A method to determine the characteristic time‐scales of quasi‐isotropic surface‐layer turbulence over complex terrain: A case‐study in the Adige Valley (Italian Alps)

2019 ◽  
Vol 145 (719) ◽  
pp. 495-512 ◽  
Author(s):  
Marco Falocchi ◽  
Lorenzo Giovannini ◽  
Massimiliano de Franceschi ◽  
Dino Zardi
Keyword(s):  
Surface Layer ◽  
Time Scales ◽  
Complex Terrain ◽  
Characteristic Time ◽  
Italian Alps

scholarly journals The impact of a foehn wind on PM10 concentrations and the urban boundary layer in complex terrain: a case study from Kraków, Poland

Tellus B ◽  
2021 ◽  
Vol 73 (1) ◽  
pp. 1-26
Author(s):  
Piotr Sekuła ◽  
Anita Bokwa ◽  
Zbigniew Ustrnul ◽  
Mirosław Zimnoch ◽  
Bogdan Bochenek
Keyword(s):  
Boundary Layer ◽  
Complex Terrain ◽  
Urban Boundary Layer ◽  
The Impact

From back-analysis to run-out prediction: a case study in the Western Italian Alps

Landslides ◽  
2011 ◽  
Vol 8 (2) ◽  
pp. 159-170 ◽  
Author(s):  
Marina Pirulli ◽  
Alessio Colombo ◽  
Claudio Scavia
Keyword(s):  
Back Analysis ◽  
Italian Alps

scholarly journals Balancing wood market demand and common property rights: a case study of a community in the Italian Alps

2014 ◽  
Vol 19 (5) ◽  
pp. 417-426 ◽  
Author(s):  
Alessandro Paletto ◽  
Isabella De Meo ◽  
Maria Giulia Cantiani ◽  
Dario Cocciardi
Keyword(s):  
Property Rights ◽  
Common Property ◽  
Market Demand ◽  
Italian Alps

scholarly journals Characteristics of Large-Scale Orographic Precipitation in a Linear Perspective

2011 ◽  
Vol 12 (1) ◽  
pp. 27-44 ◽  
Author(s):  
Michael Kunz
Keyword(s):  
Time Scales ◽  
Characteristic Time ◽  
Large Scale ◽  
Linear Perspective ◽  
Horizontal Wind ◽  
Small Scale ◽  
Orographic Precipitation ◽  
Observation Data ◽  
Horizontal Wind Speed ◽  
Precipitation Patterns

Abstract Simulations of orographic precipitation over the low mountain ranges of southwestern Germany and eastern France with two different physics-based linear precipitation models are presented. Both models are based on 3D airflow dynamics from linear theory and consider advection of condensed water and leeside drying. Sensitivity studies for idealized conditions and a real case study show that the amount and spatial distribution of orographic precipitation is strongly controlled by characteristic time scales for cloud and hydrometeor advection and background precipitation due to large-scale lifting. These parameters are estimated by adjusting the model results on a 2.5-km grid to observed precipitation patterns for a sample of 40 representative orography-dominated stratiform events (24 h) during a calibration period (1971–80). In general, the best results in terms of lowest rmse and bias are obtained for characteristic time scales of 1600 s and background precipitation of 0.4 mm h−1. Model simulations of a sample of 84 events during an application period (1981–2000) with fixed parameters demonstrate that both models are able to reproduce quantitatively precipitation patterns obtained from observations and reanalyses from a numerical model [Consortium for Small-scale Modeling (COSMO)]. Combining model results with observation data shows that heavy precipitations over mountains are restricted to situations with strong atmospheric forcings in terms of synoptic-scale lifting, horizontal wind speed, and moisture content.

scholarly journals Superconducting fluctuations and characteristic time scales in amorphous WSi

2018 ◽  
Vol 97 (17) ◽  
Author(s):  
Xiaofu Zhang ◽  
Adriana E. Lita ◽  
Mariia Sidorova ◽  
Varun B. Verma ◽  
Qiang Wang ◽  
...  
Keyword(s):  
Time Scales ◽  
Characteristic Time ◽  
Superconducting Fluctuations

Wind speed modeling over complex terrain with the artificial neural network in the measure-correlate-predict technique: A case study of Malaysia

2021 ◽  
pp. 0309524X2110558
Author(s):  
Yong Kim Hwang ◽  
Mohd Zamri Ibrahim ◽  
Marzuki Ismail ◽  
Ali Najah Ahmed ◽  
Aliashim Albani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Spatial Modeling ◽  
Complex Terrain ◽  
Simulated Data ◽  
Wind Flow ◽  
Acceptable Accuracy ◽  
Network Measure ◽  
Artificial Neural

This study aimed to create a Malaysian wind map of greater accuracy. Compared to a previous wind map, spatial modeling input was increased. The Genetic Algorithm-optimized Artificial Neural Network Measure–Correlate–Predict method was used to impute missing data, and managed to control over- or under-prediction issues. The established wind map was made more reliable by including surface roughness to simulate wind flow over complex terrain. Validation revealed that the current wind map is 33.833% more accurate than the previous wind map. Furthermore, the correlation coefficient between wind map-simulated data and observed data was high as 0.835. In conclusion, the new and improved wind map for Malaysia simulates data with acceptable accuracy.

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