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.

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 PROCESSING STRATEGY FOR NEAR REAL TIME GPS PRECIPITABLE WATER VAPOR RETRIEVAL

2007 ◽  
Vol 24 (4) ◽  
pp. 275-284
Author(s):  
Jeong-Ho Baek ◽  
Jae-Won Lee ◽  
Byung-Kyu Choi ◽  
Jung-Ho Cho
Keyword(s):  
Water Vapor ◽  
Real Time ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Processing Strategy

scholarly journals Precipitable Water Vapor Retrieval and Analysis by Multiple Data Sources: Ground-Based GNSS, Radio Occultation, Radiosonde, Microwave Satellite, and NWP Reanalysis Data

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Qin Zhang ◽  
Junhua Ye ◽  
Shuangcheng Zhang ◽  
Fei Han
Keyword(s):  
Water Vapor ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Reanalysis Data ◽  
Precipitable Water Vapor ◽  
Reanalysis Dataset ◽  
High Positive Correlation ◽  
Advantages And Disadvantages ◽  
Ecmwf Reanalysis

Precipitable water vapor (PWV) content detection is vital to heavy rain prediction; up to now, lots of different measuring methods and devices are developed to observe PWV. In general, these methods can be divided into two categories, ground-based or space-based. In this study, we analyze the advantages and disadvantages of these technologies, compare retrieved atmosphere parameters by different RO (radio occultation) observations, like FORMOSAT-3/COSMIC (Formosa Satellite-3 and Constellation Observing System for Meteorology, Ionosphere, and Climate) and FY3C (China Feng Yun 3C), and assess retrieved PWV precision with a radiosonde. Besides, we interpolate PWV from NWP (numerical weather prediction) reanalysis data for more comparison and analysis with RO. Specifically, ground-based GNSS is of high precision and continuous availability to monitor PWV distribution; in our paper, we show cases to validate and compare GNSS retrieving PWV with a radiosonde. Except GNSS PWV, we give two different radio occultation sounding results, COSMIC and FY3C, to validate the precision to monitor PWV from space in a global area. FY3C results containing Beidou (China Beidou Global Satellite Navigation System) radio occultation events need to be emphasized. So, in our study, we get the retrieved atmospheric profiles from GPS and Beidou radio occultation observations and derive atmosphere PWV by a variational retrieval method based on these data over a global area. Besides, other space-based methods, such as microwave satellite, are also useful in detecting PWV distribution situations in a global area from space; in this study, we present a case of retrieved PWV using microwave satellite observation. NWP reanalysis data ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-Interim and the new-generation reanalysis data ERA5 provide global grid atmosphere parameters, like surface temperature, different-level pressures, and precipitable water. We show cases of retrieved PWV and validate the precision with radiosonde results and compare new reanalysis dataset ERA5 with ERA-Interim, finding that ERA5 can get higher precision-retrieved atmosphere parameters and PWV. In the end, from our comparison, we find that the retrieved PWV from RO (FY3C and COSMIC) and ECMWF reanalysis data (ERA-Interim and ERA5) have a high positive correlation and that almost all R2 values exceed 0.9, compare retrieved PWV with a radiosonde, and find that whether it is RO and ECMWF reanalysis data, ground-based GNSS, or microwave satellite, they all show small biases.

scholarly journals A New Method for Refining the GNSS-Derived Precipitable Water Vapor Map

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 698 ◽  
Author(s):  
Chen Liu ◽  
Nanshan Zheng ◽  
Kefei Zhang ◽  
Junyu Liu
Keyword(s):  
Water Vapor ◽  
Interpolation Method ◽  
Geographical Location ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Virtual Sample ◽  
Weighted Mean Temperature ◽  
Sample Points ◽  
Elevation Model ◽  
A Minor

Abstract: The objective of the study was to put forth an interpolation method (the LZ method) for refining the GNSS-derived precipitable water vapor (PWV) map. We established a regional weighted mean temperature (Tm) model for this experiment, which introduced a minor difference into the resultant GNSS-derived PWV compared to the previous Tm models. The kernel of the LZ method consists of increasing the sample density via the virtual sample points. These virtual sample points originated from the digital elevation model (DEM) were constructed on the basis of the statistically significant correlation between PWV and geographical location (i.e., geographical coordinates and elevation). The LZ method was validated and compared to the conventional interpolation approach only accounting for the original sample points. The results reflect that the PWV maps generated by the LZ method showed more details than through conventional one. In addition, the prediction performance of the LZ method was better than that of the conventional method by using cross-validation.

scholarly journals Determination of zenith hydrostatic delays and the development of new global long-term GNSS-derived precipitable water vapor

2016 ◽  
Author(s):  
Xiaoming Wang ◽  
Kefei Zhang ◽  
Suqin Wu ◽  
Changyong He ◽  
Yingyan Cheng ◽  
...  
Keyword(s):  
Water Vapor ◽  
Surface Pressure ◽  
Meteorological Data ◽  
Accurate Determination ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
Average Value ◽  
Rms Errors

Abstract. Surface pressure is a vital meteorological variable for the accurate determination of precipitable water vapor (PWV) using Global Navigation Satellite Systems (GNSS). The lack of pressure observations is a big issue for the study of climate using historical GNSS observations, which is a relatively new area of GNSS applications in climatology. Hence the use of the surface pressure derived from either an empirical model (e.g. Global Pressure and Temperature 2 wet, GPT2w) or a global atmospheric reanalysis (e.g. ERA-Interim) becomes an important alternative solution. In this study, pressure derived from these two methods is compared against the pressure observed at 108 global GNSS stations for the period 2000–2013. Results show that a good accuracy is achieved from the GPT2w-derived pressure in the latitude band of −30 to 30° and the average value of Root-Mean-Square (RMS) errors across all the stations in this region is 2.4 mb. Correspondingly, an error of 5.6 mm and 1.0 mm in its resultant zenith hydrostatic delay (ZHD) and PWV is expected. In addition, GPT2w-derived pressure usually has a larger error in the cold season due to large diurnal ranges, which is not considered in the GPT2w model. The average value of the RMS errors of the ERA-Interim-derived pressure across all the 108 stations is 1.1 mb, which will lead to an equivalent error of 2.5 mm and 0.4 mm in its resultant ZHD and PWV respectively. Our research also indicates that the ERA-Interim-derived pressure has the potential to be used as a useful meteorological data source to obtain high accuracy PWV on a global scale for climate studies and the GPT2w-derived pressure can be potentially used for climatology as well although it may be only suitable for the tropical regions.

scholarly journals CONSTRUCTION OF REGIONAL WEIGHTED MEAN TEMPERATURE MODEL BASED ON OPTIMIZATION BP NEURAL NETWORK

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1099-1105
Author(s):  
Z. X. Chen ◽  
L. L. Liu ◽  
L. K. Huang ◽  
Q. T. Wan ◽  
X. Q. Mo
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Bp Neural Network ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Weighted Mean ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Single Station

Abstract. The tropospheric weighted mean temperature (Tm) is one of the key characteristic parameters in the troposphere, which plays an important role in the conversion of Zenith Wet Delay (ZWD) to atmospheric Precipitable Water Vapor (PWV). The precision of Global Navigation Satellite System (GNSS) inversion of PWV can be significantly improved with the accurate calculation of Tm. Due to the strong nonlinear mapping ability of Back Propagation (BP) neural network, the algorithm can be used to excavate the law with massive data. In term of the nonlinear and non-stationary characteristics of GNSS precipitable water vapor, in this paper, we proposes a forecast method of GNSS precipitable water vapor based on BP neural network, which can modelling the weighted mean temperature of troposphere. The traditional BP neural network has some shortcomings, such as large amount of calculation, long training time and easy to appear “over-fitting” phenomenon and so on. In order to optimize the deficiency and numerical simulation, the three characteristic values include water vapor pressure, surface pressure and surface temperature provided are selected as input parameters, named as BP_Tm. The optimal initialization parameters of the model were obtained from the 2016 radiosonde data of 89 radiosonde stations in China, and the modeling and accuracy verification were conducted with the 2017 radiosonde data，and the accuracy of the new model was compared with the common regional Tm model. The results show the BP_Tm model has good simulation accuracy, the average deviation is −0.186K, and the root mean square error is 3.144K. When simulating the weighted mean temperature of a single station, the accuracy of the four models to simulate Tm is compared and analyzed, which the BP_Tm model can obtain good accuracy and reflect better stability and reliability.

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