AtmosferaPub Date : 2022-09-29DOI: 10.20937/atm.53149
F. Dökmen, Y. Ahi, Daniyal Durmuş Köksal
{"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}
AtmosferaPub Date : 2022-08-05DOI: 10.20937/atm.53155
Bruno Guerreiro Miranda, Rogério Galante Negri, Luana Albertani Pampuch
{"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}
AtmosferaPub Date : 2022-08-02DOI: 10.20937/atm.53103
Javier De Jesús Correa Islas, Juan Manuel Romero Padilla, Paulino Pérez Rodríguez, Antonio Vázquez Alarcón
{"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}
AtmosferaPub Date : 2022-08-02DOI: 10.20937/atm.53042
Shreyas Pandit, S. Mishra, A. Mittal, Anil Kumar Devrani
{"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}