Accuration of Time Series and Spatial Interpolation Method for Prediction of Precipitation Distribution on the Geographical Information System

The Trapped sun’s thermal radiation in the earth’s atmosphere is known as the greenhouse effect. This process is considered very important since it keeps the earth warm and hence possible to live in. Greenhouse gases such as carbon dioxide (CO2) and methane (CH4) are considered very important contributors to the greenhouse effect. During the last two decades, the level of greenhouse gases has increased, which plays a major role in global warming and climate change. The Middle East is considered among the most affected areas by climate change. In the current study, Geographical Information System (GIS) has been used to create some temperature maps that could show the air temperature distribution and difference between two different periods of time (past and recent) in different stations that cover the Iraqi governorates. A spatial interpolation method has been used. This method considers known values of temperature at a given location (stations in the current study) to estimate a continuous surface map during a specific period of time. The results of this study showed no significant increase in the average air temperature values, however the area of high air temperature values is growing during the cold and hot months of the year.

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Spectral and spatial interpolation of precipitation for daily and hourly time series

10.5194/egusphere-egu2020-20520 ◽

2020 ◽

Author(s):

Masoud Mehrvand ◽

András Bárdossy

Keyword(s):

Time Series ◽

Spatial Interpolation ◽

Interpolation Method ◽

Statistical Characteristics ◽

Precipitation Time Series ◽

Spectral Interpolation ◽

Hourly Data ◽

Spatial Interpolation Method ◽

Synthetic Precipitation ◽

Rainy Days

Generating synthetic precipitation for weather generators were always a challenging issue in hydro-climate simulations because of its high variability in time and space. We present a spectral method for generating the synthetic precipitation time series which is in accordance with the observed precipitation statistical characteristics not only for the observed points, but also for any desired location by interpolating the time series spectrum. In this regard, time series spectra derived from the observed signal converting from its time domain to the corresponding frequency domain using the Fourier transform.The main problem for spectral interpolation of precipitation time series is highly occurrence of non-rainy days which can be even more inaccurate for the finer resolutions such as hourly and sub-hourly data. In order to overcome the highly frequent occurrence of non-rainy days, transformation between indicator and normal correlation has been taken into account.This method enables us to generate synthetic time series with same statistical characteristics for the observed points and also for any point of interests rather than the observed points. The introduced so called spectral and spatial interpolation method applied for daily and hourly precipitation time series for the selected stations in state Baden-W&#252;rttemberg, Germany.

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Spatio-Temporal Modeling of Ozone Distribution in Tehran, Iran Based on Neural Network and Geographical Information System

Iranian Journal of Public Health ◽

10.18502/ijph.v51i1.8312 ◽

2022 ◽

Author(s):

Leila Sherafati ◽

Hossein Aghamohammadi Zanjirabad ◽

Saeed Behzadi

Keyword(s):

Neural Network ◽

Information System ◽

Geographical Information System ◽

Meteorological Parameters ◽

Interpolation Method ◽

Temporal Analysis ◽

Geographical Information ◽

Temporal Modeling ◽

Spatiotemporal Model ◽

Ozone Distribution

Background: Air pollution is one of the most important causes of respiratory diseases that people face in big cities today. Suspended particulates, carbon monoxide, sulfur dioxide, ozone, and nitrogen dioxide are the five major pollutants of air that pose many problems to human health. We aimed to provide an approach for modeling and analyzing the spatiotemporal model of ozone distribution based on Geographical Information System (GIS). Methods: In the first step, by considering the accuracy of different interpolation methods, the Inverse distance weighted (IDW) method was selected as the best interpolation method for mapping the concentration of ozone in Tehran, Iran. In the next step, according to the daily data of Ozone pollutants, the daily, monthly, and annual mean concentrations maps were prepared for the years 2015, 2016, and 2017. Results: Spatial and temporal analysis of the distribution of ozone pollutants in Tehran was performed. The highest concentrations of O3 are found in the southwest and parts of the central part of the city. Finally, a neural network was developed to predict the amount of ozone pollutants according to meteorological parameters. Conclusion: The results show that meteorological parameters such as temperature, velocity and direction of the wind, and precipitation are influential on O3 concentration.

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Spatial interpolation of precipitation depending on elevation: Case study of the Skrapez River, Western Serbia

Glasnik srpskog geografskog drustva ◽

10.2298/gsgd0801089k ◽

2008 ◽

Vol 88 (1) ◽

pp. 89-100 ◽

Cited By ~ 1

Author(s):

Jelena Kovacevic-Majkic ◽

Dragoljub Strbac

Keyword(s):

Spatial Interpolation ◽

Interpolation Method ◽

Spatial Scales ◽

Geographical Information ◽

Mean Annual Precipitation ◽

Data Set ◽

Spatial Interpolation Method ◽

Statistical Interpolation ◽

Study Inclusion

In order to make an adequate basic dataset for a subsequent hydrological research, the study of interpolation of precipitation values from measurement stations (points) to large areas (areas) was done. Among many statistical interpolation methods that estimate precipitation values in unsampled areas (where there are no measurements or they are sparse), spatial interpolation method which includes relief (elevation) was chosen for the study. Inclusion of elevation parameter was done in order to minimize the extent of interpolation errors. Research was done in the catchments area of the Srkapez River (Western Serbia). Precipitation data were collected from 19 measurement stations for the period from 1961 to 2000. The reduction method was used for filling the missing data in the data set. The best framework for managing spatial (georeferred) data is Geographical Information System, enabling easier calculation and improved precision. Therefore, raster GIS was used for spatial interpolation of precipitation and for calculating mean annual precipitation value. Concerning the fact that the observed area is a mountainous region, where precipitation may change over short spatial scales, a DEM with a grid resolution of 100 m was used.

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