Evaluating the Uncertainty of Terrestrial Water Budget Components Over High Mountain Asia

Yoon, Yeosang; Kumar, Sujay V.; Forman, B. A.; Zaitchik, Benjamin F.; Kwon, Yonghwan; Qian, Yun; Rupper, S.; Maggioni, Viviana; Houser, Paul R.; Kirschbaum, Dalia; Richey, A.; Arendt, A. A.; Mocko, David M.; Jacob, Jossy P.; Bhanja, Soumendra N.; Mukherjee, Abhijit

doi:10.3389/feart.2019.00120

Cited by 56 publications

(63 citation statements)

References 80 publications

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“…Yoon et al [42] conducted an inter-comparison of 10 different precipitation datasets, including MERRA-2 and TRMM precipitation data, and confirmed that the existing precipitation datasets have large uncertainties and variability across HMA. All precipitation datasets agreed with respect to spatial patterns, but their magnitudes and temporal trends were significantly different.…”

Section: Data Setsmentioning

confidence: 89%

“…All precipitation datasets agreed with respect to spatial patterns, but their magnitudes and temporal trends were significantly different. Yoon et al [42] suggested the use of the Climate Hazards Group InfraRed Precipitation with Station data-version 2 (CHIRPS-2; [43]) precipitation in conjunction with other forcing fields from the European Centre for Medium-Range Weather Forecasts (ECMWF; [44]) to drive land surface models over HMA. Meanwhile, Ahmad et al [45] showed that signs of the sensitivity of SVM-based ∆T B predictions to both the forcings (i.e., MERRA-2 and ECMWF + CHIRPS-2) were comparable, and higher sensitivity values were obtained for MERRA-2.…”

Section: Data Setsmentioning

confidence: 99%

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Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

et al. 2019

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This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (ΔTB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and ΔTB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted ΔTB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic ΔTB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.

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Section: Data Setsmentioning

confidence: 89%

Section: Data Setsmentioning

confidence: 99%

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

et al. 2019

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“…All these results rely upon the support of a global observing system that continuously monitors the atmosphere, the continents, and the oceans with a unified perspective, improving over existing networks that were not originally designed for climate monitoring [61,62,66]. Satellite precipitation measurements have a key role in this scientific framework, and advances in the observing constellation are on their way as:…”

Section: Discussionmentioning

confidence: 99%

“…The melting of glaciers, and consequent intensification of the water cycle with greening ecosystems and increasing frequency of hazards, is closely linked to recent warming, especially over the Asian Third Pole, requiring investigations of every major component in the system, especially through improved observations [65]. Recent research efforts have attempted to evaluate the uncertainty of terrestrial water budget components over High Mountain Asia, which is significantly impacted by the uncertainty on the driving meteorology [66], and is of the utmost importance for the assimilation of the frozen components in land surface models [67].…”

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confidence: 99%

Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate

2019

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The water cycle is the most essential supporting physical mechanism ensuring the existence of life on Earth. Its components encompass the atmosphere, land, and oceans. The cycle is composed of evaporation, evapotranspiration, sublimation, water vapor transport, condensation, precipitation, runoff, infiltration and percolation, groundwater flow, and plant uptake. For a correct closure of the global water cycle, observations are needed of all these processes with a global perspective. In particular, precipitation requires continuous monitoring, as it is the most important component of the cycle, especially under changing climatic conditions. Passive and active sensors on board meteorological and environmental satellites now make reasonably complete data available that allow better measurements of precipitation to be made from space, in order to improve our understanding of the cycle’s acceleration/deceleration under current and projected climate conditions. The article aims to draw an up-to-date picture of the current status of observations of precipitation from space, with an outlook to the near future of the satellite constellation, modeling applications, and water resource management.

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“…The alternative boundary condition product used was an amalgamation of precipitation data taken from the Climate Hazards Group InfraRed Precipitation with Station data-version 2 (CHIRPS-2; Funk et al 2015) and all other forcings acquired from the European Centre for Medium-Range Weather Forecasts (ECMWF; Molteni et al 1996). The selection of this particular combination data-set is based on the comparative analysis of boundary conditions used for Noah-MP carried out by Yoon et al (2019). NSCs for each geophysical variable (in space and time) were calculated and compared with the corresponding MERRA-2 results.…”

Section: Influence Of Model Boundary Conditionsmentioning

confidence: 99%

Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia

2019

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Snow is an important component of the terrestrial freshwater budget in high mountain Asia (HMA) and contributes to the runoff in Himalayan rivers through snowmelt. Despite the importance of snow in HMA, considerable spatiotemporal uncertainty exists across the different estimates of snow water equivalent for this region. In order to better estimate snow water equivalent, radiative transfer models are often used in conjunction with microwave brightness temperature measurements. In this study, the efficacy of support vector machines (SVMs), a machine learning technique, to predict passive microwave brightness temperature spectral difference (Tb) as a function of geophysical variables (snow water equivalent, snow depth, snow temperature, and snow density) is explored through a sensitivity analysis. The use of machine learning (as opposed to radiative transfer models) is a relatively new and novel approach for improving snow water equivalent estimates. The Noah-MP land surface model within the NASA Land Information System framework is used to simulate the hydrologic cycle over HMA and model geophysical variables that are then used for SVM training. The SVMs serve as a nonlinear map between the geophysical space (modeled in Noah-MP) and the observation space (Tb as measured by the radiometer). Advanced Microwave Scanning Radiometer-Earth Observing System measured passive microwave brightness temperatures over snow-covered locations in the HMA region are used as training data during the SVM training phase. Sensitivity of well-trained SVMs to each Noah-MP modeled state variable is assessed by computing normalized sensitivity coefficients. Sensitivity analysis results generally conform with the known first-order physics. Input states that increase volume scattering of microwave radiation, such as snow density and snow water equivalent, exhibit a plurality of positive normalized sensitivity coefficients. In general, snow temperature was the most sensitive input to the SVM predictions. The sensitivity of each state is location and time dependent. The signs of normalized sensitivity

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Evaluating the Uncertainty of Terrestrial Water Budget Components Over High Mountain Asia

Cited by 56 publications

References 80 publications

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate

Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia

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