A Study of a Retrieval Method for Temperature and Humidity Profiles from Microwave Radiometer Observations Based on Principal Component Analysis and Stepwise Regression

2011 ◽  
Vol 28 (3) ◽  
pp. 378-389 ◽  
Author(s):  
Haobo Tan ◽  
Jietai Mao ◽  
Huanhuan Chen ◽  
P. W. Chan ◽  
Dui Wu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Water Vapor ◽  
Stepwise Regression ◽  
Microwave Radiometer ◽  
Principal Component ◽  
Component Analysis ◽  
Mean Square ◽  
Vapor Density ◽  
Retrieval Method ◽  
Brightness Temperatures

Abstract This paper discusses the application of principal component analysis and stepwise regression in the retrieval of vertical profiles of temperature and humidity based on the measurements of a 35-channel microwave radiometer. It uses the radiosonde data of 6 yr from Hong Kong, China, and the monochromatic radiative transfer model (MonoRTM) to calculate the brightness temperatures of the 35 channels of the radiometer. The retrieval of the atmospheric profile is then established based on principal component analysis and stepwise regression. The accuracy of the retrieval method is also analyzed. Using an independent sample, the root-mean-square error of the retrieved temperature is less than 1.5 K, on average, with better retrieval results in summer than in winter. Likewise, the root-mean-square error of the retrieved water vapor density reaches a maximum value of 1.4 g m−3 between 0.5 and 2 km, and is less than 1 g m−3 for all other heights. The retrieval method is then applied to the actual measured brightness temperatures by the 35-channel microwave radiometer at a station in Nansha, China. It is shown that the statistical model as developed in this paper has better retrieval results than the profiles obtained from the neural network as supplied with the radiometer. From numerical analysis, the error with the water vapor density retrieval is found to arise from the treatment of cloud liquid water. Finally, the retrieved profiles from the radiometer are studied for two typical weather phenomena during the observation period, and the retrieved profiles using the method discussed in the present paper is found to capture the evolution of the atmospheric condition very well.

An Ecology of Text: Using Text Retrieval to Study Alife on the Net

1997 ◽  
Vol 3 (4) ◽  
pp. 261-287 ◽  
Author(s):  
Michael L. Best
Keyword(s):  
Principal Component Analysis ◽  
Natural Selection ◽  
Population Ecology ◽  
Sufficient Conditions ◽  
Principal Component ◽  
Component Analysis ◽  
Latent Semantic Indexing ◽  
Text Retrieval ◽  
Semantic Indexing ◽  
Retrieval Method

I introduce a new alife model, an ecology based on a corpus of text, and apply it to the analysis of posts to USENET News. In this corporal ecology posts are organisms, the newsgroups of NetNews define an environment, and human posters situated in their wider context make up a scarce resource. I apply latent semantic indexing (LSI), a text retrieval method based on principal component analysis, to distill from the corpus those replicating units of text. LSI arrives at suitable replicators because it discovers word co-occurrences that segregate and recombine with appreciable frequency. I argue that natural selection is necessarily in operation because sufficient conditions for its occurrence are met: replication, mutagenicity, and trait/fitness covariance. I describe a set of experiments performed on a static corpus of over 10,000 posts. In these experiments I study average population fitness, a fundamental element of population ecology. My study of fitness arrives at the tinhappy discovery that a flame-war, centered around an overly prolific poster, is the king of the jungle.

scholarly journals Factores determinantes de la satisfacción laboral en España en la crisis de 2008

2016 ◽  
Vol 12 (5) ◽  
pp. 1192 ◽  
Author(s):  
María Carmen Sánchez-Sellero ◽  
Pedro Sánchez-Sellero
Keyword(s):  
Job Satisfaction ◽  
Principal Component Analysis ◽  
Economic Crisis ◽  
Stepwise Regression ◽  
Linear Models ◽  
Principal Component ◽  
Component Analysis ◽  
Stepwise Multiple Regression ◽  
Personal Variables ◽  
Degree Of Satisfaction

Purpose: We try to find out differences between personal and job-related features to know which better explain job satisfaction. This study is made in a year of economic growth and in two years of economic crisis, in order to determine if the economic crisis affects to previous results.Design/methodology: The data are from the Quality of Labour Life Survey by the Ministry of Employment and Social Security in Spain, in 2007, 2009 and 2010. We use linear models (ANOVA), principal component analysis and stepwise multiple regression. The variables are degree of satisfaction with the current job and a group of personal variables (gender, age and education level) and job-related variables (with a maximum of 14 variables depending on the method).Findings: Using linear models get the variables related to work which provide better results to explain job satisfaction, and after a stepwise regression made with factors of principal component analysis, we find out that salary is one of the last factors in this explanation. The variables that influence on job satisfaction do not depend on the economic cycle, although the hierarchies are different among them.Social implications: During the crisis, the demands of workers are lower because they prefer to have a job with low working conditions and low salary than lose their job. Reducing the degree of satisfaction with stability and wages is due to the economic situation, because labour contracts are less stable and remunerated.Originality/value: We have compared the results of stepwise regression made with the original variables and the factors of principal component analysis. The combination of these methodologies is new in studies of job satisfaction, as well as the original combination of 14 variables related to work.

scholarly journals Pemampatan Citra Pas Foto dengan Menggunakan Algoritma Kompresi Joint-Photograpic Experts Group (JPEG) dan Principal Component Analysis (PCA)

2018 ◽  
Vol 17 (1) ◽  
pp. 102
Author(s):  
M. Azman Maricar ◽  
Oka Widyantara
Keyword(s):  
Principal Component Analysis ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Component Analysis ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

Penelitian ini bertujuan untuk membandingkan hasil kompresi dari algoritma Joint-Photograpic Experts Group (JPEG) dan Principal Component Analysis (PCA) terhadap citra pas foto, guna menemukan hasil terbaik dari hasil citra kompresi yang kualitas hasilnya tidak berbeda jauh dengan citra aslinya. Alat ukur yang digunakan adalah Mean Square Error (MSE) dan Peak Signal to Noise Ratio (PSNR). Hasil yang diperoleh dalam penelitian ini adalah rata-rata MSE dan PNSR algoritma PCA dapat dikatakan tinggi jika dibandingkan dengan algoritma JPEG. Namun dari segi kualitas citra yang dihasilkan tidak jauh berbeda dengan algoritma JPEG.Dapat dikatakan bahwa algoritma JPEG mampu menghasilkan citra yang lebih baik dibandingkan algoritma PCA. Namun, algoritma PCA tidaklah buruk untuk dijadikan alternatif dalam kompresi citra pas foto.

scholarly journals The Effect of Principal Component Analysis Parameters on Solar-Induced Chlorophyll Fluorescence Signal Extraction

2021 ◽  
Vol 11 (11) ◽  
pp. 4883
Author(s):  
Zhongqiu Sun ◽  
Songxi Yang ◽  
Shuo Shi ◽  
Jian Yang
Keyword(s):  
Principal Component Analysis ◽  
Chlorophyll Fluorescence ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Infrared Region ◽  
Fluorescence Signal ◽  
Ecological Diversity ◽  
Parameter Setting ◽  
Retrieval Method

Solar-induced chlorophyll fluorescence (SIF), one of the three main releasing pathways of vegetation-absorbed photosynthetic active radiation, has been proven as an effective monitoring implementation of leaf photosynthesis, canopy growth, and ecological diversity. There exist three categories of SIF retrieval methods, and the principal component analysis (PCA) retrieval method is obtrusively eye-catching due to its brief, data-driven characteristics. However, we still lack a lucid understanding of PCA’s parameter settings. In this study, we examined if principal component numbers and retrieval band regions could have effects on the accuracy of SIF inversion under two controlled experiments. The results revealed that the near-infrared region could remarkably boost SIF’s retrieval accuracy, whereas red and near-infrared bands caused anomalous values, which subverted a traditional view that more retrieval regions might provide more photosynthetic information. Furthermore, the results demonstrated that three principal components would benefit more in PCA-based SIF retrieval. These arguments further help elucidate the more in-depth influence of the parameters on the PCA retrieval method, which unveil the potential effects of different parameters and give a parameter-setting foundation for the PCA retrieval method, in addition to assisting retrieval achievements.

scholarly journals Evaluation of Eight-Item Vancomycin Prescribing Confidence Questionnaire Among Junior Doctors

2021 ◽  
Vol 8 ◽  
Author(s):  
Lu Liu ◽  
Zhehan Jiang ◽  
Ana Xie ◽  
Weimin Wang
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Root Mean Square ◽  
Principal Component ◽  
Component Analysis ◽  
Junior Doctors ◽  
Mean Square ◽  
Extreme Response ◽  
Confirmatory Factor

Background: Assessing the preparedness of junior doctors to use vancomycin is important in medical education. Preparedness is typically evaluated by self-reported confidence surveys.Materials and Methods: An eight-item vancomycin prescribing confidence questionnaire was developed, piloted, and evaluated. The questionnaire responses were collected from 195 junior doctors and a series of statistical techniques, such as principal component analysis and confirmatory factor analysis, and were implemented to examine the validity and reliability.Results: The principal component analysis supported a one-factor structure, which was fed into a confirmatory factor analysis model resulting in a good fit [comparative fit index (CFI) = 0.99, Tucker–Lewis index (TLI) = 0.99, root mean square error of approximation (RMSEA) = 0.08, standardized root mean square residual (SRMR) = 0.04]. Ordinal-based α was 0.95, and various ωs were all above 0.93, indicating a high reliability level. The questionnaire responses were further proved to be robust to extreme response patterns via item response tree modeling. Jonckheere–Terpstra test results (z = 6.5237, p = 3.429e−11) showed that vancomycin prescribing confidence differed based on the experience in order (i.e., four ordinal independent groups: “≤10 times,” “11–20 times,” “21–30 times,” and “≥31 times”) and therefore provided external validity evidences for the questionnaire.Conclusions: The questionnaire is valid and reliable such that teaching hospitals can consider using it to assess junior doctors' vancomycin prescribing confidence. Further investigation of the questionnaire can point to the relationship between the prescribing confidence and the actual performance.

The Analysis of Atmospheric Water Vapor Based on Ground-Based Microwave Radiometer

2012 ◽  
Vol 500 ◽  
pp. 335-340
Author(s):  
Jie Ying He ◽  
Feng Lin Sun ◽  
Sheng Wei Zhang ◽  
Yu Zhang
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Microwave Radiometer ◽  
Mean Square ◽  
Vapor Density ◽  
China Meteorological Administration ◽  
Density Profiles

The paper introduces a widely used atmospheric absorption models: MPM by Liebe in 1989. Using this absorption model, the paper simulates the temperature and humidity weighting functions and brightness temperature according to the different frequencies and bandwidth of the multi-channel ground-based microwave radiometer. The results show that simulated brightness temperatures are very well agreement with the observation values with an acceptable root mean square error. This paper uses widely used retrieval method of artificial neural network to obtain the water vapor density profiles and calculates the root mean square error of each dataset. Also, to improve the accuracy of retrievals, this paper adopts multi-layers neural network which has two hidden layers. The results show that the retrievals of water vapor density profiles based on ground-based microwave radiometer are agreement with the water vapor density profile which is observed by radiosonde. Grant Nos. GYHY200906035 China Meteorological Administration nonprofit sector (meteorology) special research

Toward Ensemble Assimilation of Hyperspectral Satellite Observations with Data Compression and Dimension Reduction Using Principal Component Analysis

2019 ◽  
Vol 147 (10) ◽  
pp. 3505-3518 ◽  
Author(s):  
Yinghui Lu ◽  
Fuqing Zhang
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Data Retrieval ◽  
Component Analysis ◽  
Training Dataset ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Atmospheric Conditions ◽  
Brightness Temperatures ◽  
Atmospheric State

Abstract Satellite-based hyperspectral radiometers usually have thousands of infrared channels that contain atmospheric state information with higher vertical resolution compared to observations from traditional sensors. However, the large numbers of channels can lead to computational burden in satellite data retrieval and assimilation. Furthermore, most of the channels are highly correlated and the pieces of independent information contained in the hyperspectral observations are usually much smaller than the number of channels. Principal component analysis (PCA) was used in this research to compress the observational information content contained in the Atmospheric Infrared Sounder (AIRS) channels to a few leading principal components (PCs). The corresponding PC scores were then assimilated into a PCA-based ensemble Kalman filter (EnKF) system. In this proof-of-concept study based on simulated observations, hyperspectral brightness temperatures were simulated using the atmospheric state vectors from convection-permitting ensemble simulations of Hurricane Harvey (2017) as input to the Community Radiative Transfer Model (CRTM). The PCs were derived from a preexisting training dataset of brightness temperatures calculated from convection-permitting simulation over a large domain in the Indian Ocean representing generic atmospheric conditions over tropical oceans. The EnKF increments from assimilating many individual measurements in the brightness temperature space were compared to the EnKF increments from assimilating significantly fewer numbers of leading PCs. Results showed that assimilating about 10–20 leading PCs could yield increments that were nearly indistinguishable to that from assimilating hyperspectral measurements from orders of magnitude larger number of hyperspectral channels.

scholarly journals PENERAPAN METODE LEAST MEDIAN SQUARE-MINIMUM COVARIANCE DETERMINANT (LMS-MCD) DALAM REGRESI KOMPONEN UTAMA

2013 ◽  
Vol 2 (4) ◽  
pp. 6
Author(s):  
I PUTU EKA IRAWAN ◽  
I KOMANG GDE SUKARSA ◽  
NI MADE ASIH
Keyword(s):  
Principal Component Analysis ◽  
Covariance Matrix ◽  
Principal Component Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Mean Square ◽  
Multivariate Normal ◽  
Minimum Covariance Determinant ◽  
The Difference ◽  
Parameter Estimators

Principal Component Regression is a method to overcome multicollinearity techniques by combining principal component analysis with regression analysis. The calculation of classical principal component analysis is based on the regular covariance matrix. The covariance matrix is optimal if the data originated from a multivariate normal distribution, but is very sensitive to the presence of outliers. Alternatives are used to overcome this problem the method of Least Median Square-Minimum Covariance Determinant (LMS-MCD). The purpose of this research is to conduct a comparison between Principal Component Regression (RKU) and Method of Least Median Square - Minimum Covariance Determinant (LMS-MCD) in dealing with outliers. In this study, Method of Least Median Square - Minimum Covariance Determinant (LMS-MCD) has a bias and mean square error (MSE) is smaller than the parameter RKU. Based on the difference of parameter estimators, still have a test that has a difference of parameter estimators method LMS-MCD greater than RKU method.

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