An improved triple collocation-based integration of multiple gravity anomaly grids from satellite altimetry: Contribution of ICESat-2

Chao, Nengfang; Wang, Shuai; Ouyang, Guichong; Hwang, Cheinway; Jin, Taoyong; Zhu, Chengcheng; Abulaitijiang, Adili; Zhang, Shengjun; Yue, Lianzhe; Chen, Gang; Zhang, Yanze

doi:10.1016/j.rse.2023.113582

Cited by 6 publications

(4 citation statements)

References 34 publications

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“…The triple collocation (TC) method, an effective method for quantifying uncertainties in three independently obtained data sets (Text S1 in Supporting Information ), was utilized to calculate weights (Cao et al., 2023; Dong et al., 2020; He et al., 2023). The TC assumed that there was a linear relationship between the snow depth data set and the actual snow depth ( SD 0 ) as follows:

{SD}_{i}={\alpha }_{i}+{\beta }_{i}{SD}_{0}+{\varepsilon }_{i}

where α i and β i are regression coefficients and ε i represents the zero‐mean random error.…”

Section: Methodsmentioning

confidence: 99%

The Varied Role of Atmospheric Rivers in Arctic Snow Depth Variations

Li,

Ke,

Shen

et al. 2024

Geophysical Research Letters

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The state and fate of snow on sea ice are crucial in the mass and energy balance of sea ice. The function of atmospheric rivers (ARs) on snow depth over sea ice has not been measured thus far, limiting the understanding of the mechanism of snow depth changes. Here, the effect of ARs on snow depth changes was explored. We found that increased AR frequency is responsible for winter‐autumn snow accumulation and spring‐summer snow melting. The 2 m air temperature (T2m), rainfall, snowfall, mean net longwave radiation (NLR), mean net shortwave radiation (NSR) and cloud radiative effect (CRE) during ARs explain the changes in snow depth triggered by AR occurrence. This work helps us understand how ARs affect snow depth changes through related physical processes, promotes an understanding of climate systems and provides a theoretical basis for snow treatment in sea ice models.

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{SD}_{i}={\alpha }_{i}+{\beta }_{i}{SD}_{0}+{\varepsilon }_{i}

where α i and β i are regression coefficients and ε i represents the zero‐mean random error.…”

Section: Methodsmentioning

confidence: 99%

The Varied Role of Atmospheric Rivers in Arctic Snow Depth Variations

Li,

Ke,

Shen

et al. 2024

Geophysical Research Letters

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“…We used the weighted average method to build the ensemble learning model (hereafter, the Multi‐DL model) to combine the three networks. The weight was calculated using the triple collocation method (Cao et al., 2023; J. Dong et al., 2019, 2020).

{\sigma }_{{\varepsilon }_{1}}^{2}=\overline{{\increment}{{h}_{1}}^{\prime 2}}-\frac{\overline{{\increment}{{h}_{1}}^{\prime }{\increment}{{h}_{2}}^{\prime }}\cdot \overline{{\increment}{{h}_{1}}^{\prime }{\increment}{{h}_{3}}^{\prime }}}{\overline{{\increment}{{h}_{2}}^{\prime }{\increment}{{h}_{3}}^{\prime }}}

{\sigma }_{{\varepsilon }_{2}}^{2}=\overline{{\increment}{{h}_{2}}^{\prime 2}}-\frac{\overline{{\increment}{{h}_{1}}^{\prime }{\increment}{{h}_{2}}^{\prime }}\cdot \overline{{\increment}{{h}_{2}}^{\prime }{\increment}{{h}_{3}}^{\prime }}}{\overline{{\increment}{{h}_{1}}^{\prime }{\increment}{{h}_{3}}^{\prime }}}

σ_{ε_{3}}^{2} = \overset{∆ {h_{3}}^{' 2}}{true} - \frac{\overset{∆ {h_{1}}^{'} ∆ {h_{3}}^{'}}{true} \cdot \overset{∆ {h_{2}}^{'} ∆ {h_{3}}^{'}}{true}}{∆ {h_{1}}^{'} ...}

…”

Section: An Ensemble Learning Model For Snow Depth Predictionsmentioning

confidence: 99%

“…We used the weighted average method to build the ensemble learning model (hereafter, the Multi-DL model) to combine the three networks. The weight was calculated using the triple collocation method (Cao et al, 2023;J. Dong et al, 2019J.…”

Section: Journal Of Geophysical Research: Atmospheresmentioning

confidence: 99%

An Ensemble Learning Model Reveals Accelerated Reductions in Snow Depth Over Arctic Sea Ice Under High‐Emission Scenarios

Li,

Ke,

Shen

et al. 2024

JGR Atmospheres

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There are significant differences in snow depth predictions among different earth system models, and many models underestimate snow depth, restricting their application. Here, major factors influencing snow depth changes in the Coupled Model Intercomparison Project Phase 6 (CMIP6) were identified and evaluated. Based on satellite‐derived snow depth and CMIP6 data, an ensemble learning model based on multiple deep learning methods (hereafter referred to as the Multi‐DL model) was developed to predict future snow depth. According to satellite observations and two Operation IceBridge products, the Multi‐DL model yielded root mean square errors of 7.48, 6.20, and 6.17 cm. A continuous decrease in snow depth was observed from 2002 to 2100, and the rate of decrease accelerated with increasing emissions. Under the highest emission scenario, the first snow‐free year occurred in 2047, within the same decade as the first ice‐free year (2056). The predicted warm season snow depth was sensitive to sea ice velocity, sea ice concentration (siconc), precipitation, sea surface temperature (tos) and albedo, while the predicted cold season snow depth was sensitive to tos, air temperature, and siconc. The above parameters introduce some snow depth uncertainty. This method provides new ideas for predicting snow depth, and the generated snow depth records can provide data support for formulating Arctic‐related policies.

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“…Currently, the TC analysis has been widely used in various geographic variables, including soil moisture products [11,13,14], precipitation [15][16][17][18], the leaf-area index [19], land-based water storage [20], marine gravity [21], and ET [5]. In terms of the number of publications, compared with soil moisture and precipitation products in the water cycle, relatively few papers consider the application of the TC in the estimation of errors in ET or vapor flux products [22].…”

Section: Introductionmentioning

confidence: 99%

Discovering Optimal Triplets for Assessing the Uncertainties of Satellite-Derived Evapotranspiration Products

Wang

et al. 2023

Remote Sensing

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Information relating to errors in evapotranspiration (ET) products, including satellite-derived ET products, is critical to their application but often challenging to obtain, with a limited number of flux towers available for the sufficient validation of measurements. Triple collocation (TC) methods can assess the inherent uncertainties of the above ET products using just three independent variables as a triplet input. However, both the severity with which the variables in the triplet violate the assumptions of zero error correlations and the corresponding impact on the error estimation are unknown. This study proposed a cross-correlation analysis approach to discover the optimal triplet of satellite-derived ET products with regard to providing the most reliable error estimation. All possible triple collocation solutions for the same product were first evaluated by the extended triple collocation (ETC), among which the optimum was selected based on the correlation between ETC-based and in-situ-based error metrics, and correspondingly, a statistic experiment based on ranked triplets demonstrated how the optimal triplet was valid for all pixels of the product. Six popular products (MOD16, PML_V2, GLASS, SSEBop, ERA5, and GLEAM) that were produced between 2003 to 2018 and which cover China’s mainland were chosen for the experiment, in which the error estimates were compared with measurements from 23 in-situ flux towers. The findings suggest that (1) there exists an optimal triplet in which a product as an input of TC with other collocating inputs together violate TC assumptions the least; (2) the error characteristics of the six ET products varied significantly across China, with GLASS performing the best (median error: 0.1 mm/day), followed by GLEAM, ERA5, and MOD16 (median errors below 0.2 mm/day), while PML_V2 and SSEBop had slightly higher median errors (0.24 mm/day and 0.27 mm/day, respectively); and (3) removing seasonal variations in ET signals has a substantial impact on enhancing the accuracy of error estimations.

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An improved triple collocation-based integration of multiple gravity anomaly grids from satellite altimetry: Contribution of ICESat-2

Cited by 6 publications

References 34 publications

The Varied Role of Atmospheric Rivers in Arctic Snow Depth Variations

The Varied Role of Atmospheric Rivers in Arctic Snow Depth Variations

An Ensemble Learning Model Reveals Accelerated Reductions in Snow Depth Over Arctic Sea Ice Under High‐Emission Scenarios

Discovering Optimal Triplets for Assessing the Uncertainties of Satellite-Derived Evapotranspiration Products

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