K. G. Sandhya

Neural-Network-Based Data Assimilation to Improve Numerical Ocean Wave Forecast

This paper demonstrates the skill level of a wavelet neural network in improving numerical ocean wave predictions of significant wave height (H8) and peak wave period (Tp) having practical applications in operational centers. The study uses data of H8 and Tp for a coastal region off Puducherry located in the east coast of India, and obtained from a high-resolution wave model resulting from nesting of the SWAN model with the WW3 model. A wave rider buoy located off Puducherry provided data for a period of 25 months during the period from June 2007 until July 2009 used in this study. The time series of error between numerical and corresponding measured values was first constructed, and using a wavelet neural network, the errors were predicted for future time steps. The predicted errors when incorporated into the model values provided the updated prediction of H8 and Tp. The study signifies that numerical estimations could be significantly improved using this procedure. The results provide quite satisfactory predictions with a lead time varying from 3 to 24 h. The study points out that adequate training of the neural network is an essential prerequisite to obtain good performance and skill levels. A comparison between the suggested prediction method with the standalone neural network model trained with measured data off Puducherry showed that the former approach is preferred over the latter in obtaining a sustained prediction performance.

High Frequency Tail Characteristics in the Coastal Waters off Gopalpur, Northwest Bay of Bengal: A Nearshore Modelling Study

Over the years, continued uncertainty amid − 4 and − 5 frequency exponent representation observed in the slope of the high-frequency tail of a wind-wave frequency spectrum is a major concern. To comprehend the nature of the high-frequency tail an effort has been made to assess the slope of the high-frequency tail with measured data recorded for 3 years off Gopalpur. The study demonstrates that the high-frequency slope of the spectra varied seasonally in the range of n = − 2.13 to − 3.48. The swell and wind sea parameters calculated by separation frequency method, shows that 64.6% of waves were dominant by swell and the rest 34.9% by sea annually. Single, double and multi-peaked spectra occur 12.23, 71.80 and 15.37% annually. To simulate wave spectra, the nested WAM-SWAN model is forced with ERA-Interim winds and 1D wave spectra comparisons, when performed, proved to be encouraging. From the comparisons of measured and theoretical spectra it is concluded that JONSWAP model could not describe the high-frequency tail of measured spectrum, as indicated by the very high Scatter Index ranging from 0.24 to 1.44. Whether there exists a correct slope for the high-frequency tail is still a question. Moreover, the philosophy of a unique slope at any coastal location remains uncertain for the wave modelling community.

Modification of a linear regression-based multi-model super-ensemble technique and its application in forecasting of wave height during extreme weather conditions

ABSTRACT In this study, we focus on the improvement of wave forecast of the Indian coastal region using a multi-model ensemble technique. Generally, a number of wave forecast are available for the same region from different wave models. The main objective of this study is to merge the wave forecasts available at Indian National Centre for Ocean Information Services from different wave models to obtain an improved wave forecast using a multi-model super-ensemble method [Krishnamurti et al. 1999. Improved weather and seasonal climate forecasts from multi-model super-ensemble. Science. 285:1548–1550] during extreme weather conditions and to modify Krishnamurthy’s techniques and validate with observations for a better prediction. Here, Multi-grid WAVEWATCH III, Simulating WAves Nearshore and MIKE 21 Spectral Waves are used for the generation of wave forecast. We propose a modification of Krishnamurthy’s linear regression-based ensemble model. By using both of these ensemble techniques, we perform a multi-model ensemble forecasting of significant wave height up to 24-h lead time in the Indian Ocean for three different cyclones (Nilofar, Hudhud and Phailin) and during the southwest monsoon. A comparison of ensemble predictions and individual model predictions with the actual observations showed generally satisfactory performance of the chosen tools. At the time of severe cyclones such as Hudhud and Phailin, our modified technique shows significantly better prediction than the linear regression-based ensemble technique.

Evaluation of wave forecast in the north Indian Ocean during extreme conditions and winter monsoon

ABSTRACT The accuracy of the operational ocean state forecast system at ESSO-INCOIS during extreme events as well as calm conditions for a lead time of 24, 72 and 144 h is evaluated in this paper. Forecasted wave parameters (significant wave height, mean wave period and mean wave direction) are compared with in situ data for three cyclones (Sidr, Khai Muk and Nisha) that occurred in the Bay of Bengal (BoB). Forecasts obtained for 24 and 72 h showed good agreement with in situ data. Error statistics obtained during extreme events (SI < 30%, correlation coefficient > .75) suggests the good quality of the wave forecasts, which could be used for issuing high wave alerts. The two-dimensional energy spectra from the wave model during the Sidr cyclone period showed the capability of the model to simulate wind seas and swells even under cyclonic conditions. During winter monsoon, the error statistics obtained in the BoB suggests, good quality of 24- and 72-h wave forecasts. Thus the wave model is good at simulating the wave conditions for different wave height ranges and for a lead time of up to 72 h and hence a reliable tool for generating operational ocean state forecasts.