هواشناسی کشاورزی

هواشناسی کشاورزی

مطالعه نقش طرحواره‌های‌ همرفت و ناحیه اقلیمی در پیش‌بینی دمای ماهانه دوره سرد سال در ایران با استفاده از مدل اقلیمی RegCM4.5

نوع مقاله : مقاله پژوهشی

نویسندگان
آرزو اقبالی 1
ایمان بابائیان 2
مجید آزادی 1
آذر زرین 3
مجید حبیبی نوخندان 4
1 پژوهشگاه هواشناسی و علوم جو
2 پژوهشکده اقلیم شناسی، پژوهشگاه هواشناسی و علوم جو، مشهد
3 دانشگاه فردوسی مشهد
4 دانشیار پژوهشگاه هواشناسی تهران
10.22125/agmj.2024.446547.1166
چکیده
پیش‌بینی قابل اعتماد دمای ماهانه نقش مهمی در کشاورزی هوشمند اقلیم محور، پایداری تولید غذا و کاهش ریسک مخاطرات اقلیمی دارد. این مطالعه با هدف بهبود دقت پیش‌بینی ماهانه دمای کشور در دوره سرد سال (نوامبر تا می) که عمدتا همزمان با فصل کشت پاییزه است، انجام شد. برای این منظور کارایی طرحواره‌های مختلف همرفت مدل RegCM4.5 شامل کو (Kuo)، گرل (Grell)، امانوئل (Emanuel)، تایدیک (Tiedtke) و کین (Kain) در پیش‌بینی دمای کشور در اقلیم‌های مختلف مورد مطالعه قرار گرفت. سپس بر اساس طرحواره همرفت مناسب برای هر ناحیه اقلیمی، مدل ترکیبی همرفت-ناحیه اقلیمی برای کل کشور ارائه گردید. داده‌های شرایط مرزی و اولیه از مدل پیش‌بینی اقلیمی جهانی CFSv.2 با تفکیک افقی حدود 100×100 کیلومتر (95/0 × 95/0 درجه) در دوره 2019-2014 اخذ شدند و تا سطح 30×30 کیلومتر مقیاس‌کاهی دینامیکی شدند و نتایج با داده‌های دمای پایگاه CRU مقایسه شدند. نتایج نشان دادند که مدل پیش‌بینی تلفیقی همرفت-ناحیه اقلیمی توانسته است کارایی پیش‌بینی ماهانه را به مقدار قابل توجهی ارتقاء دهد، چنانکه شاخص‌های آماری r، RMSE و MBE به ترتیب از 96/0، 09/3 درجه سلسیوس و 74/2- درجه سلسیوس به 99/0، 59/0 درجه سلسیوس و 45/0- درجه سلسیوس بهبود یافت. یافته‌های این پژوهش نشان می‌دهد که مهارت پیش‌بینی‌های دمای ماهانه نه تنها به طرحواره‌های همرفت بلکه به ناحیه‌بندی اقلیمی منطقه مورد مطالعه بستگی دارد.
کلیدواژه‌ها
پیش‌بینی دما
دوره سرد سال
RegCM
نواحی اقلیمی
ایران

موضوعات

عنوان مقاله English

Investigating the role of convection scheme and climatic zones in predicting the monthly temperature during the cold season in Iran using the RegCM4.5 climate model

نویسندگان English

Arezu Eghbali 1
Iman Babaeian 2
majid azadi 1
Azar zarrin 3
majid hibibi nokhandan 4
1 Atmospheric Science and Meteorological Research Center
2 Climate Research Institute
3 Ferdowsi University of Mashhad
4 Atmospheric Science and Meteorological Research Center
چکیده English

Reliable prediction of monthly temperature plays an important role in climate smart agriculture sustainable food production and reducing climate-related risks. This study was carried out with the aim of improving the accuracy of the Iran's monthly temperature prediction during the cold season (November to May), which is main autumn planting season. For this purpose, the skill of different convection schemes of RegCM4.5 model, including Kuo, Grell, Emanuel, Tiedtke and Kain, in predicting the monthly temperature values in different climates across Iran was studied. Then, based on the suitable convection scheme for each climatic zone, a combined convection-climate zone model was proposed for the whole country. The initial and boundary condition data of CFSv.2 global climate prediction model with a horizontal resolution of 0.95 × 0.95 degrees in the period of 2014-2019 were downscaled to the 30 × 30 km resolution and the results were compared with the CRU temperature data. The results showed that the combined climatic zone-convection scheme approach is able to improve the accuracy of monthly forecasts, so that the statistical indices r, RMSE and MBE are improved from 0.96, 3.09 oC and -2.74 oC respectively to 0.99, 0.59 oC and -0.45 oC. The results of this research demonstrate that the sensitivity of temperature predictions by the RegCM4.5 model is influenced not only by the convection schemes but also by the climate type of the study region.

کلیدواژه‌ها English

Temperature prediction
Cold season
Climate Model
Climatic zone
Iran
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