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Barry A. Bodhaine
,
Norman B. Wood
,
Ellsworth G. Dutton
, and
James R. Slusser

Abstract

Many different techniques are used for the calculation of Rayleigh optical depth in the atmosphere. In some cases differences among these techniques can be important, especially in the UV region of the spectrum and under clean atmospheric conditions. The authors recommend that the calculation of Rayleigh optical depth be approached by going back to the first principles of Rayleigh scattering theory rather than the variety of curve-fitting techniques currently in use. A survey of the literature was conducted in order to determine the latest values of the physical constants necessary and to review the methods available for the calculation of Rayleigh optical depth. The recommended approach requires the accurate calculation of the refractive index of air based on the latest published measurements. Calculations estimating Rayleigh optical depth should be done as accurately as possible because the inaccuracies that arise can equal or even exceed other quantities being estimated, such as aerosol optical depth, particularly in the UV region of the spectrum. All of the calculations are simple enough to be done easily in a spreadsheet.

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LAURENCE W. FOSKETT
,
NORMAN B. FOSTER
,
WILLIAM R. THICKSTUN
, and
REX C. WOOD

Abstract

A recording infrared absorption hygrometer which measures the absolute humidity in a 1-meter light path is described. Record is obtained on a remote self-balancing potentiometer. Use is made of the 1.37 µ water vapor absorption band and a 1.24 µ reference band. Isolation is by means of transmission type interference band-pass light filters. Infrared detection is by means of a lead sulfide photocell and amplifier. Isolation filters are contained on a sector wheel which is rotated to chop an infrared beam. A self-balancing null system is employed whereby the energy in the absorption band is kept equal to the energy in the reference band at all times. Balance is maintained by automatically varying the temperature of the lamp supplying the infrared energy, and the temperature of the lamp is a measure of the water vapor in the sensing path. An index of the lamp temperature is obtained by means of a monitor photocell, and meter or recorder. Included is a discussion on the calibration and field tests made on the instrument at the Weather Bureau Laboratories, Washington, D. C.

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

Abstract

Presented are four winter seasons of data from an enhanced precipitation instrument suite based at the National Weather Service (NWS) Office in Marquette (MQT), Michigan (250–500 cm of annual snow accumulation). In 2014 the site was augmented with a Micro Rain Radar (MRR) and a Precipitation Imaging Package (PIP). MRR observations are utilized to partition large-scale synoptically driven (deep) and surface-forced (shallow) snow events. Coincident PIP and NWS MQT meteorological surface observations illustrate different characteristics with respect to snow event category. Shallow snow events are often extremely shallow, with MRR-indicated precipitation heights of less than 1500 m above ground level. Large vertical reflectivity gradients indicate efficient particle growth, and increased boundary layer turbulence inferred from observations of spectral width implies increased aggregation in shallow snow events. Shallow snow events occur 2 times as often as deep events; however, both categories contribute approximately equally to estimated annual accumulation. PIP measurements reveal distinct regime-dependent snow microphysical differences, with shallow snow events having broader particle size distributions and comparatively fewer small particles and deep snow events having narrower particle size distributions and comparatively more small particles. In addition, coincident surface meteorological measurements indicate that most shallow snow events are associated with surface winds originating from the northwest (over Lake Superior), cold temperatures, and relatively high surface pressures, which are characteristics that are consistent with cold-air outbreaks. Deep snow events have meteorologically distinct conditions that are accordant with midlatitude cyclones and frontal structures, with mostly southwest surface winds, warmer temperatures approaching freezing, and lower surface pressures.

Open access
Mark S. Kulie
,
Lisa Milani
,
Norman B. Wood
,
Samantha A. Tushaus
,
Ralf Bennartz
, and
Tristan S. L’Ecuyer

Abstract

The first observationally based near-global shallow cumuliform snowfall census is undertaken using multiyear CloudSat Cloud Profiling Radar observations. CloudSat snowfall observations and snowfall rate estimates from the CloudSat 2C-Snow Water Content and Snowfall Rate (2C-SNOW-PROFILE) product are partitioned between shallow cumuliform and nimbostratus cloud structures by utilizing coincident cloud category classifications from the CloudSat 2B-Cloud Scenario Classification (2B-CLDCLASS) product. Shallow cumuliform (nimbostratus) snowfall events comprise about 36% (59%) of snowfall events in the CloudSat snowfall dataset. The remaining 5% of snowfall events are distributed between other categories. Distinct oceanic versus continental trends exist between the two major snowfall categories, as shallow cumuliform snow-producing clouds occur predominantly over the oceans. Regional differences are also noted in the partitioned dataset, with over-ocean regions near Greenland, the far North Atlantic Ocean, the Barents Sea, the western Pacific Ocean, the southern Bering Sea, and the Southern Hemispheric pan-oceanic region containing distinct shallow snowfall occurrence maxima exceeding 60%. Certain Northern Hemispheric continental regions also experience frequent shallow cumuliform snowfall events (e.g., inland Russia), as well as some mountainous regions. CloudSat-generated snowfall rates are also partitioned between the two major snowfall categories to illustrate the importance of shallow snow-producing cloud structures to the average annual snowfall. While shallow cumuliform snowfall produces over 50% of the annual estimated surface snowfall flux regionally, about 18% (82%) of global snowfall is attributed to shallow (nimbostratus) snowfall. This foundational spaceborne snowfall study will be utilized for follow-on evaluative studies with independent model, reanalysis, and ground-based observational datasets to characterize respective dataset biases and to better quantify CloudSat snowfall detection and quantitative snowfall estimate uncertainties.

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Claire E. Schirle
,
Steven J. Cooper
,
Mareile Astrid Wolff
,
Claire Pettersen
,
Norman B. Wood
,
Tristan S. L’Ecuyer
,
Trond Ilmo
, and
Knut Nygård

Abstract

The ability of in situ snowflake microphysical observations to constrain estimates of surface snowfall accumulations derived from coincident, ground-based radar observations is explored. As part of the High-Latitude Measurement of Snowfall (HiLaMS) field campaign, a Micro Rain Radar (MRR), Precipitation Imaging Package (PIP), and Multi-Angle Snow Camera (MASC) were deployed to the Haukeliseter Test Site run by the Norwegian Meteorological Institute during winter 2016/17. This measurement site lies near an elevation of 1000 m in the mountains of southern Norway and houses a double-fence automated reference (DFAR) snow gauge and a comprehensive set of meteorological observations. MASC and PIP observations provided estimates of particle size distribution (PSD), fall speed, and habit. These properties were used as input for a snowfall retrieval algorithm using coincident MRR reflectivity measurements. Retrieved surface snowfall accumulations were evaluated against DFAR observations to quantify retrieval performance as a function of meteorological conditions for the Haukeliseter site. These analyses found differences of less than 10% between DFAR- and MRR-retrieved estimates over the field season when using either PIP or MASC observations for low wind “upslope” events. Larger biases of at least 50% were found for high wind “pulsed” events likely because of sampling limitations in the in situ observations used to constrain the retrieval. However, assumptions of MRR Doppler velocity for mean particle fall speed and a temperature-based PSD parameterization reduced this difference to +16% for the pulsed events. Although promising, these results ultimately depend upon selection of a snowflake particle model that is well matched to scene environmental conditions.

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Julia A. Shates
,
Claire Pettersen
,
Tristan S. L’Ecuyer
,
Steven J. Cooper
,
Mark S. Kulie
, and
Norman B. Wood

Abstract

The prevailing snowfall regimes at two Scandinavian sites, Haukeliseter, Norway, and Kiruna, Sweden, are documented using ground-based in situ and remote sensing methods. Micro Rain Radar (MRR) profiles indicate three distinct snowfall regimes occur at both sites: shallow, deep, and intermittent snowfall. The shallow snowfall regime produces the lowest mean snowfall rates and radar echo tops are confined below 1.5 km above ground level (AGL). Shallow snowfall occurs under areas of large-scale subsidence with a moist boundary layer and dry air aloft. The atmospheric ridge coinciding with shallow snowfall is highly anomalous over Haukeliseter but is more common in Kiruna where shallow snowfall was frequently observed. The shallow snowfall particle size distributions (PSDs) are broad with lower particle concentrations than other regimes, especially small particles. Deep snowfall events exhibit MRR profiles that extend above 2 km AGL and tend to be associated with weak low pressure and high relative humidity throughout the troposphere. The PSDs in deep events are narrower with high concentrations of small particles. Increasing MRR reflectivity toward the surface suggests aggregation as a possible growth process during deep snowfall events. The heaviest mean snowfall rates are associated with intermittent events that are characterized by deep MRR profiles but have variations in intensity and height. The intermittent regime is associated with anomalous, deep low pressure along the coast of Norway and enhanced relative humidity at lower levels. The PSDs reveal high concentrations of small and large particles. The analysis reveals that there are unique characteristics of shallow, deep, and intermittent snowfall regimes that are common between the sites.

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Andrew Heymsfield
,
Aaron Bansemer
,
Norman B. Wood
,
Guosheng Liu
,
Simone Tanelli
,
Ousmane O. Sy
,
Michael Poellot
, and
Chuntao Liu

Abstract

Two methods for deriving relationships between the equivalent radar reflectivity factor Ze and the snowfall rate S at three radar wavelengths are described. The first method uses collocations of in situ aircraft (microphysical observations) and overflying aircraft (radar observations) from two field programs to develop ZeS relationships. In the second method, measurements of Ze at the top of the melting layer (ML), from radars on the Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Measurement (GPM), and CloudSat satellites, are related to the retrieved rainfall rate R at the base of the ML, assuming that the mass flux through the ML is constant. Retrievals of R are likely to be more reliable than S because far fewer assumptions are involved in the retrieval and because supporting ground-based validation data are available. The ZeS relationships developed here for the collocations and the mass-flux technique are compared with those derived from level 2 retrievals from the standard satellite products and with a number of relationships developed and reported by others. It is shown that there are substantial differences among them. The relationships developed here promise improvements in snowfall-rate retrievals from satellite-based radar measurements.

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Matthew W. Christensen
,
Ali Behrangi
,
Tristan S. L’ecuyer
,
Norman B. Wood
,
Matthew D. Lebsock
, and
Graeme L. Stephens
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Steven J. Cooper
,
Tristan S. L’Ecuyer
,
Mareile Astrid Wolff
,
Thomas Kuhn
,
Claire Pettersen
,
Norman B. Wood
,
Salomon Eliasson
,
Claire E. Schirle
,
Julia Shates
,
Franziska Hellmuth
,
Bjørg Jenny Kokkvoll Engdahl
,
Sandra Vásquez-Martín
,
Trond Ilmo
, and
Knut Nygård

Abstract

The High-Latitude Measurement of Snowfall (HiLaMS) campaign explored variability in snowfall properties and processes at meteorologically distinct field sites located in Haukeliseter, Norway, and Kiruna, Sweden, during the winters of 2016/17 and 2017/18, respectively. Campaign activities were founded upon the sensitivities of a low-cost, core instrumentation suite consisting of Micro Rain Radar, Precipitation Imaging Package, and Multi-Angle Snow Camera. These instruments are highly portable to remote field sites and, considered together, provide a unique and complementary set of snowfall observations including snowflake habit, particle size distributions, fall speeds, surface snowfall accumulations, and vertical profiles of radar moments and snow water content. These snow-specific parameters, used in combination with existing observations from the field sites such as snow gauge accumulations and ambient weather conditions, allow for advanced studies of snowfall processes. HiLaMS observations were used to 1) successfully develop a combined radar and in situ microphysical property retrieval scheme to estimate both surface snowfall accumulation and the vertical profile of snow water content, 2) identify the predominant snowfall regimes at Haukeliseter and Kiruna and characterize associated macrophysical and microphysical properties, snowfall production, and meteorological conditions, and 3) identify biases in the HARMONIE-AROME numerical weather prediction model for forecasts of snowfall accumulations and vertical profiles of snow water content for the distinct snowfall regimes observed at the mountainous Haukeliseter site. HiLaMS activities and results suggest value in the deployment of this enhanced snow observing instrumentation suite to new and diverse high-latitude locations that may be underrepresented in climate and weather process studies.

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Mark S. Kulie
,
Claire Pettersen
,
Aronne J. Merrelli
,
Timothy J. Wagner
,
Norman B. Wood
,
Michael Dutter
,
David Beachler
,
Todd Kluber
,
Robin Turner
,
Marian Mateling
,
John Lenters
,
Peter Blanken
,
Maximilian Maahn
,
Christopher Spence
,
Stefan Kneifel
,
Paul A. Kucera
,
Ali Tokay
,
Larry F. Bliven
,
David B. Wolff
, and
Walter A. Petersen

Abstract

A multisensor snowfall observational suite has been deployed at the Marquette, Michigan, National Weather Service Weather Forecast Office (KMQT) since 2014. Micro Rain Radar (MRR; profiling radar), Precipitation Imaging Package (PIP; snow particle imager), and ancillary ground-based meteorological observations illustrate the unique capabilities of these combined instruments to document radar and concomitant microphysical properties associated with northern Great Lakes snowfall regimes. Lake-effect, lake-orographic, and transition event case studies are presented that illustrate the variety of snowfall events that occur at KMQT. Case studies and multiyear analyses reveal the ubiquity of snowfall produced by shallow events. These shallow snowfall features and their distinctive microphysical fingerprints are often difficult to discern with conventional remote sensing instruments, thus highlighting the scientific and potential operational value of MRR and PIP observations. The importance of near-surface lake-orographic snowfall enhancement processes in extreme snowfall events and regime-dependent snow particle microphysical variability controlled by regime and environmental factors are also highlighted.

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