Why Forecasting India’s Clean Energy Demands a Closer Look at Our Skies

Think about this for a moment: from high school Physics textbook, you already learned that classical celestial mechanics is remarkably deterministic. Thanks to Kepler and Newton, we can calculate the exact coordinates of the Sun relative to any point on Earth for any given second, years into advance. We also know that the solar flux reaching the top of our atmosphere, (defined as the solar constant) is fairly steady.

Naturally, one might ask: if incoming energy is so predictable, why is solar power generation still treated as an uncertain, intermittent resource?

That is where atmospheric physics enters the picture.

Between the top of the atmosphere and the photovoltaic panels installed on our rooftops and solar parks, there lies a dynamic atmospheric column. Air is not merely empty space; it contains water vapor, suspended aerosols, passing clouds, and active precipitation. These constituents continually absorb and scatter incoming radiation. For power grid engineers who need to balance load in real time, a sudden dip in surface insolation can destabilize the entire network.

To address this gap, our team at STORM Lab set out to quantify this attenuation. My student M.V.Suprabhat Prasad investigated the behavior of the Clear Sky Index across four distinct geographical regions in India over extended observation periods.

Here is how the research was approached, along with what the data revealed:

We first set up a direct comparison between ideal, cloud-free incoming radiation modeled using the GRASS GIS tool and the ground-level solar insolation retrieved from our own geostationary meteorological satellite, INSAT-3D. As expected, the Indian Summer Monsoon proved to be the most challenging regime. Rapid cloud microphysical changes and high-intensity rainfall introduce sharp non-linearities, making surface-level solar forecasting notoriously difficult during these months. In sharp contrast, the stable, cloud-sparse conditions of winter yielded a strong correlation coefficient of approximately 0.8 between real-sky satellite observations and clear-sky theoretical values.

The takeaway for young researchers is simple: advancing renewable energy is not just an electrical or materials engineering problem, rather it is deeply tied to atmospheric sciences and meteorological modeling. If we want a reliable green grid for the nation, we must first learn to model our skies better.

For those interested in delving into the mathematical formulation and datasets, the complete work is documented in our proceedings paper published with the American Society of Mechanical Engineers (ASME).

Present but Not Learning

A student recently died by suicide after being barred from appearing in an examination due to a shortage of attendance. The incident is deeply tragic. A young life was lost over something that, in the larger scheme of things, should never outweigh human well-being. Unsurprisingly, the news triggered widespread discussion across the country. The High Court suggested that the mandatory attendance policy be revisited. Many institutions, including IITs, initiated internal deliberations on the role of attendance requirements. One proposal was to incorporate attendance into internal assessment rather than making it a prerequisite for appearing in examinations.

Does this mean attendance should become optional? Would such a system work? It would certainly reduce one source of pressure on students.

At the beginning of every course, I explain my attendance policy to students. My policy is simple: no student should take an unauthorized absence. If they are unable to attend any number of classes, they should inform me in advance. In case of an emergency, they may inform me at the earliest appropriate opportunity. The intention is not to compel uninterested students to sit in class; rather, it is to cultivate a sense of responsibility and communication.

To examine the issue more deeply, we must ask a fundamental question: What is the relationship between attendance and academic performance? In other words, is there a significant correlation between attendance and a student's grade in a course?

The data I have collected over the years presents a mixed picture. I have seen students who attend classes irregularly yet demonstrate excellent understanding during examinations and earn the highest grades. At the same time, I have seen students with near-perfect attendance struggle to achieve similar outcomes.

So where should the line be drawn? How do we strike a balance between encouraging attendance and recognizing individual learning styles?

Through interactions with both regular attendees and those who frequently miss classes, I have arrived at one important realization: attendance is only the first step toward academic success. Attending lectures demonstrates a student's willingness to be present and engage with the course. However, physical presence alone does not guarantee understanding. A student may attend every class and still fail to grasp key concepts. If doubts remain unresolved due to hesitation or lack of engagement, good attendance may not translate into good performance.

In an earlier discussion on why students rarely ask questions in class, I wrote that silence should not be mistaken for understanding. Many students hesitate to ask questions because they fear being judged, appearing uninformed, or interrupting the flow of the lecture. As a result, they leave the classroom carrying unresolved doubts. A student may therefore have perfect attendance and yet learn very little if those doubts remain unaddressed. Attendance records presence; it does not measure comprehension.

Furthermore, attending lectures merely exposes students to the topics being discussed. Unless those ideas are internalized and connected to prior knowledge, genuine learning does not occur. Listening is not the same as learning. It is akin to consuming food without allowing time for digestion; the nourishment is available, but it has not yet been absorbed.

The most effective learning occurs when students actively engage with the material—by asking questions, discussing ideas with peers, solving problems independently, and reflecting on what they have learned. In such a learning process, attendance serves as a facilitator rather than the ultimate objective. A classroom full of silent students may satisfy attendance requirements, but it may not necessarily be a classroom where meaningful learning is taking place.

Ultimately, what matters is how effectively students assimilate knowledge and transform information into understanding. In today's academic environment, students often navigate tightly packed schedules from morning until evening, leaving little time for reflection and independent learning. Whether a student attends every class or occasionally misses one, meaningful learning requires conscious effort beyond the classroom.

Attendance, therefore, should not be viewed as an end in itself. It is a means to facilitate learning. Rather than focusing exclusively on attendance percentages, we should also consider reducing excessive course content, creating more opportunities for discussion, encouraging students to ask questions without fear of judgment, and providing them with the time and space necessary to reflect, question, and assimilate what they learn. Only then can attendance serve its true purpose—as a support for learning rather than merely a number to be recorded.

Are review articles still relevant in today’s research landscape?

With powerful AI tools now capable of generating literature reviews from a single prompt, it is tempting to answer this question with a quick yes or no. But before we rush to a binary conclusion, it is worth pausing to ask a more fundamental question: what is a literature review, and why do we do it in the first place?

When you begin a Ph.D. program (whether in India or abroad) the journey usually starts with coursework. These courses are not mere formalities; they provide the intellectual foundation on which your research career is built. Think of this process as building a house. Where would you start? If the first thing you consider is the room layout or the position of windows, you are clearly missing something essential. Any sensible construction begins with the foundation.

A strong foundation determines how much load a building can carry, how many floors it can support, and whether it can withstand natural stresses such as floods or earthquakes. In the same way, rigorous coursework equips a researcher with the conceptual strength needed to handle complex scientific problems.

But a foundation alone does not make a home livable. Once the foundation is laid, we move on to design and construction. Similarly, once coursework is completed, a researcher begins to shape a research plan. How is this done? By looking at existing structures: houses we have lived in, designs we admire, and solutions that have worked elsewhere, and by consulting experts who understand the constraints and possibilities.

This is where the literature review enters the picture. Surveying existing work helps us understand what has already been built, what design choices were made, which approaches succeeded or failed, and why. By carefully examining prior studies, their methods, assumptions, and outcomes, we learn what is feasible, what is promising, and where genuine gaps exist. This process does more than summarize knowledge, it gradually transforms a student into an expert capable of asking new and meaningful questions.

Now, can AI replace this process?

One of my research scholars recently shared an experience of using an AI tool to summarize research on a specific topic. The output was impressive—clear, structured, and even presented in neat tables. As a tool for organizing existing information, AI performs remarkably well.

However, what the AI could not do was arguably the most important part: it could not chart a future course of action. It could not weigh subtle trade-offs, question underlying assumptions, or anticipate how a field might evolve under new constraints or discoveries. Research is not merely about compiling what is known; it is about developing judgment—something that emerges from deep engagement, not automated synthesis.

AI can assist us in analyzing the past and the present. But extrapolating this knowledge into the future, identifying what should be done next, requires human intuition, creativity, and critical thinking. Whether AI will ever fully acquire this capability remains an open question. For now, review articles remain not just relevant, but essential—as exercises in thinking, not just in summarizing.

Only time will tell how far AI can go. Until then, the literature review remains the intellectual blueprint on which meaningful research is built.

Engines, Energy, and Oceans: The Story of Two Great Sciences

The story of thermodynamics and fluid mechanics is closely tied to one invention that changed the world: the steam engine.

In the early 1700s, Thomas Newcomen built a steam engine to pump water out of mines and farmlands. Later, James Watt improved it, making the engine more powerful and efficient. Watt’s design became the driving force of the Industrial Revolution, powering factories, trains, and even ships.

But the steam engine created new problems and questions. Empires at that time relied heavily on ships for trade and military expeditions. Building and running these ships required huge amounts of timber—not only for the ships themselves, but also for fuel. Wood was in short supply, so people needed to understand how energy could be used more wisely. This led to the First Law of Thermodynamics: energy cannot be created or destroyed, only changed from one form to another. With this, engineers could calculate how much work could come from burning wood or coal.

Another challenge came from the ships themselves. Even with strong engines, ships moving through water faced drag, the resistance of the water pushing back. Too much drag could cancel out the engine’s power. To study this, Leonhard Euler created equations describing how fluids move, assuming water had no friction. His theory looked great on paper but failed in reality, because real water is sticky—it has viscosity.

Later, Claude-Louis Navier and George Gabriel Stokes improved the equations by including viscosity. Their work gave us the Navier–Stokes equations, which scientists and engineers still use today to understand how fluids flow.

In short, the steam engine didn’t just give us machines—it pushed scientists to discover how energy works (thermodynamics) and how fluids behave (fluid mechanics). These discoveries shaped modern engineering, from ships and airplanes to power plants and rockets.

From Curiosity to Capability: A Student’s Journey into Machine Learning

 “Sir, do you have any project related to machine learning?”

A second-year undergraduate student asked me this question, eyes gleaming with curiosity and ambition.

I felt genuinely pleased—it’s always encouraging to see students eager to explore new areas. But alongside that, I was intrigued. Why the specific interest in machine learning?

“Is there a particular reason you’re looking for a machine learning project?” I asked.

“If I learn machine learning, I’ll get better career opportunities,” came the candid reply.

I paused to reflect. It’s a valid reason. After all, college education is often seen as a path to employability. In today’s job market, having a competitive edge is crucial—and machine learning has rapidly emerged as one of the most sought-after skills by companies offering attractive roles.

But this exchange made me think deeper—not just about skills, but about the bigger picture.

---

In the 19th century, when automobiles were first invented, every component was handcrafted and assembled manually. The process was slow but labor-intensive, offering jobs to skilled workers who knew how to build and fit parts together.

Then came Henry Ford and his revolutionary moving assembly line—a system that changed manufacturing forever. While some jobs were lost, new ones were created to design, operate, and optimize the new process. The focus shifted from craftsmanship to process efficiency.

Later, the arrival of computers triggered another transformation. Computer-Aided Manufacturing (CAM) streamlined production planning and execution. Jobs based on outdated manual skills gave way to roles that required understanding systems and automation. At every step, companies aimed to reduce costs and boost efficiency—constantly evolving to stay ahead.

Now, Artificial Intelligence—and especially Machine Learning—is doing the same.

It’s no surprise that companies are replacing employees with obsolete skills in favor of those proficient in modern tools and techniques. Disruption, though unsettling, is part of progress.

---

“Sir, I’ve taken an online course on Machine Learning,” the student added, bringing me back from my thoughts.

That’s good. Machine learning is undoubtedly a powerful skill. But here’s the question I often find myself asking:

Is learning the tool enough, or should we also understand the process it’s meant to improve?

Think back to those major technological leaps. Ford didn’t just introduce a tool—he reimagined the assembly process. John Parsons and Patrick Hanratty didn’t just use computers—they pioneered numerical control and CAD/CAM systems that reshaped manufacturing. Arthur Samuel, in 1959, created a checkers-playing program that laid the foundation for machine learning. And even earlier, in the 1950s, Alan Turing asked the bold question: Can machines think?

Machine learning, as a concept, has been around for over seven decades. So why did it take so long for industries to embrace it?

Because tools are only as useful as the understanding of where—and how—to apply them. It’s not just about knowing machine learning. It’s about knowing what problem to solve with it.

I smiled at the student and said, “Yes, I do have some problems you could work on. They may not look like typical machine learning projects at first glance, but they will help you deeply understand the underlying process. And once you do, you’ll see how machine learning can be used meaningfully to improve it.”

Why No One Asks Questions in Class — And Why They Should

It’s been twenty minutes since class began. I had just introduced a new topic and walked everyone through its applications and importance. Now came that crucial moment when I turned to the students and asked, “Any questions?”

Silence.

Not the peaceful kind. The kind that echoes. Some students buried their heads in their notebooks as if decoding a secret formula. A few tried their best to look asleep. Others just looked confused, wearing expressions that screamed, “Wait, what did he just teach us?”

I paused, giving them time to process, hoping a hand might go up. But nothing. The longer I waited, the more certain it became—they were waiting for me to just move on.

So I did what every teacher secretly dreads but knows is necessary: I pointed to students at random and asked them to explain what we’d just covered. They stammered through a few keywords, clearly unsure. When I asked a follow-up, the room returned to its natural state: silence.

This wasn’t about shyness alone. It was clear that many didn’t fully understand—but didn’t want to ask. That got me thinking.

Why do teachers ask, “Any questions?” in the first place? Is it just to check if the class is following? Or is it a clever excuse for a short break before jumping into the next topic?

And what about the students? Why don’t they ask, even when they clearly need help? Is it fear of embarrassment? Worry about sounding “dumb”? Or maybe they’ve already tuned out?

But here’s the thing: asking questions isn’t just about clearing doubts. It’s about opening a door for both the student and the teacher.

When a student asks a question, they’re inviting the teacher into their thought process. That’s a powerful thing. No one, not even the best teacher, can read minds. But a question gives us a glimpse inside. It shows where the student is struggling or what sparked their curiosity. It lets the teacher respond better, explain differently, and connect more deeply.

Over time, this kind of interaction also helps teachers understand how a student thinks. That’s incredibly valuable, especially when it comes to writing recommendation letters or helping students grow. Even if a student struggles academically, good questions show potential. And for those struggling to ask good questions? Teachers can guide them on how to get better.

All it takes is one question to start a conversation that could lead to understanding, confidence, and even opportunity.

So next time you're in class—whether you're completely lost or just a little unsure—ask the question. It might just change the way you learn.

Why It Rains Cats and Dogs: A Thermodynamic Tale

 “It’s Raining Cats and Dogs!” – What Does That Even Mean?

You’ve probably heard someone—most likely from an older generation—exclaim, “It rained cats and dogs today!” Now, if you pictured furry creatures tumbling from the sky, you're not alone. But of course, no actual pets were harmed in this expression.

So, what does it mean? Simply put, it's a quirky way to describe a heavy downpour. But have you ever wondered: why cats and dogs? Why not frogs and elephants? Or books and boots?

To uncover the strange beauty of this idiom, let’s take a surprising detour into thermodynamics and philosophy.

---

Part I: Thermodynamics and the Art of Rain

In the world of thermodynamics, we often analyze systems using two approaches:

1. Control Volume (Open System): Here, we fix our gaze on a specific region in space—say, the inside of a jet engine—and track the energy and mass flowing through it. It’s like watching what enters and leaves a room without following the guests around.
   
   

2. Control Mass (Closed System): This time, we focus on a specific chunk of matter—like a mix of air and fuel in a car engine—and observe how it transforms, wherever it goes. We follow the guests through the party, watching how they change costumes.
   
   

These two perspectives—space-focused vs. object-focused—are key to understanding both thermodynamics... and pets.

---

Part II: Cats, Dogs, and Human Nature

Now for the fun part: philosophy. If you've ever had a cat or a dog, you’ll know they behave quite differently.

• Cats are homebodies. They get emotionally tied to places. Move their favorite cushion, and you’ll hear about it.
  
  

• Dogs, on the other hand, are all about people. You could shift homes, cities, even planets—and your dog will wag its tail as long as you’re there.
  
  

So, if cats are like open systems—rooted in a particular space—then dogs are like closed systems—attached to a particular mass (you!).

Humans, too, reflect this dichotomy. Cat people tend to be inward-focused, loving their cozy corners and personal space. Dog people are more outward facing, thriving in social circles and human connections.

---

Part III: And Now... the Rain

So, where does rainfall fit in?

When someone says, “It’s raining cats and dogs,” they’re unknowingly referencing both space and mass, just like our two thermodynamic systems. It means the rain is coming down in torrents (mass) and drenching everything across the area (volume). The phrase playfully captures both the intensity and scale of the event.

So next time you hear it's raining cats and dogs, remember: it’s not just an idiom—it’s an open and closed system coming together in poetic chaos.