On the Assessment of a Numerical Weather Prediction Model for Solar Photovoltaic Power Forecasts in Cities

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
Harold Gamarro ◽  
Jorge E. Gonzalez ◽  
Luis E. Ortiz
Keyword(s):  
New York ◽  
Weather Prediction ◽  
Wrf Model ◽  
Urban Canopy ◽  
Region Of Interest ◽  
Integrated Approach ◽  
Numerical Weather Prediction Model ◽  
Recent Developments ◽  
Sky Conditions ◽  
The City

Recent developments in the weather research and forecasting (WRF) model have made it possible to accurately estimate incident solar radiation. This study couples the WRF-solar modifications with a multilayer urban canopy and building energy model (BEM) to create a unified WRF forecasting system called urban WRF–solar (uWRF-solar). This paper tests the integrated approach in the New York City (NYC) metro region as a sample case. Hourly forecasts are validated against ground station data collected at ten different sites in and around the city. Validation is carried out independently for clear, cloudy, and overcast sky conditions. Results indicate that the uWRF-solar model can forecast solar irradiance considerably well for the global horizontal irradiance (GHI) with an R2 value of 0.93 for clear sky conditions, 0.61 for cloudy sky conditions, and finally, 0.39 for overcast conditions. Results are further used to directly forecast solar power production in the region of interest, where evaluations of generation potential are done at the city scale. Outputs show a gradient of power generation produced by the potential available solar energy on the entire uWRF-solar grid. In total, the city has a city photovoltaic (PV) potential of 118 kWh/day/m2 and 3.65 MWh/month/m2.

Urban WRF-Solar Validation and Potential for Power Forecast in New York City

2018 ◽  
Author(s):  
Harold Gamarro ◽  
Jorge E. Gonzalez ◽  
Luis E. Ortiz
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Solar Power ◽  
Wrf Model ◽  
Urban Canopy ◽  
Solar Module ◽  
Ground Station ◽  
Recent Developments ◽  
Sky Conditions

Recent developments in the Weather Research and Forecasting (WRF) Model have made it possible to accurately approximate solar power through the implementation of WRF-Solar. This study couples the WRF-Solar module with a multilayer urban canopy and building energy model in New York City (NYC) to create a unified WRF forecasting model called uWRF-Solar. Hourly time resolution forecasts are validated against ground station data collected at eight different sites. The validation is carried out independently for two different sky conditions: clear and cloudy. Results indicate that the uWRF-Solar model can forecast solar irradiance considerably well for the global horizontal irradiance (GHI) with an R squared value of 0.93 for clear sky conditions and 0.76 for cloudy sky conditions. Results are further used to directly forecast solar power production in the NYC region, where a power evaluation is done at a city scale. The outputs show a gradient of power generation produced by the potential available solar energy on the entire uWRF-Solar grid. In total, for the month of July 2016, NYC had a city PV potential of 233 kW/day/m2 and 7.25 MWh/month/m2.

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 NAM-NMM Temperature Downscaling Using Personal Weather Stations to Study Urban Heat Hazards

GeoHazards ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 257-276
Author(s):  
Martina Calovi ◽  
Weiming Hu ◽  
Guido Cervone ◽  
Luca Delle Monache
Keyword(s):  
New York ◽  
Urban Areas ◽  
Fundamental Problem ◽  
Local Heating ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Ambient Conditions ◽  
Ensemble Technique ◽  
Forecast Lead Time ◽  
Weather Stations

Rising temperatures worldwide pose an existential threat to people, properties, and the environment. Urban areas are particularly vulnerable to temperature increases due to the heat island effect, which amplifies local heating. Throughout the world, several megacities experience summer temperatures that stress human survival. Generating very high-resolution temperature forecasts is a fundamental problem to mitigate the effects of urban warming. This paper uses the Analog Ensemble technique to downscale existing temperature forecast from a low resolution to a much higher resolution using private weather stations. A new downscaling approach, based on the reuse of the Analog Ensemble (AnEn) indices, resulted by the combination of days and Forecast Lead Time (FLT)s, is proposed. Specifically, temperature forecasts from the NAM-NMM Numerical Weather Prediction model at 12 km are downscaled using 83 Private Weather Stations data over Manhattan, New York City, New York. Forecasts for 84 h are generated, hourly for the first 36 h, and every three hours thereafter. The results are dense forecasts that capture the spatial variability of ambient conditions. The uncertainty associated with using non-vetted data is addressed.

scholarly journals A Novel Hybrid Model of WRF and Clearness Index-Based Kalman Filter for Day-Ahead Solar Radiation Forecasting

2019 ◽  
Vol 9 (19) ◽  
pp. 3967 ◽  
Author(s):  
Yuzhang Che ◽  
Lewei Chen ◽  
Jiafeng Zheng ◽  
Liang Yuan ◽  
Feng Xiao
Keyword(s):  
Kalman Filter ◽  
Solar Radiation ◽  
Hybrid Model ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Energy Utilization ◽  
Mean Bias Error ◽  
Bias Error ◽  
Clearness Index ◽  
Sky Conditions

Day-ahead forecasting of solar radiation is essential for grid balancing, real-time unit dispatching, scheduling and trading in the solar energy utilization system. In order to provide reliable forecasts of solar radiation, a novel hybrid model is proposed in this study. The hybrid model consists of two modules: a mesoscale numerical weather prediction model (WRF: Weather Research and Forecasting) and Kalman filter. However, the Kalman filter is less likely to predict sudden changes in the forecasting errors. To address this shortcoming, we develop a new framework to implement a Kalman filter based on the clearness index. The performance of this hybrid model is evaluated using a one-year dataset of solar radiation taken from a photovoltaic plant located at Maizuru, Japan and Qinghai, China, respectively. The numerical results reveal that the proposed hybrid model performs much better in comparison with the WRF-alone forecasts under different sky conditions. In particular, in the case of clear sky conditions, the hybrid model can improve the forecasting accuracy by 95.7% and 90.9% in mean bias error (MBE), and 42.2% and 26.8% in root mean square error (RMSE) for Maizuru and Qinghai sites, respectively.

scholarly journals A Methodology for Verifying Cloud Forecasts with VIIRS Imagery and Derived Cloud Products—A WRF Case Study

Atmosphere ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 521 ◽  
Author(s):  
Keith D. Hutchison ◽  
Barbara D. Iisager ◽  
Sudhakar Dipu ◽  
Xiaoyan Jiang ◽  
Johannes Quaas ◽  
...  
Keyword(s):  
Climate Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Region Of Interest ◽  
Weather Research And Forecasting ◽  
The North ◽  
Infrared Imager ◽  
Internal Procedures ◽  
Initial Results

A methodology is presented to evaluate the accuracy of cloud cover fraction (CCf) forecasts generated by numerical weather prediction (NWP) and climate models. It is demonstrated with a case study consisting of simulations from the Weather Research and Forecasting (WRF) model. In this study, since the WRF CCf forecasts were initialized with reanalysis fields from the North American Mesoscale (NAM) Forecast System, the characteristics of the NAM CCf products were also evaluated. The procedures relied extensively upon manually-generated, binary cloud masks created from VIIRS (Visible Infrared Imager Radiometry Suite) imagery, which were subsequently converted into CCf truth at the resolution of the NAM and WRF gridded data. The initial results from the case study revealed biases toward under-clouding in the NAM CCf analyses and biases toward over-clouding in the WRF CCf products. These biases were evident in images created from the gridded NWP products when compared to VIIRS imagery and CCf truth data. Thus, additional simulations were completed to help assess the internal procedures used in the WRF model to translate moisture forecast fields into layered CCf products. Two additional sets of WRF CCf 24 h forecasts were generated for the region of interest using WRF restart files. One restart file was updated with CCf truth data and another was not changed. Over-clouded areas in the updated WRF restart file that were reduced with an update of the CCf truth data became over-clouded again in the WRF 24 h forecast, and were nearly identical to those from the unchanged restart file. It was concluded that the conversion of WRF forecast fields into layers of CCf products deserves closer examination in a future study.

scholarly journals An algorithm for detecting the Chesapeake Bay breeze from mesoscale NWP model output

2021 ◽  
Keyword(s):  
Chesapeake Bay ◽  
Prediction Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Model Output ◽  
Detection Algorithms ◽  
Bay Breeze ◽  
Nwp Model

Abstract A novel algorithm is developed for detecting and classifying the Chesapeake Bay breeze and similar water-body breezes in output from mesoscale numerical weather prediction models. To assess the generality of the new model-based detection algorithm (MBDA), it is tested on simulations from the Weather Research and Forecasting (WRF) model and on analyses and forecasts from the High Resolution Rapid Refresh (HRRR) model. The MBDA outperforms three observation-based detection algorithms (OBDAs) when applied to the same model output. Additionally, by defining the onshore wind directions based on model land-use data, not on the actual geography of the region of interest, performance of the OBDAs with model output can be improved. Although simulations by the WRF model were used to develop the new MBDA, it performed best when applied to HRRR analyses. The generality of the MBDA is promising, and additional tuning of its parameters might improve it further.

scholarly journals An Integrated Approach to Mid- and Upper-Level Turbulence Forecasting

2006 ◽  
Vol 21 (3) ◽  
pp. 268-287 ◽  
Author(s):  
R. Sharman ◽  
C. Tebaldi ◽  
G. Wiener ◽  
J. Wolff
Keyword(s):  
Weather Prediction ◽  
Integrated Approach ◽  
Numerical Weather Prediction Model ◽  
Guidance System ◽  
Forecast Errors ◽  
Weighted Sum ◽  
Statistical Verification ◽  
Diagnostic Threshold ◽  
Overall Performance ◽  
Upper Level

Abstract An automated procedure for forecasting mid- and upper-level turbulence that affects aircraft is described. This procedure, termed the Graphical Turbulence Guidance system, uses output from numerical weather prediction model forecasts to derive many turbulence diagnostics that are combined as a weighted sum with the relative weights computed to give best agreement with the most recent available turbulence observations (i.e., pilot reports of turbulence or PIREPs). This procedure minimizes forecast errors due to uncertainties in individual turbulence diagnostics and their thresholds. Thorough statistical verification studies have been performed that focused on the probabilities of correct detections of yes and no PIREPs by the forecast algorithm. Using these statistics as a guide, the authors have been able to intercompare individual diagnostic performance, and test various diagnostic threshold and weighting strategies. The overall performance of the turbulence forecast and the effect of these strategies on performance are described.

Development and Validation of an Operational, Cloud-Assimilating Numerical Weather Prediction Model for Solar Irradiance Forecasting

2012 ◽  
Author(s):  
Patrick J. Mathiesen ◽  
Craig Collier ◽  
Jan P. Kleissl
Keyword(s):  
Numerical Weather Prediction ◽  
Solar Irradiance ◽  
San Diego ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wrf Model ◽  
Numerical Weather Prediction Model ◽  
Numerical Weather ◽  
The North ◽  
Marine Layer

For solar irradiance forecasting, the operational numerical weather prediction (NWP) models (e.g. the North American Model (NAM)) have excellent coverage and are easily accessible. However, their accuracy in predicting cloud cover and irradiance is largely limited by coarse resolutions (> 10 km) and generalized cloud-physics parameterizations. Furthermore, with hourly or longer temporal output, the operational NWP models are incapable of forecasting intra-hour irradiance variability. As irradiance ramp rates often exceed 80% of clear sky irradiance in just a few minutes, this deficiency greatly limits the applicability of the operational NWP models for solar forecasting. To address these shortcomings, a high-resolution, cloud-assimilating model was developed at the University of California, San Diego (UCSD) and Garrad-Hassan, America, Inc (GLGH). Based off of the Weather and Research Forecasting (WRF) model, an operational 1.3 km-gridded solar forecast is implemented for San Diego, CA that is optimized to simulate local meteorology (specifically, summertime marine layer fog and stratus conditions) and sufficiently resolved to predict intra-hour variability. To produce accurate cloud-field initializations, a direct cloud assimilation system (WRF-CLDDA) was also developed. Using satellite imagery and ground weather station reports, WRF-CLDDA statistically populates the initial conditions by directly modifying cloud hydrometeors (cloud water and water vapor content). When validated against the dense UCSD pyranometer network, WRF-CLDDA produced more accurate irradiance forecasts than the NAM and more frequently predicted marine layer fog and stratus cloud conditions.

scholarly journals Assessment of the GOES-16 Clear Sky Mask Product over the Contiguous USA Using CALIPSO Retrievals

2020 ◽  
Vol 12 (10) ◽  
pp. 1630
Author(s):  
Pedro A. Jiménez
Keyword(s):  
Weather Prediction ◽  
Wrf Model ◽  
Probability Of Detection ◽  
Clear Sky ◽  
The North ◽  
Attractive Option ◽  
Spatio Temporal ◽  
Sky Conditions ◽  
Cloud Mask ◽  
Horizontal Grid

Cloud initialization is a challenge in numerical weather prediction. Probably the most relevant observations for this task come from geostationary satellites. These satellites provide the cloud mask with high spatio-temporal resolution and low latencies. The low latency is an attractive option for nowcasting systems such as the solar irradiance nowcasting model MAD-WRF. In this study we examine the potential of using the cloud mask from the GOES-16 satellite over the contiguous U.S. for this particular application. With this aim, the GOES-16 cloud mask product is compared against CALIPSO retrievals during a two year period. Both the GOES-16 data and the CALIPSO retrievals are interpolated to a grid that covers the contiguous U.S. at 9 km of horizontal grid spacing that is being used in MAD-WRF nowcasts. Results indicate a probability of detection, or accuracy, of all sky conditions of 86.0%. However, the accuracy is higher for cloud detections, 90.9% than for clear sky detections 74.8%. The lower performance of clear sky retrievals is a result of missdetections during daytime. This is especially clear for summer, and for regions to the north of parallel 36 during winter. However, regions to the south of parallel 36 show acceptable performance during both daytime and nighttime. It is over these regions wherein the cloud mask product should show its largest potential to enhance the cloud initialization in the MAD-WRF model.

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