scholarly journals Wavelet-Based Methodology for the Verification of Stochastic Submeso and Meso-Gamma Fluctuations

2015 ◽  
Vol 143 (10) ◽  
pp. 4220-4235 ◽  
Author(s):  
Astrid Suarez ◽  
David R. Stauffer ◽  
Brian J. Gaudet
Keyword(s):  
Single Case ◽  
Weather Prediction ◽  
Model Performance ◽  
Numerical Weather Prediction Model ◽  
Weather Research And Forecasting ◽  
New Approach ◽  
Frequency Distributions ◽  
Use Of Data ◽  
Rock Springs ◽  
Grid Length

Abstract Numerical weather prediction model skill is difficult to assess for transient, nonstationary, nondeterministic, or stochastic motions, like submeso and small meso-gamma motions. New approaches are needed to complement traditional methods and to quantify and evaluate the variability and the errors for these high-frequency, nondeterministic modes. A new verification technique that uses the wavelet transform as a bandpass filter to obtain scale-dependent frequency distributions of fluctuations is proposed for assessing model performance or accuracy. This new approach quantifies the nondeterministic variability independent of time while accounting for the time scale and amplitude of each fluctuation. The efficacy of this wavelet decomposition technique for the verification of submeso and meso-gamma motions is first illustrated for a single case before the analysis is extended to six cases. The sensitivity of subkilometer grid-length Weather Research and Forecasting Model forecasts to the choice of three initialization strategies is assessed for both deterministic and stochastic motions using observations from a special network located at Rock Springs, Pennsylvania. It is demonstrated that the use of data assimilation in a preforecast period results in improved temperature and wind speed statistics for deterministic motions and for nondeterministic fluctuations with periods greater than ~20 min. As expected, there is little-to-no accuracy forecasting the occurrence of variability for temperature and wind in the smaller-submeso range and greater accuracy in the larger-submeso and meso-gamma ranges. Nonetheless, the model has some difficulty reproducing the observed variability with the correct amplitude. It underestimates the amplitude of observed fluctuations even for larger time scales, where better model performance could be expected.

scholarly journals Evaluation of the WRF Model to Simulate a High-Intensity Rainfall Event over Kampala, Uganda

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 873
Author(s):  
Yakob Umer ◽  
Janneke Ettema ◽  
Victor Jetten ◽  
Gert-Jan Steeneveld ◽  
Reinder Ronda
Keyword(s):  
High Intensity ◽  
Flood Hazard ◽  
Weather Prediction ◽  
Wrf Model ◽  
Numerical Weather Prediction Model ◽  
Rainfall Event ◽  
Weather Research And Forecasting ◽  
Event Triggering ◽  
Rain Events

Simulating high-intensity rainfall events that trigger local floods using a Numerical Weather Prediction model is challenging as rain-bearing systems are highly complex and localized. In this study, we analyze the performance of the Weather Research and Forecasting (WRF) model’s capability in simulating a high-intensity rainfall event using a variety of parameterization combinations over the Kampala catchment, Uganda. The study uses the high-intensity rainfall event that caused the local flood hazard on 25 June 2012 as a case study. The model capability to simulate the high-intensity rainfall event is performed for 24 simulations with a different combination of eight microphysics (MP), four cumulus (CP), and three planetary boundary layer (PBL) schemes. The model results are evaluated in terms of the total 24-h rainfall amount and its temporal and spatial distributions over the Kampala catchment using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. Rainfall observations from two gauging stations and the CHIRPS satellite product served as benchmark. Based on the TOPSIS analysis, we find that the most successful combination consists of complex microphysics such as the Morrison 2-moment scheme combined with Grell-Freitas (GF) and ACM2 PBL with a good TOPSIS score. However, the WRF performance to simulate a high-intensity rainfall event that has triggered the local flood in parts of the catchment seems weak (i.e., 0.5, where the ideal score is 1). Although there is high spatial variability of the event with the high-intensity rainfall event triggering the localized floods simulated only in a few pockets of the catchment, it is remarkable to see that WRF is capable of producing this kind of event in the neighborhood of Kampala. This study confirms that the capability of the WRF model in producing high-intensity tropical rain events depends on the proper choice of parametrization combinations.

scholarly journals Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

2018 ◽  
Author(s):  
Rochelle P. Worsnop ◽  
Michael Scheuerer ◽  
Thomas M. Hamill ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Weather Prediction ◽  
Model Performance ◽  
The United States ◽  
Numerical Weather Prediction Model ◽  
Post Processing ◽  
Skill Scores ◽  
Ramp Events

Abstract. Wind power forecasting is gaining international significance as more regions promote policies to increase the use of renewable energy. Wind ramps, large variations in wind power production during a period of minutes to hours, challenge utilities and electrical balancing authorities. A sudden decrease in wind energy production must be balanced by other power generators to meet energy demands, while a sharp increase in unexpected production results in excess power that may not be used in the power grid, leading to a loss of potential profits. In this study, we compare different methods to generate probabilistic ramp forecasts from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model with up to twelve hours of lead time at two tall-tower locations in the United States. We validate model performance using 21 months of 80-m wind speed observations from towers in Boulder, Colorado and near the Columbia River Gorge in eastern Oregon. We employ four statistical post-processing methods, three of which are not currently used in the literature for wind forecasting. These procedures correct biases in the model and generate short-term wind speed scenarios which are then converted to power scenarios. This probabilistic enhancement of HRRR point forecasts provides valuable uncertainty information of ramp events and improves the skill of predicting ramp events over the raw forecasts. We compute Brier skill scores for each method at predicting up- and down-ramps to determine which method provides the best prediction. We find that the Standard Schaake Shuffle method yields the highest skill at predicting ramp events for these data sets, especially for up-ramp events at the Oregon site. Increased skill for ramp prediction is limited at the Boulder, CO site using any of the multivariate methods, because of the poor initial forecasts in this area of complex terrain. These statistical methods can be implemented by wind farm operators to generate a range of possible wind speed and power scenarios to aid and optimize decisions before ramp events occur.

The Aerosol Modeling Testbed: A Community Tool to Objectively Evaluate Aerosol Process Modules

2011 ◽  
Vol 92 (3) ◽  
pp. 343-360 ◽  
Author(s):  
Jerome D. Fast ◽  
William I. Gustafson ◽  
Elaine G. Chapman ◽  
Richard C. Easter ◽  
Jeremy P. Rishel ◽  
...  
Keyword(s):  
Model Performance ◽  
Field Measurements ◽  
Weather Research And Forecasting ◽  
Limited Data ◽  
Test Bed ◽  
New Approach ◽  
Aerosol Process ◽  
Wide Range ◽  
Aerosol Modeling ◽  
Current Paradigm

Abstract The current paradigm of developing and testing new aerosol process modules is haphazard and slow. Aerosol modules are often tested for short simulation periods using limited data so that their overall performance over a wide range of meteorological conditions is not thoroughly evaluated. Although several model intercomparison studies quantify the differences among aerosol modules, the range of answers provides little insight on how to best improve aerosol predictions. Understanding the true impact of an aerosol process module is also complicated by the fact that other processes—such as emissions, meteorology, and chemistry—are often treated differently. To address this issue, the authors have developed an Aerosol Modeling Testbed (AMT) with the objective of providing a new approach to test and evaluate new aerosol process modules. The AMT consists of a more modular version of the Weather Research and Forecasting model (WRF) and a suite of tools to evaluate the performance of aerosol process modules via comparison with a wide range of field measurements. Their approach systematically targets specific aerosol process modules, whereas all the other processes are treated the same. The suite of evaluation tools will streamline the process of quantifying model performance and eliminate redundant work performed among various scientists working on the same problem. Both the performance and computational expense will be quantified over time. The use of a test bed to foster collaborations among the aerosol scientific community is an important aspect of the AMT; consequently, the longterm development and use of the AMT needs to be guided by users.

scholarly journals Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

2018 ◽  
Vol 3 (1) ◽  
pp. 371-393 ◽  
Author(s):  
Rochelle P. Worsnop ◽  
Michael Scheuerer ◽  
Thomas M. Hamill ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Weather Prediction ◽  
Model Performance ◽  
The United States ◽  
Numerical Weather Prediction Model ◽  
Post Processing ◽  
Skill Scores ◽  
Ramp Events

Abstract. Wind power forecasting is gaining international significance as more regions promote policies to increase the use of renewable energy. Wind ramps, large variations in wind power production during a period of minutes to hours, challenge utilities and electrical balancing authorities. A sudden decrease in wind-energy production must be balanced by other power generators to meet energy demands, while a sharp increase in unexpected production results in excess power that may not be used in the power grid, leading to a loss of potential profits. In this study, we compare different methods to generate probabilistic ramp forecasts from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model with up to 12 h of lead time at two tall-tower locations in the United States. We validate model performance using 21 months of 80 m wind speed observations from towers in Boulder, Colorado, and near the Columbia River gorge in eastern Oregon. We employ four statistical post-processing methods, three of which are not currently used in the literature for wind forecasting. These procedures correct biases in the model and generate short-term wind speed scenarios which are then converted to power scenarios. This probabilistic enhancement of HRRR point forecasts provides valuable uncertainty information of ramp events and improves the skill of predicting ramp events over the raw forecasts. We compute Brier skill scores for each method with regard to predicting up- and down-ramps to determine which method provides the best prediction. We find that the Standard Schaake shuffle method yields the highest skill at predicting ramp events for these datasets, especially for up-ramp events at the Oregon site. Increased skill for ramp prediction is limited at the Boulder, CO, site using any of the multivariate methods because of the poor initial forecasts in this area of complex terrain. These statistical methods can be implemented by wind farm operators to generate a range of possible wind speed and power scenarios to aid and optimize decisions before ramp events occur.

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