scholarly journals The Value of Meteorological Data in Optimizing the Pattern of Physical Load—A Forecast Model of Rowing Pacing Strategy

2021 ◽  
Vol 19 (1) ◽  
pp. 320
Author(s):  
Tian Yan ◽  
Xiaodong Zhu ◽  
Xuesong Ding ◽  
Liming Chen
Keyword(s):  
Meteorological Data ◽  
Great Influence ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Physical Load ◽  
Forecast Performance ◽  
Outdoor Sports ◽  
Pacing Strategy ◽  
Different Temperatures ◽  
Meteorological Features

Mastering the information of arena environment is the premise for athletes to optimize their patterns of physical load. Therefore, improving the forecast accuracy of the arena conditions is an urgent task in competitive sports. This paper excavates the meteorological features that have great influence on outdoor events such as rowing and their influence on the pacing strategy. We selected the meteorological data of Tokyo from 1979 to 2020 to forecast the meteorology during the Tokyo 2021 Olympic Games, analyzed the athletes’ pacing choice under different temperatures, humidity and sports levels, and then recommend the best pacing strategy for rowing teams of China. The model proposed in this paper complements the absence of meteorological features in the arena environment assessment and provides an algorithm basis for improving the forecast performance of pacing strategies in outdoor sports.

scholarly journals Improving Forecast Accuracy with an Auto Machine Learning Post-Correction Technique in Northern Xinjiang

2021 ◽  
Vol 11 (17) ◽  
pp. 7931
Author(s):  
Junjian Liu ◽  
Hailiang Zhang ◽  
Huoqing Li ◽  
Ali Mamtimin
Keyword(s):  
Machine Learning ◽  
Relative Humidity ◽  
Wrf Model ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Forecast Performance ◽  
Northern Xinjiang ◽  
Percentage Decrease ◽  
Post Process ◽  
Correction Technique

Reliable meteorological forecasts of temperature and relative humidity are critically important to take necessary measures to avoid potential damage and losses. An operational meteorological forecast model based on the Weather Research and Forecast (WRF) model has been built in Xinjiang. Numerical forecasts usually have significant uncertainties and errors due to imperfections in models themselves. In this study, a straightforward automated machine learning (AutoML) approach has been developed to post-process the raw forecasts of the WRF model. The method was implemented and evaluated to post-process forecasts from 13 stations in northern Xinjiang. The post-processed temperature forecasts were significantly improved from the raw forecasts, with average RMSE values in the 13 stations decreasing from 3.24 °C to 2.34 °C by a large margin of 28%. As for relative humidity, the mean RMSE at 13 stations decreased from 19.54% to 11.54%, or it showed a percentage decrease of 41%. Meanwhile, biases were also significantly decreased, with average ME values being reduced from around 2 °C to ~0.33 °C for temperature and improved from −15.6% to ~0% for relative humidity. Moreover, forecast performance values after post-correction became much closer to each other than raw forecast performance values, improving forecast applicability at regional scales.

scholarly journals Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

2017 ◽  
Vol 21 (9) ◽  
pp. 4841-4859 ◽  
Author(s):  
Sean W. D. Turner ◽  
James C. Bennett ◽  
David E. Robertson ◽  
Stefano Galelli
Keyword(s):  
System Performance ◽  
Research Effort ◽  
Forecast Accuracy ◽  
Strong Relationship ◽  
Forecast Skill ◽  
Severe Drought ◽  
Forecast Performance ◽  
Performance Improvements ◽  
Sensitivity Assessment ◽  
Seasonal Streamflow

Abstract. Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

scholarly journals Mathematical Development and Evaluation of Forecasting Models for Accuracy of Inflation in Developing Countries: A Case of Vietnam

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Nhu-Ty Nguyen ◽  
Thanh-Tuyen Tran
Keyword(s):  
Raw Materials ◽  
Arima Model ◽  
Consumer Price Index ◽  
Forecast Accuracy ◽  
Complex Problem ◽  
Forecast Performance ◽  
Forecasting Models ◽  
Out Of Sample ◽  
Forecasting Inflation ◽  
Using Data

Inflation is a key element of a national economy, and it is also a prominent and important issue influencing the whole economy in terms of marketing. This is a complex problem requiring a large investment of time and wisdom to attain positive results. Thus, appropriate tools for forecasting inflation variables are crucial significant for policy making. In this study, both clarified value calculation and use of a genetic algorithm to find the optimal parameters are adopted simultaneously to construct improved models: ARIMA, GM(1,1), Verhulst, DGM(1,1), and DGM(2,1) by using data of Vietnamese inflation output from January 2005 to November 2013. The MAPE, MSE, RMSE, and MAD are four criteria with which the various forecasting models results are compared. Moreover, to see whether differences exist, Friedman and Wilcoxon tests are applied. Both in-sample and out-of-sample forecast performance results show that the ARIMA model has highly accurate forecasting in Raw Materials Price (RMP) and Gold Price (GP), whereas, the calculated results of GM(1,1) and DGM(1,1) are suitable to forecast Consumer Price Index (CPI). Therefore, the ARIMA, GM(1,1), and DGM(1,1) can handle the forecast accuracy of the issue, and they are suitable in modeling and forecasting of inflation in the case of Vietnam.

scholarly journals Sensitivities of the Madden–Julian oscillation forecasts to configurations of physics in the ECMWF global model

2021 ◽  
Vol 21 (6) ◽  
pp. 4759-4778
Author(s):  
Jun-Ichi Yano ◽  
Nils P. Wedi
Keyword(s):  
Forecast Model ◽  
Global Model ◽  
Madden Julian Oscillation ◽  
Forecast Performance ◽  
Theoretical Investigations ◽  
Forecast Models ◽  
Forecasting System ◽  
The Rich ◽  
The Tropics ◽  
Original Motivation

Abstract. The sensitivities of the Madden–Julian oscillation (MJO) forecasts to various different configurations of the parameterized physics are examined with the global model of ECMWF's Integrated Forecasting System (IFS). The motivation for the study was to simulate the MJO as a nonlinear free wave under active interactions with higher-latitude Rossby waves. To emulate free dynamics in the IFS, various momentum-dissipation terms (“friction”) as well as diabatic heating were selectively turned off over the tropics for the range of the latitudes from 20∘ S to 20∘ N. The reduction of friction sometimes improves the MJO forecasts, although without any systematic tendency. Contrary to the original motivation, emulating free dynamics with an operational forecast model turned out to be rather difficult, because forecast performance sensitively depends on the specific type of friction turned off. The result suggests the need for theoretical investigations that much more closely follow the actual formulations of model physics: a naive approach with a dichotomy of with or without friction simply fails to elucidate the rich behaviour of complex operational models. The paper further exposes the importance of physical processes other than convection for simulating the MJO in global forecast models.

scholarly journals Comparative analysis of cycloid pump based on CFD and fluid structure interactions

2020 ◽  
Vol 12 (11) ◽  
pp. 168781402097353
Author(s):  
Wang Yanhua ◽  
Huang Longlong ◽  
Liu Yong ◽  
Xu Jingsong
Keyword(s):  
Model Building ◽  
Contact Point ◽  
Great Influence ◽  
Test Results ◽  
Fluid Structure Interactions ◽  
Fluid Structure ◽  
Mean Values ◽  
Calculation Results ◽  
Different Temperatures ◽  
Set Up

At present, in the aspect of numerical simulation of cycloid pump, most studies focused on CFD (Computational Fluid Dynamics) in analyzing the pump performance under different service conditions (such as speed, temperature, etc.). The characteristics of the pump under FSI (Fluid Solid Interaction) have not been considered yet. By means of the dynamic mesh technique in the rotating domain, the fluid structure coupling interface is set up on a cycloidal pump model building in COMSOL. The simulation results obtained by applying CFD and FSI are improved by experimental verification. The results show that: (1) the average flow rate of FSI simulation is closer to the test results, and the mean values of CFD and FSI pressure are closer to the actual outlet boundary settings; (2) by comparing the velocity and pressure of rotation region of CFD and FSI at different temperatures, it is concluded that the pressure CFD calculated in the region is more than FSI, and the velocity CFD calculated is less than FSI; (3) by comparing the pressure distribution at some contact point of the fluid structure coupling interface, it is concluded that the fluctuation value of the pressure of CFD with time is greater than that of FSI. Through the comparison, it is found that the coupling has a great influence on the calculation results. The FSI analysis of the pump makes the analysis results more real and more conducive to the analysis of the flow field and rotor dynamics characteristics of the pump.

Sea Ice Thickness Forecast Performance in the Barents Sea

2020 ◽  
Author(s):  
Laura Hume-Wright ◽  
Emma Fiedler ◽  
Nicolas Fournier ◽  
Joana Mendes ◽  
Ed Blockley ◽  
...  
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Forecast Model ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Forecast Performance ◽  
In Situ Data ◽  
The Barents Sea ◽  
Ice Conditions

Abstract The presence of sea ice has a major impact on the safety, operability and efficiency of Arctic operations and navigation. While satellite-based sea ice charting is routinely used for tactical ice management, the marine sector does not yet make use of existing operational sea ice thickness forecasting. However, data products are now freely available from the Copernicus Marine Environment Monitoring Service (CMEMS). Arctic asset managers and vessels’ crews are generally not aware of such products, or these have so far suffered from insufficient accuracy, verification, resolution and adequate format, in order to be well integrated within their existing decision-making processes and systems. The objective of the EU H2020 project “Safe maritime operations under extreme conditions: The Arctic case” (SEDNA) is to improve the safety and efficiency of Arctic navigation. This paper presents a component focusing on the validation of an adaption of the 7-day sea ice thickness forecast from the UK Met Office Forecast Ocean Assimilation Model (FOAM). The experimental forecast model assimilates the CryoSat-2 satellite’s ice freeboard daily data. Forecast skill is evaluated against unique in-situ data from five moorings deployed between 2015 and 2018 by the Barents Sea Metocean and Ice Network (BASMIN) Joint Industry Project. The study shows that the existing FOAM forecasts produce adequate results in the Barents Sea. However, while studies have shown the assimilation of CryoSat-2 data is effective for thick sea ice conditions, this did not improve forecasts for the thinner sea ice conditions of the Barents Sea.

Effect of Temperature and Humidity on Mechanical Properties of Rabbit Hair Fibers

2012 ◽  
Vol 573-574 ◽  
pp. 1230-1234
Author(s):  
Qian Yang
Keyword(s):  
Mechanical Properties ◽  
Tensile Strain ◽  
Breaking Strength ◽  
Great Influence ◽  
Effect Of Temperature ◽  
Breaking Force ◽  
Breaking Elongation ◽  
Different Temperatures ◽  
Hair Fiber ◽  
Fiber Processing

In this paper, it tests the mechanical properties of rabbit hair fibers in different temperatures and humidity. Contrast and analysis by experiment showed the temperature exerts a great influence on the tensile strain, breaking elongation, breaking force and breaking strength. In wetting condition, the tensile strain and breaking elongation increase, but the breaking force and breaking strength decrease. The rabbit hair fiber processing with too much water will make the structure easier to break and destroy.

scholarly journals Probabilistic streamflow forecasts based on hydrologic persistence and large-scale climate signals in central Texas

2010 ◽  
Vol 13 (4) ◽  
pp. 760-774 ◽  
Author(s):  
Wenge Wei ◽  
David W. Watkins
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
River System ◽  
Forecast Model ◽  
Climate Indices ◽  
Lead Times ◽  
Forecast Performance ◽  
Streamflow Forecasts ◽  
Central Texas ◽  
Multiple Predictor

Skillful streamflow forecasts at seasonal lead times may be useful to water managers seeking to provide reliable water supplies and maximize system benefits. In this study, streamflow autocorrelation and large-scale climate information are used to generate probabilistic streamflow forecasts for the Lower Colorado River system in central Texas. A number of potential predictors are evaluated for forecasting flows in various seasons, including large-scale climate indices related to the El Niño/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO) and others. Results indicate that, of the predictors evaluated, only hydrologic persistence and Pacific Ocean sea surface temperature patterns associated with ENSO and PDO provide forecasts which are statistically better than climatology. An ordinal polytomous logistic regression approach is proposed as a means of incorporating multiple predictor variables into a probabilistic forecast model. Forecast performance is assessed through a cross-validation procedure, using distribution-oriented metrics, and implications for decision making are discussed.

scholarly journals Validation of Potato Early Blight Disease Forecast Models for Colorado Using Various Sources of Meteorological Data

Plant Disease ◽  
2003 ◽  
Vol 87 (1) ◽  
pp. 78-84 ◽  
Author(s):  
David H. Gent ◽  
Howard F. Schwartz
Keyword(s):  
Early Blight ◽  
Meteorological Data ◽  
Forecast Accuracy ◽  
Weather Data ◽  
Hourly Temperature ◽  
Initial Appearance ◽  
Forecast Models ◽  
Weather Stations ◽  
Disease Forecast ◽  
Meteorological Observations

Disease forecasts from regional or remotely sensed meteorological data free growers from infield weather data monitoring and may improve disease forecast implementation. This study was initiated to validate potato early blight forecast models in Colorado and to determine the influence of sources of meteorological data on forecast accuracy. Hourly temperatures were recorded by Campbell Scientific CR-10, Pessl Instruments μMetos Model MCR300, and Spectrum Technologies Model 450 WatchDog weather stations and data loggers within potato fields, field-specific temperature estimations generated by mPOWER3/EMERGE from off-site weather stations, and regional COAGMET CR-10 weather stations. Mean hourly temperature deviations between mPOWER3/EMERGE or in-field stations and COAGMET varied from 0.93°C greater to 1.11°C less than COAGMET observations. Initial appearance of early blight lesions was predicted using a 300 physiological day threshold in commercial fields in each year from 1998 to 2001 and in experimental plots in each year from 1997 to 2001 as determined by COAGMET meteorological observations. All sources of meteorological data generated early blight forecasts within 6 days of each other across all locations and years. COAGMET weather stations should free potato growers and integrated pest management personnel from collecting in-field microclimatic data and speed the implementation of disease forecasting.

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