TY  - Data
T1  - Daily 2-meter air temperature dataset for the Yangtze and Yellow River source regions at 4-kilometer resolution, 1960–1979
A1  - WU Xiaodong
A1  - LIU Guimin
A1  - SHAO Meiqi
A1  - YAN Xuchun
DO  - 10.12072/ncdc.nieer.db7004.2025
PY  - 2025
DA  - 2025-11-07
PB  - National Cryosphere Desert Data Center
AB  - 1. This dataset addresses the need for high-precision meteorological drivers in the complex terrain regions of the Yangtze and Yellow River headwaters on the Qinghai-Tibet Plateau. It provides daily 2-meter near-surface air temperature data in NC format at the regional scale for the Yangtze and Yellow River headwaters, with a spatial resolution of 4.0 km (approximately 0.03333°). The temporal range covers January 1, 1960, to December 31, 1979.The study site is located in the heart of the Qinghai-Tibet Plateau, known as the “Water Tower of China,” primarily distributed in the Tangula and Bayan Har mountain ranges of Qinghai Province. This region has an average elevation of approximately 4,500 meters, featuring a cold, arid climate with widespread glaciers and permafrost. The Yellow River headwaters region lies upstream of the Longyangxia Reservoir on the main stem of the Yellow River. It is concentrated in the Yoguzonglie Basin on the northern foothills of the Bayankala Mountains and near Lake Zhaling and Lake Eling, with geographical coordinates between 95°55'E-98°41'E and 33°56'N-35°31'N. It exhibits a typical continental plateau climate, with an average annual temperature ranging from approximately -3°C to -4.1°C and annual precipitation typically between 300–700 mm. The Yangtze River source region, demarcated by the Zhimen Da hydrological station, lies between the Tanggula and Kunlun Mountains. Its boundaries span 90°43′E-97°45′E and 32°30′N-36°35′N. The overall climate is dry, cold, and low in precipitation, with an average annual temperature of -1.7°C to -5.5°C and annual precipitation of approximately 270–410 mm.The development process is as follows: First, a two-year (1960 and 1961) WRF simulation with a spatial resolution of 1/30° was conducted. Second, a downscaling model based on a convolutional neural network (CNN) was trained at the daily scale using the WRF simulation results. This downscaling model comprises four convolutional layers (for feature extra
DB  - NCDC
UR  - http://www.ncdc.ac.cn/portal/metadata/f6b9ca6a-8fe4-4da0-b8a4-690ef8e8bda1
ER  -