Prediction of monthly rainfall on homogeneous monsoon regions of India based on large scale circulation patterns using Genetic Programming
Highlights
► The information of ENSO and EQUINOO is used to model monthly variation of Indian Summer Monsoon Rainfall. ► Separate analyses are carried out for All India rainfall and five homogeneous monsoon regions. ► ‘Genetic Programming’ (GP) has been employed for this modeling such problem and found to capture the complex relationship. ► Monthly variation of all-India monsoon rainfall is captured with correlation coefficient being 0.866. ► Influence of ENSO and EQUINOO on Rainfall is maximum for Central North-East India and minimum for Peninsular India.
Introduction
It is scientifically and mathematically challenging to use climate signals for the prediction of basin-scale hydrologic variables, because the climatic systems are very complex and physics of many systems is still not very clearly understood. The difficulties in modeling such complex systems are considerably reduced by the recent Artificial Intelligence tools like Artificial Neural Networks (ANNs); Genetic Algorithm (GA) based evolutionary optimizer and Genetic Programming (GP). Hence such AI tools are tried nowadays for modeling complex systems like basin-scale stream-flow forecasting using the information of large-scale atmospheric circulation phenomena.
Indian Summer Monsoon Rainfall is always found to vary annually leading to profound impacts on agriculture based Indian economy. Generally meteorological forecasts are generated for three timescales, viz. short-range (1–2 days ahead), medium-range (3–10 days ahead) and long-range forecasts for monthly and seasonal scales. In India, India Meteorological Department (IMD) generates the short and long-range predictions, whereas the National Centre for Medium Range Weather Forecasting (NCMRWF), New Delhi is responsible for the medium-range predictions.
Prediction of ISMR is having a long history. It started with the work of Sir Henry Blanford in 1886, which was entirely based on Himalayan snowfall. John Eliot used extra-Indian factors, viz. Pressure of Mauritus, Zanzibar and Seychelles in the monsoon forecast of 1896. Sir Gilbert Walker proposed statistical association for monsoon forecast. He systematically examined the relationship between Indian monsoon rainfall and global circulation parameters. He selected 28 predictors to issue forecast based on regression equation during the year 1906 (Jagannathan, 1960, Rao and Rama Moorthy, 1960, Rao, 1965). Most of the Walker’s predictors (except Himalayan snow accumulation) were signs of different facets of Southern Oscillation (Shukla and Paolino, 1983). Savur (1931) showed that 7 out of the 28 parameters had lost their significance in due course of time. Since then, extensive research work has been carried out on empirical seasonal forecasting of Indian Summer Monsoon Rainfall. Some of the noteworthy studies can be listed as following: Banerjee et al., 1978, Kung and Sharif, 1982, Bhalme et al., 1986, Gowariker et al., 1989, Gowariker et al., 1991, Parthasarathy et al., 1988, Parthasarathy et al., 1991, Parthasarathy et al., 1995, Krishna Kumar et al., 1995, Krishna Kumar et al., 1997, Rajeevan et al., 2004, etc. In spite of many efforts in the long range prediction of all-India summer monsoon rainfall (AISMR), it is felt that achieved success is not adequate and there is much scope to investigate new predictors and new methodologies of ISMR prediction.
Even though forecast for ‘All India Summer Monsoon Rainfall’ is available before every monsoon nowadays, still such forecasts have limited use due to significant variation in monsoon rainfall over the country in the same season. Hence it is felt that the rainfall forecasts can be more useful if issued at regional or sub-divisional scale. Hence this work attempts to develop models for issuing medium range forecasts of monthly monsoon rainfall from June through October, at all India level, as well as for five homogeneous monsoon regions of India. It is felt that such regional forecasts will have better utility than total ISMR due to availability of the information at smaller spatio-temporal scale.
Simultaneous variations of climatic conditions and hydrologic variables over widely separated regions on the surface of earth have long been discovered and noted by the meteorologists, world over. Such recurrent patterns are commonly referred to as “hydroclimatic teleconnection”. It is established that the natural variation of hydrologic variables is linked with these large-scale atmospheric circulation pattern through hydroclimatic teleconnection (Dracup and Kahya, 1994; Eltahir, 1996; Jain and Lall, 2001; Douglas et al., 2001; Ashok et al., 2001, Ashok et al., 2004; Marcella and Eltahir, 2008; Maity and Nagesh Kumar, 2008). Indian hydrometeorology is prominently influenced by two large-scale atmospheric circulation patterns. The first is El Niño-Southern Oscillation (ENSO) from tropical Pacific Ocean and second is the Equatorial Indian Ocean Oscillation (EQUINOO) from Indian Ocean. (Gadgil et al., 2004).
El Niño-Southern Oscillation (ENSO), which is a large-scale circulation pattern from tropical Pacific Ocean, is established to influence Indian Summer Monsoon Rainfall. Another large scale circulation pattern from Indian Ocean viz. Indian Ocean Dipole Mode (IOD) also influences the Indian Summer Monsoon rainfall (Saji et al., 1999).
Equatorial Indian Ocean Oscillation (EQUINOO) is the atmospheric component of the IOD mode (Gadgil et al., 2003, Gadgil et al., 2004). Gadgil et al. (2003) had shown that the Indian Summer Monsoon Rainfall is not only associated with ENSO, but also with EQUINOO. They suggest that one can scientifically predict the Indian Summer Monsoon Rainfall by knowing the prior EQUINOO status. Equatorial zonal wind index (EQWIN) is considered as an index of EQUINOO, which is defined as negative of the anomaly of the zonal component of surface wind in the equatorial Indian Ocean Region (60°E–90°E, 2.5°S–2.5°N). Weakening of ENSO-ISMR relationship is indicated by few researchers (Krishna Kumar et al., 1999). It is also established that ENSO-ISMR is modified by the influence of Indian Ocean Dipole (IOD) mode. Consideration of these two indices is found to give better results as compared to the analyses by researchers using just ENSO index (Gadgil et al., 2004). Thus, in this study, apart from ENSO index, EQUINOO index from Equatorial Indian Ocean is considered simultaneously, which is supposed to take care the temporal change in relationship between ENSO and ISMR.
Since nearly 80% of Indian Summer Monsoon Rainfall is due to the southwest monsoon, interaction between various oceans due to ENSO and EQUINOO regulates the amount and distribution of the rainfall over the sub continent. Such association is more prominent for the large aerial scale. It is also prominent for longer temporal scale (seasonal) or smaller temporal scale (monthly).
The search for a new methodology for predicting the All India Summer Monsoon Rainfall has been continued for a long time. The search is active for both, the long term as well as short term forecasts of Indian Summer Monsoon Rainfall. In recent years, an efficient Artificial Intelligence tool Genetic Programming has been used for modeling complex systems. Hence the Genetic Programming approach has been used to predict monthly Indian Summer Monsoon Rainfall over India in this study.
Section snippets
Objectives of the work
This work intends to develop models for medium range (1 month ahead) forecasts of monthly Indian Summer Monsoon rainfall for ‘All India’, as well as for five homogeneous monsoon regions of India, by using ENSO and EQUINOO indices as large scale atmospheric circulation information, with the help of Artificial Intelligence tool Genetic Programming. The study also deals with development of models for prediction of one time ISMR forecast on end of May, for All India Summer Monsoon Rainfall and five
Data
Sea surface temperature (SST) anomaly from the Niño 3.4 region (120°W–170°W, 5°S–5°N) is used as the ‘ENSO index’ in this study. Monthly sea surface temperature data from Niño 3.4 region for the period, January 1950 to December 2006, data are obtained from the website of the National Weather Service, Climate Prediction Centre of National Oceanic and Atmospheric Administration (NOAA) (http://www.cpc.noaa.gov/data/indices/). EQWIN, the negative of zonal wind anomaly over equatorial Indian Ocean
Methodology
Artificial Intelligence tools like Artificial Neural Networks, Support Vector Machines as well as Neuro-fuzzy Systems and data driven models have become popular tools for predictions in Water Resources related research problems (Drecourt, 1999; Giustolisi and Savic, 2006; Chau, 2006; Li et al., 2006; Lin et al., 2006; Shiri and Kisi, 2011; Muttil and Chau, 2007; Partal and Kisi, 2007). Many researchers have used Genetic Programming for modeling complex systems. (Babovic, 2000; Aytek and Kisi,
Results and discussions
As stated earlier, two separate analyses are carried out in this work. The first analysis uses real time monthly ENSO and EQUINOO indices of three previous monthly time steps for prediction of rainfall, which is nothing but medium range forecast with 2 weeks lead time by using monthly data of ENSO and EQUINOO in real time. The second analysis is one time analysis at the end of month of May, for computing monthly and hence monsoon rainfall in months of June through September at the end of May. It
Conclusions
Established research works indicate an association between the large-scale circulation pattern and hydrologic variables of large spatial and temporal scale. In this study, All India Summer Monsoon Rainfall as well as regional summer monsoon rainfall in India is investigated for possible influence of the large-scale circulation patterns on it. The ENSO and EQUINOO information is used as the large-scale input, which is established to be important for Indian hydroclimatology. Genetic Programming,
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