Modeling and Analysis of Adjacent Grid Point Wind Speed Profiles within and Above a Forest Canopy.

1999 ◽  
Author(s):  
Arnold Tunick
Keyword(s):  
Wind Speed ◽  
Forest Canopy ◽  
Grid Point ◽  
Modeling And Analysis

scholarly journals The XWS open access catalogue of extreme European windstorms from 1979 to 2012

2014 ◽  
Vol 14 (9) ◽  
pp. 2487-2501 ◽  
Author(s):  
J. F. Roberts ◽  
A. J. Champion ◽  
L. C. Dawkins ◽  
K. I. Hodges ◽  
L. C. Shaffrey ◽  
...  
Keyword(s):  
Wind Speed ◽  
Grid Point ◽  
Storm Track ◽  
Wind Gust ◽  
Pressure Gradients ◽  
Valuable Resource ◽  
Model Bias ◽  
Severe Storms ◽  
High Loss ◽  
Catastrophe Models

Abstract. The XWS (eXtreme WindStorms) catalogue consists of storm tracks and model-generated maximum 3 s wind-gust footprints for 50 of the most extreme winter windstorms to hit Europe in the period 1979–2012. The catalogue is intended to be a valuable resource for both academia and industries such as (re)insurance, for example allowing users to characterise extreme European storms, and validate climate and catastrophe models. Several storm severity indices were investigated to find which could best represent a list of known high-loss (severe) storms. The best-performing index was Sft, which is a combination of storm area calculated from the storm footprint and maximum 925 hPa wind speed from the storm track. All the listed severe storms are included in the catalogue, and the remaining ones were selected using Sft. A comparison of the model footprint to station observations revealed that storms were generally well represented, although for some storms the highest gusts were underestimated. Possible reasons for this underestimation include the model failing to simulate strong enough pressure gradients and not representing convective gusts. A new recalibration method was developed to estimate the true distribution of gusts at each grid point and correct for this underestimation. The recalibration model allows for storm-to-storm variation which is essential given that different storms have different degrees of model bias. The catalogue is available at http://www.europeanwindstorms.org .

scholarly journals Machine learning classification of significant tornadoes and hail in the U.S. using ERA5 proximity soundings

2021 ◽  
Author(s):  
Vittorio A. Gensini ◽  
Cody Converse ◽  
Walker S. Ashley ◽  
Mateusz Taszarek
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Random Forest ◽  
Characteristic Curve ◽  
Grid Point ◽  
Severe Weather ◽  
Theoretic Approach ◽  
Random Forest Classification ◽  
Machine Learning Classification ◽  
Forest Classification

AbstractPrevious studies have identified environmental characteristics that skillfully discriminate between severe and significant-severe weather events, but they have largely been limited by sample size and/or population of predictor variables. Given the heightened societal impacts of significant-severe weather, this topic was revisited using over 150 000 ERA5 reanalysis-derived vertical profiles extracted at the grid-point nearest—and just prior to—tornado and hail reports during the period 1996–2019. Profiles were quality-controlled and used to calculate 84 variables. Several machine learning classification algorithms were trained, tested, and cross-validated on these data to assess skill in predicting severe or significant-severe reports for tornadoes and hail. Random forest classification outperformed all tested methods as measured by cross-validated critical success index scores and area under the receiver operating characteristic curve values. In addition, random forest classification was found to be more reliable than other methods and exhibited negligible frequency bias. The top three most important random forest classification variables for tornadoes were wind speed at 500 hPa, wind speed at 850 hPa, and 0–500-m storm-relative helicity. For hail, storm-relative helicity in the 3–6 km and -10 to -30 °C layers, along with 0–6-km bulk wind shear, were found to be most important. A game theoretic approach was used to help explain the output of the random forest classifiers and establish critical feature thresholds for operational nowcasting and forecasting. A use case of spatial applicability of the random forest model is also presented, demonstrating the potential utility for operational forecasting. Overall, this research supports a growing number of weather and climate studies finding admirable skill in random forest classification applications.

Post-processing for NWP Outputs Based on Machine Learning for 2022 Winter Olympics Games over Complex Terrain

2020 ◽  
Author(s):  
Kang Yanyan ◽  
Li Haochen ◽  
Xia Jiangjiang ◽  
Zhang Yingxin
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Complex Terrain ◽  
Weather Prediction ◽  
Grid Point ◽  
Forecast Accuracy ◽  
Post Processing ◽  
Winter Olympics ◽  
Forecast Time ◽  
Station Point

<p>    Weather forecasts play an important role in the Olympic game,especially the mountain snow projects, which will help to find a "window period" for the game. The winter Olympics track is located on very complex terrain, and a detailed weather forecast is needed. A Post-processing method based on machine learning is used for the future-10-days weather prediction with 1-km spatial resolution and 1-hour temporal resolution, which can greatly improve accuracy and refinement of numerical weather prediction(NWP). The ECWMF/RMAPS model data and the automatic weather station data(AWS) from 2015-2018 are prepared for the training data and test data, included 48 features and 4 labels (the observed 2m temperature, relative humidity , 10m wind speed and wind direction ). The model data are grid point, while the AWS data are station point. We take the nearest 9 model point to predict the station point, instead of making an interpolation between the grid point and station point. Then the feature number will be 48*9 in dataset. The interpolation error from grid point to station is eliminated,and the spatial distribution is considered to some extent. Machine leaning method we used are SVM, Random Forest, Gradient Boosting Decision Tree(GBDT) and XGBoost. We find that XGBoost method performs best, slightly better than GBDT and Random Forest. It is noted that we did some feature engineering work before training, and we found that it’s not that the more features, the better the model, while 10 features are enough. Also there is an interesting thing that the features that closely related the labels values becomes less important as the forecast time increases,such as the model outputed 2m temperature, 10m wind speed and wind direction. While some features that forecasters don’t pay attention to become more important in the 6-10 days prediction, such as latent heat flux, snow depth and so on. So it’s necessary to train the model based on dynamic weight parameters for different forecast time. Through the post-processing based on the machine learning method, the forecast accuracy has been greatly improved compared with EC model. The averaged forecast accuracy of 0-10 days for 2m relative humidity, 10m wind speed and direction has been increased by almost 15%, and the temperature accuracy has been increased by 20%~40% ( 40% for 0-3 days, and the accuracy decreased with the forecast time ). </p>

Wind Effect Modeling and Analysis for Estimation of Photovoltaic Module Temperature

2017 ◽  
Vol 140 (1) ◽  
Author(s):  
Dhiraj Magare ◽  
Oruganti Sastry ◽  
Rajesh Gupta ◽  
Birinchi Bora ◽  
Yogesh Singh ◽  
...  
Keyword(s):  
Wind Speed ◽  
Ambient Temperature ◽  
Wind Direction ◽  
Wind Effect ◽  
Photovoltaic Module ◽  
Temperature Sensitive ◽  
Modeling And Analysis ◽  
Model Based ◽  
Pv Modules ◽  
Module Efficiency

The performance of photovoltaic (PV) modules in outdoor field conditions is adversely affected by the rise in module operating temperature. Wind flow around the module affects its temperature significantly, which ultimately influences the module output power. In this paper, a new approach has been presented, for module temperature estimation of different technology PV modules (amorphous Si, hetero-junction with intrinsic thin-layer (HIT) and multicrystalline Si) installed at the site of National Institute of Solar Energy (NISE), India. The model based on presented approach incorporates the effect of wind speed along with wind direction, while considering in-plane irradiance, ambient temperature, and the module efficiency parameters. For all the technology modules, results have been analyzed qualitatively and quantitatively under different wind situations. Qualitative analysis based on the trend of module temperature variation under different wind speed and wind direction along with irradiance and ambient temperature has been presented in detail from experimental data. Quantitative results obtained from presented model showed good agreement with the experimentally measured data for different technology modules. The model based on presented approach showed marked improvement in results with high consistency, in comparison with other models analyzed for different technology modules installed at the site. The improvement was very significant in case of multicrystalline Si technology modules, which is most commonly used and highly temperature sensitive technology. Presented work can be used for estimating the effect of wind on different technology PV modules and for prediction of module temperature, which affects the performance and reliability of PV modules.

scholarly journals On the Ability of the WRF Model to Reproduce the Surface Wind Direction over Complex Terrain

2013 ◽  
Vol 52 (7) ◽  
pp. 1610-1617 ◽  
Author(s):  
Pedro A. Jiménez ◽  
Jimy Dudhia
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Complex Terrain ◽  
Grid Point ◽  
Surface Wind ◽  
Winter Season ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Surface Flow ◽  
High Horizontal Resolution

AbstractThe ability of the Weather Research and Forecasting (WRF) model to reproduce the surface wind direction over complex terrain is examined. A simulation spanning a winter season at a high horizontal resolution of 2 km is compared with wind direction records from a surface observational network located in the northeastern Iberian Peninsula. A previous evaluation has shown the ability of WRF to reproduce the wind speed over the region once the effects of the subgrid-scale topography are parameterized. Hence, the current investigation complements the previous findings, providing information about the model's ability to reproduce the direction of the surface flow. The differences between the observations and the model are quantified in terms of scores explicitly designed to handle the circular nature of the wind direction. Results show that the differences depend on the wind speed. The larger the wind speed is, the smaller are the wind direction differences. Areas with more complex terrain show larger systematic differences between model and observations; in these areas, a statistical correction is shown to help. The importance of the grid point selected for the comparison with observations is also analyzed. A careful selection is relevant to reducing comparative problems over complex terrain.

scholarly journals An improved canopy wind model for predicting wind adjustment factors and wildland fire behavior

2017 ◽  
Vol 47 (5) ◽  
pp. 594-603 ◽  
Author(s):  
W.J. Massman ◽  
J.M. Forthofer ◽  
M.A. Finney
Keyword(s):  
Wind Speed ◽  
Reynolds Stress ◽  
Forest Canopy ◽  
Wildland Fire ◽  
Fire Behavior ◽  
Canopy Structure ◽  
Ground Surface ◽  
Simple Analytical Expression ◽  
Surface Distribution ◽  
Adjustment Factors

The ability to rapidly estimate wind speed beneath a forest canopy or near the ground surface in any vegetation is critical to practical wildland fire behavior models. The common metric of this wind speed is the “mid-flame” wind speed, UMF. However, the existing approach for estimating UMF has some significant shortcomings. These include the assumptions that both the within-canopy wind speed and the canopy structure are uniform with depth (z) throughout the canopy and that the canopy roughness length (z0) and displacement height (d) are the same regardless of canopy structure and foliage density. The purpose of this study is to develop and assess a model of canopy wind and Reynolds stress that eliminates these shortcomings and thereby provide a more physically realistic method for calculating UMF. The present model can be used for canopies of arbitrary plant surface distribution and leaf area, and the single function that describes the within-canopy wind speed is shown to reproduce observed canopy wind speed profiles across a wide variety of canopies. An equally simple analytical expression for the within-canopy Reynolds stress, [Formula: see text], also provides a reasonable description of the observed vertical profiles of Reynolds stress. In turn, [Formula: see text] is used to calculate z0 and d. Tests of operational performance are also discussed.

scholarly journals Extreme Climatic Characteristics Near the Coastline of the Southeast Region of Brazil in the Last 40 Years

2021 ◽  
Author(s):  
Marilia Mitidieri Fernandes de Oliveira ◽  
Jorge Luiz Fernandes de Oliveira ◽  
Pedro José Farias Fernandes ◽  
Eric Gilleland ◽  
Nelson Francisco Favilla Ebecken
Keyword(s):  
Wind Speed ◽  
Rio De Janeiro ◽  
Grid Point ◽  
Extremal Index ◽  
Sao Paulo ◽  
Generalized Extreme Value Distribution ◽  
Brazilian Coast ◽  
São Paulo ◽  
Return Levels ◽  
Grid Points

Abstract The southeastern Brazilian coast is a vulnerable region to the development of severe storms, mainly caused by the passage of cold fronts and extratropical cyclones. In the last decades, there has been an increase in the occurrence of subtropical cyclones. This study investigates trends and climatic variations, analyzing surface meteoceanographic series at six grid points from the reanalysis databases of ERA-Interim and ERA5 (European Center for Medium-Range Weather Forecasts-ECMWF) from 1979 to 2018 over the ocean region bounded, approximately, at 18°S, 25°S and 37ºW, 45ºW (between the states of Espírito Santo, Rio de Janeiro and São Paulo). Non-parametric statistical tests and the generalized extreme value distribution are employed for annual, seasonal and daily maxima/minima. The numbers of occurrence of extreme values, as well as the extremal index are also estimated in order to better understand the behavior of extremes. Annual maximum sea-surface temperature anomalies of the ERA-Interim databases show very low negative values, mainly at the beginning of measurements (between 1979 and 1982), leading to high positive trend values. The results are compared to the updated data from ERA5 which have anomalies that are more homogeneous with positive trends but without statistical significance. The other meteorological series of the ERA-Interim does not present discrepancies. Only the maximum anomalies of air temperature have significant annual and seasonal positive trends at grid points near the coast of Rio de Janeiro and São Paulo. Despite that the analyses for pressure and wind speed anomalies do not indicate significant trends, they present increases in the interdecadal pattern of the numbers of occurrence of extreme percentiles for almost every grid point. Return levels for 10, 25, 50, 75, and 100 years are estimated at each grid point and many maximum/minimum peaks are close to the return levels for 100-year return periods. The extremal index suggests average cluster sizes associated with no predominance of clustering for the extreme percentiles, which represents weak dependence between the exceedances. These results characterize some independence between extreme meteorological events such as the event that has been taking place in the region. The occurrence of maximum daily wind speed peaks calculated in austral spring, whose values exceeded the previous ones, is identified at three grid points near the southeast Brazilian coast, caused by the passage of the subtropical cyclone “Deni,” which occurred in November 2016.

Moss leaf water content and solar radiation at the moss surface in a mature black spruce forest in central Alaska

1983 ◽  
Vol 13 (5) ◽  
pp. 860-868 ◽  
Author(s):  
O. Skre ◽  
W. C. Oechel ◽  
P. M. Miller
Keyword(s):  
Wind Speed ◽  
Vapor Pressure ◽  
Water Content ◽  
Water Loss ◽  
Forest Canopy ◽  
Black Spruce ◽  
Field Measurements ◽  
The Other ◽  
Leaf Water Content ◽  
Moss Species

In a mature black spruce (Piceamariana (Mill.) B.S.P.) forest near the University of Alaska, Fairbanks, AK, samples of four common moss species, Polytrichumcommune Hedw., Hylocomiumsplendens (Hedw.) B.S.G., Pleuroziumschreberi (Brid.) Mitt., and Sphagnumsubsecundum Nees. were collected at intervals during the 1976 season to determine the diurnal variation in leaf water content and daily water loss as functions of temperature, moisture, radiation, and wind speed. The field measurements were followed by laboratory experiments on intact cores in an open system and on excised shoots in closed cuvettes. In these experiments, water loss rates varied by species and were affected by vapor pressure deficits and wind speed; where vapor pressure deficit is more constant than these other factors, variations in light intensities had less effect on water loss rates. Polytrichumcommune, which translocates water from the soil, avoided moisture stress to a greater extent than the other moss species which were more dependent on water absorption through the leaves. Hylocomiumsplendens was below the water content for compensation for almost 50% of the July measurement period. Observed patterns of the rates of water loss and of the moisture required to reach field capacity are correlated with the moisture status of the mosses in the field. Field measurements of photosynthetically active radiation (PAR) at the moss surface and above the forest canopy showed that the transmission of diffuse radiation through the forest canopy increased with increasing cloudiness. PAR at the moss surface was above the compensation level for photosynthesis with sunny and cloudy conditions. Sunflecks (short periods of direct sunlight), which had an intensity of about 76% of the radiation incident on the forest canopy, occurred on up to 35% of the ground surface and provided a major source of the radiation received. Species varied in their pattern of attenuation of light through the moss canopy. A mixed stand of P. commune and P. schreberi and stands of H. splendens transmit light deeply. Sphagnumsubsecundum, on the other hand, shows rapid attenuation of light high in the canopy. The observed pattern of light attenuation helps explain the vertical position of the green–brown interface and the death of green material in the moss canopy.

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