Evaluating the effects of land use and cover change on the decrease of surface wind speed over China in recent 30 years using a statistical downscaling method

The changes of near-surface wind speed (SWS) were induced by the combination effects from anthropogenic activities and natural climate changes, thus the research of the long-term changes and cause of SWS is very important to recognize the effects from natural changes and anthropogenic activities to SWS. Some studies have shown that land use and cover change (LUCC) over China was very distinct in the last 30 years. However, the possible effects of LUCC to the slowdown in SWS are still uncertain. The European Centre for Medium-Range Weather Forecasts Reanalysis from January 1989 onward (to be extended back to January 1979) (ERA-Interim hereafter) dataset includes little information about LUCC, so the effects of LUCC on SWS in China during 1979–2010 have been estimated by using the difference between SWS per the ERA-Interim dataset and that from 492 meteorological stations. The results show the following. (1) The effects of LUCC on the SWS are distinct, and could have caused a decrease in the SWS of 0.12 m s–1 per decade, which could account for decreases of 0.57 and 0.30 m s–1 wind speed for large and small cities, respectively. In addition, a decrease of 0.1 m s–1 in the SWS could be induced by a 10% rise in the urbanization rate. (2) The impacts of LUCC on the SWS in the Beijing-Tianjin-Tangshan region, the Yangtze River Delta region and the Pearl River Delta region are more significant than those for the entire region. (3) The bias in the ERA-Interim dataset could cause a 51% error in the estimation for the entire region during the study period, but had a non-significant effect on the decreasing trend in the SWS. The decrease rate of SWS induced by LUCC based on the traditional observation minus reanalysis method had an error of 0.01 m s–1 decade–1. (4) The results of different methods to quantify the impacts of LUCC on the SWS are also compared in this study.

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Estimating the impact of the changes in land use and cover on the surface wind speed over the East China Plain during the period 1980–2011

Climate Dynamics ◽

10.1007/s00382-015-2616-z ◽

2015 ◽

Vol 46 (3-4) ◽

pp. 847-863 ◽

Cited By ~ 35

Author(s):

Jian Wu ◽

Jinlin Zha ◽

Deming Zhao

Keyword(s):

Land Use ◽

Wind Speed ◽

Surface Wind ◽

Surface Wind Speed ◽

East China ◽

The Impact ◽

Land Use And Cover

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Investigation of the Effects of Different Land Use and Land Cover Patterns on Mesoscale Meteorological Simulations in the Taiwan Area

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-12-0109.1 ◽

2013 ◽

Vol 52 (3) ◽

pp. 570-587 ◽

Cited By ~ 45

Author(s):

Fang-Yi Cheng ◽

Yu-Ching Hsu ◽

Pay-Liam Lin ◽

Tang-Huang Lin

Keyword(s):

Land Use ◽

Wind Speed ◽

Roughness Length ◽

Surface Wind ◽

Wrf Model ◽

Surface Wind Speed ◽

Sea Breezes ◽

Modis Data ◽

Wind Speeds ◽

Moderate Resolution Imaging Spectroradiometer

AbstractThe U.S. Geological Survey (USGS) land use (LU) data employed in the Weather Research and Forecasting (WRF) model classify most LU types in Taiwan as mixtures of irrigated cropland and forest, which is not an accurate representation of current conditions. The WRF model released after version 3.1 provides an alternative LU dataset retrieved from 2001 Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products. The MODIS data correctly identify most LU-type distributions, except that they represent western Taiwan as being extremely urbanized. A new LU dataset, obtained using 2007 Système Probatoire d’Observation de la Terre (SPOT) satellite imagery [from the National Central University of Taiwan (NCU)], accurately shows the major metropolitan cities as well as other land types. Three WRF simulations were performed, each with a different LU dataset. Owing to the overestimation of urban area in the MODIS data, WRF-MODIS overpredicts daytime temperatures in western Taiwan. Conversely, WRF-USGS underpredicts daytime temperatures. The temperature variation estimated by WRF-NCU falls between those estimated by the other two simulations. Over the ocean, WRF-MODIS predicts the strongest onshore sea breezes, owing to the enhanced temperature gradient between land and sea, while WRF-USGS predicts the weakest onshore flow. The intensity of the onshore breeze predicted by WRF-NCU is between those predicted by WRF-MODIS and WRF-USGS. Over Taiwan, roughness length is the key parameter influencing wind speed. WRF-USGS significantly overpredicts the surface wind speed owing to the shorter roughness length of its elements, while the surface wind speeds estimated by WRF-NCU and WRF-MODIS are in better agreement with the observed data.

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Correlation of PM2.5and meteorological variables in Ontario cities: statistical downscaling method coupled with artificial neural network

Air Pollution XXIV ◽

10.2495/air160201 ◽

2016 ◽

Cited By ~ 1

Author(s):

X. Su ◽

W. Gough ◽

Q. Shen

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Statistical Downscaling ◽

Meteorological Variables ◽

Statistical Downscaling Method ◽

Downscaling Method ◽

Artificial Neural

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Estimation of Ocean Surface Wind Speed and Direction From Polarimetric Radiometry Data

10.21236/ada533831 ◽

2008 ◽

Author(s):

David R. Lyzenga

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Ocean Surface ◽

Surface Wind Speed ◽

Ocean Surface Wind

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Challenges in developing a high-quality surface wind-speed data-set for Australia

Australian Meteorological and Oceanographic Journal ◽

10.22499/2.6004.001 ◽

2010 ◽

Vol 60 (04) ◽

pp. 227-236 ◽

Cited By ~ 13

Author(s):

D Jakob

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Surface Wind Speed ◽

High Quality ◽

Data Set ◽

Wind Speed Data ◽

Quality Surface ◽

High Quality Surface

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Recurrence Characteristics Analysis of Near-Surface Wind Speed Signal with Different Sampling Frequencies

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.599-601.1605 ◽

2014 ◽

Vol 599-601 ◽

pp. 1605-1609 ◽

Cited By ~ 1

Author(s):

Ming Zeng ◽

Zhan Xie Wu ◽

Qing Hao Meng ◽

Jing Hai Li ◽

Shu Gen Ma

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Surface Wind Speed ◽

Recurrence Plot ◽

Sampling Frequency ◽

Near Surface ◽

Gas Leakage ◽

Time Period ◽

Characteristics Analysis ◽

Quantification Analysis

The wind is the main factor to influence the propagation of gas in the atmosphere. Therefore, the wind signal obtained by anemometer will provide us valuable clues for searching gas leakage sources. In this paper, the Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA) are applied to analyze the influence of recurrence characteristics of the wind speed time series under the condition of the same place, the same time period and with the sampling frequency of 1hz, 2hz, 4.2hz, 5hz, 8.3hz, 12.5hz and 16.7hz respectively. Research results show that when the sampling frequency is higher than 5hz, the trends of recurrence nature of different groups are basically unchanged. However, when the sampling frequency is set below 5hz, the original trend of recurrence nature is destroyed, because the recurrence characteristic curves obtained using different sampling frequencies appear cross or overlapping phenomena. The above results indicate that the anemometer will not be able to fully capture the detailed information in wind field when its sampling frequency is lower than 5hz. The recurrence characteristics analysis of the wind speed signals provides an important basis for the optimal selection of anemometer.

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