A Parameterization Scheme for Correcting All‐Sky Surface Longwave Downward Radiation Over Rugged Terrain

Author:

Yang Feng123,Zeng Zhenzhong3ORCID,Cheng Jie1ORCID

Affiliation:

1. State Key Laboratory of Remote Sensing Science Faculty of Geographical Science Beijing Normal University Beijing China

2. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau Northwest A&F University Yangling China

3. School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen China

Abstract

AbstractAccurate surface longwave downward radiation (SLDR) is crucial for understanding mountain climate dynamics. While existing algorithms notably improve the accuracy of clear‐sky SLDR, a terrain correction algorithm that can correct remotely sensed and model‐simulated all‐sky SLDR on a large scale remains largely unexplored. Here, we propose a parameterization scheme for estimating all‐sky SLDR in rugged terrain. We primarily improve the estimation of nearby terrain thermal contribution by considering topographic asymmetry and incorporate the effects of ice cloud thermal scattering under low water vapor conditions. We validate the reliability of our model using the Discrete Anisotropic Radiative Transfer (DART) model, demonstrating a good agreement with a bias value of −12.8 W/m2 and a RMSE value of 28.2 W/m2. Further evaluation against the Essential Thermal Infrared Remote Sensing (ELITE) SLDR product at three TIPEX‐III in situ sites, located near the bottom of deep valleys with predominantly flat surfaces, indicates significant improvement in our model, reducing the mean bias by 7.4 W/m2 and the mean RMSE by 4.1 W/m2. Post‐terrain correction, the ELITE SLDR difference map exhibits a spatial pattern of “small in the northwest and large in the southeast” in the study area, with the maximum differences reaching 67 W/m2 in the daytime and 54 W/m2 at nighttime. Comparison with existing methods reveals similar improvements due to the consideration of terrain effects. Overall, our SLDR correction model shows enormous potential for correcting remotely sensed and model‐simulated SLDR products on a large scale.

Funder

National Natural Science Foundation of China

Publisher

American Geophysical Union (AGU)

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