The Use of a Monte Carlo Markov Chain Method for Snow-Depth Retrievals: A Case Study Based on Airborne Microwave Observations and Emission Modeling Experiments of Tundra Snow

Saberi, Nastaran; Kelly, Richard; Pan, Jinmei; Durand, Michael; Goh, Joslin; Scott, K. Andrea

doi:10.1109/tgrs.2020.3004594

Cited by 9 publications

(17 citation statements)

References 33 publications

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“…Such simplifications could be potentially useful for satellite SWE retrievals across Arctic tundra regions. Since Bayesian SWE optimization needs a strong first guess from regional a priori information, multiple distributions of snow depth, density and SSA presented here can be used for tundra type snow in MCMC sampling (Pan et al, 2017;Saberi et al, 2020). Additionally, a similar approach to our GP simulation can be added so the parameter can also be used as a priori information with a distribution from 0.8 to 1, since it improved TB RMSE by ~8K (Figure 8).…”

Section: Discussionmentioning

confidence: 99%

“…The impact on microwave scattering of variability of layer microstructures with snow depth was previously accounted for in Saberi et al (2020) by defining two categories, a high scattering thin snow layer (high DHF) and a thicker self-emitting layer (low DHF). Snowpack properties (layer extent, density, SSA) were related to snow depth via DHF (Figure 5) instead of using two categories.…”

Section: Dhf Predictions Using Snow Depth With Gaussian Processesmentioning

confidence: 99%

“…However, spatial distribution and heterogeneity of total snow depth and stratigraphy remains challenging to implement and is not considered for large scale monitoring of SWE in tundra environments. Rutter et al (2019) and Saberi et al (2020), using three-and two-layer models respectively, demonstrated a relationship between the ratio of depth hoar and wind slab with respect to total depth, enabling the usage of proportion of these two layers with total snow depth. Working with a simplified layer representation of a snowpack with well-defined physical properties may adequately characterize snowpack for large scale SWE retrievals.…”

Section: Introductionmentioning

confidence: 99%

“…There is potential for to be used as an effective parameter to estimate sub-pixel variability in brightness temperature. Bayesian frameworks are used in inversion schemes for SWE retrievals (Durand and Liu, 2012;Pan et al, 2017;Saberi et al, 2020) using a priori information (density, microstructure and temperature) from regional snowpack characteristics and inversion of radiative transfer models (Saberi et al, 2020). An iterative approach based on Bayesian theory is used (Takala et al, 2011) to match observed brightness temperature with modelled brightness temperature by iterating a priori information of the snowpack in order to derive snow depth and SWE.…”

Section: Introductionmentioning

confidence: 99%

“…An iterative approach based on Bayesian theory is used (Takala et al, 2011) to match observed brightness temperature with modelled brightness temperature by iterating a priori information of the snowpack in order to derive snow depth and SWE. Saberi et al (2020) conducted a case study for snow depth retrievals using a two layer model from airborne microwave observations using a Bayesian framework (or Monte Carlo Markov Chain) over tundra snow. However, high uncertainty (21.8 cm) in retrieved snow depth (via ) resulted, which suggested the use of a Gaussian Process (GP) involving snow depth instead of a uniform snow depth.…”

Section: Introductionmentioning

confidence: 99%

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Characterizing Tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

Meloche

Langlois

Rutter

et al. 2021

Preprint

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Abstract. Topography and vegetation play a major role in sub-pixel variability of Arctic snowpack properties, but are not considered in current passive microwave (PMW) satellite SWE retrievals. Simulation of sub-pixel variability of snow properties is also problematic when downscaling snow and climate models. In this study, we simplified observed variability of snowpack properties (depth, density, microstructure) in a two-layer model with mean values and distributions of two multi-year tundra dataset so they could be incorporated in SWE retrieval schemes. Spatial variation of snow depth was parametrized by a log-normal distribution with mean (μsd) values and coefficients of variation (CVsd). Snow depth variability (CVsd) was found to increase as a function of the area measured by a Remotely Piloted Aircraft System (RPAS). Distributions of snow specific area (SSA) and density were found for the wind slab (WS) and depth hoar (DH) layers. The mean depth hoar fraction (DHF) was found to be higher in Trail Valley Creek (TVC) than Cambridge Bay (CB) where TVC is at a lower latitude with a sub-arctic shrub tundra compared to CB which is a graminoid tundra. DHF were fitted with a gaussian process and predicted from snow depth. Simulations of brightness temperatures using the Snow Microwave Radiative Transfer (SMRT) model incorporating snow depth and DHF variation were evaluated with measurements from the Special Sensor Microwave/Imager and Sounder (SSMIS) sensor. Variation in snow depth (CVsd) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. Snow depth simulations using a CVsd of 0.9 best matched CVsd observations from spatial datasets for areas > 3 km2, which is comparable to the 3.125 km pixel size of the Equal-Area Scalable Earth (EASE) grid 2.0 enhanced resolution at 37 GHz.

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Section: Discussionmentioning

confidence: 99%

Section: Dhf Predictions Using Snow Depth With Gaussian Processesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characterizing Tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

Meloche

Langlois

Rutter

et al. 2021

Preprint

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Automated Calibration of a Snow‐On‐Sea‐Ice Model

Cabaj

Kushner

Petty

2023

Earth and Space Science

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Snow on Arctic sea ice has many, contrasting effects on ice thickness and extent. Furthermore, estimates of snow depth on Arctic sea ice are a key input for ice thickness estimates from satellite altimeters such as ICESat‐2. Models such as the NASA Eulerian Snow on Sea Ice Model (NESOSIM) have been recently utilized by the sea ice community to provide time‐varying basin‐wide estimates of snow depth and density on Arctic sea ice. NESOSIM is a two‐snow‐layer model with simple representations of snow accumulation, wind packing, loss due to blowing snow, and redistribution due to sea ice motion. Two free parameters in NESOSIM, which dictate the bulk effect of wind packing (densification) and blowing snow processes, lack direct observational constraints. We present an indirect calibration of these parameters using a Markov Chain Monte Carlo (MCMC) approach. NESOSIM output is calibrated to observations of snow depth from Operation IceBridge and CRREL‐Dartmouth buoys, and density from historical drifting stations. OIB measurements alone are found to more strictly constrain the blowing snow parameter, and including additional observations yields more physically reasonable density estimates. The MCMC‐calibrated model output is further used to estimate sea ice thickness and uncertainty from model parameter uncertainty using ICESat‐2 freeboard measurements. Despite visible differences in density, the change in ice thickness is minimal. We also find that the model is relatively insensitive to parameter variations, and hence, the snow model uncertainty contribution to ice thickness is small compared to the systematic uncertainty from snow in the current ICESat‐2 thickness product.

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Moderate-resolution snow depth product retrieval from passive microwave brightness data over Xinjiang using machine learning approach

Liu,

Yang,

Chen

et al. 2024

International Journal of Digital Earth

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The Use of a Monte Carlo Markov Chain Method for Snow-Depth Retrievals: A Case Study Based on Airborne Microwave Observations and Emission Modeling Experiments of Tundra Snow

Cited by 9 publications

References 33 publications

Characterizing Tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

Characterizing Tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

Automated Calibration of a Snow‐On‐Sea‐Ice Model

Moderate-resolution snow depth product retrieval from passive microwave brightness data over Xinjiang using machine learning approach

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