scholarly journals Development of an Improved Model for Prediction of Short-Term Heavy Precipitation Based on GNSS-Derived PWV

2020 ◽  
Vol 12 (24) ◽  
pp. 4101
Author(s):  
Haobo Li ◽  
Xiaoming Wang ◽  
Suqin Wu ◽  
Kefei Zhang ◽  
Xialan Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Factor Model ◽  
Precipitable Water ◽  
Heavy Precipitation ◽  
Probability Of Detection ◽  
Global Navigation Satellite Systems ◽  
New Model ◽  
Satellite Systems ◽  
Noticeable Increase ◽  
Precipitation Records

Nowadays, the Global Navigation Satellite Systems (GNSS) have become an effective atmospheric observing technique to remotely sense precipitable water vapor (PWV) mainly due to their high spatiotemporal resolutions. In this study, from an investigation for the relationship between GNSS-derived PWV (GNSS-PWV) and heavy precipitation, it was found that from several hours before heavy precipitation, PWV was probably to start with a noticeable increase followed by a steep drop. Based on this finding, a new model including five predictors for heavy precipitation prediction is proposed. Compared with the existing 3-factor model that uses three predictors derived from the ascending trend of PWV time series (i.e., PWV value, PWV increment and rate of the PWV increment), the new model also includes two new predictors derived from the descending trend: PWV decrement and rate of PWV decrement. The use of the two new predictors for reducing the number of misdiagnosis predictions is proposed for the first time. The optimal set of monthly thresholds for the new five-predictor model in each summer month were determined based on hourly GNSS-PWV time series and precipitation records at three co-located GNSS/weather stations during the 8-year period 2010–2017 in the Hong Kong region. The new model was tested using hourly GNSS-PWV and precipitation records obtained at the above three co-located stations during the summer months in 2018 and 2019. Results showed that 189 of the 198 heavy precipitation events were correctly predicted with a lead time of 5.15 h, and the probability of detection reached 95.5%. Compared with the 3-factor method, the new model reduced the FAR score by 32.9%. The improvements made by the new model have great significance for early detection and predictions of heavy precipitation in near real-time.

scholarly journals A New Method for Determining an Optimal Diurnal Threshold of GNSS Precipitable Water Vapor for Precipitation Forecasting

2021 ◽  
Vol 13 (7) ◽  
pp. 1390
Author(s):  
Haobo Li ◽  
Xiaoming Wang ◽  
Suqin Wu ◽  
Kefei Zhang ◽  
Erjiang Fu ◽  
...  
Keyword(s):  
Water Vapor ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Heavy Precipitation ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
New Method ◽  
New Model ◽  
Precipitation Records ◽  
Optimal Set

Nowadays, precipitable water vapor (PWV) retrieved from ground-based Global Navigation Satellite Systems (GNSS) tracking stations has heralded a new era of GNSS meteorological applications, especially for severe weather prediction. Among the existing models that use PWV timeseries to predict heavy precipitation, the “threshold-based” models, which are based on a set of predefined thresholds for the predictors used in the model for predictions, are effective in heavy precipitation nowcasting. In previous studies, monthly thresholds have been widely accepted due to the monthly patterns of different predictors being fully considered. However, the primary weakness of this type of thresholds lies in their poor prediction results in the transitional periods between two consecutive months. Therefore, in this study, a new method for the determination of an optimal set of diurnal thresholds by adopting a 31-day sliding window was first proposed. Both the monthly and diurnal variation characteristics of the predictors were taken into consideration in the new method. Then, on the strength of the new method, an improved PWV-based model for heavy precipitation prediction was developed using the optimal set of diurnal thresholds determined based on the hourly PWV and precipitation records for the summer over the period 2010–2017 at the co-located HKSC–KP (King’s Park) stations in Hong Kong. The new model was evaluated by comparing its prediction results against the hourly precipitation records for the summer in 2018 and 2019. It is shown that 96.9% of heavy precipitation events were correctly predicted with a lead time of 4.86 h, and the false alarms resulting from the new model were reduced to 25.3%. These results suggest that the inclusion of the diurnal thresholds can significantly improve the prediction performance of the model.

scholarly journals Estimating trends in atmospheric water vapor and temperature time series over Germany

2017 ◽  
Author(s):  
Fadwa Alshawaf ◽  
Kyriakos Balidakis ◽  
Galina Dick ◽  
Stefan Heise ◽  
Jens Wickert
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Temporal Trends ◽  
Precipitable Water ◽  
Reanalysis Data ◽  
Global Navigation Satellite Systems ◽  
Satellite Systems ◽  
Surface Measurements ◽  
Standard Deviations ◽  
Gnss Time Series

Abstract. Ground-based GNSS (Global Navigation Satellite Systems) have efficiently been used since the 1990s as a meteorological observing system. Recently scientists used GNSS time series of precipitable water vapor (PWV) for climate research. In this work, we compare the temporal trends estimated from GNSS time series with those estimated from European Center for Medium-Range Weather Forecasts Reanalysis (ERA-Interim) data and meteorological measurements. We aim at evaluating climate evolution in Germany by monitoring different atmospheric variables such as temperature and PWV. PWV time series were obtained by three methods: 1) estimated from ground-based GNSS observations using the method of precise point positioning, 2) inferred from ERA-Interim reanalysis data, and 3) determined based on daily in situ measurements of temperature and relative humidity. The other relevant atmospheric parameters are available from surface measurements of meteorological stations or derived from ERA-Interim. The trends are estimated using two methods, the first applies least squares to seasonally-adjusted time series and the second using the Theil-Sen estimator. The trends estimated at 113 GNSS sites, with 10 and 19 year temporal coverage, varies between −1.5 and 2 mm/decade with standard deviations below 0.25 mm/decade. These values depend on the length and the variations of the time series. Therefore, we estimated the PWV trends using ERA-Interim and surface measurements spanning from 1991 to 2016 (26 years) at synoptic 227 stations over Germany. The former shows positive PWV trends below 0.5 mm/decade while the latter shows positive trends below 0.9 mm/decade with standard deviations below 0.03 mm/decade. The estimated PWV trends correlate with the temperature trends.

scholarly journals Decadal variations in atmospheric water vapor time series estimated using ground-based GNSS

2016 ◽  
Author(s):  
Fadwa Alshawaf ◽  
Galina Dick ◽  
Stefan Heise ◽  
Tzvetan Simeonov ◽  
Sibylle Vey ◽  
...  
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Linear Trend ◽  
Precipitable Water ◽  
The Other ◽  
Global Navigation Satellite Systems ◽  
Positive Trend ◽  
Satellite Systems ◽  
Decadal Variations ◽  
Global Navigation Satellite

Abstract. Ground-based GNSS (Global Navigation Satellite Systems) have efficiently been used since the 1990s as a meteorological observing system. Recently scientists used GNSS time series of precipitable water vapor (PWV) for climate research. In this work, we use time series from GNSS, European Center for Medium-Range Weather Forecasts Reanalysis (ERA-Interim) data, and meteorological measurements to evaluate climate evolution in Central Europe. The assessment of climate change requires monitoring of different atmospheric variables such as temperature, PWV, precipitation, and snow cover. PWV time series were obtained by three methods: 1) estimated from ground-based GNSS observations using the method of precise point positioning, 2) inferred from ERA-Interim data, and 3) determined based on daily surface measurements of temperature and relative humidity. The other variables are available from surface meteorological stations or received from ERA-Interim. The PWV trend component estimated from GNSS data strongly correlates with that estimated from the other data sets. The linear trend is estimated by straight line fitting over 30 years of seasonally-adjusted PWV time series obtained using meteorological measurements. The results show a positive trend in the PWV time series at more than 60 GNSS sites with an increase of 0.3–0.6 mm/decade. In this paper, we compare the results of three stations. The temporal increment of the PWV correlates with the temporal increase in the temperature levels.

scholarly journals Validating HY-2A CMR Precipitable Water Vapor Using Ground-based and Shipborne GNSS Observations

2020 ◽  
Author(s):  
Zhilu Wu ◽  
Yanxiong Liu ◽  
Yang Liu ◽  
Jungang Wang ◽  
Xiufeng He ◽  
...  
Keyword(s):  
Water Vapor ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Global Navigation Satellite Systems ◽  
High Temporal Resolution ◽  
Satellite Systems ◽  
The Indian Ocean ◽  
Global Navigation Satellite ◽  
Gnss Data ◽  
Gnss Observations

Abstract. The calibration microwave radiometer (CMR) onboard Haiyang-2A satellite provides wet tropospheric delays correction for altimetry data, which can also contribute to the understanding of climate system and weather processes. Ground-based Global Navigation Satellite Systems (GNSS) provide precise PWV with high temporal resolution and could be used for calibration and monitoring of the CMR data, and shipborne GNSS provides accurate PWV over open oceans, which can be directly compared with uncontaminated CMR data. In this study, the HY-2A CMR water vapor product is validated using ground-based GNSS observations of 100 IGS stations along the coastline and 56-day shipborne GNSS observations over the Indian Ocean. The processing strategy for GNSS data and CMR data is discussed in detail. Special efforts were made to the quality control and reconstruction of contaminated CMR data. The validation result shows that HY-2A CMR PWV agrees well with ground-based GNSS PWV with 2.67 mm in RMS within 100 km. Geographically, the RMS is 1.12 mm in the polar region and 2.78 mm elsewhere. The PWV agreement between HY-2A and shipborne GNSS shows a significant correlation with the distance between the ship and the satellite footprint, with an RMS of 1.57 mm for the distance threshold of 100 km. Ground-based GNSS and shipborne GNSS agree with HY-2A CMR well with no obvious system error.

On the determination of weighted mean temperature in Indonesia

2021 ◽  
Author(s):  
Nabila Putri ◽  
Johannes Boehm ◽  
Dudy D. Wijaya ◽  
Wedyanto Kuntjoro ◽  
Zamzam Tanuwijaya ◽  
...  
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Water Vapor Pressure ◽  
Precipitable Water ◽  
Global Navigation Satellite Systems ◽  
Satellite Systems ◽  
Mean Temperature ◽  
Weather Model ◽  
Weighted Mean Temperature ◽  
Global Navigation Satellite

<p>The mean temperature weighted with water vapor pressure (Tm) is an important parameter to obtain precipitable water vapor (PWV) from the Global Navigation Satellite Systems (GNSS) observations. This study investigates the possible impacts of equatorial troposphere on Tm estimates and its relation with surface temperature Ts. We calculated Tm in Indonesia from a Numerical Weather Model at nine InaCORS sites. We used 3-hourly ERA5 pressure, temperature, and humidity profiles for the year 2019. We found that Tm and surface temperature Ts in Indonesia have low correlation, less than 0.4. Seasonal and site-specific Tm-Ts relationships have slightly higher correlation, although the values can vary significantly. The highest correlation of around 0.7 is found at site CPUT in Kalimantan. We calculated Tm at nine additional stations in Kalimantan and found that stations located farther from the coast tend to have higher correlation, independent of the seasons. This suggests that Tm is also influenced by the vicinity to the coast. Based on our findings, the use of a general Tm-Ts relationship in Indonesia may not be appropriate. Further studies are necessary to develop an improved Tm over Indonesian region.</p>

Systematic Errors in Global Radiosonde Precipitable Water Data from Comparisons with Ground-Based GPS Measurements

2008 ◽  
Vol 21 (10) ◽  
pp. 2218-2238 ◽  
Author(s):  
Junhong Wang ◽  
Liangying Zhang
Keyword(s):  
Precipitable Water ◽  
Systematic Errors ◽  
Global Navigation Satellite Systems ◽  
Radiosonde Data ◽  
Tropospheric Delay ◽  
Sampling Errors ◽  
Gps Measurements ◽  
Satellite Systems ◽  
Dry Bias

Abstract A global, 10-yr (February 1997–April 2006), 2-hourly dataset of atmospheric precipitable water (PW) was produced from ground-based global positioning system (GPS) measurements of zenith tropospheric delay (ZTD) at approximately 350 International Global Navigation Satellite Systems (GNSS) Service (IGS) ground stations. A total of 130 pairs of radiosonde and GPS stations are found within a 50-km distance and 100-m elevation of each other. At these stations, 14 types of radiosondes are launched and the following 3 types of humidity sensors are used: capacitive polymer, carbon hygristor, and goldbeater’s skin. The PW comparison between radiosonde and GPS data reveals three types of systematic errors in the global radiosonde PW data: measurement biases of the 14 radiosonde types along with their characteristics, long-term temporal inhomogeneity, and diurnal sampling errors of once- and twice-daily radiosonde data. The capacitive polymer generally shows mean dry bias of −1.19 mm (−6.8%). However, the carbon hygristor and goldbeater’s skin hygrometers have mean moist biases of 1.01 mm (3.4%) and 0.76 mm (5.4%), respectively. The protective shield over the humidity sensor boom introduced in late 2000 reduces the PW dry bias from 6.1% and 2.6% in 2000 to 3.9% and −1.14% (wet bias) in 2001 for the Vaisala RS80A and RS80H, respectively. The dry bias in Vaisala radiosondes has larger magnitudes during the day than at night, especially for RS90 and RS92, with a day–night difference of 5%–7%. The time series of monthly mean PW differences between the radiosonde and GPS are able to detect significant changes associated with known radiosonde type changes. Such changes would have a significant impact on the long-term trend estimate. Diurnal sampling errors of twice-daily radiosonde data are generally within 2%, but can be as much as 10%–15% for the once-daily soundings. In conclusion, this study demonstrates that the global GPS PW data are useful for identifying and quantifying several kinds of systematic errors in global radiosonde PW data. Several recommendations are made for future needs of global radiosonde and GPS networks and data.

scholarly journals Twelve Years of High Frequency Absolute Gravity Measurements at the UK’s Space Geodesy Facility: Systematic Signals and Comparison with SLR Heights

2021 ◽  
Author(s):  
Victoria Anne Smith ◽  
Graham Appleby ◽  
Marek Ziebart ◽  
Jose Rodriguez
Keyword(s):  
Time Series ◽  
Gravity Data ◽  
International Comparisons ◽  
Vertical Motion ◽  
Space Geodesy ◽  
Global Navigation Satellite Systems ◽  
Absolute Gravity ◽  
Soil Parameters ◽  
Satellite Systems ◽  
Gravity Measurements

AbstractAbsolute gravity measurements taken on a near-weekly basis at a single location is a rarity. Twelve years of data at the UK’s Space Geodesy Facility (SGF) provides evidence to show that the application of results from international comparisons of absolute gravimeters should be applied to data and are critical to the interpretation of theSGF gravity time series of data from 2007 to 2019. Though residual biases in the data are seen. The SGF time series comprises near weekly data, with exceptions for manufacturer services and participation in international instrument comparisons. Each data set comprises hourly data taken over 1 day, with between 100 and 200 drops per hour. Environmental modelling indicates that the annual groundwater variation at SGFof some 2 m influences the gravity data by 3.1 μGal, based upon some measured and estimated soil parameters. The soil parameters were also used in the calculation of the effect of an additional telescope dome, built above the gravity laboratory, and have been shown to be realistic. Sited in close proximity to the long-established satellite laser ranging (SLR) system and the global navigation satellite systems (GNSS) the absolute gravimetry (AG) measurements provide a complimentary geodetic technique, which is non space-based. The SLR-derived height time series provides an independent measurement of vertical motion at the site which may be used to assess the AG results, which are impacted by ground motion as well as mass changes above and below the instruments.

scholarly journals Analysis of October 23 (Mw 7.2) and November 9 (Mw 5.6), 2011 Van Earthquakes Using Long-Term GNSS Time Series

2017 ◽  
Vol 21 (3) ◽  
pp. 147-156 ◽  
Author(s):  
Ibrahim Tiryakioglu ◽  
Hakan Yavasoglu ◽  
Mehmet Ali Ugur Ugur ◽  
Caglar Ozkaymak ◽  
Mustafa Yilmaz ◽  
...  
Keyword(s):  
Time Series ◽  
Hanging Wall ◽  
Global Navigation Satellite Systems ◽  
Satellite Systems ◽  
Before And After ◽  
Wall Deformation ◽  
Van Earthquakes ◽  
Global Navigation Satellite ◽  
Seismic Deformation ◽  
Gnss Data

The eastern Anatolia provides one of the best examples of an area of rapid deformation and intense contraction that is the consequence of an active continental collision between the Arabian and Eurasian plates leading to large and devastating earthquakes. The latest evidence of the active tectonism in the region is revealed by two remarkable seismic events; Van-Tabanli (Mw 7.2, October 23, 2011) and Van-Edremit (Mw 5.6, November 9, 2011) earthquakes. The study of the earthquake cycle and observation of geodetic and seismic deformation in this region is very important to hazard assessments. In this study, the inter-seismic, co-seismic, and post-seismic movements caused by the above-mentioned earthquakes were investigated using the time series of 2300 days of Global Navigation Satellite Systems (GNSS) observations of the local stations selected from the network of the Continuously Operating Reference Stations, Turkey (CORS-TR). For the inter-seismic period, approximately 1100 daily data were obtained from 21 CORS-TR stations (prior to the earthquakes between October 1, 2008 and October 23, 2011) and evaluated using the GAMIT/GLOBK software. The behaviour of these stations was investigated by processing 1 Hz data from the GNSS stations during the earthquakes on the GAMIT/TRACK software. In addition to October 23 and November 9, the GNSS data on one day before and after the earthquakes was assessed to determine co-seismic deformations. During the October 23 earthquake, hanging-wall deformation of about 60 mm was detected in the SW direction at the MURA station. However, at the VAAN station, deformation of 200 mm (value predicted by time series) was observed in the footwall block in the NW direction. There were not any significant changes at the stations during the November 9 earthquake. For the post-seismic period, the GNSS data from 2012 to 2015 was evaluated. According to the observations, post-seismic deformation continued at the stations close to the epicenter of the earthquake.

scholarly journals The Rapid and Steady Mass Loss of the Patagonian Icefields throughout the GRACE Era: 2002–2017

2019 ◽  
Vol 11 (8) ◽  
pp. 909 ◽  
Author(s):  
Andreas Richter ◽  
Andreas Groh ◽  
Martin Horwath ◽  
Erik Ivins ◽  
Eric Marderwald ◽  
...  
Keyword(s):  
Time Series ◽  
Mass Loss ◽  
A Priori ◽  
Global Navigation Satellite Systems ◽  
Mass Change ◽  
Satellite Systems ◽  
Isostatic Adjustment ◽  
Global Navigation Satellite ◽  
Gravity Recovery ◽  
The Antarctic

We use the complete gravity recovery and climate experiment (GRACE) Level-2 monthly time series to derive the ice mass changes of the Patagonian Icefields (Southern Andes). The glacial isostatic adjustment is accounted for by a regional model that is constrained by global navigation satellite systems (GNSS) uplift observations. Further corrections are applied concerning the effect of mass variations in the ocean, in the continental water storage, and of the Antarctic ice sheet. The 161 monthly GRACE gravity field solutions are inverted in the spatial domain through the adjustment of scaling factors applied to a-priori ice mass change patterns based on published remote sensing results for the Southern and Northern Patagonian Icefields, respectively. We infer an ice mass change rate of −24.4 ± 4.7 Gt/a for the Patagonian Icefields between April 2002 and June 2017, which corresponds to a contribution to the eustatic sea level rise of 0.067 ± 0.013 mm/a. Our time series of monthly ice mass changes reveals no indication for an acceleration in ice mass loss. We find indications that the Northern Patagonian Icefield contributes more to the integral ice loss than previously assumed.

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