scholarly journals Real-Time Short-Term Forecasting Based on Information Management

2014 ◽  
Vol 04 (01) ◽  
pp. 11-21 ◽  
Author(s):  
Jamal Raiyn ◽  
Tomer Toledo
Keyword(s):  
Real Time ◽  
Information Management ◽  
Short Term ◽  
Short Term Forecasting

Short-term forecasting of GDP using large datasets: a pseudo real-time forecast evaluation exercise

2009 ◽  
Vol 28 (7) ◽  
pp. 595-611 ◽  
Author(s):  
G. Rünstler ◽  
K. Barhoumi ◽  
S. Benk ◽  
R. Cristadoro ◽  
A. Den Reijer ◽  
...  
Keyword(s):  
Real Time ◽  
Large Datasets ◽  
Forecast Evaluation ◽  
Short Term ◽  
Evaluation Exercise ◽  
Short Term Forecasting

scholarly journals Short-term forecasting of urban storm water runoff in real-time using extrapolated radar rainfall data

2013 ◽  
Vol 15 (3) ◽  
pp. 897-912 ◽  
Author(s):  
S. Thorndahl ◽  
M. R. Rasmussen
Keyword(s):  
Real Time ◽  
Water Levels ◽  
Rainfall Data ◽  
Storm Water ◽  
Water Runoff ◽  
Short Term ◽  
Radar Rainfall ◽  
Storm Water Runoff ◽  
Short Term Forecasting ◽  
Urban Storm

Model-based short-term forecasting of urban storm water runoff can be applied in real-time control of drainage systems in order to optimize system capacity during rain and minimize combined sewer overflows, improve wastewater treatment or activate alarms if local flooding is impending. A novel online system, which forecasts flows and water levels in real-time with inputs from extrapolated radar rainfall data, has been developed. The fully distributed urban drainage model includes auto-calibration using online in-sewer measurements which is seen to improve forecast skills significantly. The radar rainfall extrapolation (nowcast) limits the lead time of the system to 2 hours. In this paper, the model set-up is tested on a small urban catchment for a period of 1.5 years. The 50 largest events are presented.

scholarly journals Short-term Forecasting of Cumulative Confirmed Covid-19 Cases Pandemic in Somalia

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Dahir Abdi Ali ◽  
Muhammad Sani
Keyword(s):  
Real Time ◽  
Linear Trend ◽  
Evidence Based ◽  
Short Term ◽  
Trend Model ◽  
The Past ◽  
Death Case ◽  
The Future ◽  
The Government ◽  
Short Term Forecasting

Somalia has recorded the first confirmed Covid-19 case and first death case on March 16, and April 08, 2020, respectively. Since its arrival, it had infected 2,603 people and took the lives of 88 people while 577 patients were recovered as of 14 June, 2020. To fight this pandemic, the government requires to make the necessary plans accordingly. To plan effectively, the government needs to answer this question: what will be the effect of Covid-19 cases in the country? To answer this question accurately and objectively, forecasting the spread of confirmed Covid-19 cases will be vital. To this regard, this paper provides real times forecasts of Covid-19 cases employing Holt's linear trend model without seasonality. Provided that the data employed is accurate and the past pattern of the disease will continue in the future, this model is powerful to produce real time forecasts in the future with some degree of uncertainty. With the help of these forecasts, the government can make evidence based decisions by utilizing the scarce resource available at its disposal.

scholarly journals Short-Term Forecasting of GDP Using Large Monthly Datasets: A Pseudo Real-Time Forecast Evaluation Exercise

2008 ◽  
Author(s):  
Barhoumi Karim ◽  
Gerhard Rünstler ◽  
Riccardo Cristadoro ◽  
Ard den Reijer ◽  
Audrone Jakaitiene ◽  
...  
Keyword(s):  
Real Time ◽  
Forecast Evaluation ◽  
Short Term ◽  
Evaluation Exercise ◽  
Short Term Forecasting

scholarly journals Short-term forecasting of COVID-19 in Germany and Poland during the second wave – a preregistered study

2020 ◽  
Author(s):  
J. Bracher ◽  
D. Wolffram ◽  
J. Deuschel ◽  
K. Görgen ◽  
J.L. Ketterer ◽  
...  
Keyword(s):  
Real Time ◽  
Relative Performance ◽  
Lead Times ◽  
Single Model ◽  
Short Term ◽  
Ensemble Forecasts ◽  
Model Predictions ◽  
Short Term Forecasting ◽  
Second Wave ◽  
Pharmaceutical Interventions

AbstractWe report insights from ten weeks of collaborative COVID-19 forecasting for Germany and Poland (12 October – 19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.

REFRACTT 2006

2008 ◽  
Vol 89 (10) ◽  
pp. 1535-1548 ◽  
Author(s):  
Rita D. Roberts ◽  
Frédéric Fabry ◽  
Patrick C. Kennedy ◽  
Eric Nelson ◽  
James W. Wilson ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Real Time ◽  
Index Of Refraction ◽  
Surface Boundary ◽  
Short Term ◽  
Surface Boundary Layer ◽  
Near Surface ◽  
Large Domain ◽  
Short Term Forecasting ◽  
Moisture Variability

The Refractivity Experiment for H2O Research and Collaborative Operational Technology Transfer (REFRACTT), conducted in northeast Colorado during the summer of 2006, provided a unique opportunity to obtain high-resolution gridded moisture fields from the operational Denver Next Generation Weather Radar (NEXRAD) and three research radars using a radar-based index of refraction (refractivity) technique. Until now, it has not been possible to observe and monitor moisture variability in the near-surface boundary layer to such high spatial (4-km horizontal gridpoint spacing) and temporal (4–10-min update rates) resolutions using operational NEXRAD and provide these moisture fields to researchers and the National Weather Service (NWS) forecasters in real time. The overarching goals of REFRACTT were to 1) access and mosaic the refractivity data from the operational NEXRAD and research radars together over a large domain for use by NWS forecasters in real time for short-term forecasting, 2) improve our understanding of near-surface water vapor variability and the role it plays in the initiation of convection and thunderstorms, and 3) improve the accuracy of quantitative precipitation forecasts (QPF) through improved observations and assimilation of low-level moisture fields. This paper presents examples of refractivity-derived moisture fields from REFRACTT in 2006 and the moisture variability observed in the near-surface boundary layer, in association with thunderstorm initiation, and with a cold frontal passage.

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