>>MODIFIER:  Good morning and welcome to the morning session, data and environmental client data.  One of our speakers is not feeling well today, and he will not be able to come.  But that should not be a problem.  We have two of his colleagues here, and I think we will still be able to learn what we came for here about data in this context.  Very briefly, I'm the lecturer.  I used to work as a consultant in digital information and today we have ‑‑

Data, we listen a lot about it.  We hear a lot about it.  It's become a buzz word in recent times.  Most of the time, we hear about data being generated from internet platforms now that is something that is in the focus.  But actually, we are also told often that internet of things and developments were generated and bigger amounts of data, and this will change our future. 

However, there are organizations for which this is already a reality who have been working with data with censors, with collecting massive amounts of data and processing them for a long time.  And one such organization based in Geneva is world technological ‑‑  and they collected from satellites, land stations, and you can imagine the volume of this data. 

So today you'd like to learn from their experience to see how it can be used, what you can learn from that for other context.  But we'll start with a few basic explanations of what kind of data they will equip and how to use it.  There are questions that you may have for me, it will be interesting questions to know what kind of capacities are needed globally to use to benefit from data because that is not often talked about today. 

But if we are ‑‑ if you want to make sure that most people benefit from this, I think this is one of questions that interest me. 

So without further ado, I would like to ask Stephen to start with his presentation and then we'll have a brief break after his presentation and then a second presentation and then more questions and time for discussion.

>>SPEAKER:  I say big data, whatever that means is bread and butter for the technological community.  It pre‑dates 1953, it was founded on the realization data measurements of our planets are required to global.  And so for the community, data is a fair.  They do this every day.  But it depends how it is structured.  They are very well described. They are not unstructured data, such as Google searches.  So that's the big difference.  Although we also see the emergence of observatories, taking the temperature of using our smart phone, and there are indications of how to make data for observations. 

I will focus on the ‑‑ that contribute to weather and climate applications. 

So just one slide on what ‑‑ is.  It's the specialization of the United Nations based here in headquarters in Geneva with 191 member states, and it is the UN systems of atmosphere, its interaction with land and oceans, the weather and environment it produces and the resulting distribution of water resources. 

We work mostly with national meteorological services to make sure this is happening. 

I decided to show you this slide, which it gives you a snap shot of the situation in the Atlantic in September of this year.  It was one of the ‑‑ in the Atlantic.  It shows Hurricane Maria and tropical storm approaching the Caribbean and the coast of the U.S.  and information like this from satellite but also from buoys in the ocean and stations at land is critical to help forecast the track of these systems which pose an important threat to lives and livelihoods as you heard in the news. 

At the bottom, you see the storm tracks of all the storms in the 2017 season and forecasters in operation centers 24/7 are monitoring this and forecasting those storms. 

It is a matter of hours or minutes to get this data in time to the right people, to the right computers, to forecast these storms.  And that's by no reasons trivial. 

This is track of hurricane Sandy in 2012.  Very unusual track.  You don't see any of these tracks in 2017.  And it was due to the advanced American prediction models that the landfill of Sandy in New York City could be predicted with four days in advance, which gives very precious time for emergency services and disaster prevention, although it is to react accordingly and to evacuate where needed. 

So while the laws of lives and livelihoods was tragic in the Caribbean and the U.S., it would have been much much larger without these predictions. 

Significant measures have been made by countries all around the world in the weather and climate data value chain which goes from the observations of the state of the planet, telecommunication and my colleague will talk about this in more detail.  So quickly get the data from where it's been measured to the right place. 

Forecasting these data into something that is looking like a forecast of weather in 3, 5, 10 days and then the services from your TV program and your smart phone. 

Just the estimations estimated globally around 5 to 10 million U.S. dollars and nearly by all countries. 

It's often sited that this system and value chain is a primary example for global planning through the UN, also referred to as the world weather watch.  It's driven by advances in science and technology, as we see today.  It was started in the early 60s when the first satellites were available and also when global prediction models came about.  But countries didn't just invest in this because they felt this is an exciting opportunity.  They also saw the real benefits in investing and improving forecasts. 

I mentioned the shipping vessels, but there are many, many other applications of this, and I'll show this in my slides. 

Establishment of world weather watch was treated by a speech by U.S. president JFK in the UN assembly at the time.  So satellites are supporting these services in weather client and environment through many means.  So observation means you have a sophisticated camera up there, and it takes pictures or other measurements of the planet. 

But there are also these navigation serves driven by GPS satellites and Galileo these days, which allows you to geolocate and whatever is happening and you use this in your smart phone wherever you are.  Telecom communication services.  So if you want to make a phone call on the north pole, you use a satellite for them if you're on the ocean.  And fortunately, we would like to know what the sun is doing and how it impacts systems on earth. 

I focus mostly on that observation in this talk.  This is not the complete picture of all the satellites orbiting above our heads, but it's sort of the work horses as we say that provide the data for the meteorological community.  We call this a base base level observance system.  And these satellites are not to scale.  These are operated by agencies from countries around the world that are listed below, coordinated through the coordination group. 

I have a web link here which gives you information on all the satellites and systems that are currently flying our plan. 

Some people may not know what a satellite looks like, so I just put a picture in of a typical one.  This is a European satellite flying at about 900 kilometers above earth.  In theory, you can see it every day, but it's kind of hard to spot it's about the size of a car and 800 kilos.  There are much smaller satellites these days, even private companies embarking on this. 

So what is it all used for?  This shows ‑‑ the satellite data sets that are used for European center based in the UK are using many many more satellites over time.  It's not trivial to assimilate this data in your model to make them useful, but the effects are clear. 

At the bottom, you see the quality of forecasts which has improved over time.  If things go up, it means forecasts are getting better.  That means you compare forecasts ‑‑ you do forecasts now over the next three days and then wait three days and compare what's really happening and you correlate it.  And that gives you this draft. 

So the forecasts have improved day three, day 5, day 7 over the past 30 years, and that's largely due to the contributions of satellites, but also due to advances in modeling the earth system.  And if you just take the red curve, B plus five now, and you can see it's about at the height where this D plus three was about 15 years ago. 

This means a 5‑day forecast today is as good as a 3‑day forecast 15 years ago. 

So we're making about one day per eight years.  And that would extrapolate that forever, of course.  That's just an indication of progress in our participants.  And also the bands here, this shows not in southern hemisphere.  So the skill of forecast as it converged because we now have data from areas where there's not many observations, like in the oceans and in the southern hemisphere.  So forecast in the southern hemisphere are now as good as in the northern hemisphere, which used to be very different. 

Just another example.  It's a major seasonal weather phenomenon that you may have heard of, affecting weather patterns around the world.  This is the anomaly in the tropical ocean altimeter. 

If you fly across the pacific, you better know what the volcanos are doing in the elevations islands across just off Alaska.  They're very active volcanos, and there's lots of night and you better know have a volcano is erupting because then you may want to get you know, rewrite your flight pattern.  And the yellow is false colors but shows the presence of volcanic ash, and you would not want to fly through this. 

Taking a real holistic view of the earth, 1968. 

We need to know what we need to measure to get the whole picture of the planet, and a program called global observance system defined variables, about 55 of them, which are recommended to all around the world to be measured on a routine, systematic, and long‑term basis.  The point of this slide is to show you it's not just about the atmosphere.  It's also about the oceans, CIs, core regions, land surfaces, and ultimately about the ‑‑  so we have to observe all these things on a routine basis. 

This is the list of comet variables as we have it today and in yellow, you see the is satellite square observations.  And just one example of how it's being used. 

If you want to protect your ports and your settlements, you better know how sea level is rising over in the future and on the top right, on the top left you see regional sea level change over the past 25 years.  So red means change up to one centimeter per year and other colors mean a bit less than that.  It's not like in the bath tub where water just goes up. 

It's very variable depending on ocean currents and the gravity field.  If you know that and you know on the top right the city allowance levels which means the degree too much the cities can adapt their ports and settlements to make them more protected, then you can calculate how much it will cost per year, each of these cities to maintain such levels of protection. 

One more example.  No one is living there, really, but we say what happens at the poles doesn't stay at the poles.  Satellites are a unique way of monitoring what's happening in the arctic content and ice its ice sheets.  What you see is the velocity which glaciers move, and you can use that as a proxy formatting of the ice sheet and with the effectancy level. 

We've also mapped what observation can do to sustain and development goals of the United Nations and here are the 17 goals which are on the targets and indicators.  And on the right, you see to what extent the observation data helps to map and provide information about these things. 

So for example population distribution, of course you have national security levels and censors and all these things, but you can also see where people live and quite nicely from space.  And you can draw your conclusions upon that.  So this is available and can be used. 

I'd like to talk more about data now.  A key engine behind the progress in our discipline has been free and unrestricted international exchange of data.  And that is we no means something taken for granted.  In the mid'90s, this resolution, it's a formal statement by member states was approved.  And it created a regime in which all countries can find which data they want to exchange, called essential data, and for which data there are restrictions to exchange.  This allows each country to regulate what they have in national regulations.  But this is the instrument behind it.  So every day, every second, you have gig bites of data that are exchanged dramatically, routinely, and can be used for forecasting, based on this resolution.  It's regularly under threat because of needs and intentions to commercialize data, but so far this holds true. 

Unrestricted means nondiscriminatory and without charge. 

One slide on what we do to help countries make use of these large amounts of information.  We have region based satellite user groups.  We divide the world up into six regionen and each region, we have people who are expert ipusing satellite data for meteorological and plan service comes we bring them together regularly.  We brought training through this and learning techniques and other things to keep them up to speed on what's happening in space and how they can use these data effectively. 

We also maintain online portals and issue guidance and best practices. 

Looking ahead, some thoughts on what we see as trends that will determine our discipline.  Of course, we will have advances in modeling science.  We would like to simulate even better how the planet is changing.  And in order to do that, we want to have five best resolutions of our models.  So let's say if you want a weather forecast of Geneva, it's no use if you have information available only 100 kilometers.  It's quite coarse, especially if you have such terrain around here.  So you want high resolution down to 5, 2 kilometers.  And also you want to consider many more factors.  You want to be even more realistic to model what's happening. 

So this is where we see things going, but this has implications on computing and data because the models today we have roughly five times ten to the power of nine variables to consider, to run such a model every day.  That's the top right box here.  But the models will improve, and they will have many more great points.  Top right, you see sort of how, you know, we model what's happening on the earth. 

Many more levels, which means layers of the atmosphere and many more variables.  People want to know more and more how is the planet changing, you know.  Today, you have a forecast for temperature and rainfall.  Maybe tomorrow you want to know how is my tree changing?  How is my soil changing?  My agricultural fields.  The lake and so on.  So this is driven by society demand information and also by science. 

So you will have a factor of 2000 more data per times.  On the observation side on the left here, we also will have more data.  More satellites, more instruments, high resolution.  But the increase in data is less.  It's maybe a factor of 10 to 50 and not 2000. 

The modeling sensors need to cope with this.  This is a graph from a major modeling center, and they are simply running out of energy to run these models.  So on the bottom, you have model resolution.  And on the Y axis, you have a number of computer cause and the power consumption needed to run these models.  And you can easily see that we are getting in too narrow ‑‑ if we have a model of five‑kilometer resolution in 2025 and a single model, we will use about 100 mega watts.  Well, 50 mega watts.  So you need almost your own power station to run this.  And it cannot be really delegated to the clouds because then that will take too much time.  Being verified storms competing to do this.  And the scalability of scenic conductors is also at its end, so we need new approaches to modeling.  That's the key next year, to cope with this, and to meet the demand of society. 

My last slide, so the future trend that we see, we need some kind of data thinning or conversion while reserving main computer content in the data we use to exchange.  The volumes are just getting too large.  We need to look at how cloud computing can be useful.  It's already being done.  There are pilot projects in this era. 

And traditionally, the data will send to somebody.  And they could do something with it.  Because the data volumes are getting so large and band width is always a problem, and limitation, you're increasingly seeing people bring their code to the data.  Okay?  And then you calculate whatever you want on the data in the clouds or somewhere, and then you get results, which are not as, you know ‑‑ the volumes are smaller. 

We talk about analysis rate data.  This means that's say if you want to cook cordon bleu and some cooks they want just the best ingredients and they know how to cook it.  Other people just want the ready menu.  And it depends what you want.  We are sometimes accused we are too academic, data cannot be readily used.  People just want to know how much rain there is without asking many questions. 

In our community, we tend to say it's not that simple.  We need some processing, some science.  So the is a debate around this.  Where do you relieve your data to somebody to make use of it in an intelligent way.  The model on global and restricted data exchange needs preservation.  We're convinced of that, and we have working groups to work on principles of satellite data and other data to preserve this resolution 40. 

We need new approaches to weather modeling, because the end of modeling is nigh.  A big challenge as in many other technical areas, how to reduce the digital device infrastructure and human capacity.  How can we make sure it keeps pace with modeling on this? 

Certification of weather and climate services.  So this is ‑‑ should we have a role here or not?  If you have a smart phone in your app and you get a weather forecast, anyone can do this.  Should there be any kind of quality control or certification, a stamp of approval, by a service to make sure ‑‑ to give people confident that this is useful?  If you plan your barbecue and it rains, that's maybe not such a big deal.  But if there are serious warnings and there's confusion in the population because one app says this and another says that, who's right?  So who has the authority to say this? 

And lastly, commercialization of the weather enterprise.  There are new access entering the field.  It's much easier today to access space.  Companies want to commercialize data.  It's on going.  Thank you.

>>MODIFIER:  Thank you.  I actually have many questions here, but I wonder, are the any questions from the audience?  Yes, please.

>>AUDIENCE:  Yeah, I got a question.  My name is Amir.  I have educated in GIS so I pretty much understand about data numbers and stuff.  We got numbers and data.  We got beautiful maps for us.  But how it can be meaningful for others is still a challenge, yeah?  And so so far, what kind of efforts so far that has been done to deliver this data to communities?  That's first. 

And what about the data security?  A satellite can be hacked and also these processors can be disrupted and also data can be manipulated.  How do you deal with that?  Thank you. 

>>PANELIST:  On the delivery, so in principal, the data are made available freely to the community.  Some nations have more restrictive data policies.  Others are totally open.  And most of the data, as soon as it's no longer time critical, it's available on the web for anyone to use.  Satellite agencies are more and more moving in this area of free and open data access.  So whatever you saw on my slide with the satellites, those data are generally freely available. 

Especially they lose value with time.  So the archive data are usually freely available.  It's very fresh data and sometimes has restrictions.  We also have science groups that help us translate into something more simple.  Rainfall is a big interest for many many communities.  We have an assigned group that just looks at that and compares different estimates of rainfall to help forecasting and emergency services in case of heavy rainfall events, for example, or droughts.  We maintain science groups, and we foster data exchange.  That's the first question. 

The second one, yes, it is a challenge.  In some companies, the agency running satellites is part of the defense industry.  They are used to these kinds, but it's certainly a challenge how infrastructure can be protected and my colleague will talk about this more and we have it as a discussion point as well.  Thank you.

>>MODIFIER:  Any other questions? 

>>AUDIENCE:  When you say that data is available freely to the communities but also some nations have a more restricted policy, the data that is exchanged internationally, is forwarded to national serves, right, and then they decide what to share at a national level or not?  Do I understand that correct? 

>>PANELIST:  Yes, they decide what they share at the national level.  Usually, it's shared immediately with other governmental agencies so it's largely under government control.  But in some countries, whatever is shared in the government is immediately available to the general public and to any users.  In other countries countries, it's more restricted.

>>AUDIENCE:  But with public access to the internet, would it be possible for people to access the same information from other sources in the globe if they can get it globally.

>>PANELIST:  That is already happening in some locations, yes.

>>AUDIENCE:  And how does some interesting challenge of communities using this in some ways that are not maybe foreseen or planned, some kind of crowd sourcing applications that you've seen that may regard it interesting.

>>PANELIST:  Of course, lots of many things like this happening.  You know, people build their own service and make their own predictions almost.  You can run a little weather model on your laptop these days.  You can focus your local weather.  You can investigate pollution, for example.  Pollution levels in your local community, air quality.  People are doing this.  For example, if you want to assess the impact of air pollution on agricultural, on an ecosystem, you can do that very locally and that doesn't have to be done by the network. 

>>AUDIENCE:  You say one of the challenges is models and the computer power we need for them.  Are there any promising emerging new models or principles for modeling? 

>>PANELIST:  Absolutely.  Yeah, it's called ‑‑ one approach is called GPU rather than CPU so central and graphic processing unit.  So you delegate some of the processing through a different way of processing and also architecture and proximity of computer systems becomes more and more important.  So you have to be ‑‑ you have to code your programs in a way that this data is moved around a lot.  You have to process where the data is physically almost on your mother board to make that happen.  So the architecture of the computer system becomes more and more important. 

>>AUDIENCE:  Thank you.

>>MODIFIER:  There are no other questions for now, I think we can move to the next presentation. 

>>PANELIST:  Good morning, everyone.  My punch is information system.  What we do is all about facilitating and promoting the technical collaboration among our members in data exchange, data sharing, providing ‑‑ setting standards on data.  So I will show you a few slides of the technical systems that we coordinate of among members. 

As my colleague has shown a few minutes ago, weather coordinates several global networks which is implemented by our member countries.  I will say the most expensive system is the global of living system.  So here we have integrated global living system, which is composed of all sorts of living technologies, systems, satellites and data, buoys on the ocean, and also ‑‑  so it's a very complex system. 

You can imagine how much data those systems are generating in real time and how complex those data are in formants and levels of product.  So how to make sure they are of good quality, enough quality of our data processing systems.  And for our focus just to use and how to maintain this system in a sustainable way. 

So getting them integrated into a whole picture is the ‑‑ 

So we have GTS government information system which stands for global telecommunication system, which is one of the key component of what would watch, as was mentioned a few minutes ago.  So we have this GTS is about data exchange.

So how to make data freely and ‑‑ free and unrestricted where to flow among among our members, to make sure each member has their own share of global data that they want to use. 

And at the lighter corner we have global data processing and focusing system, namely this is composed of a long list of focus incentives in the world.  One of the most famous one is internet pacing in Europe.  It's the European with a focus in southern England. 

So we have other systems to under ping the research of our member countries to deliver weather climates and hydrological weather serves. 

So I took a look at the GTS, you can see this is a high ‑‑ which is the result of evolution since the early 1960s.  So the very beginning years, they start with weather watch.  So we have at the corner a few incentives interlinked by the main telecommunication network and then where we have a national incentative and each country ‑‑ each country has a long national energy center which is connected to this network. 

Imagine the complexity of the network.  What we are proud of is that well before the age of internet, we have very ‑‑ we already have sophisticated metro connected in theory all the members by all sorts of telecommunication means.  Look at this chart which is of the data a few years ago which is only part of the GTS which is in Asia.  So you can see while this is much better, or this is too complex maybe, yes, this is based on the legacy technology, telegraph, namely. 

But since the beginning of ‑‑ we think of the beginning of the internet age, so this chart is evolving and we now have a much more ‑‑ well, we have a flatter structure.  That is what we call it now, the WIS network which is cutting down the levels of hierarchy levels because we are benefiting from the development of internets.  Internets is crucial to our business.  Internets is connecting all our bits and systems, stations together.  So internet is a very crucial infrastructure that we base our system on. 

So government information system, what is the difference?  Trying to open the GTS.  As you can see, GTS is more oriented to professional centers, operation of centers. 

So we now have a much more data, all much more kind of data.  And our users want to get access to those data in an easier way.  So how do we make this happen?  We started some years ago this determination of the information system which is composed of three categories, center, national center, and data collection and profession centers.  Those are the major.  While most of the major data centers, meteorological data centers in the world are in this category. 

One of the forecasting centers, where that produces lots of global prediction ‑‑ global weather forecasting products is the ECM.  So those are the better production centers.  And we have a few global information system centers which are interconnected by a private network.  And one of the key rules of the centers is to keep ‑‑ to maintain a global catalogue of all things where you can get information, what we call meta data. 

So each national center, each DCTC, when they want to share more data to send the data ‑‑ to share the data with more, a new type of data with the other country, they get their meta data from that product registered with one of the global information centers.  So that is the entry for the meta data to be registered, which is crucial. 

With the synchronization between those discs we have, we maintain a most up to date global catalogue of information.  So if we want to see what kind of information, if you aring to research ‑‑ if you are doing research, a new data processing systems, you want to get back up to the global weather climate and data.  So you go to one of the discs to browse into the catalogue. 

Of course, I would make that ‑‑ it is not that easy to use.  So we're trying to open this to a much broader user community.  As we realize that in the past, we have seen too much focus on professional users.  So you know that is true in a nut. 

So just a quick show of how this works.  When an international center or a data producing center want to publish the data, they get their meta data registered so that's new and addition is the synchronized now.  And when a user wants to find data, it goes to the ‑‑ one of the discs and find the description on data.  And you can subscribe once you know what kind of data is that ‑‑ is it.  And when there's better centers, they supply their meta data to get registered and then they're ready to supply their data service.

So you can access them directly.  Of course there's data policy issue.  The data is still owned by the data center.  So of course there is data access rights.  You have to be authenticated if you want to get a steady and data in an operational way.  So there is some operation of setups needed, but if this is to facility with data discovery access and retrieval.  So just a picture. 

But now we are faced with a lot of new challenges.  Challenges both from the data and the user community because we have more and more data.  Huge, a lot of increasing in volume and complexity and user expectations are changing.  They want to get access to the information and service through common interfaces and applications.  That is seen by the different internet of course. 

And users would like to combine a mobile clouds and the technologies to get access to more information. 

As we can see, common sharing platforms and technologies are coming in mazes.  And changing data supply and user applications, as I mentioned earlier.  In the meantime, we have the opportunities.  Web service, cloud technology, search engines.  Of course, if you go to Google, you don't get much information on professional data which is still a problem. 

So we will work with search engines to try to break that barrier so that the public will have ‑‑ the public user will have a much meaningful results when they go to a search engines.  So that's a need we'll work with together with search engines like Google and bing to make that happen, which is not the case yet. 

So other technologies, of course.  We are all talking about big tators.  What does this mean to us.  And we have to think about this.  So with that, Deb alieno started to develop the strategy for the next stage of information system.  And I will go through in detail about this.  So perfectly, we are trying to take benefits of new technology to address the new requirements of our users and make our system more open and easier to use. 

So data acquisition and dissemination A. New platform which is more ‑‑ for the private partners, as we are talking about. 

And of course we will benefit a lot from the development of cloud technology.  And we will look at data and information management issue as well.  In the past, we focused very much on the data exchange phase, if you are talking about life cycle management of information.  So we will provide more guidance to our members, to our user community, on how to stall, how to maintain your archive, and how even to dispose of the data because we cannot afford to keep all data forever. 

So that's the a brief instruction of what we do in our effort to help our members to get data exchanged, to get data describe exchanged and managed.  So we are trying to move to do a better job to serve more programs like that.  In the past, we've been very much I would say weather centric.  A weather forecast.  But now climate applications for causing more challenging questions to us.  So let's face them and try to address them. 

And with that, I would leave this on the screen so it is for my colleague who was not able to come.  We regret that very much.  So just a few things to be considered for your discussion.  Thank you very much. 

>>MODIFIER:  Thank you.  This was really interesting.  This must be the first time I hear somebody speaking about planned disposal of data.  I've never had somebody planning that before, and I think that it's ‑‑ interesting topic.  Question, please. 

>>AUDIENCE: .  My name is Michael.  I really appreciate both of you separately coming and speaking with us.  It's not every day that anyone in general, much les people in the internet governance community, get to speak with actual, you know, meteorological and climate scientists.  And as a point of kind of embarrassment, I actually grew up in a climate change denying household, believe it or not, and I've since come a long way.  In fact, I mainly work on sustainability and the sustainability of ICT, et cetera.

So your presentations really hit a cord with me.  And I guess I really wanted to ask you, somewhat tan gentally, not necessarily related to your presentation but it is in that it's related to data.  And that is how would you recommend in your experience that whenever people ‑‑ whenever individuals are doubting the data and whenever people are ‑‑ specifically when it comes to climate changes ‑‑ how would you recommend that we necessarily address those points, that skepticism of what the data is saying and what the model are predicting? 

>>PANELIST:  There's no way around taking any concern seriously, no matter whether it's repeatedly issued or whether it has foundation or not.  The climate community has set up numerous websites and other outlets to address such concerns, and people take evenings and weekends and time to really go into debt of thing they see data were consensus and re‑explain them to all these concerns. 

It's a major burden on the community, and there's no other way around it.  The work we've been together with the United Nations program is to run the intergovernmental panel on climate change, and that's not just the secretariat in Geneva, it's actually thousands, 3 or 4,000 scientist around the world from all countries, almost, coming together, largely unpaid time, and producing massive assessments on the validity of the various data sets. 

And so you have to go into these reports into some detail then and there is always make a summary which is easy to read and if you want more detail, you go into the actual reports and you find that they take all these concerns about data quality very, very seriously.  So you would not find a single data point in these reports that does not have an uncertainty attached to it.  And it is another challenge of us to explain to the public what uncertainty actually means.  It's not a doubt about the data.  It's just something that is inherent to any measurement governed by the laws of physics.

>>AUDIENCE:  Thank you so much for all of the wonderful work that you all do. 

>>MODIFIER:  Do we have any other questions from the room?  Then I will ask my question. 

One of the questions I had when you try to stay close to the internet, you mentioned public internet case and exchanging data, accelerated the whole process of sharing data and then also the benefits.  You are probably aware of again waiting in those discussions of net neutrality.  Did you ever perceive this as a possible threat, if we lose net neutrality, will that impact your exchange of data?  You also mentioned some private networks.  How do you see this? 

>>PANELIST:  Well, first of all, by private network, I mean, a network which is different from internets who are lead lines, lead lines we talk about this exchange now.  It's a closed network which is not equipped with a certain level of service level agreement with the provider.  So it's ‑‑ our internet is more ‑‑ kind of ‑‑ or a similar technology.  So it's a closed network.  Demand is highly operational centers. 

So your first part of the question is about ‑‑

I think for us is a small international organization which is strong science and technological background and the data we're talking.  Of course, climate data, a bit more sensitive than weather data, maybe.  But in general I think we would welcome there a more open network of the internet technologies which is welcome. 

In fact, when I say getting the other members connected, this is our vision.  For most part of the world, this is getting easier.  But still we have those more difficult countries where the national infrastructure, the national technical infrastructure is not that good and we still have a difficulty even though we have 100 automatic stations deployed and we still have difficulty to get those data in operation in real time to be covered to the national center so that they will be available to be shared with the rest of the world. 

So that connectivity within that country and between that country with its neighboring country are internet.  So if we have a better access to internet for those national meteorological services, which will be helping a lot to improve the global service.

>>PANELIST:  Regarding the internet, so it's partly due to the heritage of our organization that the internet is not yet used for realtime exchange of data.  It's also because in case of a hurricane striking, the internet tends to go down.  So you don't want to rely on a system that isn't really designed for working 24/7, even in case of a major disaster if you want information about the disaster in that moment. 

So that's why the internet is increasingly being used by some countries, but the core networks that we described today are still largely outside the internet.  But connectivity is crucial because I talked about training.  We organize a lot of distant learning training.  People in, you know, developing countries, sometimes they just kind of connect properly to the sessions because the internet is too slow.  So that's really where it cannot build capacity if the internet isn't there.  It's not just about the data itself.  It's about training and ‑‑ so the connectivity still remain the issue side the progress that's been made.  Thanks. 

>>MODIFIER:  Unfortunately, we are coming to the end of the session.  I have some questions.  I hope you can stay five minutes informally.  Thank you very much.  Thank you everyone for your participation.  Thank you to our speakers.  I really enjoy these presentations and there's really a lot to learn. 

Enjoy the rest of the day and other sessions.