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 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 HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

2021 ◽  
Vol 13 (11) ◽  
pp. 2179
Author(s):  
Pedro Mateus ◽  
Virgílio B. Mendes ◽  
Sandra M. Plecha
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

Real-time and near real-time ZTD from a local network of low-cost dual-frequency GNSS receivers.

2021 ◽  
Author(s):  
Tomasz Hadas ◽  
Grzegorz Marut ◽  
Jan Kapłon ◽  
Witold Rohm
Keyword(s):  
Water Vapor ◽  
Real Time ◽  
Low Cost ◽  
Weather Prediction ◽  
Local Network ◽  
System Component ◽  
Dual Frequency ◽  
Lower Accuracy ◽  
Gnss Receivers ◽  
The Impact

<p>The dynamics of water vapor distribution in the troposphere, measured with Global Navigation Satellite Systems (GNSS), is a subject of weather research and climate studies. With GNSS, remote sensing of the troposphere in Europe is performed continuously and operationally under the E-GVAP (http://egvap.dmi.dk/) program with more than 2000 permanent stations. These data are one of the assimilation system component of mesoscale weather prediction models (10 km scale) for many nations across Europe. However, advancing precise local forecasts for severe weather requires high resolution models and observing system.   Further densification of the tracking network, e.g. in urban or mountain areas, will be costly when considering geodetic-grade equipment. However, the rapid development of GNSS-based applications results in a dynamic release of mass-market GNSS receivers. It has been demonstrated that post-processing of GPS-data from a dual-frequency low-cost receiver allows retrieving ZTD with high accuracy. Although low-cost receivers are a promising solution to the problem of densifying GNSS networks for water vapor monitoring, there are still some technological limitations and they require further development and calibration.</p><p>We have developed a low-cost GNSS station, dedicated to real-time GNSS meteorology, which provides GPS, GLONASS and Galileo dual-frequency observations either in RINEX v3.04 format or via RTCM v3.3 stream, with either Ethernet or GSM data transmission. The first two units are deployed in a close vicinity of permanent station WROC, which belongs to the International GNSS Service (IGS) network. Therefore, we compare results from real-time and near real-time processing of GNSS observations from a low-cost unit with IGS Final products. We also investigate the impact of replacing a standard patch antenna with an inexpensive survey-grade antenna. Finally, we deploy a local network of low-cost receivers in and around the city of Wroclaw, Poland, in order to analyze the dynamics of troposphere delay at a very high spatial resolution.</p><p>As a measure of accuracy, we use the standard deviation of ZTD differences between estimated ZTD and IGS Final product. For the near real-time mode, that accuracy is 5 mm and 6 mm, for single- (L1) and dual-frequency (L1/L5,E5b) solution, respectively. Lower accuracy of the dual-frequency relative solution we justify by the missing antenna phase center correction model for L5 and E5b frequencies. With the real-time Precise Point Positioning technique, we estimate ZTD with the accuracy of 7.5 – 8.6 mm. After antenna replacement, the accuracy is improved almost by a factor of 2 (to 4.1 mm), which is close to the 3.1 mm accuracy which we obtain in real-time using data from the WROC station.</p>

scholarly journals Near Real Time GPS Water Vapor Monitoring for Numerical Weather Prediction in Germany

2004 ◽  
Vol 82 (1B) ◽  
pp. 361-370 ◽  
Author(s):  
Gerd GENDT ◽  
Galina DICK ◽  
Christoph REIGBER ◽  
Maria TOMASSINI ◽  
Yanxiong LIU ◽  
...  
Keyword(s):  
Water Vapor ◽  
Real Time ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather

scholarly journals Antarctic Low-Tropospheric Humidity Inversions: 10-Yr Climatology

2013 ◽  
Vol 26 (14) ◽  
pp. 5205-5219 ◽  
Author(s):  
Tiina Nygård ◽  
Teresa Valkonen ◽  
Timo Vihma
Keyword(s):  
Water Vapor ◽  
Climate Models ◽  
Weather Prediction ◽  
Temperature Inversion ◽  
Statistical Characteristics ◽  
The Arctic ◽  
Radiosonde Data ◽  
Atmospheric Moisture Budget ◽  
Temperature Inversions ◽  
The Antarctic

Abstract Humidity inversions are nearly permanently present in the coastal Antarctic atmosphere. This is shown based on an investigation of statistical characteristics of humidity inversions at 11 Antarctic coastal stations using radiosonde data from the Integrated Global Radiosonde Archive (IGRA) from 2000 to 2009. The humidity inversion occurrence was highest in winter and spring, and high atmospheric pressure and cloud-free conditions generally increased the occurrence. A typical humidity inversion was less than 200 m deep and 0.2 g kg−1 strong, and a typical humidity profile contained several separate inversion layers. The inversion base height had notable seasonal variations, but generally the humidity inversions were located at higher altitudes than temperature inversions. Roughly half of the humidity inversions were associated with temperature inversions, especially near the surface, and humidity and temperature inversion strengths as well as depths correlated at several stations. On the other hand, approximately 60% of the humidity inversions were accompanied by horizontal advection of water vapor increasing with height, which is also a probable factor supporting humidity inversions. The spatial variability of humidity inversions was linked to the topography and the water vapor content of the air. Compared to previous results for the Arctic, the most striking differences in humidity inversions in the Antarctic were a much higher frequency of occurrence in summer, at least under clear skies, and a reverse seasonal cycle of the inversion height. The results can be used as a baseline for validation of weather prediction and climate models and for studies addressing changes in atmospheric moisture budget in the Antarctic.

scholarly journals European GNSS troposphere monitoring for meteorological applications

2021 ◽  
Vol 906 (1) ◽  
pp. 012058
Author(s):  
Jan Douša ◽  
Pavel Václavovic ◽  
Petr Bezdĕka ◽  
Guergana Guerova
Keyword(s):  
Real Time ◽  
Water Vapour ◽  
Weather Prediction ◽  
Double Difference ◽  
High Temporal Resolution ◽  
Water Vapour Content ◽  
Estimated Parameters ◽  
Individual Site ◽  
Standard Production

Abstract Near real-time GNSS double-difference network processing is a traditional method still used within the EUMETNET EIG GNSS Water Vapour Programme (E-GVAP) for the atmosphere water vapour content monitoring in support of Numerical Weather Prediction. The standard production relies on estimating zenith tropospheric path delays (ZTDs) for GNSS ground stations with a 1-hour time resolution and a latency of 90 minutes. The Precise Point Positioning (PPP) method in real-time mode has reached the reliability and the accuracy comparable to the near real-time solution. The effectiveness of the PPP method relies on exploiting undifferenced observations from individual receivers, thus optimal use of all tracked systems, observations and signal bands, possible in-situ processing, high temporal resolution of estimated parameters and almost without any latency. The solution may implicitly include horizontal tropospheric gradients and slant tropospheric path delays for enabling the monitoring of a local asymmetry of the troposphere around each individual site. We have been estimating ZTD and gradients in real-time continuously since 2015 with a limited number of stations. Recently, the solution has been extended to a pan-European and global production consisting of approximately 200 stations. The real-time product has been assessed cross-comparing ZTDs and horizontal gradients at 11 collocated stations and by validating real-time ZTDs with respect to the final post-processing products.

scholarly journals Progress toward High-Resolution, Real-Time Radiosonde Reports

2016 ◽  
Vol 97 (11) ◽  
pp. 2149-2161 ◽  
Author(s):  
Bruce Ingleby ◽  
Patricia Pauley ◽  
Alexander Kats ◽  
Jeff Ator ◽  
Dennis Keyser ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Numerical Weather Prediction ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Vertical Resolution ◽  
Not Given ◽  
Rounding Errors ◽  
Numerical Weather

Abstract Some real-time radiosonde reports are now available with higher vertical resolution and higher precision than the alphanumeric TEMP code. There are also extra metadata; for example, the software version may indicate whether humidity corrections have been applied at the station. Numerical weather prediction (NWP) centers and other users need to start using the new Binary Universal Form for Representation of Meteorological Data (BUFR) reports because the alphanumeric codes are being withdrawn. TEMP code has various restrictions and complexities introduced when telecommunication speed and costs were overriding concerns; one consequence is minor temperature rounding errors. In some ways BUFR reports are simpler: the whole ascent should be contained in a single report. BUFR reports can also include the time and location of each level; an ascent takes about 2 h and the balloon can drift 100 km or more laterally. This modernization is the largest and most complex change to the worldwide reporting of radiosonde observations for many years; international implementation is taking longer than planned and is very uneven. The change brings both opportunities and challenges. The biggest challenge is that the number and quality of the data from radiosonde ascents may suffer if the assessment of the BUFR reports and two-way communication between data producers and data users are not given the priority they require. It is possible that some countries will only attempt to replicate the old reports in the new format, not taking advantage of the benefits, which include easier treatment of radiosonde drift and a better understanding of instrument and processing details, as well as higher resolution.

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 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.

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