Heat Illness Related Meteorology over Chiangmai Using Automatic Weather Station Observed Data

This research is a part of study of heat illness vulnerability. Understanding heat characteristic, especially in tropical area, would decrease loss from heat deceases and also support tourism in tropical countries. The aim of this research is to apply the meteorological sensor dataset in Chiangmai (a famous city in northern Thailand), which is under the control of Thai Meteorological Department, from 2015-2017 to investigate heat index characteristic. Two elements, temperature and relative humidity, were used to calculate heat index following Steadman’s equation. Analyzed heat index would be arranged by its intensity and then applied on heat illness warning. The study demonstrated that heat index warning from Automatic Weather Station data analysis could get along with the maximum temperature historical statistics data which observed by weather stations. Local people and visitors in Chiangmai mostly suffer from heat in between March and June. The greatest vulnerability to heat illness in Chiangmai was in April and May. This extreme-heat period is consistent with the report from Department of Decease Control Thailand, an amount of heat illness patients is very high in April and May. Moreover, the results show that even if the air temperature is getting low in after summer season but heat index is still high through the year. So, people still need to be aware of heat deceases and always concern about environmental heat when doing outdoor activities. In the future, AWS data from every station over Thailand would be used to develop a real-time Heat Illness Alert System.

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Topoclimatic Zoning and Representative Areas as Determined by an Automatic Weather Station (AWS) Network in the Atacama Region, Chile

Atmosphere ◽

10.3390/atmos11060611 ◽

2020 ◽

Vol 11 (6) ◽

pp. 611

Author(s):

Donna Cortez ◽

Rodrigo Padilla ◽

Sebastián Herrera ◽

Juan Uribe ◽

Manuel Paneque

Keyword(s):

Principal Component ◽

Weather Station ◽

Automatic Weather Station ◽

Climate Information ◽

Weather Information ◽

Climate Knowledge ◽

Weather Stations ◽

And Cluster Analysis ◽

Representative Area ◽

Climatic Information

Climate information is crucial to the management and profitability of key development sectors involving agriculture, hydrologic resources, natural hazards, and energy. Climate knowledge, real-time weather information, and climate predictions reliability all contribute to the planning and management of socioeconomic activities and sustainable development. Automatic weather stations (AWSs) are remotely operated and facilitate the recording of meteorological information for unoccupied and out-of-reach areas. However, the representative area of the Atacama region is unknown, whose uniqueness is largely determined by the topography of the terrain. This paper describes the topoclimatic zoning of the Atacama region, based on the identification of homogeneous climatic and topographic areas, using climatic information, principal component analysis, and cluster analysis. Topoclimatic zoning was used to determine the representative area of the AWSs. Sixty-one regional topographic units were identified as equivalent to the representative area of the AWS. The directly represented area was estimated at 2365 km2 (3.13% of the regional total), the indirectly represented area was 8725 km2 (11.53%), and the unrepresented area was 64,561 km2 (85.34%). This large unrepresented area displays potential zones for future AWS installations, which can improve both the efficiency of the regional meteorological network and access to quality climate information.

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Comparisons of Temperature Measurements from Local Weather Stations and the Tomato Plant Canopy: Implications for Crop and Pest Forecasting

Journal of the American Society for Horticultural Science ◽

10.21273/jashs.115.5.861 ◽

1990 ◽

Vol 115 (5) ◽

pp. 861-869 ◽

Cited By ~ 1

Author(s):

Nita A. Davidson ◽

L. Theodore Wilson ◽

Michael P. Hoffmann ◽

Frank G. Zalom

Keyword(s):

Maximum Temperature ◽

Weather Station ◽

Degree Days ◽

Tomato Crop ◽

Degree Day ◽

Average Maximum ◽

Temperature Ranges ◽

The Mean ◽

Weather Stations ◽

Accumulated Degree Days

Temperatures recorded by weather stations and within the canopy of tomato (Lycopersicon esculentum Mill.) crops were compared in fields near Davis, Calif., during Summer 1983 (60 days) and 1987 (50 days). For both years, the average maximum and minimum temperatures, daily temperature ranges, degree days per day, and total accumulated degree days were compared. In 1983, the mean maximum temperature at the weather station did not differ significantly from that in the canopy, but the mean minimum temperature at the weather station was significantly lower than that in the canopy. In 1987, the mean maximum temperature at the weather station was significantly higher than that in the canopy, but mean minimum temperatures did not differ significantly. Temperature ranges were significantly narrower for the weather station toward the end of the 1983 season, and significantly wider for the weather station at midseason 1987. Comparisons of degree days per day showed significant differences between means at the weather station and in the canopy in 1983, and among those at the weather station and the two degree day calculation methods used for temperatures recorded in the canopy. Total accumulated degree days based on temperature records at the weather station were lower than those in the canopy in 1983 but higher in 1987. In 1987, the single sine degree day calculation method overestimated degree days compared to the 2-hr triangulation method. The phenology of the tomato crop as predicted by weather station temperatures indicated that tomato maturation was underestimated in 1983 and overestimated in 1987. The rate of development for hypothetical populations of Heliothis zea (Boddie) and Spodoptera exigua (Hübner) (Lepidoptera: Noctuidae) within the tomato crop was again underestimated in 1983 and overestimated in 1987, as based on temperature data of the weather station.

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Greenland bare-ice albedo from PROMICE automatic weather station measurements and Sentinel-3 satellite observations

Geological Survey of Denmark and Greenland Bulletin ◽

10.34194/geusb.v47.5284 ◽

2021 ◽

Vol 47 ◽

Author(s):

Adrien Wehrlé ◽

Jason E. Box ◽

Masashi Niwano ◽

Alexandre M. Anesio ◽

Robert S. Fausto

Keyword(s):

Time Series ◽

Snow Cover ◽

Satellite Imagery ◽

Spatial Dependence ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Satellite Observations ◽

Weather Station ◽

Automatic Weather Station ◽

Weather Stations

The Programme for Monitoring of the Greenland Ice Sheet (PROMICE) provides surface meteorological and glaciological measurements from widespread on-ice automatic weather stations since mid-2007. In this study, we use 105 PROMICE ice-ablation time series to identify the timing of seasonal bare-ice onset preceded by snow cover conditions. From this collection, we find a bare-ice albedo at ice-ablation onset (here called bare-ice-onset albedo) of 0.565 ± 0.109 that has no apparent spatial dependence among 20 sites across Greenland. We then apply this snow-to-ice albedo transition value to measure the variations in daily Greenland bare-ice area in Sentinel-3 optical satellite imagery covering the extremely low and high respective melt years of 2018 and 2019. Daily Greenland bare-ice area peaked at 153 489 km² in 2019, 1.9 times larger than in 2018 (80 220 km²), equating to 9.0% (in 2019) and 4.7% (in 2018) of the ice sheet area.

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Meteorological effects of the solar eclipse of 20 March 2015: analysis of UK Met Office automatic weather station data and comparison with automatic weather station data from the Faroes and Iceland

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2015.0212 ◽

2016 ◽

Vol 374 (2077) ◽

pp. 20150212 ◽

Cited By ~ 7

Author(s):

Edward Hanna ◽

John Penman ◽

Trausti Jónsson ◽

Grant R. Bigg ◽

Halldór Björnsson ◽

...

Keyword(s):

Wind Speed ◽

Air Temperature ◽

Solar Eclipse ◽

Temperature Drop ◽

Weather Station ◽

Automatic Weather Station ◽

Station Data ◽

Weather Station Data ◽

The Mean ◽

Weather Stations

Here, we analyse high-frequency (1 min) surface air temperature, mean sea-level pressure (MSLP), wind speed and direction and cloud-cover data acquired during the solar eclipse of 20 March 2015 from 76 UK Met Office weather stations, and compare the results with those from 30 weather stations in the Faroe Islands and 148 stations in Iceland. There was a statistically significant mean UK temperature drop of 0.83±0.63°C, which occurred over 39 min on average, and the minimum temperature lagged the peak of the eclipse by about 10 min. For a subset of 14 (16) relatively clear (cloudy) stations, the mean temperature drop was 0.91±0.78 (0.31±0.40)°C but the mean temperature drops for relatively calm and windy stations were almost identical. Mean wind speed dropped significantly by 9% on average during the first half of the eclipse. There was no discernible effect of the eclipse on the wind-direction or MSLP time series, and therefore we can discount any localized eclipse cyclone effect over Britain during this event. Similar changes in air temperature and wind speed are observed for Iceland, where conditions were generally clearer, but here too there was no evidence of an eclipse cyclone; in the Faroes, there was a much more muted meteorological signature. This article is part of the themed issue ‘Atmospheric effects of solar eclipses stimulated by the 2015 UK eclipse’.

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Relationships between macrosynoptic weather types and development of the thermal excess in suburban areas in Debrecen, Hungary

International Review of Applied Sciences and Engineering ◽

10.1556/irase.5.2014.2.9 ◽

2014 ◽

Vol 5 (2) ◽

pp. 167-172

Author(s):

S. Szegedi ◽

I. Lázár ◽

T. Tóth

Keyword(s):

Reference Station ◽

Heat Island ◽

Weather Station ◽

Automatic Weather Station ◽

Heat Islands ◽

Weather Patterns ◽

Suburban Areas ◽

Synoptic Conditions ◽

Urban Heat ◽

Weather Stations

Impacts of macrosynoptic weather patterns on the development of the thermal excess in suburban areas of Debrecen are examined in this paper. Temperature datasets have been recorded at two heights by three automatic weather stations mounted in Debrecen (east Hungary) and a small settlement in its vicinity. An additional automatic weather station is used as a reference station outside Debrecen. Urban heat island (UHI) intensities have been calculated from the raw datasets. Impacts of synoptic conditions have been analyzed on the base of Péczely’s macrosynoptic types. It has been found that anticyclone types are more favorable from the aspect of UHI development, while cyclone types, especially the passage of warm fronts can effectively hinder the formation of strong heat islands in Debrecen.

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Stochastic simulation of the spatio-temporal field of the average daily heat index in Southern Russia

Climate Research ◽

10.3354/cr01623 ◽

2020 ◽

Vol 82 ◽

pp. 149-160

Author(s):

N Kargapolova

Keyword(s):

Heat Waves ◽

Numerical Models ◽

Heat Index ◽

Real Field ◽

Gaussian Field ◽

The Real ◽

Southern Russia ◽

Weather Stations ◽

Spatio Temporal ◽

Temporal Field

Numerical models of the heat index time series and spatio-temporal fields can be used for a variety of purposes, from the study of the dynamics of heat waves to projections of the influence of future climate on humans. To conduct these studies one must have efficient numerical models that successfully reproduce key features of the real weather processes. In this study, 2 numerical stochastic models of the spatio-temporal non-Gaussian field of the average daily heat index (ADHI) are considered. The field is simulated on an irregular grid determined by the location of weather stations. The first model is based on the method of the inverse distribution function. The second model is constructed using the normalization method. Real data collected at weather stations located in southern Russia are used to both determine the input parameters and to verify the proposed models. It is shown that the first model reproduces the properties of the real field of the ADHI more precisely compared to the second one, but the numerical implementation of the first model is significantly more time consuming. In the future, it is intended to transform the models presented to a numerical model of the conditional spatio-temporal field of the ADHI defined on a dense spatio-temporal grid and to use the model constructed for the stochastic forecasting of the heat index.

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