Julie M. Thériault

How Well Are We Measuring Snow: The NOAA/FAA/NCAR Winter Precipitation Test Bed

This paper presents recent efforts to understand the relative accuracies of different instrumentation and gauges with various windshield configurations to measure snowfall. Results from the National Center for Atmospheric Research (NCAR) Marshall Field Site will be highlighted. This site hosts a test bed to assess various solid precipitation measurement techniques and is a joint collaboration between the National Oceanic and Atmospheric Administration (NOAA), NCAR, the National Weather Service (NWS), and Federal Aviation Administration (FAA). The collaboration involves testing new gauges and other solid precipitation measurement techniques in comparison with World Meteorological Organization (WMO) reference snowfall measurements. This assessment is critical for any ongoing studies and applications, such as climate monitoring and aircraft deicing, that rely on accurate and consistent precipitation measurements.

Dependence of Snow Gauge Collection Efficiency on Snowflake Characteristics

Accurate snowfall measurements are critical for a wide variety of research fields, including snowpack monitoring, climate variability, and hydrological applications. It has been recognized that systematic errors in snowfall measurements are often observed as a result of the gauge geometry and the weather conditions. The goal of this study is to understand better the scatter in the snowfall precipitation rate measured by a gauge. To address this issue, field observations and numerical simulations were carried out. First, a theoretical study using finite-element modeling was used to simulate the flow around the gauge. The snowflake trajectories were investigated using a Lagrangian model, and the derived flow field was used to compute a theoretical collection efficiency for different types of snowflakes. Second, field observations were undertaken to determine how different types, shapes, and sizes of snowflakes are collected inside a Geonor, Inc., precipitation gauge. The results show that the collection efficiency is influenced by the type of snowflakes as well as by their size distribution. Different types of snowflakes, which fall at different terminal velocities, interact differently with the airflow around the gauge. Fast-falling snowflakes are more efficiently collected by the gauge than slow-falling ones. The correction factor used to correct the data for the wind speed is improved by adding a parameter for each type of snowflake. The results show that accurate measure of snow depends on the wind speed as well as the type of snowflake observed during a snowstorm.

On the Dependence of Winter Precipitation Types on Temperature, Precipitation Rate, and Associated Features

The phase of precipitation formed within the atmosphere is highly dependent on the vertical temperature profile through which it falls. In particular, several precipitation types can form in an environment with a melting layer aloft and a refreezing layer below. These precipitation types include freezing rain, ice pellets, wet snow, and slush. To examine the formation of such precipitation, a bulk microphysics scheme was used to compare the characteristics of the hydrometeors produced by the model and observed by a research aircraft flight during the 1998 ice storm near Montreal, Canada. The model reproduced several of the observed key precipitation characteristics. Sensitivity tests on the precipitation types formed during the ice storm were also performed. These tests utilized temperature profiles produced by the North American Regional Reanalysis. The results show that small variations (±0.5°C) in the temperature profiles as well as in the precipitation rate can have major impacts on the types of precipitation formed at the surface. These results impose strong requirements on the accuracy needed by prediction models.

A Parameterization of the Microphysical Processes Forming Many Types of Winter Precipitation

Several types of precipitation, such as freezing rain, ice pellets, and wet snow, are commonly observed during winter storms. The objective of this study is to better understand the formation of these winter precipitation types. To address this issue, detailed melting and refreezing of precipitation was added onto an existing bulk microphysics scheme. These modifications allow the formation of mixed-phase particles and these particles in turn lead to, or affect, the formation of many of the other types of precipitation. The precipitation type characteristics, such as the mass content, liquid fraction, and threshold diameters formed during a storm over St John’s, Newfoundland, Canada, are studied and compared with observations. Many of these features were reproduced by the model. Sensitivity experiments with the model were carried out to examine the dependence of precipitation characteristics in this event on thresholds of particle evolution in the new parameterization.

On the Characteristics of and Processes Producing Winter Precipitation Types near 0°C

AbstractThis article examines the types of winter precipitation that occur near 0°C, specifically rain, freezing rain, freezing drizzle, ice pellets, snow pellets, and wet snow. It follows from a call by M. Ralph et al. for more attention to be paid to this precipitation since it represents one of the most serious wintertime quantitative precipitation forecasting (QPF) issues. The formation of the many precipitation types involves ice-phase and/or liquid-phase processes, and thresholds in the degree of melting and/or freezing often dictate the types occurring at the surface. Some types can occur simultaneously so that, for example, ensuing collisions between supercooled raindrops and ice pellets that form ice pellet aggregates can lead to substantial reductions in the occurrence of freezing rain at the surface, and ice crystal multiplication processes can lead to locally produced ice crystals in the subfreezing layer below inversions. Highly variable fall velocities within the background temperature and w...

Microphysical Processes Within Winter Orographic Cloud and Precipitation Systems

One of the most challenging aspects of understanding and forecasting weather in the mountainous environment is the phenomenon of orographic precipitation. While it is the dynamics of orographic flow that yield upward motion and condensation, it is the field of cloud microphysics that addresses the question “How does the liquid or solid water mass thus formed produce particles that fall to the surface?” This chapter addresses this question by reviewing general principles of cloud and precipitation microphysics, as well as aspects of this discipline that are specific to winter-time midlatitude orographic storms. It is intended to serve as an overview of the subject, to provide a sound scientific understanding to the forecaster who faces the challenge of predicting weather in mountainous locations. Topics covered include precipitation particle growth processes, interactions between orographic flows and cloud physics, the “seeder-feeder” mechanism, orographic storm life cycles, physical processes within the solid–liquid transition region, depth and density of accumulating snow, and the representation of cloud physics in numerical models used to forecast winter orographic storms.

An Improved Trajectory Model to Evaluate the Collection Performance of Snow Gauges

Recent studies have used numerical models to estimate the collection efficiency of solid precipitation gauges when exposed to the wind in both shielded and unshielded configurations. The models used computational fluid dynamics (CFD) simulations of the airflow pattern generated by the aerodynamic response to the gauge‐shield geometry. These are used as initial conditions to perform Lagrangian tracking of solid precipitation particles. Validation of the results against field observations yielded similarities in the overall behavior, but the model output only approximately reproduced the dependence of the experimental collection efficiency on wind speed. This paper presents an improved snowflake trajectory modeling scheme due to the inclusion of a dynamically determined drag coefficient. The drag coefficient was estimated using the local Reynolds number as derived from CFD simulations within a time-independent Reynoldsaveraged Navier‐Stokes approach. The proposed dynamic model greatly improves the consistency of results with the field observations recently obtained at the Marshall Field winter precipitation test bed in Boulder, Colorado.

The Collection Efficiency of Shielded and Unshielded Precipitation Gauges. Part II: Modeling Particle Trajectories

AbstractThe use of windshields to reduce the impact of wind on snow measurements is common. This paper investigates the catching performance of shielded and unshielded gauges using numerical simulations. In Part II, the role of the windshield and gauge aerodynamics, as well as the varying flow field due to the turbulence generated by the shield–gauge configuration, in reducing the catch efficiency is investigated. This builds on the computational fluid dynamics results obtained in Part I, where the airflow patterns in the proximity of an unshielded and single Alter shielded Geonor T-200B gauge are obtained using both time-independent [Reynolds-averaged Navier–Stokes (RANS)] and time-dependent [large-eddy simulation (LES)] approaches. A Lagrangian trajectory model is used to track different types of snowflakes (wet and dry snow) and to assess the variation of the resulting gauge catching performance with the wind speed. The collection efficiency obtained with the LES approach is generally lower than the on...

The Collection Efficiency of Shielded and Unshielded Precipitation Gauges. Part I: CFD Airflow Modeling

AbstractThe aerodynamic response of snow gauges when exposed to the wind is responsible for a significant reduction of their collection performance. The modifications induced by the gauge and the windshield onto the space–time patterns of the undisturbed airflow deviate the snowflake trajectories. In Part I, the disturbed air velocity field in the vicinity of shielded and unshielded gauge configurations is investigated. In Part II, the airflow is the basis for a particle tracking model of snowflake trajectories to estimate the collection efficiency. A Geonor T-200B gauge inside a single Alter shield is simulated for wind speeds varying from 1 to 8 m s−1. Both time-averaged and time-dependent computational fluid dynamics simulations are performed, based on Reynolds-averaged Navier–Stokes (RANS) and large-eddy simulation (LES) models, respectively. A shear stress tensor k–Ω model (where k is the turbulent kinetic energy and Ω is the turbulent specific dissipation rate) is used for the RANS formulation and s...

Impact of Wind Direction, Wind Speed, and Particle Characteristics on the Collection Efficiency of the Double Fence Intercomparison Reference

The accurate measurement of snowfall is important in various fields of study such as climate variability, transportation, and water resources. A major concern is that snowfall measurements are difficult and can result in significant errors. For example, collection efficiency of most gauge–shield configurations generally decreases with increasing wind speed. In addition, much scatter is observed for a given wind speed, which is thought to be caused by the type of snowflake. Furthermore, the collection efficiency depends strongly on the reference used to correct the data, which is often the Double Fence Intercomparison Reference (DFIR) recommended by the World Meteorological Organization. The goal of this study is to assess the impact of weather conditions on the collection efficiency of the DFIR. Note that the DFIR is defined as a manual gauge placed in a double fence. In this study, however, only the double fence is being investigated while still being called DFIR. To address this issue, a detailed analysis of the flow field in the vicinity of the DFIR is conducted using computational fluid dynamics. Particle trajectories are obtained to compute the collection efficiency associated with different precipitation types for varying wind speed. The results show that the precipitation reaching the center of the DFIR can exceed 100% of the actual precipitation, and it depends on the snowflake type, wind speed, and direction. Overall, this study contributes to a better understanding of the sources of uncertainty associated with the use of the DFIR as a reference gauge to measure snowfall.