All-Weather Near-Surface Air Temperature Estimation Based on Satellite Data Over the Tibetan Plateau

Wang, Wei; Zhou, Ji; Wen, Xuezhi; Zhang, Long; Zhong, Hailing; Ma, Jin; Ding, Lirong; Qi, Dongmei

doi:10.1109/jstars.2022.3161800

Cited by 6 publications

(9 citation statements)

References 38 publications

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“…There are also estimated daily mean NSAT products in the QTP. Some have an overall RMSE accuracy of 2.43 K and R 2 accuracy of 0.93 with a spatial resolution of 1 km × 1 km (W. Wang et al., 2022), and some gain an RMSE of 1.6–2.8 K and an R 2 of 0.92–0.97 with a spatial resolution of 0.05° × 0.05° for 2002–2016 (Rao et al., 2019). The RMSE of daily mean temperature products in this paper are 1.86 and 1.60 K, respectively; R 2 is 0.95 for 1981–1999 and 0.96 for 2000–2019.…”

Section: Resultsmentioning

confidence: 99%

Spatiotemporal Trends of Extreme Temperature Events Along the Qinghai‐Tibet Plateau Transportation Corridor From 1981 to 2019 Based on Estimated Near‐Surface Air Temperature

Wang,

Gao,

et al. 2023

JGR Atmospheres

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The intensification of global warming leads to the increased frequency of extreme temperature events. Many studies have reported on numerous regions facing extreme hot and cold temperatures to some degree. In this study, the daily maximum, minimum and mean near‐surface air temperatures were estimated to explore the detailed spatial trends of extreme temperature events along the Qinghai‐Tibet Plateau Transportation Corridor (QTPTC). Sixteen extreme temperature indices were used to represent extreme temperature events, and the Mann‐Kendall trend test and Sen's slope estimation method were employed to explore the spatiotemporal trends of extreme temperatures along the QTPTC during 1981–2019. The possible factors that affect the trends of extreme temperature events were also discussed, indicating that: (a) There was an exacerbation of extreme hot events, while the prevalence of extreme cold events weakened along the QTPTC. This was in accordance with the trend of global warming. (b) The trends of extreme temperature indices based on daily minimum temperature were stronger than those based on daily maximum temperature in most areas, except for the eastern region. Furthermore, as altitude increased, the amplitude of warming intensified. (c) Unlike the trend in most areas, the intensified warming trend occurred in the low‐altitude areas in the eastern region where there are more human activities and rapid urbanization, and the daily maximum temperature trend was more severe there. The results can provide references for policymakers to formulate corresponding adaptation strategies, addressing the impacts of extreme temperature events on transportation facilities with significant elevation differences.

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Section: Resultsmentioning

confidence: 99%

Spatiotemporal Trends of Extreme Temperature Events Along the Qinghai‐Tibet Plateau Transportation Corridor From 1981 to 2019 Based on Estimated Near‐Surface Air Temperature

Wang,

Gao,

et al. 2023

JGR Atmospheres

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“…As a consequence, spatial downscaling, which is a hopeful technique of generating highresolution meteorological data from the low-resolution one, grows into a hot research topic among atmospheric research. As an important component of meteorological analysis [1], [2], [3], [4], [5], spatial wind downscaling [6], [7] plays a substantial role among areas including oceanographic research [8], [9], climate analysis [10], renewable energy generation [11] and accurate meteorological forecasting [12].…”

Section: Introductionmentioning

confidence: 99%

Terrain-Guided Flatten Memory Network for Deep Spatial Wind Downscaling

Yang

Huang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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High-resolution wind analysis plays an essential role in pollutant dispersion and renewable energy utilization. This paper focuses on spatial wind downscaling. Specifically, a novel terrain guided flatten memory network (abbreviated as TIGAM) with axial similarity constraint is proposed. TIGAM consists of three elaborately designed blocks, i.e., the similarity block, the reconstruction block, and the denoise block. To achieve long-spatial dependency, the similarity block interpolates low resolution data to high resolution in an axial attention manner. Meanwhile, the reconstruction block aims to obtain a clearer high resolution representation in closed form. Taking both of the meteorological prior and network design principle into consideration, this paper also proposes a flatten memory module with learnable input for high resolution denoising. Furthermore, for accurate detail reconstruction, a terrain guided enhanced loss is presented benefitting from the high-resolution remote sensing data. This loss function integrates wind spatial distribution and terrain elegantly. Extensive quantitative and qualitative experiments demonstrate the superiority of the proposed TIGAM.

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“…In recent decades, with the intensification of global warming, the glacier melting has accelerated, and the amount of ice-snow melting water has increased. The NSAT, which is directly related to global warming, has become a research focus [5].…”

mentioning

confidence: 99%

“…Due to the unique geographical conditions and complex natural environment, the Tibetan Plateau (TP) has an important impact on the climate change in its surroundings and even the world [5], [6]. NSATLR can be used to analyze the spatial distribution characteristics of NSAT and simulate the surface environment in mountainous areas such as the TP.…”

mentioning

confidence: 99%

“…Therefore, most of the NSAT products based on remotely sensed thermal infrared surface temperature estimation cannot be used to estimate NSATLR. In the process of estimating NSAT based on remotely sensed thermal infrared surface temperature data, most methods use a variety of surface variables and in-situ NAST data as auxiliary parameters to achieve accurate NAST estimates [5], [26]. MODIS atmospheric profile products have potential advantages in estimating NSAT, which do not depend on auxiliary surface variables and in-situ NSAT data.…”

mentioning

confidence: 99%

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Estimation of Near-Surface Air Temperature Lapse Rate Based on MODIS Data Over the Tibetan Plateau

Zhong

Zhou

Tang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Near-surface air temperature lapse rate (NSATLR) is one of the key criteria for judging atmospheric stability . It is an important model parameter in glacier, hydrology, ecology, climate and meteorology in high mountains such as the Tibetan Plateau (TP). However, due to the complex interaction between the atmosphere and land surface, retrieving NSATLR from remotely sensed data is challenging. In this study, we develop a novel model based on the MODIS data to estimate the daily 5-km NSATLR over the TP. The input parameters of the model include DEM and the near-surface air temperature (NSAT) estimated from the MODIS data. By combining moving window and specified criteria, the pixel-based NASTLR with a 5-km spatial resolution over the TP is estimated. In addition, we also estimate the NSATLR based on the meteorological data over the TP (NSATLRin-situ) to evaluate the accuracy of NSATLR estimation model. Results demonstrate that the NSATLR estimate agrees well with NSATLRin-situ in the eastern TP, with an overall root mean square error (RMSE) of 1.51 °C/km and a mean bias error (MBE) of -0.78 °C/km. Larger deviation exists in the southern TP. Thanks to the evident advantages of simplicity, the availability of input parameters and the independence from the meteorological data, the developed model can be applied to larger areas.

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All-Weather Near-Surface Air Temperature Estimation Based on Satellite Data Over the Tibetan Plateau

Cited by 6 publications

References 38 publications

Spatiotemporal Trends of Extreme Temperature Events Along the Qinghai‐Tibet Plateau Transportation Corridor From 1981 to 2019 Based on Estimated Near‐Surface Air Temperature

Spatiotemporal Trends of Extreme Temperature Events Along the Qinghai‐Tibet Plateau Transportation Corridor From 1981 to 2019 Based on Estimated Near‐Surface Air Temperature

Terrain-Guided Flatten Memory Network for Deep Spatial Wind Downscaling

Estimation of Near-Surface Air Temperature Lapse Rate Based on MODIS Data Over the Tibetan Plateau

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