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The Qinghai–Tibet Plateau (QTP) is a crucial component of the global climate system, influencing the regional and global climate through complex thermal and dynamic mechanisms. The high-altitude region, which is the largest part of the extra-polar cryosphere, encompasses extensive mountain glaciers, permafrost, and seasonally frozen land, making it highly sensitive to global climate change. However, the challenging environmental conditions, such as the harsh terrain and high altitude, coupled with sparse weather station distribution and weak observatory representation, make it difficult to accurately quantify the atmospheric conditions and land–atmosphere coupling systems and their effects on the surrounding areas. To address these challenges, we utilized the Weather Research and Forecasting (WRF) model and a three-dimensional variational (3DVAR) assimilation method to create a high-resolution assimilated dataset (HRAD). The QTP-HRAD, covering the spatial range of 70 to 110°E and 25 to 40°N, was validated using both surface weather station observations and the European Center for Medium-Range Weather Forecasts Reanalysis V5, and can now be utilized for further studies on land–atmosphere interactions, water cycling and radiation energy transfer processes, and extreme weather events in the region. [ABSTRACT FROM AUTHOR]

Using the MODIS satellite data and station atmospheric turbulence observation data in Nagqu area of northern Tibetan plateau in 2008, 2010 and 2012, with the Massman retrieved model and an independent method to determine aerodynamic surface roughness, the temporal and spatial variation characteristics of the surface roughness was analyzed. The results show that the surface roughness has obvious seasonal variation characteristics. From February to August, Z0m increases constantly with the ablation of snow and vegetation growth, and the maximum value reaches 4-5cm at BJ site. From September to February, Z0m gradually decreased because of the post-monsoon over the plateau, and the values decreased to about 1-2cm. The snowfall in abnormal years is the main reason why Z0m is obviously lower than that in normal. The underlying surface can be divided into four categories according to the different values of Z0m: snow and ice, sparse grassland, lush grassland and town. Among them, lush grassland and sparse grassland account for 62.49% and 33.74% respectively in the region, which are the main categories, and their Z0m annual changes are between 2-6cm and 1-4cm. The correlation between the two methods are positively related to each other, and the retrieved data are smaller than the measured results due to the average sliding action. On the whole, Z0m calculated by satellite data retrieved algorithm is feasible, it can be applied to improve the model parameters of land surface model parameters and the accuracy of model simulation, better reveal the heat flux exchange. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) plays a critical role in influencing regional and global climate, via both thermal and dynamical mechanisms. Meanwhile, as the largest high-elevation part of the cryosphere outside the polar regions, with vast areas of mountain glaciers, permafrost and seasonally frozen ground, the TP is characterized as an area sensitive to global climate change. However, meteorological stations are sparely and biased distributed over the TP, owing to the harsh environmental conditions, high elevations, complex topography, and heterogeneous surfaces. Moreover, due to the weak representative of the stations, atmospheric conditions and the local land-atmosphere coupled system over the TP as well as its effects on surrounding regions are poorly quantified. This paper presents a long-term (2005-2016) dataset of hourly land-atmosphere interaction observations from an integrated high-elevation, cold region observation network, which is composed of six field observation and research platforms on the TP. In-situ observations, at the hourly resolution, consisting of measurements of micrometeorology, surface radiation, eddy covariance (EC), and soil temperature and soil water content profiles. Meteorological data were monitored by automatic weather station (AWS) or a planetary boundary layer (PBL) observation system composed of multiple meteorological element instruments. Multilayer soil hydrothermal data were recorded to capture vertical variations in soil temperature and water content and to study the freeze-thaw processes. In addition, to capture the high-frequency vertical exchanges of energy, momentum, water vapor and carbon dioxide within the atmospheric boundary layer, an EC system consisting of an ultrasonic anemometer and an infrared gas analyzer was installed at each station. The release of these continuous and long-term datasets with hourly time resolution represents a leap forward in scientific data sharing over the TP, and it has been partially used in the past to assist in understanding key land surface processes. This dataset is described here comprehensively for facilitating a broader multidisciplinary community by enabling the evaluation and development of existing or new remote sensing algorithms as well as geophysical models for climate research and forecasting. The whole datasets are freely available at Science Data Bank (http://www.dx.doi.org/10.11922/sciencedb.00103, Ma et al., 2020) and, additionally at the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/en/data/b9ab35b2-81fb-4330-925f-4d9860ac47c3/). [ABSTRACT FROM AUTHOR]