Session of the wmo-ioc joint Technical Commission for Oceanography and Marine Meteorology (jcomm) agreed that it would be logical to transform the wmo wave Programme into the jcomm wind Wave and Storm Surge Programme
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- Validation and Verification
- Output Parameters
- Time series
- Fields
- Output from other models
- 4.5.4 Example of forecast result
Hydrological inputRiver runoff can have a significant effect on storm surges, especially for detailed models and in estuaries. Data can come from observations or hydrological models which are used to forecast water levels and discharge of a river system. Another application of hydrological data is the interaction of storm surge, river runoff and precipitation for management of water levels in-land. In The Netherlands this kind of information is used to optimize the use of pumps in case of expected heavy precipitation and to sluice water out of the country on low tides before the rains hit.
The terms validation and verification are not always clearly distinguished and defined. They include the steps of making sure that the model does what it has been designed to do (model validation) and, in the operational phase, making a (semi-)permanent quanitative assessment of its performance (forecast verification). Validation and verification of the model are best performed on the primary output parameters of the forecasts. Nevertheless, for the assessment of the characteristics and the performance of the model in more general terms, also other parameters and locations should be considered. Observed water levels, as described for data assimilation are an excellent base, especially for the ongoing forecast verification. But, any available current data, e.g. from cruises, or satellite altimetry data could be helpful for the initial model validation. Output from a storm surge model can be generated as time series for specific locations of interest, or fields for a fixed time. Output from methods that do not directly involve a numerical storm surge model can be various, typically suited for the purpose of the method.
Typical output of a storm surge model is a time series of astronomical tide and meteorological surge for a specific location. Very often, for these specific locations, astronomical tides are also available from harmonic analyses of long time series. Especially if the resolution of the model is moderate, the harmonic analysis tide is very often better than the astronomical tide from the model. The model can then be used to generate only the surge, which already is the output from a linear model, or the difference between runs with and without meteorological forcing from a nonlinear model. The model surge added to the astronomical tide from the harmonic analysis is then the most accurate estimate of the total water level. 
Figure 4.2: Surge [m] in Sheerness for January 2007 Figure 4.2 gives an example of the surge in Sheerness on the North Sea from the British model CS3X, see POL (2007), together with observed surges, markers for the high tides (red dots) and some statistics. A parameter which can be derived from the time series and which is especially important for storm surge situations is the skew surge for a high or low tide, defined in Figure 4.3. The skew surge quantifies the maximum deviation of the absolute water level with respect to the extreme of the astronomical tide and can be used in combination with tables of the astronomical high and low tides. Due to possible time shifts, the skew surge is often different from the (extreme of the) non-tidal residual. To derive the skew surge from the model, the astronomical tide time series from the model itself should be used and not the astronomical tide from a harmonic analysis. 
Illustration 1 = Figure 8: Definition of the skew surge Figure 4.3: Skew surge
For a general overview of the situation at sea, fields of the astronomical tide and surge provide a useful tool. An example of the surge from the Dutch storm surge model DCSM (KNMI, 2007), together with the input wind and pressure fields is given in Figure 4.4. The wind field is given as flags with each long bar representing 10 kts and an additional short bar 5 kts, pressure contoured in hPa and the surge(m) in coloure code with reference to the astronomical tide. 
Figure 4.4: Surge (colour range in m) from the Dutch DCSM model with wind and pressure input
(a) (b) (c)   
Ise Bay 12hr forecast 
Osaka Bay  
Figure 4.5: Maximum surge envelopes simulated with different typhoon tracks. (unit: cm) Typhoon track used in the simulations. (b) The case in which a typhoon takes the westernmost path. (c) The same as (b) but for the easternmost path. (Figure 4.5(a)) demonstrates how the difference in the path of a typhoon changes storm surge occurrence. If a typhoon takes a path left of the forecast track, a storm surge may occur in the Osaka Bay, the western bay in the area shown in the figure (Figure 4.5(b)), while a surge may occur in Ise Bay, the eastern bay in the figure if the typhoon takes a right path (Figure 4.5(c)). To take into account the influence of typhoon track on the occurrence of storm surge, we conduct five runs of the storm surge model with five possible typhoon tracks in the JMA forecasting system. These five typhoon tracks are prescribed at the center and at four points on the forecast circle within which a typhoon is forecast to exist with a probability of 70%, and used to make meteorological fields with the empirical typhoon model mentioned above. 4.5.4 Example of forecast resultThis part gives an example of results of the storm surge forecasting system. Figure 4.6 shows the time series of storm surge at Takamatsu tide station on August 30-31, 2004 when Typhoon CHABA (T0416) passed the western part of Japan. As mentioned in the introduction, this typhoon caused storm surge disasters in the coastal areas surrounding Seto Inland Sea in the western part of Japan. Figure 4.6 also shows the predicted storm surges at 09JST on August 30, about 12 hours before the peak surge occurred. As described above, five forecast runs were executed for the five different possible typhoon tracks and the results are denoted as the five thin lines in the figure. Although the time of the peak surges predicted by the model is slightly earlier than observed, the height of the forecast peak surge is in a good agreement with the observation. Based on this model result, Takamatsu Local Meteorological Observatory issued storm surge warnings about six hours before the sea level reached its maximum. This case can be considered to be an example that demonstrates the effectiveness of the forecasting system.  (a) (b)
 
Figure 4.6: Track of Typhoon CHABA (T0416) and time series of storm surge at Takamatsu. Track of the typhoon. The thick line is the analyzed track. Two circles indicate the typhoon positions of 12-hour and 24-hour forecasts. Observed and modeled storm surges for Takamatsu tide station. The five thin lines depict the time series predicted for the five different typhoon tracks. 5. STORM SURGE PREDICTION MODELS
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