Inter‐play between Sub‐seasonal Rainfall and Global Predictors in Modulating Interannual to Multi‐decadal Predictability of the ISMR

2020 ◽  
Author(s):  
Subodh Kumar Saha ◽  
Mahen Konwar ◽  
Samir Pokhrel ◽  
Anupam Hazra ◽  
Hemantkumar S. Chaudhari ◽  
...  
Keyword(s):  
Seasonal Rainfall ◽  
Decadal Predictability

Contribution of cold fronts to seasonal rainfall in simulations over the southern La Plata Basin

2016 ◽  
Vol 68 (2-3) ◽  
pp. 243-255 ◽  
Author(s):  
EM de Jesus ◽  
RP da Rocha ◽  
MS Reboita ◽  
M Llopart ◽  
LM Mosso Dutra ◽  
...  
Keyword(s):  
Seasonal Rainfall ◽  
La Plata ◽  
Cold Fronts ◽  
La Plata Basin

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

Long-lead seasonal rainfall forecasting using time-delay recurrent neural networks: a case study

2007 ◽  
Vol 22 (2) ◽  
pp. 229-241 ◽  
Author(s):  
Mohammad Karamouz ◽  
Saman Razavi ◽  
Shahab Araghinejad
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Recurrent Neural Networks ◽  
Seasonal Rainfall ◽  
Rainfall Forecasting
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