scholarly journals 31 years of hourly spatially distributed air temperature, humidity, and precipitation amount and phase from Reynolds Critical Zone Observatory

2017 ◽  
pp. 1-11 ◽  
Author(s):  
Patrick R. Kormos ◽  
Danny G. Marks ◽  
Mark S. Seyfried ◽  
Scott C. Havens ◽  
Andrew Hedrick ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Precipitation Amount ◽  
Critical Zone ◽  
Dew Point ◽  
Elevation Gradients ◽  
Wide Range ◽  
Critical Zone Observatory ◽  
Reynolds Creek ◽  
Spatially Distributed

Thirty one years of spatially distributed air temperature, relative humidity, dew point temperature, precipitation amount, and precipitation phase data are presented for the Reynolds Creek Experimental Watershed, which is part of the Critical Zone Observatory network. The air temperature, relative humidity, and precipitation amount data are spatially distributed over a 10&amp;thinsp;m Lidar-derived digital elevation model at an hourly time step using a detrended kriging algorithm. This dataset covers a wide range of weather extremes in a mesoscale basin (237&amp;thinsp;km<sup>2</sup>) that encompasses the rain-snow transition zone and should find widespread application in earth science modeling communities. Spatial data allows for a more holistic analysis of basin means and elevation gradients, compared to weather station data measured at specific locations. Files are stored in the NetCDF file format, which allows for easy spatiotemporal averaging and/or subsetting. Data are made publicly available through an OPeNDAP-enabled THREDDS server hosted by Boise State University Libraries in support of the Reynolds Creek Critical Zone Observatory (<a href="https://doi.org/doi:10.18122/B2B59V" target ="_blank">https://doi.org/10.18122/B2B59V</a>).

scholarly journals 31 years of hourly spatially distributed air temperature, humidity, and precipitation amount and phase from Reynolds Critical Zone Observatory

2018 ◽  
Vol 10 (2) ◽  
pp. 1197-1205 ◽  
Author(s):  
Patrick R. Kormos ◽  
Danny G. Marks ◽  
Mark S. Seyfried ◽  
Scott C. Havens ◽  
Andrew Hedrick ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Precipitation Amount ◽  
Critical Zone ◽  
Dew Point ◽  
Elevation Gradients ◽  
Wide Range ◽  
Critical Zone Observatory ◽  
Reynolds Creek ◽  
Spatially Distributed

Abstract. Thirty-one years of spatially distributed air temperature, relative humidity, dew point temperature, precipitation amount, and precipitation phase data are presented for the Reynolds Creek Experimental Watershed, which is part of the Critical Zone Observatory network. The air temperature, relative humidity, and precipitation amount data are spatially distributed over a 10 m lidar-derived digital elevation model at an hourly time step using a detrended kriging algorithm. This 21 TB dataset covers a wide range of weather extremes in a mesoscale basin (238 km2) that encompasses the rain–snow transition zone and should find widespread application in earth science modeling communities. Spatial data allow for a more holistic analysis of basin means and elevation gradients, compared to weather station data measured at specific locations. Files are stored in the NetCDF file format, which allows for easy spatiotemporal averaging and/or subsetting. Data are made publicly available through an OPeNDAP-enabled THREDDS server hosted by Boise State University Libraries in support of the Reynolds Creek Critical Zone Observatory (https://doi.org/10.18122/B2B59V).

scholarly journals Dynamic process connectivity explains ecohydrologic responses to rainfall pulses and drought

2018 ◽  
Vol 115 (37) ◽  
pp. E8604-E8613 ◽  
Author(s):  
Allison E. Goodwell ◽  
Praveen Kumar ◽  
Aaron W. Fellows ◽  
Gerald N. Flerchinger
Keyword(s):  
Information Flow ◽  
Critical Zone ◽  
Dry Season ◽  
Multiple Sources ◽  
Elevation Gradients ◽  
Soil Systems ◽  
Weather Events ◽  
Critical Zone Observatory ◽  
Reynolds Creek ◽  
Time Dependencies

Ecohydrologic fluxes within atmosphere, vegetation, and soil systems exhibit a joint variability that arises from forcing and feedback interactions. These interactions cause fluctuations to propagate between variables at many time scales. In an ecosystem, this connectivity dictates responses to climate change, land-cover change, and weather events and must be characterized to understand resilience and sensitivity. We use an information theory-based approach to quantify connectivity in the form of information flow associated with the propagation of fluctuations between variables. We apply this approach to study ecosystems that experience changes in dry-season moisture availability due to rainfall and drought conditions. We use data from two transects with flux towers located along elevation gradients and quantify redundant, synergistic, and unique flow of information between lagged sources and targets to characterize joint asynchronous time dependencies. At the Reynolds Creek Critical Zone Observatory in Idaho, a dry-season rainfall pulse leads to increased connectivity from soil and atmospheric variables to heat and carbon fluxes. At the Southern Sierra Critical Zone Observatory in California, separate sets of dominant drivers characterize two sites at which fluxes exhibit different drought responses. For both cases, our information flow-based connectivity characterizes dominant drivers and joint variability before, during, and after disturbances. This approach to gauge the responsiveness of ecosystem fluxes under multiple sources of variability furthers our understanding of complex ecohydrologic systems.

scholarly journals Solar Radiation, Air Temperature, Relative Humidity, and Dew Point Study: Damak, Jhapa, Nepal

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Arun Kumar Shrestha ◽  
Arati Thapa ◽  
Hima Gautam
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Air Temperature ◽  
Gaussian Function ◽  
Great Influence ◽  
Dew Point ◽  
Upward Trend ◽  
Summer Maximum ◽  
Online Calculator ◽  
Climatic Phenomenon

Monitoring and prediction of the climatic phenomenon are of keen interest in recent years because it has great influence in the lives of people and their environments. This paper is aimed at reporting the variation of daily and monthly solar radiation, air temperature, relative humidity (RH), and dew point over the year of 2013 based on the data obtained from the weather station situated in Damak, Nepal. The result shows that on a clear day, the variation of solar radiation and RH follows the Gaussian function in which the first one has an upward trend and the second one has a downward trend. However, the change in air temperature satisfies the sine function. The dew point temperature shows somewhat complex behavior. Monthly variation of solar radiation, air temperature, and dew point shows a similar pattern, lower at winter and higher in summer. Maximum solar radiation (331 Wm-2) was observed in May and minimum (170 Wm-2) in December. Air temperature and dew point had the highest value from June to September nearly at 29°C and 25°C, respectively. The lowest value of the relative humidity (55.4%) in April indicates the driest month of the year. Dew point was also calculated from the actual readings of air temperature and relative humidity using the online calculator, and the calculated value showed the exact linear relationship with the observed value. The diurnal and nocturnal temperature of each month showed that temperature difference was relatively lower (less than 10°C) at summer rather than in winter.

scholarly journals Quantifying the impact of heat on human physical work capacity; part II: the observed interaction of air velocity with temperature, humidity, sweat rate, and clothing is not captured by most heat stress indices

2021 ◽  
Author(s):  
Josh Foster ◽  
James W. Smallcombe ◽  
Simon Hodder ◽  
Ollie Jay ◽  
Andreas D. Flouris ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Empirical Study ◽  
Air Temperature ◽  
Physical Work ◽  
Climate Index ◽  
Physical Work Capacity ◽  
Air Movement ◽  
Wide Range ◽  
Temperature And Humidity ◽  
The Impact

Abstract Increasing air movement can alleviate or exacerbate occupational heat strain, but the impact is not well defined across a wide range of hot environments, with different clothing levels. Therefore, we combined a large empirical study with a physical model of human heat transfer to determine the climates where increased air movement (with electric fans) provides effective body cooling. The model allowed us to generate practical advice using a high-resolution matrix of temperature and humidity. The empirical study involved a total of 300 1-h work trials in a variety of environments (35, 40, 45, and 50 °C, with 20 up to 80% relative humidity) with and without simulated wind (3.5 vs 0.2 m∙s−1), and wearing either minimal clothing or a full body work coverall. Our data provides compelling evidence that the impact of fans is strongly determined by air temperature and humidity. When air temperature is ≥ 35 °C, fans are ineffective and potentially harmful when relative humidity is below 50%. Our simulated data also show the climates where high wind/fans are beneficial or harmful, considering heat acclimation, age, and wind speed. Using unified weather indices, the impact of air movement is well captured by the universal thermal climate index, but not by wet-bulb globe temperature and aspirated wet-bulb temperature. Overall, the data from this study can inform new guidance for major public and occupational health agencies, potentially maintaining health and productivity in a warming climate.

scholarly journals Study of winter fog over Indian subcontinent : Climatological perspectives

MAUSAM ◽  
2021 ◽  
Vol 65 (1) ◽  
pp. 19-28
Author(s):  
G.K. SAWAISARJE ◽  
P Khare ◽  
C.Y. SHIRKE ◽  
S. DEEPAKUMAR ◽  
N.M. NARKHEDE
Keyword(s):  
Relative Humidity ◽  
Spatial Variability ◽  
Air Temperature ◽  
West Bengal ◽  
Indian Subcontinent ◽  
Winter Season ◽  
Dew Point ◽  
North India ◽  
Gangetic Plains ◽  
Long Period

Fog is localized phenomenon where horizontal visibility is reduced to less than 1000 m due to suspension of very small water droplets in the air. In fact Fog is cloud at surface level. This phenomenon matters to us in our daily activities due to its effect on life, public health, road safety, and economic prosperity.  Its knowledge is essential to meet critical societal needs. In India, radiation and advection fog are most common which occur mostly over north India in winter. This study presents results of spatial variability of average number of days having visibility less than 1000 m at 0300 UTC during winter season over Indian subcontinent (0° N-35° N and 60° E-100° E) using synoptic hour daily surface data. Analysis of Fog events based on half hourly METAR observations for winter months             (December 2010-February 2011) at selected ICAO stations of India is also presented.    The monthly spatial variability of average number of days with fog having visibility less than 1000 m indicates presence of fog over Indo-Gangetic plains on an average of 7 to 10 days during December and more than 8 days during January. South Gangetic West Bengal is susceptible to fog during February. Seasonal variation of fog shows that there is prominence of fog over Indo-Gangetic plains on an average of more than 6.5 days. Significant incidences of foggy days occur over South Interior Karnataka and Coastal Karnataka has incidence of foggy days on an average of 6.5 days. North India is susceptible to dense fog on average of 2.5 days during the season while Gangetic West Bengal, West Rajasthan and adjoining parts of east Rajasthan and East Uttar Pradesh have occurrence of fog on an average of 3.5 days. Thick fog occurs on an average of 3 days over northern India during the winter season. Analysis of time series of air temperature, dew point temperature, dew point depression, visibility, zonal and meridional components of wind and its magnitude at selected ICAO stations indicate that visibility reduces to below 1000 m while light winds are southeasterly at Ahmedabad, northerly to northwesterly at Amritsar, northerly to northeasterly at New Delhi, westerly to southwesterly at Kolkata, Guwahati and dew point depression is below 3 °C during such conditions. Long period 1971-2010 analysis of visibility conditions less than 1000 m over Indian subcontinent shows Indo-Gangetic plains region to have average wind speed to be 0.6 m/s, air temperature in the range 9 °C to 15 °C during December-February at 0300 UTC. Long period 1971- 2010 analysis of relative humidity shows Indo-Genetic plains region to have relative humidity in the range 72% to 84% at 0300 UTC during December-February.

The water intake of British Friesian cows on rations containing various forages

1975 ◽  
Vol 20 (2) ◽  
pp. 181-189 ◽  
Author(s):  
M. E. Castle ◽  
Thesca P. Thomas
Keyword(s):  
Drinking Water ◽  
Relative Humidity ◽  
Air Temperature ◽  
Daily Intake ◽  
Average Intake ◽  
Wide Range ◽  
The Mean ◽  
Daily Milk Yield ◽  
Daily Milk ◽  
Friesian Cows

SummaryThe intake of drinking water of lactating British Friesian cows in 14 herds with a total of 840 animals was measured monthly from November to April inclusive. The intake of food, the air temperature and relative humidity were also recorded. The herds which mainly contained autumn-calved cows were managed under controlled commercial conditions.The average daily intake of drinking water was 49·9 (range 20·1 to 87·1) kg/cow for animals yielding an average of 16·8 kg milk/day in an environment with a mean temperature of 8·2°C and a relative humidity of 84·8%. The amount of water drunk daily was positively and significantly related to the mean daily milk yield and the drymatter content of the ration, but not significantly related to either the mean air temperature or the relative humidity. The average intake of water was 3·70 kg per kg of dry matter consumed after subtracting the amount of water in the milk. On average, 40·0% of the total daily intake of water was drunk between 15·00 and 21·00 hr.Behaviour studies of 24 hr duration on five herds showed that drinking time ranged from 2·0 to 7·8 min/cow per day, and the rate of drinking varied from 4·5 to 14·9 kg/min.Recommendations are made for the provision of drinking water for dairy cows being offered a wide range of forages under modern conditions of housing.

scholarly journals Electrical Resistivity of Conductive Leather and Influence of Air Temperature and Humidity

Tekstilec ◽  
2021 ◽  
Vol 64 (4) ◽  
pp. 298-304
Author(s):  
Ilda Kazani ◽  
◽  
Majlinda Hylli ◽  
Pellumb Berberi ◽  
◽  
...  
Keyword(s):  
Electrical Resistivity ◽  
Electrical Properties ◽  
Relative Humidity ◽  
Air Temperature ◽  
Cold Climate ◽  
High Tech ◽  
Air Concentration ◽  
Primary Input ◽  
Smart Products ◽  
Wide Range

Leather is a material that has been used in different applications for centuries. Today, living in the era of high-tech¬nology, we are surrounded by smart products. For this reason, traditional products must be changed or im¬proved in order to support and make us more comfortable while using them. For instance, the touch screen display in electronics products is a smart phone’s or a tablet computer’s primary input device. Still, traditional leather will not function properly in a cold climate or other specific conditions. To make it conductive in such conditions, the double in-situ polymerization of the pyrrole coating method was used. The aim of this study was to observe the electrical properties of conductive leather. At the same time, it stands up to a wide range of different air temperatures, and relative and absolute humidity. These properties are essential because de¬signers and textile engineers should be familiar with them when they decide to use materials in different smart products. Electricity conductivity tests were carried out in year-round temperatures from 7.5 °C to 28.1 °C, with a relative humidity from 18% to 77% and a vapor air concentration from 2.77 g/kg to 12.46 g/kg. The so-called “multiple-step method” was used to test leather’s electrical resistivity for the first time. The method considers a material’s compressional properties and provides an indicator inherent for a material’s electrical properties, regardless of the mass and shape of samples. The results showed a strong dependence between water vapor air concentration and electrical resistivity, described using the formula ρ = 1.3103 H−1.04 Ωm, with a correlation coefficient of 0.87. There was no relation between relative humidity and electrical resistivity, and resistivity and air temperature. Also, the results confirmed again that changes in the shape of the sample used during tests did not influence the measurement’s results, but supported the appropriateness of the measuring method.

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