نوع مقاله : مقاله تحقیقی (پژوهشی)
نویسندگان
1 استادیار/پژوهشکده اقلیم شناسی مشهد
2 استادیار پژوهشگاه هواشناسی و علوم جو
3 عضو هیات علمی
4 عضو هیات علمی پژوهشکده اقلیم شناسی
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
Spatial and temporal separation of temperature 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, insufficient number of stations or inefficient spatial distribution of the stations could make calculated TLRs very uncertain. In order 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 shortage. 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 approached 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. In order to achieve the scopes, firstly monthly MODIS LST data for the period of 2002 to 2020 was downloaded. Then the MODIS LST data was averaged over the period for each month, separately, so that one LST map became available for each month. The Digital Elevation Model (DEM) map was also downloaded from USGS data set, it was then re-grided based on based on LST maps. Reanalysis daily temperature data of ERA5, CFS and MERRA models were also downloaded for the period of 1980 to 2021. The observed data of 317 weather stations were employed for validation of the results. These two data sets of LST and DEM were utilized to estimate TLRs within each block of reanalysis maps. In order to avoid unacceptable TLR values and fill missing, SDE (Standard Deviation of Elevation) threshold was used. This threshold was computed through a dynamical process. Once the TLRs were calculated for each month and for all the models, the daily reanalysis temperature data was downscaled. The downscaled and original data sets were compared with the recorded temperature at the weather stations using RMSE, MAE, R and NSE coefficients. The efficiency analysis was performed in 6 different climate region and 5 elevation levels. Overall, ERA5 original data has the best accuracy when compared with the observed data. While the results showed that improvements were resulted in all climate regions and elevation levels, the improvements were the highest for MERRA2. On average 15, 18 and 4 percent improvements were seen in RMSE, MAE and NSE, respectively.
کلیدواژهها [English]
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