ریزمقیاس نمایی دمای روزانه سه بانک اطلاعاتی بازتحلیل به تفکیک مکانی یک کیلومتر با استفاده از داده‌های سنجنده MODIS

نوع مقاله : مقاله تحقیقی‌ (پژوهشی‌)

نویسندگان

1 استادیار، پژوهشگاه هواشناسی و علوم جو، تهران، ایران

2 کارشناس پژوهشی، پژوهشگاه هواشناسی و علوم جو، تهران، ایران

چکیده

مفهوم افتآهنگ دما، ابزاری مناسب برای میان‌یابی مکانی داده‌های پراکنده دمای هوا است. با این وجود، گسستگی مکانی ایستگاه‌های سنجش دما باعث ایجاد عدم قطعیت بالایی در مفهوم افتآهنگ می‌گردد. به منظور غلبه بر این مشکل، استفاده از دمای سطح زمین که توسط ماهواره‌ها ثبت شده، به طور گسترده جهت تخمین دمای واقعی هوا در جهان استفاده شده است. یکی از این منابع دمای سطح زمین، محصولات پرکاربرد توسعه یافته سنجنده MODIS است. در این مطالعه با استفاده از داده‌های دمای سطح زمین MODIS که از دقت مکانی مناسبی برخوردار هستند، افتآهنگ دما هوای ایران در ماه‌های مختلف محاسبه شد و از آن برای ریزمقیاس‌نمایی داده‌های روزانه بازتحلیل ERA5، CFS و MERRA2 به تفکیک مکانی یک کیلومتر استفاده گردید. سپس، داده‌های ریزمقیاس شده در محل ایستگاه‌های هواشناسی در مناطق اقلیمی و طبقات ارتفاعی مختلف با داده‌های ثبت شده مقایسه گردید. بر اساس شاخص‌های ارزیابی RMSE، R و NSE داده‌های ERA5 به صورت خام و ریزمقیاس شده نسبت به دو مدل دیگر دقت بیشتری داشته است. حداکثر بهبود در شاخص‌ها پس از ریزمقیاس‌نمایی برای مدل MERRA2 و در طبقه ارتفاعی کم ارتفاع با متوسط بهبود نزدیک 17 درصد (با در نظر نگرفتن شاخص ضریب همبستگی) و در منطقه خشک معتدل با متوسط بهبود نزدیک به 20 درصد مشاهده گردید. به علاوه نتایج این مطالعه نشان داد که در تمام مناطق اقلیمی و ارتفاعی، داده‌های ریزمقیاس شده در قیاس با داده‌های خام بازتحلیل از دقت بالاتری برخوردار هستند به گونه‌ای که به طور متوسط باعث بهبود 15، 18 و 4 درصدی به ترتیب در مقادیر شاخص‌های RMSE، MAE و NSE شده است. به طور کلی نتایج حاکی از مؤثر بودن روش ریزمقیاس نمایی ارائه شده در بهبود دقت داده‌های دما است..
 

کلیدواژه‌ها


عنوان مقاله [English]

Downscaling daily temperature of three reanalysis databases at a spatial resolution of one kilometer using MODIS sensor data

نویسندگان [English]

  • Yashar Falamarzi 1
  • Ebrahim Asadi Oskouei 1
  • Zoherh Javanshiri 1
  • Morteza Pakdaman 2
1 Assistant professor, Research Institute of Meteorology and Atmospheric Science, Tehran, Iran
2 Research Expert, Research Institute of Meteorology and Atmospheric Science, Tehran, Iran
چکیده [English]

While temperature is an important climatic variable in the majority of the fields such as hydrological and climatological modelling, spatial and temporal separation of this variable is a weakness all over the world which makes challenges in its usages. Lots of studies have been conducted to solve this problem; using Temperature Laps Rate (TLR) is one popular way to handle this challenge. Although TLR is an effective tool to interpolate temperature,an insufficient number of stations or inefficient spatial distribution of the stations could make calculated TLRs very uncertain. To cope with this discontinuity in temperature, satellite-sensed temperature data have been utilized. In comparison to station-based temperature, satellite-sensed temperature data is a well choice to map the temporal and spatial pattern of temperature in a wide area. With recent developments in Moderate-resolution Imaging Spectroradiometer (MODIS), Land Surface Temperature (LST) data has been successfully employed in several areas such as earth surface radiation, evaporation, urban heat islands, climate change, hydrological modeling, sea surface and air temperature estimation. Iran is located in the arid and
semi-arid region that has been always faces with water shortages. This has been worthen with global warming which has caused increases in water demand, too. Thus, having temperature data with good spatial resolution has been always a need and challenge in the area and lots of the fields. In this study, an approach was introduced to estimate TLR utilizing MODIS LST with good spatial resolution. These estimated TLRs were then used to downscale ERA5, CFS, and MERRA2 daily reanalysis temperature data sets to 1 km spatial resolution. The downscaled data was compared with the recorded data at the stations in different climate regions and elevation clusters. The results showed that improvements resulted in all climate regions and elevation levels. On average 15, 18, and 4 percent improvements were seen in RMSE, MAE, and NSE, respectively.

کلیدواژه‌ها [English]

  • Temperature
  • Iran
  • MODIS
  • TLR
  • Downscaling
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