An Open Source Approach to Communicating Weather Risks

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An Open Source Approach to Communicating Weather Risks
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Open Source Geospatial Foundation (OSGeo)
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Portland, Oregon, United States of America

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Weather data is a critical element in the decision making process for a vast number of entities and its timely and accurate portrayal is essential. The U.S. National Weather Service has utilized a combination of Open Source projects including: OpenLayers, Qooxdoo, PostGIS and Flot among others to create a mash-up called the Enhanced Data Display or EDD ( to promote the development of a Weather Ready Nation. The EDD provides a platform to quickly communicate past, current and future weather conditions. What happens over the next couple of hours to a week dictates the agenda of everything from strategic resource placement to what to wear to work. More often than not, the weather forecast is not binary - there is always some probabilistic component that results from the inherent chaos of a 4-D fluid wrapped around a spinning sphere. Luckily, the EDD makes use of a variety of techniques that leverage Open Source technologies to present forecasts in both deterministic and probabilistic forms. The EDD contains many visual displays that refine bulky meteorological datasets into palatable forms. Whether you are looking to see what hazards you may face along a travel route or trying to find a heat map of how many people will be impacted by a tornado warning, the EDD can display this quickly. Finally, the ability to combine EDD layers with your own data makes this an extremely powerful application. EDD is a good example of how leveraging Open Source resources can result in an exquisite product.
Keywords Weather Disaster Response Situational Awareness PostGIS OpenLayers Qooxdoo Flot Mapping
Category of being Computer animation Hazard (2005 film) Machine vision
Dot product Texture mapping Service (economics) Connectivity (graph theory) 1 (number) Cartesian coordinate system Computer animation Causality Hypermedia Different (Kate Ryan album) Average Prediction Office suite Freeware
Web page Time zone Service (economics) File format Information overload Multiplication sign Interface (computing) 1 (number) Set (mathematics) Thresholding (image processing) Sphere Product (business) Type theory Message passing Process (computing) Computer animation Different (Kate Ryan album) Telecommunication Freeware Abstraction
Service (economics) Transformation (genetics) View (database) Connectivity (graph theory) Multiplication sign Zoom lens Set (mathematics) Field (computer science) Mathematics Different (Kate Ryan album) Hypermedia Cuboid Algebraic variety Addition Texture mapping Graph (mathematics) Mapping Tesselation Interface (computing) Shared memory Database Data management Computer animation Right angle Geometry
Slide rule Density functional theory Service (economics) Computer animation Digitizing 1 (number) Database Office suite Cartesian coordinate system Pole (complex analysis)
Domain name Point (geometry) Medical imaging Pixel Computer animation Information Image resolution Database Maxima and minima Field (computer science) Plug-in (computing) Power (physics)
Point (geometry) Service (economics) Open source Real number Multiplication sign Connectivity (graph theory) Design by contract Regular graph Graph coloring Depiction Computer icon Computer configuration Representation (politics) Condition number Area Dependent and independent variables Hazard (2005 film) Bit Database Line (geometry) Degree (graph theory) Type theory Proof theory Uniform resource locator Message passing Computer animation Personal digital assistant Routing
Point (geometry) Group action Greatest element Statistics Run time (program lifecycle phase) Service (economics) Multiplication sign Connectivity (graph theory) Metadata Medical imaging Goodness of fit Population density String (computer science) Touch typing Texture mapping Hazard (2005 film) Interface (computing) Polygon Total S.A. Bit Line (geometry) System call Degree (graph theory) Word Computer animation Right angle Quicksort Table (information)
Heegaard splitting Statistics Computer animation Hazard (2005 film) Natural number Decision theory Multiplication sign Polygon Database Summierbarkeit Product (business)
Arithmetic mean Computer animation Concentric Confidence interval Different (Kate Ryan album) Decision theory Right angle Diagram Line (geometry) Spectrum (functional analysis) Field (computer science) Product (business)
Point (geometry) Ocean current Service (economics) Code Plotter Multiplication sign Direction (geometry) Execution unit Time series Graph coloring Field (computer science) Product (business) Frequency Hypermedia Different (Kate Ryan album) Cuboid Arrow of time Standard deviation Dot product Texture mapping Graph (mathematics) Mapping Hazard (2005 film) Interactive television Sound effect Database Vector potential Computer animation Normal (geometry) Right angle Quicksort Window Row (database)
Area Programming paradigm Greatest element Presentation of a group Service (economics) Information Open source Multiplication sign Polygon Revision control Goodness of fit Uniform resource locator Computer animation Different (Kate Ryan album) Right angle Object (grammar) Exception handling
Point (geometry) Open source Code Online help Solid geometry Disk read-and-write head Machine vision Category of being Web application Computer animation Operator (mathematics) Chain Stability theory
Computer animation Lecture/Conference Code Self-organization Freeware
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Type theory Server (computing) Lecture/Conference Multiplication sign Feedback Sheaf (mathematics) Online help Right angle Cartesian coordinate system Event horizon
Web page Demo (music) Lecture/Conference Videoconferencing Electronic program guide Online help Right angle Bit Menu (computing) Cartesian coordinate system Mereology
are all the background and the US National Weather Service of our mission is to say what's in property so this a flying melds well at that and our vision is to build a whether the nation where we give basically everyone the ability to prepare and respond appropriately to weather related hazards and when the
service is pretty you wide and stretch so this 120 different weather forecast offices around the country and that each of these red dots on this map here so as 1 of those forecast offices and then there's also a 6 regional centers which require the most publicized 1 such as the Storm Prediction Center in Norman Oklahoma and the National Hurricane Center down in Miami Florida those ones get a lot of media attention but is a lot of other components in here but I guess the the gist of it is that all these places are generating a lot of free data that is work that can be used in GIS applications
so the US is privy to a really on hazardous hazardous flew of whether or all these different things combined to a basically cause about 650 deaths and every year in the average year of US Weather and about 15 billion in damages and they are graphic in the lower left hand corner here shows the weather related I cost a society from 1980 to 2010 and regardless of the trainer what's causing it but what is becoming more and more impactful to the country every year the so there's a few issues with the weather
data the first one being a communication and communicating risk is a very difficult process for forecasters to get their message to the public the public take the proper spots on to add potential risk and not a reasons for this are the a lot people just interpret data differently and and other people have different risk thresholds so those are the 2 big ones and then there's the of false comfort zone where basically on with something we call warning fatigue or you get 9 22 warnings for your house but nothing ever happens but on the temp time a kitchen that's all what we refer to as warning fatigue and what is a pretty abstract concept it can think four-dimensional in this worry have a transient food wrapped around a rotating sphere a lot of people don't have a hard time comprehending how all that works and it's pretty chaotic too so um of a finish that up with that data format so there's a lot of free and plentiful weather data out there but until recently it hasn't been in a very usable format especially not in a GIS format and so is getting better and that
product that I've developed is called enhanced it displayed it's taken all of the different types of data sets for the weather service offers the amount of kind of 1 place I hate seeing one-stop-shop that's kind of what is and as far as I know a related one-stop and so this is the interface if you come to that page that's the Europe there the top and that has a few the highlights of
interface and left-hand side on the green something I call quickly there's or gives you access to the most common layers which can toggle our change opacity change different on fields and their and underneath that is what I call a quite copy of additional layers with about 350 searchable filterable our weather related layers that you can add a map the fast enough you can go to the top right hand corner and I've put a little tool I can bring your own data and bring your data you can import camels shapefiles W Madison tiles services so and so I'm pretty flexible in the mountains it can do in the red box there there is at that layer manager which control the order in opacity of all layers and the very bottom center there's a little button that allows you to quickly share this view with anyone else all the layers of zoom everything on by URL and I critter tiny or else so easily shareable by social media so a lot of things by going to is kind of busy interface but
it's very powerful at the same time so the interface is built on open layers for the mapping API and that cooks to which a JavaScript API is our does a lot of the I I guess gooey manipulation and buttons where I use a flat and had just started recently using D 3 for the and so the plotting of different neurological fields in the back and is a basically oppose shares database and post press NGL does the geometric transformations of lot of those are kind of old data sets in a kind of a more spherical Mercator projection so there's a lot of components that go
into this application 1 of the biggest ones is the national digital forecast database on and DFT this data is being generated by all the forecasters at all the weather service offices and cross country I showed you a few slides back and this is an example for our last winter when we had a they got snow ice storm down in the southeast of US here the then to show you some things that are potentially possible with the
database interpre gigantic database and extends from western Africa all the way to eastern Asia and from the centre South America already to about the north pole and so it's a gigantic on database with the the 2 and a half clamor
grid so it's very high resolution SAR images use of Portland and mounted is a little pixel on 50 grit picture of maximum temperature the database goes out 78 days with power grids out 3 days it has about 3 million grid points per domain per grid with about 40 different fields and all those are updated hourly so you can imagine how much data that is actually going to this and if you want
more information on this and put a plug in for a 10 campuses talk tomorrow at 10 30 he's going over how he rapidly pushes all this data in and out of this database so that we should have said if you're interested so I leverage at database
to make different components in the ED display and 1 of them is the travel hazard forecast which shows you the hazardous weather color coded by the on along the line along travel wrote at the time that you arrive at a specific location and the way that works is you can quickly right-click to map and set a start point on point and if you want you can add a point 2 and that'll bring up but this doing actually fill up for you or alternatively you can just bring this up by itself and search your own locations and the different options this is when you wanna leave and once you actually have data in there there's a few other things that you can add on such as display options stuff but in the end you're gonna end up with a that kind of forecasts where he certain New Jersey real pass on hit Portland and what I have she displayed here is our temperature along the route and icons i denote different whether types that you can hit across away so right now the oranges as thunderstorms but being in a little more granularity that in the future and so I can't depict severe thunderstorms versus assure regular ordinary thunderstorms and then there's a little bit into the red text kind of all over in the West Virginia area I through a filter and the fur just highlighting places where Tempters above 85 degrees just for display purposes the data was making a combined a lot of really good weather but so how did the whole thing works
well users geo-location to grab the start and end points and I feed a routing service right now it's Google Maps better prior to going away cause we're losing a contract with them on and so applied to convert that to a more open source and the the thing that I said 1 that I just er to investigate and then user response from that routing service to calculate the rival times along the route that create a database at each of those points and basically color code the segments by the worst conditions on either side and on the the point and then I display the values as a feature and those but there was a point to but as you can see this kind of was a proof of concept being and I began working but then I found out that there's some flaws so this case right here there's a high wind warning out on a segment of a across a segment of your out and it didn't pick it up because of sampling on both sides of that line so what I've done is if I start sampling along the line segment between those points that while I get a lot better representation to the
public so here's an example call of doing such a thing and again I'm still developing this so still work in progress but and this grid years represents the odd to have some arrested and if degree and that's from that it is based on the line segment is 1 of those paths between the points so the 1st thing and you the is I clicked this giant rested down to this a small right around the line segment there and that using this to clip from post yes and the accordance of that line string going to that GM from text called and then from there hi intersected at the specific valid time of 1 big assumption that time along this line because of i is all about the same time that's what that were valid time is equal to such a such date and that is a pretty I'm a pretty big assumption but at same tidings is really good assumption because those line segments are generally can be less than an hour and all of our goods on our on long so that's all right so if I do intersect that with that contrasted and I put all the data points from that that Thailand future touches and then I just I unionism up by so basically merging always together and I do value counterpart of the hazards or whatever else I'm interested in along the the cool thing is the whole total execution time for that is about 3 millisecond so that very very quick
component of the interface is the impact hazards where basically what you see now under the idea uracy weather service warning is a polygon and so this 1 has a little bit more flexibility to really get the name of what it is but has vowed to whole hazard watch gooey there that you can sort n by n time population affected and so on and so forth and it's all interactive to so the mouse over 1 of these features you'll get all that metadata that socially without warning there including impact data the end there's a little bit more information get if you click on the little red target in that table it'll bring up on something that Brian Wallen wonder and his group have been working on and this is uh something that's really powerful because it shows 3 the data left with the warning but also kind a heat map of population density on the right hand side and that all a population impacted statistics on the bottom this is really useful for femur and on you can since it is an image in tweeted out by social media and get the word out really quickly to a lot of people so very powerful stuff there the way that he
does that is that he uses the LandScan database which is the 1 climber grid of the Indian population basically where people are during the day and then he also uses the 2012 a typical dataset for public venues and the National Transportation database Atlas database for highways rails national parks and he rest arises the warning polygon we haven't sums up all the data within their I had to get those impact statistics that ocean so really really neat stuff that and again this
is a pretty powerful and has to face and it's kind of built for the decision maker so 1 in they would be concerned about is hazardous material reasons or something of that nature so no around they are high split model which is a model that predicts where these on chemicals will go if there is such a release and so there's an example of say and a fictitious release from Chicago in this shows the time of arrival to certain destinations along the way
and this 1 right here it is the concentration along the again this is all within our that you can interact with anything decision
makers you told me about when they were on doubt there is they really wanna know I confidence in a forecast that we have and so I came up with this kind of plume diagram that shows at least the probability of exceeding a certain amount of rainfall visceral percentiles in there in the mean is the the solid black line there and similarly you can throw on the National Weather Service forecast and draw a couple other different fields there it isn't really give them a good idea for what our forecast is and how that relates to what the models the thing is called the UnMouse spectrum this is
all again with and and the shoppers points in the maps the clicker in making it a lot of our by the data out of the this 1 right here is really good for extreme heat and cold where the background colors are the record highs record lows normalize normal low temperatures are the dark blue dots are then to be as forecast in the box and whiskers are the model and spread at a sort of certain point for period and it's colored by the standard deviation so the warmer colors are cost a lot higher spread in the green arrow her cooler colors are well spread so when they're green shade you thinking that the pre icons forecasts were works red you may not be sure like that very last 1 over the right hand side of the purple unit you know the about same I get record heat that David was services saying you can be above normal vinyl records of really interesting stuff in there if you
click on the map just anywhere what you'll get is the sole on window here pop up and will tell you is a little gantt chart at the top shows the current active hazards and watches and warnings advisories in effect for your point you know what time period there in effect for in in the 1st half that which is gives a forecast but if you switch over to the hourly graph 10 that's where it happened that DFT database again and I plot of the media gram or basically a whether time series of that data from always different potential fear that plot I after hearing interact with so and if you have the show wind rose button checked it allow popup on a wind rose over the unit that is basically what direction of the wind coming from and what frequency what speed is coming from and after really think Nelson minor who is also in the room and for the code to do that and I can actually click and drag a swipe on the graph and it will update and all the fields and kind of summarize the data for itself and again really powerful stuff with them and I 1 of the
really cool things is that open source technology is allowing us to change the present a warning paradigm of the weather service right now we're in the like all binary warning where anything inside of this is big yellow polygon here warned anything outside of it you're unwarned and it doesn't really have a lot of good information except for the fact that and you know toward the person in the top left hand corner is the same exact chance of getting hit by the storm is the 1 on the bottom right hand corner that's a meteorologist but you know that's not true on the person in the path of the storm has the highest chance so what we're doing is we're using OpenLayers on the right-hand side to draw a threat area or recalling threat objects around a certain storm and then then were propagating that out in time using a calcium which is the typical and damage at a store and With that gives you is a whole lot more information about that so you can calculate the probability that storm hitting you and for the time of arrival of that storm so on in your specific locations so that's a huge improvement over or currently on fitting out so I just as changing the warning paradigm using open-source tools it's a bunch of us
screenshots from enhancing display stand cherry-pick these come over last year so of different things you can do with it and I the there is a mobile version but it needs a
lot of work so that others but at giant carry out there so in summary I
i've the open source communities really predator release stable and solid foundation to build a professional web application and and underlined support in that 1st bullet just because without it this thing would still be a vision in my head hadn't with all of you guys help it's really made this come to fruition so so I really think the community for all the help for the given me over the past few years the open source code also enables us to generate new data is displayed techniques and get was in your partner's hands as quickly as possible speeding up research operations and which is very important and finally down with I think this is by the most important point of all is a chain the way that we're hi community whether risks and ultimately leads to saving lives and property so the it that's all I have for you if you have
any questions and I'll be free and to the now if you want to to our code in all give you a trip to see said organ or mycosis weakened using and self so that all I have for a thanks thank Phil what this of repeated look any questions
or the we the people the for the and 1 yeah so the question is can you pull the data from the EDD into an existing system and that is
actually the works right now I a lot of data sources in this application I come from a variety of different sources and the weather service is they're almost all experimental so they're not really stable supported on but that's changing because if a set of this group of the IDP which they're taking that she took all the layers that I have in here using that as like a baseline so when it does come on line you should have access to all of them and if there's anything in particular that you're interested in such as radar data I can show you how to do that because in the help section in there it has examples of article and your application so partial here the only thing you and the the yes so the question is are some of the visual ages and visualizations are complicated and complex any he went off I have gotten around a testing them with actual users and the answer is yes I have given this demo of ad like alive demo and probably 3 dozen times to merge the managers and other decision makers and when they 1st see it they are overwhelmed by given
that I but a lot of them that have come up me afterward and start using it say the prices so on and a lot of UN special about spectrum 1 it's pretty complexes is so much data going into that but they do in the end of the day really like seeing that information and it doesn't is not too complicated for them we set told me and that's what a lot of feedback and got from this display so far has show and thank you yeah that it's really targeted for decision-makers but you know there's a lot of really whether setting people out there really appreciate this display to on and it really has a huge broad user group from hospitals to school districts to where managers to grammar he has
so why music just what the and home well what they do is there's a a feedback survey that can take in the top right hand corner and they give you 10 questions of the reliability of data how well the work on do you like it should be offered by the weather service stuff like that and they compile those reviews and then they make a decision saying she we continue this series on get rid of it or to be too by modifying in making a better and then put it up for operations just I forgot to repeat the question his question was how I was the weather service so
the use this kind of application and use did feedback for this type of application the so so that this yes the the the question is on average how times application
opened I actually don't know because the server people on that I have to go through to get this pushed out to the public I haven't given me those stats but but I suspect it probably gets a hundred thousand hits or so a day so my dad Ch any other questions then it and do our follow up and that's where the data is very and and that's depend on the weather have a day I mean if normal they at there and take out but also some big event happens and I was in the spike and other millions of hits per hour or 2nd so if any other questions yes the the not kind of I yeah so the question was if she didn't hear the livestream or me demoing this is the way to go back and get a feel for how to use it or did user features or as a paper and I there is a help section in the top right hand corner of the page and
the fact that appears to to to give it
the but it has a whole user a
demo of the ego so the help menu bar top right hand corner it has videos and how
a user application has but a paper rough draft guide kind of thing and that's about 26 pages long of how to use each individual part of it and on prominence little bit updating that yes so yes there is the any other questions where we're running out of earlier right thank you thank you