利用多元线性回归估计土耳其Kocaeli地区日照时数

IF 0.8 4区 地球科学 Q4 METEOROLOGY & ATMOSPHERIC SCIENCES Idojaras Pub Date : 2023-01-01 DOI:10.28974/idojaras.2023.3.2
Mine Tulin Zateroglu
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引用次数: 0

摘要

本研究的目的是估计和评价长期记录的日照时数特征行为。日照时数和其他气候变量,如云量、降水量、相对湿度等,在世界各地的气象站都有长期的测量。但在某些情况下,如缺少数据或没有站点,日照时数的估计起着至关重要的作用。统计模型可用于预测气候变量的日照时数。为了评价日照时数的变化规律,对不同时间尺度的气候变量进行了分析。本研究使用的数据是从一个地面气象站收集的。首先,将所有数据按月平均值、季节平均值和年平均值等不同的时间尺度进行排列。为每个时间尺度构建预测模型。本研究采用多元线性回归(MLR)建立模型,并采用Pearson相关分析确定气候要素之间的关系。所建立的估算日照时数的模型也得到了验证。结果表明,MLR可用于气候变量的日照时数预测。
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Estimating the sunshine duration using multiple linear regression in Kocaeli, Turkey
This study aims to estimate and evaluate the characteristic behavior of sunshine duration for long-term records. Sunshine duration and other climate variables such as cloudiness, precipitation, relative humidity, etc., have been measured in meteorological stations for a long time all over the world. But in some cases, such as missing data or unavailable station, the estimation of sunshine duration play a crucial role. Statistical models can be used to predict the sunshine duration over climate variables. To evaluate the behavior of sunshine duration, several climate variables were analyzed for different time scales. The data used in this study were collected from a ground-based meteorological station. In the first, all data were arranged according to different time scales as monthly, seasonal, and annual average values. Prediction models were constructed for each time scale. This study used multiple linear regression (MLR) to build the models and the Pearson correlation analysis to determine the relations between the climate elements. The created models for estimating sunshine duration were validated as well. According to the results, MLR can be utilized and recommended for the prediction of the sunshine duration over climate variables.
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来源期刊
Idojaras
Idojaras 地学-气象与大气科学
CiteScore
1.60
自引率
11.10%
发文量
9
审稿时长
>12 weeks
期刊介绍: Information not localized
期刊最新文献
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