Walnut trees, as well as their fruits, represent an important sector of the agricultural industry and their cultivation significantly contributes to the global economy. Irrigation is a key factor in walnut cultivation and the most important problem is related to accurately estimating the need for irrigation water. Walnut water use was estimated in this study through the artificial intelligence methods of Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) using meteorological data in western Türkiye, which has semi-arid climatic conditions. Probabilistic scenarios based on maximum, minimum and average temperature, wind speed and sunshine hours over the period 2016-2019 were developed and tested with ANNs and ANFIS models to estimate walnut evapotranspiration. Results indicate that the optimum performance in the training and testing for ANNs and ANFIS models was obtained from the fourth scenario with R = 0.95 and two climate parameters -sunshine duration and mean temperature-. Both ANNs and ANFIS models were able to predict crop water use obtaining high correlation and the minimum number of climatic parameters. Nevertheless, the ANFIS model had a higher predictive capacity, with smaller MSE (0.36 for training and 0.29 for testing) compared to the ANNs model.
{"title":"Crop water use estimation of drip irrigated walnut using ANNs and ANFIS models","authors":"F. Dökmen, Y. Ahi, Daniyal Durmuş Köksal","doi":"10.20937/atm.53149","DOIUrl":"https://doi.org/10.20937/atm.53149","url":null,"abstract":"Walnut trees, as well as their fruits, represent an important sector of the agricultural industry and their cultivation significantly contributes to the global economy. Irrigation is a key factor in walnut cultivation and the most important problem is related to accurately estimating the need for irrigation water. Walnut water use was estimated in this study through the artificial intelligence methods of Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) using meteorological data in western Türkiye, which has semi-arid climatic conditions. Probabilistic scenarios based on maximum, minimum and average temperature, wind speed and sunshine hours over the period 2016-2019 were developed and tested with ANNs and ANFIS models to estimate walnut evapotranspiration. Results indicate that the optimum performance in the training and testing for ANNs and ANFIS models was obtained from the fourth scenario with R = 0.95 and two climate parameters -sunshine duration and mean temperature-. Both ANNs and ANFIS models were able to predict crop water use obtaining high correlation and the minimum number of climatic parameters. Nevertheless, the ANFIS model had a higher predictive capacity, with smaller MSE (0.36 for training and 0.29 for testing) compared to the ANNs model.","PeriodicalId":55576,"journal":{"name":"Atmosfera","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67655626","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}
Bruno Guerreiro Miranda, Rogério Galante Negri, Luana Albertani Pampuch
Southeastern Brazil comprises an important geoeconomic and populous region in South America. Consequently, it is essential to analyze and understand the precipitation profiles in this region. Among different data sources and techniques available to perform such study, the use of clustering algorithms and information from the Global Precipitation Measurement (GPM) project rises as a convenient yet few exploited alternative. Precisely, this study employs the K-Means, the Hierarchical Ward, and the Self-Organizing Maps methods to cluster the annual and seasonal precipitation data from GPM project recorded from 2001 to 2019. The adopted methods are compared in terms of quantitative measures and the number of clusters defined through a well-established rule. The results demonstrate that the annual and seasonal periods are organized according to different number of clusters. Moreover, the results allow: identify the presence of a spatially heterogeneous distribution in the study area; to conclude that the K-Means algorithm is a suitable clustering method in the context of this investigation when compared to Ward’s Hierarchical and Self-Organizing Maps methods in terms of the Calinski-Harabasz and Davies-Bouldin measures; and that the spatial precipitation distribution over Southeastern Brazil is represented by 10 clusters in annual and summer periods, 11 clusters in autumn and spring and 9 clusters in winter period.
{"title":"Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazil","authors":"Bruno Guerreiro Miranda, Rogério Galante Negri, Luana Albertani Pampuch","doi":"10.20937/atm.53155","DOIUrl":"https://doi.org/10.20937/atm.53155","url":null,"abstract":"Southeastern Brazil comprises an important geoeconomic and populous region in South America. Consequently, it is essential to analyze and understand the precipitation profiles in this region. Among different data sources and techniques available to perform such study, the use of clustering algorithms and information from the Global Precipitation Measurement (GPM) project rises as a convenient yet few exploited alternative. Precisely, this study employs the K-Means, the Hierarchical Ward, and the Self-Organizing Maps methods to cluster the annual and seasonal precipitation data from GPM project recorded from 2001 to 2019. The adopted methods are compared in terms of quantitative measures and the number of clusters defined through a well-established rule. The results demonstrate that the annual and seasonal periods are organized according to different number of clusters. Moreover, the results allow: identify the presence of a spatially heterogeneous distribution in the study area; to conclude that the K-Means algorithm is a suitable clustering method in the context of this investigation when compared to Ward’s Hierarchical and Self-Organizing Maps methods in terms of the Calinski-Harabasz and Davies-Bouldin measures; and that the spatial precipitation distribution over Southeastern Brazil is represented by 10 clusters in annual and summer periods, 11 clusters in autumn and spring and 9 clusters in winter period.","PeriodicalId":55576,"journal":{"name":"Atmosfera","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656495","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}
Javier De Jesús Correa Islas, Juan Manuel Romero Padilla, Paulino Pérez Rodríguez, Antonio Vázquez Alarcón
An annual mean temperature map was calculated using the Kriging interpolation method for the north-central zone of Mexico to obtain the current aridity, as well as, possible scenarios for the near and distant future. The altitudinal gradient was estimated by linear regression and it was used to estimate the mean temperature. Climate Influence Areas (CIA) were obtained by superimposing the official precipitation layer and the annual mean temperature layer with help of Geographic Information Systems tools. Monthly databases of climatic variables were generated for each CIA and potential evapotranspiration was estimated using the Thorthwaite methodology. The Aridity Index (AI) was calculated and mapped for a base scenario (1970-2000). Subsequently, the aridity behavior of some scenarios was projected and mapped using the global climate models HADGEM 2.0, GFDLCM 3.0, MIP_ESM, and CRNMCM5. Some scenarios were predicted, in the best scenario, aridity will weaken the humid ecosystems and in the worst scenario, hyper-arid climates will appear in the study region.
{"title":"Application of Geostatistic Models for Aridity Scenarios in northern Mexico","authors":"Javier De Jesús Correa Islas, Juan Manuel Romero Padilla, Paulino Pérez Rodríguez, Antonio Vázquez Alarcón","doi":"10.20937/atm.53103","DOIUrl":"https://doi.org/10.20937/atm.53103","url":null,"abstract":"An annual mean temperature map was calculated using the Kriging interpolation\u0000 method for the north-central zone of Mexico to obtain the current aridity, as well as,\u0000 possible scenarios for the near and distant future. The altitudinal gradient was\u0000 estimated by linear regression and it was used to estimate the mean temperature. Climate\u0000 Influence Areas (CIA) were obtained by superimposing the official precipitation layer\u0000 and the annual mean temperature layer with help of Geographic Information Systems tools.\u0000 Monthly databases of climatic variables were generated for each CIA and potential\u0000 evapotranspiration was estimated using the Thorthwaite methodology. The Aridity Index\u0000 (AI) was calculated and mapped for a base scenario (1970-2000). Subsequently, the\u0000 aridity behavior of some scenarios was projected and mapped using the global climate\u0000 models HADGEM 2.0, GFDLCM 3.0, MIP_ESM, and CRNMCM5. Some scenarios were predicted, in\u0000 the best scenario, aridity will weaken the humid ecosystems and in the worst scenario,\u0000 hyper-arid climates will appear in the study region.","PeriodicalId":55576,"journal":{"name":"Atmosfera","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48566240","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}
Shreyas Pandit, S. Mishra, A. Mittal, Anil Kumar Devrani
Lightning Detection Systems (LDS) have a vital role in the real-time identification of the location of lightning strikes for the purpose of weather forecasting and issuing warning with sufficient lead time for safe operations. The spatial and temporal distribution of lightning, formulated using LDS observations, can be an objective input to infer and refine the climatology of Thunderstorm (TS) over a region. This study uses the data of Indian Air Force (IAF) LDS network to prepare climatological plots of lightning over India and to formulate location-specific TS guidance for a total of 12 Indian airports. The analysis of climatological plots reveals that there is a distinct warm-season preponderance of lightning strikes over Indian subcontinent, with pre-monsoon months receiving the maximum lightning. The most probable time of occurrence being 1200-1400 UTC during all the seasons across the country. Location-specific TS guidance not only signifies the most probable direction of occurrence of TS with respect to the airport, but also clearly brings out the favourable direction of movement. Hence, the same can be judiciously used as nowcasting aid coupled with actual LDS and Doppler Weather Radar (DWR) observations. Further, the characteristics features of lightning, like surges in flash rate, can be objectively used to define a predictor for nowcasting severe weather associated with a TS cloud. The study of these surges in lightning flash rate visa vis occurrence of Strong Surface Winds (SSW) > 60 kmph over Delhi National Capital Region(NCR), indicated that there is an increase in the number of lightning flashes prior to the occurrence of SSW. 77.5 % occurrences are preceded by surges in flash rate within 45 minutes of the occurrence of SSW, however, the probability of detection of the event with a lead time of 15 to 45 minutes is around 71%.
{"title":"Nowcasting Severity of Thunderstorm Associated with Strong Wind Flow Over Indian\u0000 Subcontinent: Resource Lightning Surges","authors":"Shreyas Pandit, S. Mishra, A. Mittal, Anil Kumar Devrani","doi":"10.20937/atm.53042","DOIUrl":"https://doi.org/10.20937/atm.53042","url":null,"abstract":"Lightning Detection Systems (LDS) have a vital role in the real-time\u0000 identification of the location of lightning strikes for the purpose of weather\u0000 forecasting and issuing warning with sufficient lead time for safe operations. The\u0000 spatial and temporal distribution of lightning, formulated using LDS observations, can\u0000 be an objective input to infer and refine the climatology of Thunderstorm (TS) over a\u0000 region. This study uses the data of Indian Air Force (IAF) LDS network to prepare\u0000 climatological plots of lightning over India and to formulate location-specific TS\u0000 guidance for a total of 12 Indian airports. The analysis of climatological plots reveals\u0000 that there is a distinct warm-season preponderance of lightning strikes over Indian\u0000 subcontinent, with pre-monsoon months receiving the maximum lightning. The most probable\u0000 time of occurrence being 1200-1400 UTC during all the seasons across the country.\u0000 Location-specific TS guidance not only signifies the most probable direction of\u0000 occurrence of TS with respect to the airport, but also clearly brings out the favourable\u0000 direction of movement. Hence, the same can be judiciously used as nowcasting aid coupled\u0000 with actual LDS and Doppler Weather Radar (DWR) observations. Further, the\u0000 characteristics features of lightning, like surges in flash rate, can be objectively\u0000 used to define a predictor for nowcasting severe weather associated with a TS cloud. The\u0000 study of these surges in lightning flash rate visa vis occurrence of Strong Surface\u0000 Winds (SSW) > 60 kmph over Delhi National Capital Region(NCR), indicated that there\u0000 is an increase in the number of lightning flashes prior to the occurrence of SSW. 77.5 %\u0000 occurrences are preceded by surges in flash rate within 45 minutes of the occurrence of\u0000 SSW, however, the probability of detection of the event with a lead time of 15 to 45\u0000 minutes is around 71%.","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":"67655794","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}
This study presents a first simultaneous trend and magnitude assessment of the air pollutants (CO, H2S, SO2, NO2, NO, NOx, O3 and PM10), and meteorological variables (rainfall or RF, relative humidity or RH, atmospheric pressure or PR, temperature or TC, wind speed or WS and wind direction or WD) in the city of Haqel and at four different locations in the city of Jeddah, in Saudi Arabia, for a continuous 5-year period (2008−2012). The spatio-temporal co-variations of air pollutants in terms of their diurnal, weekly, seasonal and annual cycles, and their relationship with meteorological conditions, along with the estimates of the weekend effect, are described. A decreasing annual trend was observed for most air pollutants analyzed except for O3 and PM10. The CO, NO2, NO and NOx displayed a strong weekend effect. A percentile-based change analysis displayed an increase in concentrations for O3 (PM10) in the lower (higher) percentiles from the first to second half of the study period. The study identified 12 cyclonic weather events during the 5-year time period associated with high PM10 concentrations (> 500 µg m-3) relative to a mean value of 102 µg m-3, with a standard deviation value of 179 µg m-3. The study also analyzed the impacts of several mid-latitude anti-cyclonic events on air pollutant concentrations and found a significant change in air pollutant concentrations (CO, SO2, NO2, NO, NOx, O3 and PM10) and meteorological variables (RH, PR, TC, WS, and WD) associated with stagnant upper air conditions during the atmospheric blocking.
{"title":"Spatio-temporal co-variability of air pollutants and meteorological variables over\u0000 Haqel and Jeddah, Saudi Arabia","authors":"Syeda Batool Tazeem, Nabeel Ahmed, A. Hussain","doi":"10.20937/atm.53100","DOIUrl":"https://doi.org/10.20937/atm.53100","url":null,"abstract":"This study presents a first simultaneous trend and magnitude assessment of the air\u0000 pollutants (CO, H2S, SO2, NO2, NO, NOx, O3 and PM10), and meteorological variables\u0000 (rainfall or RF, relative humidity or RH, atmospheric pressure or PR, temperature or TC,\u0000 wind speed or WS and wind direction or WD) in the city of Haqel and at four different\u0000 locations in the city of Jeddah, in Saudi Arabia, for a continuous 5-year period\u0000 (2008−2012). The spatio-temporal co-variations of air pollutants in terms of their\u0000 diurnal, weekly, seasonal and annual cycles, and their relationship with meteorological\u0000 conditions, along with the estimates of the weekend effect, are described. A decreasing\u0000 annual trend was observed for most air pollutants analyzed except for O3 and PM10. The\u0000 CO, NO2, NO and NOx displayed a strong weekend effect. A percentile-based change\u0000 analysis displayed an increase in concentrations for O3 (PM10) in the lower (higher)\u0000 percentiles from the first to second half of the study period. The study identified 12\u0000 cyclonic weather events during the 5-year time period associated with high PM10\u0000 concentrations (> 500 µg m-3) relative to a mean value of 102 µg m-3, with a standard\u0000 deviation value of 179 µg m-3. The study also analyzed the impacts of several\u0000 mid-latitude anti-cyclonic events on air pollutant concentrations and found a\u0000 significant change in air pollutant concentrations (CO, SO2, NO2, NO, NOx, O3 and PM10)\u0000 and meteorological variables (RH, PR, TC, WS, and WD) associated with stagnant upper air\u0000 conditions during the atmospheric blocking.","PeriodicalId":55576,"journal":{"name":"Atmosfera","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49314545","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}
Solar radiation, which is used in hydrological modeling, agricultural, solar energy systems, and climatological studies, is the most important element of the energy reaching the earth. The present study compared, the performance of two empirical equations -Angstrom and Hargreaves-Samani equations- and, three machine learning models -Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM)-. Various learning models were developed for the variables used in each empirical equation. In the present study, monthly data of six stations in Turkey, three stations receiving the most solar radiation and three stations receiving the least solar radiation, were used. In terms of the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and determination coefficient () values of each model, LSTM was the most successful model, followed by ANN and SVM. The MAE value was 2.65 with the Hargreaves-Samani equation and, decreased to 0.987 with the LSTM model while MAE was 1.24 in the Angstrom equation and decreased to 0.747 with the LSTM model. The study revealed that the deep learning model is more appropriate to use compared to the empirical equations even in cases where there is limited data.
{"title":"Comparative analysis of estimated solar radiation with different learning methods\u0000 and empirical models","authors":"Mehmet Murat Comert, Kemal Adem, Müberra Erdoğan","doi":"10.20937/atm.53131","DOIUrl":"https://doi.org/10.20937/atm.53131","url":null,"abstract":"Solar radiation, which is used in hydrological modeling, agricultural, solar\u0000 energy systems, and climatological studies, is the most important element of the energy\u0000 reaching the earth. The present study compared, the performance of two empirical\u0000 equations -Angstrom and Hargreaves-Samani equations- and, three machine learning models\u0000 -Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Long Short-Term\u0000 Memory (LSTM)-. Various learning models were developed for the variables used in each\u0000 empirical equation. In the present study, monthly data of six stations in Turkey, three\u0000 stations receiving the most solar radiation and three stations receiving the least solar\u0000 radiation, were used. In terms of the mean squared error (MSE), root mean squared error\u0000 (RMSE), mean absolute error (MAE), and determination coefficient () values of each\u0000 model, LSTM was the most successful model, followed by ANN and SVM. The MAE value was\u0000 2.65 with the Hargreaves-Samani equation and, decreased to 0.987 with the LSTM model\u0000 while MAE was 1.24 in the Angstrom equation and decreased to 0.747 with the LSTM model.\u0000 The study revealed that the deep learning model is more appropriate to use compared to\u0000 the empirical equations even in cases where there is limited data.","PeriodicalId":55576,"journal":{"name":"Atmosfera","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48604958","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}
Y. Skiba, Roberto Carlos Cruz Rodriguez, D. Filatov
The implicit and unconditionally stable numerical method proposed in Skiba (2015) is applied for solving linear advection-diffusion-reaction problems and nonlinear diffusion-reaction problems on a sphere. Numerical experiments carried out on a high-resolution spherical mesh show the effectiveness of the method in modelling linear advection-diffusion processes on a sphere (dispersion of pollution in the atmosphere), and nonlinear diffusion processes (propagation of nonlinear temperature waves, blow-up regimes of combustion, and chemical reactions in the Gray-Scott model). The method correctly describes the mass balance of a substance in forced and dissipative systems, and conserves the total mass and norm of the solution in the absence of forcing and dissipation.
{"title":"Solution of advection-diffusion-reaction problems on a sphere: high-resolution\u0000 numerical experiments","authors":"Y. Skiba, Roberto Carlos Cruz Rodriguez, D. Filatov","doi":"10.20937/atm.53172","DOIUrl":"https://doi.org/10.20937/atm.53172","url":null,"abstract":"The implicit and unconditionally stable numerical method proposed in Skiba (2015)\u0000 is applied for solving linear advection-diffusion-reaction problems and nonlinear\u0000 diffusion-reaction problems on a sphere. Numerical experiments carried out on a\u0000 high-resolution spherical mesh show the effectiveness of the method in modelling linear\u0000 advection-diffusion processes on a sphere (dispersion of pollution in the atmosphere),\u0000 and nonlinear diffusion processes (propagation of nonlinear temperature waves, blow-up\u0000 regimes of combustion, and chemical reactions in the Gray-Scott model). The method\u0000 correctly describes the mass balance of a substance in forced and dissipative systems,\u0000 and conserves the total mass and norm of the solution in the absence of forcing and\u0000 dissipation.","PeriodicalId":55576,"journal":{"name":"Atmosfera","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49010559","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}
Leandro Rodrigo Almonacid, N. Pessacg, Boris Gaston Diaz, P. Peri
Climate regionalization is essential for characterizing spatial and temporal climatic variability, producing meteorological forecasts, analyzing trends at different scales and, determining the climatic impact on human activities. The aim was to propose a climatic regionalization for Santa Cruz province, based on gridded data of rainfall and temperature (period 1995 to 2014), and subsequent characterization. To achieve this goal, we applied the non-hierarchical k-means clustering method to monthly accumulated rainfall and monthly average temperature databases. The Thornthwaite classification modified by Feddema was used to classify each cluster. Results from this study showed that Santa Cruz province is divided into 11 climatic regions based on rainfall and temperature. The driest and warmest regions are located in the center and northeast of the province and the most humid and coldest ones in the south and southwest. Regionalization is an important component of many applied climate studies and it can be used in other studies related to agriculture, energy production, water resource management, extreme weather events, and climate change, among others. This regionalization in particular can be used to examine the impacts of climate change in regional studies of climatic scale reduction in Santa Cruz province. As well as this tool can be essential in the study of drought and its impacts and contributes to a better understanding of the climatic phenomena that condition drought.
{"title":"Climate regionalization of Santa Cruz province, Argentina","authors":"Leandro Rodrigo Almonacid, N. Pessacg, Boris Gaston Diaz, P. Peri","doi":"10.20937/atm.53166","DOIUrl":"https://doi.org/10.20937/atm.53166","url":null,"abstract":"Climate regionalization is essential for characterizing spatial and temporal\u0000 climatic variability, producing meteorological forecasts, analyzing trends at different\u0000 scales and, determining the climatic impact on human activities. The aim was to propose\u0000 a climatic regionalization for Santa Cruz province, based on gridded data of rainfall\u0000 and temperature (period 1995 to 2014), and subsequent characterization. To achieve this\u0000 goal, we applied the non-hierarchical k-means clustering method to monthly accumulated\u0000 rainfall and monthly average temperature databases. The Thornthwaite classification\u0000 modified by Feddema was used to classify each cluster. Results from this study showed\u0000 that Santa Cruz province is divided into 11 climatic regions based on rainfall and\u0000 temperature. The driest and warmest regions are located in the center and northeast of\u0000 the province and the most humid and coldest ones in the south and southwest.\u0000 Regionalization is an important component of many applied climate studies and it can be\u0000 used in other studies related to agriculture, energy production, water resource\u0000 management, extreme weather events, and climate change, among others. This\u0000 regionalization in particular can be used to examine the impacts of climate change in\u0000 regional studies of climatic scale reduction in Santa Cruz province. As well as this\u0000 tool can be essential in the study of drought and its impacts and contributes to a\u0000 better understanding of the climatic phenomena that condition drought.","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":"67656387","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}
Juan Manuel Navarro Céspedes, Jesús Horacio Hernández, Pedro Camilo Alcántara Concepción, Jorge Luis Morales Martínez, Gilberto Carreño Aguilera, Francisco Padilla Benítez
Climatological data with unreliable or missing values is an important area of research, and multiple methods are available to fill in missing data and evaluate data quality. Our study aims to compare the performance of different methods for estimating missing values that are explicitly designed for precipitation and multipurpose hydrological data. The climate variable used for the analysis was daily precipitation. We considered two different climate and orographic regions to evaluate the effects of altitude, precipitation regime and percentage of missing data on the Mean Absolute Error of imputed values and using a homogeneity evaluation of meteorological stations. We excluded from the analysis meteorological stations with more than 25% missing data. In the semi-arid region, ReddPrec (optimal for 9 stations), and GCIDW (optimal for 8) were the best performing methods for the 23 stations, with average MAE values of 1.63 mm/day and 1.46 mm/day, respectively. In the humid region, GCIDW was optimal in ~59% of stations, EM in ~24%, and ReddPrec in ~17%, with average MAE values of ~6.0 mm/day, 6.5 mm/day and ~9.8 mm/day, respectively. This research makes an important contribution to identifying the most appropriate methods to impute daily precipitation in different climatic regions of Mexico based on efficiency indicators and homogeneity evaluation.
{"title":"A comparison of missing values imputation methods applied to precipitation of two\u0000 semi-arid and humid regions of México","authors":"Juan Manuel Navarro Céspedes, Jesús Horacio Hernández, Pedro Camilo Alcántara Concepción, Jorge Luis Morales Martínez, Gilberto Carreño Aguilera, Francisco Padilla Benítez","doi":"10.20937/atm.53095","DOIUrl":"https://doi.org/10.20937/atm.53095","url":null,"abstract":"Climatological data with unreliable or missing values is an important area of\u0000 research, and multiple methods are available to fill in missing data and evaluate data\u0000 quality. Our study aims to compare the performance of different methods for estimating\u0000 missing values that are explicitly designed for precipitation and multipurpose\u0000 hydrological data. The climate variable used for the analysis was daily precipitation.\u0000 We considered two different climate and orographic regions to evaluate the effects of\u0000 altitude, precipitation regime and percentage of missing data on the Mean Absolute Error\u0000 of imputed values and using a homogeneity evaluation of meteorological stations. We\u0000 excluded from the analysis meteorological stations with more than 25% missing data. In\u0000 the semi-arid region, ReddPrec (optimal for 9 stations), and GCIDW (optimal for 8) were\u0000 the best performing methods for the 23 stations, with average MAE values of 1.63 mm/day\u0000 and 1.46 mm/day, respectively. In the humid region, GCIDW was optimal in ~59% of\u0000 stations, EM in ~24%, and ReddPrec in ~17%, with average MAE values of ~6.0 mm/day, 6.5\u0000 mm/day and ~9.8 mm/day, respectively. This research makes an important contribution to\u0000 identifying the most appropriate methods to impute daily precipitation in different\u0000 climatic regions of Mexico based on efficiency indicators and homogeneity\u0000 evaluation.","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":"67655590","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}
Andrea Burgos-Cuevas, A. Ruiz‐Angulo, Carlos Alberto Palacios Morales, Karina Ramos Musalem, Cruz Daniel García Molina
Experimental lock-release gravity currents are investigated as they propagate downslope over a synthetic varying topography. We emulate and investigate the dynamics of thermally driven winds that propagate downslope while interacting with the roughness of a topographic surface. The mixing processes between the gravity currents and their surroundings are studied with Particle Image Velocimetry (PIV) and entrainment is quantified. The magnitude of the entrainment coefficient is shown to increase as the roughness of the slope increases. Shadowgraph visualizations qualitatively reproduce this behavior. Finally, pressure fields are estimated from velocity fields and the arrival of gravity currents is shown to be detected in pressure time series. This last result may be useful to detect atmospheric gravity currents using only surface pressure measurements.
{"title":"Experimental downslope gravity currents over a synthetic topography","authors":"Andrea Burgos-Cuevas, A. Ruiz‐Angulo, Carlos Alberto Palacios Morales, Karina Ramos Musalem, Cruz Daniel García Molina","doi":"10.20937/atm.53156","DOIUrl":"https://doi.org/10.20937/atm.53156","url":null,"abstract":"Experimental lock-release gravity currents are investigated as they propagate\u0000 downslope over a synthetic varying topography. We emulate and investigate the dynamics\u0000 of thermally driven winds that propagate downslope while interacting with the roughness\u0000 of a topographic surface. The mixing processes between the gravity currents and their\u0000 surroundings are studied with Particle Image Velocimetry (PIV) and entrainment is\u0000 quantified. The magnitude of the entrainment coefficient is shown to increase as the\u0000 roughness of the slope increases. Shadowgraph visualizations qualitatively reproduce\u0000 this behavior. Finally, pressure fields are estimated from velocity fields and the\u0000 arrival of gravity currents is shown to be detected in pressure time series. This last\u0000 result may be useful to detect atmospheric gravity currents using only surface pressure\u0000 measurements.","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":"67656186","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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