scholarly journals A Temperature-Scaling Approach for Projecting Changes in Short Duration Rainfall Extremes from GCM Data

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 313 ◽  
Author(s):  
Ruben Dahm ◽  
Aashish Bhardwaj ◽  
Frederiek Sperna Weiland ◽  
Gerald Corzo ◽  
Laurens M. Bouwer
Keyword(s):  
Short Duration ◽  
General Circulation ◽  
Empirical Relation ◽  
Dew Point ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Hourly Rainfall ◽  
Rainfall Extremes ◽  
Temperature Scaling ◽  
Precipitation Projections

Current and future urban flooding is influenced by changes in short-duration rainfall intensities. Conventional approaches to projecting rainfall extremes are based on precipitation projections taken from General Circulation Models (GCM) or Regional Climate Models (RCM). However, these and more complex and reliable climate simulations are not yet available for many locations around the world. In this work, we test an approach that projects future rainfall extremes by scaling the empirical relation between dew-point temperature and hourly rainfall and projected changes in dew-point temperature from the EC-Earth GCM. These projections are developed for the RCP 8.5 scenario and are applied to a case study in the Netherlands. The shift in intensity-duration-frequency (IDF) curves shows that a 100-year (hourly) rainfall event today could become a 73-year event (GCM), but could become as frequent as a 30-year (temperature-scaling) in the period 2071–2100. While more advanced methods can help to further constrain future changes in rainfall extremes, the temperature-scaling approach can be of use in practical applications in urban flood risk and design studies for locations where no high-resolution precipitation projections are available.

Using a dew point temperature scaling framework to interpret changes in hourly extremes from convection-permitting model simulations

2020 ◽  
Author(s):  
Geert Lenderink ◽  
Erik van Meijgaard ◽  
Hylke de Vries ◽  
Bert van Ulft ◽  
Renaud Barbero ◽  
...  
Keyword(s):  
Climate Change ◽  
Future Climate ◽  
Dew Point ◽  
Climate Data ◽  
Rainfall Distribution ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Temperature Scaling ◽  
Summer Rain ◽  
Analysis Environment

<p>While summer rain storms are very intermittent, chaotic and influenced by multiple atmospheric drivers, some statistics of observed short duration precipitation actually display surprisingly simple, regular behaviour. As an example, 10-min rainfall extremes derived from Dutch climate data show a dependency of 13% per degree over an almost 20-degree dew point temperature range. Similar behaviour has also been found in hourly precipitation observations. Each degree of warming reflects 6-7% more moisture in the air,  following from the well-known Clausius-Clapeyron (CC) relation which is the cornerstone to understand and quantify the influence of climate change on precipitation extremes.  According to the above finding, however, precipitation intensities may be increasing with temperature at a rate twice the commonly expected CC rate. In this presentation we will use output from a number of 10-year simulations for present-day and future climate with the convection permitting model HCLIM-AROME to investigate how hourly extremes respond to warming in both a pseudo global warming (PGW) and a GCM driven setup. In particular, we use the scaling diagram -- different percentiles of the rainfall distribution, usually the 90, and 99th conditioned on the occurrence of rain, as a function of dew  point temperature -- as a analysis environment. Focus will be on how the scaling diagram is affected by climate change, and what information can be derived from these changes in scaling. While changes in the scaling diagram between present-day and future climate are in general consistent with a CC prediction, evidence of super CC behaviour, between 10 and 14 % per degree dew point, is also present. The same applies to changes in the most extreme events from the simulations, which show super CC behaviour in both PGW and GCM driven setups when scaled with the appropriate dew point temperature change. </p>

scholarly journals Improving efficiency and lowering operating costs of evaporative cooling

2021 ◽  
Vol 338 ◽  
pp. 01027
Author(s):  
Jan Taler ◽  
Bartosz Jagieła ◽  
Magdalena Jaremkiewicz
Keyword(s):  
Electricity Consumption ◽  
Open Circuit ◽  
Dew Point ◽  
Operating Costs ◽  
Water Evaporation ◽  
Total Water ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Adiabatic Cooling ◽  
Positive Effect

Cooling towers, or so-called evaporation towers, use the natural effect of water evaporation to dissipate heat in industrial and comfort installations. Water, until it changes its state of aggregation, from liquid to gas, consumes energy (2.257 kJ/kg). By consuming this energy, it lowers the air temperature to the wet-bulb temperature, thanks to which the medium can be cooled below the ambient temperature. Evaporative solutions are characterized by continuous water evaporation (approx. 1.5% of the total water flow) and low electricity consumption (high EER). Evaporative (adiabatic) cooling also has a positive effect on the reduction of electricity consumption of cooled machines. Lowering the relative humidity (RH) by approx. 2% lowers the wet-bulb temperature by approx. 0.5°C, which increases the efficiency of the tower, operating in an open circuit, expressed in kW, by approx. 5%, while reducing water consumption and treatment costs. The use of the M-Cycle (Maisotsenko cycle) to lower the temperature of the wet thermometer to the dew point temperature will reduce operating costs and increase the efficiency of cooled machines.

scholarly journals Estimating Daily Dew Point Temperature Using Machine Learning Algorithms

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 582 ◽  
Author(s):  
Sultan Noman Qasem ◽  
Saeed Samadianfard ◽  
Hamed Sadri Nahand ◽  
Amir Mosavi ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Gene Expression Programming ◽  
Machine Learning Algorithms ◽  
Dew Point ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
M5 Model Tree ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Input Parameters

In the current study, the ability of three data-driven methods of Gene Expression Programming (GEP), M5 model tree (M5), and Support Vector Regression (SVR) were investigated in order to model and estimate the dew point temperature (DPT) at Tabriz station, Iran. For this purpose, meteorological parameters of daily average temperature (T), relative humidity (RH), actual vapor pressure (Vp), wind speed (W), and sunshine hours (S) were obtained from the meteorological organization of East Azerbaijan province, Iran for the period 1998 to 2016. Following this, the methods mentioned above were examined by defining 15 different input combinations of meteorological parameters. Additionally, root mean square error (RMSE) and the coefficient of determination (R2) were implemented to analyze the accuracy of the proposed methods. The results showed that the GEP-10 method, using three input parameters of T, RH, and S, with RMSE of 0.96°, the SVR-5, using two input parameters of T and RH, with RMSE of 0.44, and M5-15, using five input parameters of T, RH, Vp, W, and S with RMSE of 0.37 present better performance in the estimation of the DPT. As a conclusion, the M5-15 is recommended as the most precise model in the estimation of DPT in comparison with other considered models. As a conclusion, the obtained results proved the high capability of proposed M5 models in DPT estimation.

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