Recognition of Iran Air Masses by Spatial Synoptic Classification
This study deals with the spatial synoptic classification of Iran air masses through a new approach, so that up to the end of the process for weather typing calculations , the framework of SSC and SSCWE methods has been used, but the subject of recognizing the air masses and selecting the referenc...
| Main Authors: | , |
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| Format: | Article |
| Language: | fas |
| Published: |
University of Sistan and Baluchestan
2014-06-01
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| Series: | جغرافیا و توسعه |
| Subjects: | |
| Online Access: | https://gdij.usb.ac.ir/article_1551_fffae81bba3a55802637229af187c319.pdf |
| _version_ | 1797805020443312128 |
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| author | Ramin Beedel Seyed Abolfazl Masoodian |
| author_facet | Ramin Beedel Seyed Abolfazl Masoodian |
| author_sort | Ramin Beedel |
| collection | DOAJ |
| description | This study deals with the spatial synoptic classification of Iran air masses through a new approach, so that up to the end of the process for weather typing calculations , the framework of SSC and SSCWE methods has been used, but the subject of recognizing the air masses and selecting the reference days have been indicated with the new approaches. In this study, 9 (nine) factors including daily Cloudiness), Minimum and Maximum Temperature, Mean Sea Level Pressure (MSLP), Dew Point (12 GMT) deficit , Daily Temperature Range, Daily Dew Point Range, Maximum And Minimum temperature Saturation Deficit related to 63 synoptic stations in Iran were used for typing each station air or obtaining the spatial synoptic index. Subsequent to climate's seasonal division and defining seasonal windows, a matrix including stations' database (16071*15) in P mode was formed and then weather was classified by using Eigenvectors Techniques, Principal Components Analyzing (PCA) & Cluster Analysis (CA). Subsequently, through classification of various climate types at weather stations in different seasons, 13 types of seasonal air masses of distinct characteristics were identified using virtual potential temperature (VPT), a stable indicator from a meteorological point of view, as well as two new calculation methods of selecting reference days. The results indicated that different methods of selection of reference days are appropriately similar with the derived air masses having similar characteristics. Given the average characteristics of the seasonal air masses of Iran, and comparing it with American and Bergeron classifications of air masses it was made clear that the MM and DP types of the American classification and the cA, cP and mE types of the Bergeron classification couldn’t be found among the air masses of Iran and therefore those classifications do not apply. In terms of similarity of seasonal air-mass frequency patterns, 4 types of patterns could be identified, which are based on frequency of air-mass presence in specific terrains, each of which include specific masses. These are: 1) Northern Coastal type, 2) Mid-Southern and Southeastern type, 3) Southern Coastal, Northeastern and Northwestern type, and 4) Southern Alborz and Iranian Western Half type. |
| first_indexed | 2024-03-13T05:45:45Z |
| format | Article |
| id | doaj.art-61fc218e372b46a490429cfd75ea423c |
| institution | Directory Open Access Journal |
| issn | 1735-0735 2676-7791 |
| language | fas |
| last_indexed | 2024-03-13T05:45:45Z |
| publishDate | 2014-06-01 |
| publisher | University of Sistan and Baluchestan |
| record_format | Article |
| series | جغرافیا و توسعه |
| spelling | doaj.art-61fc218e372b46a490429cfd75ea423c2023-06-13T20:17:32ZfasUniversity of Sistan and Baluchestanجغرافیا و توسعه1735-07352676-77912014-06-01123511810.22111/gdij.2014.15511551Recognition of Iran Air Masses by Spatial Synoptic ClassificationRamin Beedel0Seyed Abolfazl Masoodian1دانشگاه اصفهاندانشگاه اصفهانThis study deals with the spatial synoptic classification of Iran air masses through a new approach, so that up to the end of the process for weather typing calculations , the framework of SSC and SSCWE methods has been used, but the subject of recognizing the air masses and selecting the reference days have been indicated with the new approaches. In this study, 9 (nine) factors including daily Cloudiness), Minimum and Maximum Temperature, Mean Sea Level Pressure (MSLP), Dew Point (12 GMT) deficit , Daily Temperature Range, Daily Dew Point Range, Maximum And Minimum temperature Saturation Deficit related to 63 synoptic stations in Iran were used for typing each station air or obtaining the spatial synoptic index. Subsequent to climate's seasonal division and defining seasonal windows, a matrix including stations' database (16071*15) in P mode was formed and then weather was classified by using Eigenvectors Techniques, Principal Components Analyzing (PCA) & Cluster Analysis (CA). Subsequently, through classification of various climate types at weather stations in different seasons, 13 types of seasonal air masses of distinct characteristics were identified using virtual potential temperature (VPT), a stable indicator from a meteorological point of view, as well as two new calculation methods of selecting reference days. The results indicated that different methods of selection of reference days are appropriately similar with the derived air masses having similar characteristics. Given the average characteristics of the seasonal air masses of Iran, and comparing it with American and Bergeron classifications of air masses it was made clear that the MM and DP types of the American classification and the cA, cP and mE types of the Bergeron classification couldn’t be found among the air masses of Iran and therefore those classifications do not apply. In terms of similarity of seasonal air-mass frequency patterns, 4 types of patterns could be identified, which are based on frequency of air-mass presence in specific terrains, each of which include specific masses. These are: 1) Northern Coastal type, 2) Mid-Southern and Southeastern type, 3) Southern Coastal, Northeastern and Northwestern type, and 4) Southern Alborz and Iranian Western Half type.https://gdij.usb.ac.ir/article_1551_fffae81bba3a55802637229af187c319.pdfair massesiranseed daysspatial synoptic classificationweather types |
| spellingShingle | Ramin Beedel Seyed Abolfazl Masoodian Recognition of Iran Air Masses by Spatial Synoptic Classification جغرافیا و توسعه air masses iran seed days spatial synoptic classification weather types |
| title | Recognition of Iran Air Masses by Spatial Synoptic Classification |
| title_full | Recognition of Iran Air Masses by Spatial Synoptic Classification |
| title_fullStr | Recognition of Iran Air Masses by Spatial Synoptic Classification |
| title_full_unstemmed | Recognition of Iran Air Masses by Spatial Synoptic Classification |
| title_short | Recognition of Iran Air Masses by Spatial Synoptic Classification |
| title_sort | recognition of iran air masses by spatial synoptic classification |
| topic | air masses iran seed days spatial synoptic classification weather types |
| url | https://gdij.usb.ac.ir/article_1551_fffae81bba3a55802637229af187c319.pdf |
| work_keys_str_mv | AT raminbeedel recognitionofiranairmassesbyspatialsynopticclassification AT seyedabolfazlmasoodian recognitionofiranairmassesbyspatialsynopticclassification |