Speaker 1: With Laurent's segele end from London and Gerard Reed from Berlin.
Speaker 1: This is redefining Energy.
Speaker 2: Today.
Speaker 1: On Redefining Energy, we're going to talk about the weather.
Speaker 2: Lauren, yes, job, it's September now, Simmer's gone for Lisa,
Speaker 2: Lisa in particular. As for it, I will sing a
Speaker 2: song that that's going to be at the conclusion. But
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Speaker 1: more flexible. Back to the show, when you're going to
Speaker 1: sing the song like me, because nobody will listened to
Speaker 1: the podcast again if they hear my voice singing.
Speaker 3: So actually, I just want to say. What we're not
Speaker 3: going to do is talk about the weather across Europe,
Speaker 3: which has been pretty crazy over the last few months.
Speaker 4: But we're really going to talk about the importance I've
Speaker 4: been able to predict the weather for anybody that's running
Speaker 4: energy assets across the world. This is really becoming critical.
Speaker 2: So there is this new sector called the weather forecasting service.
Speaker 2: It's a market to sizes about three billion a year
Speaker 2: and it's going extremely fast. Now. Of course, you've got
Speaker 2: sat de lights, new tech droans, new application, better granularity,
Speaker 2: use of AI, lots and lots of incredible innovation happening.
Speaker 1: Which is why we sort of talked, listen, let's go
Speaker 1: and get an expert in. So we decided to invite
Speaker 1: Martin Fengler, who's the CEO and founder of meteo Matics,
Speaker 1: which is a Swiss based weather service company.
Speaker 2: Yeah. Meteumatics is part of those ten ish startup which
Speaker 2: emerged in the past decade and competing, of course against
Speaker 2: the legacy public agency, but with a very different approach,
Speaker 2: very sharp. So yeah, it was a very interesting interview.
Speaker 2: One thing I need to say, it's very geeky. Surest Apron, Okay,
Speaker 2: let's listen to detail. Martin. Welcome to the show.
Speaker 5: Thank you soon, watching me.
Speaker 1: Great to have the weather man here. So I mean,
Speaker 1: what i'd like to do is maybe kick off by
Speaker 1: just asking just talk a little bit about the importance
Speaker 1: of the weather and the ability to predict the weather
Speaker 1: in the world of energy that were myself and RNA
Speaker 1: are living and working in every day well.
Speaker 5: Weather Yes, of course a huge impact on the energy industry.
Speaker 5: So you have, for instance, demand forecasts that are typically
Speaker 5: related to temperature, maybe radiation and wind speed, which is
Speaker 5: driving demand. But on the other hand, you have all
Speaker 5: these renewable energies like wind power, solar power, hydropower, and
Speaker 5: precise knowledge about the wind and the solar radiation, for instance,
Speaker 5: helps to predict the productions of those assets and ultimately
Speaker 5: helps you plan production much better with all the consequences
Speaker 5: or the different stakeholders in the energy sector.
Speaker 2: You've been the weather forecasting business for quite some time.
Speaker 2: Can you talk about the recent technological innovation that we've
Speaker 2: seen the PUS say fifteen twenty years.
Speaker 5: The weather industry has been actually quite old, so there
Speaker 5: have been players in the market for several decades. Especially
Speaker 5: national met services have been around for maybe on the
Speaker 5: years or even longer.
Speaker 2: But all of.
Speaker 5: Them have in common that they try to forecast weather.
Speaker 5: But it's a bottom line solving a really hard and
Speaker 5: challenging physical problem. So there are law of physics. Now,
Speaker 5: there are Stokes equations coup into feminodynamic equations, and it's
Speaker 5: really hard numerics that you've been in hands of National
Speaker 5: med Services. They had the budgets to purchase a supercompute
Speaker 5: power needed to run these models. But as we all know,
Speaker 5: the weather forecasts as we have them as if today
Speaker 5: have their flaws. So if you think of forecasts low
Speaker 5: stratus or even storms, it's quite evident that there's something
Speaker 5: missing when it comes to quality, and when you have
Speaker 5: a close look to it, then you'll start to realize, Hey,
Speaker 5: models that are used typically and computations are just to
Speaker 5: cause to resolve these small scale phenomena, and often enough
Speaker 5: you don't have a clue how the weather is right now,
Speaker 5: which is for layman typically irritating because you can just
Speaker 5: look out of the window and you see all the
Speaker 5: weather is outside. But of course the doesn't mean that
Speaker 5: the computer knows about it and knows exactly about the
Speaker 5: wind speed, wind direction and temperatures of force to do
Speaker 5: the weather forecast. And this is how matematics was born.
Speaker 5: We believe that it's time to do better and to
Speaker 5: do more high resolution weather forecasts and also to improve
Speaker 5: the initial state. And yeah, we have been working on
Speaker 5: this now for the last thirteen years by different means.
Speaker 1: So Martin, maybe just to dig into exactly what you're
Speaker 1: doing differently than national weather agencies.
Speaker 5: So national weather agencies are running their own models, but
Speaker 5: because it's their job from xpair is mainly to do
Speaker 5: it inside a specific country or to the country border.
Speaker 5: You rarely see them running really global models. There are
Speaker 5: only a few global models available and what we are
Speaker 5: doing is that we try to disrupt this. Next time
Speaker 5: we fly from Zurich to London, you need to recall
Speaker 5: that you're crossing maybe three different high resolution models from
Speaker 5: three different national met services. And this is something that
Speaker 5: we do differently. So we run, for instance, a Pan
Speaker 5: European and US high resolution model, so that is all
Speaker 5: unique and the resolution is way higher than what you
Speaker 5: can get as today from the national met services. So
Speaker 5: that's one piece a larger domain, higher resolution, and you
Speaker 5: need to invest also a lot more in getting data
Speaker 5: into your weather model that are currently not assimilated. Stimulated
Speaker 5: means that those weather information that you get from certain satellites,
Speaker 5: that you get from other measurement sources that might have
Speaker 5: also some proprietary nature, such as the meteor drones that
Speaker 5: we are working on whether whether the National met Service
Speaker 5: don't have access to and so you can improve the
Speaker 5: initial state, gets you closer to the current weather conditions
Speaker 5: and helps you also to forecast much better than the
Speaker 5: National met Services can do.
Speaker 2: Weather forecasting is the question of when and where. So
Speaker 2: when means how many days is the current state of
Speaker 2: the art, and compare with you a few years before?
Speaker 2: And where is how granular you can be from a
Speaker 2: geographical point of view? How do you compare?
Speaker 5: In our services, we provide our customers for any arbitrary
Speaker 5: that long weather data from the service up to an
Speaker 5: altitude of maybe twenty five kilometers. And this is something
Speaker 5: often enough that relies on publicly available or commercially available
Speaker 5: data such as from the European Center of Medium Range
Speaker 5: Leather forecasts from the UK Met Office from and this
Speaker 5: helps us to harmonize the existing information and to provide
Speaker 5: for instance also seasonal forecasts, maybe even climate projections or
Speaker 5: certain historical data. What Mediumatics is focusing on on top
Speaker 5: is that we run these high resolution models across Europe
Speaker 5: and yes, and for some if you other regions as well,
Speaker 5: but they are mainly focusing on the intra day day
Speaker 5: ahead and maybe three days ahead. So it's really more
Speaker 5: sort of a short term Betther model which helps us
Speaker 5: to address many applications that you come across in industry.
Speaker 1: Can I ask you to dig into this idea resolution
Speaker 1: Because I'm managing a wind farm, all I want to
Speaker 1: know is how strong the wind is going to be all.
Speaker 5: Over a period of time.
Speaker 1: That is that not all that I'm interested in.
Speaker 5: Someone who runs a wind turbine different aspects to it.
Speaker 5: So you have a rot or diameter that spands out,
Speaker 5: say fifty meters above the service to say one hundred
Speaker 5: and fifty meters, so it requires already wind information in
Speaker 5: different levels, but also temperature and air pressure that changes
Speaker 5: over time, and all of this is affecting the production
Speaker 5: of the wind speed. And it's quite obvious also the
Speaker 5: wind direction la role because of maybe shadowing or channelization
Speaker 5: effects or sea breeze. With this example, you can already
Speaker 5: see how many aspects come into the game when it's
Speaker 5: about precisely forecasting what's happening at turbine level. And if
Speaker 5: you think of multiple turbines in the park then maybe
Speaker 5: neighboring wind turbines or affecting each other because of wake effects.
Speaker 1: Okay, so this is this whole area of wind theft
Speaker 1: which we're beginning to hear in the offshore wind area. Right,
Speaker 1: Explain that to us and give us your view on this,
Speaker 1: because it seems to be a little bit strange. I go, well,
Speaker 1: I mean, it's your own fourth for wind turbines beside
Speaker 1: another wind turbine. Right, It's obvious if the wind comes
Speaker 1: in the west, you're going to steal it, or not
Speaker 1: steal it, you're going to change the weather pattern.
Speaker 2: Right.
Speaker 5: That What surprises me most is that people are surprised
Speaker 5: by it because the turbines are actually just doing what
Speaker 5: they've been designed to do, namely to collect energy out
Speaker 5: of the wind. So when you take the wind energy
Speaker 5: out of it, then now you have pretty calm winds
Speaker 5: behind on the lee of the such a wind farm.
Speaker 5: And we call these vake effects, and they can actually
Speaker 5: extend over large distances. So again layman might assume it's
Speaker 5: maybe just two hundred meters or one mile something like
Speaker 5: this that has affected it. But depending on the weather conditions,
Speaker 5: those wake effects can effect downstream wind and weather patterns
Speaker 5: in twenty thirty forty kilometers distance. And if you think
Speaker 5: of some of the wind farms that have been already
Speaker 5: built in the North Sea, for instance, and look into
Speaker 5: the plants that say how they want your expanses to
Speaker 5: till say twenty to fifty, you will see lots of
Speaker 5: discussions and in your future around this topic. So the
Speaker 5: UK wind farms in the North Sea might be here
Speaker 5: in favor because they're in Europe. We have often these
Speaker 5: westerly conditions and they getting the wind first. The others
Speaker 5: need to see what's left over.
Speaker 2: So you're using somebody else satellite. So your expertise is
Speaker 2: to possess billions of data all the time and compare
Speaker 2: with the past and everything. And also you're using, and
Speaker 2: that's proprietory what you call your drones. So can you
Speaker 2: explain a bit what they are doing and what additional
Speaker 2: quality they bring to your results.
Speaker 5: So satellites have been around and weather for maybe fifty
Speaker 5: years already, and here we are mainly typically in whether
Speaker 5: we discuss geostationary satellites. Satellites operating at an altitude of
Speaker 5: maybe thirty eight thousand kilometer distance to the airth surface
Speaker 5: and really well designed and well engineered instruments. And then
Speaker 5: you have also low earth orbiters typically operating in four
Speaker 5: hundred kilometers distance. And again these are typically operated and
Speaker 5: owned by government entities such as humids such in Europe
Speaker 5: and in NOA in the US, and all of them
Speaker 5: haven't common that they typically use optical sensors in different
Speaker 5: spectral channels and this gives you some great pixel value.
Speaker 5: And this great pixel value that you detect can be
Speaker 5: related to some radiation input and after some calibration procedure,
Speaker 5: for instance, you can determine how a certain amount of water,
Speaker 5: vapor or a cloud is then monitored with such an instrument. Now,
Speaker 5: the problem is these instruments is that they don't give
Speaker 5: you any direct measurements of the air temperature, the windspeed,
Speaker 5: in direction, air pressure, nothing even moisture is not directly measured. Therefore,
Speaker 5: those instruments are typically not that accurate. They give you
Speaker 5: a very strong signal and a super important for in
Speaker 5: the microather focus, so I'm not taking this into doubt.
Speaker 5: But when it comes to very local phenomena, especially those
Speaker 5: that are triggered by air masses close to the air surface.
Speaker 5: So in the first say three or four kilometers, then
Speaker 5: these instruments are typically not that accurate, and this is
Speaker 5: why whether stations are still important. But of course also
Speaker 5: radio signs and some other remote sensing instruments or even
Speaker 5: aircraft data, they are all super relevant because they give
Speaker 5: you direct measurement readings. And with our drones, we are
Speaker 5: actually replicating this and having a drone equipped with which
Speaker 5: logical sensors which gives us direct measurement readings.
Speaker 1: Martin, can I ask you about the economic side of it,
Speaker 1: because what I'm trying to get my head of if
Speaker 1: I was an energy trader or as at the end
Speaker 1: of the day, what I'm trying to do is make
Speaker 1: as much money as possible for as little risk as possible.
Speaker 1: So how does it turned into economics? That's really weather prediction.
Speaker 5: What we of course do is that we sell to
Speaker 5: energy and trading companies high resolution, high quality wind information
Speaker 5: and solar power information. Gives our customers some advantage that
Speaker 5: they can immediately leverage at the exchange. Now, coming back
Speaker 5: to the drone piece, for instance, it's not necessary that
Speaker 5: you have drones being installed on say every kilometer. It's
Speaker 5: absolutely not the case and the reason is, and this
Speaker 5: is where the secret source lies, is that you have
Speaker 5: a four dimensional data simulation. This means that every single
Speaker 5: profile that you do now helps you also with the
Speaker 5: next model initialization that you do, even though the measurement
Speaker 5: is one hour old, but the information is advacted with
Speaker 5: the wind. It's transported with a wind. Simple example, if
Speaker 5: you do, for instance, and a profile in Zurich, and
Speaker 5: it takes about an hour on aver that this air
Speaker 5: mass is reaching sangaland that is about seventy kilometers to
Speaker 5: the east of the ric. What it ultimately means, even
Speaker 5: if you do these soundings and a small amount of occasions,
Speaker 5: it helps you to make those sensings in many locations
Speaker 5: just because of the fact that the wind is transporting
Speaker 5: this information.
Speaker 1: Maybe just to change topic as slide, but when I
Speaker 1: listen to you, I can hear very clearly the benefits
Speaker 1: in the energy space. But I also think that if
Speaker 1: benefits in other areas. I can imagine rescue missions, military missions.
Speaker 1: You can go through a whole pile of areas where
Speaker 1: accurate weather prediction is important.
Speaker 5: Yeah, but that touches everything. I've been in this business
Speaker 5: now for nearly twenty years and I still learn at
Speaker 5: least once a week about the new use case. You
Speaker 5: have customers in the automotive industry such a Porsche may
Speaker 5: see this in w whatsoever. They all need weather for
Speaker 5: certain applications, save for entertainment in the car, for predictive maintenance,
Speaker 5: for autonomous driving. Just with these examples you already see
Speaker 5: how diverse weather applications can be. And this is just automotive.
Speaker 5: Then you have maybe aviation, where we work with companies
Speaker 5: like air Bus, with Lockheed, with Tahlis and others. And
Speaker 5: again there might be about aircraft engine optimization. Again you
Speaker 5: need high altitude weather to do that. And you can
Speaker 5: continue with all of those industries and it's super fascinating
Speaker 5: how diverse weather applications is, and it goes far beyond
Speaker 5: what we typically have when we watch the BBC Weather,
Speaker 5: which shows just the UK map and then a few
Speaker 5: items dropped on it. It's a lot more than what
Speaker 5: we typically look at.
Speaker 2: If I look at your client least you have six
Speaker 2: on clients, and some extremely prestigious like NASA. Do you
Speaker 2: know the application they're doing or you're just giving them
Speaker 2: THATA and you don't really know what they do with
Speaker 2: those data. Is there a kind of collaboration, how does
Speaker 2: it work?
Speaker 5: Often do you know about the application? In case of NASA,
Speaker 5: I can tell it's about supporting slide tests for some
Speaker 5: prototypes that they're looking at. And this is of course
Speaker 5: whether again it's affecting for instance, not just because of
Speaker 5: turbulence or maybe icing aircraft operations. But here, for instance,
Speaker 5: it's about sound propagation. So you think of ultrasonic plane
Speaker 5: and aircraft, then you have this sonic boom and the
Speaker 5: boom propagation is affected by weather again and it's again
Speaker 5: it's not just wind, but it's the combination of temperature
Speaker 5: and moisture which creates sound bending sometimes sometimes not, and
Speaker 5: just just depends on the better.
Speaker 2: How far away can you predict, because what's very very
Speaker 2: important in the energy business are mostly the dry and
Speaker 2: you know, not very windy winters versus the mild and
Speaker 2: windy winter, and that makes a lot of difference in
Speaker 2: you managed to get some long term you know, prediction
Speaker 2: when we arrive like in October, because this is really
Speaker 2: going to affect massively the all energy system for the
Speaker 2: coming months.
Speaker 5: So for these long term predictions we rely also on
Speaker 5: other sources and MWF. The European center of a medium
Speaker 5: range of the forecasting is certainly the gold standard if
Speaker 5: it comes to forecasts into the front week, three weeks,
Speaker 5: four weeks, and maybe seasonal forecasts. We utilize the output
Speaker 5: and refine it. We're adding, for instance, downscaling techniques, combining
Speaker 5: their cost grid data with high resolution terraer data and
Speaker 5: find unit. But at the moment we as me teenetics.
Speaker 5: We don't render the sort of long term forecasts. But
Speaker 5: of course our moonshot vision is to run a high
Speaker 5: resolution model, global one kilometer model one day, and we're
Speaker 5: making huge progress with this. We have been working on
Speaker 5: this for a couple of years now. When we start
Speaker 5: with it, people laughed at us. People told us, no
Speaker 5: one needs this, no one wants this. And now many
Speaker 5: of our competitors stopped laughing. I believe we have delivered
Speaker 5: now on a pan European one calumeter model on the
Speaker 5: US one calummeter model, and it's just a matter of
Speaker 5: time that we are going to deploy also a global
Speaker 5: one klumeter model.
Speaker 1: Martin, can I ask you something which is quite different,
Speaker 1: which is I'd really like to hear your opinion in
Speaker 1: and around what I call weather volatility, which is it
Speaker 1: seems that weather volatility is increasing. And listen, you've got
Speaker 1: all the data out there when you look at the weather,
Speaker 1: is our weather changing and if so, what does it
Speaker 1: mean for us?
Speaker 5: I think because this always depends on the region and
Speaker 5: all of this, but what we definitely see in the
Speaker 5: data is that the weather becomes more pronounced, more extreme
Speaker 5: from time to time. Climate change is introducing more radiation
Speaker 5: and this leads to more higher temperatures. Higher temperatures often
Speaker 5: can often lead to an increased moisture transport, and then
Speaker 5: you often see much higher intensity when it comes to storms,
Speaker 5: and there being some recent examples. I don't want to
Speaker 5: go that far and blame climate chain for that, but
Speaker 5: if you think of those extreme events that we just
Speaker 5: saw the other day in Texas or in Valencia, these
Speaker 5: are certainly extreme events that can be a consequence from
Speaker 5: this effect.
Speaker 2: Martin, you are part of the coupe of startups who
Speaker 2: are competing again national weather services. How do you see
Speaker 2: the balance between the two services going forward?
Speaker 5: We of course try to look into collaboration with National
Speaker 5: met Service wherever we can. But of course the natural
Speaker 5: question that let's call it the elephant in the room
Speaker 5: with National met Services whether the way how they operators
Speaker 5: of today is appropriate for the future. Meaning I envision
Speaker 5: that we see National met Services change over time and
Speaker 5: to become more like an authority like the Civil Aviation
Speaker 5: Authority for instance, or like a Ministry of Defense. And
Speaker 5: this means in terms of betther that they are a
Speaker 5: rulemaking organization and still have some operative element to it,
Speaker 5: like reassuring severe weather warnings, but all the tools and
Speaker 5: services they need to render the operations will be acquired
Speaker 5: from the market, as if today it is just some
Speaker 5: sort of historical accident that you still have the agencies
Speaker 5: that are doing all the programming themselves. But frankly, there
Speaker 5: is no reason to do so. If you can buy
Speaker 5: a fighter jet like an F thirty five through a
Speaker 5: bit process highly complex weapon systems in this case, when
Speaker 5: you can do this through a tenoring process, then frankly
Speaker 5: you can also purchase the weather model output from the market.
Speaker 5: You could also ask companies to run and operate weather
Speaker 5: station networks and just put some slas against it, and
Speaker 5: then you can actually buy all of this from the
Speaker 5: private sector from the market. And this is maybe something
Speaker 5: we are not going to see to change in the
Speaker 5: near future, especially not in Central Europe, but I expect
Speaker 5: that we are going to see this already in other
Speaker 5: areas of the world, that these services will be purchased
Speaker 5: from the market.
Speaker 2: Martin. When I look at the prediction, the market of
Speaker 2: weather forecasting is supposed to double the next ten years.
Speaker 2: So what technology do you have in mind and what
Speaker 2: type of result will we have in ten years that
Speaker 2: we don't have now?
Speaker 5: What I strong believe is that all the modern AI
Speaker 5: techniques that are discussed will find their way also into weather.
Speaker 5: I'm here a bit cautious because that at the moment
Speaker 5: also a lot of noise in the market around that.
Speaker 5: But there are certainly areas where AI methods can contribute.
Speaker 5: If you think of satellite data assimulation, for instance, which
Speaker 5: has always been a huge challenge in whether or if
Speaker 5: it comes to post processing. So for instance, if Jara
Speaker 5: is really interested in wind power output of a specific
Speaker 5: turbine civic location, then AI techniques helped to come up
Speaker 5: with even more precise focast because AI techniques can identify
Speaker 5: local phenomena that are not primetrized, not modeled, not reflected
Speaker 5: in another model. And this is where AI techniques can
Speaker 5: definitely add value.
Speaker 2: Well, Martin was geeky but interesting. The whole point of
Speaker 2: being experts like you is just to realize that it's
Speaker 2: not because you read three articles on the internet that
Speaker 2: you become an immediate international expert. So thank you very
Speaker 2: much for sharing your expertise with us.
Speaker 5: Thank you for having me here.
Speaker 1: Yeah, it's good, Thank you very much, Martin.
Speaker 2: Job. I found the conversation fascinating, and I'm glad we
Speaker 2: have this podcast so we can bring a real expert
Speaker 2: and not the Juninequgo expert like we are. Sometimes.
Speaker 1: I'm totally with ch and I know it's a little
Speaker 1: bit nerdy and technical, but it's really really critical. And actually,
Speaker 1: do you know the funny thing is I use her
Speaker 1: app all the time now by the prediction now right,
Speaker 1: it's really good.
Speaker 2: Okay, there was some part which I kind of knew,
Speaker 2: but learn more about wind theft which was really interesting,
Speaker 2: also about moisture and the impact of climate change on
Speaker 2: the events we have seen. Now, of course AI is
Speaker 2: going to be phenomenon, but the sentence that mark my
Speaker 2: mind is information is transported by the wind.
Speaker 1: Yeah, very good, Okay, very good. So Lauren, I think
Speaker 1: I want to talk. Thank Martin again for coming on.
Speaker 1: The show was really great, and now I'm looking forward
Speaker 1: to you singing the song.
Speaker 2: So this song is dedicated to Lisa. She knows she
Speaker 2: is and she said, Lauren, you know when you talk
Speaker 2: it's like nah, but when you sing, it's so beautiful.
Speaker 2: This one is for Lisa and it's about okay. So
Speaker 2: Maria's comment pass the enosun can never last wake me
Speaker 2: up when Septem burs.
Speaker 3: My friend, Well, I've laughing because if I had a
Speaker 3: song that now, you would have just screamed laughing during
Speaker 3: the song.
Speaker 1: No pretty good.
Speaker 2: Hello, yeah, No, I know you are more a fan
Speaker 2: of Earth, Sween and Fire than gree Day, so you
Speaker 2: would have songs. Do you remember September? Okay? Joah?
Speaker 1: Okay, my friend, you and speak next week, look forward
Speaker 1: to Thank you for listening to Redefining Energy. Don't forget
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