Good morning. My name is Nickitas Georgas. I am an oceanographer working at Stevens Institute of Technology in Hoboken. I am here to talk to you

Transcription

1 Good morning. My name is Nickitas Georgas. I am an oceanographer working at Stevens Institute of Technology in Hoboken. I am here to talk to you about an exciting and significant expansion of our Storm Surge Warning System.

2 Why do we need Storm Surge Warning Systems? Storm surge is the most dangerous and damaging aspect of Tropical and Extratropical coastal storms. A large portion of the eastern seaboard lies only 10 feet above sea level and may flood due to the passage of a Nor-Easter or hurricane. So, accurate knowledge and prediction of water levels are necessary to effectively mitigate potential damage and loss of life. A Warning System for Storm Surge, not unlike the tsunami warning system that has recently been in the news, allows for the appropriate allocation of emergency management resources for pre-storm actions, evacuations, and post-storm response.

3 So, today, I will start with an overview of the original Stevens Storm Surge Warning System, SSWS, established in This system used the latest observations from sensors to extrapolate surge 2 hours out. Then, I will quickly go through a marine awareness system we ve been developing the last 4 years, that includes a 4D forecast model of surge that could be used instead. In the main part of the talk, I will compare data-based vs. model-based methods of predicting surge, and finally describe the new, redesigned, SSWS.

4 So, in , with the proliferation of real-time environmental sensor networks, observations were utilized to augment National Weather Service surge forecast products that were based on SLOSH or Extra-Tropical Storm Surge models.

5 The water level data collected in near-real time from each gage were used to create a water level prediction as follows: Surge here is defined as the departure of the true observed water level minus the astronomical tidal prediction, possibly due to a combination of many factors. The factors we usually care about are cyclones, but other factors are present too. Even errors in the observed record themselves would be a surge under this definition, hence the quotes around true water level. Then, the difference between the current surge and the surge observed at the previous hour is used dto define the rate of change for surge for the past hour, SR.

6 That surge rate from the past hour is then extrapolated out 2 hours and added to the present surge to get an estimate for the surge 2 hours in the future. This surge estimate is added to the predicted astronomical tide to create an estimate for the total water level 2 hours out. The big underlying assumption of this method is that the surge grows or shrinks in the next 2 hours like it did in the past hour. This is not true in the graphical example to the right, where the surge contracted and the extrapolation prediction overestimated that surge. A different prediction could be made that assumes that the surge now is not going to change in the next two hours. This type of prediction is called persistence. The original SSWS used the 2 hour extrapolation method.

7 The way the system works is shown here. As water level data are coming in from the sensors, they pass through automated preliminary QA/QC algorithms used to discard obviously bad measurements. The extrapolation method was then used to make a water level prediction 2 hours out. Observations and predictions were plotted on a web interface and, if a flood level was exceeded by the 2 hour extrapolation, the warning system was activated.

8 If flooding levels are predicted, a database of emergency management personnel is accessed and a tailored text message is transmitted via .

9 The advantages and disadvantages of the original SSWS method are highlighted here. This was an observation based system, based on real data, not forecast model data, and, don t we all know how good there are It had a nice automated alert system, and a web interface for transparency. But the fact that it was an observation based system, regardless of the method chosen for the actual prediction, brings in some major issues. Real real-time data are preliminary because they undergo automated QA/QC, not human control. Real stations may go out of service when one needs them. A real station network is costly and may be limited. And, importantly, real time data are at least 15 minutes old, with big discrepancies among data sources. So, practically, the extrapolation period may be greater than 2 hours, much greater in some cases.

10 This slide was taken from a presentation in Even back then, there was talk about future work of using in SSWS water level forecasts from the model used in the New York Harbor Observing and Prediction System, the system several of you know as NYHOPS. The curvilinear grid of the model in 2005 is shown to the right. The forecast simulations were issues once daily, and included a 24 hour hindcast and a 48 hour forecast of water levels at each of the grid cells.

11 OK. So, let s fast-forward now to the present. With a little help and sustained funding from a lot of friends, the Stevens NYHOPS system has matured considerably, is used daily, and has been successfully used in several emergency responses and investigations in and around NY and NJ. 11

12 The system is a talk in itself, I am only going to go through it very quickly here. For an environmental and situation awareness system of systems such as NYHOPS, one needs to have multiple components that can both observe and forecast the environment, ground-truth observations against forecasts and vice-versa, and a front end, a website, or multiples of, that can serve information to the public 24/7 in multiple formats. And, of course, all these components have to communicate and work automatically. 12

13 The present NYHOPS does not rely into a single source of data. Instead, it benefits greatly by networks owned and operated by partners agencies and institutions. Techniques for retrieving this data include screen-scraping of web sites, text and XML data downloads from HTTP sources, and data file downloads from research partners. 13

14 All NYHOPS-supporting (or NYHOPS-generated data) are stored in the Stevens Oceanographic and Meteorological Data Repository (OMDR), and can be accessed through the NYHOPS website. 14

15 Observing is only one component of the New York Harbor Observing and Prediction system. The second major component for which I am responsible for is Forecasting, and this is accomplished by observation-forced computer forecast models that can faithfully predict the spatial and temporal variability of the environment to explain observed results with ground-truth analysis against observations in near real time. From the old NYHOPS grid, in January 2007 we transitioned to a second higher-resolution version, down to 25 meters in parts of the domain. In June 2009 we transitioned to our present 3 rd version of the forecasting model, with further improvements in physics and predictive skill. The model predicts water level 48 hours our, which is important here, but also 3D water temperature, salinity, currents, and speed of sound, waves, water fluorescence and absorption. 15

16 I have recently completed a 2 year evaluation of NYHOPS in terms of all its prognostic variables. With regard to water level in particular, each successive version of NYHOPS reduced the root-mean-square-error in the prediction bringing us closer to the center of the circle here. Post-processed version 3 water level RMSE is down to 8.3 centimeters for the 15 new SSWS stations I will be discussing next. The 0 to 24 hours forecast is only, on the average, 6% worse than the observationbased hindcast period, while the second-day forecast is 15% worse.

17 So, if we were to use the NYHOPS forecast model to trigger SSWS alerts, we would be using a system that is always available, with redundancy, since 48 hour forecasts are issued daily. The locations for alerts would only be limited by model resolution, and since forecasts do not loose their skill considerably in the first 24 hours at least, we were toying with the idea of providing longer term flood forecasts. But, the use of a computer-model-based method carries some fine print too. Model data have traditionally been considered guidance only, because, occasionally, bad forecasts of the forcing functions (meteorology, hydrology, etc.) compromise water level forecast accuracy.

18 We wanted to answer the following: Can a model such as NYHOPS accurately predict Storm surge events? Is it as accurate as using 2 hour data extrapolation? Can we expand the lead time for alerts to greater than 2 hours? Given the opportunity, we also wanted to: Expand the network of SSWS stations, include flood levels and vertical datums on time series plots, and present an interactive general flood prediction map of the area.

19 We compared water level predictions by the NYHOPS forecast model to the 2 hour extrapolation of the older SSWS, but also to longer term 6 hour and 24 hour extrapolations that could happen if data are not received in time. We also created and included 2 hour persistence-based predictions as well as 6 hour and 24 hour persistence. We did that for 2 years, February 2007 to February Importantly, all station observations were manually QA/QCed before use, so no bad data where extrapolated ltdor persisted, itdand, a 2h hour prediction is also a true 2h hour prediction (the data were there, we did not have to wait for them to enter the system). In the process, tidal datums and constituents were calculated for all stations in the NYHOPS-supporting network.

20 This is the average correlation coefficient squared statistic between the predictions from different methods and observations, averaged over 15 stations. 2 hour persistence explains more of the variance in the overall water levels than other methods, followed closely by the NYHOPS forecast, then the 2 hour extrapolation and the 6 hour persistence. The 6 hour extrapolation is, as expected, a coarser method. The comparison was reflected in the root-mean-square errors, with the 2 hour persistence having 7.3 centimeters average RMSE, 1 centimeter less than NYHOPS, and 3 centimeters less than the 2 hour extrapolation used in the original SSWS.

21 So, on average, 2 hour surge persistence, assuming good data are readily available provides better results than other methods tried, followed by NYHOPS, then 2 hour extrapolation, 6 hour persistence and, the outlier-prone 6 hour extrapolation. But, does this finding hold during flood events, when peak or near peak water levels are reached?

22 To see what happens with alerts, let s first review a contingency table and it s statistics. The table is seen on the top left. The negative sign means levels are below flood level. The positive sign means flooding is occurring and water is over the flood level. When both the observed level and the prediction are over flooding levels, this is called a True Positive or a hit. When an event occurred but was not predicted this is called a false negative or a miss. When an event is predicted but does not occur, we have a false positive or false alarm. A True Negative means correct rejection. In a prediction method, we are trying to maximize hits and minimize misses and false alarms. This can be summarized by two statistics, the true positive rate, which is the percent of true events predicted, and the positive predictive value which is the percent of alerts that are true. Having 100 percent TPR and PPV means that all of the events were predicted and there were no false alerts.

23 Now, let s look at flood events during the 2 year evaluation period. The hidden stations on the right do not have local flood levels officially defined, so forget them for now. For the 15 stations to the left we used official flood levels taken either from the NWS NJ tidal impact tables (shown in the map insert) or the NWS Advanced Hydrologic Prediction Service (AHPS) website. There is large variability for flooding among stations. From Feb 2007 to 2009, water levels exceeded minor flood levels 48 times at Cape May, NJ, with 2 days going over moderate flood levels. On the other end of the spectrum, up the Hudson River, at Albany, NY, no water level exceeded flood levels there. Sandy Hook experienced flooding 34 days.

24 Now, let s focus on Sandy Hook, NJ, an average station. These here are simulatedvs-observed water level plots for 4 methods: NYHOPS-based, 2 hour persistence, 2 hour extrapolation, and 6 hour persistence. Every point represents the maximum water level, observed and predicted, within a given 6 hour period in the 2 evaluation years. The x axis is observed, the y axis is predicted, and the diagonal would be a perfect model. The doted red lines are the flood levels. Red dots indicate floods, observed or predicted. The right part of each plot indicates observed events; the top indicates alarms that would be sent by each method. If an alarm coincides with an event, it was a true alarm. R-square and Root-mean-square-errors are exactly as mentioned before: 2 hour persistence is a better overall water level predictor than NYHOPS. It is the most tight curve, followed by NYHOPS, then the rest. However, note the shape of the blue curves. All data-based methods (top right, and bottom) are ellipses, with bigger errors for middle water levels than both high waters and low waters. Yet NYHOPS funs out, with significantly higher errors at high waters than at low waters. The reason for this is unclear. My current working hypothesis is that the model may be more fluid in higher waters than for shallower water columns translating to bigger phase errors in high waters. Regardless of the reason, high water predictions are what s important for flooding events! In fact, all data-based methods shown,,predicted more true 6 hourly exceedances than NYHOPS at Sandy Hook: 31 out of 42 for the 2 hour persistence, 29 for the 2 hour extrapolation and even the 6 hour persistence, compared to 26 for NYHOPS. In terms of false alerts, NYHOPS and persistence produced about the same number, 11 to 13 false alerts, while extrapolation methods were much more false-alert-prone.

25 This plot summarizes the quality of each method in predicting exceedances. On the X-axis is the percent of true exceedances that were predicted, and on the Y-axis is the percent of alerts that were true. The closer a method is to the top right corner, the better it is. Although there is great variability among different stations, on the average 2 hour persistence is the best, while NYHOPS, 2 hour extrapolation, and even 6 hour persistence are almost equivalent, with NYHOPS just slightly better. But, NYHOPS sends fewer false alerts, at the expense of events predicted! So, choose your poison Ensemble predictions might be helpful l if you want the poison to choose you instead, but this is to be determined.

26 This is the same plot as before, but shows averages only, and includes 24 hour NYHOPS, 24 hour persistence, and 24 hour extrapolation methods to see which one is better in longer-term predictions. Different methods are connected with lines. A 24 hour NYHOPS prediction of 6 hourly exceedances is roughly as good as any individual 6 hourly one. On the other hand, all data-based methods become considerably worse as time from the last valid observation they have used increases. Thus, in the long term advisory mode, NYHOPS wins! One should expect 4 out of 10 missed events, and 3 out of 10 false alarms.

27 So, to summarize, 2 hour surge persistence, assuming good data are readily available, provides better alerts than the other models tried and should be chosen over extrapolation. NYHOPS provides comparable results to 6 hour persistence, and wins for longer-term forecasts. But, in practice, 3 years of experience with the older SSWS have shown that bad preliminary QA/Qced observations increase the amount of false alerts and prediction gaps. The data are also never truly real time, so a 2 hour persistence might be a 2 and a half to 6 or more hours persistence, depending di on the station ti and the sensor network it belongs. So, to increase warning time and preserve accuracy, the NYHOPS model is a good alternative for surge alerts.

28 Based on all this, we redesigned SSWS to more than double the network of stations, present a general Google map of the area with flooding indicators, include flood levels and datums on time series plots, and use the NYHOPS forecasts to trigger alerts. The new system: checks stored NYHOPS water level predictions against flood levels every 6 hours for 8 hours in the future. This reduces the amount of e- mails, still providing for a 2 hour minimum warning time, but increasing the maximum warning time to 8 hours.

29 Here is a view of the new SSWS front page, with its zoomable Google map, its 22 color-coded or blinking when flooding stations, click and choose popups, and a new alert-subscription page for the 15 stations with official flood levels.

30 If you click on a station, you will be able to see the observed and predicted water level time series, with color coded flood levels. From the toolbox to the left you can change stations or select earlier times, change vertical datums from a selection of tidal and geodetic datums, change units, and even download water level observations. You can also see a time series of both the actual surge, as well as the error in the prediction to help you understand whether an event is occurring and whether NYHOPS has forecasted that event well.

31 New automated s have been designed for the subscriber in case of a flood level exceedance. They tell you the time period the alert is applicable, for which station, when the first exceedance will be per station, and provide links to each flooding station s time series page.

32 In the future, the history of NYHOPS shows that the error will decrease with better model resolution, more frequent forecast cycles, improvements in physics, better forcing forecasts from better meteorological and hydrologic models, data assimilation, and ensemble forecasts. Definition of local flood levels for all stations is something we would like, and that can perhaps be done by linking SSWS to raster topographic data for GIS flooding. In the future we also would like to combine NYHOPS surge levels to inland flood models such as this picture for a pilot Sea Grant project that Stevens and partners have proposed.

33 This is the final slide, with only some of the NYHOPS resources available to anyone with internet access. Please drop me a business card if you want us to include you in the alerting system and do not want to go to the website. I will be happy to answer any questions you may have. 33