scholarly journals Skill of Real-Time Seasonal ENSO Model Predictions During 2002–11: Is Our Capability Increasing?

2012 ◽  
Vol 93 (5) ◽  
pp. ES48-ES50 ◽  
Author(s):  
Anthony G. Barnston ◽  
Michael K. Tippett ◽  
Michelle L. L'Heureux ◽  
Shuhua Li ◽  
David G. DeWitt
1989 ◽  
Vol 33 (20) ◽  
pp. 1481-1485 ◽  
Author(s):  
Helene P. Iavecchia ◽  
Paul M. Linton ◽  
Alvah C. Bittner ◽  
James C. Byers

An empirical study was undertaken to collect real-time workload estimates of pilots and copilots performing a resupply mission in a UH-60A flight simulator. Overall and peak workload (OW and PW) ratings were collected for twelve mission segments. These ratings were compared with OW and PW values predicted by the Task Analysis/Workload (TAWL) simulation model. High correlations were found between TAWL-based predictions and crew results for OW ( r = 0.82 to 0.95; p < .01). Lower correlations were found for PW ( r = 0.62; p < .05).


2009 ◽  
Vol 13 (7) ◽  
pp. 1045-1059 ◽  
Author(s):  
J. B. Cai ◽  
Y. Liu ◽  
D. Xu ◽  
P. Paredes ◽  
L. S. Pereira

Abstract. Aiming at developing real time water balance modelling for irrigation scheduling, this study assesses the accuracy of using the reference evapotranspiration (ETo) estimated from daily weather forecast messages (ETo,WF) as model input. A previous study applied to eight locations in China (Cai et al., 2007) has shown the feasibility for estimating ETo,WF with the FAO Penman-Monteith equation using daily forecasts of maximum and minimum temperature, cloudiness and wind speed. In this study, the global radiation is estimated from the difference between the forecasted maximum and minimum temperatures, the actual vapour pressure is estimated from the forecasted minimum temperature and the wind speed is obtained from converting the common wind scales into wind speed. The present application refers to a location in the North China Plain, Daxing, for the wheat crop seasons of 2005–2006 and 2006–2007. Results comparing ETo,WF with ETo computed with observed data (ETo,obs) have shown favourable goodness of fitting indicators and a RMSE of 0.77 mm d−1. ETo was underestimated in the first year and overestimated in the second. The water balance model ISAREG was calibrated with data from four treatments for the first season and validated with data of five treatments in the second season using observed weather data. The calibrated crop parameters were used in the simulations of the same treatments using ETo,WF as model input. Errors in predicting the soil water content are small, 0.010 and 0.012 m3 m−3, respectively for the first and second year. Other indicators also confirm the goodness of model predictions. It could be concluded that using ETo computed from daily weather forecast messages provides for accurate model predictions and to use an irrigation scheduling model in real time.


2012 ◽  
Vol 93 (5) ◽  
pp. 631-651 ◽  
Author(s):  
Anthony G. Barnston ◽  
Michael K. Tippett ◽  
Michelle L. L'Heureux ◽  
Shuhua Li ◽  
David G. DeWitt

Real-time model predictions of ENSO conditions during the 2002–11 period are evaluated and compared to skill levels documented in studies of the 1990s. ENSO conditions are represented by the Niño- 3.4 SST index in the east-central tropical Pacific. The skills of 20 prediction models (12 dynamical, 8 statistical) are examined. Results indicate skills somewhat lower than those found for the less advanced models of the 1980s and 1990s. Using hindcasts spanning 1981–2011, this finding is explained by the relatively greater predictive challenge posed by the 2002–11 period and suggests that decadal variations in the character of ENSO variability are a greater skill-determining factor than the steady but gradual trend toward improved ENSO prediction science and models. After adjusting for the varying difficulty level, the skills of 2002–11 are slightly higher than those of earlier decades. Unlike earlier results, the average skill of dynamical models slightly, but statistically significantly, exceeds that of statistical models for start times just before the middle of the year when prediction has proven most difficult. The greater skill of dynamical models is largely attributable to the subset of dynamical models with the most advanced, highresolution, fully coupled ocean–atmosphere prediction systems using sophisticated data assimilation systems and large ensembles. This finding suggests that additional advances in skill remain likely, with the expected implementation of better physics, numeric and assimilation schemes, finer resolution, and larger ensemble sizes.


2020 ◽  
Author(s):  
J. Bracher ◽  
D. Wolffram ◽  
J. Deuschel ◽  
K. Görgen ◽  
J.L. Ketterer ◽  
...  

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.


Author(s):  
R. G. Desavale

In this work, vibration characteristic diagnosis of misalignment rotor in loosely fitted bearing is investigated using dimensional analysis (DA) approach. A comprehensive empirical model (EM) using nondimensional parameters is developed to diagnose the rotor-bearing system, and EM model has been validated through an experimental setup developed in-house. Experiments are performed for various defects such as misalignment and bearing looseness. The EM results can be used to monitor the real-time conditions of the rotor-bearing system. This work also presents the effect of misalignment and bearing looseness under various load and speed conditions. Further, work has been extended to predict the combined effect of bearing looseness and misalignment. It has been found that EM model predictions of the vibration amplitude are better when compared to experimental results.


2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Zhihai Zheng ◽  
Zeng-Zhen Hu ◽  
Michelle L’Heureux
Keyword(s):  

2021 ◽  
Author(s):  
Genghmun Eng

Epidemiologists have never had such high-quality real-time pandemic data. Modeling CoVID-19 pandemic data became a predictive tool instead of an afterwards analysis. How early CoVID-19 model predictions impacted US Government policies and practices is first reviewed here as an important part of the pandemic history. It spurred independent modeling efforts, such as this, to help develop a better understanding of CoVID-19 spread, and to provide a substitute for the IHME (Institute for Health Metrics & Evaluation, U. Washington) 4-month predictions for the expected pandemic evolution, which they had to revise every couple of weeks. Our alternative model, which was developed over the course of several earlier medrxiv.org preprints, is shown here to provide a good description for the entire USA CoVID-19 pandemic to date, covering: (1) the original CoVID-19 wave [3/21/20-6/07/20], (2) the Summer 2020 Resurgence [6/07/20-9/25/20], (3) the large Winter 2020 Resurgence [9/25/20-3/19/21], (4) a small Spring 2021 "Fourth Wave", [3/19/21-6/07/21], and (5) the present-day Summer 2021 "Fifth Wave" [6/07/21-present], which the USA is now in the midst of. Our analysis of the initial "Fifth Wave" data shows that this wave presently has the capacity to infect virtually all susceptible non-vaccinated persons who practice NO Mask-Wearing and minimal Social Distancing.


Author(s):  
Erida Gjini

AbstractThe SARS-CoV-2 epidemic is one of the biggest challenges healthcare systems worldwide have ever had to face. To curb transmission many countries have adopted social distancing measures and travel restrictions. Estimating the effect of these measures in each context is challenging and requires mathematical models of the transmission dynamics. Projections for the future course of the epidemic strongly rely on model predictions and accurate representation of real-time data as they accumulate. Here I develop an SEIR modeling framework for Covid-19, to evaluate reported cases and fatalities, and to enable forecasting using evidence-based Bayesian parameter estimation. This Bayesian framework offers a tool to parametrize real-time dynamics of Covid-19 cases, and explore the effect of control as it unfolds in any setting. I apply the model to Covid-19 data from Albania, where drastic control measures were put in place already on the day of the first confirmed case. Evaluating the dynamics of reported cases 9-31 March 2020, I estimate parameters and make preliminary projections. Three weeks into the measures, Albanian data already indicate a strong signature of more than 40% transmission reduction, and lend support to a progressively increasing effect of control measures rather than a static one. In the Albanian setting, the model and data match well, projecting the peak of the outbreak may be around 5-15 April, and be contained within 300 active confirmed cases if control continues with the same trend. This framework can be used to understand the quantitative effects of different control measures in real-time, and inform adaptive intervention for success in other settings.


Sign in / Sign up

Export Citation Format

Share Document