Smoke plume modeling in crisis management

Transcription

1 Smoke plume modeling in crisis management Hein Zelle, Edwin Wisse, Ágnes Mika and Tom van Tilburg Abstract The current state of numerical modeling allows detailed, accurate weather forecasting, as well as forecasting of the transport and dispersion of smoke plumes and gas clouds. However, emergency workers do not have access to such state-ofthe-art information during large incidents. BMT ARGOSS and Geodan have developed an integrated system to provide emergency workers with weather and smoke plume forecasts, in a modern crisis management system based on a geographical information system. 1 Introduction In February 2011, a large fire in the Netherlands illustrated the need for better information during incidents. A fire broke out in a chemical storage facility near Moerdijk. The smoke plume extended well into the neighboring regions, requiring the participation of fire fighters from different fire brigades (crisis response in the Netherlands is organized into regions). As the smoke plume cooled downwind, it descended and touched the ground at a considerable distance from the fire. The smoke Hein Zelle BMT ARGOSS, Voorsterweg 28, 8316 PT Marknesse, The Netherlands, hein.zelle@bmtargoss.com Edwin Wisse Geodan, President Kennedylaan 1, 1079 MB Amsterdam, The Netherlands, edwin@kandefang.com Ágnes Mika BMT ARGOSS, Voorsterweg 28, 8316 PT Marknesse, The Netherlands, agnes.mika@bmtargoss.com Tom van Tilburg Geodan, President Kennedylaan 1, 1079 MB Amsterdam, The Netherlands, tom.van.tilburg@geodan.nl 1

2 2 Hein Zelle, Edwin Wisse, Ágnes Mika and Tom van Tilburg potentially contained several chemical species from the storage facility, as well as the products of the chemical reactions in the fire. As the species and concentration levels were not well-known, estimating the health risks for people in the affected regions was difficult or impossible. The need for better information about the movement and dispersal of a smoke plume was emphasized by this particular incident. Authorities in the municipalities where the plume reached ground level had no information about the pollutants in the cloud and the concentration levels. The crisis management team at the incident location of the fire could not adequately predict where the cloud was moving. Also, there was a lack of ability to share the information effectively between affected regions and the involved crisis management teams. 1.1 Current state During an incident, an expert on dangerous chemical species is made responsible for providing information to other emergency workers and decision makers. In the Netherlands, this expert is called the advisor on hazardous substances (AGS). The expert collects information about the substances present at the incident location and, using standard procedures, reports about the consequences and gives advice on the actions that should be taken (e.g. evacuation). During a typical incident with a large fire, the following events and actions would occur: 1. Fire detected and reported to central operator (emergency number) 2. Fire crew alerted, present at location 3. Chance of emission of dangerous chemicals asserted 4. Specialist in chemical species called to incident, arrives within 30 minutes. At this point, the specialist will assess the weather conditions, the state of the fire and the possibility of dangerous chemicals being released. He uses a standard worksheet to estimate wind, emission strength and species, and obtains a template representing the possible spread of the plume. This template can then be placed on a paper map or applied on a digital map. 5. Wind direction and strength are obtained from the nearest weather station or estimated. 6. The specialist estimates fire parameters using a standard worksheet: surface area, temperature, emission strength. 7. A gas template is selected using the standard worksheet. This is an outline of a smoke plume, printed on transparent plastic, which can be overlayed on a map. The specialist can now draw affected areas into the map. He will decide where to send observation teams, which areas and access roads to block, and which areas should potentially be evacuated. One of the main disadvantages of this approach is the lack of real-time information and forecasts about the weather and the movement of the smoke plume.

3 Smoke plume modeling in crisis management Proposed solution The aim of this project is to provide emergency workers access to real time, sophisticated information about the weather and the movement of smoke and chemical species. To achieve this, a meteorological model is coupled to a chemistry-transport model. The models are integrated with a state-of-the-art crisis management system: Eagle [1, 3]. A user interface is developed which conforms to existing procedures in crisis management. Network communication is used to give all the parties involved in an incident access to the same information [2]. This also allows the fire brigade s chemical specialist to share relevant information about a smoke plume with the other parties involved, in an understandable form. 1.3 Challenges Scientific institutes that create and run numerical models work in a very different environment compared to emergency workers that deal with the direct effects of a fire or gas cloud. Numerical models are typically run in a computer lab with the availability of large meteorological data sets and strong computing power. A typical weather forecast takes several hours to compute, and can only be performed 2 4 times per day. In contrast, during a crisis situation near-real-time access to information is required. In practice this means that an information request must be answered within minutes and all model data must be readily available. In addition, the quality of a model result will be judged differently: a scientist will emphasize on physical accuracy, whereas a fire brigade may focus on timely delivery, useful presentation and simplicity of the results. The challenge of this project is to bridge the gap between models in the lab and real time situations in the field, without compromising the quality of the model results or the reliability, speed and usability of the system. 2 System description The basic system design consists of three clearly defined components: a weather model, a smoke plume model and a crisis management system. These components are linked together, and presented to the user with a dedicated user interface. In this section we first describe the intended use of the integrated system. Each individual component is then described separately. The integration of the components is described in section 2.9.

4 4 Hein Zelle, Edwin Wisse, Ágnes Mika and Tom van Tilburg 2.1 Scenario with integrated system The capabilities of the integrated system are demonstrated with a scenario of a large fire in a chemical factory. Within the crisis management system, an incident is opened and other participants are invited (e.g. the commander in charge). Opening an incident initiates a map for that incident which will be shared among the involved parties. Since the location is a chemical plant the commander decides to call the specialist: the adviser hazardous substances (AGS) to the incident location. The specialist arrives at the scene and determines that dangerous chemicals are likely being released into the smoke plume. The specialist opens the crisis management system and enters the fire and emission details in the input screen of the smoke plume module. To do this, he follows standard procedures to estimate the type of fire, the size of the source and the temperature of the fire. Based on what materials are known (or guessed) to be burning, the chemical species that are emitted are chosen. The specialist will then arrive at a rough estimate of the strength of the emission source: for example, 1 kg CO is being emitted per second. 1. Specialist enters fire parameters: location, size, temperature, emission strength. Emission strength and type are estimated using the standard worksheet and methods. 2. Model simulation is automatically started. 3. Simulation process is monitored and shown to the specialist ( 3 minutes). 4. Simulation results are shown on the map as concentration contours and wind vectors. The specialist now has access to all information regarding the smoke plume forecast. He can verify that the observed wind direction and strength match with the weather forecast. The smoke plume can be visualized in different ways, looking from above or in the form of a vertical cross-section. The plume is drawn using 3 contours representing alert, evacuate and lethal levels. In the crisis management desktop, these contours can be combined with information about sensitive buildings, number of people affected and whether the plume affects evacuation routes. 5. Specialist draws affected areas and evacuation zones into the map, using the plume forecast as a guide. Typically this is done with streets or blocks as logical boundaries, keeping access possibilities for vehicles in mind. 6. The affected areas are shared with all emergency workers that should have access. The smoke plume contours are not shared with the other (non-specialist) parties involved in the incident like municipalities, the police and medical services. A conscious decision was made not to share all information between all parties involved. The output of a smoke plume model requires expert knowledge about the processes involved, interpretation of the output and the model limitations. Therefore, the chemical specialist will only share his/her conclusions with the other parties involved, not the model results themselves. These conclusions (which areas are

5 Smoke plume modeling in crisis management 5 safe, which should be evacuated, which roads should be blocked) are shared as annotations and marked areas on the map. Communication with measurement teams in the field may also take place through the shared map. Figure 1 gives a schematic representation of the system components and the use cases during an incident. Fig. 1 Schematic for the Smoke plume use case. 2.2 Crisis management system The Eagle crisis management system is used to share information during an incident. Eagle uses a map as the shared communication layer. An incident is opened, participants are invited and from that moment on all invited parties see a shared map of the incident location. This map will show all communication messages as annotations with a location marker. The users can add symbols to the map from a predefined set of crisis management symbols. These symbols denote road blocks, location of measurements, police and fire brigade posts and current locations of vehicles. Eagle is implemented in a GIS system. Therefore a geo-spatial analysis of available information can be made and shared immediately. Different extensions have

6 6 Hein Zelle, Edwin Wisse, Ágnes Mika and Tom van Tilburg been built in Eagle, for example to estimate the number of people within a given area. Other extensions are available for the use of social media as information sources and for the display of video feeds from (unmanned) aerial vehicles. The smoke plume simulation interface has been implemented as an extension to the Eagle system. 2.3 Weather model BMT ARGOSS operates the WRF (Weather Research and Forecasting) atmosphere model for daily weather forecasting. WRF is an open-source model, developed in cooperation between several institutes and universities in the USA [4, 6]. The model is state-of-the-art, used both in research and operational weather forecasting. For Europe and the Netherlands, the model is configured with 3 nested domains, going from a large, coarse (27 km resolution) domain covering north-west Europe to a small, fine (3 km resolution) grid over the Netherlands. The nested grids allow us to refine the model quality, while keeping the required computational power limited. Figure 2 shows the WRF domain configuration. Fig. 2 The WRF weather model domain configuration. Left panel: the outer domain grid has 27 km spatial resolution. The green rectangle marks the location of the intermediate, 9 km domain. Right panel: the dots indicate the 9 km grid, the green rectangle over the Netherlands marks the finest, 3 km grid. Model forecasts are produced 4 times per day, with the following parameters: forecast times: 00, 06, 12 and 18 UTC forecast window: from +0 to +120 hours (5 days into the future) boundary forcing: NCEP GFS global forecast model nesting: fine-scale results are fed back to the coarse-scale model topography: SRTM (Shuttle Radar Topography Mission) data with 90 m spatial resolution land use: MODIS satellite imagery classified in 20 categories

7 Smoke plume modeling in crisis management 7 vertical structure: 31 levels from the surface up to approximately 20 km. Extra levels were added for more detail in the lower 200 m. convective precipitation: clouds and rainfall are explicitly resolved in the 3 km domain. The result of the model forecast is a 4-dimensional database of all atmospheric parameters. The data is post-processed to compute any derived variables, to standardize the NetCDF output files and to visualize the most important parameters on a web site. Figure 3 shows a sample of the weather charts generated from the operational weather forecast. Fig. 3 Sample weather forecasts for north-west Europe (left) and for the Netherlands (right). Top: 2 m temperature ( C). Center: 10 m wind speed (m/s). Bottom: total precipitation (mm/h).

8 8 Hein Zelle, Edwin Wisse, Ágnes Mika and Tom van Tilburg 2.4 Smoke plume model We now have an up-to-date weather forecast, at most 6 hours old, which provides weather information up to 5 days in the future. The next step is to couple the weather model to a chemistry-transport model. Given the state of the weather and an emission source (e.g. a fire), such a model will calculate the transport of chemical species, as well as their transformations due to chemical reactions. The following characteristics are required: Capable of very high detail levels: street level or better. The target resolution is 50 m. Computationally very fast. Target response time: <5 minutes. Capable of simulating extreme situations such as high concentrations, strong flows, high temperatures. Capable of simulating dynamic situations with changing weather conditions and variable emission sources. Capable of simulating vertical plume motion due to heat transport, and providing 4-dimensional output. Preferably open source with a sound scientific basis. Out of several candidates, the CalPuff model was selected [7]. This model complies with all the requirements above. CalPuff is a so-called puff model, which means it simulates a gas cloud as a collection of puffs, small packets of chemical species. These packets are transported by the wind, dispersed or affected by chemical reactions. The advantage of this system, compared to a Eulerian model with a regular box grid is that it allows very localized emission sources and it is more suitable for highly dynamic situations, such as the plume coming out of a chimney. The puff model is also much faster computationally, a big bonus for this application. CalPuff is well tested and is used operationally as well as for regulatory purposes. For specific applications such as the modeling of explosions or the dispersion of heavy gases, the use of a different, specialized model may be more appropriate. The system design is modular and allows replacement of individual components so this an be easily achieved. An extensive comparison of Calpuff with other fire models is outside the scope of this paper but the main reason for not using them is that they are computationally too slow if operated at the required space-time resolution. This is especially true for the Eulerian models. 2.5 Model coupling The coupling between the atmosphere model and the smoke plume model is relatively straightforward. The output of the WRF model consists of full 4-dimensional data fields, in NetCDF format. The only non-trivial aspect is the projection of the model grid, which is a Lambert Conical Conformal projection. The CalPuff model requires 4-dimensional wind, temperature and moisture fields. From these data it

9 Smoke plume modeling in crisis management 9 can compute all required information, notably the stability of the atmosphere, the horizontal and vertical transport and the diffusion. The CalPuff model grid uses a different map projection (Rijksdriehoek, a variant of UTM zone 31 specifically designed for the Netherlands). A custom coupling program reads each of the required variables in from the WRF data files, transforms the source grid to the target grid, and writes output files in a format specified by CalPuff. At this point the CalPuff model can be operated normally by providing it with information about the emission source, a reference to the pre-processed weather data, and other required configuration information about the simulation such as start and end times. 2.6 Validation of the coupled modeling system The WRF model and CalPuff model have both been validated intensively. Both models are used by the research community, where validation studies are regularly performed and published. The operational WRF configuration at BMT ARGOSS has been validated in previous projects using all available observation stations from the national observation network in the Netherlands [5]. This was performed for the period Extensive statistics were computed and presented. Overall, we can conclude that the model performs equally well as the HIRLAM model when compared on a large scale. At smaller scales the WRF model shows clear improvements, notably near coastal boundaries, in cities and in more difficult terrain such as hills. Fig. 4 Fire fighters at work on the fire at the trawler Willem van der Zwan, 2007 January 31.

10 10 Hein Zelle, Edwin Wisse, Ágnes Mika and Tom van Tilburg The coupled smoke plume model was validated using data from a fire on the fish trawler Willem van der Zwan (SCH 302) which took place on January 31, 2007 near Velsen in the west of the Netherlands. The incident report produced by the chemical expert during the initial stage of the fire was used to assess the emission rate of NO x. As the fire fighters arrived, measurements indicated a concentration of 20 mg/m 3 NO x in the vicinity of the vessel. Figure 5 shows the same value (orange contour) that was predicted for the time of measurement. The top panel shows the concentration contours at ground level and the bottom panel the concentration contours in the vertical along the plume center line. More measurement/prediction comparisons at different times show the same similarity indicating that the model provides plausible results. In cooperation with the regional fire brigades more validation data are collected of other incidents. Fig. 5 Estimated NO x concentration at the start of the fire incident at the trawler Willem van der Zwan, 2007 January 31. Top panel: concentration contours at ground level. Bottom panel: vertical cross-section along the plume center line.

11 Smoke plume modeling in crisis management Dealing with uncertainties It is possible, even likely, that the weather and smoke plume forecast deviate from reality. This can be due to the general uncertainty of the weather or local effects such as trees, water bodies and buildings. Even more likely are errors in the model input parameters. The emission strength may easily be wrong by a factor 10. The size and temperature of a fire are all estimated and may contain significant errors. This means a specialist will always have to interpret the information, adding uncertainty margins when drawing affected work areas before sharing them. In case of weather forecast errors, the chemical specialist may contact a meteorologist to ask for advice. The meteorologist can verify the forecast error and explain the difference (e.g. a low pressure system arrived earlier than expected). He can then provide advice on the expected developments: e.g. the real plume will likely deviate to the south compared to the forecast. Errors in the estimated fire temperature and source strength are best dealt with by the chemical expert. The current system is designed in such a way that it is easy to generate multiple forecasts: the specialist can re-do a forecast with a modified emission strength or a different fire temperature. The results can then be inter-compared to make a decision based on multiple possible outcomes. 2.8 Operation and reliability When applying a modeling system toward emergency services, it is vital that the system is always available. BMT ARGOSS faces similar requirements for its regular forecasting services. Service reliability is guaranteed by the following means: a cluster computer with resilient components partial backup computing capacity at an off-site location a job scheduling system (SMS) which plans, submits and checks each task at regular times. SMS was developed at the European Centre for Medium Ranged Weather Forecasting (ECMWF) for this purpose. It automatically deals with scheduling jobs at the right time, inter-job dependencies (job b can only run after job a is complete), error handling, sending notifications, automatic re-running of failed jobs. Round the clock system monitoring by meteorologists and technical support. These systems have proven very reliable, with very low failure rates and fast recovery in case of failure. However, for the purpose of an operational deployment for emergency services the level of reliability should likely be increased even further: a fully redundant system at a different site may be required to deal with power outage, complete internet failure or a fire.

12 12 Hein Zelle, Edwin Wisse, Ágnes Mika and Tom van Tilburg 2.9 System integration The Eagle crisis management system is used as an interface to the smoke plume modeling service. The aim is to allow the user seamless access, to enable geographical analysis combining smoke plume information with other information sources and to allow the user to share information resulting from his/her analysis with other involved parties. To achieve this integration, the Eagle system must be able to do the following: enter fire parameters (user interface component) start a remote task (run a remote simulation) request model information (smoke plume model results) display this information in its own map (contours in GIS map). Entering the fire parameters is achieved through a user interface component which was implemented as an extension to Eagle. Figure 6 shows the input form in Eagle. Fig. 6 The input form in the Eagle crisis management system Displaying the model results is made possible through the use of open geographical standards: web map services (WMS) and web feature services (WFS). Performing analyses requires access to the numerical model results: this is achieved via web coverage services (WCS). These protocols are supported by most GIS clients. The model results (contours) are delivered via the WMS or WFS protocols, and can be instantly displayed in the Eagle map. Figure 7 shows a sample smoke plume in Eagle. Finally, Eagle must be able to start a model simulation or request status information. Such communication between Eagle and the model server is implemented

13 Smoke plume modeling in crisis management 13 Fig. 7 The smoke plume contours displayed in the Eagle crisis management system. using the open Web Processing Standard (WPS) protocol. The advantage of the WPS protocol is that it allows a clear division between user interface and model implementation. This allows the implementation of alternative user interfaces or model components speaking the same protocol. A web interface with similar functionality to the Eagle map has been developed to demonstrate this, and to perform tasks such as archiving of previous model simulations. Figure 8 shows the system components and communication protocols. 3 Future and ongoing work The service is currently being evaluated by several fire departments. The next step will be to evaluate the system during live exercises where incidents with dangerous chemical species are simulated. These are planned for the end of The smoke plume is currently represented as a set of contours on a 2D map and as a vertical cross-section. There are cases in which the height of the plume is important, for example in a city with tall buildings. Full 3D visualization of the smoke plume is being explored in the project. A limiting factor lies in the representation of the common map (top down view) in the crisis management system: 3D information can not be shared and 3D visualization will remain a separate option, for now. Other options such as visualization in Google Earth are being developed. There are several highly specific types of incidents which are not yet covered by the system. These include explosions, the release of gases heavier than air and

14 14 Hein Zelle, Edwin Wisse, Ágnes Mika and Tom van Tilburg Fig. 8 Schematic of the system components. Web protocols are indicated between parentheses. Red and green blocks can be approached via the Internet. Gray blocks are implemented internally and cannot be accessed directly. high-pressure gas leaks. Work is ongoing on the implementation and validation of some of these incident types. Validation may be required in cooperation with scientific institutes. Some incident types may require specialized models for proper representation of the physical effects. These types of incidents will be added in the future. The final, main challenge for this service is the market introduction. Although the added value of the service is obvious and the market (fire brigades, emergency services) is clearly present, it is complex to introduce such services. There are many parties involved (national institutes) with responsibilities during incidents. Each of these will have to judge how the service connects to their tasks and responsibilities. Results from complementary services (e.g. this service and a national service) should not contradict each other and the working methods should be the same. Discussions with the national institutes have been planned and are ongoing. We strive to see this service in full operation in the near future. 4 Summary and Conclusions BMT ARGOSS and Geodan have developed an integrated system to provide emergency workers with smoke plume and gas cloud forecasts during incidents. The sys-

15 Smoke plume modeling in crisis management 15 tem is based on a coupled modeling system: a high resolution weather model drives a chemistry-transport model which computes the smoke plume transport. Model results are presented in Eagle, a state-of-the-art crisis management system. Eagle is a GIS-based tool, displaying all information in a common map. It allows transparent sharing of information between all parties involved in crisis management. The integrated smoke plume modeling system has been tested by several fire departments in the Netherlands. Initial responses are very positive, and further tests during live exercises are planned for fall This project has proven that it is possible to apply the state-of-the-art in weather forecasting and chemistry modeling toward real-world applications in crisis management and rescue work. We strive toward bringing this system to the market both in the Netherlands and internationally, so all emergency workers will have access to the best information possible during an incident. Acknowledgements This project is sponsored by AgentschapNL. References 1. Fruijtier, S., Van der Zee, E., Gehrels, B., Zlatanova, S., Ryan, M., Scholten, H. J.: Design, implementation and real-life evaluation of a disastermanagement architecture. In GISWORX 2009, Dubai (2009) 2. Neuvel, J. M. M., Scholten, H. J., van den Brink, A.: From Spatial Data to Synchronised Actions: The Network-centric Organisation of Spatial Decision Support for Risk and Emergency Management, Applied Spatial Analysis, Springer (2010) 3. Scholten, H. J., Fruijter, S., Dilo, A., Van Borkulo, E.: Spatial data infrastructures for emergency response in the Netherlands. In Nayak, S., Zlatanova, S. (Eds.): Remote sensing and GIS technologies for monitoring en prediction of disasters (pp ), Berlin, Springer (2008). 4. Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang, W., Powers, J. G.: A Description of the Advanced Research WRF Version 2, UCAR technical report (2005) 5. Zelle, H., Groenewoud, P., Calkoen, C.: Forecasting Services for Wind Parks, technical report, BMT ARGOSS (2010) 6. WRF model: 7. CalPuff model: