A Composite Analysis of Snowfall Modes from Four Winter Seasons in Marquette, Michigan

Pettersen, Claire; Kulie, Mark S.; Bliven, Larry F.; Merrelli, Aronne; Petersen, Walter A.; Wagner, Timothy J.; Wolff, David B.; Wood, Norman B.

doi:10.1175/jamc-d-19-0099.1

Cited by 30 publications

(62 citation statements)

References 81 publications

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“…This study utilizes an enhanced precipitation instrument suite located at the Marquette, Michigan (MQT) National Weather Service (NWS) office [19]. This long-duration dataset consists of colocated remotely sensed and in situ observations of precipitation from January 2014 through the present.…”

Section: Location and Instrumentationmentioning

confidence: 99%

“…This long-duration dataset consists of colocated remotely sensed and in situ observations of precipitation from January 2014 through the present. This region receives approximately 250 to 500 cm of measured snow accumulation per year, half of which is produced by Lake-effect Snow (LeS) processes [19]. Because this area receives both synoptically and surface-forced (e.g., LeS and orographic) snowfall, there is a wide range of observed snow event environmental conditions and snow-to-liquid ratios (SLRs).…”

Section: Location and Instrumentationmentioning

confidence: 99%

“…Additionally, for particles that are observed in more than one frame, both the horizontal and vertical motion is measured. These observations are then used to create particle size distributions (PSDs) and fall speed estimates at a one-minute resolution [16,19]. The images are subsampled by a factor of 25 to prevent repeatedly counting the same particle when calculating the PSD.…”

Section: Precipitation Imaging Packagementioning

confidence: 99%

“…Additionally, studies have used observations from the PIP (and its predecessor, the Snowflake Video Imager [16]) to produce mass-dimensional relationships of snow particles to assess the quality of radar reflectivity-derived snowfall rate relationships [13,17,18]. PIP-derived products of snow properties in conjunction with meteorological measurements and reanalysis products have been used to illuminate microphysical characteristics associated with specific types of snowfall events [19,20], and to generate parameterizations to improve space-based retrievals of precipitating snow properties [8,21].…”

Section: Introductionmentioning

confidence: 99%

“…The PIP snow rate and equivalent density retrievals are assessed through intercomparison with established retrieval methods by von Lerber et al [17] and Wood et al [30] and through evaluation with colocated ground-based observations. We achieve quality intercomparisons of snow events by leveraging the unique and comprehensive dataset collected at the National Weather Service Marquette, Michigan office [19]. The presented work separates snow events into categories of low or high snow-to-liquid ratio (SLR) and will examine the differences using observations from the PIP and bulk snow characteristics from the PIP higher-order-derived products and the von Lerber et al [17] and Wood et al [30] retrievals.…”

Section: Introductionmentioning

confidence: 99%

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The Precipitation Imaging Package: Assessment of Microphysical and Bulk Characteristics of Snow

et al. 2020

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Remote-sensing observations are needed to estimate the regional and global impacts of snow. However, to retrieve accurate estimates of snow mass and rate, these observations require augmentation through additional information and assumptions about hydrometeor properties. The Precipitation Imaging Package (PIP) provides information about precipitation characteristics and can be utilized to improve estimates of snowfall rate and accumulation. Here, the goal is to demonstrate the quality and utility of two higher-order PIP-derived products: liquid water equivalent snow rate and an approximation of volume-weighted density called equivalent density. Accuracy of the PIP snow rate and equivalent density is obtained through intercomparison with established retrieval methods and through evaluation with colocated ground-based observations. The results confirm the ability of the PIP-derived products to quantify properties of snow rate and equivalent density, and demonstrate that the PIP produces physically realistic snow characteristics. When compared to the National Weather Service (NWS) snow field measurements of six-hourly accumulation, the PIP-derived accumulations were biased only +2.48% higher. Additionally, this work illustrates fundamentally different microphysical and bulk features of low and high snow-to-liquid ratio events, through assessment of observed particle size distributions, retrieved mass coefficients, and bulk properties. Importantly, this research establishes the role that PIP observations and higher-order products can serve for constraining microphysical assumptions in ground-based and spaceborne remotely sensed snowfall retrievals.

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Section: Location and Instrumentationmentioning

confidence: 99%

Section: Location and Instrumentationmentioning

confidence: 99%

Section: Precipitation Imaging Packagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Precipitation Imaging Package: Assessment of Microphysical and Bulk Characteristics of Snow

et al. 2020

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Modeling monthly and seasonal Michigan snowfall based on machine learning: A multiscale approach

Meng

Zhu

2022

Preprint

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Snowfall has important significance in water resources management and disaster prevention worldwide. Accurate prediction of both mean and extreme snowfall is challenging because of multiple controlling mechanisms at different spatial and temporal scales. By using a 65 years long in-situ snowfall observation, we evaluated seven different machine learning algorithms for predicting monthly snowfall in the Lower Peninsula of Michigan (LPM). The Bayesian Additive Regression Trees (BART) demonstrates the best fitting (R2 = 0.88) and out-of-sample prediction skills (R2 = 0.58) for the monthly mean snowfall followed by the Random Forest model. The BART also demonstrate strong predictive skills for seasonal and the extreme

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Performance of precipitation phase partitioning methods and their impact on snowpack evolution in a humid continental climate

Leroux,

Vionnet,

Thériault

2023

Hydrological Processes

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Accurate estimations of the precipitation phase at the surface are critical for hydrological and snowpack modelling in cold regions. Precipitation phase partitioning methods (PPMs) vary in their ability to estimate the precipitation phase at around 0°C and can significantly impact simulations of snowpack accumulation and melt. The goal of this study is to evaluate PPMs of varying complexity using high‐quality observations of precipitation phase and to assess the impact on snowpack simulations. We used meteorological data collected in Edmundston, New Brunswick, Canada, during the 2021 Saint John River Experiment on Cold Season Storms (SAJESS). These data were combined with manual observations of snow depth. Five PPMs commonly used in hydrological models were tested against observations from a laser‐optical disdrometer and a Micro Rain Radar. Most PPMs produced similar accuracy in estimating only rainfall and snowfall. Mixed precipitation was the most difficult phase to predict. The multi‐physics model Crocus was then used to simulate snowpack evolution and to diagnose model sensitivity to snowpack accumulation processes (PPM, snowfall density, and snowpack compaction). Sixteen snowpack accumulation periods, including nine warm accumulation events (average temperatures above −2°C) were observed during the study period. When considering all accumulation events, simulated changes in snow water equivalent (SWE) were more sensitive to the type of PPM used, whereas simulated changes in snow depth were more sensitive to uncertainties in snowfall density. Choice of PPM was the main source of model sensitivity for changes in SWE and snow depth when only considering warm events. Overall, this study highlights the impact of precipitation phase estimations on snowpack accumulation at the surface during near‐0°C conditions.

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A Composite Analysis of Snowfall Modes from Four Winter Seasons in Marquette, Michigan

Cited by 30 publications

References 81 publications

The Precipitation Imaging Package: Assessment of Microphysical and Bulk Characteristics of Snow

The Precipitation Imaging Package: Assessment of Microphysical and Bulk Characteristics of Snow

Modeling monthly and seasonal Michigan snowfall based on machine learning: A multiscale approach

Performance of precipitation phase partitioning methods and their impact on snowpack evolution in a humid continental climate

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