scholarly journals Development of a Quality-Controlled and Homogenised Long-Term Daily Maximum and Minimum Air Temperature Network Dataset for Ireland

Climate ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 158
Author(s):  
Carla Mateus ◽  
Aaron Potito
Keyword(s):  
Quality Control ◽  
Air Temperature ◽  
Temperature Series ◽  
Daily Maximum ◽  
Control Procedure ◽  
Climate Data ◽  
Annual Series ◽  
National Network ◽  
Minimum Air Temperature

Accurate long-term daily maximum and minimum air temperature series are needed to assess the frequency, intensity, distribution, and duration of extreme climatic events. However, quality control and homogenisation procedures are required to minimise errors and inhomogeneities in climate series before the commencement of climate data analysis. A semi-automatic quality control procedure consisting of climate consistency, internal consistency, day-to-day step-change, and persistency tests was applied for 12 long-term series registered in Ireland from 1831–1968, Armagh Observatory (Northern Ireland) from 1844–2018, and for 21 short-term series dating to the mid-19th century. There were 976,786 observations quality-controlled, and 27,854 (2.9%) values flagged. Of the flagged records, 98.5% (n = 27,446) were validated, 1.4% (n = 380) corrected and 0.1% (n = 28) deleted. The historical long-term quality-controlled series were merged with the modern series quality-controlled by Met Éireann and homogenised using the software MASHv3.03 in combination with station metadata for 1885–2018. The series presented better homogenisation outcomes when homogenised as part of smaller regional networks rather than as a national network. The homogenisation of daily, monthly, seasonal, and annual series improved for all stations, and the homogenised records showed stronger correlations with the Central England long-term temperature series.

scholarly journals Spatial Accuracy of Climate Networks: A Case Study in Nebraska

2014 ◽  
Vol 53 (8) ◽  
pp. 1932-1942 ◽  
Author(s):  
Andrea J. Coop ◽  
Kenneth G. Hubbard ◽  
Martha D. Shulski ◽  
Jinsheng You ◽  
David B. Marx
Keyword(s):  
Air Temperature ◽  
Weather Data ◽  
Reference Network ◽  
Daily Maximum ◽  
Spatial Accuracy ◽  
Climate Data ◽  
Historical Climatology ◽  
Mean Square Errors ◽  
Minimum Air Temperature

AbstractClimate data are increasingly scrutinized for accuracy because of the need for reliable input for climate-related decision making and assessments of climate change. Over the last 30 years, vast improvements to U.S. instrumentation, data collection, and station siting have created more accurate data. This study explores the spatial accuracy of daily maximum and minimum air temperature data in Nebraska networks, including the U.S. Historical Climatology Network (HCN), the Automated Weather Data Network (AWDN), and the more recent U.S. Climate Reference Network (CRN). The spatial structure of temperature variations at the earth’s surface is compared for timeframes 2005–09 for CRN and AWDN and 1985–2005 for AWDN and HCN. Individual root-mean-square errors between candidate station and surrounding stations were calculated and used to determine the spatial accuracy of the networks. This study demonstrated that in the 5-yr analysis CRN and AWDN were of high spatial accuracy. For the 21-yr analysis the AWDN proved to have higher spatial accuracy (smaller errors) than the HCN for both maximum and minimum air temperature and for all months. In addition, accuracy was generally higher in summer months and the subhumid area had higher accuracy than did the semiarid area. The findings of this study can be used for Nebraska as an estimate of the uncertainty associated with using a weather station’s data at a decision point some distance from the station.

scholarly journals Reconstruction of a long‐term historical daily maximum and minimum air temperature network dataset for Ireland (1831‐1968)

2020 ◽  
Vol 7 (2) ◽  
pp. 102-115
Author(s):  
Carla Mateus ◽  
Aaron Potito ◽  
Mary Curley
Keyword(s):  
Air Temperature ◽  
Daily Maximum ◽  
Minimum Air Temperature

Acceptable limits for precision and accuracy in a long-term quality-control procedure.

1983 ◽  
Vol 29 (3) ◽  
pp. 572-573
Author(s):  
C Ricós ◽  
A Casals ◽  
S Schwartz
Keyword(s):  
Quality Control ◽  
Control Procedure ◽  
Quality Control Procedure ◽  
Precision And Accuracy

Evaluation of estimating daily maximum and minimum air temperature with MODIS data in east Africa

2012 ◽  
Vol 18 ◽  
pp. 128-140 ◽  
Author(s):  
Shengpan Lin ◽  
Nathan J. Moore ◽  
Joseph P. Messina ◽  
Mark H. DeVisser ◽  
Jiaping Wu
Keyword(s):  
East Africa ◽  
Air Temperature ◽  
Daily Maximum ◽  
Modis Data ◽  
Minimum Air Temperature

scholarly journals Incorporating climate trends in the stochastic modeling of extreme minimum air temperature series of Campinas, state of São Paulo, Brazil

Bragantia ◽  
2011 ◽  
Vol 70 (4) ◽  
pp. 952-957 ◽  
Author(s):  
Gabriel Constantino Blain
Keyword(s):  
Air Temperature ◽  
Extreme Value Distribution ◽  
Temperature Series ◽  
Time Dependent ◽  
Ratio Test ◽  
Climate Trends ◽  
Probabilistic Structure ◽  
Dependent Model ◽  
Time Dependent Model ◽  
Minimum Air Temperature

Under the hypothesis that the presence of climate trends in the annual extreme minimum air temperature series of Campinas (Tminabs; 1891-2010; 22º54'S; 47º05'W; 669 m) may no longer be neglected, the aim of the work was to describe the probabilistic structure of this series based on the general extreme value distribution (GEV) with parameters estimated as a function of a time covariate. The results obtained by applying the likelihood ratio test and the percentil-percentil and quantil-quantil plots, have indicated that the use of a time-dependent model provides a feasible description of the process under evaluation. In this non-stationary GEV model the parameters of location and scale were expressed as time-dependent functions. The shape parameter remained constant. It was also verified that although this non-stationary model has indicated an average increase in the values of the analyzed data, it does not allow us to conclude that the region of Campinas is now free from frost occurrence since this same model also reveals an increasing trend in the dispersions of the variable under evaluation. However, since the parameters of location and scale of this probabilistic model are significantly conditioned on time, the presence of climate trends in the analyzed time series is proven.

scholarly journals Climate trends in the municipality of Pelotas, state of Rio Grande do Sul, Brazil

2015 ◽  
Vol 35 (4) ◽  
pp. 769-777 ◽  
Author(s):  
Izabele B. Kruel ◽  
Monica C. Meschiatti ◽  
Gabriel C. Blain ◽  
Ana M. H. de Ávila
Keyword(s):  
Air Temperature ◽  
Rio Grande ◽  
Location Parameter ◽  
Temperature Series ◽  
Likelihood Method ◽  
Daily Maximum ◽  
Rio Grande Do Sul ◽  
The Mean ◽  
Maximum Air Temperature ◽  
Over Time

ABSTRACT Changes in the frequency of occurrence of extreme weather events have been pointed out as a likely impact of global warming. In this context, this study aimed to detect climate change in series of extreme minimum and maximum air temperature of Pelotas, State of Rio Grande do Sul, (1896 - 2011) and its influence on the probability of occurrence of these variables. We used the general extreme value distribution (GEV) in its stationary and non-stationary forms. In the latter case, GEV parameters are variable over time. On the basis of goodness-of-fit tests and of the maximum likelihood method, the GEV model in which the location parameter increases over time presents the best fit of the daily minimum air temperature series. Such result describes a significant increase in the mean values of this variable, which indicates a potential reduction in the frequency of frosts. The daily maximum air temperature series is also described by a non-stationary model, whose location parameter decreases over time, and the scale parameter related to sample variance rises between the beginning and end of the series. This result indicates a drop in the mean of daily maximum air temperature values and increased dispersion of the sample data.

Stomatal conductance in three conifer species at different elevations during summer in Wyoming

1988 ◽  
Vol 18 (2) ◽  
pp. 242-246 ◽  
Author(s):  
Gregory A. Carter ◽  
William K. Smith ◽  
Julian L. Hadley
Keyword(s):  
Air Temperature ◽  
Early Morning ◽  
Early Summer ◽  
Subalpine Forest ◽  
Daily Maximum ◽  
Interspecific Differences ◽  
Lower Elevation ◽  
Central Rocky Mountains ◽  
Maximum Conductance ◽  
Minimum Air Temperature

Stomatal conductances to water vapor diffusion in Engelmann spruce (Piceaengelmannii Parry ex Engelm.), subalpine fir (Abieslasiocarpa (Hook.) Nutt.), and lodgepole pine (Pinuscontorta Engelm.) were compared to determine environmental influences on conductance at higher (3220 m) and lower (2860 m) elevations in the central Rocky Mountains. Measurements were taken on clear days, and soil water potentials remained at or greater than −0.1 MPa. Interspecific differences were small between spruce and fir at either site, but pine conductance was generally higher than spruce or fir at 2860 m. Daily maximum conductance in spruce and fir at 3220 m did not increase above 1.0 mm s−1 until daily minimum air temperature (early morning) increased to near 1 °C in early summer. Increases in maximum conductance above 2.0 mm s−1 occurred at both elevations when minimum air temperature rose above approximately 5 °C. At the lower elevation site, increases in maximum conductance during late July and mid-August appeared to depend strongly on soil temperature increasing above 7–8 °C. The persistence of cold soil temperatures in the highest elevations of the subalpine forest may serve to inhibit stomatal opening in spruce and fir in comparison to spruce, fir, and pine in lower elevation forests.

scholarly journals Long-Term Winter Inversion Properties in a Mountain Valley of the Western United States and Implications on Air Quality

2015 ◽  
Vol 54 (12) ◽  
pp. 2339-2352 ◽  
Author(s):  
S.-Y. Simon Wang ◽  
Lawrence E. Hipps ◽  
Oi-Yu Chung ◽  
Robert R. Gillies ◽  
Randal Martin
Keyword(s):  
Air Quality ◽  
Air Temperature ◽  
Daily Maximum ◽  
Horizontal Distance ◽  
Climate Conditions ◽  
Air Temperatures ◽  
Narrow Valley ◽  
Northern Utah ◽  
Temperature Inversions

AbstractBecause of the geography of a narrow valley and surrounding tall mountains, Cache Valley (located in northern Utah and southern Idaho) experiences frequent shallow temperature inversions that are both intense and persistent. Such temperature inversions have resulted in the worst air quality in the nation. In this paper, the historical properties of Cache Valley’s winter inversions are examined by using two meteorological stations with a difference in elevation of approximately 100 m and a horizontal distance apart of ~4.5 km. Differences in daily maximum air temperature between two stations were used to define the frequency and intensity of inversions. Despite the lack of a long-term trend in inversion intensity from 1956 to present, the inversion frequency increased in the early 1980s and extending into the early 1990s but thereafter decreased by about 30% through 2013. Daily mean air temperatures and inversion intensity were categorized further using a mosaic plot. Of relevance was the discovery that after 1990 there was an increase in the probability of inversions during cold days and that under conditions in which the daily mean air temperature was below −15°C an inversion became a certainty. A regression model was developed to estimate the concentration of past particulate matter of aerodynamic diameter ≤ 2.5 μm (PM2.5). The model indicated past episodes of increased PM2.5 concentrations that went into decline after 1990; this was especially so in the coldest of climate conditions.

scholarly journals Modeling extreme minimum air temperature series under climate change conditions

2011 ◽  
Vol 41 (11) ◽  
pp. 1877-1883 ◽  
Author(s):  
Gabriel Constantino Blain
Keyword(s):  
Air Temperature ◽  
Extreme Value Distribution ◽  
Location Parameter ◽  
Kendall Test ◽  
Temperature Series ◽  
Rate Of Change ◽  
Stationary Model ◽  
Climate Conditions ◽  
Two Parameters ◽  
Minimum Air Temperature

Considering the presence of non-stationary components, such as trends, in the extreme minimum air temperature series available from three locations of the State of São Paulo-Brazil, the aim of this research was to describe the probabilistic structure of this variable by using a non-stationary model (based on the general extreme value distribution; GEV model) in which the parameters are estimated as a function of time covariate. The Mann-Kendall test has proven the presence of significant increasing trends in all analyzed series. Furthermore, according to the Pettitt (changing-point) test, 1991 is the initial year of these trends (in the three locations). The applied selection criteria indicated that a GEV model in which the location parameter is estimated as a function of time is recommended to describe the probability structure of the variable under evaluation. The others two parameters of this model remained time-independent. According to this non-stationary model, the detected trends in the climate conditions of these locations have shown the same rate of change (0.04°C per year).

Prediction of temperatures in cattle dung for estimating development times of coprophilous organisms

1988 ◽  
Vol 78 (2) ◽  
pp. 235-240 ◽  
Author(s):  
J. N. Matthiessen ◽  
M. J. Palmer
Keyword(s):  
Air Temperature ◽  
Meteorological Data ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Threshold Temperature ◽  
Cattle Dung ◽  
Daily Maximum ◽  
Daily Minimum ◽  
Maximum Temperatures ◽  
Minimum Air Temperature

AbstractIn studies in Western Australia, temperatures in air and one- and two-litre pads of cattle dung set out weekly and ranging from one to 20 days old were measured hourly for 438 days over all seasons, producing 1437 day x dung-pad observations. Daily maximum temperatures (and hence thermal accumulation) in cattle dung pads could not be accurately predicted using meteorological data alone. An accurate predictor of daily maximum dung temperature, using multiple regression analysis, required measurement of the following factors: maximum air temperature, hours of sunshine, rainfall, a seasonal factor (the day number derived from a linear interpolation of day number from day 0 at the winter solstice to day 182 at the preceding and following summer solstices) and a dung-pad age-specific intercept term, giving an equation that explained a 91·4% of the variation in maximum dung temperature. Daily maximum temperature in two-litre dung pads was 0·6°C cooler than in one-litre pads. Daily minimum dung temperature equalled minimum air temperature, and daily minimum dung temperatures occurred at 05.00 h and maximum temperatures at 14.00 h for one-litre and 14.30 h for two-litre pads. Thus, thermal summation in a dung pad above any threshold temperature can be computed using a skewed sine curve fitted to daily minimum air temperature and the calculated maximum dung temperature.

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