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 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 Joint Exploitation of SAR and GNSS for Atmospheric Phase Screens Retrieval Aimed at Numerical Weather Prediction Model Ingestion

2020 ◽  
Vol 12 (4) ◽  
pp. 654 ◽  
Author(s):  
Marco Manzoni ◽  
Andrea Virgilio Monti-Guarnieri ◽  
Eugenio Realini ◽  
Giovanna Venuti
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Fast Method ◽  
Total Delay ◽  
Dense Area ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Phase Screens

This paper proposes a simple and fast method to estimate Atmospheric Phase Screens (APSs) by jointly exploit a stack of Synthetic Aperture Radar (SAR) images and a dataset of GNSS-derived atmospheric product. The output of this processing is conceived to be ingested by Numerical Weather Prediction Models (NWPMs) to improve weather forecasts. In order to provide wide and dense area coverage and to respect requirements in terms of spatial resolution of ingestion products in NWPMs, both Permanent Scatterers (PSs) and Distributed Scatterers (DSs) are jointly exploited. While the formers are by definition stable targets, but unevenly distributed, the latter are ubiquitous but stable only within a certain temporal baseline that can vary depending on the operational frequency of the radar. The proposed method is thus particularly suited for C, L, and P band missions with low temporal baseline between two consecutive acquisitions of the same scene: these conditions, that are both necessary to provide the dense space-time coverage required by meteorologists, allow for a reliable and robust estimation of APSs thanks to the intrinsic limitation of temporal decorrelation. The proposed technique integrates Zenith Total Delay (ZTD) products computed on a very sparse grid from a network of GNSS stations to correct for SAR orbital errors and to provide the missing phase constant from the derived APS map. In this paper, the complete workflow is explained, and a comparison of the derived APSs is performed with phase screens derived from state-of-the-art SAR processing workflow (SqueeSAR®).

scholarly journals Data assimilation of stratospheric constituents: a review

2007 ◽  
Vol 7 (22) ◽  
pp. 5745-5773 ◽  
Author(s):  
W. A. Lahoz ◽  
Q. Errera ◽  
R. Swinbank ◽  
D. Fonteyn
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Chemical Constituents ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Chemical Model ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
State Of Affairs ◽  
Prediction Approach

Abstract. The data assimilation of stratospheric constituents is reviewed. Several data assimilation methods are introduced, with particular consideration to their application to stratospheric constituent measurements. Differences from meteorological data assimilation are outlined. Historically, two approaches have been used to carry out constituent assimilation. One approach has carried constituent assimilation out as part of a Numerical Weather Prediction system; the other has carried it out in a standalone chemical model, often with a more sophisticated representation of chemical processes. Whereas the aim of the Numerical Weather Prediction approach has been to improve weather forecasts, the aims of the chemical model approach have included providing chemical forecasts and analyses of chemical constituents. A range of constituent assimilation systems developed in these two areas is presented and strengths and weaknesses discussed. The use of stratospheric constituent data assimilation to evaluate models, observations and analyses, and to provide analyses of constituents, monitor ozone, and make ozone forecasts is discussed. Finally, the current state of affairs is assessed, future directions are discussed, and potential key drivers identified.

scholarly journals Data assimilation of stratospheric constituents: a review

2007 ◽  
Vol 7 (4) ◽  
pp. 9561-9633 ◽  
Author(s):  
W. A. Lahoz ◽  
Q. Errera ◽  
R. Swinbank ◽  
D. Fonteyn
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Chemical Constituents ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Chemical Model ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
State Of Affairs ◽  
Prediction Approach

Abstract. The data assimilation of stratospheric constituents is reviewed. The data assimilation method is introduced, with particular consideration to its application to stratospheric constituent measurements. Differences from meteorological data assimilation are outlined. Historically, two approaches have been used to carry out constituent assimilation. One approach has carried constituent assimilation out as part of a numerical weather prediction system; the other has carried it out in a standalone chemical model, often with a more sophisticated representation of chemical processes. Whereas the aim of the numerical weather prediction approach has been to improve weather forecasts, the aims of the chemical model approach have included providing chemical forecasts and analyses of chemical constituents. A range of constituent assimilation systems developed in these two areas is presented and strengths and weaknesses discussed. The use of stratospheric constituent data assimilation to evaluate models, observations and analyses, and to provide analyses of constituents, monitor ozone, and make ozone forecasts is discussed. Finally, the current state of affairs is assessed, future directions are discussed, and potential key drivers identified.

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.

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