intensity scaling
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2021 ◽  
Author(s):  
Karisma Yumnam ◽  
Ravi Kumar Guntu ◽  
Ankit Agarwal ◽  
Maheswaran Rathinasamy

<p>A multitude number of satellite precipitation products developed as an alternative to ground-based measurements. However, these products suffer from considerable errors and uncertainties due to their retrieval algorithms and sensor capabilities. The uncertainties vary from region to region depending on the topography and also with the rainfall intensities. This study evaluated the accuracy of Tropical Rainfall Measuring Mission (TRMM3B42), Integrated Multi-satellitE Retrievals for GPM (IMERG), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR), Climate Prediction Center morphing method (CMORPH), Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation (ERA5) during the monsoon season over the coastal Vamsadhara river basin in India. We have also developed a quantile based Bayesian model averaging (QBMA) to merge these products. QBMA is compared with traditional methods, namely, simple model averaging and one outlier removed. Two cases of merging, each with three sub-cases, were experimented: In the first case, we combined various for of TRMM (Linear Scaling bias-corrected, Local Intensity Scaling bias-corrected) PERSIANN and CMORPH. In the second case we had various combination of IMERG (Linear Scaling bias-corrected, Local Intensity Scaling bias-corrected), CHIRPS and ERA5. In all the cases, the coefficients were calibrated using 2001 to 2013 daily monsoon rainfall data and validated for 2014 to 2018. The results indicate that linear scaling bias-corrected QBMA  outperformed the other methods in the first case. For the second case, the one outlier removed method performed better in terms of the correlation coefficient. However, the relative root mean square error is lowest for linear scaling bias-corrected QBMA. The second case outperformed the first case. Our results imply that the improvement of accuracy depends on the method and products used in merging.</p>


2020 ◽  
Vol 102 (11) ◽  
Author(s):  
Robin Ekman ◽  
Tom Heinzl ◽  
Anton Ilderton

2020 ◽  
Vol 10 (23) ◽  
pp. 13395-13402
Author(s):  
Jun Sun ◽  
Xiaoping Chen ◽  
Mantang Wang ◽  
Jinlong Li ◽  
Quanlin Zhong ◽  
...  
Keyword(s):  

AIP Advances ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 065306
Author(s):  
Hiromu Kawasaki ◽  
Atsushi Sunahara ◽  
Yuta Shimada ◽  
Takeo Ejima ◽  
Weihua Jiang ◽  
...  

Authorea ◽  
2020 ◽  
Author(s):  
Jun Sun ◽  
Xiao Chen ◽  
Mantang Wang ◽  
Jinlong Li ◽  
Quan ling Zhong ◽  
...  
Keyword(s):  

2020 ◽  
Vol 127 (8) ◽  
pp. 083302
Author(s):  
G. J. Williams ◽  
S. Patankar ◽  
D. A. Mariscal ◽  
V. T. Tikhonchuk ◽  
J. D. Bude ◽  
...  

2020 ◽  
Vol 11 (S1) ◽  
pp. 322-342 ◽  
Author(s):  
Eugene Zhen Xiang Soo ◽  
Wan Zurina Wan Jaafar ◽  
Sai Hin Lai ◽  
Faridah Othman ◽  
Ahmed Elshafie ◽  
...  

Abstract Although satellite precipitation products (SPPs) increasingly provide an alternative means to ground-based observations, these estimations exhibit large systematic and random errors which may cause large uncertainties in hydrologic modeling. Three approaches of bias correction (BC), i.e. linear scaling (LS), local intensity scaling (LOCI), and power transformation (PT), were applied on four SPPs (TRMM, IMERG, CMORPH, and PERSIANN) during 2014/2015 extreme floods in Langat river basin, and the performance in terms of rainfall and streamflow were investigated. The results show that the original TRMM had a potential to predict the peak streamflow although CMORPH show the best performance in general. After performing BC, it is found that the LS-IMERG and LOCI-TRMM show the best performance at both rainfall and streamflow analysis. Generally, it is indicated that the current SPP estimations are still imperfect for any hydrological applications. Cross validation of different datasets is required to avoid the calibration effects of datasets.


Author(s):  
Irwayanti Irwayanti ◽  
Ahmad Yasser

The purpose of this research is to discover the effect of cognitive restructuring counseling technique on social media usage intensity of SMPN 1 Bantaeng students. This research uses quantitative approach with Quasi Experimental Design with the Nonequivalent Control Group Design research design. The subjects of this research were 20 students in class VIII who were identified as having high social media usage intensity. The data collection techniques used in this research are usage intensity scaling, observation, and interviews. The results of this research discovers that the implementation of cognitive restructuring technique can reduce the intensity of social media usage intensity as applied according to the procedures that have been designed through 8 meetings. The intensity of using social media before implementing cognitive restructuring technique was considered high but after implementing cognitive restructuring technique, the intensity of using social media decreased.


Fluids ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 180 ◽  
Author(s):  
Nils T. Basse

We study streamwise turbulence intensity definitions using smooth- and rough-wall pipe flow measurements made in the Princeton Superpipe. Scaling of turbulence intensity with the bulk (and friction) Reynolds number is provided for the definitions. The turbulence intensity scales with the friction factor for both smooth- and rough-wall pipe flow. Turbulence intensity definitions providing the best description of the measurements are identified. A procedure to calculate the turbulence intensity based on the bulk Reynolds number (and the sand-grain roughness for rough-wall pipe flow) is outlined.


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