scholarly journals Studies on Short-term Rainfall Prediction Using Animation of Radar Images and Estimation of Average-Rainfall-Depth over a Watershed.

1997 ◽  
pp. 13-30
Author(s):  
Tadashi Yamada ◽  
Tadashi Hibino ◽  
Makoto Nakatsugawa ◽  
Mitsuaki Fujisawa ◽  
Hiroshi Morinaga
Keyword(s):  
Short Term ◽  
Rainfall Prediction ◽  
Radar Images ◽  
Rainfall Depth ◽  
Average Rainfall

Nowcasting heavy precipitation over the Netherlands using a 13-year radar archive: a machine learning approach

2021 ◽  
Author(s):  
Eva van der Kooij ◽  
Marc Schleiss ◽  
Riccardo Taormina ◽  
Francesco Fioranelli ◽  
Dorien Lugt ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Netherlands ◽  
Heavy Rainfall ◽  
Predictive Performance ◽  
Heavy Precipitation ◽  
Early Warning Systems ◽  
Training Data ◽  
Short Term ◽  
Data Set ◽  
Radar Images

<p>Accurate short-term forecasts, also known as nowcasts, of heavy precipitation are desirable for creating early warning systems for extreme weather and its consequences, e.g. urban flooding. In this research, we explore the use of machine learning for short-term prediction of heavy rainfall showers in the Netherlands.</p><p>We assess the performance of a recurrent, convolutional neural network (TrajGRU) with lead times of 0 to 2 hours. The network is trained on a 13-year archive of radar images with 5-min temporal and 1-km spatial resolution from the precipitation radars of the Royal Netherlands Meteorological Institute (KNMI). We aim to train the model to predict the formation and dissipation of dynamic, heavy, localized rain events, a task for which traditional Lagrangian nowcasting methods still come up short.</p><p>We report on different ways to optimize predictive performance for heavy rainfall intensities through several experiments. The large dataset available provides many possible configurations for training. To focus on heavy rainfall intensities, we use different subsets of this dataset through using different conditions for event selection and varying the ratio of light and heavy precipitation events present in the training data set and change the loss function used to train the model.</p><p>To assess the performance of the model, we compare our method to current state-of-the-art Lagrangian nowcasting system from the pySTEPS library, like S-PROG, a deterministic approximation of an ensemble mean forecast. The results of the experiments are used to discuss the pros and cons of machine-learning based methods for precipitation nowcasting and possible ways to further increase performance.</p>

scholarly journals Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 668 ◽  
Author(s):  
S. Poornima ◽  
M. Pushpalatha
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Moving Average ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Rainfall Prediction ◽  
Long Short Term Memory

Prediction of rainfall is one of the major concerns in the domain of meteorology. Several techniques have been formerly proposed to predict rainfall based on statistical analysis, machine learning and deep learning techniques. Prediction of time series data in meteorology can assist in decision-making processes carried out by organizations responsible for the prevention of disasters. This paper presents Intensified Long Short-Term Memory (Intensified LSTM) based Recurrent Neural Network (RNN) to predict rainfall. The neural network is trained and tested using a standard dataset of rainfall. The trained network will produce predicted attribute of rainfall. The parameters considered for the evaluation of the performance and the efficiency of the proposed rainfall prediction model are Root Mean Square Error (RMSE), accuracy, number of epochs, loss, and learning rate of the network. The results obtained are compared with Holt–Winters, Extreme Learning Machine (ELM), Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network and Long Short-Term Memory models in order to exemplify the improvement in the ability to predict rainfall.

Short-term risk forecasts of heavy rainfall

2002 ◽  
Vol 45 (2) ◽  
pp. 121-125 ◽  
Author(s):  
W. Schmid ◽  
S. Mecklenburg ◽  
J. Joss
Keyword(s):  
Probability Distribution ◽  
Density Function ◽  
Heavy Rainfall ◽  
Heavy Precipitation ◽  
Severe Weather ◽  
Short Term ◽  
Radar Images ◽  
Error Density ◽  
Estimated Error ◽  
Near Future

Methodologies for risk forecasts of severe weather hardly exist on the scale of nowcasting (0–3 hours). Here we discuss short-term risk forecasts of heavy precipitation associated with local thunderstorms. We use COTREC/RainCast: a procedure to extrapolate radar images into the near future. An error density function is defined using the estimated error of location of the extrapolated radar patterns. The radar forecast is folded (“smeared”) with the density function, leading to a probability distribution of radar intensities. An algorithm to convert the radar intensities into values of precipitation intensity provides the desired probability (or risk) of heavy rainfall at any position within the considered window in space and time. We discuss, as an example, a flood event from summer 2000.

scholarly journals SINGULAR VECTOR METHOD ON SHORT-TERM RAINFALL PREDICTION USING RADAR FOR HYDROLOGIC ENSEMBLE PREDICTION

2011 ◽  
Vol 67 (4) ◽  
pp. I_109-I_114 ◽  
Author(s):  
Ratih Indri HAPSARI ◽  
Satoru OISHI ◽  
Kengo SUNADA ◽  
Eiichi NAKAKITA ◽  
Tetsuya SANO
Keyword(s):  
Singular Vector ◽  
Ensemble Prediction ◽  
Short Term ◽  
Vector Method ◽  
Rainfall Prediction

Use of GPS Technology for Short-term Rainfall Prediction

2003 ◽  
Author(s):  
G. V. Loganathan ◽  
S. Gorugantula ◽  
D. F. Kibler ◽  
S. J. Keighton ◽  
M. Gillen
Keyword(s):  
Short Term ◽  
Gps Technology ◽  
Rainfall Prediction
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