Heather D. Reeves

Classification of Precipitation Types during Transitional Winter Weather Using the RUC Model and Polarimetric Radar Retrievals

AbstractA new hydrometeor classification algorithm that combines thermodynamic output from the Rapid Update Cycle (RUC) model with polarimetric radar observations is introduced. The algorithm improves upon existing classification techniques that rely solely on polarimetric radar observations by using thermodynamic information to help to diagnose microphysical processes (such as melting or refreezing) that might occur aloft. This added information is especially important for transitional weather events for which past studies have shown radar-only techniques to be deficient. The algorithm first uses vertical profiles of wet-bulb temperature derived from the RUC model output to provide a background precipitation classification type. According to a set of empirical rules, polarimetric radar data are then used to refine precipitation-type categories when the observations are found to be inconsistent with the background classification. Using data from the polarimetric KOUN Weather Surveillance Radar-1988 Dopple...

A Dual-Polarization Radar Signature of Hydrometeor Refreezing in Winter Storms

AbstractPolarimetric radar measurements in winter storms that produce ice pellets have revealed a unique signature that is indicative of ongoing hydrometeor refreezing. This refreezing signature is observed within the low-level subfreezing air as an enhancement of differential reflectivity ZDR and specific differential phase KDP and a decrease of radar reflectivity factor at horizontal polarization ZH and copolar correlation coefficient ρhv. It is distinct from the overlying melting-layer “brightband” signature and suggests that unique microphysical processes are occurring within the layer of hydrometeor refreezing. The signature is analyzed for four ice-pellet cases in central Oklahoma as observed by two polarimetric radars. A statistical analysis is performed on the characteristics of the refreezing signature for a case of particularly long duration. Several hypotheses are presented to explain the appearance of the signature, along with a summary of the pros and cons for each. It is suggested that prefe...

Quasi-Vertical Profiles—A New Way to Look at Polarimetric Radar Data

AbstractA novel methodology is introduced for processing and presenting polarimetric data collected by weather surveillance radars. It involves azimuthal averaging of radar reflectivity Z, differential reflectivity ZDR, cross-correlation coefficient ρhv, and differential phase ΦDP at high antenna elevation, and presenting resulting quasi-vertical profiles (QVPs) in a height-versus-time format. Multiple examples of QVPs retrieved from the data collected by S-, C-, and X-band dual-polarization radars at elevations ranging from 6.4° to 28° illustrate advantages of the QVP technique. The benefits include an ability to examine the temporal evolution of microphysical processes governing precipitation production and to compare polarimetric data obtained from the scanning surveillance weather radars with observations made by vertically looking remote sensors, such as wind profilers, lidars, radiometers, cloud radars, and radars operating on spaceborne and airborne platforms. Continuous monitoring of the melting l...

Sources of Uncertainty in Precipitation-Type Forecasting

AbstractFive implicit precipitation-type algorithms are assessed using observed and model-forecast sounding data in order to measure their accuracy and to gauge the effects of model uncertainty on algorithm performance. When applied to observed soundings, all algorithms provide very reliable guidance on snow and rain (SN and RA). However, their skills for ice pellets and freezing rain (IP and FZRA) are comparatively low. Most misclassifications of IP are for FZRA and vice versa. Deeper investigation reveals that no method used in any of the algorithms to differentiate between IP and FZRA allows for clear discrimination between the two forms. The effects of model uncertainty are also considered. For SN and RA, these effects are minimal and each algorithm performs reliably. Conversely, IP and FZRA are strongly impacted. When the range of uncertainty is fully accounted for, their resulting wet-bulb temperature profiles are nearly indistinguishable, leading to very poor skill for all algorithms. Although curr...

Verifying Forecast Precipitation Type with mPING

AbstractIn winter weather, precipitation type is a pivotal characteristic because it determines the nature of most preparations that need to be made. Decisions about how to protect critical infrastructure, such as power lines and transportation systems, and optimize how best to get aid to people are all fundamentally precipitation-type dependent. However, current understanding of the microphysical processes that govern precipitation type and how they interplay with physics-based numerical forecast models is incomplete, degrading precipitation-type forecasts, but by how much? This work demonstrates the utility of crowd-sourced surface observations of precipitation type from the Meteorological Phenomena Identification Near the Ground (mPING) project in estimating the skill of numerical model precipitation-type forecasts and, as an extension, assessing the current model performance regarding precipitation type in areas that are otherwise without surface observations. In general, forecast precipitation type i...

A Polarimetric and Microphysical Investigation of the Northeast Blizzard of 8–9 February 2013

AbstractOn 8–9 February 2013, the northeastern United States experienced a historic winter weather event ranking among the top five worst blizzards in the region. Heavy snowfall and blizzard conditions occurred from northern New Jersey, inland to New York, and northward through Maine. Storm-total snow accumulations of 30–61 cm were common, with maximum accumulations up to 102 cm and snowfall rates exceeding 15 cm h−1. Dual-polarization radar measurements collected for this winter event provide valuable insights into storm microphysical processes. In this study, polarimetric data from the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Upton, New York (KOKX), are investigated alongside thermodynamic analyses from the 13-km Rapid Refresh model and surface precipitation type observations from both Meteorological Phenomena Identification Near the Ground (mPING) and the National Weather Service (NWS) Forecast Office in Upton, New York, for interpretation of polarimetric signatures. The storm exhibited uni...

The Dependence of QPF on the Choice of Microphysical Parameterization for Lake-Effect Snowstorms

AbstractSeveral lake-effect-snow forecasts are compared to assess how the choice of microphysical parameterization affects quantitative precipitation forecasting (QPF). Eight different schemes, with different numbers of moments and categories of hydrometeors, are considered. Half of the schemes are in the steady regime (so named because the precipitation rates are nearly constant with time), and the remaining experiments are in the unsteady regime, which has a high temporal variation in precipitation. The steady-regime members have broader precipitation shields and 24-h accumulations that range from 43 to 50 mm. In the unsteady regime, the precipitation shields are narrower, leading to higher accumulations (ranging from 55 to 94 mm). These differences are the result of lower terminal velocities υt in the steady regime, which allows for relofting or suspension of hydrometeors (assuming the vertical velocity is sufficiently large) and, hence, a longer in-cloud residence time and stronger downstream transpor...

Discrimination between Winter Precipitation Types Based on Spectral-Bin Microphysical Modeling

AbstractA new approach for distinguishing precipitation types at the surface, the spectral bin classifier (SBC), is presented. This algorithm diagnoses six categories of precipitation: rain (RA), snow (SN), a rain–snow mix (RASN), freezing rain (FZRA), ice pellets (PL), and a freezing rain–ice pellet mix (FZRAPL). It works by calculating the liquid-water fraction fw for a spectrum of falling hydrometeors given a prescribed temperature T and relative humidity profile. Demonstrations of the SBC output show that it provides reasonable estimates of fw of various-sized hydrometeors for the different categories of precipitation. The SBC also faithfully represents the horizontal distribution of precipitation type inasmuch as the model analyses and surface observations are consistent with each other. When applied to a collection of observed soundings associated with RA, SN, FZRA, and PL, the classifier has probabilities of detection (PODs) that range from 62.4% to 98.3%. The PODs do decrease when the effects of m...

The Dependence of QPF on the Choice of Boundary- and Surface-Layer Parameterization for a Lake-Effect Snowstorm

AbstractSix forecasts of a lake-effect-snow event off Lake Erie were conducted using the Weather Research and Forecasting Model to determine how the quantitative precipitation forecast (QPF) was affected when the boundary- and surface-layer parameterization schemes were changed. These forecasts showed strong variability, with differences in liquid-equivalent precipitation maxima in excess of 20 mm over a 6-h period. The quasi-normal scale elimination (QNSE) schemes produced the highest accumulations, and the Mellor–Yamada–Nakanishi–Niino (MYNN) schemes produced the lowest. Differences in precipitation were primarily due to different sensible heat flux FH and moisture flux FQ off the lake, with lower FH and FQ in MYNN leading to comparatively weak low-level instability and, consequently, reduced ascent and production of hydrometeors. The different FH and FQ were found to have two causes. In QNSE, the higher FH and FQ were due to the decision to use a Prandtl number PR of 0.72 (all other schemes use a PR of...

The Uncertainty of Precipitation-Type Observations and Its Effect on the Validation of Forecast Precipitation Type

AbstractHerein, an evaluation of the uncertainty of precipitation-type observations and its effect on the validation of forecast precipitation type is undertaken. The forms of uncertainty are instrument/observer bias and horizontal/temporal variability. Instrument/observer biases are assessed by comparing observations from the Automated Surface Observing Station (ASOS) and Meteorological Phenomena Identification Near the Ground (mPING) networks. Relative to the augmented ASOS, mPING observations are biased toward ice pellets (PL) and away from rain (RA). However, when mPING is used to validate precipitation-type algorithms, the probabilities of detection (PODs) for both RA and PL are decreased relative to those from the augmented ASOS. The decreased POD for RA is the result of numerous mPING reports of RA in the presence of a surface-subfreezing layer in the nearest observed sounding. Temporal and spatial variability effects are also assessed. The typical lifespan of transitional forms of precipitation is...