scholarly journals Comparison of rain gauge observations with modeled precipitation over Cyprus using Contiguous Rain Area analysis

2005 ◽  
Vol 5 (8) ◽  
pp. 2147-2154 ◽  
Author(s):  
N. Tartaglione ◽  
S. Mariani ◽  
C. Accadia ◽  
A. Speranza ◽  
M. Casaioli
Keyword(s):  
Mean Square Error ◽  
Rain Gauge ◽  
Mean Square ◽  
Rain Area ◽  
Verification Method ◽  
Different Types ◽  
Different Dimensions ◽  
Rain Gauge Network

Abstract. Verification of modeled rainfall with precipitation observed by a rain gauge network has been performed in a case study over the Cyprus Island. Cyprus has a relatively dense rain gauge network. The applied verification method is the Contiguous Rain Area (CRA) analysis. Some drawbacks of the CRA method are pointed out when it is applied to such a case study. Impact on the CRA results, when considering different dimensions of the verification sub-domain and different types of indicators (correlation and mean square error) used in the comparison, are discussed. Results indicate that care should be taken when verification of modeled rainfall is performed over a domain smaller than the model one.

scholarly journals Comparison of raingauge observations with modeled precipitation over Cyprus using Contiguous Rain Area analysis

2005 ◽  
Vol 5 (2) ◽  
pp. 2355-2376 ◽  
Author(s):  
N. Tartaglione ◽  
S. Mariani ◽  
C. Accadia ◽  
A. Speranza ◽  
M. Casaioli
Keyword(s):  
Mean Square Error ◽  
Rain Gauge ◽  
Mean Square ◽  
Rain Area ◽  
Verification Method ◽  
Model Domain ◽  
Different Types ◽  
Different Dimensions ◽  
Rain Gauge Network

Abstract. Verification of modeled rainfall with precipitation observed by a rain gauge network has been performed in a case study over the Cyprus Island. Cyprus has a relatively dense rain gauge network. The applied verification method is the Contiguous Rain Area (CRA) analysis. In this work some drawbacks are pointed out when CRA method is applied in such a case study. Impact on CRA results, when considering different dimensions of the compared model domain and different types of indicators (correlation and mean square error) used in the comparison, are discussed. Results indicate that care has to be taken when verification of modeled rainfall is performed over some of islands or hydrological basins.

scholarly journals Multisensor Comparison and Numerical Modeling of Atmospheric Water Fields: A VOLTAIRE Case Study over Cyprus

2008 ◽  
Vol 23 (4) ◽  
pp. 674-701 ◽  
Author(s):  
Stefano Mariani ◽  
Christophe Accadia ◽  
Nazario Tartaglione ◽  
Marco Casaioli ◽  
Marco Gabella ◽  
...  
Keyword(s):  
Water Vapor ◽  
Mediterranean Sea ◽  
Rain Gauge ◽  
Limited Area ◽  
Atmospheric Water ◽  
Limited Area Model ◽  
Trmm Pr ◽  
Rain Gauge Network ◽  
Total Column

Abstract This paper presents a study performed within the framework of the European Union’s (EU) VOLTAIRE project (Fifth Framework Programme). Among other tasks, the project aimed at the integration of the Tropical Rainfall Measuring Mission (TRMM) data with ground-based observations and at the comparison between water fields (precipitation and total column water vapor) as estimated by multisensor observations and predicted by NWP models. In particular, the VOLTAIRE project had as one of its main objectives the goal of assessing the application of satellite-borne instrument measures to model verification. The island of Cyprus was chosen as the main “test bed,” because it is one of the few European territories covered by the passage of the TRMM Precipitation Radar (PR) and it has a dense rain gauge network and an operational weather radar. TRMM PR provides, until now, the most reliable space-borne spatial high-resolution precipitation measurements. Attention is focused on the attempt to define a methodology, using state-of-the-art diagnostic methods, for a comprehensive evaluation of water fields as forecast by a limited area model (LAM). An event that occurred on 5 March 2003, associated with a slow cyclone moving eastward over the Mediterranean Sea, is presented as a case study. The atmospheric water fields were forecast over the eastern Mediterranean Sea using the Bologna Limited Area Model (BOLAM). Data from the Cyprus ground-based radar, the Cyprus rain gauge network, the Special Sensor Microwave Imager (SSM/I), and the TRMM PR were used in the comparison. Ground-based radar and rain gauge data were merged together in order to obtain a better representation of the rainfall event over the island. TRMM PR measurements were employed to range-adjust the ground-based radar data using a linear regression algorithm. The observed total column water vapor has been employed to assess the forecast quality of large-scale atmospheric patterns; such an assessment has been performed by means of the Hoffman diagnostic method applied to the entire total column water vapor field. Subsequently, in order to quantify the spatial forecast error at the finer BOLAM scale (0.09°), the object-oriented contiguous rain area (CRA) analysis was chosen as a comparison method for precipitation. An assessment of the main difficulties in employing CRA in an operational framework, especially over such a small verification domain, is also discussed in the paper.

scholarly journals Comparison of Entropy Methods for an Optimal Rain Gauge Network: A Case Study of Daegu and Gyeongbuk Area in South Korea

2020 ◽  
Vol 10 (16) ◽  
pp. 5620
Author(s):  
Taeyong Kwon ◽  
Junghyun Lim ◽  
Seongsim Yoon ◽  
Sanghoo Yoon
Keyword(s):  
Conditional Entropy ◽  
Rain Gauge ◽  
Optimal Number ◽  
Severe Drought ◽  
Prediction Errors ◽  
Joint Entropy ◽  
Spatio Temporal ◽  
Rain Gauge Network ◽  
Better Than

To reduce hydrological disasters, it is necessary to operate rain gauge stations at locations where the spatio-temporal characteristics of rainfall can be reflected. Entropy has been widely used to evaluate the designs and uncertainties associated with rain gauge networks. In this study, the optimal rain gauge network in the Daegu and Gyeongbuk area, which requires the efficient use of water resources due to low annual precipitation and severe drought damage, was determined using conditional and joint entropy, and the selected network was quantitatively evaluated using the root mean square error (RMSE). To consider spatial distribution, prediction errors were generated using kriging. Four estimators used in entropy calculations were compared, and weighted entropy was calculated by weighting the precipitation. The optimal number of rain gauge stations was determined by calculating the RMSE reduction and the reduction ratio according to the number of selected rain gauge stations. Our findings show that the results of conditional entropy were better than those of joint entropy. The optimal rain gauge stations showed a tendency wherein peripheral rain gauge stations were selected first, with central stations being added afterward.

Prediction of runoff using BPNN, FFBPNN, CFBPNN algorithm in arid watershed: A case study

2020 ◽  
Vol 24 (3) ◽  
pp. 243-251
Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Mean Square Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coefficient Of Determination ◽  
Hydraulic Structures ◽  
Mean Square ◽  
Feed Forward Back Propagation

Here, an endeavor has been made to predict the correspondence between rainfall and runoff and modeling are demonstrated using Feed Forward Back Propagation Neural Network (FFBPNN), Back Propagation Neural Network (BPNN), and Cascade Forward Back Propagation Neural Network (CFBPNN), for predicting runoff. Various indicators like mean square error (MSE), Root Mean Square Error (RMSE), and coefficient of determination (R2) for training and testing phase are used to appraise performance of model. BPNN performs paramount among three networks having model architecture 4-5-1 utilizing Log-sig transfer function, having R2 for training and testing is correspondingly 96.43 and 95.98. Similarly for FFBPNN, with Tan-sig function preeminent model architecture is seen to be 4-5-1 which possess MSE training and testing value 0.000483, 0.001025, RMSE training and testing value 0.02316, 0.03085 and R2 for training and testing as 0.9925, 0.9611, respectively. But for FFBPNN the value of R2 in training and testing is 0.8765 0.8976. Outcomes on the whole recommend that assessment of runoff is suitable to BPNN as contrasted to CFBPNN and FFBPNN. This consequence helps to plan, arrange and manage hydraulic structures of watershed.

scholarly journals A GIS methodology for the analysis of weather radar precipitation data

2003 ◽  
Vol 5 (2) ◽  
pp. 113-126 ◽  
Author(s):  
M. A. Gad ◽  
I. K. Tsanis
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Rain Gauge Data ◽  
Gis Environment ◽  
Arcview Gis ◽  
The City ◽  
Rain Gauge Network

A GIS multi-component module was developed within the ArcView GIS environment for processing and analysing weather radar precipitation data. The module is capable of: (a) reading geo-reference radar data and comparing it with rain-gauge network data, (b) estimating the kinematics of rainfall patterns, such as the storm speed and direction, and (c) accumulating radar-derived rainfall depths. By bringing the spatial capabilities of GIS to bear this module can accurately locate rainfall on the ground and can overlay the animated storm on different geographical features of the study area, making the exploration of the storm's kinematic characteristics obtained from radar data relatively simple. A case study in the City of Hamilton in Ontario, Canada is used to demonstrate the functionality of the module. Radar comparison with rain gauge data revealed an underestimation of the classical Marshal & Palmer Z–R relation to rainfall rate.

scholarly journals Assessment of the empirical methods for the development of the synthetic unit hydrograph: a case study of a semi-arid river basin

2021 ◽  
Author(s):  
Mohamedmaroof P. Shaikh ◽  
Sanjaykumar M. Yadav ◽  
Vivek L. Manekar
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
River Basin ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Optimum Number ◽  
Mean Square ◽  
Unit Hydrograph ◽  
Synthetic Unit Hydrograph

Abstract This study aims to assess various empirical synthetic unit hydrograph (SUH) methods and find the best method. Ideally, each river should have a definite rain gauge station (RGS) to get sufficient rainfall data that is available for carrying out meaningful analysis. The provisions of Indian Standard (IS) 4987:1994 determined the optimum number of RGS. In the absence of RGS, the SUH is recommended. SUHs have been developed using various methods such as Snyder's, Taylor and Schwarz, Soil Conservation Service, Mitchell's and Central Water Commission (CWC). In the present study, the Rel River Basin (RRB) is considered as the study area which has two existing RGS. IS 4987:1994 suggested that four RGS are required for more reliable rainfall data. Various efficiency criteria such as Correlation Coefficient, Pearson Coefficient, Nash Sutcliffe Efficiency, Index of Agreement, Normalized Root Mean Square Error, Mean Absolute Error, Root Mean Square Error and Kling-Gupta Efficiency have been used to compare SUH methods. The ranking of SUH methods was reported based on the compound factor (CF) through efficiency criteria. The 1.125 CF was observed as the minimum for the CWC method and recommended for determining peak discharge and timing for the study area.

scholarly journals Rainfall Estimation in the Sahel. Part II: Evaluation of Rain Gauge Networks in the CILSS Countries and Objective Intercomparison of Rainfall Products

2005 ◽  
Vol 44 (11) ◽  
pp. 1707-1722 ◽  
Author(s):  
Abdou Ali ◽  
Abou Amani ◽  
Arona Diedhiou ◽  
Thierry Lebel
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Estimation Error ◽  
Error Function ◽  
Grid Cell ◽  
Rain Gauge ◽  
Mean Square ◽  
Precipitation Climatology ◽  
Global Precipitation

Abstract This study investigates the accuracy of various precipitation products for the Sahel. A first set of products is made of three ground-based precipitation estimates elaborated regionally from the gauge data collected by Centre Regional Agrometeorologie–Hydrologie–Meteorologie (AGRHYMET). The second set is made of four global products elaborated by various international data centers. The comparison between these two sets covers the period of 1986–2000. The evaluation of the entire operational network of the Sahelian countries indicates that on average the monthly estimation error for the July–September period is around 12% at a spatial scale of 2.5° × 2.5°. The estimation error increases from south to north and remains below 10% for the area south of 15°N and west of 11°E (representing 42% of the region studied). In the southern Sahel (south of 15°N), the rain gauge density needs to be at least 10 gauges per 2.5° × 2.5° grid cell for a monthly error of less than 10%. In the northern Sahel, this density increases to more than 20 gauges because of the large intermittency of rainfall. In contrast, for other continental regions outside Africa, some authors have found that only five gauges per 2.5° × 2.5° grid cell are needed to give a monthly error of less than 10%. The global products considered in this comparison are the Climate Prediction Center (CPC) merged analysis of precipitation (CMAP), Global Precipitation Climatology Project (GPCP), Global Precipitation Climatology Center (GPCC), and Geostationary Operational Environmental Satellite (GOES) precipitation index (GPI). Several methods (scatterplots, distribution comparisons, root-mean-square error, bias, Nash index, significance test for the mean, variance, and distribution function, and the standard deviation approach for the kriging interval) are first used for the intercomparison. All of these methods lead to the same conclusion that CMAP is slightly the better product overall, followed by GPCC, GPCP, and GPI, with large errors for GPI. However, based on the root-mean-square error, it is found that the regional rainfall product obtained from the synoptic network is better than the four global products. Based on the error function developed in a companion paper, an approach is proposed to take into account the uncertainty resulting from the fact that the reference values are not the real ground truth. This method was applied to the most densely sampled region in the Sahel and led to a significant decrease of the raw evaluation errors. The reevaluated error is independent of the gauge references.

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