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 Evaluation of Precipitable Water Vapor from Five Reanalysis Products with Ground-Based GNSS Observations

2020 ◽  
Vol 12 (11) ◽  
pp. 1817 ◽  
Author(s):  
Shuaimin Wang ◽  
Tianhe Xu ◽  
Wenfeng Nie ◽  
Chunhua Jiang ◽  
Yuguo Yang ◽  
...  
Keyword(s):  
Water Vapor ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Department Of Energy ◽  
Western China ◽  
Spatial Evolution ◽  
Advantages And Disadvantages ◽  
Temporal And Spatial Evolution ◽  
Temporal And Spatial

At present, the global reliability and accuracy of Precipitable Water Vapor (PWV) from different reanalysis products have not been comprehensively evaluated. In this study, PWV values derived by 268 Global Navigation Satellite Systems (GNSS) stations around the world covering the period from 2016 to 2018 are used to evaluate the accuracies of PWV values from five reanalysis products. The temporal and spatial evolution is not taken into account in this analysis, although the temporal and spatial evolution of atmospheric flows is one of the most important information elements available in numerical weather prediction products. The evaluation results present that five reanalysis products with PWV accuracy from high to low are in the order of the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5), ERA-Interim, Japanese 55-year Reanalysis (JRA-55), National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), and NCEP/DOE (Department of Energy) according to root mean square error (RMSE), bias and correlation coefficient. The ERA5 has the smallest RMSE value of 1.84 mm, while NCEP/NCAR and NCEP/DOE have bigger RMSE values of 3.34 mm and 3.51 mm, respectively. The findings demonstrate that ERA5 and two NCEP reanalysis products have the best and worst performance, respectively, among five reanalysis products. The differences in the accuracy of the five reanalysis products are mainly attributed to the differences in the spatial resolution of reanalysis products. There are some large absolute biases greater than 4 mm between GNSS PWV values and the PWV values of five reanalysis products in the southwest of South America and western China due to the limit of terrains and fewer observations. The accuracies of five reanalysis products are compared in different climatic zones. The results indicate that the absolute accuracies of five reanalysis products are highest in the polar regions and lowest in the tropics. Furthermore, the effects of different seasons on the accuracies of five reanalysis products are also analyzed, which indicates that RMSE values of five reanalysis products in summer and in winter are the largest and the smallest in the temperate regions. Evaluation results from five reanalysis products can help us to learn more about the advantages and disadvantages of the five released water vapor products and promote their applications.

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 Improved Drought Monitoring Index Using GNSS-Derived Precipitable Water Vapor over the Loess Plateau Area

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5566 ◽  
Author(s):  
Qingzhi Zhao ◽  
Xiongwei Ma ◽  
Wanqiang Yao ◽  
Yang Liu ◽  
Zheng Du ◽  
...  
Keyword(s):  
Water Vapor ◽  
Loess Plateau ◽  
Meteorological Data ◽  
Precipitable Water ◽  
Satellite System ◽  
Reanalysis Data ◽  
Precipitable Water Vapor ◽  
Drought Monitoring ◽  
The Loess Plateau ◽  
Monitoring Index

Standardized precipitation evapotranspiration index (SPEI) is an acknowledged drought monitoring index, and the evapotranspiration (ET) used to calculated SPEI is obtained based on the Thornthwaite (TH) model. However, the SPEI calculated based on the TH model is overestimated globally, whereas the more accurate ET derived from the Penman–Monteith (PM) model recommended by the Food and Agriculture Organization of the United Nations is unavailable due to the lack of a large amount of meteorological data at most places. Therefore, how to improve the accuracy of ET calculated by the TH model becomes the focus of this study. Here, a revised TH (RTH) model is proposed using the temperature (T) and precipitable water vapor (PWV) data. The T and PWV data are derived from the reanalysis data and the global navigation satellite system (GNSS) observation, respectively. The initial value of ET for the RTH model is calculated based on the TH model, and the time series of ET residual between the TH and PM models is then obtained. Analyzed results reveal that ET residual is highly correlated with PWV and T, and the correlate coefficient between PWV and ET is −0.66, while that between T and ET for cases of T larger or less than 0 °C are −0.54 and 0.59, respectively. Therefore, a linear model between ET residual and PWV/T is established, and the ET value of the RTH model can be obtained by combining the TH-derived ET and estimated ET residual. Finally, the SPEI calculated based on the RTH model can be obtained and compared with that derived using PM and TH models. Result in the Loess Plateau (LP) region reveals the good performance of the RTH-based SPEI when compared with the TH-based SPEI over the period of 1979–2016. A case analysis in April 2013 over the LP region also indicates the superiority of the RTH-based SPEI at 88 meteorological and 31 GNSS stations when the PM-based SPEI is considered as the reference.

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.

Improved Daytime Column-Integrated Precipitable Water Vapor from Vaisala Radiosonde Humidity Sensors

2008 ◽  
Vol 25 (6) ◽  
pp. 873-883 ◽  
Author(s):  
K. E. Cady-Pereira ◽  
M. W. Shephard ◽  
D. D. Turner ◽  
E. J. Mlawer ◽  
S. A. Clough ◽  
...  
Keyword(s):  
Water Vapor ◽  
Great Plains ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Atmospheric Radiation Measurement ◽  
Atmospheric Transmittance ◽  
Dry Bias

Abstract Accurate water vapor profiles from radiosondes are essential for long-term climate prediction, weather prediction, validation of remote sensing retrievals, and other applications. The Vaisala RS80, RS90, and RS92 radiosondes are among the more commonly deployed radiosondes in the world. However, numerous investigators have shown that the daytime water vapor profiles measured by these instruments present a significant dry bias due to the solar heating of the humidity sensor. This bias in the column-integrated precipitable water vapor (PWV), along with variability due to calibration, can be removed by scaling the humidity profile to agree with the PWV retrieved from a microwave radiometer (MWR), as has been demonstrated by several previous studies. Infrared radiative closure analyses have shown that the MWR PWV does not present daytime versus nighttime differences; thus, scaling by the MWR is a possible approach for removing the daytime dry bias. However, MWR measurements are not routinely available at all radiosonde launch sites. Starting from a long-term series of sonde and MWR PWV measurements from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, the authors have developed a simple correction to the column-integrated sonde PWV, derived from an analysis of the ratio of the MWR and sonde measurements; this correction is a function of the atmospheric transmittance as determined by the solar zenith angle, and it effectively removes the daytime dry bias at all solar zenith angles. The correction was validated by successfully applying it to an independent dataset from the ARM tropical western Pacific (TWP) site.

scholarly journals Comparisons of Performance Using Data Assimilation and Data Fusion Approaches in Acquiring Precipitable Water Vapor: A Case Study of a Western United States of America Area

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2943
Author(s):  
Zhaohui Xiong ◽  
Jizhang Sang ◽  
Xiaogong Sun ◽  
Bao Zhang ◽  
Junyu Li
Keyword(s):  
United States ◽  
Water Vapor ◽  
Data Assimilation ◽  
United States Of America ◽  
Weather Prediction ◽  
Wrf Model ◽  
Precipitable Water ◽  
The United States ◽  
Satellite System ◽  
Precipitable Water Vapor

There are two main types of methods available to obtain precipitable water vapor (PWV) with high accuracy. One is to assimilate observations into a numerical weather prediction (NWP) model, for example, the Weather Research and Forecasting (WRF) model, to improve the accuracy of meteorological parameters, and then obtain the PWV with improved accuracy. The other is the direct fusion of multi-source PWV products. Regarding the two approaches, we conduct a comparison experiment on the West Coast of the United States of America with the data from May 2018, in which the WRF data assimilation (DA) system is used to assimilate the Global Navigation Satellite System (GNSS) PWV, while the method by Zhang et al. to fuse the GNSS PWV, ERA5 PWV and MODIS (moderate-resolution imaging spectroradiometer) PWV. As a result, four groups of PWV products are generated: the assimilated GNSS PWV, the unassimilated GNSS PWV, PWV from the fusion of the GNSS PWV and ECWMF (European Centre for Medium-Range Weather Forecasts) ERA5 (ECWMF Reanalysis 5) PWV, and PWV from the fusion of the GNSS PWV, ERA5 PWV and MODIS PWV. Experiments show that the data assimilation based on the WRF model (WRFDA) and adopted fusion method can generate PWV products with similar accuracy (1.47 mm vs. 1.52 mm). Assimilating the GNSS PWV into the WRF model slightly improves the accuracy of the inverted PWV by 0.18 mm. The fusion of the MODIS PWV, GNSS PWV and ERA5 PWV results in a higher accuracy than the fusion of GNSS PWV and ERA5 PWV by a margin of 0.35 mm. In addition, the inland canyon topography appears to have an influence on the inversion accuracy of both the methods.

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