Advanced Quantitative Precipitation Information (AQPI) is a synergistic project that combines observations and models to improve monitoring and forecasts of precipitation, streamflow, and coastal flooding in the San Francisco Bay Area. As an experimental system, AQPI leverages more than a decade of research, innovation, and implementation of a statewide, state-of-the-art network of observations, and development of the next generation of weather and coastal forecast models. AQPI was developed as a prototype in response to requests from the water management community for improved information on precipitation, riverine, and coastal conditions to inform their decision-making processes. Observation of precipitation in the complex Bay Area landscape of California’s coastal mountain ranges is known to be a challenging problem. But, with new advanced radar network techniques, AQPI is helping fill an important observational gap for this highly populated and vulnerable metropolitan area. The prototype AQPI system consists of improved weather radar data for precipitation estimation; additional surface measurements of precipitation, streamflow, and soil moisture; and a suite of integrated forecast modeling systems to improve situational awareness about current and future water conditions from sky to sea. Together these tools will help improve emergency preparedness and public response to prevent loss of life and destruction of property during extreme storms accompanied by heavy precipitation and high coastal water levels—especially high-moisture laden atmospheric rivers. The Bay Area AQPI system could potentially be replicated in other urban regions in California, the United States, and worldwide.

This study analyzes the economic and distributional impacts of a Production Tax Credit (PTC) on Indonesia's renewable electricity sector using a Computable General Equilibrium (CGE) model. The simulation spans 2018 to 2030, utilizing the 2018 Social Accounting Matrix (SAM). Results indicate significant reductions in electricity prices by 2025 under two scenarios of total factor productivity (TFP) growth (20% and 30%), with continued price decreases through 2030. The study reveals that while urban households and highly educated labour benefit from reduced energy costs and income gains, rural and lower-skilled workers experience fewer advantages, highlighting potential inequalities. The findings underscore the importance of inclusive renewable energy policies to ensure equitable economic outcomes during the energy transition. This research contributes to existing literature by offering new insights into the role of PTC in promoting sustainable energy development in emerging economies like Indonesia, with critical implications for policy design.

To help mitigate greenhouse gas (GHGs) generation from burning fossil fuels, many state and local governments are requiring utilities to dramatically increase the share of electricity generated from renewable sources. The City of Los Angeles has set a target of 100% renewable energy by 2045 and has formulated a plan that considers nine potential alternative scenarios that differ by technology, location, and timing. Each scenario has a unique set of local investments, operating and maintenance (O&M) costs, and concomitant rate structures. In this study we develop and apply a computable general equilibrium (CGE) model built specifically for LA to estimate and compare the economic impacts for each of the scenarios over time relative to a reference case. We find differences in economic impacts across scenarios, depending on the level and timing of investment and O&M expenditures, as well as differences in the relative rate changes across scenarios. Results show that employment and economic output are positively correlated with greater capital and O&M spending, while higher electricity rates can dampen economic activity. Several scenarios generate positive economic impacts relative to the reference case, showing that the transition need not have harmful economic impacts, and all scenarios generate a number of other positive co-benefits, such as reduced damage to health from the reduction of ordinary air pollutants. The net employment impacts from 2026 to 2045 across the scenarios range from a low of 3,600 job-year losses annually to 4,700 job-year gains, both around only 0.1% of the baseline average annual employment in the city over that period. The analysis also indicates that lower-income households are relatively more affected than others by the scenarios. Overall, even in the most negatively impactful case, the economic output and employment effects are quite small when taken in the context of the overall size of the regional economy and the large reduction in GHGs. The City of Los Angeles can confidently move forward with its transition to 100% renewable electricity without concern of significant negative aggregate economic impacts.Job impacts across all scenarios can be enhanced by attracting renewable equipment manufacturing and support industries to the City.Even the minimal adverse distributional impacts can be reduced by policies such as subsidies for home energy efficiency improvement, modifications to electricity rate structures or rebates.Both aggregate and distributional impacts can be further addressed by providing training for skilled and semi-skilled labour creating employment opportunities for City residents.

The multi-disciplinary data and information available at a community level comprise the foundation of natural hazard resilience modeling. These data enable and inform mitigation and recovery planning decisions prior to and following damaging events such as earthquakes. This paper presents a multi-disciplinary seismic resilience modeling methodology to assess the vulnerability of the built environment and economic systems. This methodology can assist decision-makers with developing effective mitigation policies to improve the seismic resilience of communities. Two complementary modeling strategies are designed to examine the impacts of scenario earthquakes from a combined engineering and economic perspective. The engineering model is developed using a probabilistic fragility-based modeling approach and is analyzed using Monte Carlo (MC) simulations subject to seismic multi-hazard, including simulated ground shaking and resulting liquefaction of the soil, to quantify the physical damage to buildings and electric power substations (EPS). The outcome of the analysis is subsequently used as input to repair and recovery models to quantify repair cost and recovery time metrics for buildings and as input to functionality models to estimate the functionality of individual buildings and substations by accounting for their interdependency. The economic model consists of a spatial computable general equilibrium (SCGE) model that aggregates commercial buildings into sectors for retail, manufacturing, services, etc., and aggregates residential buildings into a wide range of household groups. The SCGE model employs building functionality estimates to quantify the economic losses. The outcomes of this integrated modeling consist of engineering and economic impact metrics, which are used to investigate mitigation actions to help inform a community on approaches to achieve its resilience goals. An illustrative case study of Salt Lake County (SLC), Utah, developed through an extensive collaborative partnership and engagement with SLC officials, is presented. The results demonstrate the effectiveness of the proposed methodology in quantifying the loss and functional recovery of infrastructure systems, the impacts on capital stock, employment, and household income and the effect of various mitigation strategies in reducing the losses and functional recovery time subject to earthquakes with varying intensities.

El Estudio de Resiliencia de la Red de Puerto Rico y Transiciones a Energia 100% Renovable (PR100) es un analisis integral basado en amplios aportes de las partes interesadas sobre posibles caminos para que Puerto Rico alcance su meta de 100% de energía renovable para 2050. PR100 fue un esfuerzo integrado que se baso en experiencia y capacidades de los laboratorios nacionales contribuyentes que exploraron posibles caminos para que Puerto Rico logre su objetivo de 100% de energía renovable en el largo plazo (para 2050), aumente la confiabilidad y la resiliencia en el plazo inmediato (dentro de los proximos anos), y trabajar hacia la justicia energetica. El proposito del estudio es brindar apoyo a las decisiones e informar las decisiones de inversion para los implementadores de la transicion energetica de Puerto Rico. See NREL/TP-6A20-88384 for the English translation of this report.

El estudio de Resiliencia y Transiciones a 100% Energia Renovable de Puerto Rico (PR100) es un estudio de 2 anos de la Oficina de Movilizacion de la Red del Departamento de Energia y seis laboratorios nacionales para analizar exhaustivamente las rutas dirigidas por las personas interesadas hacia un futuro de energia limpia en Puerto Rico. En el Ano 1 del estudio, el equipo PR100, creo y analizo los modelos que alcanzan las metas de energia renovable para Puerto Rico y los objetivos de resiliencia energetica a corto y largo plazo. Este informe, que resume el progreso en el Ano 1, proporciona las consideraciones que pueden informar posibles decisiones de fondos e implementacion potenciales por parte de las agencias federales y locales clave y partes interesadas. El resumen de este informe sigue a la publicacion en julio 2022 de un Informe de Seis Meses de Progreso de PR100. (en ingles y espanol), asi como webinarios publicos en febrero 2022 para lanzar el estudio y julio 2022 para presentar la actualizacion a 6 meses. Un informe final por escrito y visuales por la web seran publicados a finales del 2023. Todas las publicaciones y eventos publicos asociados con el estudio estaran disponibles en ingles y espanol. This report is also available in English https://www.nrel.gov/docs/fy23osti/85018.pdf.

The Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100) is a 2-year study by the U.S. Department of Energy's (DOE's) Grid Deployment Office and six national laboratories to comprehensively analyze stakeholder-driven pathways to Puerto Rico's clean energy future. In Year 1 of the study, the PR100 team rigorously modeled and analyzed scenarios that meet Puerto Rico's renewable energy targets and achieve short-term recovery goals and long-term energy resilience. This report, which summarizes PR100 progress in Year 1, provides considerations that can inform potential funding and implementation decisions by key federal and local agencies and stakeholders. The summary report follows the publication in July 2022 of a PR100 Six-Month Progress Update (in English and Spanish), as well as public webinars in February 2022 to kick off the study and in July 2022 to present the 6-month update. A final written report and web-based visuals will be published in late 2023. All publications and public events associated with the study will be available in Spanish and English. This report is also available in Spanish https://www.nrel.gov/docs/fy23osti/85144.pdf.

Each year the U.S. government makes significant investments in improving weather forecast models. In this paper, we use a multidisciplinary approach to examine how utilities can benefit from improved wind-speed forecasts to more efficiently use wind-generated electricity and subsequently increase economic activity. Specifically, we examine how improvements to the National Oceanic and Atmospheric Administration's high-resolution rapid refresh model (HRRR) wind forecasts can provide (1) cost savings for utilities and (2) increase in real household income. To do so, we compare 12-h-ahead wind forecasts with real-time observations for two HRRR model transitions (i.e., when one model is operational, the other is being tested). We compare estimates of actual and predicted wind power under the publicly available and developmental models, with reduced forecast errors allowing for better utility decision-making and lower production costs. We then translate potential cost savings into electricity price changes, which are entered as exogenous shocks to eight regional computable general equilibrium models constructed for the U.S. Overall, we find that households would have seen a potential $60 million increase in real income for our sample (13% of all contiguous U.S. land-based turbine capacity), which had the updated HRRR models been in place during the two transition periods; applying our estimated savings for the sample of turbines to the entire array of turbines shows a potential real household income increase in approximately $384 million during these time frames.

Puerto Rico se ha comprometido a satisfacer sus necesidades de electricidad con un 100% de energia renovable para 2050, junto con el cumplimiento de objetivos intermedios del 40% para 2025, el 60% para 2040, la eliminacion gradual de la generacion a base de carbon para 2028, y una mejora del 30% en la eficiencia energetica para 2040, segun lo establecido en la Ley de Politica Publica Energetica de Puerto Rico (Ley 17). Desde los huracanes Irma y Maria en septiembre de 2017, DOE y sus laboratorios nacionales han proporcionado a las partes interesadas del sistema energetico de Puerto Rico herramientas, adiestramiento y apoyo de modelaje para permitir la planificacion y el funcionamiento de la red electrica con mas resiliencia frente a nuevas interrupciones. El 2 de febrero de 2022, DOE, FEMA y seis laboratorios nacionales lanzaron el Estudio de Resiliencia de la Red Electrica de Puerto Rico y Transicion a la Energia 100% Renovable (PR100), de dos anos de duracion, para llevar a cabo un analisis exhaustivo de las vias impulsadas por las partes interesadas para el futuro energetico de Puerto Rico. El analisis energetico, solido y objetivo, comprende cinco actividades, con enfasis en la confiabilidad del sistema electrico, la resiliencia y la planificacion de la generacion. Esta presentacion se realizo en un seminario web publico el 21 de julio del 2022, proporcionando un resumen general del progreso en los primeros seis meses del Estudio, incluyendo la presentacion de cuatro escenarios iniciales definidos mediante un rol activo de las partes interesadas. This is the Spanish translation of NREL/PR-6A20-83431.

Each year the U.S. government makes significant investments in improving weather forecasts from numerical weather prediction models that are run operationally within the National Weather Service. Although the primary purpose is saving lives and property, more accurate forecasts can create substantial efficiency gains when they elicit improved behavioral responses. One example involves commuters, who make daily decisions about when to leave for work based, in part, on expected road conditions. When workers account for potential weather delays, economic losses due to missed work time are reduced. Economists have often looked at such questions with cost-loss models, a partial equilibrium approach investigating gains to individual agents. We extend this idea to examine economy-wide effects, embedding a behavioral model of time allocation with improved information into eight regional computable general equilibrium models of the US economy. Our primary mechanism is that reductions in lost work time lead to gains in firm-level output. This study evaluates the economic impact of improvements to the High-Resolution Rapid Refresh weather forecast models over three different versions on commuting. Aggregating results from comparisons between old and new versions of the HRRR for 206 metropolitan statistical areas, we find that forecasting improvements lead to smaller losses in work time, creating notable gains to the U.S. economy.

The growing number of flood disasters worldwide and the subsequent catastrophic consequences of these events have revealed the flood vulnerability of communities. Flood impact predictions are essential for better flood risk management which can result in an improvement of flood preparedness for vulnerable communities. Early flood warnings can provide households and business owners additional time to save certain possessions or products in their buildings. This can be accomplished by elevating some of the water-sensitive components (e.g., appliances, furniture, electronics, etc.) or installing a temporary flood barrier. Although many qualitative and quantitative flood risk models have been developed and highlighted in the literature, the resolution used in these models does not allow a detailed analysis of flood mitigation at the building- and community level. Therefore, in this article, a high-fidelity flood risk model was used to provide a linkage between the outputs from a high-resolution flood hazard model integrated with a component-based probabilistic flood vulnerability model to account for the damage for each building within the community. The developed model allowed to investigate the benefits of using a precipitation forecast system that allows a lead time for the community to protect its assets and thereby decreasing the amount of flood-induced losses.

Forecasts from numerical weather prediction (NWP) models play a critical role in many sectors of the U.S. economy. Improvements to operational NWP model forecasts are generally assumed to provide significant economic savings through better decision-making. But is this true? Since 2014, several new versions of the High-Resolution Rapid Refresh (HRRR) model were released into operation within the National Weather Service. Practically, forecasts have an economic impact only if they lead to a different action than what would be taken under an alternative information set. And in many sectors, these decisions only need to be considered during certain weather conditions. We estimate the economic impacts of improvements made to the HRRR, using 12-h wind, precipitation, and temperature forecasts in several cases where they can have "economically meaningful" behavioral consequences. We examine three different components of the U.S. economy where such information matters: 1) better integration of wind energy resources into the electric grid, 2) increased worker output due to better precipitation forecasts that allow workers to arrive to their jobs on time, and 3) better decisions by agricultural producers in preparing for freezing conditions. These applications demonstrate some of the challenges in ascertaining the economic impacts of improved weather forecasts, including highlighting key assumptions that must be made to make the problem tractable. For these sectors, we demonstrate that there was a marked economic gain for the United States between HRRR versions 1 and 2 and a smaller, but still appreciable economic gain between versions 2 and 3.