scholarly journals Processing and quality control of FY-3C GNOS data used in numerical weather prediction applications

2019 ◽  
Vol 12 (5) ◽  
pp. 2679-2692 ◽  
Author(s):  
Mi Liao ◽  
Sean Healy ◽  
Peng Zhang
Keyword(s):  
Quality Control ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Satellite System ◽  
Numerical Weather ◽  
Noise Estimate ◽  
Ionosphere Model ◽  
Global Navigation ◽  
Global Navigation Satellite ◽  
Bending Angles

Abstract. The Chinese radio occultation sounder GNOS (Global Navigation Occultation Sounder) is on the FY-3C satellite, which was launched on 23 September 2013. Currently, GNOS data are transmitted via the Global Telecommunications System (GTS), providing 450–500 profiles per day for numerical weather prediction applications. This paper describes the processing of the GNOS profiles with large biases related to L2 signal degradation. A new extrapolation procedure in bending angle space corrects the L2 bending angles using a thin ionosphere model and the fitting relationship between L1 and L2. We apply the approach to improve the L2 extrapolation of GNOS. The new method can effectively eliminate about 90 % of large departures. In addition to the procedure for the L2 degradation, this paper also describes our quality control (QC) for FY-3C GNOS. A noise estimate for the new L2 extrapolation can be used as a QC parameter to evaluate the performance of the extrapolation. A statistical comparison between GNOS bending angles and short-range ECMWF (European Centre for Medium-Range Weather Forecasts) forecast bending angles demonstrates that GNOS performs almost as well as the Global Navigation Satellite System (GNSS) Receiver for Atmospheric Sounding (GRAS), especially in the core region from around 10 to 35 km. The GNOS data with the new L2 extrapolation are suitable for assimilation into numerical weather prediction systems.

scholarly journals Processing and quality control with FY-3C/GNOS data used in numerical weather prediction applications

2018 ◽  
Author(s):  
Mi Liao ◽  
Sean Healy ◽  
Peng Zhang
Keyword(s):  
Quality Control ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Noise Estimate ◽  
Ionosphere Model ◽  
Angle Space ◽  
Prediction Systems ◽  
L1 And L2

Abstract. The Chinese radio occultation sounder GNOS (Global Navigation Occultation Sounder) is on the FY-3C satellite, which was launched on September 23, 2013. Currently, GNOS data is transmitted via the Global Telecommunications System (GTS) providing 450–500 profiles per day for numerical weather prediction applications. This paper describes the processing for the GNOS profiles with large biases, related to L2 signal degradation. A new extrapolation procedure in bending angle space corrects the L2 bending angles, using a thin ionosphere model, and the fitting relationship between L1 and L2. We apply the approach to improve the L2 extrapolation of GNOS. The new method can effectively eliminate about 90 % of the large departures. In addition to the procedure for the L2 degradation, this paper also describes our quality control (QC) for FY-3C/GNOS. A noise estimate for the new L2 extrapolation can be used as a QC parameter to evaluate the performance of the extrapolation. Mean phase delays of L1 and L2 in the tangent height interval of 60 to 80 km are analysed and applied in the QC as well. A statistical comparison between GNOS and ECMWF (European Centre for Medium-Range Weather Forecasts) forecast data demonstrates that GNOS performs almost as well as GRAS, especially in the core region from around 10 to 35 km. The GNOS data with the new L2 extrapolation is suitable for assimilation into numerical weather prediction systems.

scholarly journals Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1204
Author(s):  
Weihua Bai ◽  
Nan Deng ◽  
Yueqiang Sun ◽  
Qifei Du ◽  
Junming Xia ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Satellite System ◽  
Full Time ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
High Vertical Resolution ◽  
Global Coverage ◽  
Global Navigation Satellite

The global navigation satellite system (GNSS) radio occultation (RO) technique is an atmospheric sounding technique that originated in the 1990s. The data provided by this approach are playing a consistently significant role in atmospheric research and related applications. This paper mainly summarizes the applications of RO to numerical weather prediction (NWP) generally and specifically for tropical cyclone (TC) forecast and outlines the prospects of the RO technique. With advantages such as high precision and accuracy, high vertical resolution, full-time and all-weather, and global coverage, RO data have made a remarkable contribution to NWP and TC forecasts. While accounting for only 7% of the total observations in European Centre for Medium-Range Weather Forecasts’ (ECMWF’s) assimilation system, RO has the fourth-largest impact on NWP. The greater the amount of RO data, the better the forecast of NWP. In cases of TC forecasts, assimilating RO data from heights below 6 km and from the upper troposphere and lower stratosphere (UTLS) region contributes to the forecasting accuracy of the track and intensity of TCs in different stages. A statistical analysis showed that assimilating RO data can help restore the critical characteristics of TCs, such as the location and intensity of the eye, eyewall, and rain bands. Moreover, a non-local excess phase assimilation operator can be employed to optimize the assimilation results. With denser RO profiles expected in the future, the accuracy of TC forecast can be further improved. Finally, future trends in RO are discussed, including advanced features, such as polarimetric RO, and RO strategies to increase the number of soundings, such as the use of a cube satellite constellation.

scholarly journals Multi-GNSS Combined Precise Point Positioning Using Additional Observations with Opposite Weight for Real-Time Quality Control

2019 ◽  
Vol 11 (3) ◽  
pp. 311 ◽  
Author(s):  
Wenju Fu ◽  
Guanwen Huang ◽  
Yuanxi Zhang ◽  
Qin Zhang ◽  
Bobin Cui ◽  
...  
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Precise Point Positioning ◽  
Satellite System ◽  
Convergence Time ◽  
Navigation Satellite ◽  
Positioning Accuracy ◽  
Global Navigation ◽  
Point Positioning ◽  
Global Navigation Satellite

The emergence of multiple global navigation satellite systems (multi-GNSS), including global positioning system (GPS), global navigation satellite system (GLONASS), Beidou navigation satellite system (BDS), and Galileo, brings not only great opportunities for real-time precise point positioning (PPP), but also challenges in quality control because of inevitable data anomalies. This research aims at achieving the real-time quality control of the multi-GNSS combined PPP using additional observations with opposite weight. A robust multiple-system combined PPP estimation is developed to simultaneously process observations from all the four GNSS systems as well as single, dual, or triple systems. The experiment indicates that the proposed quality control can effectively eliminate the influence of outliers on the single GPS and the multiple-system combined PPP. The analysis on the positioning accuracy and the convergence time of the proposed robust PPP is conducted based on one week’s data from 32 globally distributed stations. The positioning root mean square (RMS) error of the quad-system combined PPP is 1.2 cm, 1.0 cm, and 3.0 cm in the east, north, and upward components, respectively, with the improvements of 62.5%, 63.0%, and 55.2% compared to those of single GPS. The average convergence time of the quad-system combined PPP in the horizontal and vertical components is 12.8 min and 12.2 min, respectively, while it is 26.5 min and 23.7 min when only using single-GPS PPP. The positioning performance of the GPS, GLONASS, and BDS (GRC) combination and the GPS, GLONASS, and Galileo (GRE) combination is comparable to the GPS, GLONASS, BDS and Galileo (GRCE) combination and it is better than that of the GPS, BDS, and Galileo (GCE) combination. Compared to GPS, the improvements of the positioning accuracy of the GPS and GLONASS (GR) combination, the GPS and Galileo (GE) combination, the GPS and BDS (GC) combination in the east component are 53.1%, 43.8%, and 40.6%, respectively, while they are 55.6%, 48.1%, and 40.7% in the north component, and 47.8%, 40.3%, and 34.3% in the upward component.

scholarly journals Data assimilation of GNSS zenith total delays from a Nordic processing centre

2017 ◽  
Vol 17 (22) ◽  
pp. 13983-13998 ◽  
Author(s):  
Magnus Lindskog ◽  
Martin Ridal ◽  
Sigurdur Thorsteinsson ◽  
Tong Ning
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Bias Correction ◽  
Weather Prediction ◽  
Satellite System ◽  
Initial State ◽  
Numerical Weather ◽  
Processing Centre ◽  
The Impact ◽  
Forecast Quality

Abstract. Atmospheric moisture-related information estimated from Global Navigation Satellite System (GNSS) ground-based receiver stations by the Nordic GNSS Analysis Centre (NGAA) have been used within a state-of-the-art kilometre-scale numerical weather prediction system. Different processing techniques have been implemented to derive the moisture-related GNSS information in the form of zenith total delays (ZTDs) and these are described and compared. In addition full-scale data assimilation and modelling experiments have been carried out to investigate the impact of utilizing moisture-related GNSS data from the NGAA processing centre on a numerical weather prediction (NWP) model initial state and on the ensuing forecast quality. The sensitivity of results to aspects of the data processing, station density, bias-correction and data assimilation have been investigated. Results show benefits to forecast quality when using GNSS ZTD as an additional observation type. The results also show a sensitivity to thinning distance applied for GNSS ZTD observations but not to modifications to the number of predictors used in the variational bias correction applied. In addition, it is demonstrated that the assimilation of GNSS ZTD can benefit from more general data assimilation enhancements and that there is an interaction of GNSS ZTD with other types of observations used in the data assimilation. Future plans include further investigation of optimal thinning distances and application of more advanced data assimilation techniques.

scholarly journals Data assimilation of GNSS Zenith Total Delays from a Nordic processing centre

2017 ◽  
Author(s):  
Magnus Lindskog ◽  
Martin Ridal ◽  
Sigurdur Thorsteinsson ◽  
Tong Ning
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Bias Correction ◽  
Weather Prediction ◽  
Satellite System ◽  
Initial State ◽  
Numerical Weather ◽  
Processing Centre ◽  
The Impact ◽  
Forecast Quality

Abstract. Atmospheric moisture-related information obtained from Global Navigation Satellite System (GNSS) observations from ground-based receiver stations of the Nordic GNSS Analysis Centre (NGAA) have been used within a state-of-the-art km-scale numerical weather prediction system. Different processing techniques have been implemented to derive the the moisture-related GNSS information in the form of Zenith Total Delays (ZTD) and these are described and compared. In addition full scale data assimilation and modelling experiments have been carried out to investigate the impact of utilizing moisture related GNSS data from the NGAA processing centre on a numerical weather prediction (NWP) model initial state and on the following forecast quality. The sensitivity of results to aspects of the data processing, observation density, bias-correction and data assimilation have been investigated. Results show a benefit on forecast quality of using GNSS ZTD as an additional observation type. The results also show a sensitivity to thinning distance applied for GNSS ZTD observations but not to modifications to the number of predictors used in the variational bias correction applied. In addition it is demonstrated that the assimilation of GNSS ZTD can benefit from more general data assimilation enhancements and that there is an interaction of GNSS ZTD with other types of observations used in the data assimilation. Future plans include further investigation of optimal thinning distances and application of more advanced data assimilation techniques.

scholarly journals A Severe Weather Quick Observing System Simulation Experiment (QuickOSSE) of Global Navigation Satellite System (GNSS) Radio Occultation (RO) Superconstellations

2017 ◽  
Vol 145 (2) ◽  
pp. 637-651 ◽  
Author(s):  
S. Mark Leidner ◽  
Thomas Nehrkorn ◽  
John Henderson ◽  
Marikate Mountain ◽  
Tom Yunck ◽  
...  
Keyword(s):  
Global Navigation Satellite System ◽  
Simulation Experiment ◽  
System Simulation ◽  
Weather Prediction ◽  
Satellite System ◽  
Navigation Satellite ◽  
Global Navigation ◽  
Global Navigation Satellite ◽  
Observing System Simulation Experiment ◽  
The Impact

Global Navigation Satellite System (GNSS) radio occultations (RO) over the last 10 years have proved to be a valuable and essentially unbiased data source for operational global numerical weather prediction. However, the existing sampling coverage is too sparse in both space and time to support forecasting of severe mesoscale weather. In this study, the case study or quick observing system simulation experiment (QuickOSSE) framework is used to quantify the impact of vastly increased numbers of GNSS RO profiles on mesoscale weather analysis and forecasting. The current study focuses on a severe convective weather event that produced both a tornado and flash flooding in Oklahoma on 31 May 2013. The WRF Model is used to compute a realistic and faithful depiction of reality. This 2-km “nature run” (NR) serves as the “truth” in this study. The NR is sampled by two proposed constellations of GNSS RO receivers that would produce 250 thousand and 2.5 million profiles per day globally. These data are then assimilated using WRF and a 24-member, 18-km-resolution, physics-based ensemble Kalman filter. The data assimilation is cycled hourly and makes use of a nonlocal, excess phase observation operator for RO data. The assimilation of greatly increased numbers of RO profiles produces improved analyses, particularly of the lower-tropospheric moisture fields. The forecast results suggest positive impacts on convective initiation. Additional experiments should be conducted for different weather scenarios and with improved OSSE systems.

scholarly journals Empirical Model for Total Precipitable Water Retrieval from Ground-based GNSS Observations in Thailand

2020 ◽  
Author(s):  
Weeranat Phasamak ◽  
Seubson Soisuvarn ◽  
Yuttapong Rangsanseri
Keyword(s):  
Real Time ◽  
Global Navigation Satellite System ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Satellite System ◽  
Navigation Satellite ◽  
Total Precipitable Water ◽  
Global Navigation ◽  
Global Navigation Satellite ◽  
Gnss Observations

Retrieval of Total Precipitable Water (TPW) using ground-based Global Navigation Satellite System (GNSS) observations is a challenging task due to its real‐time and high temporal resolution. In this paper, we present a method for establishing an analytic model for retrieving the total precipitable water (TPW) based on Global Navigation Satellite System (GNSS) observations over one-year period from 12 distributed stations across Thailand. The derived zenith total delay (ZTD) at all stations agrees well with the TPW data available from Global Data Assimilation System (GDAS) Numerical Weather Prediction (NWP) model. At first, a unique relationship between the ZTD and the TPW was established by taking into account of the variation of station altitudes. In addition, the bias correction technique using probability distribution function (PDF) matching was also applied to improve the final model. The inversion model of TPW from ZTD is then easily obtained using a numerical technique. The performance of our method has been successfully evaluated on an independent test data. This model can be useful in the further near real-time TPW measurements from widely available GNSS receivers.

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