Abstract

Forecast errors with respect to wind, temperature, moisture, clouds, and precipitation largely correspond to the limited capability of current Earth system models to capture and simulate land–atmosphere feedback. To facilitate its realistic simulation in next-generation models, an improved process understanding of the related complex interactions is essential. To this end, accurate 3D observations of key variables in the land–atmosphere (L–A) system with high vertical and temporal resolution from the surface to the free troposphere are indispensable.

Recently, we developed a synergy of innovative ground-based, scanning active remote sensing systems for 2D to 3D measurements of wind, temperature, and water vapor from the surface to the lower troposphere that is able to provide comprehensive datasets for characterizing L–A feedback independently of any model input. Several new applications are introduced, such as the mapping of surface momentum, sensible heat, and latent heat fluxes in heterogeneous terrain; the testing of Monin–Obukhov similarity theory and turbulence parameterizations; the direct measurement of entrainment fluxes; and the development of new flux-gradient relationships. An experimental design taking advantage of the sensors’ synergy and advanced capabilities was realized for the first time during the Land Atmosphere Feedback Experiment (LAFE), conducted at the Atmospheric Radiation Measurement Program Southern Great Plains site in August 2017. The scientific goals and the strategy of achieving them with the LAFE dataset are introduced. We envision the initiation of innovative L–A feedback studies in different climate regions to improve weather forecast, climate, and Earth system models worldwide.

A novel synergy of scanning lidar systems as well as other in situ and remote sensing instruments provides accurate 3D measurements of numerous dynamical and thermodynamical quantities to evaluate and improve our understanding of land–atmosphere interactions.

The L–A system includes the soil, the land cover such as vegetation, and the overlying atmo-sphere (see the  appendix for a list of key acronyms used in this paper). The interaction of variables (e.g., related to the water and energy budgets) results in characteristic natural variabilities and regimes as well as their changes due to anthropogenic influences. The PBL is part of the L–A system and represents the interface between the land surface and the free troposphere. Through an exchange of momentum, energy, and water, the dynamics, the thermodynamic structure, and the evolution of the PBL affect the formation of shallow and deep clouds, convection initiation, and thus precipitation (Sherwood et al. 2010; Behrendt et al. 2011; Santanello et al. 2011; Van den Hurk et al. 2011; Wulfmeyer et al. 2015a). One of the most complex feedback loops is between soil moisture and precipitation (Seneviratne et al. 2010; Guillod et al. 2015; Santanello et al. 2018). Precipitation can be influenced directly by the surface fluxes (Ek and Holtslag 2004) and indirectly via PBL dynamics and mesoscale circulations (Taylor et al. 2012).

The PBL state and its evolution are strongly influenced by nonlinear feedbacks in the L–A system. These are due to two-way interactions between radiation, soil, vegetation, and atmospheric variables, which result in the diurnal cycles of surface fluxes. The feedbacks are relevant from local to global scales (Mahmood et al. 2014; Stéfanon et al. 2014), and their strength varies both regionally and seasonally in dependence of soil moisture, advection, and climate regimes. In locations where these feedbacks play an important role, it is likely that they will become even more important due to anthropogenic climate change (Dirmeyer et al. 2012). Thus, to improve our understanding of the state and the evolution of the L–A system as well as the dynamics and thermodynamics of the PBL, it is critical that feedbacks and fluxes between the different components, including entrainment at the top of the PBL, are well characterized and appropriately represented in weather, climate, and Earth system models (e.g., Seneviratne et al. 2010; Prein et al. 2015).

However, currently the representation of L–A feedbacks in models is far from sufficient. Errors are clearly visible in simulated diurnal cycles of soil, surface, and PBL variables in forecast models (e.g., Shin and Hong 2011; Liu et al. 2011; Santanello et al. 2013; Milovac et al. 2016; Massey et al. 2016; Dirmeyer and Halder 2017) as well as in the propagation of these errors to the simulation of clouds and precipitation (e.g., Gentine et al. 2013; Tawfik et al. 2015). Climate models must be able to reproduce the range of L–A feedbacks including their effect on extreme events (Lenderink and Van Meijgaard 2008; Zolina et al. 2013) in order to project these into the future. This level of model performance has not yet been achieved (Warrach-Sagi et al. 2013; Vautard et al. 2013; Kotlarski et al. 2014; Lorenz et al. 2016).

Model horizontal grid increments continue to improve and approach the so-called gray zone of turbulence with resolutions between ∼100 m and ∼2 km (e.g., Honnert et al. 2011) where turbulence is neither unidirectional nor isotropic and thus not fully resolved (Wyngaard 2004; Honnert et al. 2016). It is currently unclear how to represent and parameterize turbulent variables and fluxes at these scales. Generally, the existing parameterization schemes were developed for coarser numerical models over essentially flat terrain. Therefore, the turbulence parameterization schemes need to be readjusted or even new scalable ones formulated in order to bridge the existing gap between mesoscale and LES scales (e.g., Baklanov et al. 2011; Honnert et al. 2011, 2016). Adaptations to the gray zones have been made by various groups (e.g., Boutle et al. 2014; Ito et al. 2015; Shin and Hong 2015; Honnert et al. 2016), but such approaches are strongly related to the selected PBL and surface-layer schemes. Therefore, over the entire range of model scales from tens of meters to tens of kilometers, it is necessary to study the representation of L–A interactions. Otherwise, improvements in the representation of clouds and precipitation can hardly be expected.

A key component in L–A feedbacks and their simulation is the turbulent mixing of momentum, heat, and moisture between the land surface to the top of the CBL. In the atmospheric surface layer, turbulent mixing is approximated by MOST. However, MOST assumes that the surface is homogeneous and that the fluxes are stationary, and then relates the fluxes only to surface-layer properties, which in turn depend on radiation, the soil and vegetation properties, and the properties of the air that interacts with the surface (e.g., Van de Boer et al. 2014). It is currently not understood how to modify MOST in terrain with heterogeneous soil and land-cover properties, how to consider entrainment from the free troposphere down to the surface layer, or how to parameterize these effects and their dependence on model resolution.

Since turbulent mixing in the PBL is active over a range of scales that is typically much smaller than the resolution of gray-zone models, various approaches are used to parameterize this mixing from the surface to the free troposphere. Surface fluxes represent the lower boundary condition for these PBL schemes and interaction with them. Turbulence is parameterized using basically two approaches. The “local approach” allows for interaction of the turbulence between adjacent atmospheric levels only. In the “nonlocal approach,” the interactions between a single level and multiple different levels are considered. The basis for both approaches is the estimation of the turbulent flux profiles by a function that is usually proportional to the vertical gradient of the transported variable. The coefficient of proportionality is the turbulent exchange coefficient, which is a function of the length scale, the velocity scale, and stability functions. Formulations of these functions depend on the approach and closure technique deployed (e.g., Mellor and Yamada 1982; Cuijpers and Holtslag 1998; Weng and Taylor 2003; Grisogono 2010). A relatively new method that integrates both the local and nonlocal approaches has been proposed by Siebesma and Teixera (2000). Here, an eddy-diffusivity model for the local mixing is combined with an advective mass-flux approach (Siebesma et al. 2007).

The performance of turbulence parameterizations was studied by various research teams (e.g., Banks et al. 2016; Tastula et al. 2016; Han et al. 2016; Sakradzija et al. 2016), but a general understanding has still not been achieved. Sensitivity studies indicate that the nonlocal approach is more suitable for the CBL and turbulence driven primarily by buoyancy (e.g., Pleim 2007). However, the vertical mixing is often too large resulting in an overestimation of the PBL depth (e.g., Coniglio et al. 2013; Cohen et al. 2015; Milovac et al. 2016). The major challenge of the local approach is the treatment of large eddies that redistribute significant amounts of energy over the entire PBL. Underestimation of this mixing process in local schemes can result in PBLs that are too shallow, too moist, and too cold, especially in the case of dry convection (e.g., Holtslag and Boville 1993; Cuijpers and Holtslag 1998; Teixeira and Cheinet 2004). Therefore, the local approach seems to be more suitable for shear turbulence in a weakly stable PBL (e.g., Mellor and Yamada 1982).

Milovac et al. (2016) showed a strong dependence of PBL structure and fluxes on the surface flux parameterization. Different PBL turbulence parameterizations and changes in the land surface model led to similar differences in water vapor mixing ratio of ∼1–2 g kg–1 in the mixed layer. This variability is a matter of concern since it can clearly make the difference whether convection initiation occurs in the model or not (Crook 1996; Ducrocq et al. 2002). Therefore, in order to disentangle the role of surface and PBL fluxes and L–A feedback, a new, comprehensive observing strategy is necessary that provides a joint dataset of the soil–vegetation–atmosphere system.

These data must include the measurement of the mean vertical profiles of wind, temperature, and moisture; their gradients; and their turbulence fluctuations from the surface to the free troposphere. A dataset containing these observations would enable direct comparisons of simulated and measured flux profiles and their dependence on scaling variables; it would also facilitate the development of new similarity relationships for flux parameterizations (Wulfmeyer et al. 2016).

In the last decade, significant advances in ground-based remote sensing of the PBL have been realized, primarily using lidar systems [see Wulfmeyer et al. (2015a) for an overview]. These observations enable a novel class of field experiments that capitalize on the synergy of these remote sensing methods to measure the quantities needed to evaluate and improve land surface and turbulence parameterization schemes. In this work, we present the development and application of this sensor synergy in the LAFE. To the best of our knowledge, LAFE is the first field campaign dedicated to a comprehensive observation of L–A interaction and feedback. The scientific goals are introduced, and we discuss how these can be reached with the LAFE dataset. This is just a first step; ultimately, this type of field experiments needs to be conducted in different climate regimes in order to get a global insight in L–A feedback for the benefit of weather forecast, climate, and Earth system models.

PREVIOUS LAND–ATMOSPHERE FEEDBACK STUDIES.

Model studies.

The strong coupling of L–A processes and variables as well as the resulting challenges of corresponding measurements are depicted in the sidebar on “Land–atmosphere feedback” using the surface latent heat flux as an example. The latent heat flux depends not only on soil temperature and moisture, vegetation properties, and temperature and moisture profiles in the atmospheric surface layer, but also on the divergence of the latent heat flux profile in the PBL. This complex web of interactions poses great challenges in the understanding, modeling, and measurement of L–A feedback. From the “Land–atmosphere feedback” sidebar, it is evident that L–A feedback is the result of the two-way interaction of soil–vegetation–atmospheric variables, which can be used to distinguish between the terms “interaction” and “feedback.” The sidebar also demonstrates that a full observational characterization of L–A feedback requires the measurement of mean profiles of soil, land cover, and atmospheric variables; their vertical gradients; and the surface and entrainment fluxes.

LAND–ATMOSPHERE FEEDBACK ILLUSTRATED USING THE WATER-VAPOR FLUX PROFILE

The interaction of the land surface and the atmosphere is demonstrated here using typical states of the L–A system in an afternoon where a convective, entrainment-drying planetary boundary layer interacts with a negative slope of the virtual potential temperature θυ in the surface layer. The drying is due to a water-vapor flux profile 〈wq′〉(z), which has a positive slope (i.e., increases with height).

For the atmosphere (Fig. SB1), the evolution of the specific humidity q is described by the following prognostic equation:

 
formula
Fig. SB1.

Structure of the CBL.

Fig. SB1.

Structure of the CBL.

For the land–atmosphere interface (Fig. SB2), we have

 
formula
Fig. SB2.

Bare soil and canopy interface.

Fig. SB2.

Bare soil and canopy interface.

The evolution of the fluxes at the L–A interface is described by the SEB and the relation of the latent heat flux with soil, vegetation, and soil properties. This can be approximated by a flux–gradient relationship, where K is the turbulent exchange coefficient, Cwv is the bulk exchange coefficient, r is the canopy and aerodynamic resistance, is the soil and canopy saturation specifc humidity evolution, and Ts,υ is the temperature of the bare soil (s) or vegetation (υ).

For the soil layer (Fig. SB3), the temperature and moisture potential profiles are given by the heat conductivity equation and Darcy’s law (not shown). The resulting profiles are coupled with the equation above for the surface latent heat flux.

Fig. SB3.

Soil temperature and moisture profiles.

Fig. SB3.

Soil temperature and moisture profiles.

Various research strategies have been proposed to characterize L–A interactions. Many of these have been developed by the GEWEX LoCo initiative (Santanello et al. 2018). For example, Ek and Holtslag (2004) used a coupled 1D land surface–PBL model to investigate the role of soil moisture on cloud development in terms of a relative humidity tendency equation at the top of the PBL. This study demonstrated that understanding the L–A feedback requires measurements of entrainment fluxes. Findell and Eltahir (2003a) developed a framework based on a 1D coupled model to classify L–A coupling into regimes by using measures of convective triggering potential and humidity. To address model deficiencies, Santanello et al. (2009, 2011, 2013) developed and studied L–A coupling strength over the southern Great Plains in the United States, which highlighted the importance of entrainment quantification. Koster et al. (2006) and Seneviratne et al. (2006) used GLACE data from 12 forecast and regional climate models to define dry, transitional, and wet climate/soil moisture regimes based on the impact of soil moisture on the evapotranspiration variability. Using the same data, Guo et al. (2006) showed that most of the differences between the models and within a given model are associated with the surface water evaporation rate, which varies strongly and consistently with soil moisture trends and affects the L–A coupling strength. Dirmeyer et al. (2012) approached these feedbacks in terms of terrestrial versus atmospheric legs of LA coupling. Hohenegger et al. (2009) and Knist et al. (2016) showed that L–A coupling is sensitive not only to the model physics but also to the model grid increment.

Observational studies and field campaigns.

Models are only as good as the data that were used for their development and verification. Currently, our understanding of L–A feedback processes is strongly impeded by the lack of suitable observations. Therefore, it is important to develop a comprehensive observation strategy for L–A feedback studies, which must involve the simultaneous measurements of the variables of all components and the transport processes between them.

Observational data have already been used to study the fidelity of L–A feedback within numerical models. As for the modeling studies, many of these statistically based approaches are summarized by Santanello et al. (2018) and the LoCo-based research efforts. Findell and Eltahir (2003b) investigated feedback between soil moisture and moist convection over the continental United States using radiosonde measurements. Ferguson and Wood (2011) used a 7-yr satellite remote sensing data record and available radiosonde measurements to produce global maps of L–A coupling signals. Roundy et al. (2013) investigated L–A feedbacks on diurnal time scales using satellite remote sensing data. Guillod et al. (2015) analyzed spatial and temporal soil moisture effects on afternoon rainfall by combining available satellite data for precipitation, soil moisture, total evaporative stress, and radiation with data gaps filled with GPCP and ERA-Interim data. They found that positive feedbacks between soil moisture and precipitation dominate in time, but rain more often occurs over drier soils. These studies only investigated relatively small portions of the L–A feedback process chain, primarily owing to the inability of the observations used to fully characterize all of the needed components of the L–A system.

Refined and more detailed studies of PBL turbulence and L–A feedback during field campaigns are now possible because a more sophisticated synergy of instruments is available. Likely the first campaign dedicated to L–A feedback was FIFE, which was conducted in Kansas between 1987 and 1989. FIFE already considered the important role of vegetation and applied a sophisticated combination of remote sensing and in situ instruments on various platforms (Sellers et al. 1988). Further studies covered other key regions for L–A feedback such as HAPEX-Sahel (Goutorbe et al. 1994) and the extensive BOREAS conducted in central Canada mainly between 1993 and 1996 (Hall 1999). More remote sensors were applied, such as during IHOP_2002 along the central U.S. dryline in 2002 (Weckwerth et al. 2004), LITFASS around the DWD Lindenberg Observatory during 2003 (Beyrich et al. 2006), COPS performed over the low-mountain Black Forest area in Germany in 2007 (Wulfmeyer et al. 2011), the BLLAST experiment performed in southern France in 2011 (Lothon et al. 2014), HOPE performed in heterogeneous midlatitude terrain in Germany in 2013 (Macke et al. 2017), and the ScaleX campaign on scale-crossing land surface and boundary layer processes in a pre-Alpine observatory between 2015 and 2016 (Wolf et al. 2017); the study areas became even more complex, PBL transition periods were included, and were among the first to use scanning active remote sensing systems.

Furthermore, multiyear, high-resolution datasets are available and used to look at turbulent properties of the PBL (e.g., Turner et al. 2014b; Berg et al. 2017; Osman et al. 2018) from the ARM observations at the SGP (Sisterson et al. 2016) and TWP (Long et al. 2016) sites as well as various observatories such as Cabauw in the Netherlands and Lindenberg in Germany. However, during these campaigns and observations, limited knowledge about the 3D structure of the PBL and the interaction of surface and entrainment fluxes was acquired, calling for more sophisticated approaches and instruments.

Observational gaps and new observations.

To fully characterize L–A interactions, observations are needed in the soil, in the vegetation canopy, and from the surface layer to the top of the PBL. Measurements of temperature and moisture in the soil, as a function of depth from the surface to many tens of centimeters deep, are required to characterize the evolution of the land conditions. These characteristics are a strong function of soil texture and hydraulic coefficients. These observations need to be accompanied by the specification of vegetation parameters such as plant type, leaf area index, and canopy height as the vegetation can often provide a more efficient pathway to transfer water from the soil to the atmosphere. Observations of the energy balance above the canopy level are necessary, which includes the measurement of the ground net radiative flux, latent heat flux, and sensible heat flux. These observations are traditionally made by SEB stations that include the upwelling and downwelling surface longwave and shortwave radiative fluxes.

Measurements of PBL dynamics and thermodynamics are particularly critical to fully understand and characterize the L–A feedback. In the past, PBL and entrainment fluxes have been mainly determined by aircraft in situ measurements (e.g., Lenschow et al. 1994), a relatively sparse and expensive method. Moreover, with in situ sensors, it is challenging to measure profiles of mean and turbulent quantities throughout the PBL, which is essential for evaluating new flux–gradient relationships and turbulence parameterizations. The lack of high-resolution temperature and water vapor profiles, their gradients, and their turbulent fluctuations is currently a major weakness for monitoring the CBL diurnal cycle and feedback processes. This is also a severe limitation of current satellite-based retrievals of PBL properties (Wulfmeyer et al. 2015a).

Ground-based lidars have become an important and emerging tool for turbulence profiling, as the high temporal and vertical resolution of these observations allow both the mean and higher-order-moment profiles to be observed. DLID systems can profile mean winds very accurately (e.g., Klein et al. 2015; Päschke et al. 2015) and are able to provide profiles of higher-order turbulent moments of the vertical wind and dissipation (e.g., Wulfmeyer and Janjić 2005; Ansmann et al. 2010; Lenschow et al. 2012; Berg et al. 2017). Water vapor can be remotely sensed with both WVDIAL (e.g., Wagner et al. 2013) and WVRLID (Turner et al. 2002; Whiteman 2003a,b; Froidevaux et al. 2013); both of these techniques have been shown to resolve turbulent fluctuations (second to fourth moments) in the water vapor field in the CBL (Wulfmeyer 1999a; Wulfmeyer et al. 2010; Turner et al. 2014a,b; Muppa et al. 2016; Di Girolamo et al. 2017). Simultaneous observations with DLID or radio acoustic sounding systems in the same volume can provide profiles of latent heat flux as well as stability indices (Senff et al. 1994; Wulfmeyer 1999b; Giez et al. 1999; Kiemle et al. 2007; Linné et al. 2007; Behrendt et al. 2011; Corsmeier et al. 2011). The capability of the WVRLID and DLID combination collocated at the ARM SGP site is demonstrated in Fig. 1. The left panel shows latent heat flux profiles measured using the eddy covariance technique, and the right panel the corresponding variance profiles of moisture and vertical wind. Noise and sampling error bars are also shown.

Fig. 1.

(left) Latent heat flux profile measured at the ARM SGP site from 1856 to 2049 UTC 9 Nov 2012 with SRLID and a collocated DLID including noise (small caps) and sampling error bars (large caps). Lower-level flux measurements are possible but are still under investigation. (right) The corresponding vertical wind and mixing ratio variance profiles.

Fig. 1.

(left) Latent heat flux profile measured at the ARM SGP site from 1856 to 2049 UTC 9 Nov 2012 with SRLID and a collocated DLID including noise (small caps) and sampling error bars (large caps). Lower-level flux measurements are possible but are still under investigation. (right) The corresponding vertical wind and mixing ratio variance profiles.

Temperature profiles can be observed with rotational TRLID (Radlach et al. 2008; Newsom et al. 2013; Hammann et al. 2015). A particularly exciting advance is the recent ability to observe profiles of turbulent temperature fluctuations (Behrendt et al. 2015) and the measurement of sensible heat flux profiles by combined observations with DLID (Wulfmeyer et al. 2015b). To our knowledge, this was the first time profiles of sensible heat flux have been derived in the PBL with lidar systems.

While operational lidars, such as those at the ARM SGP site, are able to provide long-term measurements of turbulent profiles (Turner et al. 2014b; Berg et al. 2017), one of the weaknesses of these datasets is that the near-surface level (lowest 100 m) is not well sampled. Optical scanners can make lidar measurements in different directions and improve the sampling of the surface layer of the PBL (i.e., the lowest 100 m). Precise measurements of the mean wind using multiple scanning Doppler lidars has been demonstrated in the XPIA field study (Lundquist et al. 2017) and the ScaleX campaign (Wolf et al. 2017). Using multi-Doppler techniques, high-temporal- and high-spatial-resolution wind measurements very close to the surface are possible.

Surface-layer temperature and water vapor measurements are also possible using scanning lidar systems and have been analyzed in Froidevaux et al. (2013), Hammann et al. (2015), and Späth et al. (2016). The latter team demonstrated that surface-layer profiles can be measured with vertical resolutions of a few meters as well as accuracies of 0.1 g kg–1 and 0.2 K up to a range of several kilometers. Furthermore, they showed the capability of the WVDIAL to measure 3D water vapor fields in the lower troposphere with high spatial and temporal resolutions. With these recent advances including the potential to measure entrainment fluxes of heat and moisture, it is timely to develop new concepts for producing original, synergetic datasets for studying L–A feedback.

THE LAND–ATMOSPHERE FEEDBACK EXPERIMENT (LAFE).

Concept and overarching goals.

The previous considerations led to a new concept for L–A feedback experiments, which is depicted in Fig. 2. This configuration addresses the required combination of measurements for advanced studies of L–A feedback including the observation of the heterogeneity of land surface fluxes and entrainment at the CBL top. The sensor synergy consists of networks of surface and vegetation in situ sensors together with a combination of scanning lidar systems. At the central site, a combination of DLID, TRLID/WVRLID or WVDIAL systems is operating in vertically pointing mode and a similar combination of these systems is operating in surface scanning modes. If not all of these systems are available, they can switch between these modes. During vertically pointing operation, the lidar systems measure profiles of turbulent quantities and fluxes including entrainment in the CBL. The scanning capability down to the land surface provides high-resolution profiles in the surface layer and 2D surface measurements of latent and sensible heat fluxes over different land-cover and soil types. Thus, the heterogeneity of land surface fluxes can be captured in more detail, which is hard to achieve with energy balance station networks. During the UHOH SABLE 2014 campaign, a first attempt to demonstrate a part of this innovative sensor synergy and operation modes was made. The results were presented in Wulfmeyer et al. (2015b) and Späth et al. (2016).

Fig. 2.

The proposed concept for the combination of remote sensing systems enabling for the first time the simultaneous remote sensing of surface and entrainment fluxes. With the low-level scans, the friction velocity u* and the surface sensible and latent heat flux S and λE will be determined in combination with the vertical profiles of specific humidity q and temperature T as well as their higher-order moments and fluxes. A conical scanning Doppler lidar allows for measuring the horizontal wind profile V.

Fig. 2.

The proposed concept for the combination of remote sensing systems enabling for the first time the simultaneous remote sensing of surface and entrainment fluxes. With the low-level scans, the friction velocity u* and the surface sensible and latent heat flux S and λE will be determined in combination with the vertical profiles of specific humidity q and temperature T as well as their higher-order moments and fluxes. A conical scanning Doppler lidar allows for measuring the horizontal wind profile V.

Based on these achievements, a new campaign with an enhanced sensor synergy, LAFE, was performed in August 2017 at the reconfigured ARM SGP site in Oklahoma (Sisterson et al. 2016). LAFE represents an international collaboration of several U.S. institutions as well as various universities in the United States and Germany. Further details are found in the LAFE science plan (www.arm.gov/publications/programdocs/doe-sc-arm-16-038.pdf). The overarching goals and the research strategy are summarized in the “LAFE goal and objectives” sidebar.

LAFE GOAL AND OBJECTIVES

The overarching goal of LAFE is the study of L–A feedback in the SGP region containing different vegetation types and soil moisture conditions during summertime. Specifically, LAFE has four scientific objectives:

  1. determine profiles of turbulent moments and fluxes and investigate new similarity relationships among gradients, variances, and fluxes.

  2. map surface momentum, sensible heat, and latent heat fluxes using a synergy of scanning wind, humidity, and temperature lidar systems;

  3. characterize L–A feedback and the moisture budget at the SGP site in dependence of different soil moisture regimes; and

  4. verify LES and improve turbulence parameterizations in mesoscale models.

These objectives are addressed by the combination of the three LAFE sensor synergy components, which are presented in the “LAFE observational design” section and Table 1. The matrix of variables produced by LAFE is shown in Table 2. The research approaches to reach these four objectives and first results are demonstrated in the “Strategy to reach the objectives of LAFE” section.

LAFE is considered a starting point of a series of complementary field campaigns and configurations of observatories in different climate regions. LAFE will also contribute to national and international activities such as the HD(CP)2 project in Germany, GLASS, the GEWEX LoCo initiative of the World Climate Research Programme (WCRP), and the L–A studies within DOE’s ASR program.

Table 1.

Overview of key instruments that participated in LAFE. All lidars also measured atmospheric backscatter and extinction profiles as well as cloud properties at their corresponding wavelengths.

Overview of key instruments that participated in LAFE. All lidars also measured atmospheric backscatter and extinction profiles as well as cloud properties at their corresponding wavelengths.
Overview of key instruments that participated in LAFE. All lidars also measured atmospheric backscatter and extinction profiles as well as cloud properties at their corresponding wavelengths.
Table 2.

Variables measured by the synergy of instruments during LAFE which includes SRLID, SDLID, NDIAL, URLID, UDIAL, NOAA DLID, and HSRL. The UDIAL, URLID and NOAA DLID systems performed coordinated RHI scans. The abbreviations are introduced in the text and in the list of acronyms.

Variables measured by the synergy of instruments during LAFE which includes SRLID, SDLID, NDIAL, URLID, UDIAL, NOAA DLID, and HSRL. The UDIAL, URLID and NOAA DLID systems performed coordinated RHI scans. The abbreviations are introduced in the text and in the list of acronyms.
Variables measured by the synergy of instruments during LAFE which includes SRLID, SDLID, NDIAL, URLID, UDIAL, NOAA DLID, and HSRL. The UDIAL, URLID and NOAA DLID systems performed coordinated RHI scans. The abbreviations are introduced in the text and in the list of acronyms.

LAFE observational design.

LAFE took advantage of the newly reconfigured ARM SGP site. Figure 3 shows the layout of the new SGP domain and, in particular, the location of AERIs (Knuteson et al. 2004a,b) and DLID systems at four new profiling extended facilities (red diamonds) that are located 40–50 km away from the central facility. This configuration will allow us to study the moisture budget by adding the 2D and turbulent flux measurements at the central facility (thick black line to the NE).

Fig. 3.

The layout of the SGP site in Aug 2017 together with the surface elevation map of the region. The central LAFE instrumentation was deployed along the thick black line emanating from the central facility to the northeast (see Fig. 4). The four profiling extended facilities (red diamonds) that are approximately 50 km from the central facility, together with the NDIAL that was collocated with the 915-MHz wind profiler about 16 km south-southeast of the central facility, will provide critical information on water vapor and temperature advection. The multiple surface energy balance stations (blue squares) will be used to quantify radiation, momentum, latent heat, and sensible heat fluxes over different surface types.

Fig. 3.

The layout of the SGP site in Aug 2017 together with the surface elevation map of the region. The central LAFE instrumentation was deployed along the thick black line emanating from the central facility to the northeast (see Fig. 4). The four profiling extended facilities (red diamonds) that are approximately 50 km from the central facility, together with the NDIAL that was collocated with the 915-MHz wind profiler about 16 km south-southeast of the central facility, will provide critical information on water vapor and temperature advection. The multiple surface energy balance stations (blue squares) will be used to quantify radiation, momentum, latent heat, and sensible heat fluxes over different surface types.

Table 1 provides an overview of the LAFE instrumentation. The main remote sensing system synergy consists of three scanning lidar systems combining vertical pointing with low-level RHI scanning measurements of temperature, humidity, and wind according to Fig. 2, with two additional wind profiling lidar systems that are also performing RHI scans across the primary RHI path to create virtual towers. The setup of the LAFE instrumentation around the SGP site is presented in Fig. 4 and consists of three components.

Fig. 4.

The deployment and scanning strategy for LAFE at the ARM SGP central facility. The three scanning lidar systems (UDIAL, URLID, and NOAA DLID) were located 300 m north of the SRL and scanning in elevation above the main RHI (thick white line and planes). Four energy balance stations (including soil moisture and temperature) were installed at the tower locations 1–4 (EC1–4). Dual-Doppler lidar scans were also performed at these tower locations using a combination of the NOAA DLID and the DLIDs included with the SPARC (red planes) and CLAMPS (yellow planes). The sUAS flight track (blue) was parallel to the main RHI path. The field types are indicated by color: pasture (cyan), farm fields (which were mainly wheat stubble or soybeans; green), and conservation research program land (which has native grasses that are about 0.6 m tall; purple).

Fig. 4.

The deployment and scanning strategy for LAFE at the ARM SGP central facility. The three scanning lidar systems (UDIAL, URLID, and NOAA DLID) were located 300 m north of the SRL and scanning in elevation above the main RHI (thick white line and planes). Four energy balance stations (including soil moisture and temperature) were installed at the tower locations 1–4 (EC1–4). Dual-Doppler lidar scans were also performed at these tower locations using a combination of the NOAA DLID and the DLIDs included with the SPARC (red planes) and CLAMPS (yellow planes). The sUAS flight track (blue) was parallel to the main RHI path. The field types are indicated by color: pasture (cyan), farm fields (which were mainly wheat stubble or soybeans; green), and conservation research program land (which has native grasses that are about 0.6 m tall; purple).

First LAFE component.

The first component consists of mainly vertically pointing lidar systems (see Fig. 4), namely, the water vapor and temperature SRLID (Goldsmith et al. 1998; Turner and Goldsmith 1999; Turner et al. 2002; Ferrare et al. 2006; Newsom et al. 2009; Wulfmeyer et al. 2010; Newsom et al. 2013; Turner et al. 2014a,b; Turner et al. 2016), SDLID (e.g., Hogan et al. 2009; Klein et al. 2015; Berg et al. 2017), UDLID (Pearson et al. 2009) (see Fig. 5), and NDIAL (Spuler et al. 2015; Weckwerth et al. 2016).

Fig. 5.

Key scanning lidar systems operated during LAFE. (top left) DLIDs operated in vertically staring or six-point scanning pointing modes collocated with the SRLID. (top right) Collocation of (left to right) the NOAA DLID, the URLID, and the UDIAL at the SGP central facility all pointing northeast. (bottom left) The SPARC and (bottom right) the CLAMPS systems deployed and operated close to the SGP central facility.

Fig. 5.

Key scanning lidar systems operated during LAFE. (top left) DLIDs operated in vertically staring or six-point scanning pointing modes collocated with the SRLID. (top right) Collocation of (left to right) the NOAA DLID, the URLID, and the UDIAL at the SGP central facility all pointing northeast. (bottom left) The SPARC and (bottom right) the CLAMPS systems deployed and operated close to the SGP central facility.

SRLID and the SDLID were continuously operated in vertically pointing mode for deriving vertical turbulence as well as sensible and latent heat flux measurements. The UDLID performed a modified, continuous six-direction pointing mode for studying TKE, momentum flux, and horizontal wind profiles (Bonin et al. 2017) that are essential for studying similarity relationships and parameterizations of turbulence.

NDIAL was located mainly upstream of the SGP site with a 915-MHz wind profiler in order to study the heterogeneity of the moisture field and moisture advection (see Fig. 3). This deployment strategy allows us to study inhomogeneities in the water vapor field due to advection at three different scales: at ∼100 km by the network of four AERIs/DLIDs at the boundary facilities, along the 15-km distance between the NDIAL and the SRL, and along the 4-km RHI path observed by scanning lidar systems at the SGP site.

Second LAFE component.

The second component employs a synergy of scanning lidar systems as well as surface and airborne in situ measurements (see Fig. 4). The high-resolution NOAA DLID, UDIAL (Wagner et al. 2013; Muppa et al. 2016; Späth et al. 2016), and URLID (Hammann et al. 2015; Behrendt et al. 2015) were operated (see Fig. 5). These lidars were placed in the northeastern corner of the ARM SGP central facility and scanned toward the northeast (purple and white planes in Figs. 2 and 4, respectively). Continuous RHI scans were performed for measuring the LOS velocity, the humidity, and the temperature in a well-defined plane from the CBL top down to the canopy level. Furthermore, this component was complemented by three SEB towers from NOAA/ATDD (see Fig. 6) and one SEB station from UHOH (Wizemann et al. 2015). All four of these SEB towers included soil moisture and soil temperature measurements and were placed in a line at four locations with different field types under the RHI path. Each of the three ATDD towers included flux measurements at two heights (2 and 10 m AGL) to quantify flux divergence in addition to the standard meteorological measurements.

Fig. 6.

In situ and airborne instruments operated during LAFE. (top left) NOAA ATDD and (top right) UHOH SEB towers over soybeans and natural vegetation mix. (bottom left) The Piper Navajo at Ponca City airport. (bottom right) NOAA ATDD sUAS operating close to the SGP central facility.

Fig. 6.

In situ and airborne instruments operated during LAFE. (top left) NOAA ATDD and (top right) UHOH SEB towers over soybeans and natural vegetation mix. (bottom left) The Piper Navajo at Ponca City airport. (bottom right) NOAA ATDD sUAS operating close to the SGP central facility.

NOAA/ATDD also operated two sUAS, including the octocopter shown in Fig. 6 to obtain additional information on the spatial and vertical variability of temperature and humidity. A DJI S-1000 and a microdrone MD4–1000 sUAS were flown in two modes. Mode 1 was a vertical profiling mode to capture the rapid evolution of temperature and humidity from near the surface to its maximum allowed flight altitude of ∼365 m AGL. The sUAS flew close to tower 2 when in this mode. Mode 2 involved flight at constant altitude between points adjacent to towers 2 and 3. These flights mapped the horizontal variability of air temperature and humidity as well as land surface temperature using an infrared camera and are being used to derive surface sensible heat fluxes following a recently developed technique by Lee et al. (2017) as well as the approach introduced in Morrison et al. (2017). These sUASs help to identify any coherent spatial structures that may not be easily identifiable from the measurements made at the four towers. The horizontal legs were made at a small number of flight levels (e.g., 100 and 300 m AGL).

Third LAFE component.

The third component consists of two facilities deployed at different distances near the RHI scan path, namely, SPARC (see Fig. 5) and the NOAA/NSSL CLAMPS (see Fig. 5). Both the SPARC and CLAMPS facilities contain a vertically pointing AERI and a scanning DLID. Both DLIDs were operated in a three-direction cross-track scanning pattern (such as in the upper-left corner of Fig. 2; see also Fig. 4). The primary objective for these two facilities is to perform dual-Doppler virtual towers with the NOAA DLID up to ∼100 m above the SEB towers. SPARC sampled above towers 1, 2, and 3, whereas CLAMPS sampled above towers 2, 3, and 4. These DLID RHI scans realized crossing points of two DLID scans in different directions down to the surface in order to determine the wind profile over different land cover along the major LOS of the RHI scanning lidar systems. This is necessary for closing the MOST relationships. The XPIA field experiment demonstrated that these dual Doppler lidar scans do not need to be perfectly coordinated in time with the NOAA DLID if mean wind profiles are desired (Choukulkar et al. 2017), which greatly simplified operations. In addition, the SPARC includes an HSRL, which is also able to measure turbulent motions in the aerosol backscatter field (McNicholas and Turner 2014).

UTSI in cooperation with NOAA/ARL operated a Piper Navajo (see Fig. 6) manned fixed-wing aircraft to measure the surface temperature inhomogeneity over a large 10 km × 10 km area surrounding the ARM central facility. Its primary instrument is a downward-looking midinfrared camera. The Piper Navajo flew “lawnmower” patterns over this domain to fully characterize the surface conditions on 6 days during LAFE: 2 days at the beginning of the experiment, 2 days in the middle, and 2 days at the end. This allows the LAFE team to see how the overall soil and land-cover states evolved during the monthlong experiment and to determine the evolution of selected vegetation indices (e.g., the NDVI). On each selected day, the Piper Navajo flew three missions, in the early morning, around noon, and in the midafternoon. This sampling allows the LAFE team to characterize how the surface warms as the CBL grows as a result of insolation.

LAFE observation matrix.

Table 2 presents a summary of the variables that were observed and will be derived by the LAFE sensor synergy. The key variables temperature T, water vapor mixing ratio m, absolute humidity ρ, and vertical wind w were measured with a temporal resolution of 1–10 s and a range resolution of 30–300 m. These lidar systems also observed aerosol particle backscatter and extinction coefficients, the aerosol optical thickness (βPar, αPar, τPar), and the instantaneous CBL depth zi.

Furthermore, the horizontal wind profile V, the wind shear dV/dz, and the cloud optical thickness τC will be determined. From these results, the following variables will be derived: the TKE dissipation rate ε; profiles of the fluxes of water vapor (latent heat) and of its variance 〈wm′〉, 〈w′ρ′〉, 〈wm2〉, and 〈w′ρ′2〉; the molecular destruction rates of humidity variances εm and ερ; profiles of the temperature flux (sensible heat) and the flux of temperature variance 〈wT′〉 and 〈wT2〉; and the molecular destruction rate of temperature variance εT. The errors will be specified with respect to noise and sampling errors according to Lenschow et al. (2000), Wulfmeyer et al. (2010), Turner et al. (2014b), and Wulfmeyer et al. (2016). The friction velocity u*; the surface latent and sensible heat fluxes λE and S will be derived with MOST (as long as it can be applied) and compared with the surface fluxes measured with the SEB stations. Then the convective velocity, humidity, and temperature scales w*, q*, and T* can be deduced.

STRATEGY TO REACH THE OBJECTIVES OF LAFE AND FUTURE ADVANCED L–A FEEDBACK STUDIES.

Objective 1: Turbulence similarity relationships.

For addressing objective 1, we are taking advantage of LAFE research component 1, the synergy between the SRLID, the SDLID, and the UDLID. Using water vapor and vertical velocity time series at different heights, their fluctuations can be measured with temporal resolutions of 1–10 s, typically. By means of the technique presented in Lenschow et al. (2000), these fluctuations are used to derive higher-order moments of mixing ratio and vertical velocity as well as their covariance, and thus the latent heat flux. Wulfmeyer et al. (2010) demonstrated that mixing ratio moments can be derived up to the third order from the SRLID with a temporal resolution of 30–60 min. Figure 1 confirms the potential of the SRLID–SDLID combination to measure latent heat flux and variance profiles with reasonable noise errors. During LAFE, considerably more data were collected allowing for a deeper insight in the general capability of the SRLID–SDLID combination to measure latent heat flux profiles with sufficiently low noise and sampling errors. The SRLID temperature data will also be evaluated to see if accurate temperature variance and skewness as well as sensible heat flux profiling is possible (Newsom et al. 2013; Behrendt et al. 2015). Turner et al. (2014b) showed that the high stability and the nearly continuous operation of the SRLID allowed for first studies of the statistics of turbulent properties and how these depend on a variety of meteorological conditions. Thus, daily cycles of turbulent properties can be measured.

During several SOPs, comparisons of SRLID and UDIAL measurements were made. Figure 7 presents a simultaneous measurement of water vapor, temperature, and vertical wind time–height cross sections with resolutions of 10 s and 50–67.5 m, respectively. Clouds are marked by the black areas. Clearly the morning evolution of the CBL is resolved, and turbulent eddies below and around clouds are detected with high temporal and vertical correlation between all variables. These results demonstrate the high resolution and accuracy of active remote sensing for latent and sensible heat flux profiling.

Fig. 7.

Simultaneous measurements of water vapor, temperature, and vertical wind by UDIAL, URLID, and NOAA DLID during SOP2 on 26 Aug 2017. The temporal resolutions are 10 s. The vertical resolutions are 67.5 m for the UDIAL and URLID as well as 50 m for the DLID. Black areas are due to the presence of clouds, which can be partly penetrated by the URLID and the DLID.

Fig. 7.

Simultaneous measurements of water vapor, temperature, and vertical wind by UDIAL, URLID, and NOAA DLID during SOP2 on 26 Aug 2017. The temporal resolutions are 10 s. The vertical resolutions are 67.5 m for the UDIAL and URLID as well as 50 m for the DLID. Black areas are due to the presence of clouds, which can be partly penetrated by the URLID and the DLID.

A key scientific contribution of these data is the study of similarity relationships between gradients, variances, and fluxes. If these relations are verified and calibrated, they could represent the foundation of new turbulence parameterizations. Wulfmeyer et al. (2016) proposed how entrainment fluxes should depend on scaling variables in the interfacial layer such as the gradient Richardson number, which require the simultaneous measurement of wind shear and temperature gradients. For the first time, the LAFE dataset will provide the full information contents to verify these relationships. Similar relations can also be derived for variances and higher-order moments providing further ingredients for new turbulence parameterizations and studies of L–A feedback.

Objective 2: Remote sensing of surface fluxes.

Objective 2 is addressed by combining the LAFE research components 2 and 3. The cross-RHI scans of the NOAA DLID and the SPARC and CLAMPS DLIDs will permit the measurement of wind profiles down to the surface at their crossing points (see Figs. 2 and 4). Simultaneously, the RHI scans of the UDIAL and the URLID will deliver temperature and moisture profiles at these points as well. Consequently, along the LOS of the major RHI, the surface profiles and their gradient functions of wind, temperature, and moisture will be determined over the surface with different land-cover and soil characteristics. During LAFE, we will make the first attempt to use these gradients to measure surface fluxes entirely with remote sensing systems at the SGP site. We operated NOAA DLID, UDIAL, and URLID in RHI scanning mode for full daily cycles under various surface and mesoscale forcing conditions as well as different cloud coverage. We will use a temporal resolution of 30–60 min and a spatial resolution of 30–300 m for the derivation of these fluxes provided that MOST will be applicable. The availability of this array of remote sensing instrumentation will be a unique opportunity to advance the understanding of boundary layer processes and their dependence on surface fluxes. The measurements of surface fluxes will be verified with the four SEB stations and other SEB and eddy covariance stations at the SGP site further complemented with surface radiation as well as soil moisture and temperature measurements.

The capability of the UDIAL to study the structure of the 2D water vapor field in the surface layer and the PBL is presented in Fig. 8. The data were collected during IOP 11 on 23 August 2017, which also included an evening transition. To study both the PBL structure and the surface layer with high vertical resolution, alternating 0°–90° RHIs for 10 min and 0°–7° RHIs for 50 min were performed. The top panel of Fig. 8 shows the measurement of the 2D water vapor field in the PBL. Various turbulent eddies and mesoscale structures were resolved up to a range of 4 km. The bottom panel of Fig. 8 presents a low-level RHI from the surface to 7°. Nearly continuously microscale to mesoscale coherent structures and/or turbulent eddies were observed, which entrained water vapor from the top of the PBL down to the surface.

Fig. 8.

Alternating RHI scans of the UDIAL for studying the 2D water vapor field performed during IOP11 on 23 Aug 2017. (top) The 0°–90° RHI with scan speed of 1° s–1 and a range resolution of 200 m showing several turbulent eddies from the surface up to the PBL top at approximately 900 m AGL. (bottom) The 0°–7° RHI with scan speed of 0.1° s–1 and also a range resolution of 200 m revealing several coherent structures in the surface water vapor field with an amplitude of ≈2 g m–3 ranging from turbulent to mesogamma scales.

Fig. 8.

Alternating RHI scans of the UDIAL for studying the 2D water vapor field performed during IOP11 on 23 Aug 2017. (top) The 0°–90° RHI with scan speed of 1° s–1 and a range resolution of 200 m showing several turbulent eddies from the surface up to the PBL top at approximately 900 m AGL. (bottom) The 0°–7° RHI with scan speed of 0.1° s–1 and also a range resolution of 200 m revealing several coherent structures in the surface water vapor field with an amplitude of ≈2 g m–3 ranging from turbulent to mesogamma scales.

The surface-layer profiles can be compared with gradient functions or their vertically integrated versions provided by MOST. There are two possibilities: either accurate fits to these functions will be realized, confirming MOST over different land-cover types, or deviations from MOST are detected, which would also be a very interesting scientific result motivating refinements of MOST in heterogeneous terrain.

Within LAFE, we hypothesize that the footprints of the soil and land cover at the crossing points are homogeneous enough so that MOST is valid. In this case, a fit to the gradient functions yields the surface friction velocity, the sensible and the latent heat fluxes, and the Monin–Obukhov length. As several crossing points are realized, insight on the spatial variability and the diurnal cycles of fluxes along the major RHI path is possible. However, the strong entrainment down to the surface and the presence of coherent mesoscale structures detected in Fig. 8 may question the applicability of MOST in heterogeneous terrain.

The LAFE strategy involved sampling the surface fluxes over different types of surfaces, and thus the in situ SEB stations were placed in fields with different plant covers (e.g., soybean plants and native grasses). The Bowen ratio (i.e., the ratio of the sensible to the latent heat flux) can be markedly different over these different surface types. At the beginning of August 2017, the soil was relatively dry and thus the Bowen ratio was large; however, on 10–11 August, the SGP region received nearly 50 mm of precipitation, thus saturating the soil. Thus, by the end of the month, the Bowen ratio was significantly smaller than in the beginning of the month (Fig. 9). This variability in the surface fluxes provides a nice range of conditions for our analyses.

Fig. 9.

The distribution of Bowen ratio measured over (a) a pasture and (b) a recently harvested wheat field during the early (red) and late (blue) parts of LAFE.

Fig. 9.

The distribution of Bowen ratio measured over (a) a pasture and (b) a recently harvested wheat field during the early (red) and late (blue) parts of LAFE.

For 2 days during LAFE, the daily data-acquisition operations were expanded to include the evening transition period and to sample the nocturnal stable boundary layer and associated low-level jets (e.g., Banta 2008; Pichugina and Banta 2010). Figure 10 demonstrates the excellent information content of the six-point-scanning pointing mode of the UHOH DLID for providing the horizontal wind, TKE, and momentum flux profiles continuously during day and night. The evolution of the nocturnal jet can be studies as well as the morning transition and afternoon decay of TKE and momentum flux.

Fig. 10.

Analysis of the six-point scanning pointing mode of the UHOH DLID demonstrating the large information contents of this technique. With this setup, horizontal wind, TKE, and momentum flux profiles can be simultaneously and continuously measured from the surface layer throughout the PBL including the interfacial layer. These measurements were performed during 16 Aug 2017.

Fig. 10.

Analysis of the six-point scanning pointing mode of the UHOH DLID demonstrating the large information contents of this technique. With this setup, horizontal wind, TKE, and momentum flux profiles can be simultaneously and continuously measured from the surface layer throughout the PBL including the interfacial layer. These measurements were performed during 16 Aug 2017.

LAFE also captured a rare event: the solar eclipse of 2017. On 21 August, the sky was largely cloud-free when the moon moved across the face of the sun, resulting in about 89% obscuration during the peak of the eclipse. The large change in the downwelling shortwave flux modulated the other terms of the surface energy budget almost immediately, which then impacted the evolution of the PBL above (Turner et al. 2018). This “rapid sunset/sunrise” event makes an excellent complement to the observations collected during the normal evening/morning transition times and should prove extremely useful in teasing out the details of L–A interactions and feedbacks.

Objective 3: Feedbacks and budgets.

The combination of variables measured during LAFE, together with the temporal and vertical resolution of these observations, makes the estimation of the water vapor mean and variance budgets possible (see Table 2). Crucial parts of these budgets are the advection terms, which are very difficult to measure. Michael (1994) and Higgins et al. (2013) demonstrated the potential of scanning lidar systems for improving the estimation of advection. The LAFE design will enable us to estimate this component with improved accuracy. The 3D moisture gradient on different spatial scales will be determined using the AERI at the ARM central facility as well as the AERI and DLIDs that are part of the new SGP boundary facility network (Figs. 3 and 4). The RHI scans of the UDIAL in combination with the NOAA DLID will measure the horizontal water vapor gradient along the LOS of RHI on scales up to 4 km, whereas the NDIAL and the 915-MHz profiler will observe scales between the two above. Furthermore, the horizontal wind measurements of the UDLID, the combination of the other DLIDs used during LAFE, and adding other wind profile measurements at the SGP site will be used to get the best estimate of the horizontal wind profile.

Observations of advection, the PBL budget, and entrainment processes are key to understanding the full nature of L–A coupling and assessing model physics (e.g., LSM and PBL schemes) in interactive mode. Research as part of the LoCo project has demonstrated this, where the mixing diagram approaches of Betts (1992) and Santanello et al. (2009) are used to simultaneously quantify the behavior of surface fluxes (latent, sensible), PBL fluxes (entrainment, advection), and states (2-m temperature, humidity). However, these diagnostics have been limited in terms of model evaluation or development due to lack of PBL height, advection, and entrainment observations. The measurements proposed herein would thus have immediate impact on LoCo research by providing detailed insight as to where the development needs to be targeted.

Objective 4: Verification of LES and mesoscale models.

Objective 4 will evaluate turbulence parameterizations and perform model verification. An example for the parameterizations of entrainment fluxes and variances is presented in Wulfmeyer et al. (2016). Validation and refinement of flux–gradient relationships will eventually lead to advanced parameterizations of CBL turbulent fluxes and land surface fluxes in mesoscale models. Various model runs and outputs will be compared with the LAFE measurements based on Table 2 and investigated with respect to their accuracy.

An overview of these models is presented in Table 3. We will take advantage of the ARM LASSO project (Gustafson et al. 2017b), which will become operational soon; LAFE is very timely for these kinds of comparisons and model verification efforts. Further model runs and comparisons are planned for mesoscale models such as using the hourly updated HRRR model (Benjamin et al. 2016) run operationally at the National Centers for Environmental Prediction and WRF-Noah-MP and for various LES models such as PALM (Maronga et al. 2015), DALES (Heus et al. 2010), and COMMAS. Within the WRF-Noah-MP configuration of UHOH, ensemble runs with different turbulence parameterizations will be performed over the SGP site. High-resolution PALM ensemble simulations (10 m or less) with varying initial perturbances will also help to better separate the signature caused by the land surface heterogeneities from the general turbulence. It is envisioned that the global ICON model of the DWD with turbulence-permitting grid refinement will also be run over the SGP domain.

Table 3.

Model systems to be used for comparisons with LAFE data.

Model systems to be used for comparisons with LAFE data.
Model systems to be used for comparisons with LAFE data.

Essential for the success of model verification is the extraction of all parameters and variables from mesoscale model runs necessary for dedicated comparisons with respect to surface fluxes and turbulence parameterizations. As these variables are typically not routinely provided, the model outputs will have to be extended. If these results become accessible, a huge ensemble of equations related to other local and nonlocal turbulence parameterizations as well as for parameterizations based on mass-flux schemes can be studied and improved.

From the LES model simulations, higher-order turbulent moment profiles and flux profiles will be determined and compared against the lidar and in situ observations. The results will also allow us to give more precise estimates of the uncertainty of the turbulence measurements (Sühring and Raasch 2013). A very useful example of DLID measurements that will be used to evaluate the evolution of the PBL in LES (and other model) simulations is presented in Fig. 10 (Sathe et al. 2015). These LES profiles are not parameterized save for a subfilter-scale contribution that should be small in the bulk of the boundary layer. While LES simulations have traditionally been used for idealized and theoretical studies, these simulations will be driven by reanalysis and observational data, following Neggers et al. (2012). The obvious advantage is that these simulations can be expected to be closer to the observations, at least in a statistical sense, helping to separate errors due to initial conditions and model physics. Relationships between surface fluxes, atmospheric turbulence, entrainment, and cloud formation found in the observations are likely to show up in these simulations as well, which would mean that the LES may serve as a virtual laboratory to test hypotheses. For instance, by breaking certain couplings in the L–A, one could deduce cause and effect for these feedbacks. LES could also assist in comparisons with validations of large-scale parameterizations. Indeed, the original premise behind the test bed setup (Neggers et al. 2012) was that parameterization schemes are usually designed against ideal and idealized datasets and may not hold as well against more realistic data. This is particularly true for parameterizations of the PBL and of PBL clouds.

There are many LES model components that share the same weaknesses found in mesoscale models. For instance, subgrid-scale turbulence is still parameterized, which causes uncertainties around stable interfaces such as the surface layer and the entrainment layer—especially for coarser LES resolutions (100 m and coarser). The surface layer would be of particular interest here. This is not only because interactive soil models are relatively new in LES, and can therefore likely be optimized for the smaller scales, but also because typical MOST-based surface-layer parameterizations may not be valid in very high-resolution simulations. Datasets like the LAFE measurements will therefore be invaluable to improve the surface and soil parameterizations of LES and larger-scale models.

SUMMARY AND OUTLOOK.

In this work, we described key research activities for the improvement of the next generation of weather forecast, climate, and Earth system models. As the resolutions used in these model systems approach the gray zone of turbulence, new and scalable combinations of the parameterization of surface fluxes and PBL turbulence in heterogeneous terrain need to be developed and tested. The deficiencies in current parameterizations have to be detected and suggestions for their improvement have to be made as these problems are leading to suboptimal representations of L–A feedback and, thus, to errors in the representation of the diurnal cycle of PBL dynamics and thermodynamics including the simulation of convection initiation. As these errors are significant, research on L–A feedback is a prerequisite for a better simulation of clouds and precipitation including extreme events and its climate statistics.

We have demonstrated that advanced insight in L–A interaction and feedback requires the application of new observations, as the whole L–A system needs to be investigated from the land surface throughout the PBL into the free troposphere including the effects of entrainment. We also showed that combined measurements of dynamical and thermodynamical fields are necessary.

This is now possible with the recent development of new instrumentation and a novel synergy of these instruments. This sensor synergy mainly consists of scanning active remote sensing systems, which are able to observe wind, temperature, and water vapor profiles with fine range and temporal resolutions so that gradients and turbulence can be measured simultaneously from the surface to the interfacial layer at the top of the PBL. These key instruments are water vapor DIAL, WVRLID, TRLID, and DLID. The synergy of these instruments also permits the observation of 2D fields of surface fluxes and vertical profiles of sensible and latent heat fluxes, which will be investigated in combination with soil and vegetation measurements. Thus, we demonstrated that we have now the tools not only to simulate but also to observe the gray zone of turbulence.

The application of this sensor synergy was realized and exploited during the LAFE conducted at the ARM SGP site during August 2017. We have described the setup and operational modes of these systems. To the best of our knowledge, LAFE is the first international field campaign dedicated to a direct and almost complete observation of L–A feedback without any model input.

Four objectives of LAFE have been defined and can be addressed with the LAFE dataset: 1) the determination of turbulence and latent heat flux profiles and the investigation of new similarity relationships for fluxes and variances, particularly with respect to entrainment; 2) the mapping of surface momentum, sensible heat, and latent heat fluxes; 3) the characterization of L–A feedback and the moisture budget in dependence of different soil moisture regimes; and 4) the verification of LES runs and the improvement of turbulence parameterizations in mesoscale models. Because of this general research approach, the instrument configuration presented here can serve as a bridge to advanced approaches to evaluate/improve satellite-based PBL monitoring and may be considered as a blueprint for a new generation of experiments conducted in different climate regions in order to achieve an improved representation of L–A feedback in models globally.

ACKNOWLEDGMENTS

This work is in memoriam of Prof. Dr. Zbigniew Sorbjan, who made significant contributions to the preparation of the research activities presented in this work. LAFE is funded by the DOE, NASA, and NOAA in the United States as well as the BMBF in Germany via the HD(CP)2 project. Support concerning the preparation and performance of LAFE by the ARM program via Nicki Hickmon, Chris Martin, James Martin, Mike Ritsche, and John Schatz is highly appreciated. We acknowledge the support of LAFE by IPM UHOH staff, namely, Andrea Riede, Alexander Geißler, and Hans-Dieter Wizemann. We highly acknowledge the operation of the Piper Navajo aircraft by the UTSI team headed by Dr. Steve Brooks.

Furthermore, the field phase of LAFE was assisted by many students and scientists, particularly, Simon Kleine (UHOH); Justin Flaherty (Cleveland State University); Sarah Borg, Greg Blumberg, and Ryan Pajela (University of Oklahoma); and Coda Phillips and David Loveless (University of Wisconsin–Madison).

The data for this paper can be made available upon request from the authors. The LAFE data will be stored in the ARM data archive.

APPENDIX: LIST OF ACRONYMS.

     
  • 1D, 2D, 3D

    One-dimensional, two-dimensional, three-dimensional

  •  
  • AERI

    Atmospheric Emitted Radiance Interferometer

  •  
  • AGL

    Above ground level

  •  
  • ARL

    Air Resources Laboratory

  •  
  • ARM

    Atmospheric Radiation Measurement

  •  
  • ASR

    Atmospheric System Research

  •  
  • ATDD

    Atmospheric Turbulence and Diffusion Division

  •  
  • BLLAST

    Boundary Layer Late Afternoon and Sunset Turbulence

  •  
  • BMBF

    Federal Ministry of Education and Research of Germany

  •  
  • BOREAS

    Boreal Ecosystem–Atmosphere Study

  •  
  • CBL

    Convective planetary boundary layer

  •  
  • CIMMS

    Cooperative Institute for Mesoscale Meteorological Studies

  •  
  • CIMSS

    Cooperative Institute for Meteorological Satellite Studies

  •  
  • CIRES

    Cooperative Institute for Research in Environmental Sciences

  •  
  • CLAMPS

    Collaborative Lower Atmospheric Mobile Profiling System

  •  
  • COMMAS

    Collaborative Model for Multiscale Atmospheric Simulation

  •  
  • COPS

    Convective and Orographically Induced Precipitation Study

  •  
  • CSD

    Chemical Sciences Division

  •  
  • DALES

    Dutch Atmospheric LES

  •  
  • DIAL

    Differential absorption lidar

  •  
  • DLID

    Doppler lidar

  •  
  • DOE

    Department of Energy

  •  
  • DWD

    German Meteorological Service

  •  
  • EDMF

    Eddy-diffusivity mass flux

  •  
  • ERA

    ECMWF reanalyses

  •  
  • ERA-Interim

    European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis

  •  
  • FIFE

    First International Satellite Land Surface Climatology Project Field Experiment

  •  
  • GEWEX

    Global Energy and Water Exchanges Project

  •  
  • GLACE

    Global Land–Atmosphere Coupling Experiment

  •  
  • GLASS

    Global Land–Atmosphere System Study

  •  
  • GPCP

    Global Precipitation Climatology Project

  •  
  • GSFC

    Goddard Space Flight Center

  •  
  • HAPEX

    Hydrological Atmospheric Pilot Experiment

  •  
  • HD(CP)2

    High-Definition Clouds and Precipitation Project

  •  
  • HOPE

    HD(CP)2 Observational Prototype Experiment

  •  
  • HRRR

    High-Resolution Rapid Refresh model

  •  
  • HSRL

    High-spectral-resolution lidar

  •  
  • IBS

    Institute of Soil Science and Land Evaluation at UHOH

  •  
  • ICON

    Icosahedral nonhydrostatic (DWD model)

  •  
  • IHOP_2002

    International H2O Project

  •  
  • IL

    Interfacial layer

  •  
  • IOP

    Intensive observation period

  •  
  • IPM

    Institute of Physics and Meteorology at UHOH

  •  
  • L–A

    Land–atmosphere

  •  
  • LAFE

    Land Atmosphere Feedback Experiment

  •  
  • LASSO

    LES ARM Symbiotic Simulation and Observation

  •  
  • LES

    Large-eddy simulation

  •  
  • Lidar

    Light detection and ranging

  •  
  • LITFASS

    Lindenberg Inhomogeneous Terrain–Fluxes between Atmosphere and Surface: A Long-Term Study

  •  
  • LoCo

    Local coupling

  •  
  • LOS

    Line of sight

  •  
  • LSM

    Land surface model

  •  
  • MYNN

    Mellor–Yamada–Nakanishi–Niino

  •  
  • MOST

    Monin–Obhukov similarity theory

  •  
  • NASA

    National Aeronautics and Space Administration

  •  
  • NCAR

    National Center for Atmospheric Research

  •  
  • NDIAL

    NCAR WVDIAL

  •  
  • NDVI

    Normalized difference vegetation index

  •  
  • NOAA

    National Oceanic and Atmospheric Administration

  •  
  • Noah-MP

    Noah LSM with multiparameterization options

  •  
  • NSSL

    National Severe Storms Laboratory

  •  
  • OU

    University of Oklahoma

  •  
  • PALM

    Parallelized LES Model

  •  
  • PBL

    Planetary boundary layer

  •  
  • RAP

    Rapid-Refresh model

  •  
  • RHI

    Range–height indicator

  •  
  • RLID

    Raman lidar

  •  
  • RRL

    Rotational Raman lidar

  •  
  • RUC

    Rapid update cycle

  •  
  • SABLE

    Surface Atmospheric Boundary Layer Experiment

  •  
  • SDLID

    SGP Doppler lidar

  •  
  • SEB

    Surface energy balance

  •  
  • SGP

    Southern Great Plains

  •  
  • SOP

    Special observation period

  •  
  • SPARC

    University of Wisconsin–Madison SSEC Portable Atmospheric Research Center

  •  
  • SRLID

    SGP Raman lidar

  •  
  • SSEC

    Space Science and Engineering Center

  •  
  • sUAS

    Small unmanned aircraft system

  •  
  • TESSEL

    Tiled ECMWF Scheme for Surface Exchanges over Land

  •  
  • TKE

    Turbulent kinetic energy

  •  
  • TRLID

    Temperature Raman lidar

  •  
  • TWP

    Tropical western Pacific

  •  
  • UDIAL

    UHOH WVDIAL

  •  
  • UDLID

    UHOH Doppler lidar

  •  
  • UHOH

    University of Hohenheim

  •  
  • URLID

    UHOH temperature and water vapor Raman lidar

  •  
  • UTSI

    University of Tennessee Space Institute

  •  
  • WCRP

    World Climate Research Programme

  •  
  • WRF

    Weather Research and Forecasting Model

  •  
  • WMO

    World Meteorological Organization

  •  
  • WVDIAL

    Water vapor differential absorption lidar

  •  
  • WVRLID

    Water vapor Raman lidar

  •  
  • WWRP

    World Weather Research Programme

  •  
  • YSU

    Yonsei University

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Footnotes

Publisher’s Note: On 6 September 2018 this article was modified to correct reference and citation errors.

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