Investigation of Ocean- and Land-Atmosphere Feedbacks using a new Synergy of Active Remote Sensing Systems.
Syed Saqlain Abbas
Institute of Physics and Meteorology, University of Hohenheim
Supervisor: Prof. Dr. Volker Wulfmeyer
Summary:
Investigating the dynamics of atmospheric boundary (ABL) layers is very essential for air quality studies, and study of energy and water cycle, wind energy and for weather and climate models. The ABL is the lowermost part of the atmosphere over land and ocean surface, and it is directly influenced by the Earth’s surface forcing in the time scale of an hour. The structure of the marine atmospheric boundary layer is characterizing by the sea surface temperature (Hollis et al. 2005), and air is advected across a strong sea surface temperature. Therefore, this give rise a strong variability in the thermos-dynamical structure of marine atmospheric boundary layer, surface flux and cloud properties (Hollis et al. 2005). Hence, accurate measurement of momentum flux, sensible heat flux and latent flux between the atmosphere and the ocean/land surface are essential for understanding reginal water cycles in the Earth system. This interaction must be understood accurately so that uncertainties in weather, seasonal and climate models’ simulations may be minimized. For this, the accurate measurement of atmospheric temperature, moisture and wind profiles is essential because these three parameters are crucial for the formation of clouds and precipitation.
The land-atmosphere feedbacks, horizontal and vertical transport processes in the convective boundary layer (CBL) will be investigated comprehensively using the long-term time series data obtained from very advance and sophisticated laser scanning instruments. This novel remote sensing synergy consist of Raman Temperature and Humidity Sounder (ARTHUS), Doppler lidars and Doppler Cloud Radar, these are the excellent tools for observing high spatial and temporal resolution data of water vapor, temperature, wind atmospheric profiles and cloud types. ARTHUS performs exceptional observations in the ABL (Lange et al. 2019), and Doppler lidars are the best remote sensing instruments to measure accurate wind and turbulent properties. In this project, different datasets will be analyzed in detail, these datasets are recorded during Eurec4a 2020 campaigns in the Caribbean, Land Atmosphere Feedback Experiment (LAFE) in Oklahoma, USA and the Land Atmosphere Feedback Observatory (LAFO) at University of Hohenheim. The aim of the LAFO campaign is to understand the connection between CBL, formation of clouds and precipitation in the weather, climate, and earth system models.
Field campaigns:
The Land-Atmosphere Feedback Experiment (LAFE, Wulfmeyer et al., 2018) field studies have been performed in August 2017 in Oklahoma, USA. The main objective was investigation of the land surface fluxes and horizontal and vertical transport process within the mixing layer. Many state-of-the-art remote sensing instruments were deployed at Atmospheric Radiation Measurement (ARM) facility Southern Great Plains (SGP). These instruments include scanning Doppler lidar system, water vapor and temperature Raman lidar and water vapor DIAL operated primarily in the vertical-staring mode.
Another very important filed study in the area of atmospheric and marine sciences is elucidating the role of clouds-circulation coupling in climate (EUREC4A), It took place from 20 January to 20 February 2020 in the Caribbean. As a part of this global initiative the measurements of the lidar systems on board the research vessel Maria S. Merian were carried out by the Remote Sensing Department of the Institute of Physics and Meteorology (IPM) at the University of Hohenheim (https://www.uni-hohenheim.de). In this field study the world-wide best remote sensing systems namely Doppler lidars and ARTHUS were deployed on non-stabilized moving research vessel Maria S. Merian to probe an oceanic trade-wind region near the equator. This trade wind region acts as a thermal shield to regulate the temperature for the atmosphere by the presence of trade wind cumulus clouds. In this region the clouds form in the trade wind layer and dissipate again, therefore, this region is very sensitive to climate change, a very small feedback effects may cause a significant rise is temperature.
Very high temporal and spatial resolution is currently being collected at LAFO test site, it is located to south of Stuttgart, nearby the Stuttgart airport approximately between 48° 42' North Latitude and 9°11' East Longitude (https://lafo.uni-hohenheim.de). Principally, the LAFO field measurement concept is based on LAFE field campaign. A novel scanning lidar systems synergy installed at LAFO, this comprise water vapor differential absorption lidar (DIAL), a water vapor and temperature rotational Raman lidar (RRL) and Doppler Cloud Radar in vertical-staring mode, two Doppler lidars (DL) are being operated as one of Doppler lidar collecting data in vertical-staring and the other Doppler lidar accumulating data continuously in scanning mode.
Objectives:
The first objective is to provide the full assessment of the momentum flux, sensible heat flux and latent heat flux including measurement uncertainties in the ocean-land atmospheric boundary layer. The critical turbulent variables (variance profiles of vertical wind, temperature and moisture fluctuations) in the convective boundary layer are measured continuously using Doppler lidar and Raman lidars, and this gives valuable data for land-surface flux model verification and testing turbulence parametrization (Behrendt et al., 2020).
The second objective is to study water budgets in the marine and the atmospheric boundary layer over the ocean land in different climate zones. The measured fluxes profiles are very important to understand the distribution of temperature, humidity and vertical wind in the atmospheric boundary layer (ABL), and further gives information of atmospheric stability and formation of clouds and precipitation (Behrendt et al., 2020).
The final objective is the development of parameterizations of surface and atmospheric variables in the convective boundary layer (CBL). These variables include entrainment fluxes, higher-order moments of humidity, potential temperature and vertical wind.
Methodology:
In this study, the large data will be analyzed which were obtained from different fields campaigns (LAFE, 2017, EUREC4A 2020 and LAFO). The data obtained from Doppler lidars (both in six-beam scanning and staring mode) and Raman lidar (vertical profiles of temperature, water vapor mixing ratio, and optical properties of aerosol and cloud particles at 355 nm) must be preprocessed because lidar data is prone to instrumental errors. The instrumental errors must be eliminated from atmospheric turbulent fluctuations. For this, Lenschow et al. (2000) has presented a very important method which separates efficiently the random instrumental noise from higher order moments for temperature, vertical wind, and humidity. Wulfmeyer et al. (2016) introduced further important refinements which were automated spike detection and proper choice of lags in the autocovariance method (Behrendt et al., 2020). Finally, the correlation of temperature and vertical wind fluctuations provides the profiles of sensible heat. Similarly, the latent heat flux profiles can be obtained from the correlation of humidity fluctuation and vertical wind fluctuation. The accurate determination of these fluxes is highly critical for the turbulence parametrization in weather and climate models (Wulfmeyer et al. 2016).
For the EUREC4A 2020 campaign the remote sensing instruments were installed on non-stabilized platform, this has increased the effect of ship motion and continuously changing orientation (roll, pitch and yaw) in the observation of Doppler lidar wind data. An appropriate algorithm is already developed to retrieve the corrected horizontal wind profiles. This algorithm effectively removes stripes from horizontal wind profiles and also from the six-beam analysis. In addition, the contamination of horizontal wind, ship velocity and ship orientation were removed from vertical wind profiles but still the moment arm correction need to be done. These steps are very crucial to determine accurate sensible, latent and momentum fluxes. Usually, the effect of non-stabilized platform is quite significant on Doppler wind data.
Typically, the momentum fluxes will be determined using six-beam method presented by Sathe et al. (2015), as per this method one of the Doppler lidar must be in scanning mode at particular six azimuth angles and fixed elevation say 45°. This method principally uses the radial velocity variances of each of the beam independently, and further computes Reynolds stress tensor components in the CBL. After preprocessing high temporal resolution lidar data in the mixing layer by taking account of instrumental random errors, the momentum fluxes and horizontal wind profiles and turbulent kinetic energy will be retrieved and analyzed.
Bonin et al. (2018) has developed a novel composite fuzzy logic approach to determine the mixing height by applying the composite techniques using combination of various scan types of Doppler lidar data.
Expected results:
In all cases (LAFE, LAFO and EUREC4A), the high temporal resolution data is obtained because of instrument automated mode. This data is very important and valuable for model verification and turbulence parametrization (Behrendt et al., 2020). The successful accomplishment of this project will improve the existing dataset and advanced parameterizations of fluxes in high-resolution land-atmosphere models. In detail, the following accomplishments will be achieved.
- Developing the ship motion and lidar pointing errors (ship roll, pitch and yaw angles) correction algorithm for Doppler lidar wind data for the EUREC4A campaign
- Flux profiles from all campaigns
- Studies of local water and heat budgets and their links to evapotranspiration in order to optimize agricultural practices
- Derivation of the parameterization of fluxes to optimize weather and climate models for better prediction of extreme events
- Relate the water budget analyses to agricultural activities
References:
Behrendt, A., Wulfmeyer, V., Senff, C., Muppa, S. K., Späth, F., Lange, D., Kalthoff, N., & Wieser, A. (2020). Observation of sensible and latent heat flux profiles with lidar. Atmospheric Measurement Techniques, 13(6), 3221–3233. https://doi.org/10.5194/amt-13-3221-2020
Lenschow, D. H., Wulfmeyer, V., & Senff, C. (2000). Measuring Second- through Fourth-Order Moments in Noisy Data. Journal of Atmospheric and Oceanic Technology, 17(10), 1330–1347. doi.org/10.1175/1520-0426(2000)017<1330:MSTFOM>2.0.CO;2
Sathe, A., Mann, J., Vasiljevic, N., & Lea, G. (2015). A six-beam method to measure turbulence statistics using ground-based wind lidars. Atmospheric Measurement Techniques, 8(2), 729–740. doi.org/10.5194/amt-8-729-2015
Wulfmeyer, V., Muppa, S. K., Behrendt, A., Hammann, E., Späth, F., Sorbjan, Z., Turner, D. D., & Hardesty, R. M. (2016). Determination of Convective Boundary Layer Entrainment Fluxes, Dissipation Rates, and the Molecular Destruction of Variances: Theoretical Description and a Strategy for Its Confirmation with a Novel Lidar System Synergy. Journal of the Atmospheric Sciences, 73(2), 667–692. https://doi.org/10.1175/jas-d-14-0392.1
Wulfmeyer, V. and Turner, D. (2016). Land-Atmosphere Feedback Experiment (LAFE) Science Plan. [online] Available at: https://www.arm.gov/publications/programdocs/doe-sc-arm-16-038.pdf
Pyatt, H. E., Albrecht, B. A., Fairall, C., Hare, J. E., Bond, N., Minnis, P., & Ayers, J. K. (2005). Evolution of Marine Atmospheric Boundary Layer Structure across the Cold Tongue–ITCZ Complex. Journal of Climate, 18(5), 737–753. doi.org/10.1175/jcli-3287.1
Lange, D., Behrendt, A., & Wulfmeyer, V. (2019). Compact Operational Tropospheric Water Vapor and Temperature Raman Lidar with Turbulence Resolution. Geophysical Research Letters, 46(24), 14844–14853. https://doi.org/10.1029/2019gl085774
Bonin, T. A., Carroll, B. J., Hardesty, R. M., Brewer, W. A., Hajny, K., Salmon, O. E., & Shepson, P. B. (2018). Doppler Lidar Observations of the Mixing Height in Indianapolis Using an Automated Composite Fuzzy Logic Approach. Journal of Atmospheric and Oceanic Technology, 35(3), 473–490. https://doi.org/10.1175/jtech-d-17-0159.1
Manninen, A. J., Marke, T., Tuononen, M., & O’Connor, E. J. (2018). Atmospheric Boundary Layer Classification with Doppler Lidar. Journal of Geophysical Research: Atmospheres, 123(15), 8172–8189. doi.org/10.1029/2017jd028169