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State of New York Public Service Commission STATE OF NEW YORK PUBLIC SERVICE COMMISSION __________________________________________ : In the Matter of Department of Public Service : Staff Investigation into the Utilities’ : Preparation for and Response to August 2020 : Case 20-E-0586 Tropical Storm Isaias and Resulting Electric : Power Outages : ___________________________________________ AFFIDAVIT OF BRIAN CERRUTI ON BEHALF OF CONSOLIDATED EDISON COMPANY OF NEW YORK, INC. I, Brian Cerruti, being duly sworn, depose and say: 1. My name is Brian Cerruti. My business address is 4 Irving Place, New York, New York 10003. My official title is Project Specialist, but I perform the functions of a meteorologist. I have been employed by Consolidated Edison Company of New York, Inc., (Con Edison or the Company) for seven years. 2. My responsibilities include creating custom weather forecasts for the Company, leading weather discussions on storm preparation conference calls, responding to questions from operating personnel before and during a storm, overseeing contracts with weather information vendors, developing, calibrating, verifying, and implementing outage prediction models for Con Edison and Orange and Rockland Utilities, Inc. (O&R), and providing subject matter expertise to Con Edison and O&R as needed. I am also the lead on the Company’s Probabilistic Load Forecasting Project, which is a tool co-developed with a vendor, TESLA, that quantifies weather uncertainty in the Company’s electric and steam load forecasts. 3. I earned a Bachelor of Science degree in Meteorology from Rutgers University’s George H. Cook School of Environmental and Biological Sciences and a Master of Science 1 degree in Atmospheric Science from Rutgers University’s Graduate School of Atmospheric Science. My Master’s thesis was entitled “A Statistical Forecast Model of Weather-Related Damage to a Major Electric Utility.” This thesis was also accepted for peer-reviewed publication by the Journal of Applied Meteorology and Climatology in February 2012. 4. Before working at Con Edison, I worked as a contractor at the Meteorological Development Laboratory National Weather Service Headquarters in Silver Spring, Maryland as a meteorologist. My job responsibilities included developing probabilistic wind speed forecasts for over a thousand weather stations across North America using the Ensemble Kernel Density model output statistics technique. I also applied my subject matter expertise in precipitation type forecasting and algorithms to convert several scripts into Fortran for the Short Range Ensemble Forecasting System Winter Guidance project. While working at the Meteorological Development Laboratory, I published another graduate school research paper by the peer- reviewed Bulletin of the American Meteorological Society, entitled “The Local Winter Storm Scale: A Measure of the Intrinsic Ability of Winter Storms to Disrupt Society.” Prior to that, I was the Head Forecaster for the Rutgers University Public Service Electric and Gas (PSE&G) Undergraduate Forecasting Program where my responsibilities included developing, calibrating, verifying, and implementing a damage prediction model for PSE&G’s overhead electrical distribution system, supervising undergraduate forecasters creating forecasts for PSE&G, leading weather discussions on conference calls in advance of adverse weather to aid in storm preparation, and providing subject matter expertise as needed. Purpose of Affidavit 5. The purpose of my affidavit is to describe my forecasts for Tropical Storm Isaias and to explain why they were reasonable. I will discuss important weather forecasting concepts, 2 different weather models, my Isaias forecasts, and respond to specific statements in the Order1 and the Department of Public Service (Department) Report.2 Weather Forecasting Concepts 6. A weather forecast is a snapshot prediction of the future state of the atmosphere. Meteorologists develop weather forecasts using many sources, including radar, satellite, and weather station data, numerical weather prediction models (weather models), and model output statistics. Meteorologists apply their experience and expertise to such data to produce a weather forecast. A weather forecast is typically comprised of temperature, precipitation, and wind forecasts. In addition, meteorologists can also develop track forecasts, which predict the path of tropical storms and sometimes nor’easters. 7. Numerical weather prediction models have become an integral part of developing a weather forecast. For example, a single weather forecast may derive information from many numerical weather prediction model forecasts. Numerical weather prediction models generally use differential equations to predict the future state of the atmosphere based on the initial conditions of the atmosphere. 8. Meteorologists compare numerical weather prediction model forecasts to observed conditions to assess the strengths and weaknesses of a given model’s predictions. Meteorologists typically assess observed conditions by analyzing satellite, radar, and surface weather station data. Often, meteorologists use historical weather model forecast performance as 1Case 20-E-0586, In the Matter of Department of Public Service Staff Investigation into the Utilities’ Preparation for and Response to August 2020 Tropical Storm Isaias and Resulting Electric Power Outages, Order to Commence Proceeding and Show Cause (issued November 19, 2020) (Order). 2 Id., New York State Department of Public Service Staff Interim Investigation Report on Tropical Storm Isaias (issued November 19, 2020) (Report). 3 a general guide on which models to favor. However, models also have inherent biases due to the physics or horizontal resolution, the distance between model calculation nodes, implemented within each model. As a result, post-processing of “raw” numerical weather prediction model output can result in improved weather predictions. This post-processing technique can take many forms, the most popular of which is called model output statistics. A meteorologist will use all this information - numerical weather prediction model forecasts, model output statistics forecasts, satellite data, radar data, surface observations, and his or her own experience – to develop a weather forecast. 9. The United States’ National Hurricane Center, which is part of the National Weather Service (through the National Centers for Environmental Prediction), is the primary source for information about tropical cyclones in the Atlantic Basin. The National Hurricane Center develops hurricane-specific numerical weather prediction models and statistical models to assist with tropical cyclone forecasting. The National Hurricane Center also develops consensus models to improve track and intensity forecasts of tropical cyclones. The National Hurricane Center produces forecasts of tropical cyclone track and intensity using these tools. It also analyzes observational data to determine the location, intensity, and structure of tropical cyclones for input into other numerical weather model simulations. 10. The National Weather Service develops local weather forecasts, which may use numerical weather prediction models as inputs. The National Weather Service uses the National Hurricane Center’s track and intensity forecasts as inputs to its own weather forecasts when tropical storms or hurricanes threaten a local area. 11. An “ensemble” is a collection of multiple numerical weather prediction model forecasts. Collectively, the group of forecasts helps to better capture the variability of the 4 atmosphere more completely than a single forecast. Often, ensembles are developed by slightly varying the atmospheric initial conditions in a numerical weather prediction model and then running that same model over the various initial conditions to generate multiple forecasts for given steps forward in time, in essence capturing the natural chaos within the atmosphere. Alternatively, the same initial conditions can be used in similar numerical weather prediction models where the variability in the atmosphere is captured by the differing model physics, parameterizations, and computational schemes. 12. Spaghetti plots3 are used to visualize the output from numerical weather prediction ensembles. They take many forms. The two most common are meteograms and spatial maps. Meteogram spaghetti plots generally show the variability of ensemble members for a specific location and weather variable over time, such as temperature forecasts from ensemble members for a single weather station. Such forecasts are beneficial for diagnosing confidence in the forecast for a specific location for a specific weather parameter. Another common spaghetti plot is in the form of a map. For example, a map can be generated to show the track predictions of cyclone centers over time from all ensemble members. Such forecasts are beneficial for diagnosing confidence in where a tropical cyclone will track. Comparison of Weather Models 13. The main “global” models are the American (Global Forecast System, or GFS), European (European Centre for Medium-Range Weather Forecasts Integrated Forecast System, or ECMWF) and Canadian (Canadian Meteorological Centre’s Global Environmental Multiscale, or GEM) models. In my experience, the European model is better than the American 3 Spaghetti plots is the nickname given to the computer model images that show potential tropical cyclone paths. When shown together, the individual model tracks can somewhat resemble strands of spaghetti noodles. 5 and Canadian models at
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