Effect of shadow on atmospheric and topographic processed NDSI values in Chenab basin, western Himalayas

Jasrotia, Avtar Singh; Kour, Retinder; Singh, Komal

doi:10.1016/j.coldregions.2022.103561

Cited by 4 publications

(1 citation statement)

References 71 publications

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“…In addition, Stillinger et al (2023) show that the application of the normalized difference snow index (NDSI) -which is used in Snow CCI products -shows certain limitations over mountainous areas, whereas spectral unmixing techniques could provide finer estimates of the SCF. Last but not least, the presence of shadows can also affect the NDSI retrievals (Jasrotia et al, 2022). All these factors contribute to the uncertainty of SCF retrievals in the Snow CCI datasets, more observations should be used in the future to assess the impact of these uncertainties.…”

Section: Land-atmosphere Feedbacksmentioning

confidence: 99%

Reducing the High Mountain Asia cold bias in GCMs by adaptingsnow cover parameterization to complex topography areas

Lalande

Ménégoz

Krinner

et al. 2023

Preprint

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Abstract. The influence of topography on the snow cover fraction (SCF) is investigated in this study with 5 different parameterizations. These SCF parameterizations are evaluated using the High Mountain Asia Snow Reanalysis (HMASR). Then, they are implemented in the ORCHIDEE land surface model (LSM) of the Institut Pierre Simon Laplace (IPSL) general circulation model (GCM) to quantify their skill in global land-atmosphere coupled simulations. SCF varies as a function to snow depth (SD), with a relationship that differs between flat and mountainous areas in HMASR. SCF parameterizations that do not include a dependency on the topography lead to large snow cover overestimations. Furthermore, a hysteresis between SCF and SD is found in HMASR, with a rapid snow cover increase during accumulation and a slower retreat of patchy snow occurring during ablation periods, discarding parameterizations not considering this effect. The application of the parametrizations in global simulations shows contrasting results depending on the location because other processes also explain the snow biases. Nevertheless, the snow cover overestimation in mountain areas is reduced by about 5 to 10 % on average when we include a dependency on the subgrid topography in our SCF parameterizations, which in turn allows to decrease the surface cold bias from −1.8 °C to about −1 °C in the High Mountain Asia (HMA) region. However, persisting snow cover biases remain in these experiments, with a SCF overestimation in HMA, as well as a SCF underestimation in several other regions (e.g., the Rockies mountains). Further calibration considering other regions and multiple datasets would allow to improve the SCF parameterizations. Assessing SCF parameterizations is challenging as it requires both realistic snowfall and snowpack in model experiments, and combined SCF, SD, and SWE – or snow density – observations, that are generally limited and uncertain in mountainous regions.

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Section: Land-atmosphere Feedbacksmentioning

confidence: 99%

Reducing the High Mountain Asia cold bias in GCMs by adaptingsnow cover parameterization to complex topography areas

Lalande

Ménégoz

Krinner

et al. 2023

Preprint

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Surface spectral irradiance and irradiance partitioning in a complex mountain environment: understanding location-dependent topographic effects in satellite imagery

Bishop,

Young,

Colby

2023

Geocarto International

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Numerical Modeling and Parameter Sensitivity Analysis for Understanding Scale-Dependent Topographic Effects Governing Anisotropic Reflectance Correction of Satellite Imagery

2022

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Anisotropic reflectance correction (ARC) of satellite imagery is required to remove multi-scale topographic effects in imagery. Commonly utilized ARC approaches have not effectively accounted for atmosphere-topographic coupling. Furthermore, it is not clear which topographic effects need to be formally accounted for. Consequently, we simulate the direct and diffuse-skylight irradiance components and formally account for multi-scale topographic effects. A sensitivity analysis was used to determine if characterization schemes can account for a collective treatment of effects, using our parameterization scheme as a basis for comparison. We found that commonly used assumptions could not account for topographic modulation in our simulations. We also found that the use of isotropic diffuse irradiance and a topographic shielding parameter also failed to characterize topographic modulation. Our results reveal that topographic effects govern irradiance variations in a synergistic way, and that issues of ARC need to be formally addressed given atmosphere-topography coupling. Collectively, our results suggest that empirical ARC methods cannot be used to effectively address topographic effects, given inadequate parameterization schemes. Characterizing and removing spectral variation from multispectral imagery will most likely require numerical modeling efforts. More research is warranted to develop/evaluate parameterization schemes that better characterize the anisotropic nature of atmosphere-topography coupling.

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Effect of shadow on atmospheric and topographic processed NDSI values in Chenab basin, western Himalayas

Cited by 4 publications

References 71 publications

Reducing the High Mountain Asia cold bias in GCMs by adaptingsnow cover parameterization to complex topography areas

Reducing the High Mountain Asia cold bias in GCMs by adaptingsnow cover parameterization to complex topography areas

Surface spectral irradiance and irradiance partitioning in a complex mountain environment: understanding location-dependent topographic effects in satellite imagery

Numerical Modeling and Parameter Sensitivity Analysis for Understanding Scale-Dependent Topographic Effects Governing Anisotropic Reflectance Correction of Satellite Imagery

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