scholarly journals Fine-Resolved, Near-Coastal Spatiotemporal Variation of Temperature in Response to Insolation

2013 ◽  
Vol 52 (5) ◽  
pp. 1208-1220 ◽  
Author(s):  
Nikki Vercauteren ◽  
Georgia Destouni ◽  
Carl Johan Dahlberg ◽  
Kristoffer Hylander
Keyword(s):  
Model Development ◽  
Temperature Response ◽  
Lag Time ◽  
Spatiotemporal Variation ◽  
Eastern Coast ◽  
Atmospheric Conditions ◽  
Near Surface ◽  
Average Temperature ◽  
Monthly Average ◽  
Diurnal Temperature Variation

AbstractThis study uses GIS-based modeling of incoming solar radiation to quantify fine-resolved spatiotemporal responses of monthly average temperature, and diurnal temperature variation, at different times and locations within a field study area located on the eastern coast of Sweden. Near-surface temperatures are measured by a network of temperature sensors during the spring and summer of 2011 and then used as the basis for model development and testing. The modeling of finescale spatiotemporal variation considers topography, distance from the sea, and observed variations in atmospheric conditions, accounting for site latitude, elevation, surface orientation, daily and seasonal shifts in sun angle, and effects of shadows from surrounding topography. The authors find a lag time between insolation and subsequent temperature response that follows an exponential decay from coastal to inland locations. They further develop a linear regression model that accounts for this lag time in quantifying fine-resolved spatiotemporal temperature evolution. This model applies in the considered growing season for spatial distribution across the studied near-coastal landscape.

scholarly journals Seasonal Influence of Insolation on Fine-Resolved Air Temperature Variation and Snowmelt

2014 ◽  
Vol 53 (2) ◽  
pp. 323-332 ◽  
Author(s):  
Nikki Vercauteren ◽  
Steve W. Lyon ◽  
Georgia Destouni
Keyword(s):  
Solar Radiation ◽  
Snow Cover ◽  
Eastern Coast ◽  
Seasonal Influence ◽  
Near Surface ◽  
Average Temperature ◽  
Monthly Average ◽  
Air Temperatures ◽  
Low Performance ◽  
Snow Cover Duration

AbstractThis study uses GIS-based modeling of incoming solar radiation to quantify fine-resolved spatiotemporal responses of year-round monthly average temperature within a field study area located on the eastern coast of Sweden. A network of temperature sensors measures surface and near-surface air temperatures during a year from June 2011 to June 2012. Strong relationships between solar radiation and temperature exhibited during the growing season (supporting previous work) break down in snow cover and snowmelt periods. Surface temperature measurements are here used to estimate snow cover duration, relating the timing of snowmelt to low performance of an existing linear model developed for the investigated site. This study demonstrates that linearity between insolation and temperature 1) may only be valid for solar radiation levels above a certain threshold and 2) is affected by the consumption of incoming radiation during snowmelt.

Southern Hemisphere Continental Temperature Responses to Major Volcanic Eruptions Since 1883 in CMIP5 Models

2021 ◽  
Author(s):  
Pamela J Harvey ◽  
Stefan W Grab
Keyword(s):  
Southern Hemisphere ◽  
Temperature Response ◽  
Volcanic Eruptions ◽  
Lag Time ◽  
Cmip5 Models ◽  
Seasonal Temperature ◽  
Near Surface ◽  
Santa Maria ◽  
Temperature Responses ◽  
Austral Autumn

Abstract Although global and Northern Hemisphere (NH) temperature responses to volcanic forcing have been extensively investigated, knowledge of such responses over Southern Hemisphere (SH) continental regions is still limited. Here we use an ensemble of CMIP5 models to explore SH temperature responses to four major volcanic eruptions: Krakatau (1883), Santa Maria (1902), Agung (1963) and Pinatubo (1991). Focus is on near-surface temperature responses over southern continental landmasses including southern South America (SSA), southern Africa (SAF) and Australia and their seasonal differences. Findings indicate that for all continents, temperature responses were strongest and lasted longest following the Krakatau eruption. Responses in Australia had the shortest lag time, strongest maximum seasonal response, as well as the most significant monthly anomalies. In contrast, SSA records the longest lag time, weakest maximum seasonal temperature response, and lowest number of monthly negative anomalies following these eruptions. In most cases, the strongest single-season response occurred in austral autumn or winter, and the weakest in summer or spring. We tentatively propose that cooler temperature responses are likely caused, at least in part, by the intensification of the westerlies and associated mid-latitude cyclones and anti-cyclones.

scholarly journals Spatial, Temporal, and Spatiotemporal Variation of Malaria Incidence and Risk Factors in West Gojjam Zone From 1 July 2013- 30 June 2018, Northwest Ethiopia, 2019.

2020 ◽  
Author(s):  
Eniyew Tegegne Bayih ◽  
Kassahun Alemu Gelaye ◽  
Awrajaw Dessie Zeleke ◽  
Alebachew Shimelash Damtew ◽  
Biachew Asmare Asmare ◽  
...  
Keyword(s):  
Hot Spots ◽  
Malaria Incidence ◽  
Space Time ◽  
Spatiotemporal Variation ◽  
Average Temperature ◽  
Monthly Average ◽  
Average Rainfall ◽  
West Gojjam ◽  
Average Relative Humidity ◽  
West Gojjam Zone

Abstract Background: Malaria is a life-threatening acute febrile illness which is affecting the lives of millions globally. Its distribution is characterized by spatial, temporal, and spatiotemporal heterogeneity making detection of the space-time distribution and mapping high-risk areas useful to effectively targeting hot spots of malaria for intervention. Methods : Time series cross sectional study was conducted using data obtained from weekly malaria surveillance reports stored in the Amhara Public Health Institute from 1 July 2013-30 June 2018. Climatic variables were obtained from West Amhara Meteorological Agency. All districts were included and geo-coded and the spatial data was created in ArcGIS10.2.2 software. Global and local spatial autocorrelation were used to test the hypothesis and to detect hot spots respectively. The Poisson model was fitted to determine the purely spatial, temporal, and space-time clusters using SaTScan™9.6 software. Spearman correlation, bivariate, and multivariable negative binomial regressions were used to analyze the relation of the climatic factors to count of malaria incidence. Result: The study revealed spatial, temporal, and spatiotemporal heterogeneity of malaria incidence. Jabitenan, Quarit, Sekela, Bure, and Wonberma were high rate spatial cluster of malaria incidence hierarchically. Spatiotemporal clusters were detected. A temporal scan statistic identified one risk period from 1 July 2013 to 30 June 2015. Monthly average temperature was positively but monthly average rainfall and monthly average relative humidity were negatively correlated to count of malaria incidence at all lag-months. The adjusted incidence rate ratio showed that monthly average temperature and monthly average rainfall were independent predictors for malaria incidence at all lag-months. Monthly average relative humidity was significant at 2 months lag. Conclusion: Malaria incidence had shown spatial, temporal, spatiotemporal variability in West Gojjam zone. Mean monthly temperature and rainfall were directly and inversely associated to count of malaria incidence respectively. Considering these space-time variations and risk factors (temperature and rainfall) would be useful for the prevention and control and ultimately achieve elimination. Keywords: Spatiotemporal Variation, Malaria Incidence, Risk Factor, West Gojjam, Ethiopia

Acoustic Modeling of Meteorological Effects on Roadway Noise

2021 ◽  
pp. 036119812199458
Author(s):  
Roger L. Wayson ◽  
Kenneth Kaliski
Keyword(s):  
Road Traffic ◽  
Traffic Noise ◽  
Model Development ◽  
The United States ◽  
Noise Model ◽  
Road Traffic Noise ◽  
Atmospheric Conditions ◽  
Noise Propagation ◽  
Transportation Research ◽  
Meteorological Effects

Modeling road traffic noise levels without including the effects of meteorology may lead to substantial errors. In the United States, the required model is the Traffic Noise Model which does not include meteorology effects caused by refraction. In response, the Transportation Research Board sponsored NCHRP 25-52, Meteorological Effects on Roadway Noise, to collect highway noise data under different meteorological conditions, document the meteorological effects on roadway noise propagation under different atmospheric conditions, develop best practices, and provide guidance on how to: (a) quantify meteorological effects on roadway noise propagation; and (b) explain those effects to the public. The completed project at 16 barrier and no-barrier measurement positions adjacent to Interstate 17 (I-17) in Phoenix, Arizona provided the database which has enabled substantial developments in modeling. This report provides more recent information on the model development that can be directly applied by the noise analyst to include meteorological effects from simple look-up tables to more precise use of statistical equations.

Experiments on wind-driven heat exchange processes over melting snow

2021 ◽  
Author(s):  
Michael Haugeneder ◽  
Tobias Jonas ◽  
Dylan Reynolds ◽  
Michael Lehning ◽  
Rebecca Mott
Keyword(s):  
Snow Cover ◽  
Surface Wind ◽  
Infrared Camera ◽  
Snow Accumulation ◽  
Internal Boundary ◽  
Small Scale ◽  
Snow Melt ◽  
Two Dimensional ◽  
Atmospheric Conditions ◽  
Near Surface

<p>Snowmelt runoff predictions in alpine catchments are challenging because of the high spatial variability of t<span>he snow cover driven by </span>various snow accumulation and ablation processes. In spring, the coexistence of bare and snow-covered ground engages a number of processes such as the enhanced lateral advection of heat over partial snow cover, the development of internal boundary layers, and atmospheric decoupling effects due to increasing stability at the snow cover. The interdependency of atmospheric conditions, topographic settings and snow coverage remains a challenge to accurately account for these processes in snow melt models.<br>In this experimental study, we used an Infrared Camera (VarioCam) pointing at thin synthetic projection screens with negligible heat capacity. Using the surface temperature of the screen as a proxy for the air temperature, we obtained a two-dimensional instantaneous measurement. Screens were installed across the transition between snow-free and snow-covered areas. With IR-measurements taken at 10Hz, we capture<span> the dynamics of turbulent temperature fluctuations</span><span> </span>over the patchy snow cover at high spatial and temporal resolution. From this data we were able to obtain high-frequency, two-dimensional windfield estimations adjacent to the surface.</p><p>Preliminary results show the formation of a stable internal boundary layer (SIBL), which was temporally highly variable. Our data suggest that the SIBL height is very shallow and strongly sensitive to the mean near-surface wind speed. Only strong gusts were capable of penetrating through this SIBL leading to an enhanced energy input to the snow surface.</p><p>With these type of results from our experiments and further measurements this spring we aim to better understand small scale energy transfer processes over patch snow cover and it’s dependency on the atmospheric conditions, enabling to improve parameterizations of these processes in coarser-resolution snow melt models.</p>

scholarly journals Global source attribution of sulfate concentration and direct and indirect radiative forcing

2017 ◽  
Vol 17 (14) ◽  
pp. 8903-8922 ◽  
Author(s):  
Yang Yang ◽  
Hailong Wang ◽  
Steven J. Smith ◽  
Richard Easter ◽  
Po-Lun Ma ◽  
...  
Keyword(s):  
Southern Hemisphere ◽  
Northern Hemisphere ◽  
Radiative Forcing ◽  
Dimethyl Sulfide ◽  
Sulfur Source ◽  
Atmospheric Conditions ◽  
So2 Emissions ◽  
Near Surface ◽  
Indirect Radiative Forcing ◽  
Global Source

Abstract. The global source–receptor relationships of sulfate concentrations, and direct and indirect radiative forcing (DRF and IRF) from 16 regions/sectors for years 2010–2014 are examined in this study through utilizing a sulfur source-tagging capability implemented in the Community Earth System Model (CESM) with winds nudged to reanalysis data. Sulfate concentrations are mostly contributed by local emissions in regions with high emissions, while over regions with relatively low SO2 emissions, the near-surface sulfate concentrations are primarily attributed to non-local sources from long-range transport. Regional source efficiencies of sulfate concentrations are higher over regions with dry atmospheric conditions and less export, suggesting that lifetime of aerosols, together with regional export, is important in determining regional air quality. The simulated global total sulfate DRF is −0.42 W m−2, with −0.31 W m−2 contributed by anthropogenic sulfate and −0.11 W m−2 contributed by natural sulfate, relative to a state with no sulfur emissions. In the Southern Hemisphere tropics, dimethyl sulfide (DMS) contributes 17–84 % to the total DRF. East Asia has the largest contribution of 20–30 % over the Northern Hemisphere mid- and high latitudes. A 20 % perturbation of sulfate and its precursor emissions gives a sulfate incremental IRF of −0.44 W m−2. DMS has the largest contribution, explaining −0.23 W m−2 of the global sulfate incremental IRF. Incremental IRF over regions in the Southern Hemisphere with low background aerosols is more sensitive to emission perturbation than that over the polluted Northern Hemisphere.

scholarly journals Interannual dynamics in intensity of mesoscale hydroxyl nightglow variations over Almaty

2018 ◽  
Vol 4 (2) ◽  
pp. 63-68 ◽  
Author(s):  
А. Попов ◽  
A. Popov ◽  
Николай Гаврилов ◽  
Nikolay Gavrilov ◽  
А. Андреев ◽  
...  
Keyword(s):  
Gravity Waves ◽  
Emission Intensity ◽  
Rotational Temperature ◽  
Internal Gravity Waves ◽  
Average Intensity ◽  
Interannual Variations ◽  
Average Temperature ◽  
Monthly Average ◽  
Seasonal And Interannual Variations

The method of digital difference filters is applied to the data analysis of SATI observations of hydroxyl nightglow intensity and rotational temperature at altitudes 85–90 km over Almaty (43°03' N, 76°58' E), Kazakhstan, in 2010–2017. We examine seasonal and interannual variations in monthly average values and standard deviations of variations with periods 0.4–5.4 hrs, which may be associated with internal gravity waves in the mesopause region. The monthly average temperature near the mesopause has a maximum in winter and a minimum in June. The monthly average intensity has an additional maximum in June. Standard deviation of mesoscale rotational temperature variations and characteristics of internal gravity waves are maximum in spring and autumn. The spring maximum of mesoscale OH emission intensity variations is shifted to June. Interannual variations and multi-year trends of OH rotational temperature and emission intensity may differ in detail. This may be connected with seasonal and long-term variations in the complex system of the photochemical processes, which produce the OH nightglow.

scholarly journals The effect of atmospheric turbulence and chamber deployment period on autochamber CO<sub>2</sub> and CH<sub>4</sub> flux measurements in an ombrotrophic peatland

2012 ◽  
Vol 9 (2) ◽  
pp. 1439-1482 ◽  
Author(s):  
D. Y. F. Lai ◽  
N. T. Roulet ◽  
E. R. Humphreys ◽  
T. R. Moore ◽  
M. Dalva
Keyword(s):  
Atmospheric Turbulence ◽  
Concentration Gradient ◽  
Co2 Concentration ◽  
Time Of Day ◽  
Reliable Estimate ◽  
Organic Soils ◽  
Atmospheric Conditions ◽  
Near Surface ◽  
Ch4 Flux ◽  
Flux Measurements

Abstract. Accurate quantification of soil-atmosphere gas exchange is essential for understanding the magnitude and controls of greenhouse gas emissions. We used an automatic closed dynamic chamber system to measure the fluxes of CO2 and CH4 for several years at the ombrotrophic Mer Bleue peatland near Ottawa, Canada and found that atmospheric turbulence and chamber deployment period had a considerable influence on the observed flux rates. With a short deployment period of 2.5 min, CH4 flux exhibited strong diel patterns and both CH4 and nighttime CO2 effluxes were highly and negatively correlated with friction velocity as were the CO2 concentration gradients in the top 20 cm of peat. This suggests winds were flushing the very porous and relatively dry near surface peat layers, altering the concentration gradient and resulting in a 9 to 57% underestimate of CH4 flux at any time of day and a 13 to 21% underestimate of nighttime CO2 fluxes in highly turbulent conditions. Conversely, there was evidence of an overestimation of ~100% of CH4 and nighttime CO2 effluxes in calm atmospheric conditions possibly due to enhanced near-surface gas concentration gradient by mixing of chamber headspace air by fans. These problems were resolved by extending the deployment period to 30 min. After 13 min of chamber closure, the flux rate of CH4 and nighttime CO2 became constant and were not affected by turbulence thereafter, yielding a reliable estimate of the net biological fluxes. The measurement biases we observed likely exist to some extent in all chamber flux measurements made on porous and aerated substrate, such as peatlands, organic soils in tundra and forests, and snow-covered surfaces, but would be difficult to detect unless high frequency, semi-continuous observations are made.

scholarly journals Wind plants can impact long-term local atmospheric conditions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicola Bodini ◽  
Julie K. Lundquist ◽  
Patrick Moriarty
Keyword(s):  
Wind Speed ◽  
Urban Areas ◽  
Weather Prediction ◽  
Atmospheric Conditions ◽  
Near Surface ◽  
Direct Observations ◽  
Stable Conditions ◽  
Weather And Climate ◽  
Central United States

AbstractLong-term weather and climate observatories can be affected by the changing environments in their vicinity, such as the growth of urban areas or changing vegetation. Wind plants can also impact local atmospheric conditions through their wakes, characterized by reduced wind speed and increased turbulence. We explore the extent to which the wind plants near an atmospheric measurement site in the central United States have affected their long-term measurements. Both direct observations and mesoscale numerical weather prediction simulations demonstrate how the wind plants induce a wind deficit aloft, especially in stable conditions, and a wind speed acceleration near the surface, which extend $$\sim 30$$ ∼ 30  km downwind of the wind plant. Turbulence kinetic energy is significantly enhanced within the wind plant wake in stable conditions, with near-surface observations seeing an increase of more than 30% a few kilometers downwind of the plants.

scholarly journals PROMICE automatic weather station data

2021 ◽  
Author(s):  
Robert S. Fausto ◽  
Dirk van As ◽  
Kenneth D. Mankoff ◽  
Baptiste Vandecrux ◽  
Michele Citterio ◽  
...  
Keyword(s):  
Surface Energy Balance ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Atmospheric Conditions ◽  
Production Chain ◽  
Near Surface ◽  
Surface Mass ◽  
Future Assessment

Abstract. The Programme for Monitoring of the Greenland Ice Sheet (PROMICE) has been measuring climate and ice sheetproperties since 2007. Currently the PROMICE automatic weather station network includes 25 instrumented sites in Greenland.Accurate measurements of the surface and near-surface atmospheric conditions in a changing climate is important for reliablepresent and future assessment of changes to the Greenland ice sheet. Here we present the PROMICE vision, methodology,and each link in the production chain for obtaining and sharing quality-checked data. In this paper we mainly focus on thecritical components for calculating the surface energy balance and surface mass balance. A user-contributable dynamic webbaseddatabase of known data quality issues is associated with the data products at (https://github.com/GEUS-PROMICE/PROMICE-AWS-data-issues/). As part of the living data option, the datasets presented and described here are available atDOI: 10.22008/promice/data/aws, https://doi.org/10.22008/promice/data/aws (Fausto and van As, 2019).

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