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Abstract High-resolution historical climate grids are readily available and frequently used as inputs for a wide range of regional management and risk assessments including water supply, ecological processes, and as baseline for climate change impact studies that compare them to future projected conditions. Because historical gridded climates are produced using various methods, their portrayal of landscape conditions differ, which becomes a source of uncertainty when they are applied to subsequent analyses. Here we tested the range of values from five gridded climate datasets. We compared their values to observations from 1,231 weather stations, first using each dataset’s native scale, and then after each was rescaled to 270-meter resolution. We inputted the downscaled grids to a mechanistic hydrology model and assessed the spatial results of six hydrological variables across California, in 10 ecoregions and 11 large watersheds in the Sierra Nevada. PRISM was most accurate for precipitation, ClimateNA for maximum temperature, and TopoWx for minimum temperature. The single most accurate dataset overall was PRISM due to the best performance for precipitation and low air temperature errors. Hydrological differences ranged up to 70% of the average monthly streamflow with an average of 35% disagreement for all months derived from different historical climate maps. Large differences in minimum air temperature data produced differences in modeled actual evapotranspiration, snowpack, and streamflow. Areas with the highest variability in climate data, including the Sierra Nevada and Klamath Mountains ecoregions, also had the largest spread for Snow Water Equivalent (SWE), recharge and runoff.


Author(s):  
Robert Freer ◽  
Dursun Ekren ◽  
Tanmoy Ghosh ◽  
Kanishka Biswas ◽  
Pengfei Qiu ◽  
...  

Abstract This paper presents tables of key thermoelectric properties, which define thermoelectric conversion efficiency, for a wide range of inorganic materials. The 12 families of materials included in these tables are primarily selected on the basis of well established, internationally-recognised performance and their promise for current and future applications: Tellurides, Skutterudites, Half Heuslers, Zintls, Mg-Sb Antimonides, Clathrates, FeGa3–type materials, Actinides and Lanthanides, Oxides, Sulfides, Selenides, Silicides, Borides and Carbides. As thermoelectric properties vary with temperature, data are presented at room temperature to enable ready comparison, and also at a higher temperature appropriate to peak performance. An individual table of data and commentary are provided for each family of materials plus source references for all the data.


Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 435
Author(s):  
Nebiyu Girgibo ◽  
Anne Mäkiranta ◽  
Xiaoshu Lü ◽  
Erkki Hiltunen

Suvilahti, a suburb of the city of Vaasa in western Finland, was the first area to use seabed sediment heat as the main source of heating for a high number of houses. Moreover, in the same area, a unique land uplift effect is ongoing. The aim of this paper is to solve the challenges and find opportunities caused by global warming by utilizing seabed sediment energy as a renewable heat source. Measurement data of water and air temperature were analyzed, and correlations were established for the sediment temperature data using Statistical Analysis System (SAS) Enterprise Guide 7.1. software. The analysis and provisional forecast based on the autoregression integrated moving average (ARIMA) model revealed that air and water temperatures show incremental increases through time, and that sediment temperature has positive correlations with water temperature with a 2-month lag. Therefore, sediment heat energy is also expected to increase in the future. Factor analysis validations show that the data have a normal cluster and no particular outliers. This study concludes that sediment heat energy can be considered in prominent renewable production, transforming climate change into a useful solution, at least in summertime.


2022 ◽  
Vol 24 (1) ◽  
Author(s):  
BALJEET KAUR ◽  
NAVNEET KAUR ◽  
K. K. GILL ◽  
JAGJEEVAN SINGH ◽  
S. C. BHAN ◽  
...  

The long-term air temperature data series from 1971-2019 was analyzed and used for forecasting mean monthly air temperature at the district level. The Augmented Dickey-Fuller test, Kwiatkowski-Phillips-Schmidt-Shin test, and Mann-Kendall test were employed to test the stationarity and trend of the time series. The mean monthly maximum air temperature did not show any significant variation while an increasing trend of 0.04°C per annum was observed in mean monthly minimum air temperature, which was detrended. Box-Jenkins autoregressive integrated moving–averages were used to forecast the forthcoming 5 years (2020-2024) air temperature in the district Jalandhar of Punjab. The goodness of fit was tested against residuals, the autocorrelation function, and the histogram. The fitted model was able to capture dynamics of the time series data and produce a sensible forecast.


Author(s):  
Diljit Dutta ◽  
Rajib Kumar Bhattacharjya

Abstract Global climate models (GCMs) developed by the numerical simulation of physical processes in the atmosphere, ocean, and land are useful tools for climate prediction studies. However, these models involve parameterizations and assumptions for the simulation of complex phenomena, which lead to random and structural errors called biases. So, the GCM outputs need to be bias-corrected with respect to observed data before applying these model outputs for future climate prediction. This study develops a statistical bias correction approach using a four-layer feedforward radial basis neural network – a generalized regression neural network (GRNN) to reduce the biases of the near-surface temperature data in the Indian mainland. The input to the network is the CNRM-CM5 model output gridded data of near-surface temperature for the period 1951–2005, and the target to the model used for bias correcting the input data is the gridded near-surface temperature developed by the Indian Meteorological Department for the same period. Results show that the trained GRNN model can improve the inherent biases of the GCM modelled output with significant accuracy, and a good correlation is seen between the test statistics of observed and bias-corrected data for both the training and testing period. The trained GRNN model developed is then used for bias correction of CNRM-CM5 modelled projected near-surface temperature for 2006–2100 corresponding to the RCP4.5 and RCP8.5 emission scenarios. It is observed that the model can adapt well to the nature of unseen future temperature data and correct the biases of future data, assuming quasi-stationarity of future temperature data for both emission scenarios. The model captures the seasonal variation in near-surface temperature over the Indian mainland, having diverse topography appreciably, and this is evident from the bias-corrected output.


2022 ◽  
Author(s):  
Xiaoyu Chen ◽  
Junlai Liu ◽  
et al.

S1: Analytical Methods; Table S1: Summary of Mineral assemblages, microstructures and temperature data; Table S2: Zircon U-Pb LA-ICP-MS data of the granitic rocks from the Chong Shan structural belt.


2022 ◽  
Author(s):  
Xiaoyu Chen ◽  
Junlai Liu ◽  
et al.

S1: Analytical Methods; Table S1: Summary of Mineral assemblages, microstructures and temperature data; Table S2: Zircon U-Pb LA-ICP-MS data of the granitic rocks from the Chong Shan structural belt.


2022 ◽  
Vol 4 (1) ◽  
Author(s):  
Taly Purwa ◽  
Barbara Ngwarati

Air temperature is an important data for several sectors. The demand of fast, exact and accurate forecast on temperature data is getting extremely important since it is useful for planning of several important sectors. In order to forecast mean daily temperature data at 1st and 2nd Perak BMKG Station in Surabaya, this study used the univariate method, ARIMA model and multivariate method, VARIMA model with outlier detection. The best ARIMA model was selected using in-sample criteria, i.e. AIC and BIC. While for VAR model, the minimum information criterion namely AICc value was considered. The RMSE values of several forecasting horizons of out-sample data showed that the overall best model for mean daily temperature at 1st and 2nd Perak Station was the multivariate model, i.e. VARX (10,1) with four outliers incorporated in the model, indicated that it was necessary to consider the temperature from the nearest stations to improve the forecasting performance. This study recommends performing the overall best model only for short term forecasting, i.e. two weeks at maximum. By using the one week-step ahead and one day-step ahead forecasting scheme, the forecasting performance is significantly improved compared to default the k-step ahead forecasting scheme.


Author(s):  
Pusapati Laxmi Narasimha Raju ◽  
Chalumuru Manas ◽  
Harish Rajan

Similar to an IC (Internal combustion) engine which requires cooling to operate at optimum temperature for better efficiency; electric vehicles do require a similar system. There are various methods used in the current market for thermal management of batteries, of these our paper focuses on phase change materials (PCM). This cooling strategy can store an enormous amount of heat produced inside a battery because of its high latent heat capability. A 3D model of the battery using the multi-scale multi-dimension model (MSMD) for battery simulation and Solidification/melting models were used to showcase the melting of PCM due to the heat generated from a cell. ANSYS fluent was used to carry out the simulations. These computations are carried out at different C-rate to find the time taken for a battery to discharge and to find the impact of C-rate on PCM performance. Besides, temperature data for the cell was recorded before and after PCM was involved to compare the temperature difference between various PCM's.


2022 ◽  
Vol 1212 (1) ◽  
pp. 012047
Author(s):  
Yanshori ◽  
D W Nugraha ◽  
D Santi

Abstract The main objective of this paper is to design an IoT (Internet of Things) to monitor temperature and humidity for smart gardens. Temperature sensors and humidity sensors measure environmental conditions and are processed by a microcontroller. The actuator used is a spray pump that is used to spray water into the air to lower the temperature. Data from the sensors and status from the actuators are sent to the server and can be monitored via a smartphone. The data collected can be analyzed for various purposes. The result obtained is the effect of spraying on temperature reduction.


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