiles of the particle backscatter coefficient and the particle extinction coefficient from the rotational Raman signals at 355 nm with much better resolution than a conventional vibrational Raman lidar.&lt;/p&gt;&lt;p&gt;The observation of atmospheric moisture and temperature profiles is essential for the understanding and prediction of earth system processes. These are fundamental components of the global and regional energy and water cycles, they determine the radiative transfer through the atmosphere, and are critical for the cloud formation and precipitation (Wulfmeyer, 2015). Also, as confirmed by case studies, the assimilation of high-quality, lower tropospheric WV and T profiles results in a considerable improvement of the skill of weather forecast models particularly with respect to extreme events.&lt;/p&gt;&lt;p&gt;Very stable and reliable performance was demonstrated during more than 3000 hours of operation experiencing a huge variety of weather conditions, including seaborne operation during the EUREC4A campaign (Bony et al., 2017, Stevens et al., 2020). ARTHUS provides temperature profiles with resolutions of 10-60 s and 7.5-100 m vertically in the lower free troposphere. During daytime, the statistical uncertainty of the WV mixing ratio is &lt;2 % in the lower troposphere for resolutions of 5 minutes and 100 m. Temperature statistical uncertainty is &lt;0.5 K even up to the middle troposphere. Consequently, ARTHUS fulfills the stringent WMO breakthrough requirements on nowcasting and very short-range forecasting (see www. wmo&amp;#8208;sat.info/oscar/observingrequirements).&lt;/p&gt;&lt;p&gt;This performance serves very well the next generation of very fast rapid-update-cycle data assimilation systems. Ground-based stations and networks can be set up or extended for climate monitoring, verification of weather, climate and earth system models, data assimilation for improving weather forecasts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Bony et al., 2017, https://doi.org/10.1007/s10712-017-9428-0&lt;/p&gt;&lt;p&gt;Lange et al., 2019, https://doi.org/10.1029/2019GL085774&lt;/p&gt;&lt;p&gt;Stevens et al. 2020, submitted to ESSD&lt;/p&gt;&lt;p&gt;Wulfmeyer et al., 2015, doi:10.1002/2014RG000476&lt;/p&gt;

scholarly journals A Weighted Mean Temperature Model with Nonlinear Elevation Correction Using China as an Example

2021 ◽  
Vol 13 (19) ◽  
pp. 3887
Author(s):  
Hai Zhu ◽  
Kejie Chen ◽  
Guanwen Huang
Keyword(s):  
Vertical Direction ◽  
Reference Value ◽  
Precipitable Water ◽  
Reanalysis Data ◽  
Radiosonde Data ◽  
Tropospheric Delay ◽  
Weighted Mean ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Elevation Correction

The weighted mean temperature (Tm) is a crucial parameter for determining the tropospheric delay in transforming precipitable water vapor. We used the reanalysis data provided by European Centre for Medium-Range Weather Forecasts (ECMWF) to analyze the distribution characteristics of Tm in the vertical direction in China. To address the problem that the precision of the traditional linear function model is limited in fitting the Tm profile, a scheme using the linear and Fourier functions to fit the Tm profile was constructed. Based on the least squares principle (LSQ) to fit the change in its coefficients over time, a Tm model for China with nonlinear elevation correction (CTm-h) was constructed. The experimental results show that, using ECMWF and radiosonde data to evaluate the precision of the CTm-h model, the RMS is 3.43 K and 4.64 K, respectively. Compared to GPT2w, the precision of the CTm-h model in China is increased by about 26.8%. The CTm-h model provides a significant improvement in the correction effect of Tm in the vertical direction, and the Tm profile calculated by the model is closer to the reference value.

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