Improving Land Surface Temperature Simulation of NOAH-MP on the Tibetan Plateau

He, Qing; Lu, Hui; Yang, Kun; Zhao, Long; Zou, Minmin

doi:10.1109/igarss47720.2021.9555059

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…over semiarid regions [4][5][6][7], due to the great complexity of land surface physics and limited observations, the soil temperature forecast directly produced by LSMs has received little attention. Research efforts have been made recently in demonstrating surface ST improvement over greater time scales through semi-manually corrected LSMs with several key sensitive land surface parameters (i.e., leaf area index, known as LAI, and the coefficient of the roughness length of heat, known as CZIL) [8,9], but many other LSM land parameters that could affect medium-range ST simulations are still unaddressed, e.g., the varied flux physics related to thermal and/or hydraulic diffusion and conductivity over arid and semiarid lands can determine STs' tendencies. Therefore, identifying the most robust corrected LSM by comparing different calibration schemes could be of great significance for mitigating efforts in ST forecast improvement over semiarid regions.…”

Section: Introductionmentioning

confidence: 99%

Calibration for Improving the Medium-Range Soil Temperature Forecast of a Semiarid Region over Tibet: A Case Study

Guo,

Yuan,

et al. 2024

Atmosphere

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The high complexity of the parameter–simulation problem in land surface models over semiarid areas makes it difficult to reasonably estimate the surface simulation conditions that are important for both weather and climate in different regions. In this study, using the dense site datasets of a typical semiarid region over Tibet and the Noah land surface model with the constrained land parameters of multiple sites, an enhanced Kling–Gupta efficiency criterion comprising multiple objectives, including variable and layer dimensions, was obtained, which was then applied to calibration schemes based on two global search algorithms (particle swarm optimization and shuffled complex evaluation) to investigate the site-scale spatial complexities in soil temperature simulations. The calibrations were then compared and further validated. The results show that the Noah land surface model obtained reasonable simulations of soil moisture against the observations with fine consistency, but the negative fit and huge spatial errors compared with the observations indicated its weak ability to simulate the soil temperature over regional semiarid land. Both calibration schemes significantly improved the soil moisture and temperature simulations, but particle swarm optimization generally converged to a better objective than shuffled complex evaluation, although with more parameter uncertainties and less heterogeneity. Moreover, simulations initialized with the optimal parameter tables for the calibrations obtained similarly sustainable improvements for soil moisture and temperature, as well as good consistency with the existing soil reanalysis. In particular, the soil temperature simulation errors for particle swarm optimization were unbiased, while those for the other method were found to be biased around −3 K. Overall, particle swarm optimization was preferable when conducting soil temperature simulations, and it may help mitigate the efforts in surface forecast improvement over semiarid regions.

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Section: Introductionmentioning

confidence: 99%

Calibration for Improving the Medium-Range Soil Temperature Forecast of a Semiarid Region over Tibet: A Case Study

Guo,

Yuan,

et al. 2024

Atmosphere

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“…Development of satellite‐observed land surface products such as land surface temperature (Wan, 2013), SM (Entekhabi et al., 2010; Kerr et al., 2001; Njoku et al., 2003), and vegetation indices (e.g., LAI, Fractional Vegetation Cover (FVC), etc.) (Cohen et al., 2006; Garrigues et al., 2008; Xiao et al., 2016) facilitates improvement of land surface modeling by providing real‐time and realistic land surface information (He et al., 2021; Kolassa et al., 2020; Kumar et al., 2015, 2019; Li et al., 2019). Data assimilation (DA) technology is one typical way of incorporating satellite data sets to improve land surface simulations (Gettelman et al., 2022; Yang, Chen, et al., 2020, Yang, Zhao, et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Global Optimization of Soil Texture Maps From Satellite‐Observed Soil Moisture Drydowns and Its Implementation in Noah‐MP Land Surface Model

He,

Lu,

Yang

et al. 2024

J Adv Model Earth Syst

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Soil moisture (SM) plays an important role in regulating regional weather and climate. However, the simulations of SM in current land surface models (LSMs) contain large biases and model spreads. One primary reason contributing to such model biases could be the misrepresentation of soil texture in LSMs, since current available large‐scale soil texture data are often generated from extrapolation algorithm based on a scarce number of in‐situ geological measurements. Fortunately, recent advancements in satellite technology provide a unique opportunity to constrain the soil texture data sets by introducing observed information at large spatial scales. Here, two major soil texture baseline data sets (Global Soil Data sets for Earth system science, GSDE and Harmonized World Soil Data from Food and Agriculture Organization, HWSD) are optimized with satellite‐estimated soil hydraulic parameters. The optimized soil maps show increased (decreased) sand (clay) content over arid regions. The soil organic carbon (SOC) content increases globally especially over regions with dense vegetation cover. The optimized soil texture data sets are then used to run simulations in one example LSM, that is, Noah LSM with Multiple Parameters. Results show that the simulated SM with satellite‐optimized soil texture maps is improved at both grid and in‐situ scales. Intercase comparison analyses show the SM improvement differs between simulations using different soil maps and soil hydraulic schemes. Our results highlight the importance of incorporating observation‐oriented calibration on soil texture in current LSMs. This study also joins the call for a better soil profile representation in the next generation of Earth System Models (ESMs).

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“…SOLA, 2023, Vol. 19, 111-114(TBA), doi:10.2151/sola.2023 3 3 The Noah-MP scheme is still being improved for accurate temperature prediction (e.g., Tomasi et al 2017;He et al 2021;Li et al 2022). However, most previous researchers have proposed schemes to optimize and parameterize the surface properties, e.g., the roughness length and albedo, but have not modified thermodynamic calculation schemes.…”

Section: Introductionmentioning

confidence: 99%

Reevaluating the Surface Energy Balance and Soil Thermal Diffusion Equations in the Noah Multi-Parameterization (Noah-MP) Scheme

Nakanishi

2023

SOLA

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The Noah multi-parameterization (Noah-MP) scheme is one of several land surface schemes implemented in the Weather Research and Forecasting (WRF) model. Our simulations by the WRF model with the Noah-MP scheme show that surface air temperatures in the morning and evening tend to be higher and lower, respectively, than observed, and that the temperatures at an urban station with snow cover increase little from 0 ℃ even in the daytime. The former depends on surface energy balance in the skin layer and the latter results from snow cover assumed to be uniform over a grid cell. These weaknesses are improved by considering the partial transmission of the solar radiation through the skin layer to the soil layer, the heat capacity of the vegetation canopy, and a mixture of soil layers with and without snow cover. The present scheme will contribute to an improvement of the Noah-MP scheme.

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Improving Land Surface Temperature Simulation of NOAH-MP on the Tibetan Plateau

Cited by 4 publications

References 24 publications

Calibration for Improving the Medium-Range Soil Temperature Forecast of a Semiarid Region over Tibet: A Case Study

Calibration for Improving the Medium-Range Soil Temperature Forecast of a Semiarid Region over Tibet: A Case Study

Global Optimization of Soil Texture Maps From Satellite‐Observed Soil Moisture Drydowns and Its Implementation in Noah‐MP Land Surface Model

Reevaluating the Surface Energy Balance and Soil Thermal Diffusion Equations in the Noah Multi-Parameterization (Noah-MP) Scheme

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