 It's a man-law called Public Affairs Centre and I've been talking about climate data in India and the curfew sort of analysis that I think are interesting. So Public Affairs Centre is a small organisation with primary new works of governance and public services and around two years ago we started a small group for the environmental governance group. So primarily as a part of the group we tried to look at how to promote resilience to environmental and climate changes in local areas as a country. And as a part of this, so a large focus of ours is on climate change adaptation, promoting it from the bottom up to sort of hopefully complement big national schemes that are doing the rounds. So as a part of this, what we found was that we had to, when you go down to a small area, we were working in Vayanad and Kerala in the Gulf of Mandar, like Rameshwaram in Tamil Nadu. And like we're all in Bangalore so we've been dressed up in our things happen here. So one thing we found was that before you start talking about climate change, climate change, impacts, carbon dioxide going up, all that, we don't have enough awareness about what our climate really is. So like I contrast the Indian way of Indian style of education with say the American where they start from their town and county and state and then go up to the world where their world geography is rubbish but they know their local areas really well. In India we start from the country and then we go up to the world and it's sort of almost degrading to study about Karnataka or you know a district in school. And so it's like that spills over into a lot of our thinking. So when you want to understand climate, climate of India does not make too much sense to me. I mean it's a very large thing if you're studying the macro effects of the monsoon show but what about the climate of Bangalore, what about the climate of Mysore, Ramnagaram. We need that level of granularity to really start making sense. So I'll just do a very brief overview of how climate data is structured in India. I mean who generates it, who has it. The Indian Meteorological Department is the hegemon. They are the overlords. They are a very old organization around 170, 180 years old and they've been collecting a lot of data across the country since then. So in fact India has one of the best instrumental records in the world but the problem is there's lots of data but there's no data really available in the public or very, very little. So I would again temper this statement by saying that things are getting better at a rapid rate. Lots of meetings like this, lots of people who are trying to put pressure, get more out. So the Indian Meteorological Department maintains a lot of weather stations. So they collect all kinds of data starting from rainfall and temperature and moving on to humidity and the list goes on. For reduction especially for the reason or they can stick to rainfall and temperature and actually I'll be talking mostly about rain. Apart from the IMD, almost everybody, every state department that you can think of will have a rain gauge somewhere. I mean they'll be doing it for somebody else but you have rain gauges across Karnataka and schools and town municipality buildings in all kinds of places. So you won't get good quality data everywhere. I mean you have somebody who is responsible for manually jotting down the number at the end of the day. So that's how data is collected. So apart from that you also have the intracit farmers. You have people who are so interested in the climate and they want to know how their local weather is so they often do collect a lot of it. So when it comes to the type of data of the source, so you have station data. This is your primary data that's collected of the source and then you have gridded products. So when you want to do big analysis you want to sort of generate homogenous data for a grid of any size. The conventional size is one degree by one degree which is roughly like 110, 115 kilometers in size. So it's a little bigger than say the district of Bangalore if you're looking for that. So the latest trend is satellite products. So you have American satellites. There's one called TRMM, Propical Rainfall, Meteorological Mission or something like that. And you even have some Indian satellites which collect a lot of data now through imaging where they can deduce the amount of rainfall that actually occurs in real time. I mean there's some fascinating work and India recently launched a satellite called the Megatropics with France. It's sad that while NASA is extremely good about open data and puts a lot of it up to the public, Megatropics still doesn't really, I mean it has a very functional website which is half in French, half in English. So this is one data set that I came across a year ago called the Aphrodite thing called data set. Don't ask me why they named it that, but so here what some scientists in Japan have bought station data from across Asia and they want to, they've started creating these gridded data products. And like I said, India has one of the best station densities that you can imagine. I mean it's the best in Asia outside of Japan and that's remarkable and unfortunately that doesn't always get reflected in our literature and our scientific understanding. In fact, I want to make a caveat that this, the points that are plotted here are a subset of what really existed in India, only around 2000. So from that scale, I'll go down to one farmer in Bayonard who was like one of the first few BSE agriculture holders in the district and in 1983 he decided, you know what, I'm going to buy a small little rain gauge which is essentially a calibrated funnel and I'm going to start collecting rainbow data. It's been 27, 28 years now, he hasn't stopped since. Amazing quantity of data. And when we met him last year, we were the first people ever to have expressed an interest in this data. So he was delighted that someone was willing to recognize his hard work. This is Mr. Vimal Kumar. He's actually a Karnataka by the way. So that gives you a sense of what scales one can deal with. So this is again a snapshot of the Japanese data set. So here on a typical day they give the number of rain gauges that exist across India. So Bangalore comes somewhere here and so we have many, I mean they have, on good days they have six or seven in that little square box. So one of the things that I want to sort of talk about here is, so okay, going back, given that there are a lot of people here who are a lot more savvy with big data analysis than I am, this gridded data is now available for non-commercial use. Anyone can download it. So IMD has its own gridded data sets which are more robust because there are more station data, but it's not really in the public domain. As a non-profit or an institution, you can buy it, you can use it and publish, but you cannot disseminate it in the public domain. Why would the number of rain gauges vary in a day? No, they might not have collected, they might not have reported data errors and every station data, I mean it costs money. So they don't want to, so they have algorithms to spend as little money as possible and get as much data as possible. So if you notice, the only missing points are usually, they correlate very highly with the Naxal effected forested areas of India. So one thing you can do is a lot of math-based visualization and this is stuff that I'm not good at, but I know that there are people here who are very good at it and this is an open data set that people can use. What I am very interested in is going to... What time series is this in India? This is 1951 to 2007. So it's daily raven. Why does it stop at 2007? Well, the data set is around two years old. Oh, what you have but... We have a very funny problem in India. In the whole request for gradation of technology, if you actually look at rain gauges and weather stations in India, they sort of drop off after a while in the 2000s because you've had historic continuity for 80-100 years and suddenly they decide, okay, let's build an automated weather station. The old record gets tossed out. So you'll have artifacts coming in. So I think most data sets end in the middle of the 2000s. Hopefully they'll get updated soon. Is that really... Very rich information. So the first theme I want to look at is going local. So what I've done is taken data from this particular grid box. It's a 0.25 by 0.25 degree grid box. So that's around just 25 by 25 kilometers roughly. So this particular grid, so they also had station data but they don't mention what stations they take from. I mean, they have to sign some clauses there. But the two prominent stations in the Bangalore region are the one in Maharani's College and the one at the Old Bangalore Airport. So most likely they would have taken from those. And this gridded data is like an underestimate of the station data from what I can gather. So whatever I show you, the reality is that you can take it for granted that it's a bit more extreme by and large. So this is something that personally I have a lot of fun with. The joy of working with daily rainfall data. So whenever people talk about rainfall profiles, this is what you see. Pick any place. So you have rainfall on one axis and by months they'll tell you how much rainfall you're seeing. So with Bangalore, you can see that there is one summer rainfall period and a monsoon rainfall period. But this tells you very, very little. So I just sort of squished it down to rainfall per day where it comes to an average of 5 millimetres per day in the rainiest month. But then, so this is 57 years of data. So if I take daily averages, since I average all the January first, all the February first and so on, it gives me a very, very different picture. And this is what, I mean this, the first time I saw it, it was mind blowing. It shows you that not only do we have, we can, we almost have three rainfall regimes almost. This period between June and September in Bangalore, where it really takes, picks up end of September, October, it rains a lot there. And then you have this period, the summer monsoon period. And then this distinct drop in labor. I mean we conventionally think that monsoon comes in the first of June or the fifth of June or whatever and we think it's raining throughout. But in Bangalore, I mean this is 50 years average. So this is very statistically significant that we have this mini drop period at the end of June. So it's nuggets of information like this. I'm sure that any region of India, you plot this and you will get something new that you would not have found from a monthly average. So from, so these are average rainfalls. This graph just shows you the probability of taking 50 years, all the number of days where you get more than zero or more than two millimeters of rainfall. So it shows you that on no day in Bangalore, you get, you have a chance of more than around 40-50%. So, and this sort of is there in our psyche now. We think that we never know when it's going to end. We have a very significant percentage of rain, given that it rained the previous day. We can do that. I mean, I haven't. I think that would be more interesting when we see higher peaks. Sure. I mean, we can do that. I haven't done it in my presentation. That's certainly something we can explore. But the point is, even this, you do not, I mean, I hadn't come across a daily analysis before. So this influences our psyche, right? Where we think that there's a 50-50 chance even in the rainy season. We don't know whether it will rain or not today. But if you look at the probability of it raining even one day in a week, it's almost 80-90%. It's not a good period of the time. So if you go from thinking, I don't know whether it will rain or not to it will rain this week. It changes the way you behave. It changes the way you carry your umbrella or everything. And if you do your analysis, then it will, you know, tell us even more. So coming back to this. So this is the average, right? So it includes days where it never rained. So if you actually look at the amount of rain that fell when it rained, so the amount of rain per rainy day, it shows you a very different picture. So it sort of goes by this, you know, when it rains at fours. So there are many days, especially in February and March, it rains very rarely. When it does, it's quite a problem. So this gives you, again, a lot of very different kinds of information. And when you go to the maximum recorded rainfall on any given day, it shows you that starting April all the way till December, you've had days where it's almost touched 100 mm. And here I really want to stress that this is an underestimate. If you look at station data, I can show you much more. I mean, I think recorded data, 180 millimeters is the maximum recorded in Bangalore's history. So anytime anyone tells you that, oh, we didn't expect 100 mm of rainfall this day in Bangalore and so all the catastrophes are because we never expected it, sorry, it's a very bad excuse in public policy. We know that it's going to rain this way about once a year or 1.1 times a year. You get one day where it's 100 mm and people die. You're not aware of it always, but so it's completely preventable. This is not the Mumbai deluge of 2005, I think 2006, sorry, when 1,000 centimeters of rainfall. It was 2005, 26 July. So that was one meter of rainfall. At high tide in within about two hours, right? So, I mean, that is from a public policy angle that's excusable. That was a freak occurrence. Exactly. But 100 mm in Bangalore is not a freak occurrence. That's the point I'm trying to make. And if we don't have our infrastructure in place where we can even sort of handle 100 mm in a place like Bangalore where runoff is very easy, I mean, what does this flow out very easily? I mean, it's terrible, pathetic. So, another data source that I didn't mention in the back is so recently the INV is set up these automated weather stations and they've also set up automated rain gauges in a much larger number. So, there's one in Bangalore which gives you hourly data, pressure, temperature, rainfall, everything. And the only thing is they give you data for the last seven days and then that keeps changing. So, what I did last year was I just went every week, copied, I mean, very manually, just copied data week on week. And so, this is just a picture from 10th August to 19th, 28th September last year in the monsoon. So, there was one day, 16th August, I don't know how many of you remember, it actually rained quite a bit, over 100 mm, just after Independence Day and a bunch of things happened. So, what I'm going to show you here is not stuff I think happened but these are actual documented news stories that took place in those days. So, I tried to just correlate the two. So, the moment it rained 100 mm, those of you who are familiar with Bangalore, there's a Gali Anjaneya temple out on Mysore Road that gets flooded. Anytime a decent amount of rain falls that gets flooded. Now, that in itself is not a big deal but to me it's like a barometer. Once that gets flooded, the slums that are next to it get flooded, walls get washed down, people are homeless, some die. All our storm water rains overflow. And like I said before, this 100 mm thing is not a free occurrence, it happens all the time. So, I've actually put this at 10 mm, I think it's a little lower, even 5-6 mm of rainfall and our traffic is disrupted. Think of the opportunity cost and the economic cost of that. This was really a free occurrence where there was only 10 mm of rainfall recorded at the Bangalore station. It might have been 20, 25 at the location, I think this was close to Ghabar, where in an illegal tenement on a construction site, two people die. The 30 mm of rainfall maximum estimate, if people die, it's a very serious problem. 1,000, 1 meter of rainfall over Bombay if people die. Okay, I mean we can understand that this, I'm not very comfortable with this. So, while all this is happening, I mean what does our city do? On this day in September, the VVMP decides to start fixing portals. And after all this storm water rains have been flooded and they're overflowing, people discover storm water rain irregularities in contracts and then money spent. By the way, Bangalore has spent over 400 crores in the past 5-6 years on storm water rains. You tell me what's the result? So, the point I'm trying to make here is that all these daily rainfall analyses are not just cute, meant for people who love data, but they have serious policy implications as well. So, this is something different. This is something, if any of you are interested, I'd love your help. It's something I did in Weynard where you map this daily rainfall pattern over the traditional calendar. From what Vishwanath told me, even the GKBK here in Bangalore has done it. Where a lot of farmers sort of understand rainfall as having occurred on certain nakshatra in certain months and on certain star signs and so on. And a lot of that is very, very scientific and true. I mean they know from experience. But there's a big disconnect between that and modern scientific knowledge and research and so on. So, if you can bridge that gap, so I've just done it for Weynard. And so here the economy is such that people's lives revolve around the climate. I mean important festivals like Vishu are, I mean if enough rainfall doesn't happen then the festival cannot be a success. Can this be correlated to let's say data in the last 50 years versus data in the more recent 50 years? That could also show that there has been some change in the climate pattern on the rainfall pattern. And maybe this thing is more applicable in some extent. Yes, we can look at that with Weynard. We did sort of look at it. I mean we had only 28 years of data so we did what we could with what we had. And you don't have dramatic changes everywhere. They have very subtle changes happening. And what the traditional calendar gives you is an excellent benchmark to give causal links. You say you have a data change and you have something happening on the ground. People changing farming patterns. This gives you a metric by which to vet traditional knowledge and community knowledge. So if this can be done across India, I mean it could be a phenomenal thing. And I just want to leave you with this where I have shown you all these nice looking daily averages everywhere. But each year is fundamentally unique. These are just running monthly averages from Weynard where it shows you that each year it's like a signature. It's completely different. So I mean we need to sort of keep that in mind when we do all of our analysis. I just want to end by saying that so we have an initiative that's sort of being incubated called knowyourclimate.org. We eventually want to have like a visualization interface for a lot of these graded data products. And I'm looking for people who volunteer and work with us. Thank you.