A long-term 0.01° surface air temperature dataset of Tibetan Plateau

The surface air temperature (Ta) dataset of the Tibetan Plateau is obtained by downscaling the China regional surface meteorological feature dataset (CRSMFD). It contains the daily mean Ta and 3-hourly instantaneous Ta. This dataset has a spatial resolution of 0.01°. Its time range for surface air temperature dataset is from 2000 to 2015. Spatial dimension of data: 73°E–106°E, 40°N–23°N. The Ta with a 0.01° can serve as an important input for the modeling of land surface processes, such as surface evapotranspiration estimation, agricultural monitoring, and climate change analysis.


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The surface air temperature (T a ) dataset of the Tibetan Plateau is obtained by downscaling the China regional surface meteorological feature dataset (CRSMFD). It contains the daily mean T a and 3-hourly instantaneous T a . This dataset has a spatial resolution of 0.01°. Its time range for surface air temperature dataset is from 2000 to 2015. Spatial dimension of data: 73°E-106°E, 40°N-23°N. The T a with a 0.01°can serve as an important input for the modeling of land surface processes, such as surface evapotranspiration estimation, agricultural monitoring, and climate change analysis. &

Value of the Data
It can contribute to better modeling the radiation balance and energy budget and water cycle over the Tibetan Plateau.
It can serve as an important input parameter for the modeling of land surface processes, such as surface evapotranspiration estimation.
It can provide long-term T a dataset with acceptable accuracy and medium spatial resolution for climate change study.

Data
As the highest plateau in the world, the Tibetan Plateau has the largest glaciers except the Arctic and Antarctic. Due to complex natural environment, the Tibetan Plateau has significant impacts on climate change of the surrounding areas and even the whole world. Because of its special geographical location and topography, the radiation balance and energy budget and water cycle examinations of the Tibetan Plateau are particularly important. Thus, the scientific communities is requiring a long-term T a dataset with acceptable accuracy and medium spatial resolution.
We use the China regional surface meteorological feature dataset (CRSMFD) [1,2] as the basis dataset. We develop a practical method to downscale the CRSMFD from 0.1°to 0.01°. The temporal resolution of this dataset is consistent with CRSMFD. It have better consistency with the ground measured T a than original CRSMFD in Tibetan Plateau. It has higher spatial resolution than most of the current long-term T a dataset for the Tibetan Plateau. In addition, T a with a 0.01°resolution can reflect more spatial details of T a when compared with the original CRSMFD. The T a at some time is shown as an example in Fig. 1, and the 0.01°T a of local areas is shown as an example in Fig. 2 (area A and area B are shown in Fig. 1). Thus, this dataset is able to meet the ever-increasing demand for related studies and applications.

Experimental design, materials, and methods
The linear relationship between T a and its influencing factors, T a can be expressed as: where T a,daily and T a,ins are the daily mean and instantaneous T a in K, respectively; f daily and f ins are the statistical functions for the daily mean T a and instantaneous T a , respectively; H, X 1 , X 2 are the elevation, latitude, and longitude, respectively; λ, a, and b are the corresponding coefficients; and c is  the intercept. It is evident that λ is the lapse rate (LR) of T a [3,4]. Note that the longitude is not contained in Eq. (1) due to its ignorable ability in explaining daily mean T a . Based on Eqs. (1) and (2), the flowchart of the proposed method for T a downscaling is shown in Fig. 3. The first stage for T a downscaling is to calculate LR. The DEM data at 90-m is aggregated to 0.01°. The mean elevation of the 10 Â 10 pixels is calculated and used as the elevation of the pixel at 0.1°that containing these 10 Â 10 pixels. The spatial distribution of LR can be divided into eight regions, i.e.  [5]. In this division scheme, each region has similar regional climatic characteristics and a range of elevation changes. This division scheme is utilized by this method. To better address the intra-annual variations of LR, the LR values of instantaneous T a at every 3 h and the daily mean T a on every day are calculated.
The second stage is to determine and optimize the initial value of T a at the target resolution. The T a value at the native resolution (i.e. 0.1°) is taken as the initial value of the pixel at the target resolution (i.e. 0.01°). At the target resolution, a moving window approach is employed to refine the initial T a of the central pixel. For each pixel at the target resolution, the window size is set to 11 Â 11 pixels and the current pixel under consideration is the center of the window. If the current pixel is on the edge of the image, the window is not complete and the existing pixels are selected. Pixels with valid T a and elevation in the moving window are selected as valid pixels [6]. Then the mean T a of the valid pixels in the moving window is calculated as the optimized T a of the central pixel as follows: where T a 0 is optimized initial value of the T a ; T a-initial (i) is the initial T a the i-th pixel at the target resolution within the window; and m is the number of valid pixels in the window.
The third stage is to determine the final value of T a at the target resolution. According Eqs. (1) and (2), the T a difference between the central pixel and the mean T a of moving window can be expressed as: ΔT a;daily ¼ λðH À H win ÞþaðX 1 À i À X 1 À win Þ ð 4Þ ΔT a;ins ¼ λðH À H win ÞþaðX 1 À i À X 1 À win ÞþbðX 2 À i À X 2 À win Þ ð 5Þ where ΔT a,daily and ΔT a,ins are daily mean T a difference and instantaneous T a difference in K; H, X 1 À i and X 2 À i are the elevation, latitude, and longitude of the central pixel of the moving window, respectively; H win , X 1 À win and X 2 À win are the mean elevation, latitude, and longitude of the moving window, respectively. X 1 À i and X 1 À win , X 2 À i , and X 2 À win can be considered to be approximately equal. Thus, Eqs. (4) and (5) can be simplified as: where ΔT is the T a difference in K.
Then the final T a of the central pixel is: Finally, the 0.01°T a data of Tibetan Plateau was obtained by this downscaling method.