Can Deep Learning Replace Numerical Weather Prediction?

By Oliver Peckham

March 3, 2021

Numerical weather prediction (NWP) is a mainstay of supercomputing. Some of the first applications of the first supercomputers dealt with climate modeling, and even to this day, the largest climate models are heavily constrained by the scale of the supercomputers that run them. While some wait for the exascale era – and beyond – to brute force punishingly accurate and complex climate models into existence, others are looking for a deep learning-powered shortcut to the same results. In a paper for Philosophical Transactions of the Royal Society, eight researchers from the Jülich Supercomputing Center explored whether deep learning could ever actually beat numerical weather prediction at its own game – and if so, how and when that might happen.

Status quo

The weather and climate supercomputing community is no stranger to deep learning, but it has hitherto mostly been used to augment NWP approaches (e.g. in resolving post-processing issues). These modelers, however, are reticent to incorporate deep learning in more meaningful capacities.

“[There] are still reservations about DL in this community,” the authors write. “Two core arguments in this regard are the lack of explainability of deep [neural networks] and the lack of physical constraints. Furthermore, some scepticism prevails due to the fact that researchers have experimented with rather simple [neural networks] which were clearly unsuited to capture the complexity of meteorological data and feedback processes, but then extrapolate these results to discredit any [neural network] application including the much more powerful [deep learning] systems.”

While the  paper explores whether deep learning could eventually replace significant elements of a major NWP model, it’s perhaps more interested in whether deep learning could replace the whole thing.

That bar, of course, is extraordinarily high.

“Over the past decades, the ability of NWP models to predict the future atmospheric state has continuously improved,” the paper reads. “Contemporary global NWP models are not only able to predict the synoptic-scale weather pattern for several days, but they have also reached remarkable accuracy in forecasting end-user relevant meteorological quantities such as the 2m temperature and regional-scale precipitation events.”

But deep learning hasn’t been standing still, either – far from it. Steep increases in available computational power also benefit deep learning applications, which are also boosted by increased data availability and a rapidly expanding library of neural network architectures. 

Comparison of NWP, deep learning, and hybrid workflows for NWP. Image courtesy of the authors.

In fact, some researchers have already carried out NWP-mimicking deep learning tests – but, the authors note, these studies have been extremely limited in scope, focusing on forecasting by up to a day. 

Learning curves

The authors suggest that any eventual deep learning replacement for an NWP would likely consist of several neural networks trained on subsets of forecast products, allowing deep learning techniques to excel by focusing on specific tasks. Key to this approach, they say, is understanding the distributions of meteorological and climatological variables, which can be both complex and crucial: by way of example, they discuss sea ice, which might change very little over the course of a typical forecast, but which produces profound effects in the medium- to long-term.

Challenges facing deep learning as it tackles weather prediction. Image courtesy of the authors.

A number of challenges face deep learning as it climbs toward NWP. For instance, rare extreme weather events are difficult in terms of training and testing, though the authors report some success across various studies in accounting for this gap. Data availability is another problem: NWP typically uses satellite data where missing values are interpolated, but using such filled-in data with deep learning models poses a serious risk of concept drift, where an assumption made early on leads to cascading built-in biases.

Indeed, the authors say that with respect to data preparation generally, “best practices differ between the meteorological and ML communities.” Machine learning development, they explain, typically involves three datasets: a training dataset, a validation dataset and a test dataset, all of which should be independent from one another. But there’s a problem here, at least for weather prediction: the data is auto-correlated, meaning the datasets aren’t truly independent. 

Furthermore, neural networks, the authors say, may need to be directly taught the relationships between certain variables, as short- to medium-term datasets are unlikely to teach a model to understand longer-term variations like El Nino or climate change. This need for intervention extends to limiting factors, as well: deep learning models might be inspired to produce physically impossible forecasts or establish scientifically unsound correlation-causation links. The authors say that some studies have introduced such physical restraints to general success.

“It may be useful to reflect on the potential and necessity of physically constraining [deep learning] models from an abstract point of view,” they add. “In spite of their complexity and dimensionality, [deep learning] models still adhere to the fundamental principles of (data-driven) statistical modelling. This implies that there must be some rules in place to constrain the future, because otherwise extrapolation will be unbound.”

Finally, the authors touch on uncertainty estimation. Ensemble models, which use a series of runs to estimate the relative likelihood of various outcomes, have become more or less the norm in top-of-the-line NWP. However, ensemble approaches introduce exorbitant computational costs for deep learning models. The authors discuss Bayesian deep learning as a reasonable alternative, noting that it has already been tested for weather forecasting applications.

Prognostication

So: where is deep learning-powered weather prediction heading?

“We expect that the field of ML in weather and climate science will grow rapidly in the coming years as more and more sophisticated ML architectures are becoming available and can easily be deployed on modern computer systems,” the authors write. “We [also] expect that the success of [deep learning] weather forecast applications will hinge on the consideration of physical constraints in the [neural network] design. Taken to the extreme, portions or variants of current numerical models could eventually end up as regulators in the latent space of deep neural weather forecasting networks.”

“So, to answer the question posed in the title of this article,” they conclude, “we can only say that there might be potential for end-to-end [deep learning] weather forecast applications to produce equal or better quality forecasts for specific end-user demands, especially if these systems can exploit small-scale patterns in the observational data which are not resolved in the traditional NWP model chain.”

“Whether [deep learning] will evolve enough to replace most or all of the current NWP systems cannot be answered at this point.”

About the research

The paper discussed in this article, “Can deep learning beat numerical weather prediction?“, was published in the February 2021 issue of Philosophical Transactions of the Royal Society. The paper was written by M. G. Schultz, C. Betancourt, B. Gong, F. Kleinert, M. Langguth, L. H. Leufen, A. Mozaffari and S. Stadtler, all of the Jülich Supercomputing Center in Germany.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

ISC21 Cluster Competition Bracketology

June 18, 2021

For the first time ever, cluster competition experts have gathered together for an actual seeding reveal for the ISC21 Student Cluster Competition. What’s this, you ask? It’s where bona fide student cluster competi Read more…

OSC Enables On-Demand HPC for Automotive Engineering Firm

June 18, 2021

In motorsports, vehicle designers are constantly looking for the tiniest sliver of time to shave off through some clever piece of engineering – but as the low-hanging fruit gets snatched up, those advances are getting Read more…

PNNL Researchers Unveil Tool to Accelerate CGRA Development

June 18, 2021

Moore’s law is in decline due to the physical limits of transistor chips, putting an expiration date on a hitherto-perennial exponential trend in computing power – and leaving hardware developers scrambling to contin Read more…

TU Wien Announces VSC-5, Austria’s Most Powerful Supercomputer

June 17, 2021

Austria is getting a new top supercomputer: VSC-5, the latest iteration of the Vienna Scientific Cluster. The news was announced by VSC-5’s soon-to-be home, TU Wien (also known as the Vienna University of Technology). Read more…

Supercomputing Helps Advance Hydrogen Energy Research

June 16, 2021

Hydrogen energy has long remained an elusive target of the renewable energy industry, promising clean, carbon-free energy that would allow for rapid refueling, unlike current battery-based electric vehicles. Hydrogen-bas Read more…

AWS Solution Channel

Accelerating research and development for new medical treatments

Today, more than 290,000 researchers in France are working to provide better support and care for patients through modern medical treatment. To fulfill their mission, these researchers must be equipped with powerful tools. Read more…

FF4EuroHPC Initiative Highlights Results of First Open Call

June 16, 2021

EuroHPC is kicking into high gear, with seven of its first eight systems detailed – and one of them already operational. While the systems are, perhaps, the flashiest endeavor of the European Commission’s HPC effort, Read more…

TU Wien Announces VSC-5, Austria’s Most Powerful Supercomputer

June 17, 2021

Austria is getting a new top supercomputer: VSC-5, the latest iteration of the Vienna Scientific Cluster. The news was announced by VSC-5’s soon-to-be home, T Read more…

Catching up with ISC 2021 Digital Program Chair Martin Schulz

June 16, 2021

Leibniz Research Centre (LRZ)’s content creator Susanne Vieser interviews ISC 2021 Digital Program Chair, Prof. Martin Schulz to gain an understanding of his ISC affiliation, which is outside his usual scope of work at the research center and the Technical University of Munich. Read more…

Intel Debuts ‘Infrastructure Processing Unit’ as Part of Broader XPU Strategy

June 15, 2021

To boost the performance of busy CPUs hosted by cloud service providers, Intel Corp. has launched a new line of Infrastructure Processing Units (IPUs) that take Read more…

ISC Keynote: Glimpse into Microsoft’s View of the Quantum Computing Landscape

June 15, 2021

Looking for a dose of reality and realistic optimism about quantum computing? Matthias Troyer, Microsoft distinguished scientist, plans to do just that in his ISC2021 keynote in two weeks – Quantum Computing: From Academic Research to Real-world Applications. He notes wryly that classical... Read more…

A Carbon Crisis Looms Over Supercomputing. How Do We Stop It?

June 11, 2021

Supercomputing is extraordinarily power-hungry, with many of the top systems measuring their peak demand in the megawatts due to powerful processors and their c Read more…

Honeywell Quantum and Cambridge Quantum Plan to Merge; More to Follow?

June 10, 2021

Earlier this week, Honeywell announced plans to merge its quantum computing business, Honeywell Quantum Solutions (HQS), which focuses on trapped ion hardware, Read more…

ISC21 Keynoter Xiaoxiang Zhu to Deliver a Bird’s-Eye View of a Changing World

June 10, 2021

ISC High Performance 2021 – once again virtual due to the ongoing pandemic – is swiftly approaching. In contrast to last year’s conference, which canceled Read more…

Xilinx Expands Versal Chip Family With 7 New Versal AI Edge Chips

June 10, 2021

FPGA chip vendor Xilinx has been busy over the last several years cranking out its Versal AI Core, Versal Premium and Versal Prime chip families to fill customer compute needs in the cloud, datacenters, networks and more. Now Xilinx is expanding its reach to the booming edge... Read more…

AMD Chipmaker TSMC to Use AMD Chips for Chipmaking

May 8, 2021

TSMC has tapped AMD to support its major manufacturing and R&D workloads. AMD will provide its Epyc Rome 7702P CPUs – with 64 cores operating at a base cl Read more…

Intel Launches 10nm ‘Ice Lake’ Datacenter CPU with Up to 40 Cores

April 6, 2021

The wait is over. Today Intel officially launched its 10nm datacenter CPU, the third-generation Intel Xeon Scalable processor, codenamed Ice Lake. With up to 40 Read more…

Berkeley Lab Debuts Perlmutter, World’s Fastest AI Supercomputer

May 27, 2021

A ribbon-cutting ceremony held virtually at Berkeley Lab's National Energy Research Scientific Computing Center (NERSC) today marked the official launch of Perlmutter – aka NERSC-9 – the GPU-accelerated supercomputer built by HPE in partnership with Nvidia and AMD. Read more…

Google Launches TPU v4 AI Chips

May 20, 2021

Google CEO Sundar Pichai spoke for only one minute and 42 seconds about the company’s latest TPU v4 Tensor Processing Units during his keynote at the Google I Read more…

CERN Is Betting Big on Exascale

April 1, 2021

The European Organization for Nuclear Research (CERN) involves 23 countries, 15,000 researchers, billions of dollars a year, and the biggest machine in the worl Read more…

Iran Gains HPC Capabilities with Launch of ‘Simorgh’ Supercomputer

May 18, 2021

Iran is said to be developing domestic supercomputing technology to advance the processing of scientific, economic, political and military data, and to strengthen the nation’s position in the age of AI and big data. On Sunday, Iran unveiled the Simorgh supercomputer, which will deliver.... Read more…

HPE Launches Storage Line Loaded with IBM’s Spectrum Scale File System

April 6, 2021

HPE today launched a new family of storage solutions bundled with IBM’s Spectrum Scale Erasure Code Edition parallel file system (description below) and featu Read more…

Quantum Computer Start-up IonQ Plans IPO via SPAC

March 8, 2021

IonQ, a Maryland-based quantum computing start-up working with ion trap technology, plans to go public via a Special Purpose Acquisition Company (SPAC) merger a Read more…

Leading Solution Providers

Contributors

10nm, 7nm, 5nm…. Should the Chip Nanometer Metric Be Replaced?

June 1, 2020

The biggest cool factor in server chips is the nanometer. AMD beating Intel to a CPU built on a 7nm process node* – with 5nm and 3nm on the way – has been i Read more…

AMD Launches Epyc ‘Milan’ with 19 SKUs for HPC, Enterprise and Hyperscale

March 15, 2021

At a virtual launch event held today (Monday), AMD revealed its third-generation Epyc “Milan” CPU lineup: a set of 19 SKUs -- including the flagship 64-core, 280-watt 7763 part --  aimed at HPC, enterprise and cloud workloads. Notably, the third-gen Epyc Milan chips achieve 19 percent... Read more…

Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?

January 13, 2021

The rapid adoption of Julia, the open source, high level programing language with roots at MIT, shows no sign of slowing according to data from Julialang.org. I Read more…

Can Deep Learning Replace Numerical Weather Prediction?

March 3, 2021

Numerical weather prediction (NWP) is a mainstay of supercomputing. Some of the first applications of the first supercomputers dealt with climate modeling, and Read more…

GTC21: Nvidia Launches cuQuantum; Dips a Toe in Quantum Computing

April 13, 2021

Yesterday Nvidia officially dipped a toe into quantum computing with the launch of cuQuantum SDK, a development platform for simulating quantum circuits on GPU-accelerated systems. As Nvidia CEO Jensen Huang emphasized in his keynote, Nvidia doesn’t plan to build... Read more…

Microsoft to Provide World’s Most Powerful Weather & Climate Supercomputer for UK’s Met Office

April 22, 2021

More than 14 months ago, the UK government announced plans to invest £1.2 billion ($1.56 billion) into weather and climate supercomputing, including procuremen Read more…

African Supercomputing Center Inaugurates ‘Toubkal,’ Most Powerful Supercomputer on the Continent

February 25, 2021

Historically, Africa hasn’t exactly been synonymous with supercomputing. There are only a handful of supercomputers on the continent, with few ranking on the Read more…

The History of Supercomputing vs. COVID-19

March 9, 2021

The COVID-19 pandemic poses a greater challenge to the high-performance computing community than any before. HPCwire's coverage of the supercomputing response t Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire