High Resolution Weather Forecasting

Risk Reduction Category

Modeling and Simulation

Technology Description

The key enabler for high resolution weather forecasting is data. Current weather conditions are the input to the prediction models, so the more accurate, spatially dense, and timely this data, the better the prediction. New forecasts can be generated on demand to keep pace with rapidly evolving weather scenarios. This is not possible using commodity data products, which are generated on a rigid, fixed production schedule.

Instead, a dedicated deployment of multiple strategically-located weather stations can provide the data needed at the desired update rate. This is known as a mesonet, a mesh network of weather stations containing various sensors that blanket a region to account for perturbed atmospherics due to significant changes in the terrain. Researchers predicted that feeding a forecasting model that is tailored for a particular region would provide predictions that are far superior to commodity weather forecasts, which may be prone to false positives. A mesonet can aid in preventing such false positives in addition to providing more accurate magnitude, type, duration, location, and timing of events. The atmospheric conditions at a single weather station within a mesonet can be gathered frequently—such as once per minute—to ensure that changes in weather determinants are accurately represented in the model. Data from a mesonet is quality controlled periodically—such as every five minutes. Quality-assurance tests are also conducted periodically to ensure that mesonet data is reliable. [1]

In addition to more common measurements like temperature, humidity, wind speed, and solar radiation, weather stations can transmit data from external sensors nearby such as precipitation gauges, ultrasonic snow depth sensors, and buried sensors that monitor soil temperature and moisture. Additionally, web cameras can monitor sky conditions.

With weather station technology and mesonets fairly established as of 2023, the cutting edge technology is not the hardware itself, but the applications and use cases that are enabled by this hardware.

Public Safety Power Shutoff

Due to the extensiveness of the electric grid, it is not technically or economically feasible to fully eliminate risks through grid hardening. Electric utilities therefore also use proactive de-energization of certain high-risk pathways, widely known as Public Safety Power Shutoffs (PSPS), during critical fire weather, to avoid or minimize fire risks. Unfortunately, PSPS events coupled with an extreme fire danger may negatively impact and introduce new risks to society and to vulnerable customers. Emergency and community support services (e.g., emergency communication, transportation for potential evacuation, water delivery systems for firefighting, medical support services) may be in high demand during these events and difficult to access [2]. Because of these societal disruptions, utilities are cautious to exercise this option. Intelligence feeding this decision is therefore critical. Precise location of storm activity, its strength, and direction are critical pieces of information that feed the decision. With accurate storm information, utilities can localize their efforts more precisely than without it. [Andre]

Strategic Undergrounding

San Diego Gas and Electric (SDGE) gathers data from hundreds of weather stations within its service territory. From this data, they obtain knowledge on wind gust potential. This historical record can be used to determine when and where to remediate via undergounding as well as PSPS. [Andre]

Overloaded Transmission Lines

Another use case for high resolution wind data is related to overloading of transmission lines. Where lines are overloaded in areas of steady and moderate wind (e.g. 3 mph), the cooling effect of the wind reduces negative effects of overloading. However, during extended periods of lower than normal wind, heating in the lines increases sagging, and, under more extreme conditions, can cause annealing of the conductor. [Andre]

Fire Spread Warnings

High resolution data on windspeed and direction can also feed fire spread prediction models and assess risk to downstream communities. [Andre]

Technical Readiness (Commercial Availability)

Following are examples of professional and research grade weather station hardware currently available commercially.

Campbell Scientific

Implementations / Deployments

The topic of interest is not the weather station technology itself as much as it is about the deployment of mesonets and how data from those mesonets can inform utility decision-making. The number of mesonets is growing in the USA, with several state-wide mesonets existing as of 2023.

A listing of statewide mesonets in the USA is provided at https://nationalmesonet.us/nmp-partners/

A few examples from that website are:

Kentucky: https://www.kymesonet.org/

North Carolina: https://econet.climate.ncsu.edu/

Delaware: http://www.deos.udel.edu/

New York: http://www.nysmesonet.org/

Hawaii: https://www.hawaii.edu/climate-data-portal/hawaii-mesonet/

Nearly 100 Campbell Scientific weather stations monitor meteorological parameters as part of New Zealand’s fire weather network. The stations record temperature, relative humidity, wind speed and direction, and rainfall. Both hourly and daily data from the stations are stored. The data is entered into the Fire Weather Index component of the New Zealand Fire Danger Rating System. The stations’ ability to operate unattended on low power and withstand environmental extremes make them ideal for this kind of monitoring.[3]

Innovations as of Mid 2023

Regarding PSPS, Pacific Northwest National Lab (PNNL) is working on ways to identify exactly what needs to be shut off. Using historical weather data and records of past warnings, PNNL seeks to learn how much of a county optimally would have been affected.

Potential Enrichment Work Opportunity

Integrating external “enrichment” data into their existing (but disparate) datasets confers greater foresight because of the immense volume of data available from system sensors, consumer reports from social media and other channels in the public domain, and data from weather services, just to name a few. In addition, visualizing analyzed data—often in layers—and subsequently presenting the data to operators and other stakeholders enhance planning, tactical operations, and communication to customers, to regulators, and especially to workers with boots on the ground. Data visualization improves system-restoration efforts in a service territory that may span many square miles—even in several service territories that straddle multiple states.[1]

References

[1] Ameren Missouri Tackles Storm Forecasting to Anticipate System Damage and Accelerate Restoration of Service. EPRI. Palo Alto, CA: September 2015. 3002006638.

[2] Wildfires and Public Safety Power Shutoffs: Distributed Energy Resources for Community Electricity Resilience. EPRI. Palo Alto, CA: December 2021. 3002017505.

[3] https://www.campbellsci.com/new-zealand-fire