GRNN Prediction Model for Temperature-Induced Deformation of CRTS II Unballasted Slab Track

2021 ◽  
Author(s):  
Kitisak Kanjanun ◽  
Yan Bin ◽  
Yao Shuang'ao ◽  
Sakda Katawaethwarag
Keyword(s):  
Mean Squared Error ◽  
Sunshine Duration ◽  
Absolute Error ◽  
Temperature Fields ◽  
General Regression Neural Network ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Sample Mean

The General Regression Neural Network (GRNN) is one of the algorithms of artificial neural networks (ANN) that receives much attention in prediction applications. This research used the GRNN to predict the temperatureinduced deformation of unballasted track structures based on experimental data considering external weather conditions, such as sunshine duration, rain conditions, daily maximum temperature, daily minimum temperature, and daily average wind speed. The GRNN network predicts the average absolute error of the prediction results (0.0318 ℃), the maximum absolute error (1.7729 ℃), and the GRNN prediction sample mean squared error (0.070701). The average relative error is 0.32%. The finding of this study shows that the GRNN prediction method has good accuracy and robustness. Furthermore, it can promote the research of unballasted track temperature fields that are related to concrete structures.

scholarly journals Joint Occurrence of Daily Temperature and Precipitation Extreme Events over Canada

2014 ◽  
Vol 53 (9) ◽  
pp. 2148-2162 ◽  
Author(s):  
Bárbara Tencer ◽  
Andrew Weaver ◽  
Francis Zwiers
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Heavy Precipitation ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Negative Consequences ◽  
Temperature And Precipitation ◽  
Joint Occurrence

AbstractThe occurrence of individual extremes such as temperature and precipitation extremes can have a great impact on the environment. Agriculture, energy demands, and human health, among other activities, can be affected by extremely high or low temperatures and by extremely dry or wet conditions. The simultaneous or proximate occurrence of both types of extremes could lead to even more profound consequences, however. For example, a dry period can have more negative consequences on agriculture if it is concomitant with or followed by a period of extremely high temperatures. This study analyzes the joint occurrence of very wet conditions and high/low temperature events at stations in Canada. More than one-half of the stations showed a significant positive relationship at the daily time scale between warm nights (daily minimum temperature greater than the 90th percentile) or warm days (daily maximum temperature above the 90th percentile) and heavy-precipitation events (daily precipitation exceeding the 75th percentile), with the greater frequencies found for the east and southwest coasts during autumn and winter. Cold days (daily maximum temperature below the 10th percentile) occur together with intense precipitation more frequently during spring and summer. Simulations by regional climate models show good agreement with observations in the seasonal and spatial variability of the joint distribution, especially when an ensemble of simulations was used.

scholarly journals Observed Trends in Indices of Daily Temperature Extremes in South America 1960–2000

2005 ◽  
Vol 18 (23) ◽  
pp. 5011-5023 ◽  
Author(s):  
L. A. Vincent ◽  
T. C. Peterson ◽  
V. R. Barros ◽  
M. B. Marino ◽  
M. Rusticucci ◽  
...  
Keyword(s):  
Climate Change ◽  
South America ◽  
Climate Extremes ◽  
Daily Temperature ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Increasing Trends

Abstract A workshop on enhancing climate change indices in South America was held in Maceió, Brazil, in August 2004. Scientists from eight southern countries brought daily climatological data from their region for a meticulous assessment of data quality and homogeneity, and for the preparation of climate change indices that can be used for analyses of changes in climate extremes. This study presents an examination of the trends over 1960–2000 in the indices of daily temperature extremes. The results indicate no consistent changes in the indices based on daily maximum temperature while significant trends were found in the indices based on daily minimum temperature. Significant increasing trends in the percentage of warm nights and decreasing trends in the percentage of cold nights were observed at many stations. It seems that this warming is mostly due to more warm nights and fewer cold nights during the summer (December–February) and fall (March–May). The stations with significant trends appear to be located closer to the west and east coasts of South America.

Assessing the contribution of multiple forcings to changes in temperature extremes 1981–2020 using CMIP6 climate models

2021 ◽  
Author(s):  
Mastawesha Misganaw Engdaw ◽  
Andrew Ballinger ◽  
Gabriele Hegerl ◽  
Andrea Steiner
Keyword(s):  
Climate Change ◽  
Greenhouse Gases ◽  
Climate Models ◽  
Temporal Trends ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Cold Temperatures

<p>In this study, we aim at quantifying the contribution of different forcings to changes in temperature extremes over 1981–2020 using CMIP6 climate model simulations. We first assess the changes in extreme hot and cold temperatures defined as days below 10% and above 90% of daily minimum temperature (TN10 and TN90) and daily maximum temperature (TX10 and TX90). We compute the change in percentage of extreme days per season for October-March (ONDJFM) and April-September (AMJJAS). Spatial and temporal trends are quantified using multi-model mean of all-forcings simulations. The same indices will be computed from aerosols-, greenhouse gases- and natural-only forcing simulations. The trends estimated from all-forcings simulations are then attributed to different forcings (aerosols-, greenhouse gases-, and natural-only) by considering uncertainties not only in amplitude but also in response patterns of climate models. The new statistical approach to climate change detection and attribution method by Ribes et al. (2017) is used to quantify the contribution of human-induced climate change. Preliminary results of the attribution analysis show that anthropogenic climate change has the largest contribution to the changes in temperature extremes in different regions of the world.</p><p><strong>Keywords:</strong> climate change, temperature, extreme events, attribution, CMIP6</p><p> </p><p><strong>Acknowledgement:</strong> This work was funded by the Austrian Science Fund (FWF) under Research Grant W1256 (Doctoral Programme Climate Change: Uncertainties, Thresholds and Coping Strategies)</p>

scholarly journals Monthly Temperature Observations for Uganda

2013 ◽  
Vol 52 (10) ◽  
pp. 2363-2372 ◽  
Author(s):  
John R. Christy
Keyword(s):  
Time Series ◽  
Public Access ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Record Keeping ◽  
Monthly Average ◽  
Weather Observations ◽  
Temperature Observations

AbstractThe International Surface Temperature Initiative is a worldwide effort to locate weather observations, digitize them for public access, and attach provenance to them. As part of that effort, this study sought documents of temperature observations for the nation of Uganda. Although scattered reports were found for the 1890s, consistent record keeping appears to have begun in 1900. Data were keyed in from images of several types of old forms as well as accessed electronically from several sources to extend the time series of 32 stations with at least 4 yr of data back as far as data were available. Important gaps still remain; 1979–93 has virtually no observations from any station. Because many stations were represented by more than one data source, a scheme is described to extract the “best guess” values for each station of monthly averages of the daily maximum, minimum, and mean temperature. A preliminary examination of the national time series indicates that, since the early twentieth century, it appears that Uganda experienced essentially no change in monthly-average daily maximum temperature but did experience a considerable rise in monthly-average daily minimum temperature, concentrated in the last three decades. Because there are many gaps in the data, it is hoped that readers with information on extant data that were not discovered for this study will contact the author or the project so that the data may be archived.

scholarly journals Effects of diurnal warming on soil respiration are not equal to the summed effects of day and night warming in a temperate steppe

2009 ◽  
Vol 6 (8) ◽  
pp. 1361-1370 ◽  
Author(s):  
J. Xia ◽  
Y. Han ◽  
Z. Zhang ◽  
Z. Zhang ◽  
S. Wan
Keyword(s):  
Soil Respiration ◽  
Temperature Increase ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Temperate Steppe ◽  
Positive Effects ◽  
Diurnal Warming ◽  
Night Warming

Abstract. The magnitude of daily minimum temperature increase is greater than that of daily maximum temperature increase under climate warming. This study was conducted to examine whether changes in soil respiration under diurnal warming are equal to the summed changes under day and night warming in a temperate steppe in northern China. A full factorial design with day and night warming was used in this study, including control, day (06:00 a.m.–06:00 p.m., local time) warming, night (06:00 p.m.–06:00 a.m.) warming, and diurnal warming. Day warming showed no effect on soil respiration, whereas night warming significantly increased soil respiration by 7.1% over the 3 growing seasons in 2006–2008. The insignificant effect of day warming on soil respiration could be attributable to the offset of the direct positive effects of increased temperature by the indirect negative effects via aggravating water limitation and suppressing ecosystem C assimilation. The positive effects of night warming on soil respiration were largely due to the stimulation of ecosystem C uptake and substrate supply via overcompensation of plant photosynthesis. Changes in both soil respiration (+20.7 g C m−2 y−1) and GEP (−2.8 g C m−2 y−1) under diurnal warming are smaller than their summed changes (+40.0 and +24.6 g C m−2 y−1, respectively) under day and night warming. Our findings that the effects of diurnal warming on soil respiration and gross ecosystem productivity are not equal to the summed effects of day and night warming are critical for model simulation and projection of climate-carbon feedback.

Germination of the Salt-Tolerant Grass Diplachne fusca. II. Salinity Responses

1989 ◽  
Vol 37 (3) ◽  
pp. 239 ◽  
Author(s):  
BA Myers ◽  
WC Morgan
Keyword(s):  
Germination Percentage ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Distilled Water ◽  
Nacl Solution ◽  
Salt Tolerant ◽  
Similar Reduction ◽  
Threshold Temperatures

The responses -of germination of the salt-tolerant grass Diplachne fusca (L.) Beauv. to salinity and various temperature regimes are described. At temperatures of 30/20°C (12 h light and dark periods), final germination was 70% in distilled water, decreased to 50% in 175 mol m-3 NaCl (π = - 0.8 MPa) and 7% in 380 mol m-3 NaCl (π = -1.8 MPa). Increasing salinity from 0-130mol m-3 NaCl decreased the final germination percentage, but did not modify the threshold temperatures (day or night temperature > 27°C) at which germination occurred. Presoaking in distilled water or 1% CaCl2· 2H20 solution did not significantly affect the final germination percentage of seeds which were subsequently placed in solutions with a range of salinities from 0-210 mol m-3 NaCl (*#960 = 0 to - 1.0 MPa). How- ever, addition of CaCl2 to NaCl solution increased the final germination percentage compared with that in pure NaCl solution. Presoaking in concentrated (400 mol m-3) NaCl solution caused a decrease in subsequent germinability of 20 or 40% in 0 and 40 mol m-3 NaCl, respectively. Under field conditions (in soil with mean daily maximum temperature of 33°C and mean daily minimum temperature of 15°C), rates of seedling establishment were similar (16% of seed sown) in soils irrigated with 0 or 50 mol m-3 NaCl, and were 1% in those irrigated with 100 mol m-3 NaCl. The inhibition of germination in NaCl solution was largely an osmotic effect since there was a similar reduction in the final germination percentage in iso-osmotic solutions of NaCl and mannitol. However, the proportion of seeds germinating in NaCl solution was enhanced by adding calcium. The inhibition of germination was greater in sulfate solutions compared with that in chloride solutions and, to a lesser degree, in potassium compared with sodium solutions. The practical implications of our results are discussed. The incorporation of gypsum into the soil and measures to leach salts from the topsoil are recommended before D. fusca is sown on saline land.

scholarly journals Time Series Construction of Summer Surface Temperatures for Alabama, 1883–2014, and Comparisons with Tropospheric Temperature and Climate Model Simulations

2016 ◽  
Vol 55 (3) ◽  
pp. 811-826 ◽  
Author(s):  
John R. Christy ◽  
Richard T. McNider
Keyword(s):  
Time Series ◽  
Climate Model ◽  
Spatial Scales ◽  
Heat Content ◽  
Time Frame ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Tropospheric Temperature

AbstractThree time series of average summer [June–August (JJA)] daily maximum temperature (TMax) are developed for three interior regions of Alabama from stations with varying periods of record and unknown inhomogeneities. The time frame is 1883–2014. Inhomogeneities for each station’s time series are determined from pairwise comparisons with no use of station metadata other than location. The time series for the three adjoining regions are constructed separately and are then combined as a whole assuming trends over 132 yr will have little spatial variation either intraregionally or interregionally for these spatial scales. Varying the parameters of the construction methodology creates 333 time series with a central trend value based on the largest group of stations of −0.07°C decade−1 with a best-guess estimate of measurement uncertainty from −0.12° to −0.02°C decade−1. This best-guess result is insignificantly different (0.01°C decade−1) from a similar regional calculation using NOAA’s divisional dataset based on daily data from the Global Historical Climatology Network (nClimDiv) beginning in 1895. Summer TMax is a better proxy, when compared with daily minimum temperature and thus daily average temperature, for the deeper tropospheric temperature (where the enhanced greenhouse signal is maximized) as a result of afternoon convective mixing. Thus, TMax more closely represents a critical climate parameter: atmospheric heat content. Comparison between JJA TMax and deep tropospheric temperature anomalies indicates modest agreement (r2 = 0.51) for interior Alabama while agreement for the conterminous United States as given by TMax from the nClimDiv dataset is much better (r2 = 0.86). Seventy-seven CMIP5 climate model runs are examined for Alabama and indicate no skill at replicating long-term temperature and precipitation changes since 1895.

scholarly journals Changes of Extreme Temperature and Its Influencing Factors in Shiyang River Basin, Northwest China

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1171
Author(s):  
Junju Zhou ◽  
Jumei Huang ◽  
Xi Zhao ◽  
Li Lei ◽  
Wei Shi ◽  
...  
Keyword(s):  
Global Warming ◽  
River Basin ◽  
Minimum Temperature ◽  
Extreme Temperature ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Extreme Temperatures ◽  
Shiyang River Basin

The increase in the frequency and intensity of extreme weather events around the world has led to the frequent occurrence of global disasters, which have had serious impacts on the society, economic and ecological environment, especially fragile arid areas. Based on the daily maximum temperature and daily minimum temperature data of four meteorological stations in Shiyang River Basin (SRB) from 1960 to 2015, the spatio-temporal variation characteristics of extreme temperature indices were analyzed by means of univariate linear regression analysis, Mann–Kendall test and correlation analysis. The results showed that the extreme temperatures warming indices and the minimum of daily maximum temperature (TXn) and the minimum of daily minimum temperature (TNn) of cold indices showed an increasing trend from 1960 to 2016, especially since the 1990s, where the growth rate was fast and the response to global warming was sensitive. Except TXn and TNn, other cold indices showed a decreasing trend, especially Diurnal temperature (DTR) range, which decreased rapidly, indicating that the increasing speed of daily min-temperature were greater than of daily max-temperature in SRB. In space, the change tendency rate of the warm index basically showed an obvious altitude gradient effect that decreased with the altitude, which was consistent with Frost day (FD0) and Cool nights (TN10p) in the cold index, while Ice days (ID0) and Cool days (TX10p) are opposite. The mutation of the cold indices occurred earlier than the warm indices, illustrating that the cold indices in SRB were more sensitive to global warming. The change in extreme temperatures that would have a significant impact on the vegetation and glacier permafrost in the basin was the result of the combined function of different atmospheric circulation systems, which included the Arctic polar vortex, Western Pacific subtropical high and Qinghai-tibet Plateau circulation.

scholarly journals Quality of the Governing Temperature Variables in WRF in relation to Simulation of Primary Biological Aerosols

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
C. A. Skjøth ◽  
M. Werner ◽  
M. Kryza ◽  
B. Adams-Groom ◽  
A. Wakeham ◽  
...  
Keyword(s):  
Land Surface ◽  
Invasive Plant ◽  
Birch Pollen ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Ash Dieback ◽  
Crop Science

We have evaluated three prognostic variables in Weather Research and Forecasting (WRF) model, mean daily temperature, daily maximum temperature, and daily minimum temperature using 9 months of model simulations at 36 and 12 km resolution, and compared the results with 1182 observational sites in north and central Europe. The quality of the results is then determined in the context of the governing variables used in crop science, forestry, and aerobiological models. We use the results to simulate the peak of the birch pollen season (aerobiology), growth of barley (crop science), and development of the invasive plant pathogenHymenoscyphus pseudoalbidus(the cause of ash-dieback). The results show that the crop and aerobiological models are particularly sensitive to grid resolution and much higher quality is obtained from the 12 km simulations compared to 36 km. The results also show that the summer months have a bias, in particular for maximum and minimum temperatures, and that the low/high bias is clustered in two areas: continental and coastal influenced areas. It is suggested that the use of results from meteorological models as an input into biological models needs particular attention in the quality of the modelled surface data as well as the applied land surface modules.

scholarly journals Establishment and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS)

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1555 ◽  
Author(s):  
Xianyong Meng ◽  
Hao Wang ◽  
Chunxiang Shi ◽  
Yiping Wu ◽  
Xiaonan Ji
Keyword(s):  
Assessment Tool ◽  
Swat Model ◽  
Specific Humidity ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Infiltration Capacity ◽  
Daily Average ◽  
Soil And Water

We describe the construction of a very important forcing dataset of average daily surface climate over East Asia—the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool model (CMADS). This dataset can either drive the SWAT model or other hydrologic models, such as the Variable Infiltration Capacity model (VIC), the Soil and Water Integrated Model (SWIM), etc. It contains several climatological elements—daily maximum temperature (°C), daily average temperature (°C), daily minimum temperature (°C), daily average relative humidity (%), daily average specific humidity (g/kg), daily average wind speed (m/s), daily 24 h cumulative precipitation (mm), daily mean surface pressure (HPa), daily average solar radiation (MJ/m2), soil temperature (K), and soil moisture (mm3/mm3). In order to suit the various resolutions required for research, four versions of the CMADS datasets were created—from CMADS V1.0 to CMADS V1.3. We have validated the source data of the CMADS datasets using 2421 automatic meteorological stations in China to confirm the accuracy of this dataset. We have also formatted the dataset so as to drive the SWAT model conveniently. This dataset may have applications in hydrological modelling, agriculture, coupled hydrological and meteorological modelling, and meteorological analysis.

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