Spatial Distribution of Near-surface Air Temperature in the Glacierized Areas of the Tibetan Plateau

doi:10.26050/WDCC/GATP

Wang, Tianyun et al.

ExperimentDOI
Summary
This dataset contains spatial distribution of near-surface air temperature in the glacierized areas of the Tibetan Plateau, which has been processed using meteorological observation data, thermal infrared remote sensing data and glacier boundaries from China's Second Glacier Inventory. The data covers a high-altitude area of the Qinghai-Tibet Plateau for seven glacier regions (Guliya, Aru, Naimona'nyi, Gagze, Dunde, Parlung, and XiaodongKemadi), with a spatial resolution of 30 meters, spanning elevations of 3,924-6,078 m and temperatures of -42°C to +21°C. The original temperature data were obtained from multiple sources, including thermal infrared remote sensing data from Landsat 8 (L8) and Landsat 9 (L9) Collection 2 (C2) Level 2 (L2) products, as well as ground station measurements from National Tibetan Plateau/Third Pole Environment Data Center. Missing values were reconstructed through masked nearest-neighbor interpolation, followed by spatial smoothing to minimize residual inaccuracies during the temperature field updating process. This approach (gap-filling, bias-correction, smoothing) ensured robust spatialization of near-surface air temperature data. Technical validations are detailed in Accuracy and Completeness Reports. The dataset provides temperature distributions for glacier-covered areas of the Qinghai-Tibet Plateau. This dataset is suitable for climate research, environmental monitoring, and cryosphere.

The experiment's temporal coverage is discontinuous; the eight datasets have different time ranges. The temporal coverage includes Southeastern data (2023-01 to 2023-03), Xiaodongkemadi data (2012-05 to 2015-08), and Aru, Dagze, Dunde, Guliya, Naimona'nyi, and Parlung data (all from 2019-01, with end dates ranging from 2019-10 to 2019-11).
Southeastern data: 2023-01 to 2023-03
Xiaodongkemadi data: 2012-05 to 2015-08
Aru data: 2019-01 to 2019-10
Dagze data: 2019-01 to 2019-10
Dunde data: 2019-01 to 2019-11
Guliya data: 2019-01 to 2019-10
Naimona'nyi data: 2019-01 to 2019-10
Parlung data: 2019-01 to 2019-10
Project
HISISP-QTP (Heat Island Intensity Prediction in an Intelligent Sponge Urban System in the Qinghai-Tibet Plateau)
Contact
Dr. Tianyun Wang (
 tywang_iem@null163.com
0000-0001-8498-1536)
Spatial Coverage
Longitude 80 to 100 Latitude 27 to 39
Temporal Coverage
2012-05-21 to 2023-03-22 (gregorian)
Use constraints
Creative Commons Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/)
Data Catalog
World Data Center for Climate
Size
12.82 GiB (13762078086 Byte)
Format
NetCDF
Status
completely archived
Creation Date
Future Review Date
2035-06-21
Cite as
Wang, Tianyun; Yang, Lu; Zhang, Deyuan; Zhou, Juncheng; Song, Haolin; An, Yiming; Wang, Jinyi (2025). Spatial Distribution of Near-surface Air Temperature in the Glacierized Areas of the Tibetan Plateau. World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/GATP

BibTeX RIS
Funding
Shenyang University of Technology
Grant/Award No: LJ200080773 - Intelligent Urban Rainwater Collection Module System Application
Description
This report evaluates the accuracy of near-surface air temperature datasets across Tibetan Plateau glaciers using meteorological station observations and thermal infrared remote sensing data aligned to identical grids. The spatial modeling framework integrates gap-filling via masked nearest-neighbor interpolation for missing values and outliers, bias correction to address observational discrepancies, and spatial smoothing to minimize residuals during temperature field updates. This methodology ensures continuous temperature estimates for glaciated regions while maintaining computational efficiency. The deviation between the station estimates (spatial interpolation) and the raw station observations was quantified using root mean squared error (RMSE) and standard deviation (SD) analysis.The results indicate that the spatial surface air temperature has a root mean squared error (RMSE) and standard deviation (SD) of 0.01 to 0.5 degrees Celsius at most test station locations. The use of thermal infrared remote sensing data enhances the spatial resolution and coverage of the surface temperature.The accuracy of the models may vary depending on the specific environmental conditions and the quality of the input data. The Dunde dataset contains a relatively higher number of gaps (exceeding 100 missing observations). These gaps, along with outliers or missing value, were filled using Kriging interpolation. After applying Kriging interpolation followed by nearest-neighbor combined with triangular interpolation and smoothing, the maximum RMSE and SD values stayed under 1.6, with most cases below 0.95. In contrast, using Kriging alone (without triangular interpolation) resulted in higher maximum RMSE and SD values exceeding 3, and a greater proportion of cases below 0.95.
Description
This report evaluates the temporal and spatial completeness of near-surface air temperature datasets collected from glacierized regions across the Tibetan Plateau, spanning fragmented periods between 2012 and 2023, with most data focusing on 2019. The datasets primarily consist of ground-based meteorological stations interpolated with Landsat 8 LST data at 30 m resolution, except for the coarse (5000 m) China SE QTP (2023) dataset, which is unsuitable for fine-scale glacier studies.For high-resolution glacier studies, the Aru, Naimona'nyi, and Guliya datasets are most reliable due to their near-complete 2019 coverage (January–October) and robust station networks (6–8 stations each), enabling accurate 30 m interpolation. The Parlung dataset is valuable for September–October when AWS validation is available, but its January–August data should be used cautiously. Despite its five-station limitation, Dunde's dataset benefits from even spatial distribution and a confined interpolation area, making it suitable for regional-scale trends. Dagze's sparse network (2 stations) introduces interpolation uncertainties but can still support regional analyses, while the patchy Xiaodongkemadi (2012–2015) records are best for process studies. The coarse China SE QTP dataset (5000 m) is only viable for large-scale winter-to-spring analyses. These distinctions emphasize aligning dataset selection with specific spatial and temporal resolution needs.
Description
Summary:
Findable: 6 of 7 level;
Accessible: 2 of 3 level;
Interoperable: 3 of 4 level;
Reusable: 6 of 10 level
Method
F-UJI online v3.5.0 automated
Method Description
Checks performed by WDCC. Metrics documentation: https://doi.org/10.5281/zenodo.15045911 Metric Version: metrics_v0.8
Method Url
Result Date
2025-07-10
Result Date
2025-06-30
Description
1. Number of data sets is correct and > 0: passed;
2. Size of every data set is > 0: passed;
3. The data sets and corresponding metadata are accessible: passed;
4. The data sizes are controlled and correct: passed;
5. The temporal coverage description (metadata) is consistent to the data: passed;
6. The format is correct: passed;
7. Variable description and data are consistent: passed
Method
WDCC-TQA checklist
Method Description
Checks performed by WDCC. The list of TQA metrics are documented in the 'WDCC User Guide for Data Publication' Chapter 8.1.1
Method Url
Result Date
2025-06-30
Contact typePersonORCIDOrganization
-

Is documented by

[1] Engebretson, Chris. (2022). Landsat 8-9 Operational Land Imager (OLI)-Thermal Infrared Sensor (TIRS) Collection 2 (C2) Level 2 (L2) Data Format Control Book (DFCB). https://www.usgs.gov/media/files/landsat-8-9-olitirs-collection-2-level-2-data-format-control-book

Is related to

[1] DOI Guo, Wanqin; Liu, Shiyin; Xu, Junli; Wu, Lizong; Shangguan, Donghui; Yao, Xiaojun; Wei, Junfeng; Bao, Weijia; Yu, Pengchun; Liu, Qiao; Jiang, Zongli. (2015). The second Chinese glacier inventory: data, methods and results. doi:10.3189/2015jog14j209

Is derived from

[1] Wei,Yang(National Tibetan Plateau / Third Pole Environment Data Center). (2021). Temperature data of six glaciers in high altitude area of Qinghai Tibet Plateau (2019). doi.org/10.11888/Cryos.tpdc.271916
[2] Xu, B. (2018). Glacier temperature dataset of XiaodongKemadi (2012-2015). https://doi.org/10.11888/Glacio.tpdc.270019. https://cstr.cn/18406.11.Glacio.tpdc.270019.
[3] Zhang, D. (2024). Typical glacier front meteorological data, river water level data, and typical lake area observation data (2021). https://doi.org/10.11888/Cryos.tpdc.301611. https://cstr.cn/18406.11.Cryos.tpdc.301611.
[4] Liu, S.; Guo, W.; Xu, J. (2012). The second glacier inventory dataset of China (version 1.0) (2006-2011). https://doi.org/10.3972/glacier.001.2013.db.

Attached Datasets ( 8 )

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Additional Info

Details for selected entry
[Entry acronym: GATP] [Entry id: 5285857]