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At least 73 records · Page 4

Model Evaluation by Measurements from Collocated Remote Sensors in Complex Terrain

Model improvement efforts involve an evaluation of changes in model skill in response to changes in model physics and parameterization. When using wind measurements from various remote sensors to determine model forecast accuracy, it is important to understand the effects of measurement-uncertainty differences among the sensors resulting from differences in the methods of measurement, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. Here we quantify instrument measurement variability in 80-m wind speed during WFIP2 and its impact on the calculated errors and the change in error from one model version to another. Here the model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m. Model errors were found to be 2–3 m s –1 . Differences in errors as determined by various instruments at each site amounted to about 10% of this value, or 0.2–0.3 m s –1 . Changes in model skill due to physics or grid-resolution updates also differed depending on the instrument used to determine the errors; most of the instrument-to-instrument differences were ~0.1 m s –1 , but some reached 0.3 m s –1 . All instruments at a given site mostly showed consistency in the sign of the change in error. In two examples, though, the sign changed, illustrating a consequence of differences in measurements: errors determined using one instrument may show improvement in model skill, whereas errors determined using another instrument may indicate degradation. This possibility underscores the importance of having accurate measurements to determine the model error.

54 ENVIRONMENTAL SCIENCES↗

Truly horizontal finite difference scheme in the WRF model FY18 Q1 Update

A main goal of the Wind Forecasting Improvement Project 2 (WFIP2) is to improve performance of the Weather Research and Forecasting (WRF) model over complex terrain. Two methods for improving model performance were outlined in the LLNL WFIP 2 annual operating plan. The first is modification to WRF’s horizontal finite difference scheme to utilize an improved numerical stencil. A second proposed model improvement is use of the immersed boundary method within the WRF model to facilitate improved simulations over complex terrain. In Q1, work focused on implementing an alternative finite difference scheme, with a deliverable of a project update. The alternative finite difference method was implemented for the temperature variable and shown to improve our selected test case, and recommendations are made to continue the work in Q2. In Q2 the focus should be on extending the method to additional model variables, mainly pressure, as well as to real test cases, such as the WFIP 2 cold pool test case. Additionally, use of the immersed boundary method will be pursed in Q2 and Q3, with a second project update due at the end of Q3.

17 WIND ENERGY↗

Evaluating the 3D PBL scheme for wind energy applications in the complex terrain of Altamont Pass, CA

In this study, the 3D PBL scheme, developed and tested during previous WETO projects, was used to simulate daily speedup flows in the Altamont Pass Wind Resource Area of California. These flows were observed as part of the WFIP2 companion experiment HilFlowS, which examined potential power output from taller turbines in the Altamont region. The regularity of the observed speedup events during the summer months, combined with the importance of terrain-induced wind acceleration, makes them a useful case study for evaluating mesoscale forecast models, which often underperform in regions of complex terrain. Compared to a traditional one-dimensional PBL scheme, the 3D PBL scheme shows evidence of improved wind speed predictions, including an ability to capture atypical wind speed profiles and reduce model overestimates of wind speed across the turbine rotor region. Such improvements will be necessary for wind farm siting and power forecasting, especially as taller turbines are deployed in complex terrain.

17 WIND ENERGY↗

Sensitivity of Near-Surface Variables in the RUC Land Surface Model in the Weather Research and Forecasting Model

In this study, we investigate the parametric sensitivity of near-surface variables, such as sensible heat flux, latent heat flux, ground heat flux, hub-height wind speed and land surface temperature, to the parameters used in the Rapid Update Cycle (RUC) land surface model (LSM) during a wintertime and summertime period. The model simulations are compared with observations collected from the second Wind Forecast Improvement Project (WFIP2) field campaign. The results suggest that parameters related to snow/ice and thermal processes can have significant impact on the simulated near-surface variables. Out of the 11 examined parameters, only 6 of them have considerable influences on the model behaviors and explain about 60 ~ 80 % of the estimated total variance of the simulated variables. In addition, the magnitude of the parametric sensitivity varies with season. For instance, parameters associated with snow/ice processes are dominant during the wintertime whereas those associated with thermal processes are more important during the summertime. Furthermore, the impact of the identified parameters on the simulated variables is highly related to the topography. There is a high degree of sensitivity to the parameter values over the slope region. This points out the importance of collecting field observations over steep areas to better quantity the appropriate values of key parameters. Overall, our findings provide a better understanding of the RUC LSM behavior associated with parameter uncertainties and can be used to improve the forecasting skill of land surface processes via calibration of the most uncertain model parameters.

17 WIND ENERGY↗

Sodar - NREL Scintex SFAS Wind Profiler, Condon - Raw Data

The dataset includes 15-minute average wind speed and direction records from 10 m to 250 m above ground level (AGL) in 5-m range gates. Data were collected by a Scintec SFAS wind profiler installed at the Condon State Airport in Oregon, about 1.8 km northeast of the center of Condon, Ore., and are intended for validating WFIP2 model improvements.

17 WIND ENERGY↗

Sodar - NREL Scintec MFAS Wind Profiler, Decker Ranch Airstrip - Raw Data

The dataset includes 15-minute average wind speed and direction records from 30 m to 330 m above ground level (AGL) in 10-m range gates. Data were collected by a Scintec MFAS wind profiler installed at the Decker Ranch in Oregon, about 4.4 km southeast of Kent, Ore., and are intended for validating WFIP2 model improvements.

17 WIND ENERGY↗

Lidar - ND Halo Scanning Doppler, Boardman - Raw Data

The University of Notre Dame (ND) scanning lidar dataset used for the WFIP2 Campaign is provided. The raw dataset contains the radial velocity and backscatter measurements along with the beam location and other lidar parameters in the header.

17 WIND ENERGY↗

Sodar - Vaisala Triton Wind Profiler, AON8 - Raw Data

This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area.

17 WIND ENERGY↗

Sodar - Vaisala Triton Wind Profiler, AON7 - Raw Data

This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area.

17 WIND ENERGY↗

Sodar - Vaisala Triton Wind Profiler, AON5 - Raw Data

This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area.

17 WIND ENERGY↗

Sodar - Vaisala Triton Wind Profiler, AON4 - Raw Data

This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area.

17 WIND ENERGY↗

Sodar - Vaisala Triton Wind Profiler, AON2 - Raw Data

This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area.

17 WIND ENERGY↗

Sodar - Vaisala Triton Wind Profiler, AON6 - Raw Data

This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area.

17 WIND ENERGY↗

Sodar - Vaisala Triton Wind Profiler, AON3 - Raw Data

This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area.

17 WIND ENERGY↗

Sodar - Vaisala Triton Wind Profiler, AON1 - Raw Data

This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area.

17 WIND ENERGY↗

Sodar - Vaisala Triton Wind Profiler, AON9 - Raw Data

This dataset contains measurements from eight different Vaisala Triton Wind Profiler instruments. The Triton Wind Profiler is a sodar wind profiler that measures wind speed, direction, and turbulence intensity at heights from 30 m to 200 m above ground every 10 minutes. The eight Tritons are located at various sites around the WFIP2 study area.

17 WIND ENERGY↗