THE Surf Report

Hodgepodge.

SURF:

Great surf this week- especially in SD- and some Santa Ana conditions to boot. That has been replaced by a low pressure system today- the one with the big question mark last week- and we've got a hodgepodge of swells because of it. For Friday and Saturday, look for background SW groundswell and a continuation of W wind/groundswell for chest high+ sets. Saturday may be dicey though as the low pressure finally comes ashore with showers and more wind. 

Sunday cleans up with new shoulder high W wind/groundswell. And here are the tides, sun, and water temps for this weekend:

• Sunrise and sunset:
• 6:22 AM sunrise
• 4:46 AM sunset

• Water temps are in the low 60's due to the offshore winds this past week.

• Tides are starting to mellow out this weekend:
• about 3' at sunrise
• up to 5' at lunch
• and down to 2' at sunset

FORECAST:

High pressure is in control next week which will give us great conditions for our upcoming swells. 

First is a late season SW again for chest high surf towards N County SD and the OC by Tuesday. 

After that, more shoulder high WNW swell is headed our way for Black Friday with more clean conditions. 

And models show more shoulder high WNW late next weekend- with no rain in sight. If anything changes between now and then, make sure to follow North County Surf on Twitter.

WEATHER:

Not a bad rain maker yesterday with up to 2" in the mountains and about 1" along the SD/OC coast. We have another shot of rain late Friday into Saturday- but with smaller amounts- most likely 0.25". After that high pressure sets up with Santa Ana Conditions next week. Here's what we have on tap:

• Friday-Saturday: Cloudy late Friday and showers Saturday. Temps 65/60.
• Sunday: Mostly sunny and cool. Temps 63/56.
• Monday and beyond: Potential Santa Ana conditions. Sunny and 70/55.

BEST BET:

• Friday with combo swell and clean conditions.
• Tuesday with small but fun SW and Santa Ana's
• Next weekend with fun NW and a continuation of nice weather. 

NEWS OF THE WEEK:

All the rage right now is Artificial Intelligence. Will AI be used for good? Will AI be used for bad? Can AI help me pick lottery numbers (that's what I'm hoping for). But for practical applications like weather forecasting (and eventually surf forecasting), AI may actually be useful to surfers. Here's what Wired Magazine had to say about the subject recently:

Machine learning algorithms that digested decades of weather data were able to forecast 90 percent of atmospheric measures more accurately than Europe’s top weather center (the 'gold standard' of forecast models). In September, researchers at Google’s DeepMind AI unit in London were paying unusual attention to the weather across the pond. Hurricane Lee was at least 10 days out from landfall—eons in forecasting terms—and official forecasts were still waffling between the storm landing on major Northeast cities or missing them entirely. DeepMind’s own experimental software had made a very specific prognosis of landfall much farther north. “We were riveted to our seats,” says research scientist Rémi Lam. A week and a half later, on September 16, Lee struck land right where DeepMind’s software, called GraphCast, had predicted days earlier: Long Island, Nova Scotia—far from major population centers. It added to a breakthrough season for a new generation of AI-powered weather models, including others built by Nvidia and Huawei, whose strong performance has taken the field by surprise. Veteran forecasters told WIRED earlier this hurricane season that meteorologists’ serious doubts about AI have been replaced by an expectation of big changes ahead for the field.

This week, Google shared new, peer-reviewed evidence of that promise. In a paper published in Science, DeepMind researchers report that its model bested forecasts from the European Centre for Medium-Range Weather Forecasting (ECMWF), a global giant of weather prediction, across 90 percent of more than 1,300 atmospheric variables such as humidity and temperature. Better yet, the DeepMind model could be run on a laptop and spit out a forecast in under a minute, while the conventional models require a giant supercomputer.

Standard weather simulations make their predictions by attempting to replicate the physics of the atmosphere. They’ve gotten better over the years, thanks to better math and by taking in fine-grained weather observations from growing armadas of sensors and satellites. They’re also cumbersome. Forecasts at major weather centers like the ECMWF or the US National Oceanic and Atmospheric Association (NOAA) can take hours to compute on powerful servers.

When Peter Battaglia, a research director at DeepMind, first started looking at weather forecasting a few years ago, it seemed like the perfect problem for his particular flavor of machine learning. DeepMind had already taken on local precipitation forecasts with a system, called NowCasting, trained with radar data. Now his team wanted to try predicting weather on a global scale.

Battaglia was already leading a team focused on applying AI systems called graph neural networks, or GNNs, to model the behavior of fluids, a classic physics challenge that can describe the movement of liquids and gases. Given that weather prediction is at its core about modeling the flow of molecules, tapping GNNs seemed intuitive. While training these systems is heavy-duty, requiring hundreds of specialized graphics processing units, or GPUs, to crunch tremendous amounts of data, the final system is ultimately lightweight, allowing forecasts to be generated quickly with minimal computer power.

GNNs represent data as mathematical “graphs”—networks of interconnected nodes that can influence one another. In the case of DeepMind’s weather forecasts, each node represents a set of atmospheric conditions at a particular location, such as temperature, humidity, and pressure. These points are distributed around the globe and at various altitudes—a literal cloud of data. The goal is to predict how all the data at all those points will interact with their neighbors, capturing how the conditions will shift over time.

Training software to make good predictions requires the right data. DeepMind trained its networks to accurately predict how any given set of weather conditions will evolve using 39 years of observations collected and processed by the ECMWF. The process is meant to teach the software how an initial set of atmospheric patterns can be expected to shift over six-hour increments. Each forecast is then fed into the next prediction, eventually producing a long-term outlook that can stretch over a week.

Lam and Battaglia say they see the remarkable performance of their forecasting model as a starting point. Because it can compute any type of forecast with such ease, they believe it could be possible to tweak versions to perform even better for certain kinds of weather conditions, like precipitation or extreme heat or hurricane tracks, or to provide more detailed forecasts for specific regions. Google also says it is exploring how to add GraphCast into its products. (The company recently added a different AI model, designed for nearer-term forecasting, into its weather forecasts shown on mobile devices.)

Matthew Chantry, who works on machine learning forecasting at the ECMWF, says that Google DeepMind’s GraphCast has emerged as the strongest of the AI contenders. “Over time it will consistently be just a little bit better,” he says. “That’s really exciting.” The other benefit, he adds, is that the software is the only AI weather predictor to offer precipitation forecasts—a particularly difficult task for the AI models, because the physics that produces rain tends to happen at a much finer resolution than is supported by the data used to train them.

Despite Google’s strong results, weather forecasting is far from solved. Its AI model isn’t designed to provide ensemble forecasts, which detail multiple potential outcomes for a storm or other weather system, along with a range of probabilities that can be especially useful for major events like hurricanes. AI models also tend to low-ball the strength of some of the most significant events, such as Category 5 storms. That’s possibly because their algorithms favor predictions closer to average weather conditions, making them wary of forecasting extreme scenarios. The GraphCast researchers also reported that their model fell short of the ECMWF’s predictions for conditions in the stratosphere—the upper part of the atmosphere—though they’re not yet sure why.

Relying on historical data for training involves a potentially serious weakness: What if the weather of the future looks nothing like the weather of the past? Because traditional weather models rely on laws of physics, they are thought to be somewhat robust to changes in Earth’s climate. The weather changes, but the rules that govern it don’t. Battaglia says that the DeepMind system’s ability to predict a wide variety of weather systems, including hurricanes, despite having seen relatively few of each type in its training data, suggests it has internalized the physics of the atmosphere. Still, it’s one reason to train the model on data that’s as current as possible, Battaglia says.

Last month, when Hurricane Otis struck Acapulco, Mexico, its intensification and path over millions of people evaded the foresight of all weather models—including those powered by AI. Such storms are “outliers among outliers,” says Brian McNoldy, a meteorologist at the University of Miami. Forecasters are still figuring out why that happened, including by looking at gaps in understanding how unusual ocean conditions or processes deep within a storm can drive it to strengthen rapidly. Whatever new insights and data are acquired will flow back into the conventional weather physics models—and also the datasets that power the newer AI-based models like Google’s GraphCast. The ECMWF is creating its own AI weather forecasting model, inspired by GraphCast, betting the agency’s savvy with the physics of the atmosphere can help design a model that works even better. It aims to launch AI-powered forecasts in the coming year or two. Chantry hopes the machine learning community will keep throwing its researchers, industry money, and GPUs into improving weather forecasts, too.

BEST OF THE BLOG:

Looking for a holiday party that makes a difference? You've come to the right place then: The 2023 North County Board Meeting Hollowday Party is being held on Friday, December 1st at 5:30 PM. This year's charity event, presented by our friends at Venture LLP, will be at Master's Kitchen & Cocktail in culinary hotspot Oceanside, CA. An industrial space with classic history, Masters was once a well remembered drag car shop. The revamped restaurant kept its authentic character, and is the perfect spot to get you in the holiday spirit. And with this year's fundraiser, we'll be benefiting our fellow Hawaiian surfers through the Maui Strong Fund, supporting the rebuilding of historic Lahaina town. So come join us for a night of dinner, entertainment, drinks, and pick up some holiday gifts during our legendary live auction. Tickets are just $100 per person- and we're filling up fast- so get your tickets NOW by emailing northcountyboardmeeting@gmail.com.

AND... we're always looking for auction items- big or small- to help support this cause. Please email if you can help. 

Just a reminder we're postponing tomorrow's Surf Meeting due to the the dirty water from yesterday's rain runoff. We'll hit the water in January instead.

PIC OF THE WEEK:

In case you've been busy with work or school this week, you may have not noticed the iconic Coldwater Classic has been running at Steamer Lane in Santa Cruz the past few days. Since the mid-80's, the longest running contest in North America has given surfers around the world a proving ground in challenging conditions. Unfortunately, not the biggest surf is on tap for the contest this week, but make sure to watch it at the World Surf League if you have some downtime, and check out some of California's up and coming talent. 

Keep Surfing, 
Michael W. Glenn
Chock Full O' Swagger
Introduced Travis To Taylor
2nd Place, 1990 Coldwater Classic