scholarly journals A comparison of two seasonal rainfall forecasting systems for Australia

2010 ◽  
Vol 61 (01) ◽  
pp. 15-23 ◽  
Author(s):  
R Fawcett ◽  
R Stone
Keyword(s):  
Seasonal Rainfall ◽  
Rainfall Forecasting

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

scholarly journals Seasonal rainfall forecasting in West Africa

Nature ◽  
1974 ◽  
Vol 248 (5448) ◽  
pp. 464-464 ◽  
Author(s):  
Derek Winstanley
Keyword(s):  
West Africa ◽  
Seasonal Rainfall ◽  
Rainfall Forecasting

scholarly journals Use of ENSO-Based Seasonal Rainfall Forecasting for Informed Cropping Decisions by Farmers in the SAT India

2007 ◽  
pp. 165-180 ◽  
Author(s):  
V. Nageswara Rao ◽  
P. Singh ◽  
J. Hansen ◽  
T. Giridhara Krishna ◽  
S. K. Krishna Murthy
Keyword(s):  
Seasonal Rainfall ◽  
Rainfall Forecasting

Long-term seasonal rainfall forecasting: efficiency of linear modelling technique

2018 ◽  
Vol 77 (7) ◽  
Author(s):  
Iqbal Hossain ◽  
H. M. Rasel ◽  
Monzur Alam Imteaz ◽  
Fatemeh Mekanik
Keyword(s):  
Seasonal Rainfall ◽  
Modelling Technique ◽  
Rainfall Forecasting ◽  
Linear Modelling

scholarly journals Incorporating large-scale atmospheric variables in long-term seasonal rainfall forecasting using artificial neural networks: an application to the Ping Basin in Thailand

2016 ◽  
Vol 48 (3) ◽  
pp. 867-882 ◽  
Author(s):  
M. S. Babel ◽  
T. A. J. G. Sirisena ◽  
N. Singhrattna
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Large Scale ◽  
Resource Planning ◽  
Seasonal Rainfall ◽  
Annual Rainfall ◽  
Rainfall Forecasting ◽  
Artificial Neural ◽  
Atmospheric Variables

Understanding long-term seasonal or annual or inter-annual rainfall variability and its relationship with large-scale atmospheric variables (LSAVs) is important for water resource planning and management. In this study, rainfall forecasting models using the artificial neural network technique were developed to forecast seasonal rainfall in May–June–July (MJJ), August–September–October (ASO), November–December–January (NDJ), and February–March–April (FMA) and to determine the effects of climate change on seasonal rainfall. LSAVs, temperature, pressure, wind, precipitable water, and relative humidity at different lead times were identified as the significant predictors. To determine the impacts of climate change the predictors obtained from two general circulation models, CSIRO Mk3.6 and MPI-ESM-MR, were used with quantile mapping bias correction. Our results show that the models with the best performance for FMA and MJJ seasons are able to forecast rainfall one month in advance for these seasons and the best models for ASO and NDJ seasons are able do so two months in advance. Under the RCP4.5 scenario, a decreasing trend of MJJ rainfall and an increasing trend of ASO rainfall can be observed from 2011 to 2040. For the dry season, while NDJ rainfall decreases, FMA rainfall increases for the same period of time.

scholarly journals Seasonal rainfall forecasting for the Yangtze River basin using statistical and dynamical models

2019 ◽  
Vol 40 (1) ◽  
pp. 361-377 ◽  
Author(s):  
Shuni Qian ◽  
Jie Chen ◽  
Xiangquan Li ◽  
Chong‐Yu Xu ◽  
Shenglian Guo ◽  
...  
Keyword(s):  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Seasonal Rainfall ◽  
The Yangtze River ◽  
Dynamical Models ◽  
Rainfall Forecasting ◽  
The Yangtze River Basin

scholarly journals Monthly and Seasonal Rainfall Forecasting in Southern Brazil Using Multiple Discriminant Analysis

2016 ◽  
Vol 31 (6) ◽  
pp. 1947-1960 ◽  
Author(s):  
Denilson Ribeiro Viana ◽  
Clóvis Angeli Sansigolo
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Skill Score ◽  
Seasonal Rainfall ◽  
Rainfall Time Series ◽  
Southern Brazil ◽  
Multiple Discriminant Analysis ◽  
Rainfall Forecasting ◽  
Global Precipitation Climatology Centre

Abstract A multiple discriminant analysis was employed to forecast monthly and seasonal rainfall in southern Brazil. The methodology used includes six steps: data acquisition, preprocessing, feature extraction, feature selection, classification, and evaluation. The predictors (atmospheric, surface, and oceanic variables) and predictand (rainfall) were obtained from the Twentieth Century Reanalysis (version 2), as well as from the HadISST1 (Met Office Hadley Centre) and Global Precipitation Climatology Centre (GPCC) databases. The definition of key regions (feature extraction step) was performed using spatial principal component analysis. In the selection step, the rainfall time series were allocated into terciles, which were related to the predictors via multiple discriminating analyses. The results revealed that ⅓ of the predictors are associated with atmospheric pressure and also emphasized the role of atmospheric circulation over the Antarctic region and its surroundings. Surface variables (albedo and soil moisture) were also of great importance in the forecasting. The average skill score (gain over climatology) was 29%. It is concluded that the proposed model is a reliable alternative for use in forecasting monthly and seasonal rainfall over southern Brazil.

scholarly journals Toward Accurate and Reliable Forecasts of Australian Seasonal Rainfall by Calibrating and Merging Multiple Coupled GCMs

2013 ◽  
Vol 141 (12) ◽  
pp. 4554-4563 ◽  
Author(s):  
Andrew Schepen ◽  
Q. J. Wang
Keyword(s):  
General Circulation ◽  
Bayesian Model Averaging ◽  
Forecast Accuracy ◽  
Joint Probability ◽  
General Circulation Models ◽  
Seasonal Rainfall ◽  
Equal Weight ◽  
Rainfall Forecasting ◽  
Probability Models ◽  
Seasonal Rainfall Forecasts

Abstract The majority of international climate modeling centers now produce seasonal rainfall forecasts from coupled general circulation models (GCMs). Seasonal rainfall forecasting is highly challenging, and GCM forecast accuracy is still poor for many regions and seasons. Additionally, forecast uncertainty tends to be underestimated meaning that forecast probabilities are statistically unreliable. A common strategy employed to improve the overall accuracy and reliability of GCM forecasts is to merge forecasts from multiple models into a multimodel ensemble (MME). The most widely used technique is to simply pool all of the forecast ensemble members from multiple GCMs into what is known as a superensemble. In Australia, seasonal rainfall forecasts are produced using the Predictive Ocean–Atmosphere Model for Australia (POAMA). In this paper, the authors demonstrate that mean corrected superensembles formed by merging forecasts from POAMA with those from three international models in the ENSEMBLES dataset remain poorly calibrated in many cases. The authors propose and evaluate a two-step process for producing MMEs. First, forecast calibration of the individual GCMs is carried out by using Bayesian joint probability models that account for parameter uncertainty. The calibration leads to satisfactory forecast reliability. Second, the individually calibrated forecasts of the GCMs are merged through Bayesian model averaging (BMA). The use of multiple GCMs results in better forecast accuracy, while maintaining reliability, than using POAMA only. Compared with using equal-weight averaging, BMA weighting produces sharper and more accurate forecasts.

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