Sensitivity of Seasonal Snowfall Attribution to Atmospheric Rivers and Their Reanalysis‐Based Detection

Huning, Laurie S.; Guan, Bin; Waliser, Duane E.; Lettenmaier, Dennis P.

doi:10.1029/2018gl080783

Cited by 30 publications

(28 citation statements)

References 27 publications

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“…ERA5 precipitation estimates were added up from an hourly to a daily time scale, and the AR data set from a 6-hourly to a daily time scale. Similar to Huning et al (2017Huning et al ( , 2019, a grid in the daily map is labeled as an AR if at least one of the four 6-hourly masks indicates an AR for that day for that grid. This may lead to a slight overestimation.…”

Section: Methodsmentioning

confidence: 99%

Global Intercomparison of Atmospheric Rivers Precipitation in Remote Sensing and Reanalysis Products

et al. 2020

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Atmospheric rivers (ARs) play an important role in the total annual precipitation regionally and globally, delivering precious freshwater to many arid/semiarid regions. On the other hand, they may cause intense precipitation and floods with huge socioeconomic effects worldwide. In this study, we investigate AR-related precipitation using 18 years (2001-2018) of globally gridded AR locations derived from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). AR precipitation features are explored regionally and seasonally using remote sensing (Integrated Multi-satellitE Retrievals for GPM version 6 [IMERG V6], daily Global Precipitation Climatology Project version 1.3 [GPCP V1.3], bias-adjusted CPC Morphing Technique version 1 [CMORPH V1], and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks [PERSIANN-CDR]) and reanalysis (MERRA-2 and ECMWF Reanalysis 5th Generation [ERA5]) precipitation products. The results show that most of the world (except the tropics) experience more intense precipitation from AR-related events compared to non-AR events. Over the oceans (especially the Southern Ocean), the contribution of ARs to the total precipitation and extreme events is larger than over land. However, some coastal areas over land are highly affected by ARs (e.g., the western and eastern United States and Canada, Western Europe, North Africa, and part of the Middle East, East Asia, and eastern South America and part of Australia). Although spatial correlations for pairs of IMERG/CMORPH and GPCP/PERSIANN-CDR are fairly high, considerable discrepancies are shown in their estimation of AR-related events (i.e., overall IMERG and CMORPH show a higher fraction of AR-related precipitation). It was found that the degree of consistency between reanalysis and satellite-based products is highly regionally dependent, partly due to the uneven distribution of in situ measurements.

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Section: Methodsmentioning

confidence: 99%

Global Intercomparison of Atmospheric Rivers Precipitation in Remote Sensing and Reanalysis Products

et al. 2020

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“…This reflects higher transition elevations during atmospheric-river events, leading to less snowfall and at times resulting in rain-on-snow events at lower elevations. Along with the intense precipitation during atmospheric-river events (Huning et al 2019;Lettenmaier 2019), the additional snowmelt with warmer conditions enhances runoff and can increase the threat of flooding.…”

Section: Variabilities Of Rain-snow-transition Elevation and Zonementioning

confidence: 99%

Detecting Rain–Snow-Transition Elevations in Mountain Basins Using Wireless Sensor Networks

Cui

Bales

Rice

et al. 2020

Journal of Hydrometeorology

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To provide complementary information on the hydrologically important rain-snow-transition elevation in mountain basins, this study provides two estimation methods using ground measurements from basin-scale wireless-sensor networks: one based on wet-bulb temperature (Twet), and the other based on snow-depth measurements of accumulation and ablation. With data from 17 spatially distributed clusters (178 nodes) from two networks, in the American and Feather River basins of California’s Sierra Nevada, we analyzed transition elevation during 76 storm events in 2014-18. A Twet threshold of 0.5 °C best matched the transition elevation defined by snow depth. Transition elevations using Twet in upper elevations of the basins generally agreed with atmospheric snow level from radars located at lower elevations, while radar snow level was ~100 m higher due to snow-level lowering on windward mountainsides during orographic lifting. Diurnal patterns of the difference between transition elevation and radar snow level were observed in the American, related to diurnal ground-temperature variations. However, these patterns were not found in the Feather due to complex terrain and higher uncertainties in transition-elevation estimates. The American tends to exhibit 100-m higher transition elevations than does the Feather, consistent with the Feather being about 1° latitude further north. Transition elevation averaged 155-m higher in intense atmospheric-river events than in other events, meanwhile, snow-level lowering was enhanced with a 90-m larger difference between radar snow level and transition elevation. On-the-ground continuous observations from distributed sensor networks can complement radar data and provide important ground-truth and spatially resolved information on transition elevations in mountain basins.

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“…Wehner et al, 2018). Research focused on atmospheric rivers (ARs) in particular has contributed a great deal to our understanding of the water cycle (Zhu and Newell, 1998;Sellars et al, 2017), atmospheric dynamics (Hu et al, 2017), precipitation variability (Dong et al, 2018), precipitation extremes (Dong et al, 2018), impacts, meteorological controls on the cryosphere (Gorodetskaya et al, 2014;Huning et al, 2017Huning et al, , 2019, and uncertainty in projections of precipitation in future climate change scenarios (Gershunov et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Detection of Atmospheric Rivers with Inline Uncertainty Quantification: TECA-BARD v1.0

O’Brien¹,

Risser²,

Loring³

et al. 2020

Preprint

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Abstract. It has become increasingly common for researchers to utilize methods that identify weather features in climate models. There is an increasing recognition that the uncertainty associated with choice of detection method may affect our scientific understanding. For example, results from the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) indicate that there are a broad range of plausible atmospheric river (AR) detectors, and that scientific results can depend on the algorithm used. There are similar examples from the literature on extratropical cyclones and tropical cyclones. It is therefore imperative to develop detection techniques that explicitly quantify the uncertainty associated with the detection of events. We seek to answer the question: given a ‘plausible’ AR detector, how does uncertainty in the detector quantitatively impact scientific results? We develop a large dataset of global AR counts, manually identified by a set of 8 researchers with expertise in atmospheric science, which we use to constrain parameters in a novel AR detection method. We use a Bayesian framework to sample from the set of AR detector parameters that yield AR counts similar to the expert database of AR counts; this yields a set of 'plausible' AR detectors from which we can assess quantitative uncertainty. This probabilistic AR detector has been implemented in the Toolkit for Extreme Climate Analysis (TECA), which allows for efficient processing of petabyte-scale datasets. We apply the TECA Bayesian AR Detector, TECA-BARD v1.0, to the MERRA2 reanalysis and show that the sign of the correlation between global AR count and El Nino Southern Oscillation depends on the set of parameters used.

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Sensitivity of Seasonal Snowfall Attribution to Atmospheric Rivers and Their Reanalysis‐Based Detection

Cited by 30 publications

References 27 publications

Global Intercomparison of Atmospheric Rivers Precipitation in Remote Sensing and Reanalysis Products

Global Intercomparison of Atmospheric Rivers Precipitation in Remote Sensing and Reanalysis Products

Detecting Rain–Snow-Transition Elevations in Mountain Basins Using Wireless Sensor Networks

Detection of Atmospheric Rivers with Inline Uncertainty Quantification: TECA-BARD v1.0

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