{"id":186662,"date":"2025-05-05T10:23:22","date_gmt":"2025-05-05T14:23:22","guid":{"rendered":"https:\/\/www.hajim.rochester.edu\/senior-design-day\/?p=186662"},"modified":"2026-05-04T08:45:30","modified_gmt":"2026-05-04T12:45:30","slug":"benchmark-labs-point-specific-wave-height-forecasting-using-time-series-analysis","status":"publish","type":"post","link":"https:\/\/www.hajim.rochester.edu\/senior-design-day\/benchmark-labs-point-specific-wave-height-forecasting-using-time-series-analysis\/","title":{"rendered":"Benchmark Labs: Point-Specific Wave Height Forecasting Using Time Series Analysis"},"content":{"rendered":"\r\n

DSCC383 Team 10: Brennan Kalinowski, Tarun Paravasthu, Sean Tian, Madeleine Johnson<\/p>\r\n\r\n\r\n\r\n

Advisor: <\/strong>Cantay Caliskan, Ph.D Sponsor:<\/strong> Benchmark Labs<\/p>\r\n\r\n\r\n\r\n

Introduction<\/h1>\r\n\r\n\r\n\r\n

Background:<\/strong> Organizations like the National Weather Service use numerical weather models that divide the Earth’s surface into a grid of uniform-sized cells to make weather predictions. Each grid box represents an average value of atmospheric conditions over a specific area. [1] However, in reality, within each of these grid boxes, there is a variety of distinct microclimates localized areas where weather conditions differ significantly due to factors like elevation, vegetation, urban structures, or bodies of water.\u00a0<\/p>\r\n\r\n\r\n\r\n

Benchmark Labs aims to create point specific wave models to extend forecasting beyond 1 hour.\u00a0<\/p>\r\n\r\n\r\n\r\n

Method & Result<\/strong>: We evaluated multiple forecasting models, including ARIMA, XGBoost, and Long Short-Term Memory (LSTM) neural networks. Our analysis demonstrated that these models yielded reliable performance for short-term forecasts (1\u201312 hours). However, further refinement is necessary to enhance predictive accuracy for longer-term horizons extending beyond 24 hours.<\/p>\r\n\r\n\r\n\r\n

Data Collection<\/h1>\r\n\r\n\r\n\r\n

Our primary data source was the National Data Buoy Center (NDBC), part of the National Weather Service. The data consists of publicly sourced worldwide buoy data reported hourly. To account for missing values, we merged the data with the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset which aims to create a complete historical record of past weather and climate data. The final dataset contains variables explaining wind, wave and atmospheric behaviour. [3]\r\n\r\n\r\n\r\n

\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
NDBC Variable<\/strong><\/td>\r\nDescription<\/strong><\/td>\r\n<\/tr>\r\n
WDIR<\/strong><\/td>\r\nWind direction (o<\/sup>C)<\/td>\r\n<\/tr>\r\n
WSPD<\/strong><\/td>\r\nWind Speed (m\/s), averaged every 8 min<\/td>\r\n<\/tr>\r\n
GST<\/strong><\/td>\r\nPeak gust speed (m\/s) over 5-8 seconds<\/td>\r\n<\/tr>\r\n
WVHT<\/strong><\/td>\r\nSignificant Wave Height (m), average of highest \u2153 waves (20 mins)<\/td>\r\n<\/tr>\r\n
DPD<\/strong><\/td>\r\nDominant wave period (s)<\/td>\r\n<\/tr>\r\n
APD<\/strong><\/td>\r\nAverage wave period (s)<\/td>\r\n<\/tr>\r\n
MWD<\/strong><\/td>\r\nDominant wind direction<\/td>\r\n<\/tr>\r\n
PRES<\/strong><\/td>\r\nSea level pressure (hPa)<\/td>\r\n<\/tr>\r\n
ATMP<\/strong><\/td>\r\nAir temperature (o<\/sup>C)<\/td>\r\n<\/tr>\r\n
WTMP<\/strong><\/td>\r\nSea surface temperature (o<\/sup>C)<\/td>\r\n<\/tr>\r\n
DEWP<\/strong><\/td>\r\nDew point temperature (o<\/sup>C)<\/td>\r\n<\/tr>\r\n
VIS<\/strong><\/td>\r\nVisibility (nautical miles)<\/td>\r\n<\/tr>\r\n
PTDY<\/strong><\/td>\r\nPressure tendency (hPa) over last 3 hours<\/td>\r\n<\/tr>\r\n
TIDE<\/strong><\/td>\r\nWater level (ft) relative to Mean Lower Low Water<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/figure>\r\n\r\n\r\n\r\n
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
ERA5 Variable<\/strong><\/td>\r\nDescription<\/strong><\/td>\r\n<\/tr>\r\n
swh<\/strong><\/td>\r\nSignificant wave height (m)<\/td>\r\n<\/tr>\r\n
hmax<\/strong><\/td>\r\nMaximum individual wave height (m)<\/td>\r\n<\/tr>\r\n
mwp<\/strong><\/td>\r\nMean wave period (s), average wave period over all waves<\/td>\r\n<\/tr>\r\n
mvd<\/strong><\/td>\r\nMean wave direction (o<\/sup> from true North)<\/td>\r\n<\/tr>\r\n
ppld<\/strong><\/td>\r\nPeak wave period (s)<\/td>\r\n<\/tr>\r\n
sp<\/strong><\/td>\r\nSurface pressure (Pa), atmospheric pressure at sea level<\/td>\r\n<\/tr>\r\n
sst<\/strong><\/td>\r\nSea surface temperature (o<\/sup>C)<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/figure>\r\n\r\n\r\n\r\n

Feature Importance<\/h1>\r\n\r\n\r\n\r\n
\"\"<\/figure>\r\n\r\n\r\n\r\n

Model Structure<\/h1>\r\n\r\n\r\n\r\n
\"\"<\/figure>\r\n\r\n\r\n\r\n

Model Results<\/h1>\r\n\r\n\r\n\r\n
\"\"<\/figure>\r\n\r\n\r\n\r\n
\"\"<\/figure>\r\n\r\n\r\n\r\n
\"\"<\/figure>\r\n\r\n\r\n\r\n

Financial Impact<\/h1>\r\n\r\n\r\n\r\n
\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Forecast Horizon<\/strong><\/td>\r\nAverage RMSE \u00a0 \u00a0 (in meters)<\/strong><\/td>\r\nEstimated Downtime Reduction<\/strong>[2]<\/sup><\/strong><\/td>\r\n<\/tr>\r\n
1-4 Hours<\/strong><\/td>\r\n0.184<\/td>\r\n2-3%<\/td>\r\n<\/tr>\r\n
5-8 Hours<\/strong><\/td>\r\n0.31<\/td>\r\n1%<\/td>\r\n<\/tr>\r\n
9-12 Hours<\/strong><\/td>\r\n0.403<\/td>\r\n0.05%<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/figure>\r\n\r\n\r\n\r\n

Assumptions for Financial Model\u00a0<\/strong><\/p>\r\n\r\n\r\n\r\n

1) $800,000:<\/strong> Daily installation cost loss from weather delays.\u00a0<\/p>\r\n\r\n\r\n\r\n

2) 30: <\/strong>Average Number of days impacted by severe weather per year for each wind farm<\/p>\r\n\r\n\r\n\r\n

3) \u00a0 5:<\/strong> Model will be applied to five wind farms.<\/p>\r\n\r\n\r\n\r\n

\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Forecast Horizon<\/strong><\/td>\r\nEstimated Downtime Reduction<\/strong>[2]<\/sup><\/strong><\/td>\r\nSavings for 5 wind farms per year<\/strong><\/td>\r\n<\/tr>\r\n
1-4 Hours<\/td>\r\n2-3%<\/td>\r\n$2.4-$3.6 Million\u00a0<\/td>\r\n<\/tr>\r\n
5-8 Hours<\/td>\r\n1-2%<\/td>\r\n$1.2-$2.4 Million<\/td>\r\n<\/tr>\r\n
9-12 Hours<\/td>\r\n0.05%<\/td>\r\n$0.6 Million<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<\/figure>\r\n\r\n\r\n\r\n
\"\"<\/figure>\r\n\r\n\r\n\r\n

Conclusion<\/h1>\r\n\r\n\r\n\r\n

While both models performed similarly, the CNN+LSTM model outperformed XGBoost at longer forecast horizons and seemed to produce smoother forecasts, making it the better choice.<\/p>\r\n\r\n\r\n\r\n

We were able to achieve high accuracy for the first 5 hours, with R2 <\/sup>ranging from as high as 0.96 to around 0.8 for these predictions and RMSE below 0.3. Expanding to 10-12 hours, we saw a reduction in accuracy to around 0.6 R2<\/sup> and 0.4 RMSE, which can still be useful. However, going beyond that point is unreliable and requires more work to predict accurately.<\/p>\r\n\r\n\r\n\r\n

Next Steps<\/h1>\r\n\r\n\r\n\r\n

Going forward, there are several opportunities to optimize our models. First, additional fine-tuning may yield further improvements. Second, exploring variables with stronger correlations to wave height, such as salinity, could enhance accuracy. Finally, a full VMD-LSTM framework without data leakage remains a promising direction for future work if given more time.<\/p>\r\n\r\n\r\n\r\n

Acknowledgements<\/h1>\r\n\r\n\r\n\r\n

We gratefully acknowledge Ulrik Soderstrom and the entire Benchmark Labs team for their guidance and continued support throughout this project. We also extend our sincere thanks to Professor Cantay Caliskan for his insights & constructive feedback during our meetings.<\/p>\r\n\r\n\r\n\r\n

References<\/h1>\r\n\r\n\r\n\r\n

1. National Oceanic and Atmospheric Administration (NOAA). Weather Models. JetStream – An Online School for Weather. https:\/\/www.noaa.gov\/jetstream\/upper-air-charts\/weather-models<\/a><\/p>\r\n\r\n\r\n\r\n

2. Song, T., Han, R., Meng, F., Wang, J., Wei, W., & Peng, S. (2022). A significant wave height prediction method based on deep learning combining the correlation between wind and wind waves. Frontiers in Marine Science, 9, 983007 https:\/\/doi.org\/10.3389\/fmars.2022.983007<\/a><\/p>\r\n\r\n\r\n\r\n

3. National Data Buoy Center (NDBC). Measurement Descriptions and Units. National Oceanic and Atmospheric Administration. https:\/\/www.ndbc.noaa.gov\/faq\/measdes.shtml<\/a>.\u00a0<\/p>\r\n","protected":false},"excerpt":{"rendered":"

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