A5051056254- Meteorological Phenomena and Simulations
- Climate variability and models
- Atmospheric chemistry and aerosols
- Atmospheric and Environmental Gas Dynamics
- Air Quality Monitoring and Forecasting
- Energy Load and Power Forecasting
- Tropical and Extratropical Cyclones Research
- Wind and Air Flow Studies
- Air Quality and Health Impacts
- Atmospheric aerosols and clouds
- Hydrological Forecasting Using AI
- Atmospheric Ozone and Climate
- Wind Energy Research and Development
- Solar Radiation and Photovoltaics
- Flood Risk Assessment and Management
- Cryospheric studies and observations
- Hydrology and Watershed Management Studies
- Precipitation Measurement and Analysis
- Oceanographic and Atmospheric Processes
- Electric Power System Optimization
- Remote Sensing and Land Use
- Neural Networks and Applications
- Hydrology and Drought Analysis
- Landslides and related hazards
- Geophysics and Gravity Measurements
Scripps Institution of Oceanography
2019-2025
University of California, San Diego
2019-2025
Scripps Research Institute
2023
Universidad Católica Santo Domingo
2022
University of California, Los Angeles
2022
NSF National Center for Atmospheric Research
2011-2020
NOAA National Centers for Environmental Prediction
2020
National Oceanic and Atmospheric Administration
2020
University of Colorado Boulder
2020
European Centre for Medium-Range Weather Forecasts
2020
Abstract This study explores an analog-based method to generate ensemble [analog (AnEn)] in which the probability distribution of future state atmosphere is estimated with a set past observations that correspond best analogs deterministic numerical weather prediction (NWP). An analog for given location and forecast lead time defined as prediction, from same model, has similar values selected features current model forecast. The AnEn evaluated 0–48-h probabilistic predictions 10-m wind speed...
Abstract A barrier to utilizing machine learning in seasonal forecasting applications is the limited sample size of observational data for model training. To circumvent this issue, here we explore feasibility training various approaches on a large climate ensemble, providing long set with physically consistent realizations. After thousands seasons simulations, models are tested producing forecasts across historical period (1980-2020). For large-scale spatial patterns precipitation western...
Abstract Two new postprocessing methods are proposed to reduce numerical weather prediction’s systematic and random errors. The first method consists of running a algorithm inspired by the Kalman filter (KF) through an ordered set analog forecasts rather than sequence in time (ANKF). forecast for given location is defined as past prediction that matches selected features current forecast. second weighted average observations verified when 10 best analogs were valid (AN). ANKF AN tested 10-m...
Wind power forecasting can enhance the value of wind energy by improving reliability integrating this variable resource and economic feasibility. The National Center for Atmospheric Research (NCAR) has collaborated with Xcel Energy to develop a multifaceted prediction system. Both day-ahead forecast that is used in trading short-term are critical decision making. This system includes high resolution ensemble modeling capabilities, data assimilation, now-casting, statistical postprocessing...
México has cities (e.g., City and Puebla City) located at elevations > 2,000 m above the elevation ceiling below which local climates allow dengue virus mosquito vector Aedes aegypti to proliferate. Climate warming could raise this place high-elevation risk for transmission. To assess Ae. determine potential using weather/climate parameters predict abundance, we surveyed 12 communities along an elevation/climate gradient from Veracruz (sea level) (∼2,100 m). was commonly encountered up...
Abstract The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this scale provide opportunities for enhanced application-focused capabilities complement existing weather and climate services products. There is, however, “knowledge–value” gap, where lack evidence awareness potential socioeconomic benefits S2S forecasts limits their wider uptake. To address here we present first...
Abstract Exposure to unhealthy air causes millions of premature deaths and damages crops sufficient feed a large portion the South Asian population every year. However, little is known about how future quality in Asia will respond changing human activities. Here we examine combined effect changes climate pollutant emissions projected by Representative Concentration Pathways (RCP) 8.5 RCP6.0 on 2050 using state‐of‐the‐science Nested Regional Climate model with Chemistry (NRCM‐Chem). RCP8.5...
Abstract Ensemble Forecast Operations (EFO) is a risk‐based approach of reservoir flood control operations that incorporates ensemble streamflow predictions (ESPs) made by the California‐Nevada River Center. Reservoir for each member an ESP are individually modeled to forecast system conditions and calculate risk reaching critical operational thresholds. release decisions simulated manage forecasted with respect established tolerance levels. EFO was developed Lake Mendocino, 111,000...
Abstract This study tests the utility of convolutional neural networks as a postprocessing framework for improving National Center Environmental Prediction's Global Forecast System's integrated vapor transport forecast field in Eastern Pacific and western United States. Integrated is characteristic atmospheric rivers, which provide over 65% yearly precipitation at some U.S. locations. The method reduces full‐field root‐mean‐square error (RMSE) leads from 3 hr to seven days (9–17% reduction),...
Abstract California experienced a historic run of nine consecutive landfalling atmospheric rivers (ARs) in three weeks’ time during winter 2022/23. Following years drought from 2020 to 2022, intense ARs across December 2022–January 2023 were responsible for bringing reservoirs back historical averages and producing damaging floods debris flows. In recent years, the Center Western Weather Water Extremes collaborating institutions have developed routinely provided end users peer-reviewed...
Abstract A methodology combining Bayesian inference with Markov chain Monte Carlo (MCMC) sampling is applied to a real accidental radioactive release that occurred on continental scale at the end of May 1998 near Algeciras, Spain. The source parameters (i.e., location and strength) are reconstructed from limited set measurements release. Annealing adaptive procedures implemented ensure robust effective parameter-space exploration. simulation setup similar an emergency response scenario,...
CORRESPONDING AUTHOR: Cristina L. Archer, University of Delaware, College Earth, Ocean, and Environment, Newark, DE 19716, E-mail: carcher@udel.eduA supplement to this article is available online (10.1175/BAMS-D-13-00108.2).
Unlike deterministic forecasts, probabilistic predictions provide estimates of uncertainty, which is an additional value for decision-making. Previous studies have proposed the analog ensemble (AnEn), a technique to generate uncertainty information from purely forecast. The objective this study improve AnEn performance wind power forecasts by developing static and dynamic weighting strategies, optimize predictor combination with brute-force continuous ranked probability score (CRPS)...
The National Center for Atmospheric Research (NCAR) recently updated the comprehensive wind power forecasting system in collaboration with Xcel Energy addressing users’ needs and requirements by enhancing expanding integration between numerical weather prediction machine-learning methods. While original was designed primary focus on day-ahead support of trading, enhanced provides short-term unit commitment economic dispatch, uncertainty quantification speed probabilistic forecasting, extreme...
Abstract. This study applies the Gridpoint Statistical Interpolation (GSI) 3D-Var assimilation tool originally developed by National Centers for Environmental Prediction (NCEP), to improve surface PM2.5 predictions over contiguous United States (CONUS) assimilating aerosol optical depth (AOD) and in version 5.1 of Community Multi-scale Air Quality (CMAQ) modeling system. An optimal interpolation (OI) method implemented earlier (Tang et al., 2015) CMAQ system is also tested same period (July...
Abstract Particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5) is a critical air pollutant important impacts on human health. It essential provide accurate quality forecasts alert people avoid reduce exposure high ambient levels of PM2.5. The NOAA National Air Quality Forecasting Capability (NAQFC) provides numerical forecast guidance surface PM2.5 for the United States. However, NAQFC has exhibited substantial seasonal biases, overpredictions in winter and...