Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

Zhou, Wang; Peng, Bin; Shi, Jiancheng; Wang, Tianxing; Dhital, Yam Prasad; Yao, Ruohe; Yu, Yuechi; Lei, Zhongteng; Zhao, Rongcai

doi:10.3390/rs9090959

Cited by 36 publications

(16 citation statements)

References 70 publications

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“…Lastly, it can be concluded that using TEM and DFM based on the MODIS nighttime LST and with averaged RMSDs of 0.19 and 0.21 °C/100 m, respectively, are expected to generally produce errors for T air interpolation at 1.9 and 2.1 °C in the TP, respectively. Since the errors are generally ~2 °C for daily T air estimation in high mountain areas based on remote sensing products and climate reanalysis data sets (H. Zhang, Zhang, Ye, et al, ; Zhou et al, ), the T air interpolation accuracies from using the MODIS‐estimated TLR are considered to be acceptable for daily T air estimation in the TP. However, the accuracies of the MODIS‐estimated TLR may not be accepted for climatic analysis considering that the climatic warming rates are generally less than 0.1 °C/year for the TP (X. Liu & Chen, ).…”

Section: Resultsmentioning

confidence: 99%

How Accurately Can the Air Temperature Lapse Rate Over the Tibetan Plateau Be Estimated From MODIS LSTs?

Zhang

et al. 2018

JGR Atmospheres

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The air temperature lapse rate (TLR) is a key parameter for interpolating air temperature data in high mountainous regions such as the Tibetan Plateau (TP). The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) has been frequently used for estimating air temperature during the past decade, but its performance in estimating the TLR in the TP has seldom been investigated. This study employed two methods in estimating the TLR based on MODIS LSTs compared with the “observed” TLR derived from 86 stations across the TP. The two methods include a method for directly computing the lapse rate from MODIS LST (DFM) and a second method for calculating the lapse rate of estimated air temperatures based on air temperature estimation from MODIS LST (TEM). The results show that the MODIS LST‐estimated TLR for daily mean air temperature (Tmean) using both DFM and TEM is more accurate than that for daily minimum and maximum air temperatures. When using MODIS nighttime LSTs, both DFM and TEM show acceptable accuracies for estimating the TLR of Tmean with averaged root‐mean‐square deviations of 0.21 and 0.19 °C/100 m, respectively. The spatial and seasonal patterns of MODIS LST‐estimated TLRs of Tmean from both DFM and TEM are found to be highly consistent with the observed TLRs. This study can help alleviate the data‐sparse problem in downscaling air temperature or hydrological modeling studies in ungauged areas, especially for the western TP where station data are extremely scarce.

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Section: Resultsmentioning

confidence: 99%

How Accurately Can the Air Temperature Lapse Rate Over the Tibetan Plateau Be Estimated From MODIS LSTs?

Zhang

et al. 2018

JGR Atmospheres

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“…MOD11A2 has been validated in dataset comparison studies (Göttsche and Hulley, 2012; Yao et al ., 2018) and investigations reporting high correlation with point data from ground temperature stations (Jain et al ., 2008; Zheng et al ., 2017). These have been used in snow‐melt modelling (Jain et al ., 2010; Shrestha et al ., 2012) and permafrost distribution (Zhou et al ., 2017). More information is available at https://lpdaac.…”

Section: Methods and Datamentioning

confidence: 99%

Topoclimatic zones and characteristics of the upper Ganga basin, Uttarakhand, India

Yadav

Thayyen

Jain

2020

Intl Journal of Climatology

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The upper Ganga basin (UGB) constituting Bhagirathi and Alakananda basins has been considered as a single hydrological unit so far with comparable climate forcing. Here, we show three distinct “topoclimatic zones” of this basin having characteristically different temperature, precipitation, and orographic processes. The orographic discontinuity forced by a mountain ridge with an average elevation of 5,200 m a.s.l. is instrumental in the development of these topoclimatic zones. The northern region of the basin is identified as high elevation and high temperature zone (HE‐HT) with monsoon moisture deficit. This monsoon deficit region is characterized by higher land surface temperature lapse rate (LSTLR) during July and August (JA) derived from MODIS‐LST (11.00°C/km) as compared to much lower LSTLR of monsoon topoclimatic zone (5.78°C/km). The nival‐glacier regimes of the monsoon dominant and monsoon deficit regions constituted the third topoclimatic zone characterized by high elevation‐low temperature zone (HE‐LT). This zone has comparatively lower temperature lapse rate (5.26°C/km) than the immediately lower elevations. The coldest points identified in the basin are mostly placed in monsoon deficit regions with higher LSTLR. The 10–12°C isotherm in the monsoon deficit zone runs at 5,300 m a.s.l. as compared to 3,200 m a.s.l. in monsoon dominant zone. It is proposed that 80% of glacier area in the UGB is in the monsoon deficit zone that is regulated by the northern region orographic processes rather than southern slopes. Glacier change in the southern and northern zones show significant difference during the period of 1994–2016/2017, with significantly higher glacier area loss in the HE‐HT zone. The study highlights the importance of these topoclimatic considerations while evaluating the climate and hydrology of UGB.

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“…Existing researches reported that about 99% of the LWDR comes from the atmosphere under 700 hPa (Darnell et al, 1983), indicating that the near surface level of the atmosphere is the main contributor of the total LWDR. Fortunately, many researches noted that near-surface air temperature has a good correlation with LST, and the LST was also widely used to estimate or as the surrogate variable of spatially distributed air temperature (Pepin et al, 2016;Zhang et al, 2016;Zhou et al, 2017;Zhu et al, 2017). In addition, according to the Stephen-Boltzmann's law, LST is one of the major components to calculate the LWUP, which shows high correlation with LWDR (Cheng et al, 2017;Zhou & Cess, 2001).…”

Section: Sensor-independent Nonlinear Regression Methodsmentioning

confidence: 99%

New Methods for Deriving Clear‐Sky Surface Longwave Downward Radiation Based on Remotely Sensed Data and Ground Measurements

Zhou

Shi

Wang

et al. 2019

Earth and Space Science

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Accurate knowledge of spatiotemporal characteristics of land surface energy budget is essential to understand the Earth's surface system. To some extent, the continuous development of remote sensing technology makes the estimation of global high-resolution surface energy budget possible. However, it is still challenging to accurately derive the global longwave downward radiation (LWDR) from space. In this study, two advanced methods were proposed to estimate clear-sky LWDR based on Moderate-resolution Imaging Spectroradiometer (MODIS) products and corresponding ground measured LWDR from several global distributed radiation networks. One is a sensor-based Random Forest (RF) method that uses MODIS's surface elevation, sensor zenith angle, column precipitable water vapor (PWC), top of atmosphere radiance of band 28, 31, 34, and 36 as inputs; the other is a sensor-independent nonlinear regression method, which only uses the land surface temperature (LST) and PWC as inputs. The validation results show that the root mean square errors of RF and nonlinear regression method are less than 25.5 W/m 2 and bias are less than 0.5 W/m 2 under different tenfold cross-validation schemes. In addition, the sensitivity analysis indicates that the two models are very stable, and the retrieval errors are independent of changes of LST and PWC, and the accuracy does not rely on variations of land cover types, different observing angles, and different seasons. The comparison analysis indicates that the proposed methods are comparable or even better than existing algorithms. More importantly, the proposed sensor-independent regression method was designed for generality purpose so can easily be utilized with reasonable accuracy. Key Points:• A general scheme to estimate LWDR from space based only on LST and column precipitable water vapor (PWC) was provided • An alternative sensor-based Random Forest (RF) method with improved accuracy for deriving LWDR based on MODIS data was also proposed • Both methods are proved accurate (RMSE <25.5 W/m 2 , bias <0.5 W/m 2 ) and the model errors are independent of LST and PWC Supporting Information:• Supporting Information S1

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Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

Cited by 36 publications

References 70 publications

How Accurately Can the Air Temperature Lapse Rate Over the Tibetan Plateau Be Estimated From MODIS LSTs?

How Accurately Can the Air Temperature Lapse Rate Over the Tibetan Plateau Be Estimated From MODIS LSTs?

Topoclimatic zones and characteristics of the upper Ganga basin, Uttarakhand, India

New Methods for Deriving Clear‐Sky Surface Longwave Downward Radiation Based on Remotely Sensed Data and Ground Measurements

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