Application and Improvement of the Unmanned Aerial System SUMO for atmospheric boundary layer studies Stephanie Mayer Geophysical Institute University of Bergen, Norway A thesis submitted for the degree of Philosophiae Doctor (PhD) June 2011 Coming together is a beginning; keeping together is progress; working together is success. (Henry Ford) This thesis is written in LATEX. ii Contents 1 Introduction 3 1.1 In-situ & remote measurement methods . . . . . . . . . . . . . . . 6 1.1.1 Profiling the atmospheric boundary layer . . . . . . . . . . 6 1.2 Numerical weather prediction models . . . . . . . . . . . . . . . . 9 1.2.1 A short historical overview . . . . . . . . . . . . . . . . . . 9 1.2.2 Parameterizationsofphysicalsubgrid-scaleprocessesinnu- merical weather prediction models . . . . . . . . . . . . . . 10 1.2.3 The Weather Research and Forecasting model WRF . . . . 11 1.3 Motivation & Objectives . . . . . . . . . . . . . . . . . . . . . . . 12 1.4 The Small Unmanned Meteorological Observer SUMO . . . . . . 15 1.4.1 The airframe . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.4.2 Autopilot system . . . . . . . . . . . . . . . . . . . . . . . 15 1.4.3 Meteorological sensors . . . . . . . . . . . . . . . . . . . . 17 1.4.4 SUMO operation . . . . . . . . . . . . . . . . . . . . . . . 17 1.5 Current rules and regulations for unmanned aerial systems . . . . 19 1.6 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 References 25 2 Publications 29 2.1 Summary of Papers . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.1.1 Paper I . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.1.2 Paper II . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.1.3 Paper III . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 iii CONTENTS iv Acknowledgements First of all I would like to thank my supervisors Prof. Dr. Joachim Reuder and Dr. Anne D. Sandvik for supporting and encouraging me during the course of this Ph.D. thesis. My gratitude belows Mar- ius O. Jonassen for being a great fellow during the last years for discussing both technical and scientific topics in a very relaxed and patientway. ManythanksgototheFLOHOFandSvalbardcampaign team members for making both field campaigns a success. Without the Paparazzi project mainly provoked by Dr. Pascal Brisset and the working enthusiasm of the model aircraft constructors and pilots Mar- tin Mu¨ller and Christian Lindenberg, the realization of SUMO would not have been possible. GFI’s Ph.D. students have developed a great social environment dur- ing the last years, especially the Friday’s football workout was making ’hard’ office work more bearable and mind refreshing. I would like to thank Kristin Richter for being an enjoyable office mate and coffee drinking fellow during the last five years. Last but not least, I would like to thank my family in Germany for taking care of my daughter while I was spending many weeks abroad on field work and conferences. Abstract This synthesis and collection of Papers is submitted for the degree of Philosophiae Doctor (Ph.D.) in experimental meteorology at the Geophysical Institute, University of Bergen, Norway. It consists of two main parts. In the first part, the reader finds an introduction providing the motivation for the study and an overview of measure- ment methods of the atmospheric boundary layer followed by a brief review on numerical weather prediction modelling. This part also includes a detailed description of the used measurement tool, the Small Unmanned Meteorological Observer SUMO and an overview of Unmanned Aerial Systems used in atmospheric science including regulatory issues. The second part consists of three scientific Papers covering measurements with SUMO and simulations with a numerical weather prediction model. In Paper I and II model and measurements are compared. SUMO system improvements related to wind measure- ments are addressed in Paper III. 1 Introduction In this Ph.D. thesis a combination of measurements and integrations with a nu- merical weather prediction model is presented. The new measurement tool called Small Unmanned Meteorological Observer (SUMO) has been developed for ap- plications in the atmospheric boundary layer (ABL) and numerical model inte- grations have been performed with the Weather Research and Forecasting model WRF. With these tools the focus of this study is set on the lowest part of the at- mosphere adjacent to the Earth’s surface where exchange processes of energy and mass between the surface and the free atmosphere above take place. The air in this layer directly feels the influence of different surface types (sea, land, snow, water, ice) by heating, cooling and friction on a time scale in the order of one hour. According to Stull (1988), it typically reaches from bottom up to 100-3000 m and hence represents roughly the lowest 10 % of the troposphere (see Figure 1.1). The ABL is of special interest because it is the part of the atmo- sphere where forcing mechanisms such as heat transport from and to the ground, frictional drag, evaporation and transpiration, terrain-induced flow modification and pollution emission take place and thereby affect our daily life Stull (1988). The atmosphere and in particular the ABL are a complex dynamical system thatcannotbepredictedwithouttheuseofnumericalmodels. Numericalweather prediction (NWP) models are computer codes based on simplified mathematical representationsofphysicalprocesses. TheoutputandresultsoflimitedareaNWP models provide knowledge for daily weather forecasts as well as regional climate 3 1. INTRODUCTION 50 km 104 stratosphere troposphere 103 1 km m] [ ht 102 atmospheric eig boundarylayer h 10 10 m turbulent surfacelayer 1 roughnesslayer 0 0 Figure 1.1: Sketch of the vertical structure of the atmosphere in the lowest 50 km (adapted from Oke (1987)). projections for future decades which become increasingly important for planning and adaptation purposes. Hence, the skill and the performance of NWP models are of vital interest for the atmospheric science community as well as for decision makers and planning purposes. The quality of forecasts is highly dependent on reliable observational data for • the definition of initial and boundary conditions • the understanding of the physical processes to be mathematically described in the models on an appropriate scale • the corresponding choice of parameterizations of physical subgrid-scale pro- cesses • the validation, evaluation and further improvement of model physics by intercomparison with measurements. Large data centres, such as the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP), operationally use data from surface-based automatic weather stations, 4 released radiosondes and satellite-based sensors to monitor the state of the at- mosphere. Besides environmental monitoring, the quality of these data sets are crucial for the initialization of NWP models and operational implementation of observational data in data assimilation cycles (e.g. Hollingsworth et al. (1986) and Daley (1991)). Observations Hypothesis Measurement/ Experiment Validation Theory Model Figure 1.2: Sketch of the observational and theoretical cycle - showing the equal importance of measurements and models. Ingeneral, thebasic understandingofphysical processesin dailyweatherorig- inates from meteorological measurements. Observations provide the fundament fornewhypothesesinvestigatedintargetedmeteorologicalfieldexperiments. Ide- ally, observational findings can be used to raise new theories or to verify already existing theories, and if necessary to improve such. The theoretical work finds its application in NWP models. To validate NWP models, measurements are always required because they represent the unique source of information on the ’true’ state of the atmosphere. However, one should be aware of the measurement methods’ limitations and the uncertainties they are afflicted with. Figure 1.2 sketches the continuous interaction between observations, models and theory as tools. The use of measurements and the development and application of models can be considered to be of equal importance in atmospheric sciences (Warner, 2011). 5 1. INTRODUCTION The scope of this work is directed towards the meteorological application and improvement of a new measurement tool. This is the unmanned aerial system SUMO. A detailed description of SUMO is provided in section 1.4 followed by an overview of data measured during several field campaigns listed in Table 1.4. These data have been used in case studies for Iceland (Paper I) and Svalbard (Paper II), for evaluation of ABL schemes embedded in the Weather Research and Forecasting model WRF (Paper II) and as a basis for several tests of a new wind algorithm for small unmanned aerial systems (Paper III). 1.1 In-situ & remote measurement methods Avastvarietyofmeasurementmethodsforatmosphericscienceshasgrownduring the last 140 years. Depending on the demanded variable, available measurement technologies and financial budgets, one can choose from a pool of measurement methods. Atmospheric measurement methods can be divided into two main tech- niques: i) in-situ and ii) remote techniques as listed in Figure 1.3 which can be surface, airborne or space-based. 1.1.1 Profiling the atmospheric boundary layer The knowledge of temperature, humidity, wind speed, wind direction and pres- sure build up the pillars of atmospheric science by being the backbone of the basic governing equations also called the primitive equations. They consist of the equation of state (ideal gas law), the conservation of mass (continuity equation), the conservation of momentum (Newton’s second law), the conservation of mois- ture, the conservation of heat (first law of thermodynamics) and the conservation of a scalar quantity (e.g. Holton (1992)). Measurements on the Earth’s surface as well as continuous profiles of temper- ature, humidity and wind in the vertical dimension are indispensable to moni- tor and understand physical processes in the atmosphere and to verify, evaluate and improve NWP models. By measuring these variables, the ABL’s stability and height can be determined and mean fluxes of heat and momentum can be 6