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  • Author or Editor: M. Ravichandran x
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Kalpesh Patil
,
M. C. Deo
, and
M. Ravichandran

Abstract

The prediction of sea surface temperature (SST) in real-time or online mode has applications in planning marine operations and forecasting climate. This paper demonstrates how SST measurements can be combined with numerical estimations with the help of neural networks and how reliable site-specific forecasts can be made accordingly. Additionally, this work demonstrates the skill of a special wavelet neural network in this task. The study was conducted at six different locations in the Indian Ocean and over three time scales (daily, weekly, and monthly). At every time step, the difference between the numerical estimation and the SST measurement was evaluated, an error time series was formed, and errors over future time steps were forecasted. The time series forecasting was affected through neural networks. The predicted errors were added to the numerical estimation, and SST predictions were made over five time steps in the future. The performance of this procedure was assessed through various error statistics, which showed a highly satisfactory functioning of this scheme. The wavelet neural network based on the particular basic or mother wavelet called the “Meyer wavelet with discrete approximation” worked more satisfactorily than other wavelets.

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S. Sivareddy
,
M. Ravichandran
, and
M. S. Girishkumar

Abstract

The quality of daily gridded Advanced Scatterometer (ASCAT; DASCAT) blended winds is examined in the tropical Indian Ocean using 3-day running mean gridded Quick Scatterometer (QuikSCAT; QSCAT) winds and in situ daily winds from the Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA). The primary objective of this study is to examine whether DASCAT is a reliable replacement for the widely used QSCAT wind products. Spatial distributions of DASCAT and QSCAT winds show good agreement in speed and direction, except over a few localized regions. The study finds a significant spatial coherence between rainfall and the regions of discrepancy between DASCAT and QSCAT. Comparison of DASCAT and QSCAT wind products with RAMA moorings indicates that DASCAT better captures the overall wind variability compared to QSCAT, especially during rainy and low wind (<5 m s−1) conditions. The root-mean-square of the RAMA–DASCAT (RAMA–QSCAT) difference during rainfall in the zonal and meridional winds is 1.4 and 1.6 m s−1 (2.7 and 2.0 m s−1), respectively. The present study indicates that the DASCAT blended wind product is a reliable alternative to QSCAT in the tropical Indian Ocean.

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Tim Boyer
,
V. V. Gopalakrishna
,
Franco Reseghetti
,
Amit Naik
,
V. Suneel
,
M. Ravichandran
,
N. P. Mohammed Ali
,
M. M. Mohammed Rafeeq
, and
R. Anthony Chico

Abstract

Long time series of XBT data in the Bay of Bengal and the Arabian Sea are valuable datasets for exploring and understanding climate variability. However, such studies of interannual and longer-scale variability of temperature require an understanding, and, if possible, a correction of errors introduced by biases in the XBT and expendable conductivity–temperature–depth (XCTD) data. Two cruises in each basin were undertaken in 2008/09 on which series of tests of XBTs and XCTDs dropped simultaneously with CTD casts were performed. The XBT and XCTD depths were corrected by comparison with CTD data using a modification of an existing algorithm. Significant probe-to-probe fall-rate equation (FRE) velocity and deceleration coefficient variability was found within a cruise, as well as cruise-to-cruise variability. A small (∼0.01°C) temperature bias was also identified for XBTs on each cruise. No new FRE can be proposed for either the Bay of Bengal or the Arabian Sea for XBTs. For the more consistent XCTD, basin-specific FREs are possible for the Bay of Bengal, but not for the Arabian Sea. The XCTD FRE velocity coefficients are significantly higher than the XCTD manufacturers’ FRE coefficient or those from previous tests, possibly resulting from the influence of temperature on XCTD FRE. Mean temperature anomalies versus a long-term mean climatology for XBT data using the present default FRE have a bias (which is positive for three cruises and negative for one cruise) compared to the mean temperature anomalies for CTD data. Some improvement is found when applying newly calculated cruise-specific FREs. This temperature error must be accounted for in any study of temperature change in the regions.

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