Air pollution is the most important environmental problem in Zonguldak, Turkey due to excessive coal combustion and thermal power plant emissions. The city center is located on a complex terrain near the Black Sea shore. There exist some previous studies about PM10 pollution in the study area, but none of them is related to the spatial distribution of the pollutant. This air quality modeling study aims to fill this gap in the literature. Firstly, PM10 emission inventory has been prepared for point, line, and area sources for the year 2011. For that period, bituminous coal was the principal fuel for domestic heating in houses and generating electricity in thermal power plants, therefore particulate matter (PM10) was the leading air pollutant. Emission inventory calculations revealed that 2710.2 tons of PM10 have been emitted to the atmosphere from all sources in the study area. Then, the air quality modeling has been performed for PM10 by using two air quality models: AERMOD and CALPUFF. According to the modeling results, the PM10 pollution levels may pose a health threat to the inhabitants of Zonguldak. The maximum PM10 concentrations predicted by the CALPUFF model were higher than that of the AERMOD model. The model predicted values plus background concentration were validated against the PM10 measurements by using fractional bias, index of agreement, geometric mean bias, and geometric mean-variance. According to the model performance analysis, CALPUFF showed slightly better performance as compared to AERMOD.
{"title":"Comparison of two air quality models in complex terrain near sea shore","authors":"Özgür Zeydan, A. Karademir","doi":"10.20937/atm.53118","DOIUrl":"https://doi.org/10.20937/atm.53118","url":null,"abstract":"Air pollution is the most important environmental problem in Zonguldak, Turkey due\u0000 to excessive coal combustion and thermal power plant emissions. The city center is\u0000 located on a complex terrain near the Black Sea shore. There exist some previous studies\u0000 about PM10 pollution in the study area, but none of them is related to the spatial\u0000 distribution of the pollutant. This air quality modeling study aims to fill this gap in\u0000 the literature. Firstly, PM10 emission inventory has been prepared for point, line, and\u0000 area sources for the year 2011. For that period, bituminous coal was the principal fuel\u0000 for domestic heating in houses and generating electricity in thermal power plants,\u0000 therefore particulate matter (PM10) was the leading air pollutant. Emission inventory\u0000 calculations revealed that 2710.2 tons of PM10 have been emitted to the atmosphere from\u0000 all sources in the study area. Then, the air quality modeling has been performed for\u0000 PM10 by using two air quality models: AERMOD and CALPUFF. According to the modeling\u0000 results, the PM10 pollution levels may pose a health threat to the inhabitants of\u0000 Zonguldak. The maximum PM10 concentrations predicted by the CALPUFF model were higher\u0000 than that of the AERMOD model. The model predicted values plus background concentration\u0000 were validated against the PM10 measurements by using fractional bias, index of\u0000 agreement, geometric mean bias, and geometric mean-variance. According to the model\u0000 performance analysis, CALPUFF showed slightly better performance as compared to\u0000 AERMOD.","PeriodicalId":55576,"journal":{"name":"Atmosfera","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48033120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Etesian winds constitute an important climatological phenomenon, which does not only moderate the heat during the summer in the Aegean Sea, but provide a source of clean renewable energy as well. Even though several papers have attempted to explain their dynamical and the physical characteristics, the respective processes that drive the diurnal variation of the wind speed are not fully understood. The objective of this paper is to identify the processes responsible for diurnal variation with observed maximum wind speed around noon and minimum around midnight. Analytical solutions of a primitive equation set in Eulerian form, after introducing suitable conditions and approximations, reveal an inertial oscillation over the Aegean Sea. Data based on direct observations, ECMWF IFS high resolution analyses and high-resolution simulations with the Weather Research and Forecasting (WRF) model are utilized to find out the type and the structure of the Planetary Boundary Layer (PBL) over the Aegean Sea. This PBL appears to be of a marine character and turbulent mostly during the day but less during the night. The direct impact of the local and regional thermally-driven circulations is found to be the main cause of the diurnal variation of the observed wind and partly the inertial oscillation. Results from numerical simulations certify these findings. Furthermore, the momentum and Newtonian heating exchanges by the physical processes inside the PBL, where the gradient wind together with smaller scales of atmospheric motions exist, are also necessary for explaining the variability of the Etesian winds.
{"title":"The Planetary Boundary Layer physical processes, the secondary thermal baroclinic\u0000 circulation and inertial oscillation contribution to diurnal variation of the Etesian\u0000 winds over the Aegean Sea","authors":"N. G. Prezerakos","doi":"10.20937/atm.53039","DOIUrl":"https://doi.org/10.20937/atm.53039","url":null,"abstract":"The Etesian winds constitute an important climatological phenomenon, which does\u0000 not only moderate the heat during the summer in the Aegean Sea, but provide a source of\u0000 clean renewable energy as well. Even though several papers have attempted to explain\u0000 their dynamical and the physical characteristics, the respective processes that drive\u0000 the diurnal variation of the wind speed are not fully understood. The objective of this\u0000 paper is to identify the processes responsible for diurnal variation with observed\u0000 maximum wind speed around noon and minimum around midnight. Analytical solutions of a\u0000 primitive equation set in Eulerian form, after introducing suitable conditions and\u0000 approximations, reveal an inertial oscillation over the Aegean Sea. Data based on direct\u0000 observations, ECMWF IFS high resolution analyses and high-resolution simulations with\u0000 the Weather Research and Forecasting (WRF) model are utilized to find out the type and\u0000 the structure of the Planetary Boundary Layer (PBL) over the Aegean Sea. This PBL\u0000 appears to be of a marine character and turbulent mostly during the day but less during\u0000 the night. The direct impact of the local and regional thermally-driven circulations is\u0000 found to be the main cause of the diurnal variation of the observed wind and partly the\u0000 inertial oscillation. Results from numerical simulations certify these findings.\u0000 Furthermore, the momentum and Newtonian heating exchanges by the physical processes\u0000 inside the PBL, where the gradient wind together with smaller scales of atmospheric\u0000 motions exist, are also necessary for explaining the variability of the Etesian\u0000 winds.","PeriodicalId":55576,"journal":{"name":"Atmosfera","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67655567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nitrogen dioxide (NO2) is one of the most important atmospheric pollutants, affecting human health (increasing susceptibility to respiratory infections) and the environment (soil and water acidification). In many regions of Brazil, NO2 measurements at ground level meet difficulties because there are few and unevenly distributed monitoring stations. Satellite observations combined with machine learning models can mitigate this lack of data. This paper report results from an investigation on the ability of a machine learning approach (a non-linear statistical Random Forest algorithm, hereafter RF) to reconstruct the long-term spatiotemporal ground-level NO2 from 2006 to 2019 using as input parameters NO2 data retrieved from the Ozone Monitoring Instrument (OMI) sensor aboard AURA satellite, besides meteorological covariates and localized ground-level NO2 measurements. Results show that the RF model predicts NO2 with an accuracy expressed by an R2=0.68 correlation based on a 10-fold cross-validation. The model also predicted a mean NO2 concentration of 18.73 μg∕m3 (± 3.86 μg∕m3). The total NO2 concentration over the entire region analyzed showed a decreasing trend (2.9 μg/𝑚3 𝑦𝑟−1), being 2006 the year with the higher concentrations and 2017 with the lowest. This study suggests that non-linear statistical algorithm reconstructions using RF can be complementary tools to in situ and satellite observations to NO2 mapping.
{"title":"Satellite-based estimation of NO2 concentrations using a machine-learning model: a\u0000 case study on Rio Grande do Sul, Brazil","authors":"A. Becerra-Rondon, J. Ducati, R. Haag","doi":"10.20937/atm.53116","DOIUrl":"https://doi.org/10.20937/atm.53116","url":null,"abstract":"Nitrogen dioxide (NO2) is one of the most important atmospheric pollutants,\u0000 affecting human health (increasing susceptibility to respiratory infections) and the\u0000 environment (soil and water acidification). In many regions of Brazil, NO2 measurements\u0000 at ground level meet difficulties because there are few and unevenly distributed\u0000 monitoring stations. Satellite observations combined with machine learning models can\u0000 mitigate this lack of data. This paper report results from an investigation on the\u0000 ability of a machine learning approach (a non-linear statistical Random Forest\u0000 algorithm, hereafter RF) to reconstruct the long-term spatiotemporal ground-level NO2\u0000 from 2006 to 2019 using as input parameters NO2 data retrieved from the Ozone Monitoring\u0000 Instrument (OMI) sensor aboard AURA satellite, besides meteorological covariates and\u0000 localized ground-level NO2 measurements. Results show that the RF model predicts NO2\u0000 with an accuracy expressed by an R2=0.68 correlation based on a 10-fold\u0000 cross-validation. The model also predicted a mean NO2 concentration of 18.73 μg∕m3 (±\u0000 3.86 μg∕m3). The total NO2 concentration over the entire region analyzed showed a\u0000 decreasing trend (2.9 μg/𝑚3 𝑦𝑟−1), being 2006 the year with the higher concentrations\u0000 and 2017 with the lowest. This study suggests that non-linear statistical algorithm\u0000 reconstructions using RF can be complementary tools to in situ and satellite\u0000 observations to NO2 mapping.","PeriodicalId":55576,"journal":{"name":"Atmosfera","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67655661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Howlett, G. González Abad, Christopher E. Chan Miller, C. Nowlan, Zolal Ayazpour, Lei Zhu
Formaldehyde (HCHO) is measured from space using backscattered ultraviolet sun-light. Because of HCHO’s short lifetime, space-based observations of HCHO can serve as a proxy for volatile organic compounds, helping to characterize their global emissions and distributions. HCHO satellite observations rely on Air Mass Factor (AMF) calculations to transform fitted slant columns into vertical column densities. Most HCHO satellite products do not explicitly consider the presence of snow on the ground during the calculation of AMFs. In this study, we leverage information from the MODIS bidirectional reflectance distribution function (BRDF), MODIS snow cover information, and the Interactive Multisensor Snow and Ice Mapping System to evaluate the impact of ground snow on Ozone Monitoring Instrument (OMI) HCHO retrievals. We focus our analysis on the year 2005. We compare AMFs computed using daily MODIS BRDF to AMFs computed using OMI’s surface reflectance climatology, the baseline for NASA’s OMHCHO product. Over snow-covered regions, both sets of AMFs show significant differences. We observe two different behaviors. Regions with permanent snow cover (Greenland and Antarctica) show smaller AMFs calculated with MODIS BRDF than with the OMI climatology resulting in a 6% median annual increase of HCHO VCDs. Over regions with seasonal snow cover, the situation is more complex with more variability in the differences during the year. For example, a February 2005 case study over Europe shows that the NASA OMHCHO VCDs (calculated using the OMI Lambertian climatology) are on average 16% larger than HCHO columns retrieved using daily MODIS BRDF information.
{"title":"The influence of snow cover on Ozone Monitor Instrument formaldehyde\u0000 observations","authors":"C. Howlett, G. González Abad, Christopher E. Chan Miller, C. Nowlan, Zolal Ayazpour, Lei Zhu","doi":"10.20937/atm.53134","DOIUrl":"https://doi.org/10.20937/atm.53134","url":null,"abstract":"Formaldehyde (HCHO) is measured from space using backscattered ultraviolet\u0000 sun-light. Because of HCHO’s short lifetime, space-based observations of HCHO can serve\u0000 as a proxy for volatile organic compounds, helping to characterize their global\u0000 emissions and distributions. HCHO satellite observations rely on Air Mass Factor (AMF)\u0000 calculations to transform fitted slant columns into vertical column densities. Most HCHO\u0000 satellite products do not explicitly consider the presence of snow on the ground during\u0000 the calculation of AMFs. In this study, we leverage information from the MODIS\u0000 bidirectional reflectance distribution function (BRDF), MODIS snow cover information,\u0000 and the Interactive Multisensor Snow and Ice Mapping System to evaluate the impact of\u0000 ground snow on Ozone Monitoring Instrument (OMI) HCHO retrievals. We focus our analysis\u0000 on the year 2005. We compare AMFs computed using daily MODIS BRDF to AMFs computed using\u0000 OMI’s surface reflectance climatology, the baseline for NASA’s OMHCHO product. Over\u0000 snow-covered regions, both sets of AMFs show significant differences. We observe two\u0000 different behaviors. Regions with permanent snow cover (Greenland and Antarctica) show\u0000 smaller AMFs calculated with MODIS BRDF than with the OMI climatology resulting in a 6%\u0000 median annual increase of HCHO VCDs. Over regions with seasonal snow cover, the\u0000 situation is more complex with more variability in the differences during the year. For\u0000 example, a February 2005 case study over Europe shows that the NASA OMHCHO VCDs\u0000 (calculated using the OMI Lambertian climatology) are on average 16% larger than HCHO\u0000 columns retrieved using daily MODIS BRDF information.","PeriodicalId":55576,"journal":{"name":"Atmosfera","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67655986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Soto, José Luis Alanís, J. Pech, Jorge Luis Chagoya
Sierra de Otontepec Ecological Reserve is an isolated mountain in the Coastal Plain of Gulf of Mexico with great scientific importance. However, it lacks climatological information to support the studies of the ecosystems that have been carried out in recent years. Through GIS modelling its climatology was characterized, and climate variability scenarios were created for the period 1981-2010. From temperature and precipitation data recorded in situ, vertical gradients of both variables were obtained in relation to the orography of the zone. The records were correlated with data from nearby weather stations so that by using a DEM it was possible to obtain raster layers with a resolution of 15 m per pixel for temperature, in the summer and winter seasons; and for precipitation, in the rainy and dry seasons; the annual value was also calculated for both variables. The climatic variability detected in the zone indicates a gradual increase in air temperature over time and a spatial variation in the distribution of precipitation. The spatial resolution of the modelling is precisely adjusted to the relief of the mountain, allowing the flora and fauna elements found within each pixel to be analysed with a good level of detail. This work represents the most viable and effective alternative to estimate temperature and precipitation values in mountain systems lacking climatological stations; demonstrating that beyond providing climate information, which is increasingly necessary for ecosystems, it is possible to model their spatio-temporal dynamics to better understand the complex climate variability of our days.
Sierra de Otontepec生态保护区是墨西哥湾沿岸平原上一座孤立的山脉，具有重要的科学意义。然而，它缺乏气候信息来支持近年来进行的生态系统研究。通过GIS建模对其气候学进行了表征，并建立了1981—2010年的气候变率情景。从现场记录的温度和降水资料中，得到了这两个变量与该地区地形的垂直梯度。这些记录与附近气象站的数据相关联，因此通过使用DEM，可以在夏季和冬季获得分辨率为每像素15米的温度栅格层;对于降水，在雨季和旱季;还计算了这两个变量的年值。观测到的气候变率表明气温随时间逐渐升高，降水分布存在空间差异。模型的空间分辨率精确地调整到山的浮雕，允许在每个像素内发现的动植物元素进行良好的细节分析。这项工作代表了在缺乏气象站的山区系统中估计温度和降水值的最可行和有效的替代方法;这表明，除了提供对生态系统越来越必要的气候信息之外，还可以对生态系统的时空动态进行建模，以更好地了解当今复杂的气候变化。
{"title":"Distribution and spatio-temporal variation of temperature and precipitation in the\u0000 Sierra de Otontepec Ecological Reserve, Veracruz, Mexico through GIS modelling","authors":"V. Soto, José Luis Alanís, J. Pech, Jorge Luis Chagoya","doi":"10.20937/atm.53124","DOIUrl":"https://doi.org/10.20937/atm.53124","url":null,"abstract":"Sierra de Otontepec Ecological Reserve is an isolated mountain in the Coastal\u0000 Plain of Gulf of Mexico with great scientific importance. However, it lacks\u0000 climatological information to support the studies of the ecosystems that have been\u0000 carried out in recent years. Through GIS modelling its climatology was characterized,\u0000 and climate variability scenarios were created for the period 1981-2010. From\u0000 temperature and precipitation data recorded in situ, vertical gradients of both\u0000 variables were obtained in relation to the orography of the zone. The records were\u0000 correlated with data from nearby weather stations so that by using a DEM it was possible\u0000 to obtain raster layers with a resolution of 15 m per pixel for temperature, in the\u0000 summer and winter seasons; and for precipitation, in the rainy and dry seasons; the\u0000 annual value was also calculated for both variables. The climatic variability detected\u0000 in the zone indicates a gradual increase in air temperature over time and a spatial\u0000 variation in the distribution of precipitation. The spatial resolution of the modelling\u0000 is precisely adjusted to the relief of the mountain, allowing the flora and fauna\u0000 elements found within each pixel to be analysed with a good level of detail. This work\u0000 represents the most viable and effective alternative to estimate temperature and\u0000 precipitation values in mountain systems lacking climatological stations; demonstrating\u0000 that beyond providing climate information, which is increasingly necessary for\u0000 ecosystems, it is possible to model their spatio-temporal dynamics to better understand\u0000 the complex climate variability of our days.","PeriodicalId":55576,"journal":{"name":"Atmosfera","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67655825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The occurrence probabilities of heavy rainfalls brought about flood events have an essential role in designing water-related structures and water resource management. In many cases, data for analysis are either not available or are insufficient for reliable design of water-related structures. Regional frequency analysis is frequently preferred to provide design information at sites with especially inadequate data available. Our study implemented L-moment procedures to annual maximum rainfall series from 70 gauging stations in the Middle Black Sea Region (MBSR) in Turkey to estimate regional rainfall quantiles. The first attempt for regionalization was to evaluate the entire area as an only homogeneous region. The sub-regions were initially defined with the ward’s clustering algorithm due to the presence of discordant sites under a presumption of a single homogeneous region. In compliance with the results of the discordancy and heterogeneity measures, the most promising classification was achieved with 6 clusters (sub-regions). The sub-regions have satisfied the homogeneity condition as “acceptably homogeneous.” The GEV and GLO distributions in five sub-regions, GNO distribution in four sub-regions, and PE3 distribution in three sub-regions were decided to be acceptable as a regional frequency distribution. In comparison, GPA is a candidate distribution in none of six sub-regions.
{"title":"Regional extreme rainfall estimation in Middle Black Sea Region, Turkey","authors":"K. Yürekli, M. Enginsu, Müberra Erdoğan","doi":"10.20937/atm.53024","DOIUrl":"https://doi.org/10.20937/atm.53024","url":null,"abstract":"The occurrence probabilities of heavy rainfalls brought about flood events have an essential role in designing water-related structures and water resource management. In many cases, data for analysis are either not available or are insufficient for reliable design of water-related structures. Regional frequency analysis is frequently preferred to provide design information at sites with especially inadequate data available. Our study implemented L-moment procedures to annual maximum rainfall series from 70 gauging stations in the Middle Black Sea Region (MBSR) in Turkey to estimate regional rainfall quantiles. The first attempt for regionalization was to evaluate the entire area as an only homogeneous region. The sub-regions were initially defined with the ward’s clustering algorithm due to the presence of discordant sites under a presumption of a single homogeneous region. In compliance with the results of the discordancy and heterogeneity measures, the most promising classification was achieved with 6 clusters (sub-regions). The sub-regions have satisfied the homogeneity condition as “acceptably homogeneous.” The GEV and GLO distributions in five sub-regions, GNO distribution in four sub-regions, and PE3 distribution in three sub-regions were decided to be acceptable as a regional frequency distribution. In comparison, GPA is a candidate distribution in none of six sub-regions.","PeriodicalId":55576,"journal":{"name":"Atmosfera","volume":"20 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67655348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ida Pramuwardani, Hartono, Sunarto, Arhasena Sopaheluwakan
Tropical Rainfall Measuring Mission (TRMM) and ERA-Interim forecast data analyzed using second-order autoregressive AR(2) and space-time-spectra analysis methods (respectively) revealed contrasting results for predicting Madden Julian Oscillation (MJO) and Convectively Coupled Equatorial Waves (CCEW) phenomena over Indonesia. This research used the same 13-year series of daily TRMM 3B42 V7 derived datasets and ERA-Interim reanalysis model datasets from the European Center for Medium-Range Weather Forecasts (ECMWF) for precipitation forecasts. Three years (2016 to 2018) of the filtered 3B42 and ERA-Interim forecast data was then used to evaluate forecast accuracy by looking at correlation coefficients for forecast leads from day +1 through day +7. The results revealed that rainfall estimation data from 3B42 provides better results for the shorter forecast leads, particularly for MJO, equatorial Rossby (ER), mixed Rossby-gravity (MRG), and inertia-gravity phenomena in zonal wavenumber 1 (IG1), but gives poor correlation for Kelvin waves for all forecast leads. A consistent correlation for all waves was achieved from the filtered ERA-Interim precipitation forecast model, and although this was quite weak for the first forecast leads it did not reach a negative correlation in the later forecast leads except for IG1. Furthermore, Root Mean Square Error (RMSE) was also calculated to complement forecasting skills for both data sources, with the result that residual RMSE for the filtered ERA-Interim precipitation forecast was quite small during all forecast leads and for all wave types. These findings prove that the ERA-Interim precipitation forecast model remains an adequate precipitation model in the tropics for MJO and CCEW forecasting, specifically for Indonesia.
{"title":"Comparison of forecasting accuracy for Madden Julian Oscillation (MJO) and Convectively Coupled Equatorial Waves (CCEW) using Tropical Rainfall Measuring Mission (TRMM) and ERA-Interim precipitation forecast data for Indonesia","authors":"Ida Pramuwardani, Hartono, Sunarto, Arhasena Sopaheluwakan","doi":"10.20937/atm.53009","DOIUrl":"https://doi.org/10.20937/atm.53009","url":null,"abstract":"Tropical Rainfall Measuring Mission (TRMM) and ERA-Interim forecast data analyzed using second-order autoregressive AR(2) and space-time-spectra analysis methods (respectively) revealed contrasting results for predicting Madden Julian Oscillation (MJO) and Convectively Coupled Equatorial Waves (CCEW) phenomena over Indonesia. This research used the same 13-year series of daily TRMM 3B42 V7 derived datasets and ERA-Interim reanalysis model datasets from the European Center for Medium-Range Weather Forecasts (ECMWF) for precipitation forecasts. Three years (2016 to 2018) of the filtered 3B42 and ERA-Interim forecast data was then used to evaluate forecast accuracy by looking at correlation coefficients for forecast leads from day +1 through day +7. The results revealed that rainfall estimation data from 3B42 provides better results for the shorter forecast leads, particularly for MJO, equatorial Rossby (ER), mixed Rossby-gravity (MRG), and inertia-gravity phenomena in zonal wavenumber 1 (IG1), but gives poor correlation for Kelvin waves for all forecast leads. A consistent correlation for all waves was achieved from the filtered ERA-Interim precipitation forecast model, and although this was quite weak for the first forecast leads it did not reach a negative correlation in the later forecast leads except for IG1. Furthermore, Root Mean Square Error (RMSE) was also calculated to complement forecasting skills for both data sources, with the result that residual RMSE for the filtered ERA-Interim precipitation forecast was quite small during all forecast leads and for all wave types. These findings prove that the ERA-Interim precipitation forecast model remains an adequate precipitation model in the tropics for MJO and CCEW forecasting, specifically for Indonesia.","PeriodicalId":55576,"journal":{"name":"Atmosfera","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46415032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Earth’s surface temperature oscillated greatly throughout time. From near congelation during “snowball Earth” 2.9Gya to an ice-free world in the Paleocene-Eocene Thermal maximum 55Mya. These changes have been forced by internal (e.g. changes in the chemical composition of the atmosphere) or external (e.g. changes in solar luminosity) drivers that varied through time. Thus, if we understand how the radiation budget evolved in different times, we can closely calculate past global climate; a fundamental comparison to situate current climate change in the context Earth’s history. Here I present an analytical framework employing a simple energy balance derived from the Stephan-Boltzmann law, that allows for quick comparison between drivers of global temperature and at multiple moments in the history of our planet. My results show that current rates of increase in global temperature are at least four times faster than any previous warming event.
{"title":"A breviary of Earth’s climate changes using Stephan-Boltzmann law","authors":"Guillermo Nicolás Murray Tortarolo","doi":"10.20937/atm.53102","DOIUrl":"https://doi.org/10.20937/atm.53102","url":null,"abstract":"Earth’s surface temperature oscillated greatly throughout time. From near congelation during “snowball Earth” 2.9Gya to an ice-free world in the Paleocene-Eocene Thermal maximum 55Mya. These changes have been forced by internal (e.g. changes in the chemical composition of the atmosphere) or external (e.g. changes in solar luminosity) drivers that varied through time. Thus, if we understand how the radiation budget evolved in different times, we can closely calculate past global climate; a fundamental comparison to situate current climate change in the context Earth’s history. Here I present an analytical framework employing a simple energy balance derived from the Stephan-Boltzmann law, that allows for quick comparison between drivers of global temperature and at multiple moments in the history of our planet. My results show that current rates of increase in global temperature are at least four times faster than any previous warming event.","PeriodicalId":55576,"journal":{"name":"Atmosfera","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49164909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Haze pollution, mainly characterized by low visibility, is one of the main environmental problems currently faced by China. Accurate haze forecasts facilitate the implementation of preventive measures to control the emission of air pollutants and, thereby mitigate haze pollution. However, it is not easy to accurately predict the low visibility events induced by haze, which requires not only accurate prediction for weather elements, but also refined and real-time updated source emission inventory. In order to obtain reliable forecasting tools, this paper studies the usability of several popular machine learning methods, such as support vector machine, k-nearest neighbor, random forest, as well as several deep learning methods, on the visibility forecasting. Starting from the main factors related to visibility, the relationships between wind speed, wind direction, temperature, humidity, and visibility are discussed. Training and forecasting were performed using the machine learning methods. The accuracy of these methods in visibility forecasting was confirmed through several parameters (i.e., root-mean-square error, mean absolute error, and mean absolute percentage error). The results show that: (1) Among all meteorological parameters, wind speed was the best at reflecting the visibility change patterns; (2) RNN LSTM, and GRU methods performs almost equally well on short-term visibility forecasts(i.e. 1h, 3h, and 6h); (3) A classical machine learning method (i.e. the SVM) performs well in mid- and long-term visibility forecasts; (4) The machine learning methods also have a certain degree of forecast accuracy even for long time periods (e.g. of 72h).
{"title":"Research on the usability of different machine learning methods in visibility forecasting","authors":"P. Chan, Wu Wen, Lei Li","doi":"10.20937/atm.53053","DOIUrl":"https://doi.org/10.20937/atm.53053","url":null,"abstract":"Haze pollution, mainly characterized by low visibility, is one of the main environmental problems currently faced by China. Accurate haze forecasts facilitate the implementation of preventive measures to control the emission of air pollutants and, thereby mitigate haze pollution. However, it is not easy to accurately predict the low visibility events induced by haze, which requires not only accurate prediction for weather elements, but also refined and real-time updated source emission inventory. In order to obtain reliable forecasting tools, this paper studies the usability of several popular machine learning methods, such as support vector machine, k-nearest neighbor, random forest, as well as several deep learning methods, on the visibility forecasting. Starting from the main factors related to visibility, the relationships between wind speed, wind direction, temperature, humidity, and visibility are discussed. Training and forecasting were performed using the machine learning methods. The accuracy of these methods in visibility forecasting was confirmed through several parameters (i.e., root-mean-square error, mean absolute error, and mean absolute percentage error). The results show that: (1) Among all meteorological parameters, wind speed was the best at reflecting the visibility change patterns; (2) RNN LSTM, and GRU methods performs almost equally well on short-term visibility forecasts(i.e. 1h, 3h, and 6h); (3) A classical machine learning method (i.e. the SVM) performs well in mid- and long-term visibility forecasts; (4) The machine learning methods also have a certain degree of forecast accuracy even for long time periods (e.g. of 72h).","PeriodicalId":55576,"journal":{"name":"Atmosfera","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42731031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Study of long term variability of temperature and rainfall in the context of climate change is of much importance particularly in the region where rainfed agriculture is predominant. Long term trends of temperature and rainfall have been investigated over Kolkata, India, a tropical region using gridded monthly precipitation and temperature data obtained from Global Precipitation and Climate Centre (GPCC V7) with 0.5° X 0.5° resolution for the period 1901 to 2014. Precipitation concentration index, coefficient of variation, rainfall anomaly have been calculated and Palmer drought severity index data have been analyzed. Furthermore, Mann-Kendall test and sen’s slope estimator have been used to detect time series trend. Annual temperature and rainfall have been increased with a rate of 0.0082°C/ year and 0.03 mm/ year respectively. Statistically significant increasing trend has been observed for most of the months for temperature and rainfall. Winter and monsoon period shows highest and lowest inter-annual variability respectively. Rainfall with high precipitation concentration index (16-20) has been observed for the period 1951-1975 and 1976-2000. It has been observed that the number of years with dry conditions have been increased. However, the intensity of dryness is very near to zero. The information from this study will be helpful for the farmers to plan for resilient farming.
{"title":"Time Series Trend Analysis of Rainfall and Temperature over Kolkata and Surrounding Region","authors":"A. De, Srishty Shreya, Neel Sarkar, A. Maitra","doi":"10.20937/atm.53059","DOIUrl":"https://doi.org/10.20937/atm.53059","url":null,"abstract":"Study of long term variability of temperature and rainfall in the context of climate change is of much importance particularly in the region where rainfed agriculture is predominant. Long term trends of temperature and rainfall have been investigated over Kolkata, India, a tropical region using gridded monthly precipitation and temperature data obtained from Global Precipitation and Climate Centre (GPCC V7) with 0.5° X 0.5° resolution for the period 1901 to 2014. Precipitation concentration index, coefficient of variation, rainfall anomaly have been calculated and Palmer drought severity index data have been analyzed. Furthermore, Mann-Kendall test and sen’s slope estimator have been used to detect time series trend. Annual temperature and rainfall have been increased with a rate of 0.0082°C/ year and 0.03 mm/ year respectively. Statistically significant increasing trend has been observed for most of the months for temperature and rainfall. Winter and monsoon period shows highest and lowest inter-annual variability respectively. Rainfall with high precipitation concentration index (16-20) has been observed for the period 1951-1975 and 1976-2000. It has been observed that the number of years with dry conditions have been increased. However, the intensity of dryness is very near to zero. The information from this study will be helpful for the farmers to plan for resilient farming.","PeriodicalId":55576,"journal":{"name":"Atmosfera","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45390878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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