scholarly journals Snow density along the route traversed by the Japanese-Swedish Antarctic Expedition 2007/08

2012 ◽  
Vol 58 (209) ◽  
pp. 529-539 ◽  
Author(s):  
Shin Sugiyama ◽  
Hiroyuki Enomoto ◽  
Shuji Fujita ◽  
Kotaro Fukui ◽  
Fumio Nakazawa ◽  
...  
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Coastal Region ◽  
Snow Density ◽  
Syowa Station ◽  
Near Surface ◽  
A Value ◽  
Surface Snow ◽  
The Mean ◽  
The Antarctic

AbstractDuring the Japanese-Swedish Antarctic traverse expedition of 2007/08, we measured the surface snow density at 46 locations along the 2800 km long route from Syowa station to Wasa station in East Antarctica. The mean snow density for the upper 1 (or 0.5) m layer varied from 333 to 439 kg m-3 over a region spanning an elevation range of 365-3800 ma.s.l. The density variations were associated with the elevation of the sampling sites; the density decreased as the elevation increased, moving from the coastal region inland. However, the density was relatively insensitive to the change in elevation along the ridge on the Antarctic plateau between Dome F and Kohnen stations. Because surface wind is weak in this region, irrespective of elevation, the wind speed was suggested to play a key role in the near-surface densification. The results of multiple regression performed on the density using meteorological variables were significantly improved by the inclusion of wind speed as a predictor. The regression analysis yielded a linear dependence between the density and the wind speed, with a coefficient of 13.5 kg m-3 (m s-1)-1. This relationship is nearly three times stronger than a value previously computed from a dataset available in Antarctica. Our data indicate that the wind speed is more important to estimates of the surface snow density in Antarctica than has been previously assumed.

scholarly journals Spatiotemporal Characteristics of Near-Surface Wind in Shenzhen

2020 ◽  
Vol 12 (2) ◽  
pp. 739 ◽  
Author(s):  
Cheng Liu ◽  
Qinglan Li ◽  
Wei Zhao ◽  
Yuqing Wang ◽  
Riaz Ali ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Surface Wind ◽  
Prevailing Wind ◽  
Average Wind Speed ◽  
Near Surface ◽  
Average Wind ◽  
Prevailing Wind Direction ◽  
Seasonal Wind ◽  
The Mean

The spatiotemporal characteristics of near-surface wind in Shenzhen were investigated in this study by using hourly observations at 92 automatic weather stations (AWSs) from 2009 to 2018. The results show that during the past 10 years, most of the stations showed a decreasing trend in the annual mean of the 10 min average wind speed (avg-wind) and the mean of the 3 s average wind speed (gust wind). Over half of the decreasing trends at the stations were statistically significant (p < 0.05). Seasonally, the decrease in wind speed was the most severe in spring, followed by autumn, winter, and summer. The distribution of wind speed tends to be greater in the east and coastal areas for both avg-wind and gust wind. From September to March of the following year, the prevailing wind direction in Shenzhen was northerly, and from April to August, the prevailing wind direction was southerly. The seasonal wind speed distribution exhibited two different types, spring–summer type and autumn–winter type, which may be induced by their different prevailing wind directions. The analysis by the empirical orthogonal function (EOF) method confirmed the previous findings that the mean wind speed was decreasing in Shenzhen and that two different seasonal wind speed spatial distribution patterns existed. Such a study could provide references for wind forecasting and risk assessment in the study area.

scholarly journals Wind speed characteristics and implications for wind power generation: Cape regions, South Africa

2017 ◽  
Vol 113 (7/8) ◽  
Author(s):  
Marc A. Wright ◽  
Stefan W. Grab
Keyword(s):  
South Africa ◽  
Power Generation ◽  
Wind Speed ◽  
Coastal Zone ◽  
Temporal Dynamics ◽  
Surface Wind ◽  
Eastern Cape ◽  
Near Surface ◽  
Wind Speeds ◽  
The Mean

Spatio-temporal dynamics of near-surface wind speeds were examined across the Northern, Western and Eastern Cape regions of South Africa. The regions assessed were geographically subdivided into three zones: coastal, coastal hinterland and inland. Wind speed data (10 m) were evaluated at monthly, seasonal, annual and zonal resolutions, with the aim to establish wind speed attributes and trends. Data from 19 weather stations with high-resolution wind records between 1995 and 2014 were evaluated. The majority of stations (79%) recorded a decrease in mean annual wind speed over the study period. The mean rate of decrease across all stations over the 20-year period equates to -1.25%, quantifying to an annual decrease of -0.002 m/s/year (-0.06% pa). The largest seasonal decline of -0.006 m/s/year (-0.15% pa) was recorded in summer. Statistically significant declines in mean annual wind speed are somewhat more pronounced for the coastal zone (-0.003 m/s/year, -0.08% pa) than over interior regions (-0.002 m/s/year, -0.06% pa) for the study period. The largest decrease (-0.08% pa) was recorded for the coastal zone, followed by the inland zone (-0.06% pa), equating to an annual reduction in available energy of 0.18% pa and 0.09% pa, respectively. When considering all stations over the study period, the mean inter-annual variability is 3.11%. Despite such decreases in wind speed, the variance identified in this study would not have posed any risk to power generation from wind across the assessed stations, based on the period 1995 to 2014.

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

2014 ◽  
Vol 599-601 ◽  
pp. 1605-1609 ◽  
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.

scholarly journals Evaluation of the AMPS Boundary Layer Simulations on the Ross Ice Shelf, Antarctica, with Unmanned Aircraft Observations

2017 ◽  
Vol 56 (8) ◽  
pp. 2239-2258 ◽  
Author(s):  
Jonathan D. Wille ◽  
David H. Bromwich ◽  
John J. Cassano ◽  
Melissa A. Nigro ◽  
Marian E. Mateling ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Relative Humidity ◽  
High Wind ◽  
Ice Shelf ◽  
High Wind Speed ◽  
Near Surface ◽  
Ross Ice Shelf ◽  
Low Cloud ◽  
The Antarctic

AbstractAccurately predicting moisture and stability in the Antarctic planetary boundary layer (PBL) is essential for low-cloud forecasts, especially when Antarctic forecasters often use relative humidity as a proxy for cloud cover. These forecasters typically rely on the Antarctic Mesoscale Prediction System (AMPS) Polar Weather Research and Forecasting (Polar WRF) Model for high-resolution forecasts. To complement the PBL observations from the 30-m Alexander Tall Tower! (ATT) on the Ross Ice Shelf as discussed in a recent paper by Wille and coworkers, a field campaign was conducted at the ATT site from 13 to 26 January 2014 using Small Unmanned Meteorological Observer (SUMO) aerial systems to collect PBL data. The 3-km-resolution AMPS forecast output is combined with the global European Centre for Medium-Range Weather Forecasts interim reanalysis (ERAI), SUMO flights, and ATT data to describe atmospheric conditions on the Ross Ice Shelf. The SUMO comparison showed that AMPS had an average 2–3 m s−1 high wind speed bias from the near surface to 600 m, which led to excessive mechanical mixing and reduced stability in the PBL. As discussed in previous Polar WRF studies, the Mellor–Yamada–Janjić PBL scheme is likely responsible for the high wind speed bias. The SUMO comparison also showed a near-surface 10–15-percentage-point dry relative humidity bias in AMPS that increased to a 25–30-percentage-point deficit from 200 to 400 m above the surface. A large dry bias at these critical heights for aircraft operations implies poor AMPS low-cloud forecasts. The ERAI showed that the katabatic flow from the Transantarctic Mountains is unrealistically dry in AMPS.

Field measurements of windbreak effects on airflow, turbulent exchanges and microclimates

2002 ◽  
Vol 42 (6) ◽  
pp. 665 ◽  
Author(s):  
H. A. Cleugh
Keyword(s):  
Wind Speed ◽  
Water Vapour ◽  
Incident Angle ◽  
Surface Wind ◽  
Field Measurements ◽  
Experimental Program ◽  
Near Surface ◽  
Speed Reduction ◽  
Temperature And Humidity ◽  
The Impact

While there has been considerable research into airflow around windbreaks, the interaction of this airflow with the exchanges of heat and water vapour has received far less attention. Yet, the effects of windbreaks on microclimates, water use and agricultural productivity depend, in part, on this interaction. A field and wind tunnel experimental program was conducted to quantify the effects of windbreaks on microclimates and evaporation fluxes. This paper describes the field measurements, which were conducted over a 6-week period at a tree windbreak site located in undulating terrain in south-east Australia. The expected features of airflow around porous windbreaks were observed despite the less than ideal nature of the site. As predicted from theory, the air temperature and humidity were elevated, by day, in the quiet zone and the location of the peak increase in temperature and humidity coincided with the location of the minimum wind speed. However, this increase in temperature and humidity was small in size and restricted to the zone within 10 windbreak heights (H) of the windbreak. This pattern contrasts with that for the near surface wind speeds, which were reduced by up to 80% in a sheltered zone that extended from 5 H upwind to over 25 H downwind of the windbreak. Similar differences were found between the turbulent scalar (heat, water vapour) and velocity terms. While both are reduced in the quiet zone, the turbulent scalar terms near the surface were substantially enhanced at the location where the wake zone begins. Here the mean wind speed is reduced by 50% and the turbulent velocity terms return to their upwind values. Wind speed reductions varied linearly with [cos (90 – α)], where α is the incident angle of the wind, for sites located 6 H downwind. This means that the spatial pattern of wind speed reduction applies to all wind directions, provided that distance downwind is expressed in terms of streamwise distance. However, shelter in the near-break region is slightly increased as the wind blows more obliquely towards the windbreak. The atmospheric demand in the quiet zone was reduced when the humidity of the upwind air was low. In such conditions, windbreaks can 'protect' growing crops from the impact of dry air with high atmospheric demand. The corollary is that in humid conditions, the atmospheric demand in the quiet zone can be increased as a result of shelter.

A preliminary study on the influence of Beijing urban spatial morphology on near-surface wind speed

Urban Climate ◽  
2020 ◽  
Vol 34 ◽  
pp. 100703
Author(s):  
Yonghong Liu ◽  
Yongming Xu ◽  
Fangmin Zhang ◽  
Wenjun Shu
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Near Surface ◽  
Spatial Morphology ◽  
Preliminary Study

scholarly journals Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 738 ◽  
Author(s):  
Wenqing Xu ◽  
Like Ning ◽  
Yong Luo
Keyword(s):  
Wind Speed ◽  
Decision Tree ◽  
Wind Power ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Gradient Boosting ◽  
Post Processing ◽  
Near Surface ◽  
Wind Power Forecasting ◽  
Numerical Weather

With the large-scale development of wind energy, wind power forecasting plays a key role in power dispatching in the electric power grid, as well as in the operation and maintenance of wind farms. The most important technology for wind power forecasting is forecasting wind speed. The current mainstream methods for wind speed forecasting involve the combination of mesoscale numerical meteorological models with a post-processing system. Our work uses the WRF model to obtain the numerical weather forecast and the gradient boosting decision tree (GBDT) algorithm to improve the near-surface wind speed post-processing results of the numerical weather model. We calculate the feature importance of GBDT in order to find out which feature most affects the post-processing wind speed results. The results show that, after using about 300 features at different height and pressure layers, the GBDT algorithm can output more accurate wind speed forecasts than the original WRF results and other post-processing models like decision tree regression (DTR) and multi-layer perceptron regression (MLPR). Using GBDT, the root mean square error (RMSE) of wind speed can be reduced from 2.7–3.5 m/s in the original WRF result by 1–1.5 m/s, which is better than DTR and MLPR. While the index of agreement (IA) can be improved by 0.10–0.20, correlation coefficient be improved by 0.10–0.18, Nash–Sutcliffe efficiency coefficient (NSE) be improved by −0.06–0.6. It also can be found that the feature which most affects the GBDT results is the near-surface wind speed. Other variables, such as forecast month, forecast time, and temperature, also affect the GBDT results.

scholarly journals Evaluation of the Ocean Surface Wind Speed Change following the Super Typhoon from Space-Borne GNSS-Reflectometry

2020 ◽  
Vol 12 (12) ◽  
pp. 2034 ◽  
Author(s):  
Hongsu Liu ◽  
Shuanggen Jin ◽  
Qingyun Yan
Keyword(s):  
Wind Speed ◽  
Correlation Coefficient ◽  
Surface Wind ◽  
Ocean Surface ◽  
Surface Wind Speed ◽  
Wind Speeds ◽  
Mean Error ◽  
Super Typhoon ◽  
The Mean ◽  
Ocean Surface Wind

Ocean surface wind speed is an essential parameter for typhoon monitoring and forecasting. However, traditional satellite and buoy observations are difficult to monitor the typhoon due to high cost and low temporal-spatial resolution. With the development of spaceborne GNSS-R technology, the cyclone global navigation satellite system (CYGNSS) with eight satellites in low-earth orbit provides an opportunity to measure the ocean surface wind speed of typhoons. Though observations are made at the extremely efficient spatial and temporal resolution, its accuracy and reliability are unclear in an actual super typhoon case. In this study, the wind speed variations over the life cycle of the 2018 Typhoon Mangkhut from CYGNSS observations were evaluated and compared with European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-5 (ERA-5). The results show that the overall root-mean-square error (RMSE) of CYGNSS versus ECMWF was 4.12 m/s, the mean error was 1.36 m/s, and the correlation coefficient was 0.96. For wind speeds lower and greater than 15 m/s, the RMSE of CYGNSS versus ECMWF were 1.02 and 4.36 m/s, the mean errors were 0.05 and 1.61 m/s, the correlation coefficients were 0.91 and 0.90, and the average relative errors were 9.8% and 11.6%, respectively. When the typhoon reached a strong typhoon or super typhoon, the RMSE of CYGNSS with respect to ERA-5 from ECMWF was 5.07 m/s; the mean error was 3.57 m/s; the correlation coefficient was 0.52 and the average relative error was 11.0%. The CYGNSS estimation had higher precision for wind speeds below 15 m/s, but degraded when the wind speed was above 15 m/s.

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