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Snow

Snow is a solid precipitation that forms in the atmosphere when water vapor condenses directly into ice crystals.
It is a common and important feature of many winter landscapes, often playing a crucial role in ecosystems and human activities.
The study of snow encompasses a wide range of scientific disciplines, including meteorology, climatology, hydrology, and ecology.
Researchers may investigate the physical properties of snow, its formation and depostition processes, or its impact on the environment and human communities.
Understanding the complexities of snow is essential for accurate weather forecasting, water resource management, and assessing the effects of climate change.
Pubcompare.ai, an AI-driven platform, can optimie snow research by helping scientists locate the best protocols and products from literature, preprints, and patents using AI-driven comparisons to enhance reproducibilty and accuracy.
Leverage the power of artificial intelligence to take your snow research to new hieghts.

Most cited protocols related to «Snow»

Comprehensive North American Climate Variables

The baseline dataset contains 36 primary monthly climate variables. For applications in ecology, we provide many additional biologically relevant climate variables. Many of these additional variables need to be calculated using daily climate data, which are not available in ClimateNA. We estimated these variables based on empirical or mechanistic relationships between these variables calculated using daily observations and monthly climate variables from weather stations across the entire North America. We called these variables “derived climate variables”. Some of them have been developed in previous studies for smaller regions at the annual scale [12 (link), 13 ]. In this study, we developed the derived climate variables at monthly scale, then summed up to seasonal and annual scales. The steps included: 1) calculating derived climate variables for each month (e.g., degree days) from daily weather station data; 2) building relationships (or functions) between the derived climate variables and observed (or calculated) monthly climate variables; 3) applying the functions in ClimateNA to estimate derived climate variables using monthly climate variables generated by ClimateNA.
Observed daily climate data were obtained from 4,891 weather stations in North America from the Daily Global Historical Climatology Network (http://www.ncdc.noaa.gov). The distribution of the weather stations is shown in Fig 1. Due to the wide range of variation in climate in North America, no single linear, polynomial or nonlinear function was found to adequately reflect the relationships between degree-days and monthly climate variables. We therefore applied piecewise functions, which combine a linear function and a nonlinear function, to model these relationships between various forms of monthly degree-day variables and monthly temperatures. The degree-day variables include degree-days below 0°C (DD < 0), degree-days above 5°C (DD>5), degree-days below 18°C (DD<18) and degree-days above 18°C (DD>18). The general form of the piecewise functions of all degree-days (DD_m) is:

{D D}_{m} = {\begin{array}{l} i f T_{m} > k, \frac{a}{1 + e^{- (\frac{T_{m} - T_{0}}{b})}} \\ i f T_{m} \leq k, c + β T_{m} \end{array}

where, T_m is the monthly mean temperature for the m month; k, a, b, T₀, c and β are the six parameters to be optimized.
For number of frost-free days (NFFD) and precipitation as snow (PAS), a sigmoid function was used to model the relationship between these monthly variables and monthly temperatures:

{N F F D}_{m} (o r P A S) = \frac{a}{1 + e^{- (\frac{T_{m} - T_{0}}{b})}}

where, T_m is the monthly minimum temperature for the m month; a, b and T₀ are the three parameters to be optimized.
To estimate the length of the frost-free period (FFP), the beginning the frost-free period (bFFP) and the end of the frost-free period (eFFP), we used the same polynomial functions as ClimateWNA [12 (link)] for bFFP and eFFP while the parameters were estimated based on observations from all weather stations in North America.
For extreme minimum temperature (EMT) and extreme maximum temperature (EXT) expected over a 30-year period, polynomial functions were used as follows:

E M T = a + b T m i n 01 + {c T m i n 01}^{2} + {d T m i n 12}^{2} + {e T D}^{2}

E X T = a + b T m a x 07 + {c T m a x 07}^{2} + d T m a x 08 + {e T m a x 08}^{2} + f T D

where, a, b, c, d, e and f are the parameters to be optimized; Tmin01 and Tmin12 are monthly minimum temperature for January and December; Tmax07 and Tmax08 are monthly maximum temperature for July and August, respectively; and TD is continentality (the difference between the mean temperatures of the warmest and coldest months).
Monthly average relative humidity (RH %) is calculated from the monthly maximum and minimum air temperature following [21 ]. Monthly reference evaporation (Eref_m mm) is calculated from the monthly air temperature using the Hargreaves 1985 method [12 (link), 22 (link)]. It was evaluated against the ASCE Standardized Reference Evapotranspiration (ASCE EWRI 2005). If the monthly average air temperature is less than 0°C then Eref_m = 0. The monthly climatic moisture deficit (CMD_m mm) is 0 if Eref_m< P_m, where P_m is the monthly precipitation (mm), otherwise

{C M D}_{m} = {E r e f}_{m} - P_{m}

Wang T., Hamann A., Spittlehouse D, & Carroll C. (2016). Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS ONE, 11(6), e0156720.

Publication 2016

Climate CMDM Cold Temperature Humidity Sigmoid Colon Snow

Imaging Kinesin Dynamics in C. elegans

Microscopy images were acquired using a custom-built epi-illuminated wide-field fluorescence microscope operated by a MicroManager software interface (μManager, MicroManager 1.4, www.micromanager.org; Edelstein et al. 2014 ) and built around an inverted microscope body (Eclipse Ti; Nikon, Amsterdam, Netherlands) fitted with a 60× water-immersion objective (CFI Plan Apo IR 60× water immersion, numerical aperture 1.27; Nikon). Excitation light was provided by a diode-pumped solid-state laser (Calypso 50, 491 nm; Cobolt, Solna, Sweden). Images were captured with an electron-multiplying charge-coupled device camera (iXon 897; Andor, Belfast, UK). One camera pixel corresponded to 92 nm × 92 nm in the image plane.
The C. elegans strain expressing EGFP-tagged OSM-3 kinesin motor proteins (Snow et al. 2004 (link)) was a kind gift of Jonathan M. Scholey (University of California, Davis, Davis, CA). Fluorescence imaging in living C. elegans was performed by anesthetizing adult worms (maintained at 20°C) in M9 containing 5 mM levamisole (tetramisole hydrochloride, L9756; Sigma-Aldrich, St. Louis, MO) and immobilizing them between a 2% agarose pad and a coverslip. Samples were imaged at room temperature (21°C) at 152 ms/frame.

Mangeol P., Prevo B, & Peterman E.J. (2016). KymographClear and KymographDirect: two tools for the automated quantitative analysis of molecular and cellular dynamics using kymographs. Molecular Biology of the Cell, 27(12), 1948-1957.

Publication 2016

Adult Electrons Fluorescence Helminths Kinesin Levamisole Light Light Microscopy Medical Devices Microscopy Microscopy, Fluorescence OSM-3 protein, C elegans Reading Frames Sepharose Snow Somatotype Strains Submersion Tetramisole thiacloprid

Mapping Norovirus Capsid B-Cell Epitopes

To examine the relationship between amino acid substitutions and B-cell epitopes, we constructed structural models of the capsid protein and their predicted B-cell conformational epitopes. We made two models with the strains Hu/GII/JP/2010/GII.2/Ehime43 (one of the strains with highest identity score to the 2016 strains in the past GII.2 strains) and Hu/GII/JP/2016/GII.P16-GII.2/Kawasaki129 (2016 strain). The homology modeling was based on the crystal structures of the 1IHM and 4RPB (Protein Data Bank accession numbers). After aligning these data using MAFFTash (Katoh et al., 2002 (link)), the models were constructed using MODELER v9.15 (Webb and Sali, 2014 (link)) and adjusted using Swiss PDB Viewer v4.1 (Guex and Peitsch, 1997 (link)). Then, they were confirmed by Ramachandran plot analysis using the RAMPAGE server (Lovell et al., 2003 (link)), and the residues of putative B-cell epitopes and amino acid substitutions were mapped on the predicted structure using Chimera v1.10.2 (Pettersen et al., 2004 (link)).
Next, we predicted B-cell conformational epitopes in the capsid protein in the structural models by DiscoTope2.0 (Kringelum et al., 2012 (link)), BEPro (Sweredoski and Baldi, 2008 (link)), EPCES (Liang et al., 2007 (link)), and EPSVR (Liang et al., 2010 (link)). We used these tools with cut-offs of -3.7 (DiscoTope2.0), 1.3 (BEPro), and 70 (EPCES, EPSVR). We accepted sites as B-cell conformational epitopes when they were inferred by three or more methods. Amino acid substitutions of the strain Hu/GII/JP/2016/GII.P16-GII.2/Kawasaki129 that corresponded to the strain Hu/GII/JP/2010/GII.2/Ehime43 and predicted epitopes were mapped onto the model. Moreover, to estimate whether the amino acid substitutions in the predicted epitopes were characteristic for the 2016 strains, we examined the number of strains with these amino acid substitutions among all present GII.2 strains (186 strains), compared to Snow mountain strain.

Nagasawa K., Matsushima Y., Motoya T., Mizukoshi F., Ueki Y., Sakon N., Murakami K., Shimizu T., Okabe N., Nagata N., Shirabe K., Shinomiya H., Suzuki W., Kuroda M., Sekizuka T., Suzuki Y., Ryo A., Fujita K., Oishi K., Katayama K, & Kimura H. (2018). Genetic Analysis of Human Norovirus Strains in Japan in 2016–2017. Frontiers in Microbiology, 9, 1.

Publication 2018

Amino Acid Substitution Capsid Proteins Chimera Epitopes Epitopes, B-Lymphocyte Snow Strains

Analyzing Global Night Sky Brightness

A collection of night sky brightness observations taken using handheld and vehicle-mounted SQMs was assembled using data provided by both professional researchers and citizen scientists. The data were filtered to remove instances of twilight or moonlight, as well as observations where observers reported problematic conditions (for example, snow or mist). After this process, 20,865 observations remained, with the largest individual contributions from areas near Catalonia (7400), Madrid [see (40 )] (5355), and Berlin (2371). Globe at Night [see (41 (link))] provided a total of 4114 observations, including locations from every continent, with about 20% coming from outside North America or Europe. To reduce the influence of locations with large numbers of observations (for example, 10 or more observations on a single night), we binned the data according to a 30–arcsec grid and assigned an “effective weight” of

{(n_{e} \sqrt{N_{T}})}^{- 1}

, where N_T is the total number of nights on which observations were made and n_e is the number of observations taken on the same night.
Multiple observations taken on a single night are not independent. Although they do provide some information about the change in sky radiance over the night, they provide much less information than would an equivalent number of independent observations at widely separated locations. The maximum contribution to the data set from a location with many observations on a single night is therefore set equivalent to a single independent observation. On the other hand, a location where observations are reported on many different nights is likely to include data taken under different atmospheric conditions, days of the week, times, and seasons. These data provide a better description of the typical skyglow at the location than does a single observation, but still not as much information as would an equivalent number of observations from truly independent locations. The uncertainty on the standard deviation (SD) of the mean skyglow radiance should decrease with the square of the number of independent observations, so the weight of the combined observations was increased by a proportional amount. As an example, a single location with five observations taken on four nights contributes the same equivalent weight to the data set as two observations made at two widely separated locations. This weighting procedure led to a total of 10,441 “effective observations.”
Observations were adjusted to estimate the artificial sky brightness component by subtracting the natural component computed with a model of V-band natural sky brightness (42 ). The model was customized to the location, date, and time of each observation, and predicted the combined brightness from the Milky Way, zodiacal light, and natural airglow, as measured by an SQM-L instrument aimed at the zenith. The brightness of natural airglow for a given date was predicted on the basis of its relation with solar activity, following the work of Krisciunas et al. (43 ).

Falchi F., Cinzano P., Duriscoe D., Kyba C.C., Elvidge C.D., Baugh K., Portnov B.A., Rybnikova N.A, & Furgoni R. (2016). The new world atlas of artificial night sky brightness. Science Advances, 2(6), e1600377.

Publication 2016

Eye Light Milk Skyglow Snow Solar Activity

Physical Activity Resource Assessment

The physical activity resource assessment instrument was developed over a nine month period. The instrument was pilot tested and revised numerous times to achieve the final form. Reliability tests of a 10% overlap showed good reliability (rs > .77).
After neighborhoods were selected, the physical activity resource census was developed using the above described method. Trained field assessors (three doctoral candidates in psychology) used the instrument to systematically describe each physical activity resource. All assessments were conducted during daylight hours in the spring, summer and fall seasons when the ground was free from snow or ice. Assessors were accompanied by a second student for safety reasons in the housing development neighborhoods, and procedures included safety protocols in case of imminent perceived danger. Field assessments typically took about 10 minutes to complete; however, in a few cases of larger resources (e.g., a large park) the instrument could take up to 30 minutes to complete. Data were entered and proofed by trained graduate assistants. All analyses were conducted using SPSS [29 ].

Lee R.E., Booth K.M., Reese-Smith J.Y., Regan G, & Howard H.H. (2005). The Physical Activity Resource Assessment (PARA) instrument: Evaluating features, amenities and incivilities of physical activity resources in urban neighborhoods. The International Journal of Behavioral Nutrition and Physical Activity, 2, 13.

Publication 2005

Physical Examination Physicians Safety Snow Student

Most recents protocols related to «Snow»

Characterization of Climatic Regions in Western Anatolia and Black Sea

This climatic region is characterized by frozen soil in winter and the persistence of snow cover for several months of the year, and its average temperature in the coldest months of the year is less than 3 °C below zero, and its average exceeds 10 °C in the warmest of those months. Within the study area, the following sub-regions and sub-classifications appeared within this group:
Dsa: hot, summer, humid continental climate influenced by the Mediterranean region. The coldest month has an average of less than 0 °C, the average temperature of the warmest month is above 22 °C, and at least 4 months have an average of 10 °C. Precipitation comes in the winter months, and the summer months are dry, with the driest month in the summer receiving less than 30 mm.
Dsb: a continental climate with warm, humid summers influenced by the Mediterranean region. The coldest month has an average of less than 0 °C, the average temperature of the warmest month is less than 22 °C, and at least 4 months have an average above 10 °C. The precipitation comes during the winter months, and the summer months are the driest. The driest month in the summer receives less than 30 mm of precipitation.
Dsc: subarctic climate influenced by the Mediterranean region. The coldest months average below 0 °C and 1–3 months average above 10 °C. The precipitation is coming in the winter months, while the driest months are coming during the summer. The driest month in summer receives less than 30 mm of precipitation.
Dfb: warm-summer humid continental climate. The coldest months average less than −0 °C, all months have average temperatures below 22 °C, and at least 4 months average 10 °C. There is not much difference in precipitation between seasons.
Dfc: arctic climate. The coldest months average below 0 °C and 1–3 months average above 10 °C. There is no significant difference in precipitation between seasons.
The Western Anatolia region comprises (See Fig. 1) the sub-classes such as B (dry) BSk, C (moderate) Csa, Csb, and D (continental) Dsa, Dsb, Dsc, Dfb. Around 70.06% (52,615.16 km²) from the Western Anatolia region appeared in the B dry group, 23.36% (17,545.09 km²) appeared in the D (continental) group, while only 6.57% (4936.64 km²) from the area appeared C (moderate) group. On the other hand, The Western Black Sea region (See Fig. 1) contains the same climate groups (B (dry), C (moderate), and D (continental)), but consisting of different sub-classes such as BSk, Csa, Csb, Cfa, Cfb, Dsb, Dsc, Dfb, and Dfc. Approximately, 31.56% (23,332.30 km²) from the Western Black Sea Region appeared in the B dry climate group, 26.26% (19,410.84 km²) from the area appeared in the C (moderate) climate group, and 42.17 % (31,170.42 km²) from the region appeared in the D (continental) climate group.
In addition, the internal location of the Western Anatolia region and located in the shadow area of the continentals worked as a barrier against air movement. Thus, the air contamination is assumed to be different from the Western Black Sea Region. The Western Black Sea Region is generally located in the face of the air source coming from the Black Sea.

Ulutaş K., Abujayyab S.K., Abu Amr S.S., Alkarkhi A.F, & Duman S. (2023). The effect of air quality parameters on new COVID-19 cases between two different climatic and geographical regions in Turkey. Theoretical and Applied Climatology, 152(1-2), 801-812.

Publication 2023

Air Movements Climate Cold Temperature Face Freezing Snow

Satellite-Derived Ocean Color Analysis

The Chl a concentration maps are extracted from the Ocean and Land Colour Instrument (OLCI) Level-2 full resolution Near Real Time (NRT) product, obtained by the EUMETCast broadcast system. The OLCI sensors on board ESA Sentinel-3A and B satellites launched in February 2016 respectively April 2018, and have large swath widths (~1270 km) covering large regions with high temporal resolution (approximately 1.5-day global coverage with both sensors). The OC4ME algorithm which uses a polynomial approach of a maximum band ratio algorithm of 4 reflectances at 443, 490 and 510 nm over the 560 nm was extracted directly from OLCI Level 2 products and gives the Chl a pigment concentration in [mg/m3]. The cloud free chlorophyll orbit tiles are binned and averaged on a daily base to obtain one image per day. This leads to a very good resolution, coverage in reasonable timeframe and meets the requirements of monitoring water dynamics also on smaller spatial scales.
The analysis of satellite-derived ocean color was complemented using SeaWiFs (1997–2002, https://oceandata.sci.gsfc.nasa.gov/SeaWiFS/) and MODIS (2003–2020, https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/) level 3 Chl a data processed with the default chlorophyll algorithm (chlor_a) which employs the standard OC3/OC4 (OCx) band ratio algorithm merged with the color index (CI) in 8-day and 9 km resolution.
First, the long-term analysis of the bloom presence (Fig. 2a) was performed from January 1997 to December 2020, covering the ocean-color satellite era. We defined the bloom presence when the 8-day mean satellite-derived Chl a, averaged over 4°–8° E and 67.8°–68.4° S, was larger than the 23-year long mean Chl a + 1 standard deviation over that area (i.e., 1.14 mg m⁻³, Fig. 2a).
In addition, the bloom duration in 2019 was calculated as the period between the first occurrence of the bloom during the Austral summer 2019 and its end, before the following Austral winter. The long-term median of the average Chl a concentration over the area 4°–8° E and 67.8°–68.4° S was used as a threshold to detect the bloom onset and end above which we considered a phytoplankton bloom present. Because of the presence of clouds and sea ice, satellite-derived ocean color did not detect any pixel with Chl-a data in the bloom area before January 9, 2019, and after March 14, 2019, which we, thus, defined as the bloom duration. It is possible, however, that the bloom started earlier and terminated later than our conservative estimate.
Daily sea ice concentration (1997–2020) were obtained from the NASA’s Nimbus‐7 Scanning Multichannel Microwave Radiometer (SMMR) and Defense Meteorological Satellite Program (DMSP)‐F13, ‐F17, and ‐F18 Special Sensor Microwave/Imager (SSM/I). Data with a spatial resolution of 25 km were provided by the National Snow and Ice Data Centre, University of Colorado in Boulder, CO (http://nsidc.org), with prior processing using the NASA team algorithms⁶².
Daily east-west and north-south wind components at the sea surface were obtained for the study region from the ERA-Interim global atmospheric reanalysis⁶³.

Moreau S., Hattermann T., de Steur L., Kauko H.M., Ahonen H., Ardelan M., Assmy P., Chierici M., Descamps S., Dinter T., Falkenhaug T., Fransson A., Grønningsæter E., Hallfredsson E.H., Huhn O., Lebrun A., Lowther A., Lübcker N., Monteiro P., Peeken I., Roychoudhury A., Różańska M., Ryan-Keogh T., Sanchez N., Singh A., Simonsen J.H., Steiger N., Thomalla S.J., van Tonder A., Wiktor J.M, & Steen H. (2023). Wind-driven upwelling of iron sustains dense blooms and food webs in the eastern Weddell Gyre. Nature Communications, 14, 1303.

Publication 2023

Chlorophyll Microtubule-Associated Proteins Microwaves Orbit Phytoplankton Pigmentation Satellite Viruses Sea Ice Cover Snow Temporal Lobe Wind

Institutional Review and Participant Consent

The study was reviewed and approved by John Snow Inc. Institutional Review Board (#20–34), the Ghana Health Service Ethics Review Committee (GHS-ERC013/10/20), and the Nepal Health Research Council (177/2021 P). All participants gave written (or thumb print) consent before interviews and observations.

Singh A., Torres K.A., Maharjan N., Shrestha J., Agbozo F., Abubakari A., Abdul-Rahman L, & Mukuria-Ashe A. (2023). Learning from health system actor and caregiver experiences in Ghana and Nepal to strengthen growth monitoring and promotion. PLOS ONE, 18(3), e0282807.

Publication 2023

Ethics Committees, Research Snow Thumb

Sampling Method for Reaching Hidden MSM Community

Purposive sampling to identify the eleven data collection points frequented by the MSM community, such as the safe spaces/drop-in centres, hotels and massage parlors was used. Based on previous work in Kenya and elsewhere that indicates high mobility of this population, and given that hidden populations have networks within which they operate, data collections points which were likely to capture the same respondents or respondents with similar characteristics were not included in the study [34 (link)–40 (link)]. Snow balling or chain-referral sampling was then used to reach the interviewees [36 (link),37 (link),39 (link)]. This method, despite its inherent flaws, was the most optimal to use to identify and reach this hidden population. The method has been previously used to reach similar populations elsewhere [41 –43 ]. The study reached 391 MSM who responded to the self-administered questionnaires out of which 345 completed the information. The missing data was handled through the listwise approach that omit those cases with the missing data and analyze the remaining data. This approach is known as the complete case or listwise deletion. Listwise is a default option for analysis in most statistical software packages including the Statistical Package for the Social sciences (SPSS), we used for the analysis. However, if the assumption of missing is completely at random (MCAR) is satisfied, a listwise deletion is known to produce unbiased estimates and conservative results [44 ]. The missing data for this study was random, hence produced unbiased estimates as demonstrated in the probability plot of normality tests graphs.

Ndungu K., Gichangi P, & Temmerman M. (2023). Evaluation of factors associated with HIV self-testing Acceptability and Uptake among the MSM community in Nairobi, Kenya: A cross sectional study. PLOS ONE, 18(3), e0280540.

Publication 2023

Deletion Mutation Massage Population Group Range of Motion, Articular Snow

Narrative Case Study of Student Entrepreneur

The researcher adopted a purposive sampling procedure, using the snow-ball approach to select the participant for the study. Through the ALC DE Student Representative Council (SRC) president, the researcher located a student possessing the characteristics required for the study. The researcher intentions was to capture the detailed inner world of the participant experiences in pursuing a higher education programme and operating a printing shop, and learning about the enterprise through NFE programmes. As a qualitative researcher, and conducting a narrative case study research type, I knew the kind of information I was looking for, and found the participant most appropriate for the study. However, this narrative study is guided by the theory of human capital lens, and undergirded by the seven research questions stated above.

, & Biney I.K. (2023). Adult education and entrepreneurship: getting young adults involved. Journal of Innovation and Entrepreneurship, 12(1), 13.

Publication 2023

Homo sapiens Lens, Crystalline Programmed Learning Snow Student

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More about "Snow"

Explore the fascinating world of snow, a captivating natural phenomenon that captures the imagination of scientists and enthusiasts alike.
Snow, a solid form of precipitation, is the result of water vapor condensing directly into ice crystals in the atmosphere.
This intricate process is studied by experts in a diverse range of disciplines, including meteorology, climatology, hydrology, and ecology.
Beyond its sheer beauty, snow plays a crucial role in many ecosystems, influencing water resources, wildlife habitats, and human activities.
Understanding the complexities of snow is essential for accurate weather forecasting, effective water management, and assessing the impacts of climate change.
Researchers investigating snow may delve into its physical properties, such as its crystalline structure, albedo, and insulating abilities.
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