A global weighted mean temperature model based on empirical orthogonal function analysis

Li, Qinzheng; Chen, Peng; Sun, Langlang; Ma, Xiaping

doi:10.1016/j.asr.2017.12.031

Cited by 14 publications

(8 citation statements)

References 16 publications

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“…One unique attribute of an EOF analysis is that EOF base functions are derived from the original dataset through decomposition. This produces EOF modes that preserve the inherent characteristics of the original dataset and converge rapidly [68]. A detailed description about the EOF analysis method can be found in Hannachi, et al [69] and Monahan, et al [70].…”

Section: Empirical Orthogonal Functionmentioning

confidence: 99%

Investigations into Synoptic Spatiotemporal Characteristics of Coastal Upper Ocean Circulation Using High Frequency Radar Data and Model Output

Ren

Chu

et al. 2020

Remote Sensing

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Numerical models and remote sensing observation systems such as radars are useful for providing information on surface flows for coastal areas. Evaluation of their performance and extracting synoptic characteristics are challenging and important tasks. This research aims to investigate synoptic characteristics of surface flow fields through undertaking a detailed analysis of model results and high frequency radar (HFR) data using self-organizing map (SOM) and empirical orthogonal function (EOF) analysis. A dataset of surface flow fields over thirteen days from these two sources was used. A SOM topology map of size 4 × 3 was developed to explore spatial patterns of surface flows. Additionally, comparisons of surface flow patterns between SOM and EOF analysis were carried out. Results illustrate that both SOM and EOF analysis methods are valuable tools for extracting characteristic surface current patterns. Comparisons indicated that the SOM technique displays synoptic characteristics of surface flow fields in a more detailed way than EOF analysis. Extracted synoptic surface current patterns are useful in a variety of applications, such as oil spill treatment and search and rescue. This research provides an approach to using powerful tools to diagnose ocean processes from different aspects. Moreover, it is of great significance to assess SOM as a potential forecasting tool for coastal surface currents.

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Section: Empirical Orthogonal Functionmentioning

confidence: 99%

Investigations into Synoptic Spatiotemporal Characteristics of Coastal Upper Ocean Circulation Using High Frequency Radar Data and Model Output

Ren

Chu

et al. 2020

Remote Sensing

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“…S‐mode PCA (or empirical orthogonal function [EOF] analysis) is one variant that focuses on identifying spatial patterns in the different eigenvectors. It is used extensively to analyse meteorological variability, including rainfall (Smith and Phillips, 2013; Yu and Lin, 2015), wind (Álvarez‐García et al ., 2020; Farjami and Hesari, 2020), and temperature (Li et al ., 2018). PCA has also been used to explore ABL variables, such as boundary‐layer turbulence (Wilson, 1996; Lin et al ., 2008), urban heat island characteristics (Vicente‐Serrano et al ., 2005; Qiao et al ., 2018), and air quality (Henry et al ., 1991; Chan and Mozurkewich, 2007; Fleming et al ., 2012; Rogula‐Kozłowska et al ., 2015; Gupta et al ., 2018).…”

Section: Introductionmentioning

confidence: 99%

Spatial variability of forward modelled attenuated backscatter in clear‐sky conditions over a megacity: Implications for observation network design

Warren

Charlton‐Perez

Lean

et al. 2022

Quart J Royal Meteoro Soc

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Sensors that measure the attenuated backscatter coefficient (e.g., automatic lidars and ceilometers [ALCs]) provide information on aerosols that can impact urban climate and human health. To design an observational network of ALC sensors for supporting data assimilation and to improve prediction of urban weather and air quality, a methodology is needed. In this study, spatio-temporal patterns of aerosol-attenuated backscatter coefficient are modelled using Met Office numerical weather prediction (NWP) models at two resolutions, 1.5 km (UKV) and 300 m (London Model [LM]), for 28 clear-sky days and nights.Initially, attenuated backscatter coefficient data are analysed using S-mode principal component analysis (PCA) with varimax rotation. Four to seven empirical orthogonal functions (EOFs) are produced for each model level, with common EOFs found across different heights (day and night) for both NWP models.EOFs relate strongly to orography, wind, and emissions source location, highlighting these as critical controls of attenuated backscatter coefficient spatial variability across the megacity. Urban-rural differences are largest when wind speeds are low and vertical boundary-layer dynamics can more effectively distribute near-surface aerosol emissions vertically. In several night-time EOFs, gravity-wave features are found for both NWP models. Increasing the horizontal resolution of native ancillaries (model input parameters) and improving the urban surface scheme in the LM may enhance the urban signal in the EOFs. PCA output, with agglomerative Ward cluster analysis (CA), minimises intra-group variance. The UKV and LM CA shape and size results are similar and strongly related to orography. PCA-CA is a simple, but adaptable methodology, allowing close alignment with observation network design goals. Here, CA is used with wind roses to suggest the optimised ALC deployment is one in the city to observeThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The objective of this study was to analyze the variability of climate comfort at annual and seasonal scales in China, which will be of great significance for the human settlement improvement. Using the empirical orthogonal function (EOF) analysis, the dominant spatial variations and associated temporal trends of annual and seasonal CCI were extracted [12,13]. To improve the physical interpretation of these patterns, the spatial patterns were interpolated using the inverse distance weighting (IDW) interpolation method, and the temporal series were analyzed with the 3-year moving average method.…”

Section: Introductionmentioning

confidence: 99%

Long-term trends and spatiotemporal variations of climate comfort in China during 1966-2016

Yang

Shen

et al. 2020

Therm sci

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Climate comfort and its variability are of great importance to human comfort, health and well-being, as humans may suffer dire consequences when they are exposed to the environments with heat or cold stress. The climate comfort index represented the integrated effects of meteorological variables on the human thermal sensation. The annual and seasonal climate comfort index values were calculated based on the monthly data of the temperature, relative humidity, and wind speed from 591 stations in China between 1966 and 2016. Using the empirical orthogonal function analysis, the dominant modes of climate comfort index variations were extracted by the first two modes, which accounted for more than 50% of the total variance. The results showed that the annual and seasonal climate comfort index values displayed a latitudinal gradient, and increased towards the south except for the Qinghai-Tibet Plateau. The most frequently perceived thermal sensations were labeled as ?cold?, ?comfortable?, ?cold? and ?extremely cold? conditions from spring to winter, respectively. For annual and seasonal climate comfort index, the consistent increasing trend was detected in most regions of China in the first mode. The sensitive areas were mainly located in the central, eastern and southern China in winter, while in the northern and western China in summer. In the second mode, the fluctuations between upward and downward trends were observed. The sensitive areas were located in the central China in summer, in the southwestern and southern China in autumn, and in the northern China in winter. This study provides the important information for the improvement of human settlement comfort.

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A global weighted mean temperature model based on empirical orthogonal function analysis

Cited by 14 publications

References 16 publications

Investigations into Synoptic Spatiotemporal Characteristics of Coastal Upper Ocean Circulation Using High Frequency Radar Data and Model Output

Investigations into Synoptic Spatiotemporal Characteristics of Coastal Upper Ocean Circulation Using High Frequency Radar Data and Model Output

Spatial variability of forward modelled attenuated backscatter in clear‐sky conditions over a megacity: Implications for observation network design

Long-term trends and spatiotemporal variations of climate comfort in China during 1966-2016

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