scholarly journals Can monthly precipitation interpolation error be reduced by adding periphery climate stations? A case study in China's land border areas

2016 ◽  
Vol 8 (1) ◽  
pp. 102-113
Author(s):  
Jianyong Ma ◽  
Xiaodong Yan ◽  
Shanshan Hu ◽  
Yan Guo
Keyword(s):  
Absolute Error ◽  
Smoothing Splines ◽  
Interpolation Error ◽  
Monthly Precipitation ◽  
Continuous Precipitation ◽  
Interpolation Accuracy ◽  
Border Areas ◽  
Thin Plate Smoothing Splines ◽  
Climate Studies

Minor interpolation error of spatially continuous precipitation is increasingly in demand to support many climate studies. In this paper, based on the thin-plate smoothing splines (ANUSPLIN), we studied the effects of adding periphery stations on monthly precipitation interpolation errors in China with 184 stations from neighboring countries during 1971–2000. Here, we show that with the exception of the northern piedmont of the Himalayas, the interpolation accuracy of monthly precipitation was improved greatly in China's border areas. Mean absolute error was reduced by an average of 2.8 mm month−1 across 21 withheld stations. By incorporating 184 foreign stations into interpolation, the overestimated precipitation in the northern piedmont of the Himalayas can be primarily attributed to the drawback that ANUSPLIN had difficulty estimating sharply varying rain shadows in the Qinghai–Tibetan Plateau. Overall, these results mentioned above emphasized the importance of periphery stations to generate gridded precipitation datasets and the limitation of ANUSPLIN to simulate terrain-induced climate transitions.

scholarly journals On the Horizontal Scale of Elevation Dependence of Australian Monthly Precipitation

2005 ◽  
Vol 44 (12) ◽  
pp. 1850-1865 ◽  
Author(s):  
Jason J. Sharples ◽  
Michael F. Hutchinson ◽  
Damian R. Jellett
Keyword(s):  
Smoothing Splines ◽  
Climatic Conditions ◽  
Monthly Precipitation ◽  
Digital Elevation ◽  
Australian Continent ◽  
Version 2.0 ◽  
Thin Plate Smoothing Splines ◽  
Local Averages ◽  
Insight Into

Abstract Determination of the scale of the interaction between precipitation and topography is important for the accurate interpolation of rainfall in mountainous areas and also provides insight into the physical processes involved. In this paper, trivariate thin-plate smoothing splines are used to investigate the scale of interaction between monthly precipitation and topography by interpolating monthly rainfall over three subregions of the Australian continent, incorporating different climatic conditions and rainfall types. The interpolations are based upon elevations derived from digital elevation models (DEMs) of various resolutions. All of the DEMs are local averages of version 2.0 of the 9-s-resolution DEM of Australia. The results suggest that the optimal scale of the interaction between precipitation and topography, as it pertains to the elevation-dependent interpolation of monthly precipitation in Australia, is between 5 and 10 km. This is in agreement with results of similar studies that addressed daily precipitation over Switzerland.

scholarly journals Border Regions and Infrastructure Networks in the European Integration Process

1993 ◽  
Vol 11 (4) ◽  
pp. 431-446 ◽  
Author(s):  
P Nijkamp
Keyword(s):  
European Integration ◽  
Development Strategies ◽  
Integration Process ◽  
Infrastructure Networks ◽  
Indigenous Development ◽  
Border Regions ◽  
Development Scenarios ◽  
Border Areas ◽  
Peripheral Areas

This paper is focused on development strategies of peripheral areas. In particular, the issue of integration effects on (internal) border regions after the completion of the internal European market is addressed. After a review of recent EC policies, it is claimed that there is an urgent need for the design and evaluation of active development strategies for former internal border areas. A case study on two Dutch (peripheral) border provinces is described to see how the indigenous development potential of such areas can be exploited as a strategic vehicle for enhancing their international competitiveness. A multiple criteria analysis is used to identify the most plausible and desirable development scenarios for these regions.

Investigation of Heat Transfer for Shell-Tube Fuel-Cooled Heat Exchanger

2013 ◽  
Vol 378 ◽  
pp. 459-465
Author(s):  
Ya Guo Lu ◽  
Peng Fei Zhu
Keyword(s):  
Heat Transfer ◽  
Heat Exchanger ◽  
Relative Error ◽  
Absolute Error ◽  
Heat Transfer Characteristics ◽  
Shell Side ◽  
Transfer Characteristics ◽  
Aero Engine ◽  
The Absolute

A calculate method based on ε-NTU model for heat transfer characteristics of shell-tube fuel-cooled heat exchanger of aero-engine lubrication system was built. The heat convection coefficient was obtained by a dimensionless curve (Re~StPr2/3), which was detailed introduced as well. A case study was executed at last. The absolute error of the outlet lubrication of the tube side and the shell side between the value of calculation and experiment was less than ±10°C, and the relative error was less than 6.5%. The absolute error of the heat transferred between calculation and experiment was less than ±0.9kW, and the relative error was less than 7.4%. It indicates that the mothod is available for the investigation of heat transfer characteristics of shell-tube fuel-cooled heat exchanger.

scholarly journals Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters

2020 ◽  
Vol 12 (19) ◽  
pp. 3258
Author(s):  
Jiangsan Zhao ◽  
Dmitry Kechasov ◽  
Boris Rewald ◽  
Gernot Bodner ◽  
Michel Verheul ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Hyperspectral Image ◽  
Absolute Error ◽  
Solid Content ◽  
Image Resolution ◽  
Soluble Solid Content ◽  
Quality Properties ◽  
Tomato Quality ◽  
Rgb Images

Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond.

scholarly journals Seawater intrusion vulnerability evaluation and prediction: a case study of Qeshm Island, Iran

2020 ◽  
Author(s):  
Amin Zeynolabedin ◽  
Reza Ghiassi ◽  
Moharram Dolatshahi Pirooz
Keyword(s):  
Seawater Intrusion ◽  
Moving Average ◽  
Arima Model ◽  
Chloride Ion ◽  
Absolute Error ◽  
Ion Concentration ◽  
Qeshm Island ◽  
Autoregressive Integrated Moving Average ◽  
Dangerous Condition

Abstract Seawater intrusion is one of the most serious issues to threaten coastal aquifers. Tourian aquifer, which is selected as the case study, is located in Qeshm Island, Persian Gulf. In this study, first the vulnerability of the region to seawater intrusion is assessed using chloride ion concentration value, then by using the autoregressive integrated moving average (ARIMA) model, the vulnerability of the region is predicted for 14 wells in 2018. The results show that the Tourian aquifer experiences moderate vulnerability and the area affected by seawater intrusion is wide and is in danger of expanding. It is also found that 0.95 km2 of the region is in a state of high vulnerability with Cl concentration being in a dangerous condition. The prediction model shows that ARIMA (2,1,1) is the best model with mean absolute error of 13.3 mg/L and Nash–Sutcliffe value of 0.81. For fitted and predicted data, mean square error is evaluated as 235.3 and 264.3, respectively. The prediction results show that vulnerability is increasing through the years.

scholarly journals Precipitation Atlas for Germany (GePrA)

Atmosphere ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 737
Author(s):  
Christopher Jung ◽  
Dirk Schindler
Keyword(s):  
Spatial Resolution ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Spline Interpolation ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Small Scale ◽  
Monthly Precipitation ◽  
New Approach ◽  
Very High Spatial Resolution

A new approach for modeling daily precipitation (RR) at very high spatial resolution (25 m × 25 m) was introduced. It was used to develop the Precipitation Atlas for Germany (GePrA). GePrA is based on 2357 RR time series measured in the period 1981–2018. It provides monthly percentiles (p) of the large-scale RR patterns which were mapped by a thin plate spline interpolation (TPS). A least-squares boosting (LSBoost) approach and orographic predictor variables (PV) were applied to integrate the small-scale precipitation variability in GePrA. Then, a Weibull distribution (Wei) was fitted to RRp. It was found that the mean monthly sum of RR ( R R ¯ s u m ) is highest in July (84 mm) and lowest in April (49 mm). A great dependency of RR on the elevation (ε) was found and quantified. Model validation at 425 stations showed a mean coefficient of determination (R2) of 0.80 and a mean absolute error (MAE) of less than 10 mm in all months. The high spatial resolution, including the effects of the local orography, make GePrA a valuable tool for various applications. Since GePrA does not only describe R R ¯ s u m , but also the entire monthly precipitation distributions, the results of this study enable the seasonal differentiation between dry and wet period at small scales.

scholarly journals Regional Precipitation Model Based on Geographically and Temporally Weighted Regression Kriging

2020 ◽  
Vol 12 (16) ◽  
pp. 2547 ◽  
Author(s):  
Wei Zhang ◽  
Dan Liu ◽  
Shengjie Zheng ◽  
Shuya Liu ◽  
Hugo A. Loáiciga ◽  
...  
Keyword(s):  
Temporal Distribution ◽  
Absolute Error ◽  
Weighted Regression ◽  
Distribution Model ◽  
Monthly Precipitation ◽  
Regression Kriging ◽  
Time Step ◽  
Predictive Skill ◽  
Precipitation Model ◽  
Meteorological Modeling

High-resolution precipitation field has been widely used in hydrological and meteorological modeling. This paper establishes the spatial and temporal distribution model of precipitation in Hubei Province from 2006 through 2014, based on the data of 75 meteorological stations. This paper applies a geographically and temporally weighted regression kriging (GTWRK) model to precipitation and assesses the effects of timescales and a time-weighted function on precipitation interpolation. This work’s results indicate that: (1) the optimal timescale of the geographically and temporally weighted regression (GTWR) precipitation model is daily. The fitting accuracy is improved when the timescale is converted from months and years to days. The average mean absolute error (MAE), mean relative error (MRE), and the root mean square error (RMSE) decrease with scaling from monthly to daily time steps by 36%, 56%, and 35%, respectively, and the same statistical indexes decrease by 13%, 15%, and 14%, respectively, when scaling from annual to daily steps; (2) the time weight function based on an exponential function improves the predictive skill of the GTWR model by 3% when compared to geographically weighted regression (GWR) using a monthly time step; and (3) the GTWRK has the highest accuracy, and improves the MAE, MRE and RMSE by 3%, 10% and 1% with respect to monthly precipitation predictions, respectively, and by 3%, 10% and 5% concerning annual precipitation predictions, respectively, compared with the GWR results.

scholarly journals Monthly Precipitation Patterns in a Region Vulnerable to Climate-Related Hazards—A Case Study from Poland

Water ◽  
2016 ◽  
Vol 8 (9) ◽  
pp. 362 ◽  
Author(s):  
Wiesława Kasperska-Wołowicz ◽  
Karolina Smarzyńska ◽  
Zygmunt Miatkowski ◽  
Tymoteusz Bolewski ◽  
Ryszard Farat
Keyword(s):  
Monthly Precipitation ◽  
Precipitation Patterns
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