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Abstract. Since 2005, one-hour temperature forecasts for the Calabria region (southern Italy), modelled by the Regional Atmospheric Modeling System (RAMS), have been issued by CRATI/ISAC-CNR (Consortium for Research and Application of Innovative Technologies/Institute for Atmospheric and Climate Sciences of the National Research Council) and are available online at http://meteo.crati.it/previsioni.html (every six hours). Beginning in June 2008, the horizontal resolution was enhanced to 2.5 km. In the present paper, forecast skill and accuracy are evaluated out to four days for the 2008 summer season (from 6 June to 30 September, 112 runs). For this purpose, gridded high horizontal resolution forecasts of minimum, mean, and maximum temperatures are evaluated against gridded analyses at the same horizontal resolution (2.5 km). Gridded analysis is based on Optimal Interpolation (OI) and uses the RAMS first-day temperature forecast as the background field. Observations from 87 thermometers are used in the analysis system. The analysis error is introduced to quantify the effect of using the RAMS first-day forecast as the background field in the OI analyses and to define the forecast error unambiguously, while spatial interpolation (SI) analysis is considered to quantify the statistics' sensitivity to the verifying analysis and to show the quality of the OI analyses for different background fields. Two case studies, the first one with a low (less than the 10th percentile) root mean square error (RMSE) in the OI analysis, the second with the largest RMSE of the whole period in the OI analysis, are discussed to show the forecast performance under two different conditions. Cumulative statistics are used to quantify forecast errors out to four days. Results show that maximum temperature has the largest RMSE, while minimum and mean temperature errors are similar. For the period considered, the OI analysis RMSEs for minimum, mean, and maximum temperatures vary from 1.8, 1.6, and 2.0 °C, respectively, for the first-day forecast, to 2.0, 1.9, and 2.6 °C, respectively, for the fourth-day forecast. Cumulative statistics are computed using both SI and OI analysis as reference. Although SI statistics likely overestimate the forecast error because they ignore the observational error, the study shows that the difference between OI and SI statistics is less than the analysis error. The forecast skill is compared with that of the persistence forecast. The Anomaly Correlation Coefficient (ACC) shows that the model forecast is useful for all days and parameters considered here, and it is able to capture day-to-day weather variability. The model forecast issued for the fourth day is still better than the first-day forecast of a 24-h persistence forecast, at least for mean and maximum temperature. The impact of using the RAMS first-day forecast as the background field in the OI analysis is quantified by comparing statistics computed with OI and SI analyses. Minimum temperature is more sensitive to the change in the analysis dataset as a consequence of its larger representative error.

Knowing the maximum temperatures attained by stonework during past fires in historical buildings is important to understand the damage caused to the materials and the subsequent weathering history after the fire. Thermoluminescence (TL) provides a tool to assess such temperature but different protocols exist. TL has been tested to assess the maximum temperature reached by a past fire on the surface of calcitic sandstone (frequently used in historical buildings in Saragossa, Central- Eastern Spain). We have prepared subsamples of this sandstone annealed from 200°C to 700°C. Quartz extracts from such samples were used for testing different TL protocols, from measuring the erosion of the whole TL glow curve of quartz extracted to test the Thermal Activation Characteristic (TAC) and other more recent approaches, such as comparing the sensitization of the integrated 110, 200 and 250-400°C peaks at different irradiation doses or a full predose protocol. The erosion of the TL glow curve of quartz seems to underestimate the attained temperature while the other tested protocols indicate that the temperature reached was 400°C. ; This research was partly funded by Regional Government of Madrid Geomateriales S2009/MAT-1629/GEOMATERIALES and by a PICATA postdoctoral fellowship of the Moncloa Campus of International Excellence (UCM-UPM, CSIC). ; Peer reviewed

Abstract. Crops growing in the Iberian Peninsula may be subjected to damagingly high temperatures during the sensitive development periods of flowering and grain filling. Such episodes are considered important hazards and farmers may take insurance to offset their impact. Increases in value and frequency of maximum temperature have been observed in the Iberian Peninsula during the 20th century, and studies on climate change indicate the possibility of further increase by the end of the 21st century. Here, impacts of current and future high temperatures on cereal cropping systems of the Iberian Peninsula are evaluated, focusing on vulnerable development periods of winter and summer crops. Climate change scenarios obtained from an ensemble of ten Regional Climate Models (multimodel ensemble) combined with crop simulation models were used for this purpose and related uncertainty was estimated. Results reveal that higher extremes of maximum temperature represent a threat to summer-grown but not to winter-grown crops in the Iberian Peninsula. The study highlights the different vulnerability of crops in the two growing seasons and the need to account for changes in extreme temperatures in developing adaptations in cereal cropping systems. Finally, this work contributes to clarifying the causes of high-uncertainty impact projections from previous studies.

Predicting the maximum temperature is of great importance because it is related to various aspects of life, starting from people's lives and their comfort, passing through the medical, industrial, agricultural and commercial fields, as well as concerning global warming and what can result from it. Thus, the historical observations of maximum and minimum air temperature, wind speed and relative humidity were analyzed in this work. In Baghdad, the climatic variables were recorded on clear sky days dawn at 0300 GMT for the period between (2005-2020). Using weather station's variables multiple linear regression equation, their correlation coefficients were calculated to predict the daily maximum air temperature for any day during the transitional seasons (autumn, spring). By analyzing the results, a comparison was made between the expected and recorded maximum air temperature to improve the equation. The bias was tracked by analyzing the number of relative frequencies of the occurrence of these errors. (0.2 ) for the autumn season and (0.15 ) for the spring season was added to the multiple linear regression equation as a correction value.

Abstract
In this study grid data of daily maximum and minimum air temperatures taken from the NCEP/NCAR reanalysis for the territory of Poland for the years 1951-2005 have been used as a basis for an analysis of the spatial distribution of daily maximum and minimum air temperature, the frequency of characteristic days and the variability of these parameters in the period analysed. The results obtained were then compared to the variability in atmospheric circulation in Europe, described by the North Atlantic Oscillation (NAO) index.

AbstractHigh temperatures have been shown to affect human cognition and decision-making in a variety of settings. In this paper, we explore the extent to which higher temperatures affect judicial decision-making in India. We use data on judicial decisions from the Indian eCourt platform, merged with high-resolution gridded daily weather data. We estimate causal effects by leveraging a fixed effects framework. We find that high daily maximum temperatures raise the likelihood of convictions and these results are robust to numerous controls and specifications. Our findings contribute to a growing literature that documents that the negative impacts of rising temperatures are often more severe in low- and middle-income countries.

Climate change is a phenomenon has been based on the available evidence from a very long time ago and now its existence is very probable. The speed and nature of climate parameters changes at the middle of twentieth century has been different and its quickness more than the before and its trend changed to some extent comparing to the past. Climate change issue now regarded as not only one of the most common scientific topic but also a social political one, is not a new issue. Climate change is a complicated atmospheric oceanic phenomenon on a global scale and long-term. Precipitation pattern change, fast decrease of snowcovered resources and its rapid melting, increased evaporation, the occurrence of destroying floods, water shortage crisis, severe reduction at the rate of harvesting agricultural products and, so on are all the significant of climate change. To cope with this phenomenon, its consequences and events in which public instruction is the most important but it may be climate that no significant cant and effective action has been done so far. The present article is included a part of one surrey about climate change in Fars. The study area having annually mean temperature 14 and precipitation 320 mm .23 stations inside the basin with a common 37 year statistical period have been applied to the meteorology data (1974-2010). Man-kendal and change factor methods are two statistical methods, applying them, the trend of changes and the annual mean average temperature and the annual minimum mean temperature were studied by using them. Based on time series for each parameter, the annual mean average temperature and the mean of annual maximum temperature have a rising trend so that this trend is clearer to the mean of annual maximum temperature.

Abstract. Climate changes seriously affect people's daily life. Temperature and precipitation have been the focus of many studies dealing with regional climate changes in recent decades. In this study, the climatic trends in Xinjiang, northwest of China during 1961–2008 are analysed in detail on the basis of several parameters: annual and seasonal mean precipitation, and annual mean, minimum, and maximum temperatures. The results show a significant increasing trend of 7.40 mm decade−1 in the annual mean precipitation and a relatively minimal upward trend of 1.45 mm decade−1 in winter in Xinjiang. The annual mean temperature increases significantly in Xinjiang with an increasing trend of 0.30 °C decade−1. There is an increasing trend of 0.25 °C decade−1 in the annual maximum temperature and an increasing trend of 0.52 °C decade−1 in the annual minimum temperature.

Climate change can have profound impact on weather conditions around the world such as heavy rainfall, drought, global warming and so on. Understanding and predicting these natural variations is now a key research challenge for disaster-prone country like Bangladesh. This study focuses on the north eastern part of Bangladesh which is a hilly region, plays an important role in the ecological balance of the country along with socio-economic development. Present study analyses the behavior of maximum temperature and dry days using different statistical tools. Pearson's correlation matrix and Man-Kendall's tau are used to correlate monthly dry days with monthly maximum temperature, and also their annual trend. A moderate correlation was found mostly in dry summer months. In addition, a positive trend was observed in Man Kendall's trend test of yearly temperature which might be an indication of global warming in this region.

When the feedwater valve at the outage loop of the floating nuclear power plant leaks, thermal stratification occurs in the steam generator. It causes lower water temperature in the outage loop. The extent of hazard of this phenomenon cannot be directly determined by the existing measurement parameters, which poses a threat to the operational safety of the reactor. Therefore, this study adopts two routes: data-driven combined with safety analysis system (DSAS) and mechanism model-driven combined with safety analysis system (MSAS), to propose the prediction methods for the minimum temperature of the outage loop and the maximum power caused by the low-temperature coolant. Then, the actual data are used to verify these methods and the prediction results under different initial conditions are analyzed. The results show that both the DSAS method and the MSAS method can predict the minimum temperature of the steam generator in the outage loop and the maximum power when the outage loop is put into operation, but the DSAS method has better performance under certain conditions. These methods can provide guidance to the operators to avoid reactivity insertion accident.