Important topics in land–atmosphere (L–A) feedback research are water and energy balances and heterogeneities of fluxes at the land surface and in the atmospheric boundary layer (ABL). To target these questions, the Land–Atmosphere Feedback Observatory (LAFO) has been installed in southwestern Germany. The instrumentation allows comprehensive and high-resolution measurements from the bedrock to the lower free troposphere. Grouped into three components, atmosphere, soil and land surface, and vegetation, the LAFO observation strategy aims for simultaneous measurements in all three compartments. For this purpose the LAFO sensor synergy contains lidar systems to measure the atmospheric key variables of humidity, temperature and wind. At the land surface, eddy covariance stations are operated to record the energy distribution of radiation, sensible, latent and ground heat fluxes. Together with a water and temperature sensor network, the soil water content and temperature are monitored in the agricultural investigation area. As for vegetation, crop height, leaf area index and phenological growth stage values are registered.
The observations in LAFO are organized into operational measurements and intensive observation periods (IOPs). Operational measurements aim for long time series datasets to investigate statistics, and we present as an example the correlation between mixing layer height and surface fluxes. The potential of IOPs is demonstrated with a 24 h case study using dynamic and thermodynamic profiles with lidar and a surface layer observation that uses the scanning differential absorption lidar to relate atmospheric humidity patterns to soil water structures.
Both IOPs and long-term observations will provide new insight into exchange processes and their statistics for improving the representation of L–A feedbacks in climate and numerical weather prediction models. The lidar component in particular will support the investigation of coupling to the atmosphere.
Land–atmosphere (L–A) feedbacks are the result of interacting processes related to the exchange of momentum, energy and mass in the L–A system. This system consists of the compartments soil, land cover (such as vegetation) and the lower troposphere, encompassing the atmospheric boundary layer (ABL) and the surface layer (SL), including their modifications due to human activities.
A profound understanding and representation of L–A feedbacks contributes to an improvement in the skill of both weather (Holt et al., 2006) and medium-range to sub-seasonal forecasts (Van den Hurk et al., 2012). L–A feedbacks have been identified as one of the key science topics for advancing regional climate simulations (Jacob et al., 2020). Dirmeyer et al. (2018) found clear underrepresentation of the feedback of surface fluxes on boundary layer properties (atmospheric coupling leg) and an overrepresentation of the connection between soil moisture and surface fluxes (terrestrial leg). L–A feedbacks influence the effects of historic, current and future land use and land cover changes (LUCC) on regional hydrology, weather and climate (Davin et al., 2020; Devanand et al., 2020; Stevens et al., 2021). It is of particular importance to understand these interactions over agricultural landscapes in order to maintain high crop yields and food security (Singh et al., 2018; McDermid et al., 2019). L–A feedbacks over agricultural landscapes are also critically important because they modify the impacts of climate change over land (Seneviratne et al., 2006, 2010; Dirmeyer et al., 2012). L–A feedbacks play a very important role in the evolution and strength of heat waves and droughts (Jaeger and Seneviratne, 2011; Zhou et al., 2019a; Miralles et al., 2014, 2019). These extremes are expected to be sensitive to (and amplified by) climate change (Vogel et al., 2017, 2018; IPCC, 2021) and can only be simulated correctly if the feedbacks and reactions of the vegetation due to water and heat stress are well understood (Nolan et al., 2018; Anderegg et al., 2019; Zhou et al., 2019b). Advanced understanding of L–A feedback enables the consideration of bio-geoengineering efforts for mitigating the impacts of climate change (e.g., Davin et al., 2014; Branch and Wulfmeyer, 2019).
At any time and location, the quantities of the variables characterizing the L–A system, such as the soil, canopy, and ABL temperature and moisture values, as well as their fluxes and partitioning at the land surface, are a result of these feedback processes (Santanello et al., 2018; Wulfmeyer et al., 2018; Helbig et al., 2021). With a typical depth ranging from several hundreds of meters during nighttime to several kilometers during the daytime, the convective ABL (CBL) plays a very important role, as it rapidly responds (30–60 min) to changes in land surface properties by vertical mixing (Betts et al., 2004; Ek and Holtslag, 2004). For instance, van Heerwaarden et al. (2009) demonstrated that dry-air entrainment in the CBL increases surface evaporation under all conditions. The strength of the L–A coupling depends on the incoming radiation, the land surface properties and the large-scale synoptic forcing. Therefore, improved understanding of L–A feedbacks requires resolving their diurnal cycle and comprehending how they depend upon large-scale conditions and the evolving land cover over a vegetation period. These interactions become particularly complex when clouds and precipitation develop, feeding back to soil moisture, vegetation photosynthesis and the surface energy balance (e.g., Betts et al., 2007; Gentine et al., 2013; Vilà-Guerau de Arellano et al., 2014; Chen and Dirmeyer, 2017).
To improve the understanding of these feedback processes, observations of energy and water fluxes in the soil and at the land surface are necessary. L–A interactions at the surface are studied globally based on observations using eddy covariance (EC) stations. Many stations are organized into networks, e.g., FLUXNET (Baldocchi et al., 2001), AmeriFlux (Novick et al., 2018), ICOS (Franz et al., 2018) or TERENO (Zacharias et al., 2011), for which long-term datasets are available. Helbig et al. (2021) recently recommended to extend these observational stations with instruments for atmospheric measurements, which will certainly facilitate better understanding of the feedback of the land surface with the ABL and the lower free troposphere.
In addition to ground-based EC stations, remote sensing systems from aircraft or satellites can also observe the land surface. Measurements of atmospheric variables with high temporal and spatial resolutions, however, are even more challenging. Fields of wind, temperature and moisture must be provided at the same time, as these variables are entangled in all standard SL schemes. Recently, a synergy of scanning lidar systems for wind, temperature and water vapor became available that fulfills these measurement needs. Doppler lidar (DL) systems use the coherent detection technique for high-resolution line-of-sight wind measurements. The required range-resolved temperature measurements are realized with temperature rotational Raman lidar (TRL) (Hammann et al., 2015; Behrendt et al., 2015, 2020), and the necessary measurements of the moisture field are provided either from a water vapor Raman lidar (WVRL) (Turner et al., 2002; Wulfmeyer et al., 2010) or from a water vapor differential absorption lidar (WVDIAL) (Wulfmeyer, 1999; Wagner et al., 2013; Wulfmeyer et al., 2016; Muppa et al., 2016; Späth et al., 2016). It was demonstrated that fluxes at the land surface and in the ABL can be measured. Tests of model parameterizations against lidar observations were executed by Milovac et al. (2016) and Muppa et al. (2016).
In 2017, the Land–Atmosphere Feedback Experiment (LAFE, Wulfmeyer et al., 2018) was, to the best of our knowledge, the first dedicated experiment to investigate L–A feedback with the deployment of a novel scanning lidar synergy. It took place at the ARM Southern Great Plains site in Oklahoma, USA, and complemented the existing suit of instruments with a combination of Doppler lidars, Raman lidars and differential absorption lidars to measure the wind velocity, humidity and temperature from the land surface through the ABL and up to the lower troposphere (Späth et al., 2022a). Operating scanning lidars were the key to obtaining measurements in the surface layer region to overcome the near-range gap of vertical-pointing lidars that deliver no data within the surface layer (Späth et al., 2022a).
The campaign-based observations are time limited and thus allow only a few
meteorological situations to be captured. For statistically significant
results and robust conclusions, long-term observations are required. For
this reason, the Land–Atmosphere Feedback Observatory (LAFO) was set up as a
research facility at the University of Hohenheim (UHOH) in southwestern Germany. The
measurement data are comprehensive, highly resolved and very precise, meaning
that new parameterizations of land–atmosphere exchange processes between
soil, vegetation and the lower troposphere can be developed, implemented,
and tested (e.g., the applicability of the Monin–Obukhov similarity theory, MOST, for natural heterogeneous terrain). The design and operation of LAFO is connected to an international project of the World Climate Research
Program (WCRP) within the Global Land/Atmosphere System Study (GLASS) panel
(see
The overarching scientific goal of LAFO is to explore the L–A feedback with a novel synergy of energy balance and eddy covariance stations, soil and vegetation measurements, and scanning lidar systems. LAFO is a platform to bring together existing and unique instruments to seek observations of the soil, the land surface, the vegetation and the lower troposphere simultaneously.
To achieve the overarching goal, LAFO follows three scientific objectives:
determining the water and energy balances and the land–atmosphere feedback as a function of the conditions of the soil, vegetation, and atmosphere in a study region with agricultural fields; investigating the heterogeneity of the fluxes at the land surface and in the boundary layer; developing new parameterizations of the fluxes at the land surface taking into account the vegetation dynamics and the turbulence in the ABL.
The LAFO measurement design follows the LAFE instrumentation and benefits
from previous projects, e.g., the research unit “Agricultural Landscapes
under Global Climate Change – Processes and Feedbacks on a Regional Scale”
of the German research foundation, using the existing long-term and ongoing EC observations in Kraichgau and the Swabian Jura area, southwestern Germany (Wizemann et al., 2015; Weber et al., 2022). This project also contained the Surface–Atmosphere–Boundary–Layer Exchange (SABLE) field campaign to test
lidar observations in the surface layer (Späth et al., 2016). Both the
LAFE and the new LAFO design with their instrument synergies have already
made important contributions to this. By comparing observations to models, (e.g., MOST) natural heterogeneous land surfaces can be
investigated or new model parameterizations can be developed (Lee et al., 2019; Lee and Buban, 2020; Späth et al., 2022a).
This paper presents LAFO and is structured in the following manner: the observation strategy of the LAFO setup with the study area and its sensor synergy is presented in Sect. 2, the instrumentation is presented in Sect. 3, and measurement examples are presented in Sect. 4. Finally, we summarize and give an outlook on future developments.
To accomplish our LAFO objectives, we aim for a synergistic sensor network to simultaneously monitor a wide variety of system properties and state variables from the bedrock to the lower free troposphere. The sensor synergy consists of the three components: (1) atmospheric measurements, (2) land surface measurements and (3) vegetation measurements. The interactions between the components are depicted in Fig. 1, and the measured variables are listed in Table 1. The observations illustrated in Fig. 1 show the envisioned full suite of instrumentation for the full extent of observations to study L–A feedbacks as proposed by the GLASS panel. To observe the ABL and the interfacial layer, the combination of scanning lidars plays an important role in capturing the relevant variables with the required resolution to resolve the processes. In the surface layer, EC stations with 2 and 10 m measurement levels with several meteorological measurements can be complemented by temperature sensing along optical fibers (FODS) and low-elevation scanning lidar. In the sub-surface and soil regime, measurements of soil moisture, temperature, and matric potential and their profiles are important variables. Here several hydrological observations complement the information to characterize soil properties. The evapotranspiration of soil and vegetation can be sampled with the eddy covariance method or lysimeters. Further measurements, such as sap flow or other vegetation-characterizing measurements, are helpful to determine the contributions of soil and vegetation. It is noted that some depicted measurements can be obtained continuously and perhaps be automated while others are obtained better during field experiments and intensive observation periods (IOPs).
Measured variables with the LAFO sensor synergy. Instruments in bold are operated continuously. For these instruments we also give the temporal and spatial resolutions; otherwise we refer to the instrument descriptions in Sect. 3 and the corresponding references. RHI scan refers to the range height indicator scanning operation to measure vertical cross sections.
LAFO sensor synergy with the following elements labeled: (I) planetary boundary layer top; (II) mesoscale vortex; (III) flux
footprint; (1) satellite remote sensing; (2, 3, 4) vertically pointing and
scanning Doppler, water vapor, and temperature lidar systems; (5) 3D fiber
optic distributed temperature sensing (FODS) in combination with distributed
wind and trace gas sensors; (6) energy balance and eddy covariance stations;
(7) uncrewed aerial vehicle (UAV); (8) time domain reflectometers (TDRs); (9) leaf area index (LAI) measurement; (10) gas exchange system for photosynthesis and transpiration rate measurements; (11) tensiometers; (12) in situ crop measurements, such as root and shoot biomass, as well as canopy height; (13) soil moisture, temperature, and matric potential network; (14) leaf cuvette; (15) open rainfall sampler; (16) gas exchange chamber; (17) throughfall sampler; (18) groundwater well; (19) in situ soil water probes (14–19 are all coupled to a water isotope analyzer); (20) IR gas analyzer; (21) mini lysimeter; (22) canopy temperature, water vapor, and
In the following, we explain the target variables and introduce the sensor synergy and our experimental site. The available and operated instruments in LAFO are described in Sect. 3.
The key atmospheric variables of absolute humidity
To study vegetation status and spatial pattern in standing crops we will
record information about biomass, plant height
The Water and Temperature Sensor Network (WaTSeN) monitors the spatial
variability of soil water content
With these data, the scientific objectives of LAFO can be achieved using the
following methods.
We have adopted a two-level observation strategy: long-term time series and IOPs. These provide us with parsimonious yet highly informative datasets obtained from our sensor synergy network.
In the long term, we operate a number of instruments in operational mode to obtain continuous year-round time series. As such, since the beginning of LAFO in 2018, WaTSeN has been continuously supplying time series to characterize the spatial heterogeneity of soil water contents and temperatures at the Heidfeldhof. Similarly, two EC stations have been deployed. At the lidar site, two Doppler lidars, a Doppler cloud radar, a micro rain radar and an optical disdrometer have been continuously operated since May 2020. The vegetation status is registered on all plots with WaTSeN stations.
LAFO experimental site at the Heidfeldhof, showing the locations of the two eddy covariance stations, the 22 soil water content and temperature stations of WaTSeN, and remote sensing equipment at the lidar site and the extents of the agricultural fields. The inlet shows the location of LAFO in southwestern Germany (basemap sources: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community).
In addition to the operationally collected data, intensive observation periods will be organized as field experiments, thereby complementing the operational instrumentation with additional sensors and measurement systems to get an almost full picture of the situation in the atmosphere, at the land surface, in the vegetation layer, and in the soil. This extended instrumentation is operated temporarily to target a certain aspect of L–A interaction. Additional instruments of UHOH are DIAL, RRL, BreedVision, drones, and EM38. Interesting sensors to add are further DLs for SL sampling; fiber optic distributed sensing (FODS) to study the temperature distribution; canopy sensors to observe temperature, humidity, radiation, and wind inside and at the top of the canopy stand; and isotope measurements to discriminate between evaporation and transpiration. Interested external partners can benefit from our sensor synergy and join through collaboration. For example, LAFO was one observation site in the frame of the field campaign Swabian MOSES that took place in spring 2021 (Kunz et al., 2022). During this field experiment the Atmospheric Raman Temperature and Humidity Sounder (ARTHUS) was operated to capture atmospheric temperature and humidity profiles next to wind and cloud observations with DL and a Doppler cloud radar.
The LAFO site is located approximately 10 km south of Stuttgart and 3 km
north of Stuttgart airport, near the University of Hohenheim, Stuttgart (48
In addition to the LAFO measurements, additional observations are made close by. At a 2 km distance from the site, the Hohenheim weather and climate station has been recording data since 1878 (Wulfmeyer and Henning-Müller, 2005). The meteorological observations made from Stuttgart airport (METAR code: ESSD) are located only 3 km to the south. The official weather station Stuttgart-Schnarrenberg operated by the German weather service (DWD) with radiosonde launches (two launches per day) is located 13 km to the Northwest. Finally, the water level of the small Körsch creek next to the study area is recorded 8 km downstream to the east in Denkendorf.
The three LAFO components of the LAFO synergy are dedicated to the three compartments atmosphere, soil and land surface, and vegetation and are described in the following, listed in Table 1 and have their locations marked in Fig. 2.
The first component consists of three scanning lidar systems, a water vapor differential absorption lidar (DIAL), a water vapor and temperature rotational Raman lidar (RRL), and Doppler lidars (DLs) for wind measurements that are located at the lidar site (Fig. 2). The DIAL and RRL instruments have been developed and designed at the Institute of Physics and Meteorology of UHOH, and the deployed Doppler lidars are manufactured by Halo Photonics Ltd. (UK, Pearson et al., 2009). With this combination, it is possible to measure 2D to 3D structures of these important atmospheric variables from the ground through the boundary layer to the lower free troposphere (about 3–4 km altitude). The coherent Doppler lidars are limited to regions with atmospheric scatter (aerosols, particles), which restricts the measurement range mostly to the ABL and thin clouds, but they have a nominal measurement range of 10 km. The range of DIAL and RRL reaches 5–6 km depending on the resolution. The DIAL is mounted on a mobile platform with a frequency-doubled Nd:YAG-pumped Ti:sapphire laser transmitter at 818 nm with an output power of up to 10 W (Wagner et al., 2013; Metzendorf, 2018). The 3D scanner is equipped with an 80 cm primary mirror and allows scanning of the full upper hemisphere enabled for the first 3D humidity measurements (Späth et al., 2016). Raw signals are recorded with 0.1 s and 15 m frequency and result in provided data resolutions of 1–10 s in time and 60–300 m in range. The RRL is also a mobile, 3D scanning lidar system. The laser transmitter is based on a frequency-tripled Nd:YAG laser at the eye-safe wavelength of 355 nm and a 40 cm receiving telescope with a two-mirror 3D scanner (Radlach et al., 2008). A very efficient receiver separates the components of rotational Raman scattering for temperature and vibrational Raman scattering for water vapor mixing ratio (WVMR) measurements (Hammann et al., 2015). Meanwhile, a more compact and automated Raman lidar for temperature and humidity measurements (Atmospheric Raman Temperature and HUmidity Sounder – ARTHUS, Lange et al., 2019) has been developed and is available at LAFO for collecting vertical profiles of temperature and humidity. The temporal and spatial resolutions are 1–10 s and 100–300 m. Wulfmeyer et al. (2015) demonstrated that the DIAL and the RRL are currently the most accurate and highest-resolution water vapor and temperature remote sensing systems in the world. The RRL was the first to measure inversions at the top of the boundary layer and turbulent fluctuations in daytime temperature (Behrendt et al., 2015). As research instruments, the scanning DIAL and RRL are not automated; thus, manual operation is only affordable for certain time periods during dedicated intensive observations periods. In contrast, ARTHUS is a fully automated system for collecting vertical profiles. When ARTHUS is not involved in other field campaigns, it is operated in LAFO.
The humidity and temperature measurements are complemented by wind measurements of two DLs that are operated continuously. One DL is dedicated to observing the vertical wind with a turbulent resolving resolution of 1 s and 30 m in a constant vertical steering mode. The other DL is operated in six-beam VAD (velocity azimuth display) mode to detect profiles of horizontal wind (Choukulkar et al., 2017). These measurements also have turbulence resolution with 90 s and 21 m. Wulfmeyer et al. (2016) showed that this measurement combination of DIAL, RRL and DL is crucial for measuring fluxes through the ABL and for developing new turbulence parameterizations. In combination with additional cross-track-scanning DLs the measurement setup for SL profiling (Späth et al., 2016) can be achieved and used for mapping land surface fluxes. Additional instruments, e.g., DLs for cross-track scanning, will be provided by project partners.
During cloudy and rainy weather situation one Doppler cloud radar (DCR) is
operated in vertical stare mode. The DCR is a MIRA-36 of Metek GmbH
(Görsdorf et al., 2015). The recorded DCR data have resolutions of 1 s and 30 m. To characterize rain in more detail a micro rain radar (MRR, Metek GmbH) and an optical disdrometer (ParSiVel) are installed as well. All instruments except DIAL and RRL are operated continuously and quicklooks of measurements are available in near real-time on the LAFO website
The second component combines sensors that determine energy fluxes at the land surface and state variables of soil.
Temporal variations in spatial average soil water content (
Surface energy fluxes are derived from long-term eddy covariance measurements at two stations at the Heidfeldhof (Fig. 2). The stations are each fully equipped with a 3 m mast and sensors to measure
Beginning in June 2018, a Water and Temperature Sensor Network (WaTSeN) was
installed to quantify the spatial and temporal patterns of precipitation, soil
water content and temperature using a total of 22 Aquaflex II universal
sensors (Streat Instruments Ltd) to monitor the soil water content and
temperature, as well as 22 rain gauges (Pronamic), in November 2020, covering the
entire Heidfeldhof (HFH; see Fig. 2) area of 0.84 km
In Fig. 3, the spatiotemporal variability of soil water content (SWC), its standard deviation and the time series of precipitation for the 2-year period 2019–2020 are shown. The SWC shows the typical wetting and drying cycles during the year reaching field capacity (the water content which can be held by capillary forces against gravity) in winter and subsequently long periods of drying between April and October as a result of net evapotranspiration being larger than precipitation over this period. SWC values above field capacity are only observable during strong infiltration events, and subsequent rapid drainage leads to plateaus in the observations, which testifies to the high (un)saturated soil hydraulic conductivity. From the state space of the standard deviation of SWC (
So far we have not detected strong coupling between the soil hydrological variables and fluxes at the land surface during the growing seasons. Latent and sensible heat fluxes show no significant correlation with SWC or water potential at any depth. Thus far, soil variables have not been shown to control the fluxes at the land surface during the growing season, and heat fluxes are radiation driven. This can be beneficial since it allows for the identification of the plant state as the principal control for spatial surface flux heterogeneity at the land surface.
This component consists of equipment for characterizing vegetation. These include UAVs for recording plant characteristics, which now represent an innovative alternative to traditional remote sensing with manned aircraft or satellites due to the inexpensive and short-term availability of high spatial resolution sensor data. The higher spatial resolution in particular allows for applications using smaller field plots. Several spectral bands are necessary (e.g., 670, 700, 740 and 780 nm) for the calculation of the red edge inflection point (REIP), which is used for the determination of vegetation indices used for the derivation of biomass and nitrogen supply. The spectral analysis was performed with a UAV equipped with converted industrial four-sensor camera for REIP imaging by means of suitable interference filters (Geipel et al., 2014). In addition, the camera has been coupled to an external light meter to dynamically adjust the exposure time to the solar irradiance. The camera is individually programmable and also allows image processing in near real time.
In cooperation with the Osnabrück University of Applied Sciences, the
State Seed Breeding Institute (LSA) at the UHOH has developed the “BreedVision” phenotyping platform (Busemeyer et al., 2013). It consists of
a carrier vehicle and a sensor module. The carrier vehicle is a high-wheel
tractor especially adapted to the requirements of the sensor module. Both
the track width and the height of the carrier vehicle can be adjusted
hydraulically. The sensor module includes sensors with different morphological or spectral selectivity, such as light grids, laser distance
sensors, multi-reflective ultrasonic sensors, digital cameras, a plenoptic
camera and a hyperspectral camera. For example, light grids create a shadow
image from the transmitted light through the plants of the plots, from which
plant height
Further, we routinely register the leaf area index (LAI) with a LAI2200C sensor from LI-COR Biosciences Inc. (USA) and the crop height and phenomenological growth stage using the BBCH code (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und Chemische Industrie; Meier, 2018) at our EC and WaTSeN stations throughout the main vegetation period (biweekly). Furthermore, we have a set of canopy sensors to investigate temperature and humidity at different levels inside and right above the canopy top.
The LAFO equipment and its data products are listed in Table 1. The list contains the currently operationally available instruments and measurement data but can be extended in the future or for measurement campaigns with collaborators complementing instrumentation for certain time periods.
For studying dynamics and thermodynamics in the ABL at LAFO we use the
synergy of DLs and ARTHUS. As explained in Sect. 2.2, we measure dynamic
profiles of vertical wind with one constantly vertical pointing DL and
retrieve the horizontal wind from DL in six-beam VAD mode. In Fig. 4 we show 24 h wind measurements for 27 June 2021. Figure 4a–d show vertical profiles of vertical and horizontal wind and profiles of the backscatter coefficient. The resolution of the vertical wind and backscatter coefficient is 1 s and 30 m. The horizontal wind is retrieved from the six-beam VAD mode with a sinusoidal fit and results in a 90 s and 21 m temporal and spatial resolution (Bonin et al., 2017). To reduce the effect of the convective eddies for the horizontal wind, an additional gliding average of 21 min was applied. In the vertical wind plot (Fig. 4a) the development of the CBL started around 07:00 UTC, which is indicated by the stronger up and down draft starting to grow from the surface into the boundary layer. The CBL stayed convective until around 17:00 UTC and reached a height of around 1 km. Before and after that period only low vertical wind was observed, which is typical for the nighttime boundary layer. While there was nearly no vertical motion at the beginning of the day, a low-level jet (LLJ) in the horizontal wind up to 8 m s
Measurements of the vertical and horizontal wind from 27 June 2021. In addition to the wind itself, the backscatter coefficient, vertical wind variance, TKE and momentum flux are also shown.
Water vapor mixing ratio and temperature time series for 27 June 2021 are plotted in
The thermodynamic profiling was performed with ARTHUS. The time series of WVMR and temperature profiles of 27 June 2021 are shown in Fig. 5a and b. The data show the moistening and warming of boundary layer in the cause of the day when the boundary layer became convective. In combination with vertical wind measurements the latent and sensible heat fluxes through the boundary layer can be calculated with the covariances of humidity and temperature fluctuations and vertical wind (Behrendt et al., 2020) and are plotted in Fig. 5c and d. When including horizontal wind, the water vapor budget in the BL can be determined, as demonstrated for the first time for a LAFE case. This measurement synergy demonstrates the atmospheric link for the investigation of energy and water budget, as aimed for in LAFO objective 1.
The turbulent motion in the BL can be captured with the wind observation. The depth of the CBL is related to the energy intake by solar radiation and its partitioning at the land surface. Thus, we determined the mixing height for the 3-month period from 1 May to 31 July 2021 and correlate them to the surface fluxes measured with the EC station.
For determining the mixing layer height (MLH) we use vertically pointing
Doppler lidar and Doppler cloud radar data and a fuzzy logic approach to weight the different data (Bonin et al., 2018). The DL measures the vertical
wind
Statistics of the diurnal cycle between 06:00 and 18:00 UTC of
mixing layer height (MLH)
The MLH is calculated for each day for the time period between 06:00 and
18:00 UTC, which covers daytime between sunrise and sunset when turbulent
mixing is expected. The analysis is based on the 1 s data. Nighttime MLH
is usually very low, and mixing is caused by friction between air and land
surface. These situations cannot be captured with our vertical pointing instruments as the lowest range bin is not lower than 100 m. Data of the 1 s MLH data are averaged for 30 min intervals as the surface fluxes are
calculated likewise for half-hour time slots. In Fig. 6a the statistics of the diurnal cycle of the MLH are plotted as a box and whisker plot. The box and whiskers provide a clear picture of data distribution. The box itself represents the range of 50 % central data (between 25 % and 75 %), also known as interquartile range (IQR). In the box the red line gives the median of the data. The dashed lines (whiskers) extending from the box mark the remaining data, ranging from the minimum to the maximum values. Data points outside of these ranges are considered outliers, which are defined as
The statistics include the full 3-month period covering all cases including clear sky, clouds, rain and thunderstorms. On convective days the
MLH reached a mean of up to around 1.2 km and up to more than
2 km overall; however, on cloudy days the MLH stays about 400 m lower. This is also
represented in the flux data when the surface heating is low on cloudy days.
The sensible heat flux varies from 20 to 70 W m
Correlation of the mixing layer height MLH with the surface sensible and latent heat fluxes (
This example illustrates the use of a long-term dataset to evaluate statistics and relationships between variables of different compartments like atmosphere and the land surface. Such relationships are the first step to develop new parameterizations or metrics for the description of land surface models or to test existing models (as outlined in LAFO objective 3).
Scanning WV DIAL measurements over the agricultural field of Heidfeldhof on 20 October 2020 between 14:23 and 14:31 UTC. Panel
To observe the direct link between atmosphere and land surface we make use
of the scanning capability of our lidar instruments. With low-elevation scanning lidar measurements we are able to observe surface layer profiles of
horizontal wind, humidity content and temperature (Späth et al., 2022a).
Figure 8 demonstrates humidity measurements with the scanning WV DIAL across the agricultural fields in the northwestern direction. The scan direction and a photograph are shown in Fig. 8a. For the measurement the lidar scanning
unit (Fig. 8a) moved with 0.2
In the region with low atmospheric humidity, inter-tillage in a small growing stage was present on the fields below. Closer and further away, the fields were made up of bare soil where higher evaporation is enabled according to the higher soil water content. This observation displays how heterogenic landscapes and corresponding atmospheric structures can be observed with scanning lidars and the soil sensor network within the LAFO instrumentation. These kinds of observations allow for work on LAFO objective 2 regarding surface fluxes in heterogeneous terrain.
Important topics in L–A feedback research are water and energy balances and heterogeneities of fluxes at the land surface and in the ABL. To target these questions, the land–atmosphere feedback observatory LAFO has been installed as the first GEWEX LAFO. LAFO is dedicated to L–A feedback research, is located in southwestern Germany in a midlatitude continental climate, and demonstrates the GLASS panel proposed instrumentation allowing comprehensive and high-resolution measurements from the bedrock to the lower free troposphere. Grouped into three components (atmosphere, soil and land surface, and vegetation), the LAFO observation strategy aims for simultaneous measurements in all three compartments. For this reason, the LAFO sensor synergy contains lidar systems to measure the atmospheric key variables of humidity, temperature and wind. At the land surface, two EC stations are operated to record the energy distribution of radiation, sensible, latent and ground heat fluxes. Profiles of soil water content, soil temperature and matric potential are measured in the soil under the EC stations. With WaTSeN the soil water and temperature are monitored in the agricultural investigation area. The vegetation status is registered by crop height, LAI measurements and phenological growth stage (BBCH).
The observations in LAFO are organized into those from operational measurements and those from intensive observation periods (IOPs). Operational measurements aim for long time series datasets to investigate statistics like the demonstrated correlation between MLH and surface fluxes. Furthermore, the long-term datasets from the EC stations are interesting to use for machine learning approaches to investigate new SL relationships, as demonstrated by Wulfmeyer et al. (2022). During IOPs, non-automated instruments complement the operational instrumentation for extended analysis, e.g., analysis of sensible and latent heat fluxes in the ABL (with humidity and temperature measurements from ARTHUS) or SL observations of humidity (with the scanning capability of the WV DIAL) to relate atmospheric moisture distribution to soil water structures. The three measurement examples illustrate how the LAFO instrumentation will be used to target the three LAFO objectives to investigate LA feedback.
Further, some of LAFO's measurement technology has significant commercialization potential. In particular, the development of compact and operational water vapor and temperature profilers (ARTHUS, Lange et al., 2019; NCAR DIAL, Spuler et al., 2015) with high resolution and accuracy has long been requested for use in national and international networks to improve weather forecasting (Adam et al., 2016; Weckwerth et al., 2016; Thundathil et al., 2020, 2021), climate monitoring, verification of models and satellite measurements, and data assimilation. LAFO can serve as a platform to test and develop new devices of this kind and support the GLASS panel vision to setup GLAFOs in all climate regions.
In the upcoming months and years, the observational dataset will not only
continuously grow but also be made publicly available. Our database is based
on the available open-source data portal software of TEODOOR (Kunkel et al.,
2013) and connected to the European Network of Hydrological Observatories
(ENOHA,
The presented data are available under
VW, AB and TS designed the observation strategy, and FS, VR, TKDW, SM, JI and DL carried out the observation. FS, VR, TKDW, SM, DL and SSA analyzed the data. FS, VR and TKDW prepared the manuscript with contributions from all co-authors.
The contact author has declared that none of the authors has any competing interests.
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We thank the Carl Zeiss Foundation for funding the setup phase of LAFO as part of its program to strengthen research infrastructures at universities. Further, we kindly thank Herbert Stelz, Stefan Pilz, Timo Keller, Thomas Schreiber, Christian Schade, Jacky Schulz and Arne Poyda for their different levels of support and measurements.
This paper was edited by Fernando Nardi and reviewed by Manuel Helbig and one anonymous referee.