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Model Evaluation by Measurements from Collocated Remote Sensors in Complex Terrain

Journal Article · · Weather and Forecasting
 [1];  [1];  [2];  [1];  [2];  [2];  [2];  [1];  [3];  [4];  [5]
  1. University of Colorado, Boulder, CO (United States); National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States)
  2. National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States)
  3. National Renewable Energy Laboratory (NREL), Golden, CO (United States); University of Colorado, Boulder, CO (United States)
  4. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
  5. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
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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.

Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office; National Oceanic and Atmospheric Administration (NOAA)
Grant/Contract Number:
AC05-76RL01830; AC36-08GO28308; AC52-07NA27344
OSTI ID:
1895367
Alternate ID(s):
OSTI ID: 1899960
OSTI ID: 2281473
Report Number(s):
PNNL-SA--175552; NREL/JA--5000-83549; LLNL--JRNL-858942
Journal Information:
Weather and Forecasting, Journal Name: Weather and Forecasting Journal Issue: 10 Vol. 37; ISSN 0882-8156
Publisher:
American Meteorological SocietyCopyright Statement
Country of Publication:
United States
Language:
English

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Figures / Tables (20)

Figure 1(p. 5)figure Figure 1
Table 1(p. 6)table Table 1
Table 2(p. 10)table Table 2
Table 3(p. 10)table Table 3
Table 4(p. 12)table Table 4
Figure 2(p. 13)figure Figure 2
Table 5(p. 14)table Table 5
Figure 3(p. 15)figure Figure 3
Figure 4(p. 17)figure Figure 4
Figure 5(p. 19)figure Figure 5
Figure 6(p. 20)figure Figure 6
Figure 7(p. 21)figure Figure 7
Figure 8(p. 22)figure Figure 8
Table 6(p. 23)table Table 6
Figure 9(p. 24)figure Figure 9
Figure 10(p. 25)figure Figure 10
Fig. 11(p. 27)figure Fig. 11
Fig. 12(p. 28)figure Fig. 12
Fig. 13(p. 29)figure Fig. 13
Figure 14(p. 31)figure Figure 14

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