scholarly journals Development of a Short-term Rainfall Forecasting Model Using Weather Radar Data

2008 ◽  
Vol 41 (10) ◽  
pp. 1023-1034 ◽  
Author(s):  
Gwang-Seob Kim ◽  
Jong-Pil Kim
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Forecasting Model ◽  
Rainfall Forecasting ◽  
Short Term

scholarly journals AutoNowP: An Approach Using Deep Autoencoders for Precipitation Nowcasting Based on Weather Radar Reflectivity Prediction

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1653
Author(s):  
Gabriela Czibula ◽  
Andrei Mihai ◽  
Alexandra-Ioana Albu ◽  
Istvan-Gergely Czibula ◽  
Sorin Burcea ◽  
...  
Keyword(s):  
Binary Classification ◽  
Weather Radar ◽  
Predictive Performance ◽  
Weather Conditions ◽  
Radar Data ◽  
Probability Of Detection ◽  
Radar Reflectivity ◽  
Classification Model ◽  
Precipitation Forecast ◽  
Short Term

Short-term quantitative precipitation forecast is a challenging topic in meteorology, as the number of severe meteorological phenomena is increasing in most regions of the world. Weather radar data is of utmost importance to meteorologists for issuing short-term weather forecast and warnings of severe weather phenomena. We are proposing AutoNowP, a binary classification model intended for precipitation nowcasting based on weather radar reflectivity prediction. Specifically, AutoNowP uses two convolutional autoencoders, being trained on radar data collected on both stratiform and convective weather conditions for learning to predict whether the radar reflectivity values will be above or below a certain threshold. AutoNowP is intended to be a proof of concept that autoencoders are useful in distinguishing between convective and stratiform precipitation. Real radar data provided by the Romanian National Meteorological Administration and the Norwegian Meteorological Institute is used for evaluating the effectiveness of AutoNowP. Results showed that AutoNowP surpassed other binary classifiers used in the supervised learning literature in terms of probability of detection and negative predictive value, highlighting its predictive performance.

The Application of Bayesian Neural Network in Rainfall Forecasting

2010 ◽  
Vol 439-440 ◽  
pp. 1300-1305
Author(s):  
Chuan Jin Jiang
Keyword(s):  
Forecasting Model ◽  
Coastal Forest ◽  
Antecedent Rainfall ◽  
Rainfall Forecasting ◽  
Bayesian Neural Network ◽  
Short Term ◽  
Analysis Techniques ◽  
Proposed Model ◽  
Highly Nonlinear ◽  
Temporal And Spatial

The process of Rainfall Forecasting very complex and highly nonlinear and exhibits both temporal and spatial variability’s, In this article, a Rainfall Forecasting model using the Bayesian neural networks (BNN) is proposed for Rainfall Forecasting. The study uses the data from a coastal forest catchment. This article studies the accuracy of the short-term rainfall forecast obtained by BNN time-series analysis techniques and using antecedent rainfall depths and stream flow as the input information. The verification results from the proposed model indicate that the approach of BNN Rainfall Forecasting model presented in this paper shows a reasonable agreement in Rainfall Forecasting modeling with high accuracy.

scholarly journals SHORT-TERM RAINFALL FORECASTING BY RADAR DATA

1997 ◽  
Vol 41 ◽  
pp. 155-160
Author(s):  
Xianyun CHENG ◽  
Masato NOGUCHI
Keyword(s):  
Radar Data ◽  
Rainfall Forecasting ◽  
Short Term

scholarly journals An Image Similarity Evaluation in Rainfall Forecasting Model

2020 ◽  
Vol 9 (5) ◽  
Author(s):  
Prattana Deeprasertkul
Keyword(s):  
Image Matching ◽  
Image Similarity ◽  
Forecasting Model ◽  
Original Image ◽  
Rainfall Forecasting ◽  
Matching Method ◽  
Short Term ◽  
History Data ◽  
Term Forecast ◽  
Image Hash

The Global Satellite Mapping of Precipitation or GSMaP data which is used to display the rainfall data was used to analyze and create the rainfall forecasting model. This work is the evaluation of this rainfall forecasting model which is the short-term forecast. The GSMaP forecasting data were matched with the GSMaP history data and calculate their similarity values by applying the original image matching method. The modification of Rainfall Forecasting Model and its evaluation that applied the original image instead of the image hash improve the accuracy of rainfall forecasted results.

scholarly journals Evaluation of RadVil, a Radar-Based Very Short-Term Rainfall Forecasting Model

2006 ◽  
Vol 7 (1) ◽  
pp. 178-189 ◽  
Author(s):  
Brice Boudevillain ◽  
Hervé Andrieu ◽  
Nadine Chaumerliac
Keyword(s):  
Spatial Scales ◽  
Temporal Structure ◽  
Forecast Model ◽  
Radar Data ◽  
Rainfall Forecasting ◽  
Short Term ◽  
Precipitation Forecasting ◽  
Short Lead Time ◽  
Rain Events ◽  
Measurement Strategies

Abstract A very short-term rainfall forecast model is tested on actual radar data. This model, called RadVil, takes advantages of voluminal radar data through vertically integrated liquid (VIL) water content measurements. The model is tested on a dataset collected during the intensive observation period of the Mesoscale Alpine Program (MAP). Five rain events have been studied during this experiment. The results confirm the interest of VIL for quantitative precipitation forecasting at very short lead time. The evaluation is carried out in qualitative and quantitative ways according to Nash and correlation criteria on forecasting times ranging from 10 to 90 min and spatial scales from 4 to 169 km2. It attempts to be consistent with the hydrological requirements concerning the rainfall forecasting, for instance, by taking account of the relation between the catchments' size, their response time, and the required forecasting time. Several versions of RadVil corresponding to several VIL measurement strategies have been tested. Improvements offered by RadVil depend on meteorological situations. They are related to the spatial and temporal evolution of the VIL field structure and the validity of the models assumptions. Finally, a relationship between the temporal structure of VIL fields and forecast quality is established.

scholarly journals Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3204
Author(s):  
Michał Sabat ◽  
Dariusz Baczyński
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Forecasting Model ◽  
Short Term ◽  
Complex Evaluation ◽  
Proposed Model ◽  
Energy Demand Forecasting ◽  
Pareto Fronts ◽  
Naive Approach

Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

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