Due to the chaotic nature of the atmosphere, weather forecasts, even with ever-improving numerical weather prediction models, eventually lose all skill. Meteorologists have a strong desire to better understand this process as they try to trace forecast error back to observational gaps and to provide a means for improvement.

Root mean square error (rms, or its square, the variance distance) is often used to measure differences between simulated and observed fields. In this case, scientists measured the distance between a model forecast field within its grid and the verifying analysis field that represents all real-world observations. However, one must consider that atmospheric features, like fronts and pressure systems, are three-dimensional weather features in space that supercomputer models displace and also structurally distort as the numerical forecast moves away from initiation. Variance or rms error metrics do not quantify the displacement and distortion of weather systems.

In a recently published paper in Advances in Atmospheric Sciences, a team of scientists with the National Oceanic and Atmospheric Administration (NOAA), the Massachusetts Institute of Technology (MIT), and the University of Connecticut set out to find a general approach to assess the positional and structural components of the total difference between two fields. Essentially, meteorologists want to assess the accuracy of many different weather features within a model forecast compared to a verifying analysis based on real-world observations.

Sai Ravela from MIT, a co-author of this study, previously developed a Field Alignment method. In this case, his approach aligns the model forecast field with the observationally-based analysis in a smooth fashion so their difference is minimized (Step 1 in the schematic diagram, see also example map). Next, small-scale errors from uncertain origins are removed from all three fields (the original and aligned forecast as well as the verifying analysis, or proxy for observations) through a process called spatial filtering or smoothing (Step 2). The total variance distance, or difference, is then partitioned into three unique components (Step 3). Positional error, which is the variance distance between the smoothed original model forecast and smoothed aligned forecast fields, and structural error that is the variance distance between the smoothed aligned forecast and the smoothed verifying analysis fields, are two sides of the right-angle triangle in Fig. 1, and fine-scale noise, which are the uncertain small-scale errors removed from the original model forecast and verifying analysis, or observation fields (see smoothing arrows orthogonal to the triangle in Fig. 1). 

This method outputs the three orthogonal error components as scalar fields, as well as a vector field (Fig. 2) indicating the large-scale displacement of the forecast compared to the observational analysis field. Interestingly, throughout all regions and lead times that the team studied, more than half of the total error variance is associated with the misplacement of weather features. Therefore, displacement is more pronounced than distortion in forecast fields: only about 25% error variance is associated with structural inaccuracies of the partially predictable features, such as fronts and low-pressure systems. The rest of the error variance remains unexplained or unpredictable variability or noise. 

"How noise grows in error variance as a function of forecast lead time, and whether a positional-structural-noise decomposition of the spread among an ensemble of perturbed forecasts captures forecast error components is the subject of ongoing studies," said Dr. Jankov from NOAA, the lead author of the study.

Scientists at Raytheon BBN Technologies have developed a new way to detect a single photon, or particle of light, a development with big applications for sensors, communications, and exponentially more powerful quantum computer processors.

The team has published its work, which centers on the use of a component called a Josephson junction, in the academic journal Science. The discovery builds on the same team’s previous research into a microwave radiation detector 100,000 times more sensitive than existing systems.

“A Josephson junction in quantum computing is analogous to a transistor for modern electronics, so they are super important,” said Kin Chung Fong, a quantum information processing scientist at Raytheon BBN Technologies and a research associate at Harvard University. “Our new device enables this basic unit in quantum computing to communicate through as little as one photon. It will improve the speed in the communication and can make quantum networking and sensing possible.” 

Researchers and labs around the world have started building larger quantum computers, seeking to unlock the promise of faster processing.

“In theory, quantum computers can take over where traditional computers would run out of processing power,” said Brad Tousley, president of Raytheon BBN Technologies. “Quantum computers are particularly good at solving critical optimization problems. One example would be for a computer-aided design of a large system like an aircraft. Quantum computing allows for a more finite analysis of something like a wing shape than ever before. Fundamental everyday processing optimization is the first problem we’d like to tackle with quantum computing.”

The technical limitation has been the background noise that causes qubits to lose memory, creating errors in the processing. While other researchers see the noise as a problem, Fong and his team see opportunity.

Their method works a little like a highway, where superconducting charges play the role of cars. In principle, they can move very fast without bumping into each other. Background noise is like a broken-down car in the center lane – it breaks the flow of traffic.

“The interruption could destroy the data in quantum computing applications,” Fong said. “However, we can utilize this same phenomenon to detect a single photon, allowing the traffic to continue to speed along.”

The discovery is part of a research effort at Raytheon BBN Technologies, a subsidiary of Raytheon Intelligence & Space. Raytheon BBN has been providing advanced technology research and development for more than 70 years, often serving as a crucial link between the military and researchers at universities. As an example, it was one of the first nodes in the ARPANET, the precursor of the internet funded by the Defense Advanced Research Projects Agency, or DARPA. Scientists at Raytheon BBN work in broad-reaching portfolios, while quantum engineering and supercomputing continue to show promise for next-generation capabilities.

“This discovery is going to open up quantum processors to be connected like never before,” Tousley said. “The next step is characterizing performance and scaling up to more than one device in parallel or linking multiple devices.”

The Raytheon BBN team believes they have the systems engineering expertise to take this basic research to more practical applications.

“We’ve filled a technological void with the first Josephson junction to detect a single photon,” said Fong. “It’s an enabling technology for networking, communication, and computation. We are really just scratching the surface.”