USING SIX THERMAL AMPLITUDE MODELS FOR ESTIMATING THE DAILY GLOBAL SOLAR RADIATION AS A SOURCE OF CLEAN ENERGY FROM MEASURED TEMPERATURES OVER SAUDI ARABIA

LiDAR-derived topography and forest structure predict fine-scale variation in daily surface temperatures in oak savanna and conifer forest landscapes

The application of trivariate thin-plate smoothing splines to the interpolation of daily weather data is investigated. The method was used to develop spatial models of daily minimum and maximum temperature and daily precipitation for all of Canada, at a spatial resolution of 300 arc s of latitude and longitude, for the period 1961–2003. Each daily model was optimized automatically by minimizing the generalized cross validation. The fitted trivariate splines incorporated a spatially varying dependence on ground elevation and were able to adapt automatically to the large variation in station density over Canada. Extensive quality control measures were performed on the source data. Error estimates for the fitted surfaces based on withheld data across southern Canada were comparable to, or smaller than, errors obtained by daily interpolation studies elsewhere with denser data networks. Mean absolute errors in daily maximum and minimum temperature averaged over all years were 1.1° and 1.6°C, respectively. Daily temperature extremes were also well matched. Daily precipitation is challenging because of short correlation length scales, the preponderance of zeros, and significant error associated with measurement of snow. A two-stage approach was adopted in which precipitation occurrence was estimated and then used in conjunction with a surface of positive precipitation values. Daily precipitation occurrence was correctly predicted 83% of the time. Withheld errors in daily precipitation were small, with mean absolute errors of 2.9 mm, although these were relatively large in percentage terms. However, mean percent absolute errors in seasonal and annual precipitation totals were 14% and 9%, respectively, and seasonal precipitation upper 95th percentiles were attenuated on average by 8%. Precipitation and daily maximum temperatures were most accurately interpolated in the autumn, consistent with the large well-organized synoptic systems that prevail in this season. Daily minimum temperatures were most accurately interpolated in summer. The withheld data tests indicate that the models can be used with confidence across southern Canada in applications that depend on daily temperature and accumulated seasonal and annual precipitation. They should be used with care in applications that depend critically on daily precipitation extremes.

The accuracy of reconstructing missing daily temperature extremes in the Jaffna climatological station, situated in the northern part of the dry zone of Sri Lanka, is presented. The adopted method utilizes standard departures of daily maximum and minimum temperature values at four neighbouring stations, Mannar, Anuradhapura, Puttalam and Trincomalee to estimate the standard departures of daily maximum and minimum temperatures at the target station, Jaffna. The daily maximum and minimum temperatures from 1966 to 1980 (15 years) were used to test the validity of the method. The accuracy of the estimation is higher for daily maximum temperature compared to daily minimum temperature. About 95% of the estimated daily maximum temperatures are within ±1.5 °C of the observed values. For daily minimum temperature, the percentage is about 92. By calculating the standard deviation of the difference in estimated and observed values, we have shown that the error in estimating the daily maximum and minimum temperatures is ±0.7 and ±0.9 °C, respectively. To obtain the best accuracy when estimating the missing daily temperature extremes, it is important to include Mannar which is the nearest station to the target station, Jaffna. We conclude from the analysis that the method can be applied successfully to reconstruct the missing daily temperature extremes in Jaffna where no data is available due to frequent disruptions caused by civil unrests and hostilities in the region during the period, 1984 to 2000.

This research presents an analysis of the estimated solar radiation using Maximum and minimum daily temperatures (Tdmax and Tdmin), and by applying three models. The estimated solar radiation was calibrated by the recently developed CSR model, and extensive digital data sets derived from satellite observations edited in the Atlas Solar Radiation for the Saudi Arabia edited by KACST with the collaboration of Center for renewable energy resources of Colorado. The model performance was analyzed using four statistical models (RMSE, ME, R2, MAE). This study seeks to analyze the variability and determine the best model of estimating the daily solar radiation using the temperature data over 1985-2018. This study relied on daily dataset of extreme temperatures collected during 34 years (1985-2018) in (Abha 41112), (Makkah 41030), (Tabouk 40375), (Yanbu 40439), (Qaysumah 40373), (Dammam 40417), (Al Jouf 40361), (Qasim 40405) and (Najran, 41118) meteorology stations supervised by the National Center of Meteorology (NCM). The methodology of this study deals with the analysis of the statistic distribution of dataset of the selected (Tx), Minimum daily (Tm) and Daily average (T’) during the period 1985-2018 by applying the KolmogorovSmirnov test. The statistical significance of the Trends variance of daily temperatures was analyzed using the Semi-averages method and T-student test. The results of the statistical analysis were also represented on thematic graphs. This study revealed the spatial variations of the daily temperatures and their trends among the selected meteorology stations. The funding show many effective differences in spatial distribution of the daily temperatures. In the context of Trends analysis, the T-student test revealed the clearly differences between the semi-averages during the two periods of 1985-2001 (X’1) and 2002-2018 (X’2). In general, the differences (X’2–X’1) of temperatures (Minimum and Maximum) are smaller than the (2 SE) over different months in the selected stations. The mean of daily temperature had an increased not significantly trends in the studied stations. This study was able to represent the variation of the spatial distribution of daily temperatures using the statistical tests for determining the significance of the trends during the period 1985- 2018. The integrated employment of the methods can give more accurate results in inferring climate change indicators over Saudi Arabia regions.

Based on the daily maximum and minimum temperature observational data during 1971–2020, the variabilities of the maximum and minimum temperature of Mount Qomolangma are analyzed. The daily maximum temperature is 25.8 °C and the daily minimum temperature is −31.4 °C during the study period in Mount Qomolangma. Overall, there has been an upward trend with decadal laps for both maximum and minimum temperature. On monthly, seasonal, and annual scales, neither maximum temperature nor minimum temperature time series exhibit an increasing trend from 1971 to 2020. The increasing trends in monthly minimum temperature are even more pronounced than those in maximum temperature. Abrupt changes are noted in both monthly, seasonal, and annual maximum and minimum temperature time series. Specifically, an abrupt change in annual maximum temperature occurred in the 1980s, while an abrupt change in annual minimum temperature occurred in the 1990s. Differences between the north and south slope of Mount Qomolangma are evident, with temperature fluctuations of the north slope being more extreme than those of south slope. The seasonal and annual maximum temperature of the north slope is higher than that of the south slope, except for winter, and the seasonal and annual minimum temperatures of the north slope are all lower than those of the south slope. The tendences of maximum and minimum temperatures in the north slope are more dominant than those in the south slope. The findings are beneficial for understanding the characteristics of local climate change on the Tibetan plateau and to underscore the significant role of Mount Qomolangma in the context of global warming.

ABSTRACTTrends in indices based on daily temperature and precipitation are examined for two periods: 1948–2016 for all stations in Canada and 1900–2016 for stations in the south of Canada. These indices, a number of which reflect extreme events, are considered to be impact relevant. The results show changes consistent with warming, with larger trends associated with cold temperatures. The number of summer days (when daily maximum temperature >25°C) has increased at most locations south of 65°N, and the number of hot days (daily maximum temperature >30°C) and hot nights (daily minimum temperature >22°C) have increased at a few stations in the most southerly regions. Very warm temperatures in both summer and winter (represented by the 95th percentile of their daily maximum and minimum temperatures, respectively) have increased across the country, with stronger trends in winter. Warming is more pronounced for cold temperatures. The frost-free season has become longer with fewer frost days, consecutive frost days, and ice days. Very cold temperatures in both winter and summer (represented by the 5th percentile of their daily maximum and minimum temperatures, respectively) have increased substantially across the country, again with stronger trends in the winter. Changes in other temperature indices are consistent with warming. The growing season is now longer, and the number of growing degree-days has increased. The number of heating degree-days has decreased across the country, while the number of cooling degree-days has increased at many stations south of 55°N. The frequency of annual and spring freeze–thaw days shows an increase in the interior provinces and a decrease in the remainder of the country. Changes in precipitation indices are less spatially coherent. An increase in the number of days with rainfall and heavy rainfall is found at several locations in the south. A decrease in the number of days with snowfall and heavy snowfall is observed in the western provinces, while an increase is found in the north. There is no evidence of significant changes in the annual highest 1-day rainfall and 1-day snowfall. The maximum number of consecutive dry days has decreased, mainly in the south.

Seasonal and annual trends in Australian minimum and maximum temperatures were studied. Records of daily minimum and maximum temperatures averaged over each month, extending as far back as 1856 were examined. Over 1/2 million monthly temperature values were retrieved from the Australian Bureau of Meteorology for 299 stations. Each station had an average of 89 years of observations. Significant step discontinuities affected the maximum temperature data in the 19th century when Stevenson screens were installed. The temperature trends were found after such spurious data were removed and averaged over all stations. The resulting trend in the minimum (maximum) daily temperature was 0.67 ± 0.19 (0.58 ± 0.26) oC per century for the period 1907-2014. Decadal fluctuations were evident in the maximum daily temperature with most of the increase occurring in the late 20th century. The minimum and maximum daily temperature trends were also found for the various seasons. The minimum daily temperature trend exceeded the maximum daily temperature trend for all seasons except during June to August. The largest increases in minimum temperature as well as the smallest maximum temperature increases were found for the region north of 30 oS latitude and east of 140 oE longitude. There was also evidence that urban stations had greater increases in maximum daily temperature than those located in a rural environment.

A number of multilinear regression equations were developed to predict the relationship between global solar radiations with one or more combinations of the following weather parameters: clearness index, mean of daily temperature, ratio of maximum and minimum daily temperature, relative humidity and relative sunshine duration for Iseyin Nigeria for five years (1995 - 1999). Using the Angstrom model as the base, other regression equations were developed by modifying Angstrom equation. The value of correlation coefficient (r) and value of Root Mean Square Error (RMSE), Mean Bias Error (MBE) and Mean Percentage Error (MPE) were determined for each equation. The equation with the highest value of r and least value of RMSE, MPE and MBE is given as: where H/Ho is the cleanness index, RH is the relative humidity, S/Smax is the relative sunshine duration, θ is ratio of minimum and maximum daily temperature, T is the monthly average daily temperature. The developed model can be used for estimating global solar radiation on horizontal surfaces. Key words: Solar global, solar radiation, relative humidity, temperature, relative sunshine duration.

This study aims to examine the actual status of the urban heat island in Daegu by analyzing the data of 17 automatic weather stations installed in the Daegu area. And the results can be summarized as follows: First, regarding the temperature distribution in Daegu by summer time zones, for the 31 days(August 1st till 31st), 18 days showed daily maximum temperature over <TEX>$30^{\circ}C$</TEX>, and 11 days indicated daily minimum temperature over <TEX>$25^{\circ}C$</TEX>. The day that showed the highest daily maximum temperature was August 5th, which indicated <TEX>$36^{\circ}C$</TEX>. Second, about the spatial distribution of time ratio exceeding <TEX>$30^{\circ}C$</TEX> and <TEX>$25^{\circ}C$</TEX>, the area with the highest time ratio exceeding <TEX>$30^{\circ}C$</TEX> is mostly the downtown(central area), eastern area, and northern area. Meanwhile, regarding the time ratio exceeding <TEX>$25^{\circ}C$</TEX>, the downtown area centering around the central area were high as over 70%, and the outskirts were low as under 65%. Third, considering the temporal distribution of daily maximum temperature and daily minimum temperature, daily maximum temperature was shown around 14:00 to 15:00 while the daily minimum temperature was indicated around 17:00 to 18:00. Daily maximum and minimum temperature were appeared at northeast and downtown, respectively. Fourth, regarding the spatial distribution of tropical days and tropical night days, tropical days showed 77% and tropical night days indicated 42% before and after the 24th and also the 13th each. Tropical days were occurred up to 24 days at northeastern area. And the southwestern area of Daegu showed under 22 days. The downtown showed the 14 days of the tropical night. However, the outskirts indicated relatively few days as under 10 days. Fifth, about the spatial distribution of the average daily temperature range (the difference between the highest temperature and lowest temperature), the central area, the central part of the city, showed the smallest as <TEX>$7.2^{\circ}C$</TEX>, and as it was closer to the northern area, it became larger, so in the eastern and northern area, it was over <TEX>$8.8^{\circ}C$</TEX> or so.

Non-linear effects of mean temperature and relative humidity on dengue incidence in Guangzhou, China

Recent studies have shown that, since 1900, mean annual temperature over southern Canada has increased by an average of 0.9°C, with the largest warming during winter and early spring. Every season was associated with greater increases in minimum temperature as opposed to maximum, thus resulting in a significant decrease in the daily temperature range (DTR). The second half of the twentieth century was associated with significant winter and spring warming in the south and west, and cooling in the northeast. However, no significant changes in DTR were observed during this period. This investigation goes beyond the annual/seasonal scales by examining trends and variability in daily minimum and maximum temperature with particular emphasis on extremes. Using recently updated, homogenized daily data, spatial and temporal characteristics of daily and extreme temperature-related variables are analyzed on a seasonal basis for the periods of 1900–98 (southern Canada), and 1950–98 (the entire country). From 1900 to 1998, the majority of southern Canada shows significantly increasing trends to the lower and higher percentiles of the daily minimum and maximum temperature distribution. The findings translate into fewer days with extreme low temperature during winter, spring, and summer and more days with extreme high temperature during winter and spring. No consistent trends are found for the higher percentiles of summer daily maximum temperature, indicating little change to the number of extreme hot summer days. Over the southwest, increases are larger to the left-hand side of the daily minimum and maximum temperature distribution, resulting in significant decreases to the intraseasonal standard deviation of daily temperature. The 1950–98 results are somewhat different from the entire century, especially, during winter and spring. This result includes significant increases to the low and high percentiles over the west, and decreases over the east. This analysis reveals that the largest individual daily temperature trends (both minimum and maximum) occur during winter and early spring, when substantial warming is observed. For summer, increases are only associated with daily minimum temperature. Autumn displays varying results, with some late season cooling, mainly over western regions. The observed warming trends have a substantial effect on several economically sensitive indices. This effect includes significant increases in the number of growing and cooling degree days and significant decreases in heating degree days. In addition, the length of the frost-free period is significantly longer over most of the country.

TPS 661: Climate change: temperature effects 1, Exhibition Hall, Ground floor, August 26, 2019, 3:00 PM - 4:30 PM Introduction: Few studies have examined the impact of heat wave on mortality in developing countries most at risk due to warmer climate. Methods: We collected daily data on all-cause mortality and air temperature in Ahmedabad, India from 1987 to 2017. We used quasi-Poisson regression models to estimate associations of mortality-heat wave and mortality-temperature over lags of 0-2 days, adjusting nonlinear confounding effects of relative humidity, long-term trend and seasonality. We considered different heat wave definitions as a period of ≥ 2 or 3 consecutive days with daily maximum, minimum, or average temperature exceeding the 95th, 96th, 97th, 98th, or 99th percentile. Models performance was evaluated by one-step time series cross-validation. Effect modifications by age, gender, and residential zone were examined. Results: The model with daily maximum and minimum temperature, and without the indicator for heat wave gave the lowest root mean square error of cross-validation. The maximum and minimum temperature at lag 0 and lag 1-2 periods were statistically significantly associated with all-cause mortality. When the analysis was restricted to days with maximum temperature above 42 °C and minimum temperature above 28 °C, where dose-response curves were almost linear, an increase of 1 °C in maximum and minimum temperature at lag 0 were associated with increases in all-cause mortality of 9.6% [95% confidence interval (CI): 6.6%-12.6%] and 9.8% (95% CI: 6.3%, 13.4%), respectively. Females, older subjects, and those living in South Zone were more susceptible to high temperatures. Conclusions: Results indicate that including an indicator for heat wave in the model lead to overfitting. Both daily maximum and minimum temperature had substantial adverse effects on mortality, particularly above certain thresholds. These findings may have important policy implications on developing threshold for activating excessive heat warning system, and designing of new plans for underdeveloped regions lacking the systems.

Spatial and temporal variability of daily temperature in the Yangtze River Delta, China

This study evaluates the performance of the Conformal Cubic Atmospheric Model (CCAM) in dynamically downscaling fifth major global reanalysis produced by European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5) reanalysis data from 1985 to 2014, following a 5-yr spinup period. It focuses on daily maximum and minimum temperatures and daily precipitation, comparing CCAM to ERA5 and the Australian Gridded Climate Data (AGCD). The CCAM effectively reduces warm biases in daily minimum temperatures but struggles with cold biases in daily maximum temperatures, particularly in northern Australia during the wet season, possibly due to high-level cloud overestimation. Precipitation tends to be overestimated, especially in extreme rainfall events, though offset by an underestimation of low rainfall. The study showcases improvements in the annual minimum of daily minimum temperatures across most of Australia, while identifying challenges in forecasting cooler extreme temperatures. It adds value to annual maximum daily maximum temperatures in southern Australia but less so in the north. The analysis of the 5% annual exceedance probability (AEP5%) yields mixed results influenced by location and potential ocean temperature changes. Some coastal areas exhibit lost value, possibly linked to ocean temperature shifts. Furthermore, CCAM’s representation of maximum annual daily and 5-day rainfall reveals lost value, particularly in eastern Australia due to an overestimate of extreme rainfall. Despite the challenges of comparing a dynamical downscaling model like CCAM to ERA5, this study highlights its benefits in reducing biases, especially in temperature representation. Given the larger biases in phase 6 of Coupled Model Intercomparison Project (CMIP6) global climate models, CCAM appears suitable for dynamic downscaling in climate projections, emphasizing the need for ongoing model enhancements, including addressing biases related to ephemeral water bodies and extreme rainfall. Significance Statement This study critically assesses the performance of the Conformal Cubic Atmospheric Model (CCAM) in dynamically downscaling ERA5 reanalysis data from 1985 to 2014, offering valuable insights into climate modeling. Focusing on temperature and precipitation, CCAM proves effective in mitigating warm biases in daily minimum temperatures but encounters challenges with cold biases in daily maximum temperatures, particularly in northern Australia. The analysis reveals the overestimation of precipitation, especially in extreme events, yet identifies improvements in annual minimum daily minimum temperatures across Australia. The study underscores CCAM’s potential in reducing biases compared to CMIP6 global climate models, making it a promising tool for dynamic downscaling in climate projections. It emphasizes the necessity for ongoing model enhancements, particularly addressing biases related to ephemeral water bodies and extreme rainfall.

Proportion of oleic acid in olive oil as influenced by the dimensions of the daily temperature oscillation