**1. Introduction**

The Qinghai–Tibet Plateau (QTP) is regarded as the Earth's "Third Pole" and "Asian Water Tower" [1,2]. It is the highest plateau in the world with an average elevation of over 4000 m. The strong dynamic and thermodynamic effects [3] of the QTP significantly affect atmospheric circulations in the northern hemisphere, as well as the Asian monsoon process and the climate patterns in East Asia [4,5], and have extremely important impacts on global climate change [1–14]. Glaciers, frozen soil, meadows, snow, and wetlands are widely distributed in the alpine region of the plateau, where the headwaters of China's major rivers are located. The alpine region of the plateau is an important area of ecological barrier, but it is also an area of harsh climatic conditions [6] and fragile ecological environments [9]

**Citation:** Huang, X.; Han, S.; Shi, C. Evaluation of Three Air Temperature Reanalysis Datasets in the Alpine Region of the Qinghai–Tibet Plateau. *Remote Sens.* **2022**, *14*, 4447. https:// doi.org/10.3390/rs14184447

Academic Editors: Massimo Menenti, Yaoming Ma, Li Jia and Lei Zhong

Received: 11 July 2022 Accepted: 1 September 2022 Published: 6 September 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

with low levels of economic development [5]. Climate change in the QTP and the various impacts it brings have become a frontier and hotspot in earth system science research, which has attracted extensive attention within the scientific community [1,2,14]. Surface air temperature is a key variable in the land-surface–atmosphere interaction and energy exchange, as well as in water cycle processes. It is also an important basis [7,8] for the studies of glacial melting, soil freeze-thaw and desertification, and ecosystems and climate change in the plateau. Due to the vast area of the QTP, the restrictions of transport, and the terrain environment, weather stations are only sparsely distributed in the QTP and mainly concentrated in the eastern and southern parts of the QTP [15]; few stations are located in the western and northern parts of the plateau [16]. To complicate matters furthers, many stations in the QTP are established late and with short sequences of observations, which makes the temperature observations unable to fully reflect the state of the surface air temperature over the entire plateau. Therefore, reanalysis products of temperatures with spatiotemporal continuity and broad applicability are required to provide critical data support [15–19] for climate change and impact studies over the QTP [20,21].

In recent years, several research institutions in the United States, the European Union, China, Japan, and other countries have successfully developed a series of land surface reanalysis systems and multi-source data fusion analysis systems [22–24]. Great progress has been made in the land surface reanalysis dataset. Compared with atmospheric reanalysis datasets, land surface reanalysis products have higher spatiotemporal resolutions and wider application. At present, the most advanced land surface reanalysis datasets include the ECMWF Fifth Generation Land Surface Reanalysis (ERA5L) [25–30], the NASA Global Land Data Assimilation System (GLDAS) [31–33], and the China Meteorological Administration Land Surface Data Assimilation System (CLDAS) [34–38]. These datasets include surface meteorological elements and soil information. A series of research results have been achieved based on the application of these datasets in studies of weather and climate prediction, water resources managemen<sup>t</sup> and water cycle, etc.

Due to differences in input data, numerical assimilation models, parameterization schemes, and the spatiotemporal resolutions of final products, these reanalysis datasets demonstrate quite different performances in different regions. Therefore, accuracy evaluation and applicability analysis of various reanalysis datasets are a prerequisite for their application. Several studies have evaluated the applicability of CLDAS, ERA5L, and GLDAS in the QTP [39–42]. For example, Han et al. [40] compared surface air temperature from CLDAS and GLDAS with observations collected at 2380 weather stations in China over the period 2010–2015. Their results indicate that surface air temperatures in the two reanalysis datasets are lower than observations in the QTP, while the accuracy and correlation of CLDAS with station observations are better than GLDAS. On different temporal and spatial scales, Huang et al. [41] verified CLDAS, ERA5L, and GLDAS against observations collected at 2265 weather stations in China during 2017–2019. They found that the three aforementioned reanalysis datasets can represent the characteristic temperature changes in the QTP well, although they are lower than observations. CLDAS is highly consistent with station observations, and its accuracy is significantly better than the other two reanalysis datasets. GLDAS is better than ERA5L. Liu et al. [42] selected 32,552 assessment stations that have been fused into the CLDAS system and 12,403 non-assessment stations that are non-fused into the CLDAS system as the data sources for evaluation and conducted dependent and independent verifications of CLDAS hourly temperature data in different regions of China. Results of both dependent and independent verification confirm that CLDAS has a relatively high accuracy and applicability in the QTP. Wang et al. [43] compared GLDAS with China's gridded surface air temperature dataset in the QTP and surrounding areas. They found that GLDAS performs better in arid regions than in sub-humid areas, and that thedataaremoreaccurateduring1979–1994thanduring2000–2007.

 In summary, the three aforementioned reanalysis datasets all demonstrate a relatively high applicability in the QTP and thus have potential values for weather and climate studies. We also found that the previous applicability studies of temperature reanalysis

datasets often use observations collected at operational weather stations of the China Meteorological Administration as reference data, and, while these observations have high accuracy and reliability, the following issues need to be addressed: (1) Many national-level meteorological station observations have been included in the international exchange list, and many of the data have been used as input for assimilation and/or data fusion to produce various reanalysis datasets. Therefore, it is hard to achieve independent results using these data to verify reanalysis products. (2) Most of these weather stations are located in suburbs of cities or areas along highways that are easily accessible. Their coverage of QTP topography and landform types is limited, which makes the evaluation results have limited reference value for assessing the reliability of the reanalysis datasets in the QTP. Based on the aforementioned discussion, the present study uses in-situ observations provided by "China Alpine Region Surface Process and Environmental Monitoring Research Network" [44,45] to evaluate surface air temperature from CLDAS, ERA5L, and GLDAS. The present study reveals some important similarities and differences in comparison to previous studies. Results of the study will be helpful in studies of the special atmospheric, hydrological, and ecological processes in the alpine region of the QTP [46].

### **2. Data and Methods**

*2.1. Data*


CLDAS is a land-surface data assimilation system developed in the National Meteorological Information Center of the China Meteorological Administration (CMA) [22,35]. Advanced fusion technology is combined with independent innovations proposed in CMA during the development of CLDAS. Multi-grid variational analysis, spatial grid stitching, discrete ordinate shortwave radiation remote sensing retrievals, terrain correction, ensemble simulations of multiple land surface models (CLM, Noah-MP, CoLM), etc., are combined to produce surface pressure, ground precipitation, temperature, humidity, UV winds, shortwave radiation, surface air temperature and humidity, soil moisture and temperature, etc. The China Land-surface Data Assimilation System Version 2 (CLDAS-V2.0) [22] was released in 2015 and upgraded in 2018. This system can efficiently fuse observations collected at nearly 60,000 weather stations in China with numerical prediction data and satellite remote sensing data, and can release a real-time fused land surface data analysis product on 0.05◦ × 0.05◦ grids at 1 h intervals. This product has been widely applied in meteorological and agricultural studies [36,37,41].

2. ERA5L dataset

ERA5L is a high spatiotemporal resolution global land surface reanalysis dataset produced by ECMWF for global land areas. It is a component of the fifth-generation European Reanalysis Product (ERA5) [28] that was developed within the framework of the European Commission Copernicus Climate Change Service (C3S). Based on outputs of numerical simulations of the ECMWF land-surface model, ERA5L is a downscaled dataset from the ERA5 climate reanalysis, and elevation correction for near-surface thermal states is conducted to ensure consistent evolvement of water and energy cycles over the land [27]. ERA5L can be applied for trend and anomaly analysis. ECMWF released the ERA5L product in 2019, which contains data from 1981 until present, with ongoing updates. The historical dataset over 1950–1980 was released in September 2021. With a high spatial resolution of 0.1◦ × 0.1◦ and temporal resolution of 1h, as well as long data sequences and data consistency, ERA5L provides a strong support in hydrological study and numerical weather/climate model initialization. It is also widely applied in studies of water resources and land and environment managemen<sup>t</sup> [29,30,47], etc.

3. GLDAS dataset

GLDAS is produced by the NASA Goddard Space Flight Center (GSFC) and the National Centers for Environmental Prediction (NCEP) of National Oceanic and Atmospheric

Administration (NOAA). Surface observations and satellite remote sensing retrievals are assimilated into the land surface models of Noah, Mosaic, CLM, and VOC to simulate global surface variables [32] (such as soil moisture, land surface temperature, etc.) and fluxes (such as evaporation, sensible heat flux, etc.). GLDAS has been widely applied to global climate change studies and comparative studies with other remote sensing products. GLDAS provides two versions of the dataset (GLDAS-1 [48] and GLDAS-2 [49]). The present study uses GLDAS-2, and the spatial and temporal resolutions of the dataset are 1h and 0.25◦, respectively.

Table 1 lists the attributes of the datasets evaluated in the present study, including their spatial and temporal resolutions, coverages, and data download sources.


**Table 1.** Characteristics of the reanalysis temperature datasets.

#### 2.1.2. In-Situ Temperature Observations in the Alpine Region of the QTP

The in-situ temperature observations used to evaluate the reanalysis datasets are provided by the Tibetan Plateau Data Center of China. The data were downloaded from http://data.tpdc.ac.cn/ (accessed on 3 January 2022). These observations are collected at 17 field observation sites (Figure 1), which are evenly distributed in the alpine region of the QTP. Temperature, precipitation, wind speed and wind direction, relative humidity, radiation, evaporation, etc., are measured. Long-term surface processes and environmental changes are continuously monitored to understand patterns of climate and water resource changes in the headwater areas of the Yangzi River and the Yellow River. This information will be helpful to reveal the changes in ecosystem structure and function, build ecological protection barriers, and grasp the mechanism for the occurrence of natural disasters such as ice and snow freezing and thawing [44]. All the in-situ temperature observations used in the present study are daily mean temperature. Table 2 lists the properties of the in-situ observation sites and related information [45].
