1. Introduction
Wind energy has experienced a drastic growth during the most recent decade, exceeding hydropower as the nation’s largest renewable energy source [U.S. Department of Energy (DOE); DOE 2018]. Accompanying such rapid development is a growing demand for more accurate wind forecasts for the wind energy sectors (Veers et al. 2019). However, uncertainties in weather forecasts continue to pose a challenge to the wind industry (Lundquist et al. 2019; Veers et al. 2019). This could be partially attributed to the fact that less emphasis has been devoted to improving the forecasts of wind at heights of 50–200 m above ground level (AGL), compared to the traditional high-impact weather events (Olson et al. 2019). Therefore, understanding and improving the numerical forecasts of rotor-layer (40–120 m AGL) wind will be critical for a sustainable growth of wind energy.
In 2015, the DOE and the National Oceanic and Atmospheric Administration (NOAA) launched the second Wind Forecast Improvement Project (WFIP2) with the specific goal of improving the representation of boundary layer physics and mesoscale processes in numerical models for better wind and wind power forecasts in complex terrain (Shaw et al. 2019; Wilczak et al. 2019; Olson et al. 2019; Bianco et al. 2019; Draxl et al. 2021; Xia et al. 2021; Djalalova et al. 2020; Pichugina et al. 2020). The Columbia Basin of eastern Oregon and Washington was selected as the targeted region and an extensive field campaign was conducted to collect observations in support of numerical weather prediction (NWP) model development. Through WFIP2, significant forecast improvements were achieved by improving the treatment of complex terrain, vertical mixing between the surface and the upper atmosphere, and the treatment of turbulent mixing in the horizontal as well as vertical (Olson et al. 2019). Despite these successes, there are additional known significant sources of rotor-layer forecast errors that have not yet been investigated, such as the errors arising from land surface model (LSM).
The LSMs are coupled to various schemes, such as the surface-layer, radiation, microphysics, and convective schemes, together with the land’s state variables and land surface properties, to provide heat and moisture fluxes over land and sea ice points. These fluxes provide a lower boundary condition for the vertical transport computed in the planetary boundary layer (PBL) schemes. Multiple modeling studies have indicated that land surface process plays a key role in regulating regional weather and climate (e.g., Sobel et al. 2008; Chen et al. 2014; Sun et al. 2017, 2020; Ma et al. 2017; Lee et al. 2019; Zhuo et al. 2019; Grachev et al. 2020; Zhang et al. 2020). However, not many studies focused on the rotor-layer wind, which is of significant importance for wind renewable energy. Wharton et al. (2015) examined the role of surface energy exchange for simulating wind turbine inflow. They found that there is a relationship between surface energy partitioning and near-surface wind shear and the relationship is stronger during the summer than during the autumn. However, their research focused on wind shear rather than wind speed and their model simulation was performed as a single continuous run without frequent reinitialization, which is not a common practice for wind energy forecasts.
By leveraging the multiscale dataset from the WFIP2, the goal of this paper is to evaluate and compare the skills of three LSMs applied in WRF: Noah, Noah with multiphysics (NoahMP) and Rapid Update Cycle (RUC), in simulating the hub-height wind speed (100 m AGL). The results will provide useful information about the relative performance of these LSMs for wind energy forecasts and suggest physical processes that may require further evaluations. In this study, three cases of distinctly different soil regimes are selected to conduct model simulations, which will further address the impact of the land surface on hub-height wind under different soil conditions.
The paper is organized as follows. Section 2 introduces the case selections, validation data, model configuration and land surface schemes tested. Section 3 discusses the model results and their evaluations with observations. Section 4 examines the factors responsible for the differences between model simulations and observation, followed by the conclusions in section 5.
2. Data and methodology
a. Case selection
Three cases with distinctively different soil regimes are selected for this study. The soil regime is first determined by Grachev et al. (2020) using the soil moisture (SM) and soil temperature (ST) measurements from the WFIP2 field campaign. The first case, which represents the dry soil condition, ranges from 13 to 19 August 2016. During this period, an upper level ridge persisted over the Cascades and eastern Columbia River Gorge and the atmosphere was relatively stable. The second case represents wet soil conditions from 4 to 9 November 2016. During this period, a Pacific storm was approaching the northwestern coast, causing falling pressure west of the Cascades. This created an offshore (~4 hPa) pressure gradient across the Cascades, which resulted in easterly gap flows. Cold pools were developed during the overnight hours due to radiational cooling. The third case represents the frozen soil condition, lasting from 16 to 21 February 2017. In this case, a low pressure dominated the flow and moved slowly eastward over the campaign region. Even though upper-level westerlies persisted, the near surface was weakly decoupled with the overlying atmosphere due to a persistent cold pool. All the weather patterns described here are documented in the WFIP2 event log [Atmosphere to Electrons (A2e); A2e 2015]. Interested readers can refer to that document for more details.
To further confirm that the surface conditions during these three periods are indeed representative of dry, wet and frozen soil conditions at our study region, we examine the time series of observed ST and SM measurements at the Physics Site 3 (PS03; Fig. 1). Note that the observed ST and SM are measured at the depth of 5 cm beneath the soil surface. For the dry soil case, the ST is around 30°C while the SM exhibits a constant line at 0.12 m3 m−3. During this period, the SM measurements have reached the low limitation value of the measurement technique whereas in reality, the actual SM content should be less than shown in Fig. 1. For the wet soil case, the ST is around 10°C while the SM is nearly constant at around 0.43 m3 m−3, indicating that the soil is very wet during this period. For the frozen soil case, the ST is about 0°C while the SM increases from 0.33 to 0.50 m3 m−3. This suggests that the top soil layer is thawing and the thawed water drained down the sandy silty soil where the sensor was situated, allowing the measured SM to elevate over the next six days.
Time series of observed soil temperature (°C) and soil moisture (m3 m−3) at the Physics Site 03 for the three case periods. The purple line indicates the dry soil case from 13 to 19 Aug 2016. The blue line indicates the wet soil case from 4 to 10 Nov 2016. The orange line indicates the frozen soil case from 16 to 22 Feb 2017.
Citation: Monthly Weather Review 149, 9; 10.1175/MWR-D-20-0363.1
Overall, the observations clearly confirm that our selected case days are representative of dry, wet and frozen soil conditions.
b. WFIP2 observations
1) Observation sites
At both sites, the soil type is sandy silt loam which can be very loose when dry. The vegetation type is the soft white winter wheat which is normally planted in mid-October. In August (dry soil case), there is no green vegetation covering the surface. In November, the crop would probably be up and green, but it would have been only about 5–10 cm tall and thus not cover much of the soil surface. The wheat is pretty much dormant throughout the winter and will be green again after fertilization by early March. Therefore, the measured LH flux is negligible during the dry soil case whereas during the wet and frozen soil cases virtually all LH was from evaporation from the soil rather than transpiration from the plants (Grachev et al. 2020).
2) Surface flux data
The Energy Balance Bowen Ratio (EBBR) system from PS03 produces 30-min estimates of the vertical fluxes of sensible and latent heat at the local surface (ARM 2015). Key instrumental components include Radiation and Energy Balance Systems (REBS) net radiometer, REBS soil moisture probes, REBS soil heat flow plates and REBS soil temperature probes. Surface energy fluxes are calculated from the collected observations, and are quality checked to remove suspicious spikes that are greater than 1000 W m−2 or smaller than −200 W m−2. The data are later compiled into 1-h averages in order to compare with the model output. The uncertainty of the measurement is on the order of 10% for the surface fluxes and that is determined based on differences in measurements using the same technique by different investigators at the same location (Twine et al. 2000; Jiang et al. 2004; Liu et al. 2013). Note that the observed GH flux is defined as the soil heat flow plate measurement at 5 cm under the ground, adjusted for the change in energy storage in the soil above the heat flow plate. More details about instrumentation, data quality and data uncertainty can be found from the user handbook (Cook and Sullivan 2018).
3) SoDAR data
The Vaisala Triton SoDAR wind profiler (Vaisala 2015) at PS01 measures wind speed, direction, and turbulence intensity at heights from 30 to 200 m above ground every 10 min. Two automated procedures have been applied to remove erroneous data due to precipitation and measurement error. Similar to the surface flux data, the observed wind speeds are also compiled into hourly averages. Because this study focuses on renewable wind energy, wind speeds at common hub-heights (100 m AGL) are used in this analysis.
c. Model simulation
1) Simulation design
The simulation design is similar as employed in Xia et al. (2021), and the following text is derived from there with minor modifications.
The Weather Research and Forecasting (WRF) Model version 4.1.2 is used to conduct the model simulations in this study (Skamarock and Klemp 2008; Powers et al. 2017). The boundary and initial conditions are derived from the National Centers for Environmental Prediction/North American Mesoscale Forecast System 12-km analysis (NAM; https://doi.org/10.5065/G4RC-1N91).
The simulations are performed with three nested domains centered on the WFIP2 campaign region (Fig. 2). The first domain consists of 95 × 80 grid points with a horizontal grid spacing of 9 km, the second domain consists of 88 × 85 grid points with a grid spacing of 3 km while the innermost domain consists of 91 × 88 grid points with a grid spacing of 1 km. Corresponding topography, soil characteristics and the MODIS-based land cover dataset are used to match domain grid spacing, respectively. Physical packages that are applied include Rapid Radiative Transfer Model for shortwave and longwave radiation (Iacono et al. 2008), Thompson aerosol-awareness microphysics scheme (Thompson et al. 2008; Thompson and Eidhammer 2014), Mellor–Yamada–Nakanishi–Niino (MYNN) Level 2.5 planetary boundary layer scheme and MYNN surface layer scheme (Mellor and Yamada 1982; Nakanishi and Niino 2009). Note that cumulus convection is treated explicitly for second and third domains while it is parameterized for the outermost domain using the Kain–Fritsch scheme (Kain and Fritsch 1990, 1993; Kain 2004). A total number of 52 vertical levels is employed with finer resolution at lower levels (16 within the lowest 200 m) and coarser resolution at higher levels. The lowest model level height is 5 m AGL. Note that the reference grid point for PS01 and PS03 is the same, represented by the black point in the center of the innermost domain.
Topography (m) over the WRF Model domain. The center black dot indicates the geographical location of the Physics Site 03 and Physics Site 01.
Citation: Monthly Weather Review 149, 9; 10.1175/MWR-D-20-0363.1
Three LSMs, the Noah, NoahMP, and RUC, are examined in this study. The differences between these LSMs are described in detail in the next section. For each LSM, simulations were conducted for all three case periods using the 3-day reinitialization method, in which the first day of each 3-day run is discarded as spinup and the next two days are retained for further analysis (Xia et al. 2017, 2019).
2) Land surface models
Several important features of these LSMs, including vegetation types, soil levels, snow layers and canopy separation, are compared in Table 1. The Noah LSM has four soil layers with a total soil depth of 2 m and a surface layer of vegetation and soil surface to consider biophysical and carbon cycling processes (Mahrt and Ek 1984; Mahrt and Pan 1984; Pan and Mahrt 1987; Ek and Mahrt 1991; Chen et al. 1996; Koren et al. 1999; Chen and Dudhia 2001a,b; Ek et al. 2003). This scheme relies on soil and vegetation processes to simulate SM and ST in the soil layer, water and snow stored on the canopy and surface flux exchange between the land and the atmosphere.
Comparison of the Noah, NoahMP, and RUC land surface schemes in WRF.
The NoahMP LSM is an improved version of Noah LSM in terms of better representation of biophysical and hydrological processes (Niu et al. 2011; Yang et al. 2011). The default options of each physical process parameterization subscheme were adopted in this experiment (https://www.jsg.utexas.edu/noah-mp/scheme-options/). Major improvements include but are not limited to 1) a vegetation canopy layer separated from the original surface layer; 2) a Topography based hydrological model (TOPMODEL) based runoff scheme (Niu et al. 2005) and a simple groundwater model (Niu et al. 2007) for soil hydrology; and 3) an introduction of a more permeable frozen soil (Cai 2015).
The RUC LSM was originally developed for NOAA weather prediction (Benjamin et al. 2004a,b) but has now been incorporated into the WRF and High-Resolution Rapid Refresh (HRRR; Smith et al. 2008) models. It has nine soil levels with a default soil depth of 3 m. The vegetation processes as well as surface flux calculation are treated similarly to Noah LSM following the concept developed by Pan and Mahrt (1987). The frozen soil processes are included to improve snow treatment and phase change in soil (Smirnova et al. 1997, 2000, 2016; Benjamin et al. 2016).
d. Quantifying the impact of LSMs on hub-height wind speed
In WRF, the simulated wind speed is determined by solving a prognostic set of nonlinear equations which includes multiple physical processes. Even though it is impossible to assess the contribution of a single process (e.g., LSM) on hub-height wind speed, we can still assess its potential influence on hub-height wind by establishing an idealized physical framework based on our current understanding of the process.
At nighttime, the land surface is mostly decoupled from the atmosphere. Therefore, only measurements from daytime hours are used in this analysis. In addition, the daytime hours are separated into three periods, morning (0600–0900 LT), noon (1000–1300 LT), and afternoon (1400–1700 LT), to further demonstrate the temporal variability of such connection.
3. Results
a. Comparing simulated hub-height wind speeds with observations
Figure 3 shows the time series of the observed and simulated hub-height wind speed from the three soil cases. Note that about 20% and 40% of the observations are missing for the wet and frozen soil cases, respectively, due to measurement errors and the WRF wind speed is vertically interpolated to the 100 m AGL in order to compare with the observations. The simulated wind speeds between the three LSMs are in good agreement with one another. However, there are certainly some discrepancies between the simulated wind speed and observations, especially for the wet and frozen soil cases. Figure 4 shows the Taylor diagram to further quantify how well the simulated wind speed matches with the observations for these three cases, in terms of their root-mean-square difference (RMSD), temporal correlation as well as variances (Taylor 2001). The simulated wind speed during the dry soil case has a much higher correlation with the observations than those from the wet and frozen soil cases. With respect to standard deviation, the closer the dot is to the reference line (red), the better the agreement with the observation in terms of wind speed variability. For the dry soil case, the three LSMs closely cluster around the reference line, indicating similar variability between the observed and simulated wind speed. For the wet and frozen soil cases, the simulated wind speed from the three LSMs exhibit larger variability than the observations. As for RMSD, the values from the dry soil case are significantly smaller than those from the wet and frozen soil cases, indicating the difference between the observed and simulated wind speed is smallest under the dry soil condition. Overall, the simulated hub-height wind speeds from all three LSMs are in much better agreement with observations during the dry soil case than the wet and frozen soil cases.
Time series of observed and simulated hub-height wind speed from (a) the dry soil case, (b) the wet soil case, and (c) the frozen soil. The black, blue, orange, and purple lines indicate the results from the observations, Noah, NoahMP, and RUC, respectively.
Citation: Monthly Weather Review 149, 9; 10.1175/MWR-D-20-0363.1
Taylor diagram comparing simulated hub-height wind speeds (blue for Noah, orange for NoahMP, and purple for RUC) with observations for (a) the dry soil case, (b) the wet soil case, and (c) the frozen soil case. The red curved line indicates the standard deviation from the observed hub-height wind speed. The closer to the reference line, the lesser difference in variances.
Citation: Monthly Weather Review 149, 9; 10.1175/MWR-D-20-0363.1
b. Comparing the simulated surface energy budget with the observations
The measured and simulated NR, SH, LH, and GH fluxes are shown in Figs. 5–7 for each soil case, respectively, to illustrate how well each LSM simulated the full surface energy budget. Table 2 shows both the observed and simulated mean midday (1100–1400 LT) Bowen ratio, which is the ratio between SH and LH flux, to further describe surface energy partitioning.
Measured (black) and simulated (blue for Noah, orange for NoahMP, and purple for RUC) (a) net radiation (NR), (b) sensible heat (SH), (c) latent heat (LH), and (d) ground heat (GH) fluxes during dry soil case (August 2016). Positive SH and LH fluxes indicate net energy transfer to the atmosphere. Positive GH fluxes indicate net energy transfer to the ground surface. Time is given in coordinated universal time (UTC).
Citation: Monthly Weather Review 149, 9; 10.1175/MWR-D-20-0363.1
As in Fig. 5, but for the wet soil case (November 2016).
Citation: Monthly Weather Review 149, 9; 10.1175/MWR-D-20-0363.1
As in Fig. 5, but for the frozen soil case (February 2017).
Citation: Monthly Weather Review 149, 9; 10.1175/MWR-D-20-0363.1
Observed and simulated mean midday (1100–1400 LT) Bowen ratio from each soil case.
For the dry soil case, the SH flux is the most dominant surface forcing from both the observations and model simulation (Fig. 5a). Since the magnitude of the SH flux is significantly larger than the LH flux, this results in a large Bowen ratio, indicating a very dry surface condition. The RUC has the largest Bowen ratio because it has the weakest LH flux (Fig. 5c). All the LSMs are able to reproduce the temporal variability of the observed SH fluxes. However, they tend to underestimate the peak daytime value with Noah having the most significant underestimation by about 50 W m−2. The simulated daytime LH fluxes are generally weak with near zero value from RUC and around 20 W m−2 from Noah and NoahMP, but the observations suggest high variability. This is because the EBBR system, by definition, forces the energy balance to equal to the net radiation. Therefore, in the case of dominant SH fluxes, the observed LH flux could very likely be overestimated and appear with the wrong sign. In reality, however, the site area is quite dry in August so the LH flux may actually be near zero, which is very similar to what the LSMs predicted. The simulated GH flux, on the other hand, differs significantly from the observations, in terms of both magnitude and pattern. All the LSMs tend to simulate stronger GH flux at daytime but weaker GH flux at nighttime, as compared with observations. In addition, the daytime peak of the simulated GH flux seems to occur too early compared with the observations.
For the wet soil case, the magnitude of the SH flux decreases during the daytime while the LH flux increases. As a result, both the observed and simulated Bowen ratios drop significantly, suggesting a semiarid surface condition. In this particular case, the Noah and NoahMP produce a larger midday Bowen ratio than the observation while the value from RUC is smaller. All the LSMs manage to reproduce the variability of the observed SH flux. However, only the RUC manages to capture the observed LH flux. Note that a precipitation event occurred on 6 November 2016 and the RUC is capable of reproducing the observed LH flux fairly well. This could be attributed to the fact that the soil layers and drivers of water flux exchanges are based on atmospheric temperature and humidity instead of physiologically driven controls (Wharton et al. 2015). The simulated precipitation from simulations using all three LSMs are very similar to each other in terms of both the spatial pattern and intensity. Figure S1 in the online supplemental material shows the observed and simulated hourly precipitation at Goldendale, which is located near the center of domain 3, and all three simulations manage to qualitatively capture the precipitation event. The problem associated with reproducing the observed GH flux is still evident for all three LSMs. Compared with the observations, the simulated GH fluxes are too large during daytime and too small during nighttime.
The frozen soil case exhibits the largest difference in terms of the total energy budget between the observations and model simulations (Fig. 7a). This discrepancy is mainly contributed by the LH and GH fluxes during the daytime. Compared with the observations, all the LSMs tend to overestimate these two fluxes. As a result, the simulated Bowen ratios are larger than the observed value. Note that even though the RUC predicts the most accurate LH fluxes during the wet soil case, it also overestimates the LH flux by far for the frozen soil case. The observed GH flux is essentially zero throughout the case period whereas the simulated values from all three LSMs exhibit large variability. Over frozen soil, the difference in GH flux between observations and model simulations is greater than that of the wet and dry soil, regardless of the LSM used. A more detailed discussion about this issue is provided in section 4.
c. Examining the impacts of LSMs on hub-height wind speed
Following section 2d, the impact of LSMs on hub-height wind speed is examined by illustrating the relationship between surface flux, hub-height wind speed and wind shear during the daytime. If the land surface plays an important role in determining the hub-height wind speed, we will expect a significant physical relationship associated with surface flux as well as wind speed and shear.
Figures 8 and 9 show the scatterplots of SH flux versus hub-height wind speed and wind shear, respectively, from both the observations and LSMs for the dry soil case. From the observations, there is a strong physical connection, suggesting that stronger SH fluxes correspond with weaker wind shear and lower wind speeds. This is consistent with our hypothesis that the land surface has a significant impact on hub-height level over the dry soil. During the morning hours, the SH flux is weak but wind shear and wind speed at the hub-height are strong. However, the strong wind shear and wind speed should rather be more associated with the decay of the nocturnal boundary layer. As time progresses toward noon, the SH flux drastically increases, reaching the maximum value of 450 W m−2 around solar noon time (Fig. 5b). During this period, the corresponding wind shear and wind speed are smallest because of vigorous near-surface turbulent mixing in the boundary layer. As the afternoon progresses, the surface fluxes decrease, indicating a weaker coupling between the land surface and the hub-height level. This is accompanied by a slight recovery of stronger wind shear and hub-height wind speed. The results from the various LSMs general depict a similar picture, except that the simulated wind shear shows no difference or slightly decreases in the afternoon as compared to noon. In addition, the variability of simulated wind shear during the daytime is smaller than that from the observations.
Scatterplots of hub-height wind speed and surface fluxes (LH + SH) from both the observations and model simulations for the dry soil case period (August 2016). The blue dot indicates the mean value of the morning hours from 0600 to 0900 LT, the orange dot indicates the mean value of the noon hours from 1000 to 1300 LT, and the purple dot indicates the mean value of the afternoon hours from 1400 to 1700 LT. The horizonal and vertical lines represent the standard deviation of surface flux and wind speed for those examined hours.
Citation: Monthly Weather Review 149, 9; 10.1175/MWR-D-20-0363.1
Scatterplots of the near-surface wind shear (40–120 m) and surface fluxes (LH and SH) from both the observations and model simulations for the dry soil case period (August 2016). The blue dot indicates the mean value of the morning hours from 0600 to 0900 LT, the orange dot indicates the mean value of the noon hours from 1000 to 1300 LT, and the purple dot indicates the mean value of the afternoon hours from 1400 to 1700 LT. The horizonal and vertical lines represent the standard deviation of surface flux and wind speed for those examined hours.
Citation: Monthly Weather Review 149, 9; 10.1175/MWR-D-20-0363.1
Figures 10 and 11 show the similar plots but for the wet soil case. In this case, the observations suggest that stronger SH flux corresponds with weaker hub-height wind but stronger wind shear. This differs from our original hypothesis that wind shear should get weaker as surface fluxes get stronger. Certainly, the impact of land surface on hub-height wind for the wet soil case is smaller than that from the dry soil case. Similar to the dry soil case, the weak surface flux corresponds with strong wind speed and wind shear during morning hours. However, that is more associated with the decay of the nocturnal boundary layer as previously mentioned. Between noon and afternoon, the Noah and RUC LSM manage to mostly reproduce the temporal variability of the observed wind shear and wind speed. However, changes in surface fluxes from the NoahMP do not seem to capture such impact.
As in Fig. 8, but for the wet soil case period (November 2016).
Citation: Monthly Weather Review 149, 9; 10.1175/MWR-D-20-0363.1
As in Fig. 9, but for the wet soil case period (November 2016).
Citation: Monthly Weather Review 149, 9; 10.1175/MWR-D-20-0363.1
For the wet soil case, the LH flux becomes a much more important surface forcing. To examine the impact from LH flux, Figs. S2 and S3 show similar plots as Figs. 10 and 11 but for LH flux. Evidently, most of the identified relationships are determined by the SH flux rather than LH flux. This is not a surprise because the SH flux is the buoyancy flux which mainly drives the turbulence mixing at the near surface. Even though the magnitude of the LH flux is comparable to the SH flux, it has a negligible impact on determining the magnitude and variability of hub-height wind speed and wind shear.
The weaker coupling between the surface and hub-height winds for the wet soil case can be attributed to two main factors. The first is the drastically reduced magnitude of the SH flux. Note that the magnitude of the SH flux from the wet soil case is only about one-third of that from the dry soil case. This significantly weakens the near-surface turbulence mixing, thus reducing the coupling between the land surface and hub-height level. The second factor is probably the influence of large-scale disturbance. There was a Pacific storm approaching the WFIP2 region during the case period, resulting in rainfall on 6 November 2016 (Fig. 6c; A2e 2015). Such disturbance will definitely have an influence on the distribution of hub-height level wind speed and wind shear.
Figures 12 and 13 show the results from the frozen soil case. Overall, the observations suggest very limited impacts of the land surface on hub-height wind speed and wind shear. The results from the morning hours are generally similar to those from the dry and wet soil cases. From noon to afternoon, the observed hub-height wind speed and wind shear increase but there is very little change in the SH flux. Note that the magnitude of the SH flux from the frozen soil case is also the weakest among the three cases. This suggests that the land surface is very likely decoupled from the overlying atmosphere during the frozen soil case period. The WFIP2 observational team has documented a cold pool event happening during this period which could contribute substantially to this decoupling.
As in Fig. 8, but for the frozen soil case period (February 2017).
Citation: Monthly Weather Review 149, 9; 10.1175/MWR-D-20-0363.1
As in Fig. 9, but for the frozen soil case period (February 2017).
Citation: Monthly Weather Review 149, 9; 10.1175/MWR-D-20-0363.1
The results from the model simulations indicate a different story. From noon to afternoon, the decrease in simulated SH flux is associated with an increase in the simulated hub-height wind speed but a decrease in wind shear whereas LH does not play a significant role (Figs. S4 and S5). Similar to the wet soil case, this indicates a weak coupling between the land surface and hub-height level wind. Overall, the observations indicate almost nonexistent impact of the land surface on hub-height wind speed and near-surface wind shear during the frozen soil period whereas the simulations suggest a weak impact. The differences between model and observation could be partially attributed to the difficulty in simulating cold pool in the current numerical model (Olson et al. 2019).
4. Discussion
The preceding evaluation indicates a better agreement of the modeled hub-height wind speed with observations for the dry soil case than the wet and frozen soil cases, and that is largely insensitive to the choice of LSM. Over the dry soil, there is a strong physical connection between the land surface and hub-height level due to near-surface turbulent mixing. However, the relationship is weaker over wet soil and almost nonexistent over frozen soil.
There are many possible reasons responsible for the discrepancy between the model simulations and observations. In this paper, two factors are discussed in detail because these are the two most obvious limiting factors that stood out from the analysis. The first is the insufficient model physics description of the surface energy flux, especially the GH flux and the second is the inaccuracy of soil states, such as ST and SM.
Regardless of whichever soil surface, all LSMs have difficulties in predicting the observed GH flux. For the dry and wet soil cases, the simulated GH flux is either too large during daytime or too small during nighttime, as compared to the observations. For the frozen soil case, the observed GH flux is almost zero throughout the entire period but none of the LSMs are able to capture that. Previous studies have also indicated the discrepancies between the observed and simulated GH flux (Smirnova et al. 1997; Wharton et al. 2015; Zhang et al. 2020). Unfortunately, most LSM studies focus on discussing the impacts of LH and SH fluxes, with very little emphasis on GH flux. However, atmospheric features that are relevant for wind energy are generally close to the ground. Therefore, getting the correct energy partitioning is essential. Even though the GH flux does not directly influence hub-height level wind speed, it can still indirectly influence wind speed predictions by altering ST and thus changing the near-surface turbulence mixing.
From an observational perspective, measuring the GH flux is not an easy task because of the large temperature gradients at the surface. To solve this issue, such as that from EBBR, measurements are taken at 5 cm beneath the soil layer using soil heat flow plates (adjusted with the soil heat conductivity, which is determined from the soil texture and water content) and then adding the energy storage in the soil above the soil heat flow plate (determined from the change in ST with time in the 0–5-cm strata of the soil). The simulated and observed GH fluxes show large discrepancies for all the three soil cases. Understanding the exact reasons for the causes of these differences is beyond the scope of this study. However, it is possibly related to the difference between the observed and simulated ST.
For the dry and wet soil cases, the simulated ST from the three LSMs manages to capture the general magnitude and temporal variability of the observed ST (Fig. 14). To best compare with the observations, the simulated ST from the top soil layer (10 cm beneath the ground) is used for Noah and NoahMP whereas the averaged ST over the top three soil layers (0–4 cm beneath the ground) is used for RUC. Notice that both the maximum and minimum simulated ST are generally smaller than the observed. This cold bias in surface temperature has also been documented in the literature (Chen et al. 2017; Johannsen et al. 2019). As for the frozen soil case, the simulated ST differs significantly from the observations. The observed ST is almost zero throughout the entire period. However, model simulations show large variations, especially for the RUC. The simulated ST from Noah and NoahMP match well with the observations for the first three days but still show large variations for the last three days. In addition, the observations suggest that the correlation between the GH flux and the ST is largest for the dry soil case, it is weaker for the wet soil case, and is weakest for the frozen soil case (Table 3). This indicates that the GH flux has a strong dependency on ST and this relationship gets weaker as temperature decreases. For Noah and NoahMP, the corresponding correlation is much smaller than that from the observations, possibly indicating that the observed connection between the observed GH and ST is not well reflected in the parameterization. As for the RUC, the correlation is very high regardless of the soil regime. Note that the high correlation during the frozen soil case can be misleading because it is caused by the anomalously high ST, which is absent from the observations. This result suggests that the simulated GH flux from RUC is strongly correlated with the ST, which is similar to the observations. However, the parameterization is not very sensitive to soil water content which could be a potential limiting factor.
Time series of observed and simulated soil temperature (°C) from (a) the dry soil case, (b) the wet soil case, and (c) the frozen soil case. The black, blue, orange, and purple lines indicate the results from the observations, Noah, NoahMP, and RUC, respectively.
Citation: Monthly Weather Review 149, 9; 10.1175/MWR-D-20-0363.1
Correlation coefficient between ground heat flux and soil temperature from both the observations and model simulation. The bold value indicates that the correlation is statistically significant at least at the 95% level.
Other than the challenge of simulating GH fluxes, the other limiting factor for predicting hub-height wind speed could be associated with the inaccurate soil states. The above discussion has demonstrated the difference between the simulated and observed ST while the following section will address the issue with SM. For the dry soil case, the simulated SM from all the three LSMs is smaller than the observations (Fig. 15a). Note that the observed SM is a constant line because it has reached the low limitation value of the measurement technique. It is likely that the actual SM is smaller than the observed values. Therefore, for this particular case, the simulated SM should be closer to reality than the observations. For the wet soil case, the simulated and observed SM shows large discrepancies as the observed SM is significantly greater than the simulations. For instance, the observed SM is around 0.42 m3 m−3, while the simulation is only about 0.2 m3 m−3. For the frozen soil case, no LSM is able to capture the variability of the observed SM and the simulated values are significantly smaller than the observations. Note that the difference in simulated SM between Noah and NoahMP is greater than that from the dry soil and wet soil cases. Figure S6 shows the time series of the simulated SM from Noah and NoahMP for the first set of 3-day simulation of the frozen soil case. The first day (left side of the black line) is treated as spin up while the next two days (right side of the black line) are used for analysis. Evidently, the simulated SM from NoahMP drops significantly during the first day while that from Noah stays constant. The difference might be associated with the treatment of run off and frozen soil physics between these two LSMs. Since the soil moisture content is associated with soil heat conductivity, the deficiency in accurately predicting the SM will certainly impact the calculation of GH flux as well.
As in Fig. 14, but for soil moisture (m3 m−3).
Citation: Monthly Weather Review 149, 9; 10.1175/MWR-D-20-0363.1
Overall, the simulated SM and ST during the dry soil case match best with the observations. During the wet soil case, the simulated SM differs greatly from the observations whereas both the simulated ST and SM show large discrepancies from the observations during the frozen soil case. This certainly points out the importance of accurate soil conditions in predicting hub-height wind speed because the analysis has also indicated that the simulated hub-height wind speeds match best with the observations during the dry soil case.
As this analysis is mostly relying on observations from a single physics site, the spatial representativeness of the measurements, especially in the case of ST and SM, can be questionable (Bell et al. 2013; Diamond et al. 2013). To address this issue, ST and SM measurements from PS01 are obtained from NOAA and compared with those from PS03. Overall, the ST measurements between these two sites are similar. However, there is a systematic bias of about 0.3 m3 m−3 in the SM measurements for the wet and frozen soil periods. Note that the instruments used to measure SM are different at these two sites. Even though this does introduce uncertainties into our results, it does not affect the main conclusion as the measured SM at PS01 is also vastly different from the model simulations for the wet and frozen soil periods.
5. Conclusions
To understand the impact of LSMs on short-term wind forecasting, this study evaluates and compares the performance of three LSMs (Noah, NoahMP and RUC) coupled with WRF in simulating hub-height wind speed under three distinctly different soil conditions (dry, wet and frozen). The simulated hub-height wind speed, surface energy budget and soil properties are compared with the observations collected from the WFIP2 field campaign to examine the LSM’s capability in simulating these variables and providing potential guidance for improvements of these LSMs. The primary findings are summarized as follows:
For the selected three case periods, the impact of LSMs on hub-height wind speed are sensitive to the soil states but not so much to the choice of LSM.
The simulated hub-height wind speed is in much better agreement with the observations for the dry soil case than the wet and frozen soil cases.
Over the dry soil, there is a strong physical connection between the land surface and hub-height wind speed through near-surface turbulent mixing. Over the wet soil, the simulated hub-height wind speed is less impacted by land surface because of weaker surface fluxes and large-scale synoptic disturbance. Over the frozen soil, the LSM seems to have limited impacts on hub-height wind speed because of decoupling of the land surface from the hub-height level.
Two main sources of uncertainties are identified to explain the differences between the observations and model simulations. The first is the insufficient model physics representing the surface energy budget, especially the ground heat flux, and the second is the inaccuracy of soil states, such as ST and SM.
Acknowledgments
The authors thank the WFIP2-experiment participants who aided in the deployment and the collection of remote sensing data and our colleagues who monitored, quality controlled, and provided data to the DAP (https://a2e.energy.gov/about/dap). The research was performed using computational resources sponsored by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory. This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract DE-AC36-08GO28308. Funding was provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office. PNNL is operated by DOE by the Battelle Memorial Institute under Contract DE-A06-76RLO 1830. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. government. A portion of the research was performed using computational resources sponsored by the Department of Energy’s Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory.
Data availability statement
The NAM reanalysis data used in this study are publicly available from the NCAR/UCAR Research Data Archive at https://rda.ucar.edu/datasets/ds609.0/. Due to privacy and ethical concerns, observational data used in this study cannot be made publicly available. Further information about the data is available from the Data Archive and Portal (DAP; https://a2e.energy.gov/about/dap).
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