Real-time operational ionospheric specification and forecast models

1995 ◽  
Author(s):  
Robert Daniell, E, Jr ◽  
David Anderson
Keyword(s):  
Real Time ◽  
Forecast Models

scholarly journals How Can Existing Ground-Based Profiling Instruments Improve European Weather Forecasts?

2019 ◽  
Vol 100 (4) ◽  
pp. 605-619 ◽  
Author(s):  
A. J. Illingworth ◽  
D. Cimini ◽  
A. Haefele ◽  
M. Haeffelin ◽  
M. Hervo ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Positive Impact ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Time Data ◽  
Weather Forecasts ◽  
Forecast Models ◽  
Wind Lidar ◽  
Microwave Radiometers

Abstract To realize the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog, and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few kilometers are hardly accessible by satellite, especially in dynamically active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real time to forecast centers. The three classes of instruments are i) automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog, and volcanic ash, the last two being especially important for air traffic control; ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind gusts, and low-level jets; and iii) microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. The project includes collaboration from 22 European countries and 15 European national weather services, which involves the implementation of common operating procedures, instrument calibrations, data formats, and retrieval algorithms. Currently, data from 265 ceilometers in 19 countries are being distributed in near–real time to national weather forecast centers; this should soon rise to many hundreds. One wind lidar is currently delivering real time data rising to 5 by the end of 2019, and the plan is to incorporate radiometers in 2020. Initial data assimilation tests indicate a positive impact of the new data.

scholarly journals A Dynamical Initialization Scheme for Real-Time Forecasts of Tropical Cyclones Using the WRF Model*

2013 ◽  
Vol 141 (3) ◽  
pp. 964-986 ◽  
Author(s):  
Dong-Hyun Cha ◽  
Yuqing Wang
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Initial Conditions ◽  
Wrf Model ◽  
Forecast Model ◽  
Initial Position ◽  
Northwest Pacific ◽  
Forecast System ◽  
Positive Effects ◽  
Forecast Models

Abstract To improve the initial conditions of tropical cyclone (TC) forecast models, a dynamical initialization (DI) scheme using cycle runs is developed and implemented into a real-time forecast system for northwest Pacific TCs based on the Weather Research and Forecasting (WRF) Model. In this scheme, cycle runs with a 6-h window before the initial forecast time are repeatedly conducted to spin up the axisymmetric component of the TC vortex until the model TC intensity is comparable to the observed. This is followed by a 72-h forecast using the Global Forecast System (GFS) prediction as lateral boundary conditions. In the DI scheme, the spectral nudging technique is employed during each cycle run to reduce bias in the large-scale environmental field, and the relocation method is applied after the last cycle run to reduce the initial position error. To demonstrate the effectiveness of the proposed DI scheme, 69 forecast experiments with and without the DI are conducted for 13 TCs over the northwest Pacific in 2010 and 2011. The DI shows positive effects on both track and intensity forecasts of TCs, although its overall skill depends strongly on the performance of the GFS forecasts. Compared to the forecasts without the DI, on average, forecasts with the DI reduce the position and intensity errors by 10% and 30%, respectively. The results demonstrate that the proposed DI scheme improves the initial TC vortex structure and intensity and provides warm physics spinup, producing initial states consistent with the forecast model, thus achieving improved track and intensity forecasts.

scholarly journals Landslide early warning based on failure forecast models: the example of the Mt. de La Saxe rockslide, northern Italy

2015 ◽  
Vol 15 (7) ◽  
pp. 1639-1644 ◽  
Author(s):  
A. Manconi ◽  
D. Giordan
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Northern Italy ◽  
Surface Displacements ◽  
Forecast Models ◽  
Landslide Early Warning ◽  
Unstable Slope ◽  
Critical Phases

Abstract. We apply failure forecast models by exploiting near-real-time monitoring data for the La Saxe rockslide, a large unstable slope threatening Aosta Valley in northern Italy. Starting from the inverse velocity theory, we analyze landslide surface displacements automatically and in near real time on different temporal windows and apply straightforward statistical methods to obtain confidence intervals on the estimated time of failure. Here, we present the result obtained for the La Saxe rockslide, a large unstable slope located in Aosta Valley, northern Italy. Based on this case study, we identify operational thresholds that are established on the reliability of the forecast models. Our approach is aimed at supporting the management of early warning systems in the most critical phases of the landslide emergency.

The real time runoff forecast models for the River Dijle

1989 ◽  
Vol 3 (1) ◽  
pp. 1-9 ◽  
Author(s):  
W. Bauwens ◽  
G. L. Vandewiele
Keyword(s):  
Real Time ◽  
The Real ◽  
Forecast Models ◽  
Runoff Forecast

Recursive water quality forecasting models for urban catchments

1987 ◽  
Vol 14 (2) ◽  
pp. 221-229
Author(s):  
Gilles G. Patry
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Moving Average ◽  
Integrated Control ◽  
Oxygen Demand ◽  
Lead Times ◽  
Urban Water Quality ◽  
Combined Sewer ◽  
Forecast Models ◽  
Urban Catchments

Urban water quality forecast models for use in real-time integrated control of combined sewer systems are developed and applied to a small combined sewer system in Hamilton, Ontario. Water quality forecasts for lead times ranging from 5 to 60 min are provided for both suspended solids and chemical oxygen demand. Two modelling approaches are examined: (a) a statistical approach based on the formulation of autoregressive moving-average models with exogenous inputs and (b) a two-stage deterministic/stochastic model based on the first-order surface pollutant washoff model. While both groups of model yield comparable forecasts in terms of the mean absolute percent error in water quality forecasts, statistically based models were found to provide definite operational advantages. Key words: adaptative modelling, real-time forecasting, statistical model, stochastic system, urban hydrology, water quality modelling.

A real time forecast of the electron fluxes measured by GOES 16

2021 ◽  
Author(s):  
Richard Boynton ◽  
Michael Balikhin ◽  
Hualiang Wei
Keyword(s):  
Solar Wind ◽  
Real Time ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Time System ◽  
Real Time System ◽  
Forecast Models ◽  
Forecasting System ◽  
Solar Wind Parameters ◽  
Nonlinear Autoregressive

<p>A real time system is developed to forecast the electron fluxes measured by GOES R spacecraft. Forecast models are developed using the system identification/machine learning methodology based on Nonlinear Autoregressive Moving Average exogenous (NARMAX) models. NARMAX algorithms use past input-output data to automatically deduce a model of the system. Here, the solar wind parameters are used as inputs and the electron fluxes measured by GOES 16 are used as the outputs to deduce the models. The models are then implemented in a real time forecasting system. The forecasting system uses real time solar wind data from ACE, DSCOVR, and ENLIL, which are then processed into the correct format for the NARMAX models to provide a forecast of the electron fluxes at geostationary orbit. </p>

scholarly journals A data based mechanistic real-time flood forecasting module for NFFS FEWS

2012 ◽  
Vol 9 (6) ◽  
pp. 7271-7296 ◽  
Author(s):  
D. Leedal ◽  
A. H. Weerts ◽  
P. J. Smith ◽  
K. J. Beven
Keyword(s):  
Real Time ◽  
Forecast Model ◽  
Open Shell ◽  
Flood Forecasting ◽  
Time Data ◽  
Flood Alert ◽  
Interaction Interface ◽  
Forecast Models ◽  
Forecasting System ◽  
The Uk

Abstract. The data based mechanistic (DBM) approach for identifying and estimating rainfall to level, and level to level models has been shown to perform well for flood forecasting in several studies. The DELFT-FEWS open shell operational flood forecasting system provides a framework linking hydrological/meteorological real-time data, real-time forecast models, and a human/computer interaction interface. This infrastructure is used by the UK National Flood Forecasting System (NFFS) and the European Flood Alert System (EFAS) among others. The open shell nature of the FEWS framework has been specifically designed to make it easy to add new forecasting models written as FEWS modules. This paper shows the development of the DBM forecast model as a FEWS module and presents results for the Eden catchment (Cumbria UK) as a case study.

scholarly journals Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore

2016 ◽  
Vol 124 (9) ◽  
pp. 1369-1375 ◽  
Author(s):  
Yuan Shi ◽  
Xu Liu ◽  
Suet-Yheng Kok ◽  
Jayanthi Rajarethinam ◽  
Shaohong Liang ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Policy Decision ◽  
Forecast Models
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