Bias Correction of 10m Wind Forecasts near a Coastal Estuary in East Central Florida BRYAN P. HOLMAN, STEVEN M. LAZARUS, MICHAEL E. SPLITT, JEFFREY A. COLVIN, ROBERT J. WEAVER, PEYMAN TAEB, ATOUSA SABERI 96 TH A MERICAN M ETEOROLOGICAL S OCIETY A NNUAL M EETING 14 TH S YMPOSIUM ON THE C OASTAL E NVIRONMENT 11 J ANUARY 2015

The Indian River Lagoon (IRL) BIAS CORRECTION OF 10M WIND FORECASTS NEAR A COASTAL ESTUARY IN EAST CENTRAL FLORIDA 2 IMAGE SOURCE: UCF Physiological Ecology and Bioenergetics Lab [ 1.The lagoon is long (250 km), skinny (2 - 8 km wide), and shallow (1 - 3 m in depth). 2.Fetch is limited in most wind directions, save from the NNW (340°) and the SSE (160°). 3.Flow in the lagoon is primarily wind-driven, especially in the northern lRL. 250 km 2-8 km

Hydrodynamic Modeling BIAS CORRECTION OF 10M WIND FORECASTS NEAR A COASTAL ESTUARY IN EAST CENTRAL FLORIDA 3 ADCIRC + SWAN set-upHs

Operational Models and the IRL BIAS CORRECTION OF 10M WIND FORECASTS NEAR A COASTAL ESTUARY IN EAST CENTRAL FLORIDA 4 GFS NAM SREF

Sensitivity to Wind Direction BIAS CORRECTION OF 10M WIND FORECASTS NEAR A COASTAL ESTUARY IN EAST CENTRAL FLORIDA 5 160° >50km 140° <10km

Some Approaches ◦Use WRF at a resolution that resolves the IRL. ◦Downside: computationally expensive! ◦We have done this for a frontal passage in March, and have very interesting results, see Taeb et al. “Sensitivity of Modeled Wave Field to Temporal and Spatial Resolution of Meteorological Forcing” on Thursday afternoon (Talk 8.3)! ◦Use statistical post-processing methods to identify and correct for biases inherent in these ensembles. ◦Much cheaper, but literature shows mixed results on effectiveness w.r.t. winds. ◦Can bias correction be useful for wind direction and wind speed forecasts over the IRL? BIAS CORRECTION OF 10M WIND FORECASTS NEAR A COASTAL ESTUARY IN EAST CENTRAL FLORIDA 6

Bias Correction Component based bias correction (COMP) BIAS CORRECTION OF 10M WIND FORECASTS NEAR A COASTAL ESTUARY IN EAST CENTRAL FLORIDA 7 Direction / Speed based bias correction (DRSP) MODEL U & V – MODEL U & V –> MODEL DIR & SPD – OBS DIR & SPD OBS DIR & SPD –> OBS U & V BIAS

Bias Correction Methods ◦Window Lengths: how long to look in the past to establish biases? ◦7 Days (Cheng & Steenburgh, 2007) ◦5, 10, 15, 20, 25, 30 days (Engel and Ebert, 2007) ◦7, 14, 21, 28, 35, and 42 days (Bao et al., 2010) ◦What mathematical method to use? ◦Running-mean average ◦Decaying average ◦Kalman Filtering ◦To get started, I chose a simple running-mean average and tested the same windows lengths used in Bao et al. (2010), and found a window length of 42 days to reduce MAE the most. BIAS CORRECTION OF 10M WIND FORECASTS NEAR A COASTAL ESTUARY IN EAST CENTRAL FLORIDA 8

Data BIAS CORRECTION OF 10M WIND FORECASTS NEAR A COASTAL ESTUARY IN EAST CENTRAL FLORIDA 9 ASOS at KMLB, KVRB, and KFPR WeatherFlow stations at Parrish Park and Jensen Beach Florida Tech station at Sebastian Inlet (Part of NOAA NDBC Network) Six observation towers from the Kennedy Space Center Weather Tower Network 14 SREF grid points Bilinear interpolation was used to provide SREF 10m wind output at each observation location.

Data BIAS CORRECTION OF 10M WIND FORECASTS NEAR A COASTAL ESTUARY IN EAST CENTRAL FLORIDA 10 ◦Bias-corrected forecasts produced from 22 Sep 2014 – 28 July ◦Forecast hours 3 through 72 were used from each of the four daily SREF runs. ◦Weighted averaging of the four runs was done as in Cheng & Steenburgh (2007) to show bias error (BE) and mean absolute error (MAE) in a readable, meaningful way: Day 1 Day 2 Day 3 Day 4 03Z 09Z 15Z 21Z AVG

Results – Wind Direction BIAS CORRECTION OF 10M WIND FORECASTS NEAR A COASTAL ESTUARY IN EAST CENTRAL FLORIDA 11 There is a slight diurnal signal in the MAE, with peaks at 18Z. COMP method reduces MAE by ~ 2°, on average. DRSP method does not reduce MAE compared to the raw SREF ensemble. Day 1 Day 2 Day 3 Day 4

Results – Wind Speed BIAS CORRECTION OF 10M WIND FORECASTS NEAR A COASTAL ESTUARY IN EAST CENTRAL FLORIDA 12 Greatest MAE in SREF mean is during the afternoon. In contrast to wind direction, DRSP method is much more effective at reducing MAE than the COMP method. DRSP method reduces MAE the most in the mornings, but produces little to no change in MAE at 15Z. Day 1 Day 2 Day 3 Day 4

Results – Wind Speed BIAS CORRECTION OF 10M WIND FORECASTS NEAR A COASTAL ESTUARY IN EAST CENTRAL FLORIDA 13 SREF ensemble underforecasts wind speed, with greatest amplitude of BE occurring during early afternoon (18Z). BE of COMP method retains the shape of the raw SREF, but is displaced positively by ~ 0.1 m/s. DRSP methods also underforecasts wind speed, but the amplitude of the diurnal variation within the BE is greatly reduced. Day 1 Day 2 Day 3 Day 4

Conclusions – The component method improves wind direction forecasts better than the direction / speed method, while … – The direction / speed method improves wind speed forecasts better than the component method. – All methods of bias correction tried so far still underforecast wind speeds over the lagoon... so what to try next? BIAS CORRECTION OF 10M WIND FORECASTS NEAR A COASTAL ESTUARY IN EAST CENTRAL FLORIDA 14

Going forward … potential winds Use surface roughness length calculations to calculate wind speed biases as if the observation locations on land were actually next to water. BIAS CORRECTION OF 10M WIND FORECASTS NEAR A COASTAL ESTUARY IN EAST CENTRAL FLORIDA 15

Bias correction with potential winds BIAS CORRECTION OF 10M WIND FORECASTS NEAR A COASTAL ESTUARY IN EAST CENTRAL FLORIDA 16 Day 1 Day 2 Day 3 Day 4 To see a comparison of forcing a coupled ADCIRC SWAN with ensemble mean winds, bias-corrected winds, and bias-corrected potential winds, see Weaver et al.’s talk 6.2 this Wednesday afternoon!

Acknowledgements BIAS CORRECTION OF 10M WIND FORECASTS NEAR A COASTAL ESTUARY IN EAST CENTRAL FLORIDA 17 NOAA Collaborative Science, Technology, and Applied Research (CSTAR) Program grant #NA14NWS Department of Marine and Environmental Systems at the Florida Institute of Technology WeatherFlow Inc.

References Bao, L., T. Gneiting, E. P. Grimit, P. Guttorp, and A. E. Raftery, 2010: Bias correction and bayesian model averaging for ensemble forecasts of surface wind direction. Mon. Wea. Rev., 138, Cheng, W. Y. Y., and W. J. Steenburgh, 2007: Strengths and weaknesses of MOS, running-mean bias removal, and Kalman filter techniques for improving model forecasts over the western United States. Wea. Forecasting, 22, Engel, C. and Ebert, E., Performance of hourly operational consensus forecasts (OCFs) in the Australian region. Wea. Forecasting, 22, BIAS CORRECTION OF 10M WIND FORECASTS NEAR A COASTAL ESTUARY IN EAST CENTRAL FLORIDA 18