Prediction of seasonal summer monsoon rainfall over homogenous regions of India using dynamical prediction system

Ramu, Dandi A.; Rao, Suryachandra A.; Pillai, Prasanth A.; Pradhan, Maheswar; George, Gibies; Rao, D. Nagarguna; Mahapatra, S.; Pai, D. S.; Rajeevan, M.

doi:10.1016/j.jhydrol.2017.01.010

Cited by 37 publications

(19 citation statements)

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“…Additionally, we have examined the average number of rainy days (≥2.5 mm day −1 ) over Indian landmass and five homogeneous regions of India viz. NWI (North‐West India), NEI (North‐East India), CNEI (Central North East India), WCI (West Central India), and SPI (Southern Peninsular India) (Ramu et al., 2017) from the model simulations and the observation. Information on the number of rainy days during the monsoon season is crucial from an agricultural and hydrological perspective.…”

Section: Systematic Error In Simulated Rainfall Distribution and Iav Of Ismrmentioning

confidence: 99%

“…The high‐resolution Climate Forecast System version 2 (CFSv2) was set up at the Indian Institute of Tropical Meteorology to provide experimental forecasts of the ISMR since 2009 and is now operational in India Meteorological Department since 2017. The model has a reasonably high skill in predicting ISMR (Ramu et al., 2016; Rao et al., 2019) and the homogenous regions of the Indian sub‐continent (Ramu et al., 2017) at the longer lead time (i.e., February Initial conditions). Based on the MM phase I's success, the MM phase II was launched with an emphasis on developing applications based on the skillful seasonal forecasts of ISMR.…”

Section: Introductionmentioning

confidence: 99%

“…The meteorological inputs as seasonal means from coupled general circulation models (CGCMs) are not enough for the proper planning of the entire crop season (Capa‐Morocho et al., 2016). Seasonal forecasts of all India averaged rainfall limit its applicability to agriculture, hydrology, energy, and other sectors (Manzanas et al., 2018; Ramu et al., 2017; Sabeerali et al., 2013; S. K. Saha et al., 2016). Many crop or hydrology models are configured at smaller regional scales and hence can be significantly affected by the biases of the dynamical models, and country averaged information has little value.…”

Section: Introductionmentioning

confidence: 99%

“…The meteorological inputs as seasonal means from coupled general circulation models (CGCMs) are not enough for the proper planning of the entire crop season (Capa-Morocho et al, 2016). Seasonal forecasts of all India averaged rainfall limit its applicability to agriculture, hydrology, energy, and other sectors (Manzanas et al, 2018;Ramu et al, 2017;Sabeerali et al, 2013;S. K. Saha et al, 2016).…”

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confidence: 99%

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Reducing Systematic Biases Over the Indian Region in CFS V2 by Dynamical Downscaling

Prasad

Ramu

Rao

et al. 2021

Earth and Space Science

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The usefulness of dynamical downscaling of seasonal reforecasts of Indian Monsoon is explored to address the seasonal mean biases in the reforecasts. Almost all the current generation global coupled models, including the Climate Forecast System version 2 (CFSv2, T126 ∼110 km), exhibit systematic mean dry bias over the central Indian region during the summer monsoon season. Cold sea surface temperature (SST) biases in the Indian Ocean and a weak monsoon circulation due to a colder tropospheric temperature contribute to this dry bias. Such systematic biases restrict the use of skillful forecasts from these models in climate applications (such as agriculture or hydrology). Dynamical downscaling of seasonal forecasts (∼110 km resolution) using the Weather Research and Forecasting (WRF) model coupled to a simple ocean mixed layer model (OML; WRFOML) at 38 km resolution significantly reduces the majority of the systematic biases reported earlier. The seasonal mean dry bias reduces to 16% in WRFOML as compared to 44% (33%) in the CFSv2‐T126 (WRFCTL) over the Indian land region. Warmer SSTs in the Indian Ocean and a more robust monsoon circulation emanating from a realistic simulation of the tropospheric temperature reduced the systematic biases in WRFOML compared to CFSv2‐T126 and WRFCTL. Additionally, category‐wise rainfall distributions are also improved drastically in the downscaled simulations (WRFOML). Downscaled reforecasts with reduced systematic biases have better suitability for climate applications.

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Section: Systematic Error In Simulated Rainfall Distribution and Iav Of Ismrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Reducing Systematic Biases Over the Indian Region in CFS V2 by Dynamical Downscaling

Prasad

Ramu

Rao

et al. 2021

Earth and Space Science

Self Cite

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“…The influence of the Indian ocean dipole and the tropical Atlantic Ocean on the ISM in contrast is far more subtle and at times used to understand the noncanonical ISM-ENSO seasons (Krishnamurthy and Kirtman, 2009;Yadav et al, 2018). Nonetheless, despite these teleconnections, the seasonal prediction of the ISM continues to be a challenge (Ramu et al, 2017;Pillai et al, 2018).…”

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confidence: 99%

The impact of varying seasonal lengths of the rainy seasons of India on its teleconnections with tropical sea surface temperatures

Misra

Bhardwaj

2020

Atmospheric Science Letters

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We present in this paper the interannual variability of seasonal temperature and rainfall in the Indian meteorological subdivisions (IMS) for boreal winter and summer seasons that take in to account the varying length of the seasons. Our study reveals that accounting for the variations in the length of the seasons produces stronger teleconnections between the seasonal anomalies of surface temperature and rainfall over India with corresponding sea surface temperature anomalies of the tropical Oceans (especially over the northern Indian and the equatorial Pacific Oceans) compared to the same teleconnections from fixed length seasons over the IMS. It should be noted that the IMS show significant spatial heterogeneity in these teleconnections.

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Automated rain fall prediction enabled by optimized convolutional neural network‐based feature formation with adaptive long short‐term memory framework

Ananthajothi¹,

Karthick

2022

Concurrency and Computation

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The main concept of this article is to plan for the intelligent rainfall prediction using the combination of deep learning models. The dataset is gathered from the standard publically available dataset concerning the Tamil Nadu state. The collected data is given to the feature extraction, in which few features such as; "minimum value, maximum value, mean, median, standard deviation, kurtosis, entropy, skewness, variance, and zero cross" are extracted. Additionally, the extracted features are applied to the optimal feature formation, in which optimized convolutional neural network (O-CNN) is employed for the final feature formation. Here, the activation function, count of pooling layer, and count of hidden neurons are tuned with the intention of minimizing the correlation between the selected features. Once the optimal features are selected with less correlation, adaptive long short-term memory (A-LSTM) is adopted for the prediction model. Here, the enhancement is concentrated on minimizing the function concerning the error through the optimization of the hidden neurons of A-LSTM. The improvement of both the deep learning models O-CNN and A-LSTM is performed by the improved sun flower optimization (I-SFO). The research results reveal superior performance to existing techniques that offer novel thinking in rainfall prediction area with optimal rate of prediction. K E Y W O R D Sadaptive long-short term memory, automated rainfall prediction, improved sun flower optimization, optimized convolutional neural network INTRODUCTIONThe rainfall 1 represents a climatic feature, which damages several human activities such as tourism, forestry, power generation, construction, and agricultural production, among others. 2 The prediction of rainfall 3 is needed because this variable contains much correlation with undesirable natural events like avalanches, mass movements, flooding, and landslides. The society is being damaged by these incidents over the years. 4,5 Hence, an accurate technique for the rainfall 6 prediction considers the mitigation and preventive measures for these natural calamities. 7 The precipitation probability is predicted in a specific region and the future forecasting is predicted by estimating the rainfall 8 amount in particular regions. 9 It considers the rainfall volume estimation, error in prediction, and accuracy of prediction together with the rainfall 10 probability in that particular region. The forecasters prepare it through, "collecting, analyzing, verifying, modeling, simulating and doing research" on distinct parameters present. 11 Few constraints consist of relative humidity, and average mean monthly temperature (maximum and minimum). 12 The intensity

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Prediction of seasonal summer monsoon rainfall over homogenous regions of India using dynamical prediction system

Cited by 37 publications

References 29 publications

Reducing Systematic Biases Over the Indian Region in CFS V2 by Dynamical Downscaling

Reducing Systematic Biases Over the Indian Region in CFS V2 by Dynamical Downscaling

The impact of varying seasonal lengths of the rainy seasons of India on its teleconnections with tropical sea surface temperatures

Automated rain fall prediction enabled by optimized convolutional neural network‐based feature formation with adaptive long short‐term memory framework

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