scholarly journals An Analysis of the Reliability of a New Dataset of Transmission Line Icing Thickness in Southern China

2019 ◽  
Vol 58 (2) ◽  
pp. 413-426 ◽  
Author(s):  
Jiazheng Lu ◽  
Li Li ◽  
Xunjian Xu ◽  
Tao Feng
Keyword(s):  
Transmission Line ◽  
Observational Data ◽  
Historical Data ◽  
Southern China ◽  
Correlation Coefficients ◽  
Climate Data ◽  
Observation Station ◽  
Orthogonal Function ◽  
Temporal And Spatial ◽  
Rotated Empirical Orthogonal Function

AbstractBased on ERA-Interim data, gauge observations, transmission line icing observational data, and hindcasted predictors from a numerical forecast system of transmission line icing, a new transmission line icing thickness (TLIT) dataset was constructed to solve the problem of limited historical data. The reliability of the dataset was analyzed using case studies and climate data. The results showed that the descriptions of three icing events in southern China by the TLIT were consistent with the actual observational data, and the icing thickness differences were less than 2 mm. The spatial distribution of annual icing days and icing thickness calculated using meteorological observation station icing data (OIT) and the TLIT data had a similar pattern, with small differences in the numerical values. A rotated empirical orthogonal function (REOF) decomposition was conducted for 67 transmission line icing events. It was found that the spatial distributions of the first three characteristic vectors of the TLIT and OIT data were similar, and the correlation coefficients for the time coefficients of the first three characteristic vectors were 0.801, −0.443, and 0.576, respectively. Three key areas were identified based on the first three patterns of REOF, and the average icing thickness of 67 events in southern China and the three key areas was calculated. The correlation coefficients of icing thickness calculated by the TLIT and OIT data for these areas were 0.648, 0.384, 0.565, and 0.599, respectively. The results illustrate that the TLIT data can reflect the temporal and spatial variations of ice thickness in southern China.

scholarly journals Research on Micro-terrain Classification of Transmission Line Galloping

2021 ◽  
Vol 898 (1) ◽  
pp. 012014
Author(s):  
Li Li ◽  
Xunjian Xu ◽  
Jun Guo ◽  
Zhou Jian
Keyword(s):  
Transmission Line ◽  
Historical Data ◽  
Terrain Classification ◽  
Mountain Area ◽  
Orthogonal Function ◽  
Spatial Modes ◽  
Windward Slope ◽  
Topographic Relief ◽  
East West

Abstract Micro-terrain and micro-weather have an important impact on transmission line galloping. In order to carry out galloping prediction of micro-terrain, the classification of galloping micro-terrain is studied in this work. Firstly, we collect historical data of 1537 galloping points from the State Grid Corporation of China, and select 208 galloping points located in the micro-terrain area by analyzing the altitude and the topographic relief characteristics around each galloping point. Then the galloping micro-terrain types are extracted by Empirical Orthogonal Function method, the first four spatial modes of galloping micro-terrain are the windward slope of east-west mountain area, the windward slope of north-south mountain area, the independent hill, and the saddle back of mountain/hill. Finally, the regional characteristics of typical micro-terrain are analyzed according to the actual lines.

Rainfall Prediction in South-Eastern Part of Bangladesh by Linear Regression Method

2018 ◽  
Vol 6 (6) ◽  
pp. 119
Author(s):  
Mohammad Shohidul Islam ◽  
Sultana Easmin Siddika ◽  
S M Injamamul Haque Masum
Keyword(s):  
Linear Regression ◽  
Correlation Coefficients ◽  
Rain Gauge ◽  
Regression Method ◽  
Linear Regression Method ◽  
Climate Data ◽  
Rainfall Forecasting ◽  
Statistical Parameters ◽  
Time Duration ◽  
Rainfall Prediction

Rainfall forecasting is very challenging task for the meteorologists. Over the last few decades, several models have been utilized, attempting the successful analysing and forecasting of rainfall. Recorded climate data can play an important role in this regard. Long-time duration of recorded data can be able to provide better advancement of rainfall forecasting. This paper presents the utilization of statistical techniques, particularly linear regression method for modelling the rainfall prediction over Bangladesh. The rainfall data for a period of 11 years was obtained from Bangladesh Meteorological department (BMD), Dhaka i.e. that was surface-based rain gauge rainfall which was acquired from 08 weather stations over Bangladesh for the years of 2001-2011. The monthly and yearly rainfall was determined. In order to assess the accuracy of it some statistical parameters such as average, meridian, correlation coefficients and standard deviation were determined for all stations. The model prediction of rainfall was compared with true rainfall which was collected from rain gauge of different stations and it was found that the model rainfall prediction has given good results.

scholarly journals Diurnal variations in middle-atmospheric water vapor by ground-based microwave radiometry

2013 ◽  
Vol 13 (14) ◽  
pp. 6877-6886 ◽  
Author(s):  
D. Scheiben ◽  
A. Schanz ◽  
B. Tschanz ◽  
N. Kämpfer
Keyword(s):  
Water Vapor ◽  
Observational Data ◽  
Climate Model ◽  
Correlation Coefficients ◽  
Diurnal Variations ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water ◽  
Data Set ◽  
Diurnal Variability ◽  
Time Periods

Abstract. In this paper, we compare the diurnal variations in middle-atmospheric water vapor as measured by two ground-based microwave radiometers in the Alpine region near Bern, Switzerland. The observational data set is also compared to data from the chemistry–climate model WACCM. Due to the small diurnal variations of usually less than 1%, averages over extended time periods are required. Therefore, two time periods of five months each, December to April and June to October, were taken for the comparison. The diurnal variations from the observational data agree well with each other in amplitude and phase. The linear correlation coefficients range from 0.8 in the upper stratosphere to 0.5 in the upper mesosphere. The observed diurnal variability is significant at all pressure levels within the sensitivity of the instruments. Comparing our observations with WACCM, we find that the agreement of the phase of the diurnal cycle between observations and model is better from December to April than from June to October. The amplitudes of the diurnal variations for both time periods increase with altitude in WACCM, but remain approximately constant at 0.05 ppm in the observations. The WACCM data are used to separate the processes that lead to diurnal variations in middle-atmospheric water vapor above Bern. The dominating processes were found to be meridional advection below 0.1 hPa, vertical advection between 0.1 and 0.02 hPa and (photo-)chemistry above 0.02 hPa. The contribution of zonal advection is small. The highest diurnal variations in water vapor as seen in the WACCM data are found in the mesopause region during the time period from June to October with diurnal amplitudes of 0.2 ppm (approximately 5% in relative units).

scholarly journals Relationships between Meteorological Parameters and Particulate Matter in Mae Hong Son Province, Thailand

2018 ◽  
Vol 15 (12) ◽  
pp. 2801 ◽  
Author(s):  
Wissanupong Kliengchuay ◽  
Aronrag Cooper Meeyai ◽  
Suwalee Worakhunpiset ◽  
Kraichat Tantrakarnapa
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Meteorological Parameters ◽  
Warning System ◽  
Correlation Coefficients ◽  
Pm10 Concentration ◽  
P Value ◽  
Climate Data ◽  
High Concentration ◽  
Ambient Concentrations

Meteorological parameters play an important role in determining the prevalence of ambient particulate matter (PM) in the upper north of Thailand. Mae Hong Son is a province located in this region and which borders Myanmar. This study aimed to determine the relationships between meteorological parameters and ambient concentrations of particulate matter less than 10 µm in diameter (PM10) in Mae Hong Son. Parameters were measured at an air quality monitoring station, and consisted of PM10, carbon monoxide (CO), ozone (O3), and meteorological factors, including temperature, rainfall, pressure, wind speed, wind direction, and relative humidity (RH). Nine years (2009–2017) of pollution and climate data obtained from the Thai Pollution Control Department (PCD) were used for analysis. The results of this study indicate that PM10 is influenced by meteorological parameters; high concentration occurred during the dry season and northeastern monsoon seasons. Maximum concentrations were always observed in March. The PM10 concentrations were significantly related to CO and O3 concentrations and to RH, giving correlation coefficients of 0.73, 0.39, and −0.37, respectively (p-value < 0.001). Additionally, the hourly PM10 concentration fluctuated within each day. In general, it was found that the reporting of daily concentrations might be best suited to public announcements and presentations. Hourly concentrations are recommended for public declarations that might be useful for warning citizens and organizations about air pollution. Our findings could be used to improve the understanding of PM10 concentration patterns in Mae Hong Son and provide information to better air pollution measures and establish a warning system for the province.

scholarly journals Diurnal variations in middle atmospheric water vapor by ground-based microwave radiometry

2013 ◽  
Vol 13 (2) ◽  
pp. 3859-3880 ◽  
Author(s):  
D. Scheiben ◽  
A. Schanz ◽  
B. Tschanz ◽  
N. Kämpfer
Keyword(s):  
Water Vapor ◽  
Observational Data ◽  
Climate Model ◽  
Correlation Coefficients ◽  
Diurnal Variations ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water ◽  
Data Set ◽  
Diurnal Variability ◽  
Time Periods

Abstract. In this paper, we compare the diurnal variations in middle atmospheric water vapor as measured by two ground-based microwave radiometers in the Alpine region near Bern, Switzerland. The observational data set is also compared to data from the chemistry-climate model WACCM. Due to the small diurnal variations of usually less than 1%, averages over extended time periods are required. Therefore, two time periods of five months each, December to April and June to October, were taken for the comparison. The diurnal variations from the observational data agree well with each other in amplitude and phase. The linear correlation coefficients range from 0.8 in the upper stratosphere to 0.5 in the upper mesosphere. The observed diurnal variability is significant at all pressure levels within the sensitivity of the instruments. Comparing our observations with WACCM, we find that the agreement of the phase of the diurnal cycle between observations and model is better from December to April than from June to October. The amplitudes of the diurnal variations for both time periods increase with altitude in WACCM, but remain approximately constant at 0.05 parts per million in the observations. The WACCM data is used to separate the processes that lead to diurnal variations in middle atmospheric water vapor above Bern. The dominating processes were found to be meridional advection below 0.1 hPa, vertical advection between 0.1 and 0.02 hPa and (photo-)chemistry above 0.02 hPa. The contribution of zonal advection is small. The highest diurnal variations in water vapor are found in the mesopause region during the time period from June to October with diurnal amplitudes of 0.2 ppm (approximately 5% in relative units).

scholarly journals Assessing the reproducibility of high temporal and spatial resolution dynamic contrast-enhanced magnetic resonance imaging in patients with gliomas

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Woo Hyeon Lim ◽  
Joon Sik Park ◽  
Jaeseok Park ◽  
Seung Hong Choi
Keyword(s):  
Spatial Resolution ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Voxel Size ◽  
Dce Mri ◽  
Intraclass Correlation Coefficients ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Temporal And Spatial ◽  
Arterial Input Functions

AbstractTemporal and spatial resolution of dynamic contrast-enhanced MR imaging (DCE-MRI) is critical to reproducibility, and the reproducibility of high-resolution (HR) DCE-MRI was evaluated. Thirty consecutive patients suspected to have brain tumors were prospectively enrolled with written informed consent. All patients underwent both HR-DCE (voxel size, 1.1 × 1.1 × 1.1 mm3; scan interval, 1.6 s) and conventional DCE (C-DCE; voxel size, 1.25 × 1.25 × 3.0 mm3; scan interval, 4.0 s) MRI. Regions of interests (ROIs) for enhancing lesions were segmented twice in each patient with glioblastoma (n = 7) to calculate DCE parameters (Ktrans, Vp, and Ve). Intraclass correlation coefficients (ICCs) of DCE parameters were obtained. In patients with gliomas (n = 25), arterial input functions (AIFs) and DCE parameters derived from T2 hyperintense lesions were obtained, and DCE parameters were compared according to WHO grades. ICCs of HR-DCE parameters were good to excellent (0.84–0.95), and ICCs of C-DCE parameters were moderate to excellent (0.66–0.96). Maximal signal intensity and wash-in slope of AIFs from HR-DCE MRI were significantly greater than those from C-DCE MRI (31.85 vs. 7.09 and 2.14 vs. 0.63; p < 0.001). Both 95th percentile Ktrans and Ve from HR-DCE and C-DCE MRI could differentiate grade 4 from grade 2 and 3 gliomas (p < 0.05). In conclusion, HR-DCE parameters generally showed better reproducibility than C-DCE parameters, and HR-DCE MRI provided better quality of AIFs.

scholarly journals Spectral Empirical Orthogonal Function Analysis of Weather and Climate Data

2019 ◽  
Vol 147 (8) ◽  
pp. 2979-2995 ◽  
Author(s):  
Oliver T. Schmidt ◽  
Gianmarco Mengaldo ◽  
Gianpaolo Balsamo ◽  
Nils P. Wedi
Keyword(s):  
Frequency Domain ◽  
Empirical Orthogonal Function ◽  
Southern Oscillation ◽  
Function Analysis ◽  
Reanalysis Data ◽  
Climate Data ◽  
Quasi Biennial Oscillation ◽  
Linear Dynamical Systems ◽  
Orthogonal Function ◽  
Spectral Density Matrix

Abstract We apply spectral empirical orthogonal function (SEOF) analysis to educe climate patterns as dominant spatiotemporal modes of variability from reanalysis data. SEOF is a frequency-domain variant of standard empirical orthogonal function (EOF) analysis, and computes modes that represent the statistically most relevant and persistent patterns from an eigendecomposition of the estimated cross-spectral density matrix (CSD). The spectral estimation step distinguishes the approach from other frequency-domain EOF methods based on a single realization of the Fourier transform, and results in a number of desirable mathematical properties: at each frequency, SEOF yields a set of orthogonal modes that are optimally ranked in terms of variance in the L2 sense, and that are coherent in both space and time by construction. We discuss the differences between SEOF and other competing approaches, as well as its relation to dynamical modes of stochastically forced, nonnormal linear dynamical systems. The method is applied to ERA-Interim and ERA-20C reanalysis data, demonstrating its ability to identify a number of well-known spatiotemporal coherent meteorological patterns and teleconnections, including the Madden–Julian oscillation (MJO), the quasi-biennial oscillation (QBO), and the El Niño–Southern Oscillation (ENSO) (i.e., a range of phenomena reoccurring with average periods ranging from months to many years). In addition to two-dimensional univariate analyses of surface data, we give examples of multivariate and three-dimensional meteorological patterns that illustrate how this technique can systematically identify coherent structures from different sets of data. The MATLAB code used to compute the results presented in this study, including the download scripts for the reanalysis data, is freely available online.

scholarly journals How Well Can IMERG Products Capture Typhoon Extreme Precipitation Events over Southern China?

2019 ◽  
Vol 11 (1) ◽  
pp. 70 ◽  
Author(s):  
Chaoying Huang ◽  
Junjun Hu ◽  
Sheng Chen ◽  
Asi Zhang ◽  
Zhenqing Liang ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Southern China ◽  
Correlation Coefficients ◽  
Heavy Precipitation ◽  
Spatiotemporal Variability ◽  
Bias Analysis ◽  
Extreme Precipitation Events ◽  
Precipitation Measurement ◽  
Global Precipitation ◽  
Precipitation Events

This study assesses the performance of the latest version 05B (V5B) Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) Early and Final Runs over southern China during six extremely heavy precipitation events brought by six powerful typhoons from 2016 to 2017. Observations from a dense network composed of 2449 rain gauges are used as reference to quantify the performance in terms of spatiotemporal variability, probability distribution of precipitation rates, contingency scores, and bias analysis. The results show that: (1) both IMERG with gauge calibration (IMERG_Cal) and without gauge correction (IMERG_Uncal) generally capture the spatial patterns of storm-accumulated precipitation with moderate to high correlation coefficients (CCs) of 0.57–0.87, and relative bias (RB) varying from −17.21% to 30.58%; (2) IMERG_Uncal and IMERG_Cal capture well the area-average hourly series of precipitation over rainfall centers with high CCs ranging from 0.78 to 0.94; (3) IMERG_Cal tends to underestimate precipitation especially the rainfall over the rainfall centers when compared to IMERG_Uncal. The IMERG Final Run shows promising potentials in typhoon-related extreme precipitation storm applications. This study is expected to give useful feedbacks about the latest V5B Final Run IMERG product to both algorithm developers and the scientific end users, providing a better understanding of how well the V5B IMERG products capture the typhoon extreme precipitation events over southern China.

Stochastic comparison of climate model outputs to observed relative humidity fields

2020 ◽  
Author(s):  
Apostolos Koumakis ◽  
Panayiotis Dimitriadis ◽  
Theano Iliopoulou ◽  
Demetris Koutsoyiannis
Keyword(s):  
Relative Humidity ◽  
Climate Models ◽  
Climate Model ◽  
Historical Data ◽  
Spatial Scales ◽  
Stochastic Methods ◽  
Dependence Structure ◽  
Stochastic Comparison ◽  
Planning And Design ◽  
Temporal And Spatial

&lt;p&gt;Stochastic comparison of climate model outputs to observed relative humidity fields&lt;/p&gt;&lt;p&gt;We compare the stochastic behaviour of relative humidity outputs of climate models for the 20&lt;sup&gt;th&lt;/sup&gt; century to the historical data (stations and reanalysis fields) at several temporal and spatial scales. In particular we examine the marginal distributions and the dependence structure with emphasis on the Hurst-Kolmogorov behaviour. The comparison aims to contribute to the quantification of reliability and predictive uncertainty of relative humidity climate model outputs over different scales in a framework of assessing their relevance for engineering planning and design.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;(Acknowledgement: This research is conducted within the frame of the course &quot;Stochastic Methods&quot; of the National Technical University of Athens (NTUA). The School of Civil Engineering of NTUA provided moral support for the participation of the students in the Assembly.)&lt;/p&gt;

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