scholarly journals The Meteorological Development Laboratory’s Aviation Weather Prediction System

2010 ◽  
Vol 25 (4) ◽  
pp. 1027-1051 ◽  
Author(s):  
Judy E. Ghirardelli ◽  
Bob Glahn
Keyword(s):  
Communication Networks ◽  
Weather Prediction ◽  
Virgin Islands ◽  
Prediction System ◽  
Probabilistic Forecasts ◽  
The Future ◽  
Ceiling Height ◽  
Gfs Model ◽  
Model Output Statistics ◽  
Aviation Weather

Abstract The Meteorological Development Laboratory (MDL) has developed and implemented an aviation weather prediction system that runs each hour and produces forecast guidance for each hour into the future out to 25 h covering the major forecast period of the National Weather Service (NWS) Terminal Aerodrome Forecast. The Localized Aviation Model Output Statistics (MOS) Program (LAMP) consists of analyses of observations, simple advective models, and a statistical component that updates the longer-range MOS forecasts from the Global Forecast System (GFS) model. LAMP, being an update to GFS MOS, is shown to be an improvement over it, as well as improving over persistence. LAMP produces probabilistic forecasts for the aviation weather elements of ceiling height, sky cover, visibility, obstruction to vision, precipitation occurrence and type, and thunderstorms. Best-category forecasts are derived from these probabilities and their associated thresholds. The LAMP guidance of sensible weather is available for 1591 stations in the contiguous United States, Alaska, Hawaii, Puerto Rico, and the Virgin Islands. Probabilistic guidance of thunderstorms is also available on a grid. The LAMP guidance is available to the entire weather enterprise via NWS communication networks and the World Wide Web. In the future, all station guidance will be gridded and be made available in a form compatible with the NWS’s National Digital Forecast Database.

scholarly journals A Comparative Verification of Localized Aviation Model Output Statistics Program (LAMP) and Numerical Weather Prediction (NWP) Model Forecasts of Ceiling Height and Visibility

2010 ◽  
Vol 25 (4) ◽  
pp. 1161-1178 ◽  
Author(s):  
David E. Rudack ◽  
Judy E. Ghirardelli
Keyword(s):  
United States ◽  
Short Range ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Weather Research And Forecasting ◽  
Model Output ◽  
Probabilistic Forecasts ◽  
Ceiling Height ◽  
Nwp Model ◽  
Model Output Statistics

Abstract In an effort to support aviation forecasting, the National Weather Service’s Meteorological Development Laboratory (MDL) has recently redeveloped the Localized Aviation Model Output Statistics (MOS) Program (LAMP) system. LAMP is designed to run hourly in NWS operations and produce short-range aviation forecast guidance at 1-h projections out to 25 h. This paper compares and contrasts LAMP ceiling height and visibility forecasts with forecasts produced by the 20-km Rapid Update Cycle model (RUC20), the Weather Research and Forecasting Nonhydrostatic Mesoscale Model (WRF-NMM), and the Short-Range Ensemble Forecast system (SREF). RUC20 and WRF-NMM forecasts of continuous ceiling height and visibility were interpolated to stations and converted into categorical forecasts. These interpolated forecasts were also categorized into instrument flight rule (IFR) or lower conditions and verified against LAMP forecasts at stations in the contiguous United States. LAMP and SREF probabilistic forecasts of ceiling height and visibility from LAMP and the SREF system were also verified. This study demonstrates that for the 0000 and 1200 UTC cycles over the contiguous United States, LAMP station-based categorical forecasts of ceiling height, visibility, and IFR conditions or lower are more accurate than the RUC20 and WRF-NMM ceiling height and visibility forecasts interpolated to stations. Moreover, for the 0900 and 2100 UTC forecast cycles and verification periods studied here, LAMP ceiling height and visibility probabilities exhibit better reliability and skill than the SREF system.

A Unified Scheme of Stochastic Physics and Bias Correction in an Ensemble Model to Reduce Both Random and Systematic Errors

2019 ◽  
Vol 34 (6) ◽  
pp. 1675-1691 ◽  
Author(s):  
Yu Xia ◽  
Jing Chen ◽  
Jun Du ◽  
Xiefei Zhi ◽  
Jingzhuo Wang ◽  
...  
Keyword(s):  
Bias Correction ◽  
Weather Prediction ◽  
Systematic Errors ◽  
Ensemble Prediction ◽  
Ensemble Model ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Probabilistic Forecasts ◽  
Ensemble Mean ◽  
Stochastic Physics

Abstract This study experimented with a unified scheme of stochastic physics and bias correction within a regional ensemble model [Global and Regional Assimilation and Prediction System–Regional Ensemble Prediction System (GRAPES-REPS)]. It is intended to improve ensemble prediction skill by reducing both random and systematic errors at the same time. Three experiments were performed on top of GRAPES-REPS. The first experiment adds only the stochastic physics. The second experiment adds only the bias correction scheme. The third experiment adds both the stochastic physics and bias correction. The experimental period is one month from 1 to 31 July 2015 over the China domain. Using 850-hPa temperature as an example, the study reveals the following: 1) the stochastic physics can effectively increase the ensemble spread, while the bias correction cannot. Therefore, ensemble averaging of the stochastic physics runs can reduce more random error than the bias correction runs. 2) Bias correction can significantly reduce systematic error, while the stochastic physics cannot. As a result, the bias correction greatly improved the quality of ensemble mean forecasts but the stochastic physics did not. 3) The unified scheme can greatly reduce both random and systematic errors at the same time and performed the best of the three experiments. These results were further confirmed by verification of the ensemble mean, spread, and probabilistic forecasts of many other atmospheric fields for both upper air and the surface, including precipitation. Based on this study, we recommend that operational numerical weather prediction centers adopt this unified scheme approach in ensemble models to achieve the best forecasts.

scholarly journals Spatially coherent postprocessing of cloud cover ensemble forecasts

2021 ◽  
Author(s):  
Y. Dai ◽  
S. Hemri
Keyword(s):  
Cloud Cover ◽  
Weather Prediction ◽  
Generative Adversarial Networks ◽  
Dependence Structure ◽  
Ensemble Forecasts ◽  
Adversarial Networks ◽  
Probabilistic Forecasts ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics ◽  
Modern Machine

AbstractStatistical postprocessing is commonly applied to reduce location and dispersion errors of probabilistic forecasts provided by numerical weather prediction (NWP) models. If postprocessed forecast scenarios are required, the combination of ensemble model output statistics (EMOS) for univariate postprocessing with ensemble copula coupling (ECC) or the Schaake shuffle (ScS) to retain the dependence structure of the raw ensemble is a state-of-the-art approach. However, modern machine learning methods may lead to both, a better univariate skill and more realistic forecast scenarios. In this study, we postprocess multi-model ensemble forecasts of cloud cover over Switzerland provided by COSMO-E and ECMWF-IFS using (a) EMOS + ECC, (b) EMOS + ScS, (c) dense neural networks (dense NN) + ECC, (d) dense NN + ScS, and (e) conditional generative adversarial networks (cGAN). The different methods are verified using EUMETSAT satellite data. Dense NN shows the best univariate skill, but cGAN performed only slightly worse. Furthermore, cGAN generates realistic forecast scenario maps, while not relying on a dependence template like ECC or ScS, which is particularly favorable in the case of complex topography.

Detection and Ignoring of Blackhole Attack in Vanets Networks

2016 ◽  
Vol 6 (2) ◽  
pp. 1-10
Author(s):  
Chaima Bensaid ◽  
Sofiane Boukli Hacene ◽  
Kamel Mohamed Faraoun
Keyword(s):  
Black Hole ◽  
Communication Networks ◽  
Network Performance ◽  
Vehicular Networks ◽  
Data Communication ◽  
Main Idea ◽  
Black Hole Attack ◽  
Malicious Nodes ◽  
The Future ◽  
Detailed Simulation

Vehicular networks or VANET announce as the communication networks of the future, where the mobility is the main idea. These networks should be able to interconnect vehicles. The optimal goal is that these networks will contribute to safer roads and more effective in the future by providing timely information to drivers and concerned authorities. They are therefore vulnerable to many types of attacks among them the black hole attack. In this attack, a malicious node disseminates spurious replies for any route discovery in order to monopolize all data communication and deteriorate network performance. Many studies have focused on detecting and isolating malicious nodes in VANET. In this paper, the authors present two mechanisms to detect this attack. The main goal is detecting as well as bypass cooperative black hole attack. The authors' approaches have been evaluated by the detailed simulation study with NS2 and the simulation results shows an improvement of protocol performance.

scholarly journals Southern High-Latitude Ensemble Data Assimilation in the Antarctic Mesoscale Prediction System

2005 ◽  
Vol 133 (12) ◽  
pp. 3431-3449 ◽  
Author(s):  
D. M. Barker
Keyword(s):  
Data Assimilation ◽  
Sampling Error ◽  
Forecast Error ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Prediction System ◽  
Spatial Covariance ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
The Antarctic

Abstract Ensemble data assimilation systems incorporate observations into numerical models via solution of the Kalman filter update equations, and estimates of forecast error covariances derived from ensembles of model integrations. In this paper, a particular algorithm, the ensemble square root filter (EnSRF), is tested in a limited-area, polar numerical weather prediction (NWP) model: the Antarctic Mesoscale Prediction System (AMPS). For application in the real-time AMPS, the number of model integrations that can be run to provide forecast error covariances is limited, resulting in an ensemble sampling error that degrades the analysis fit to observations. In this work, multivariate, climatologically plausible forecast error covariances are specified via averaged forecast difference statistics. Ensemble representations of the “true” forecast errors, created using randomized control variables of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) three-dimensional variational (3DVAR) data assimilation system, are then used to assess the dependence of sampling error on ensemble size, data density, and localization of covariances using simulated observation networks. Results highlight the detrimental impact of ensemble sampling error on the analysis increment structure of correlated, but unobserved fields—an issue not addressed by the spatial covariance localization techniques used to date. A 12-hourly cycling EnSRF/AMPS assimilation/forecast system is tested for a two-week period in December 2002 using real, conventional (surface, rawinsonde, satellite retrieval) observations. The dependence of forecast scores on methods used to maintain ensemble spread and the inclusion of perturbations to lateral boundary conditions are studied.

scholarly journals High-Resolution Seeded Simulations of Western North Pacific Ocean Tropical Cyclones in Two Future Extreme Climates

2018 ◽  
Vol 32 (2) ◽  
pp. 309-334
Author(s):  
J. G. McLay ◽  
E. A. Hendricks ◽  
J. Moskaitis
Keyword(s):  
High Resolution ◽  
Pacific Ocean ◽  
Tropical Cyclones ◽  
North Pacific ◽  
Western North Pacific ◽  
North Pacific Ocean ◽  
Weather Prediction ◽  
Bootstrap Analysis ◽  
Western North Pacific Ocean ◽  
The Future

ABSTRACT A variant of downscaling is devised to explore the properties of tropical cyclones (TCs) that originate in the open ocean of the western North Pacific Ocean (WestPac) region under extreme climates. This variant applies a seeding strategy in large-scale environments simulated by phase 5 of the Coupled Model Intercomparison Project (CMIP5) climate-model integrations together with embedded integrations of Coupled Ocean–Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC), an operational, high-resolution, nonhydrostatic, convection-permitting numerical weather prediction (NWP) model. Test periods for the present day and late twenty-first century are sampled from two different integrations for the representative concentration pathway (RCP) 8.5 forcing scenario. Then seeded simulations for the present-day period are contrasted with similar seeded simulations for the future period. Reinforcing other downscaling studies, the seeding results suggest that the future environments are notably more conducive to high-intensity TC activity in the WestPac. Specifically, the future simulations yield considerably more TCs that exceed 96-kt (1 kt ≈ 0.5144 m s−1) intensity, and these TCs exhibit notably greater average life cycle maximum intensity and tend to spend more time above the 96-kt intensity threshold. Also, the future simulations yield more TCs that make landfall at >64-kt intensity, and the average landfall intensity of these storms is appreciably greater. These findings are supported by statistical bootstrap analysis as well as by a supplemental sensitivity analysis. Accounting for COAMPS-TC intensity forecast bias using a quantile-matching approach, the seeded simulations suggest that the potential maximum western North Pacific TC intensities in the future extreme climate may be approximately 190 kt.

scholarly journals Verification of Forecast Weather Surface Variables over Vietnam Using the National Numerical Weather Prediction System

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Tien Du Duc ◽  
Lars Robert Hole ◽  
Duc Tran Anh ◽  
Cuong Hoang Duc ◽  
Thuy Nguyen Ba
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Model Performance ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Prediction System ◽  
Regional Models ◽  
Global Spectral Model ◽  
Numerical Weather ◽  
The Us

The national numerical weather prediction system of Vietnam is presented and evaluated. The system is based on three main models, namely, the Japanese Global Spectral Model, the US Global Forecast System, and the US Weather Research and Forecasting (WRF) model. The global forecast products have been received at 0.25- and 0.5-degree horizontal resolution, respectively, and the WRF model has been run locally with 16 km horizontal resolution at the National Center for Hydro-Meteorological Forecasting using lateral conditions from GSM and GFS. The model performance is evaluated by comparing model output against observations of precipitation, wind speed, and temperature at 168 weather stations, with daily data from 2010 to 2014. In general, the global models provide more accurate forecasts than the regional models, probably due to the low horizontal resolution in the regional model. Also, the model performance is poorer for stations with altitudes greater than 500 meters above sea level (masl). For tropical cyclone performance validations, the maximum wind surface forecast from global and regional models is also verified against the best track of Joint Typhoon Warning Center. Finally, the model forecast skill during a recent extreme rain event in northeast Vietnam is evaluated.

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