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 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 A Fingerprinting Technique for Major Weather Events

2007 ◽  
Vol 46 (7) ◽  
pp. 1053-1066 ◽  
Author(s):  
Benjamin Root ◽  
Paul Knight ◽  
George Young ◽  
Steven Greybush ◽  
Richard Grumm ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Numerical Weather Prediction ◽  
Clustering Algorithm ◽  
Weather Prediction ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Middle Atlantic ◽  
Weather Events ◽  
Operational Forecasting ◽  
Multimodel Ensembles

Abstract Advances in numerical weather prediction have occurred on numerous fronts, from sophisticated physics packages in the latest mesoscale models to multimodel ensembles of medium-range predictions. Thus, the skill of numerical weather forecasts continues to increase. Statistical techniques have further increased the utility of these predictions. The availability of large atmospheric datasets and faster computers has made pattern recognition of major weather events a feasible means of statistically enhancing the value of numerical forecasts. This paper examines the utility of pattern recognition in assisting the prediction of severe and major weather in the Middle Atlantic region. An important innovation in this work is that the analog technique is applied to NWP forecast maps as a pattern-recognition tool rather than to analysis maps as a forecast tool. A technique is described that employs a new clustering algorithm to objectively identify the anomaly patterns or “fingerprints” associated with past events. The potential refinement and applicability of this method as an operational forecasting tool employed by comparing numerical weather prediction forecasts with fingerprints already identified for major weather events are also discussed.

scholarly journals SINGV – the Convective-Scale Numerical Weather Prediction System for Singapore

2019 ◽  
Vol 36 (3) ◽  
Author(s):  
Xiang-Yu Huang ◽  
Dale Barker ◽  
Stuart Webster ◽  
Anurag Dipankar ◽  
Adrian Lock ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Extreme Rainfall ◽  
Prediction System ◽  
Local Data ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
The United Kingdom ◽  
Convective Scale ◽  
The Impact

Extreme rainfall is one of the primary meteorological hazards in Singapore, as well as elsewhere in the deep tropics, and it can lead to significant local flooding. Since 2013, the Meteorological Service Singapore (MSS) and the United Kingdom Met Office (UKMO) have been collaborating to develop a convective-scale Numerical Weather Prediction (NWP) system, called SINGV. Its primary aim is to provide improved weather forecasts for Singapore and the surrounding region, with a focus on improved short-range prediction of localized heavy rainfall. This paper provides an overview of the SINGV development, the latest NWP capabilities at MSS and some key results of evaluation. The paper describes science advances relevant to the development of any km-scale NWP suitable for the deep tropics and provides some insights into the impact of local data assimilation and utility of ensemble predictions.

scholarly journals Improvement in the Forecasting of Heavy Rainfall over South China in the DSAEF_LTP Model by Introducing the Intensity of the Tropical Cyclone

2020 ◽  
Vol 35 (5) ◽  
pp. 1967-1980
Author(s):  
Ding Chenchen ◽  
Fumin Ren ◽  
Yanan Liu ◽  
John L. McBride ◽  
Tian Feng
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
South China ◽  
Numerical Weather Prediction ◽  
Heavy Rainfall ◽  
Prediction Models ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Landfalling Typhoon ◽  
Numerical Weather Prediction Models

AbstractThe intensity of the tropical cyclone has been introduced into the Dynamical-Statistical-Analog Ensemble Forecast (DSAEF) for Landfalling Typhoon (or tropical cyclone) Precipitation (DSAEF_LTP) model. Moreover, the accumulated precipitation prediction experiments have been conducted on 21 target tropical cyclones with daily precipitation ≥ 100 mm in South China from 2012 to 2016. The best forecasting scheme for the DSAEF_LTP model is identified, and the performance of the prediction is compared with three numerical weather prediction models (the European Centre for Medium-Range Weather Forecasts, the Global Forecast System, and T639). The forecasting ability of the DSAEF_LTP model for heavy rainfall (accumulated precipitation ≥ 250 and ≥100 mm) improves when the intensity of the tropical cyclone is introduced, giving some advantages over the three numerical weather prediction models. The selection of analog tropical cyclones with a maximum intensity (during precipitation over land) equaling to or higher than the initial intensity of the target tropical cyclone gives better forecasts. The prediction accuracy for accumulated precipitation is higher for tropical cyclones with higher intensity and higher observed precipitation, with in both cases positive linear correlations with the threat score.

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 Probabilistic forecasting of shallow, rainfall-triggered landslides using real-time numerical weather predictions

2008 ◽  
Vol 8 (2) ◽  
pp. 349-357 ◽  
Author(s):  
J. Schmidt ◽  
G. Turek ◽  
M. P. Clark ◽  
M. Uddstrom ◽  
J. R. Dymond
Keyword(s):  
Soil Moisture ◽  
New Zealand ◽  
Real Time ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Hydrologic Model ◽  
Local Scale ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Infinite Slope Stability Model

Abstract. A project established at the National Institute of Water and Atmospheric Research (NIWA) in New Zealand is aimed at developing a prototype of a real-time landslide forecasting system. The objective is to predict temporal changes in landslide probability for shallow, rainfall-triggered landslides, based on quantitative weather forecasts from numerical weather prediction models. Global weather forecasts from the United Kingdom Met Office (MO) Numerical Weather Prediction model (NWP) are coupled with a regional data assimilating NWP model (New Zealand Limited Area Model, NZLAM) to forecast atmospheric variables such as precipitation and temperature up to 48 h ahead for all of New Zealand. The weather forecasts are fed into a hydrologic model to predict development of soil moisture and groundwater levels. The forecasted catchment-scale patterns in soil moisture and soil saturation are then downscaled using topographic indices to predict soil moisture status at the local scale, and an infinite slope stability model is applied to determine the triggering soil water threshold at a local scale. The model uses uncertainty of soil parameters to produce probabilistic forecasts of spatio-temporal landslide occurrence 48~h ahead. The system was evaluated for a damaging landslide event in New Zealand. Comparison with landslide densities estimated from satellite imagery resulted in hit rates of 70–90%.

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 AROME-MetCoOp: A Nordic Convective-Scale Operational Weather Prediction Model

2017 ◽  
Vol 32 (2) ◽  
pp. 609-627 ◽  
Author(s):  
Malte Müller ◽  
Mariken Homleid ◽  
Karl-Ivar Ivarsson ◽  
Morten A. Ø. Køltzow ◽  
Magnus Lindskog ◽  
...  
Keyword(s):  
Prediction Model ◽  
Weather Prediction ◽  
Satellite System ◽  
Surface Drag ◽  
Cooperative Effort ◽  
Weather Forecasts ◽  
Convective Scale ◽  
Wind Events ◽  
Reflectivity Data ◽  
Global Navigation Satellite

Abstract Since October 2013 a convective-scale weather prediction model has been used operationally to provide short-term forecasts covering large parts of the Nordic region. The model is now operated by a bilateral cooperative effort [Meteorological Cooperation on Operational Numerical Weather Prediction (MetCoOp)] between the Norwegian Meteorological Institute and the Swedish Meteorological and Hydrological Institute. The core of the model is based on the convection-permitting Applications of Research to Operations at Mesoscale (AROME) model developed by Météo-France. In this paper the specific modifications and updates that have been made to suit advanced high-resolution weather forecasts over the Nordic regions are described. This includes modifications in the surface drag description, microphysics, snow assimilation, as well as an update of the ecosystem and surface parameter description. Novel observation types are introduced in the operational runs, including ground-based Global Navigation Satellite System (GNSS) observations and radar reflectivity data from the Norwegian and Swedish radar networks. After almost two years’ worth of experience with the AROME-MetCoOp model, the model’s sensitivities to the use of specific parameterization settings are characterized and the forecast skills demonstrating the benefit as compared with the global European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (ECMWF-IFS) are evaluated. Furthermore, case studies are provided to demonstrate the ability of the model to capture extreme precipitation and wind events.

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