scholarly journals VALIDATION TECHNIQUE FOR SIMPLE OPERATIONAL USE (INDRAMAYU, SUMEDANG, AND MAJALENGKA DISTRICTS CASE OF 2003) TEKNIK VALIDASI UNTUK PENGGUNAAN OPERASI SEDERHANA (KASUS KABUPATEN INDRAMAYU, SUMEDANG, DAN MAJALENGKA TAHUN 2003)

Agromet ◽  
2009 ◽  
Vol 23 (2) ◽  
pp. 148
Author(s):  
Yunus S. Swarinoto
Keyword(s):  
Root Mean Square Error ◽  
Sea Level ◽  
Rainy Season ◽  
Wavelet Transformation ◽  
Model Output ◽  
Mean Square ◽  
Transformation Technique ◽  
Mean Sea Level ◽  
Rainfall Total ◽  
Validation Technique

The model output of rainfall total prediction has to be validated before being applied to the operational use. After understanding the accuracy of this rainfall total prediction output, one has to make decision whether applying it in the field or not. This depends upon the value of accuracy as well. Validation technique for simple operational use can be made by applying Pearson’s correlation coefficient (r), Root Mean Square Error (RMSE), and spatial rainfall defferentiation values (ΔRR). The Wavelet Transformation Technique (WTT) for providing rainfall total prediction output of rainy and transition seasons 2003 had been applied to the domain of interest Indramayu, Sumedang, and Majalengka districts. Results show that during January 2003 (rainy season respectively) r = 0.65 with RMSE = 296 mm and 75.39% spatial disagreement area; but within April 2003 (transition season respectively) r = 0.49 with RMSE = 152 mm and 43.55% spatial disagreement area. Topography condition has play a role to the rainfall deferenciation values, especially for Lee-ward location. This is described by higher differentiation values of rainfall total prediction after reaching the top of elevation above mean sea level.

Improving Regional Rainfall Forecasts using Convolutional-Neural Networks

2021 ◽  
Author(s):  
Andrew Barnes ◽  
Nick McCullen ◽  
Thomas Rodding Kjeldsen
Keyword(s):  
Neural Networks ◽  
Root Mean Square Error ◽  
Sea Level ◽  
Air Temperature ◽  
Mean Square ◽  
Sea Level Pressure ◽  
Mean Sea Level ◽  
The Uk ◽  
Ecmwf Model ◽  
Regional Rainfall

<p>Traditional weather forecasting approaches utilize numerous numerical simulations and empirical models to produce a gridded estimate of rainfall, the cells of which often span multiple regions and struggle to capture extreme events. The approach presented here combines the power of modern meteorological forecasts from the ECMWF C3S seasonal forecasts service with convolutional neural networks (CNNs) to improve the forecasting of total monthly regional rainfall in the UK. The CNN is trained using mean sea-level pressure and 2m air temperature forecasts from the ECMWF C3S service using three lead-times: one month, three months and six months. The training is supervised using the equivalent true rainfall data provided by the CEH-GEAR (Centre for Ecology and Hydrology, gridded estimates of areal rainfall). The resulting predictions are then compared with the total monthly regional rainfall values calculated from the precipitation forecasts provided by the ECMWF C3S service. The results of this comparison show the new CNN model out-performs the ECMWF model  across all three leadtimes. This performance is calculated using the root-mean square error between the predicted rainfall values for each region and the true values calculated from the CEH-GEAR dataset. The largest gap is found at a one month leadtime where the CNN model scores a root-mean square error (RMSE) 13% lower than the ECMWF model (RMSEs: 46.5 and 53.4 respectively), the smallest gap is found at a six month leadtime where the CNN scores an RMSE only 2.2% lower than the ECMWF model (RMSEs: 48.5 and 49.6 respectively). However, these differences are exacerbated at the extremes with the CNN producing errors 26% lower than the ECMWF model at a one-month leadtime, 19% lower at a three-month leadtime and 3% at a six-month leadtime. These results are then extended to show how the CNN made the predictions and by comparing the attribution patterns of North West and South East England we are able to show a reliance on both the mean sea-level pressure to the west of the UK and the 2m air temperature to the south west of the UK and over the European continent.</p>

LQ-Warn – Entwicklung optimierter Vorhersagen verschiedener Luftqualitätsparameter

2021 ◽  
Author(s):  
Sabine Robrecht ◽  
Robert Osinski ◽  
Ute Dauert ◽  
Andreas Lambert ◽  
Stefan Gilge ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Model Output ◽  
Mean Square ◽  
Post Processing ◽  
Eine Hohe ◽  
Erste Ergebnisse ◽  
Model Output Statistics ◽  
Monitoring Service

<p>Schlechte Luftqualität gefährdet die Gesundheit der Bevölkerung. Zur Information und zur Ergreifung kurzfristiger Maßnahmen zur Luftqualitätsverbesserung (z.B. Verkehrslenkung) ist eine möglichst genaue und – insbesondere in städtischen Gebieten – möglichst räumlich hochaufgelöste Luftqualitätsvorhersage notwendig. Numerische Luftqualitätsmodelle haben für diese Aufgabe in der Regel eine zu geringe räumliche Auflösung.</p> <p>Daher ist es Ziel des Projektes „LQ-Warn“ die Luftqualitätsvorhersage insbesondere im Hinblick auf die Überschreitung von Grenzwerten zu verbessern. Basierend auf den Modellergebnissen für Luftqualitätsparameter des Copernicus Atmospheric Monitoring Service (CAMS) werden zwei Ansätze verfolgt: Einerseits werden Vorhersagen mit dem regionalen chemischen Transportmodell „REM-CALGRID“ (RCG) unter Einbeziehung von CAMS-Ergebnissen und regionalen Emissionsdaten berechnet. Dabei kann eine hohe horizontale Auflösung von 2 km erzielt werden und Prognosen können für verschiedene Luftschadstoffe in stündlicher Auflösung mit bis zu 72 Stunden Vorlaufzeit erstellt werden, unter anderem für NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub> und PM<sub>2.5</sub>. Andererseits wird die statistische Post-Processing-Methode „Model Output Statistics“ (MOS) angewandt, um Punktvorhersagen für die Massenkonzentration der Spezies NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub> und PM<sub>2.5</sub> mit einer Vorlaufzeit von bis zu 96 Stunden zu berechnen. Dafür werden luftqualitätsbezogene Messungen, CAMS-Modellergebnisse und meteorologische Parameter aus dem numerischen Wettervorhersagemodell des ECMWF als Prädiktoren verwendet.</p> <p>Es werden erste Ergebnisse der mit den o.g. Ansätzen errechneten Vorhersagen präsentiert und die Vor- und Nachteile der jeweiligen Verfahren hervorgehoben. Durch die statistische Post-Processing-Methode MOS wird an den Vorhersagepunkten vor allem für die Massenkonzentration von O<sub>3 </sub>und NO<sub>2</sub> eine signifikante Verringerung des RMSE (Root Mean Square Error) im Vergleich zu den Vorhersagen des numerischen CAMS-Modells erreicht. Diese deutliche Verbesserung der Luftqualitätsvorhersage sinnvoll auf die Fläche auszudehnen ist jedoch noch eine Herausforderung. Im Gegensatz dazu zeigt die Vorhersage mit dem RCG-Modell eine geringere Verbesserung der Vorhersagegüte an einzelnen Vorhersagepunkten als der MOS-Ansatz. Stattdessen bietet das RCG-Modell zeitlich und räumlich konsistente Vorhersagen an allen Modellgitterpunkten. Kleinskalige Konzentrationsunterschiede können aufgrund der höheren Modellauflösung deutlich realistischer vorhergesagt werden als mit den CAMS-Vorhersagen. Ein weiterführendes Ziel des LQ-Warn-Projektes ist es die beiden Ansätze zu kombinieren, um die Vorteile beider nutzen zu können und eine präzise Luftqualitätsvorhersage flächendeckend für Deutschland bereitstellen zu können.</p>

scholarly journals MODEL SIMULASI PRAKIRAAN CH BULANAN PADA WILAYAH RIAU DENGAN MENGGUNAKAN INPUT DATA SOI, SST, NINO 3.4, DAN IOD

2013 ◽  
Vol 14 (2) ◽  
pp. 95
Author(s):  
Aristya Ardhitama ◽  
Rias Sholihah
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Input Data ◽  
Model Output ◽  
Mean Square

INTISARI  Saat ini, kondisi cuaca di Pekanbaru dewasa ini begitu cepat perubahannya sehingga sulit diprediksi. Fenomena ini menuntut  prakiraan untuk meningkatkan kualitas hasil prakiraan sehingga lebih cepat, tepat, dan akurat untuk hasil yang diinginkan tersebut. Simulasi prakiraan jumlah curah hujan dengan menggunakan input data prediktor SOI, SST, Nino 3.4 dan IOD dengan parameter cuaca di Kota Pekanbaru telah  dilakukan menggunakan model persamaan regresi linear berganda. Prediktor tersebut digunakan untuk memprediksi curah hujan (CH) tahun 2011 dan 2012.Selain itu berfungsi untuk mengecek kebenaran hasil prakiraan jumlah curah hujan dengan model persamaan regresi linear berganda menggunakan rumus Root Mean Square Error (RMSE) dan Standar Deviasi (SD).Serta kajian penelitian ini berfungsi untuk membuktikan faktor prediktor (SOI, SST, Nina 3.4 dan IOD) yang paling mempengaruhi kondisi curah hujan di Pekanbaru.Data yang digunakan dalam kajian ini adalah data curah hujan sebaran normal dari tahun 1981-2010 pada stasiun wilayah Pekanbaru-Provinsi Riau. Data jumlah curah hujan tahun 2011 dan 2012 hasil observasi dianggap sebagai pembanding untuk verifikasi dan validasi nilai curah hujan (CH) hasil model output simulasi.Berdasarkan penelitian yang telah dilakukan maka dapat disimpulkan bahwa data dari SOI, SST, Nino 3.4 dan IOD memiliki pengaruh terhadap curah hujan di wilayah Pekanbaru Provinsi Riau.Kondisi cuaca terutama curah hujan untuk wilayah Pekanbaru dipengaruhi oleh factor global, regional dan lokal.Dari hasil penelitian terlihat hubungan yang memiliki tingkat korelasi yang tinggi terhadap curah hujan (CH) adalah prediktor SOI.Selain itu, dengan menggunakan RMSE membuktikan bahwa nilai kebenaran pada tahun 2011 lebih baik dibandingkan pada tahun 2012.  

scholarly journals A Segmental Approach with SWT Technique for Denoising the EOG Signal

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Naga Rajesh
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Wavelet Transformation ◽  
Power Line ◽  
Mean Square ◽  
Eye Disorders ◽  
Quality Diagnosis ◽  
Segmental Approach

The Electrooculogram (EOG) signal is often contaminated with artifacts and power-line while recording. It is very much essential to denoise the EOG signal for quality diagnosis. The present study deals with denoising of noisy EOG signals using Stationary Wavelet Transformation (SWT) technique by two different approaches, namely, increasing segments of the EOG signal and different equal segments of the EOG signal. For performing the segmental denoising analysis, an EOG signal is simulated and added with controlled noise powers of 5 dB, 10 dB, 15 dB, 20 dB, and 25 dB so as to obtain five different noisy EOG signals. The results obtained after denoising them are extremely encouraging. Root Mean Square Error (RMSE) values between reference EOG signal and EOG signals with noise powers of 5 dB, 10 dB, and 15 dB are very less when compared with 20 dB and 25 dB noise powers. The findings suggest that the SWT technique can be used to denoise the noisy EOG signal with optimum noise powers ranging from 5 dB to 15 dB. This technique might be useful in quality diagnosis of various neurological or eye disorders.

A Quantitative Approach to Measure Webpage Aesthetics

2020 ◽  
Vol 16 (2) ◽  
pp. 53-68
Author(s):  
Ranjan Maity ◽  
Samit Bhattacharya
Keyword(s):  
Root Mean Square Error ◽  
Prediction Model ◽  
Computational Model ◽  
Support Vector Regression ◽  
Mean Square Error ◽  
Cross Validation ◽  
Support Vector ◽  
Mean Square ◽  
Validation Technique ◽  
Fold Cross Validation

Aesthetics measurement is important in determining and improving the usability of a webpage. Wireframe models, the collection of the rectangular objects, can approximate the size and positions of the different webpage elements. The positional geometry of these objects is primarily responsible for determining aesthetics as shown in studies. In this work, the authors propose a computational model for predicting webpage aesthetics based on the positional geometry features. In this study, the authors found that ten out of the thirteen reported features are statistically significant for webpage aesthetics. Using these ten features, the authors developed a computational model for webpage aesthetics prediction. The model works on the basis of support vector regression. The authors rated the wireframe models of 209 webpages by 150 participants. The average users' ratings and the ten significant features' values were used to train and test the aesthetics prediction model. Five-fold cross-validation technique shows the model can predict aesthetics with a Root Mean Square Error (RMSE) of only 0.42.

scholarly journals A 3D map of englacial attenuation rate from radar reflections at Law Dome, East Antarctica

2020 ◽  
Author(s):  
Syed Abdul Salam ◽  
Jason L. Roberts ◽  
Felicity S. McCormack ◽  
Richard Coleman ◽  
Jacqueline A. Halpin
Keyword(s):  
Root Mean Square Error ◽  
Sea Level ◽  
Mean Square Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Radar Data ◽  
Mean Square ◽  
Ice Cap ◽  
Attenuation Rate ◽  
Median Absolute Error

Abstract. The East Antarctic Ice Sheet (EAIS) is the largest source of potential sea-level rise, containing approximately 52 m of sea-level equivalent. To constrain estimates of sea-level rise into the future requires knowledge of ice-sheet properties and geometry and ice-penetrating radar offers a means to estimate these properties (e.g. ice thickness, englacial temperatures). One of the regions that have been extensively surveyed using ice-penetrating radar from the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project in East Antarctica is Law Dome, a small independent ice cap situated to the west of Totten Ice Shelf. The ice cap is slow-moving, has a low melt-rate at the surface and moderate wind speeds, making it a useful study site for estimating the radar attenuation. A new method is proposed for the estimation of attenuation rate from radar data which is mathematically modelled as a constrained regularised l2 minimisation problem. In the proposed method, only radar data is required and the englacial reflectors are automatically detected from the radar data itself. To validate our methodology, attenuation differences at flight crossover points are calculated and statistical analyses performed to assess the accuracy of the results. For spatial analyses, the errors are of the order 22.6 %, 15.2 %, and 32.8 % for mean absolute error, median absolute error, and root mean square error respectively. Also, for the depth analyses, up to the depth of 800 m, the errors are under 29.9 %, 24.2 %, and 38.8 % for mean absolute error, median absolute error, and root mean square error respectively. A final product of 3D attenuation rates and uncertainty estimates is provided. The generated dataset is publicly available at https://doi.org/10.25959/5e6851e266f4a (Abdul Salam, 2020).

scholarly journals Automatic Designation of the Melting Layer with a Polarimetric Prototype of the WSR-88D Radar

2008 ◽  
Vol 47 (5) ◽  
pp. 1354-1364 ◽  
Author(s):  
Scott E. Giangrande ◽  
John M. Krause ◽  
Alexander V. Ryzhkov
Keyword(s):  
Numerical Model ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Detection Algorithm ◽  
Model Output ◽  
Mean Square ◽  
Temperature Analysis ◽  
Melting Layer ◽  
Model Temperature ◽  
Melting Level

Abstract A new polarimetric melting layer detection algorithm (MLDA) is utilized to estimate the top (melting level) and bottom boundaries of the melting layer and is tailored for operational deployment. Melting layer designations from a polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D) in central Oklahoma are validated using radiosonde and model temperature analysis. It is demonstrated that the MLDA estimates the top of the melting layer with a root-mean-square error of about 200 m within 60 km of the radar. There is evidence that the polarimetric radar might yield better spatial and temporal designation of the melting layer within the storm than that obtained from existing numerical model output and soundings.

Note on Selection of Coordinate Systems for Geodynamics

1975 ◽  
Vol 26 ◽  
pp. 395-407
Author(s):  
S. Henriksen
Keyword(s):  
Sea Level ◽  
The Other ◽  
Coordinate Systems ◽  
Mean Sea Level ◽  
The Earth ◽  
The One ◽  
Static Systems ◽  
Selection Of

The first question to be answered, in seeking coordinate systems for geodynamics, is: what is geodynamics? The answer is, of course, that geodynamics is that part of geophysics which is concerned with movements of the Earth, as opposed to geostatics which is the physics of the stationary Earth. But as far as we know, there is no stationary Earth – epur sic monere. So geodynamics is actually coextensive with geophysics, and coordinate systems suitable for the one should be suitable for the other. At the present time, there are not many coordinate systems, if any, that can be identified with a static Earth. Certainly the only coordinate of aeronomic (atmospheric) interest is the height, and this is usually either as geodynamic height or as pressure. In oceanology, the most important coordinate is depth, and this, like heights in the atmosphere, is expressed as metric depth from mean sea level, as geodynamic depth, or as pressure. Only for the earth do we find “static” systems in use, ana even here there is real question as to whether the systems are dynamic or static. So it would seem that our answer to the question, of what kind, of coordinate systems are we seeking, must be that we are looking for the same systems as are used in geophysics, and these systems are dynamic in nature already – that is, their definition involvestime.

scholarly journals Deterministic approach for multiple-source tsunami hazard assessment for Sines, Portugal

2015 ◽  
Vol 15 (11) ◽  
pp. 2557-2568 ◽  
Author(s):  
M. Wronna ◽  
R. Omira ◽  
M. A. Baptista
Keyword(s):  
Sea Level ◽  
Wave Height ◽  
Hazard Assessment ◽  
Test Site ◽  
Tsunami Hazard ◽  
Deterministic Approach ◽  
Flow Depth ◽  
Mean Sea Level ◽  
Tsunami Hazard Assessment ◽  
The Impact

Abstract. In this paper, we present a deterministic approach to tsunami hazard assessment for the city and harbour of Sines, Portugal, one of the test sites of project ASTARTE (Assessment, STrategy And Risk Reduction for Tsunamis in Europe). Sines has one of the most important deep-water ports, which has oil-bearing, petrochemical, liquid-bulk, coal, and container terminals. The port and its industrial infrastructures face the ocean southwest towards the main seismogenic sources. This work considers two different seismic zones: the Southwest Iberian Margin and the Gloria Fault. Within these two regions, we selected a total of six scenarios to assess the tsunami impact at the test site. The tsunami simulations are computed using NSWING, a Non-linear Shallow Water model wIth Nested Grids. In this study, the static effect of tides is analysed for three different tidal stages: MLLW (mean lower low water), MSL (mean sea level), and MHHW (mean higher high water). For each scenario, the tsunami hazard is described by maximum values of wave height, flow depth, drawback, maximum inundation area and run-up. Synthetic waveforms are computed at virtual tide gauges at specific locations outside and inside the harbour. The final results describe the impact at the Sines test site considering the single scenarios at mean sea level, the aggregate scenario, and the influence of the tide on the aggregate scenario. The results confirm the composite source of Horseshoe and Marques de Pombal faults as the worst-case scenario, with wave heights of over 10 m, which reach the coast approximately 22 min after the rupture. It dominates the aggregate scenario by about 60 % of the impact area at the test site, considering maximum wave height and maximum flow depth. The HSMPF scenario inundates a total area of 3.5 km2.

scholarly journals Applying Ateb–Gabor Filters to Biometric Imaging Problems

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 717
Author(s):  
Mariia Nazarkevych ◽  
Natalia Kryvinska ◽  
Yaroslav Voznyi
Keyword(s):  
Wavelet Transformation ◽  
Symmetric Functions ◽  
Gabor Filter ◽  
Signal To Noise Ratio ◽  
The Other ◽  
Mean Square ◽  
Image Transformation ◽  
Gabor Filtering ◽  
Signal To Noise ◽  
Filtration Method

This article presents a new method of image filtering based on a new kind of image processing transformation, particularly the wavelet-Ateb–Gabor transformation, that is a wider basis for Gabor functions. Ateb functions are symmetric functions. The developed type of filtering makes it possible to perform image transformation and to obtain better biometric image recognition results than traditional filters allow. These results are possible due to the construction of various forms and sizes of the curves of the developed functions. Further, the wavelet transformation of Gabor filtering is investigated, and the time spent by the system on the operation is substantiated. The filtration is based on the images taken from NIST Special Database 302, that is publicly available. The reliability of the proposed method of wavelet-Ateb–Gabor filtering is proved by calculating and comparing the values of peak signal-to-noise ratio (PSNR) and mean square error (MSE) between two biometric images, one of which is filtered by the developed filtration method, and the other by the Gabor filter. The time characteristics of this filtering process are studied as well.

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