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Full-Text Articles in Engineering
Interannual Variability And Seasonal Predictability Of Wind And Solar Resources, Nir Krakauer, Daniel S. Cohen
Interannual Variability And Seasonal Predictability Of Wind And Solar Resources, Nir Krakauer, Daniel S. Cohen
Publications and Research
Solar and wind resources available for power generation are subject to variability due to meteorological factors. Here, we use a new global climate reanalysis product, Version 2 of the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA-2), to quantify interannual variability of monthly-mean solar and wind resource from 1980 to 2016 at a resolution of about 0.5 degrees. We find an average coefficient of variation (CV) of 11% for monthly-mean solar radiation and 8% for wind speed. Mean CVs were about 25% greater over ocean than over land and, for land areas, were greatest at high latitude. The correlation …
Ensemble Forecasts: Probabilistic Seasonal Forecasts Based On A Model Ensemble, Hannah Aizenman, Michael D. Grossberg, Nir Y. Krakauer, Irina Gladkova
Ensemble Forecasts: Probabilistic Seasonal Forecasts Based On A Model Ensemble, Hannah Aizenman, Michael D. Grossberg, Nir Y. Krakauer, Irina Gladkova
Publications and Research
Ensembles of general circulation model (GCM) integrations yield predictions for meteorological conditions in future months. Such predictions have implicit uncertainty resulting from model structure, parameter uncertainty, and fundamental randomness in the physical system. In this work, we build probabilistic models for long-term forecasts that include the GCM ensemble values as inputs but incorporate statistical correction of GCM biases and different treatments of uncertainty. Specifically, we present, and evaluate against observations, several versions of a probabilistic forecast for gridded air temperature 1 month ahead based on ensemble members of the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 …
Sefo: A Package For Generating Probabilistic Forecasts From Nmme Predictive Ensembles, Nir Krakauer
Sefo: A Package For Generating Probabilistic Forecasts From Nmme Predictive Ensembles, Nir Krakauer
Publications and Research
Long-range weather forecasts based on output from ensembles of computer simulations are attracting increasing interest. A variety of methods have been proposed to convert the ensemble outputs to calibrated probabilistic forecasts. The package presented here (SeFo, for Seasonal Forecasting) implements a number of methods for producing forecasts of monthly surface air temperature anomalies up to 9 months in advance using output from the North American Multi-Model Ensemble (NMME). The package contains modules for downloading and reading past observations and ensemble output; producing forecast probability distributions; and verifying and calibrating a user-determined subset of methods using arbitrary past periods. By changing …