ALBERT

Anmerkung: Ersch. unregelmäßig , Beiträge teilweise in Englisch

Anmerkung: Contents Summary 1. Introduction 1.1 Background 1.1.1 Precipitation changes 1.1.2 Large-scale atmospheric patterns 1.2 Objectives and research questions 1.3 Thesis outline and author contribution High spatial and temporal organization of changes inprecipitation over Germany for 1951–2006 2.1 Introduction 2.2 Data 2.3 Methods 2.3.1 Threshold between wet and dry days 2.3.2 Derivation of time series of precipitation characteristics 2.3.3 Trend analyses under consideration of temporal and spatial correlation 2.3.4 Visualization of results 2.4 Results and discussion 2.4.1 Changes in total precipitation 2.4.2 Changes in mean, variability, and heavy precipitation indicators 2.4.3 Transition probabilities 2.4.4 Seven-day precipitation amount with return period 100 years 2.5 Conclusions Can local climate variability be explained by weatherpatterns? A multi-station evaluation for the Rhine basin 3.1 Introduction 3.2 Data 3.3 Methods 3.3.1 Weather pattern classification 3.3.2 Finding optimal classification parameters 3.3.3 Evaluation of classifications 3.4 Results 3.4.1 Stratification of local climate variables 3.4.2 Performance of GCMs 3.5 Discussion 3.5.1 On the optimal classification 3.5.2 On the skill of GCMs 3.6 Conclusions 3.7 Data availability 3.A Appendix Do changing weather types explain observed climatictrends in the Rhine basin? An analysis of within andbetween-type changes 4.1 Introduction 4.2 Data and weather pattern classification 4.3 Methods 4.3.1 Relationship of WPs and large-scale circulation modes 4.3.2 Trend detection methods 4.3.3 Relative share of between- and within-type changes 4.4 Results 4.4.1 Attribution of WPs to large-scale circulation modes 4.4. 2Between-Type Changes 4.4.3 Within-Type Changes 4.4.4 Relative share of between- and within-type changes 4.5 Discussion and conclusions 4.A Appendix 4.S Supplementary Discussion and conclusions 5.1 Main results 5.2 Discussion and directions for further research 5.2.1 Weather pattern classification for downscaling 5.2.2 Limitations for downscaling 5.3 Concluding remarks Bibliography

Anmerkung: Foreword xiii Preface xv Acknowledgments xxi About the Authors xxiii Part I: Data Science with Hadoop-An Overview 1 Chapter 1: Introduction to Data Science 3 What Is Data Science? 3 Example: Search Advertising 4 A Bit of Data Science History 5 Becoming a Data Scientist 8 Building a Data Science Team 12 The Data Science Project Life Cycle 13 Managing a Data Science Project 18 Summary 18 Chapter 2: Use Cases for Data Science 19 Big Data-A Driver of Change 19 Business Use Cases 21 Summary 29 Chapter 3: Hadoop and Data Science 31 What Is Hadoop? 31 Hadoop's Evolution 37 Hadoop Tools for Data Science 38 Why Hadoop Is Useful to Data Scientists 46 Summary 51 Part II: Preparing and Visualizing Data with Hadoop 53 Chapter 4: Getting Data into Hadoop 55 Hadoop as a Data Lake 56 The Hadoop Distributed File System (HDFS) 58 Direct File Transfer to Hadoop HDFS 58 Importing Data from Files into Hive Tables 59 Importing Data into Hive Tables Using Spark 62 Using Apache Sqoop to Acquire Relational Data 65 Using Apache Flume to Acquire Data Streams 74 Manage Hadoop Work and Data Flows with Apache Oozie 79 Apache Falcon 81 What's Next in Data Ingestion? 82 Summary 82 Chapter 5: Data Munging with Hadoop 85 Why Hadoop for Data Munging? 86 Data Quality 86 The Feature Matrix 93 Summary 106 Chapter 6: Exploring and Visualizing Data 107 Why Visualize Data? 107 Creating Visualizations 112 Using Visualization for Data Science 121 Popular Visualization Tools 121 Visualizing Big Data with Hadoop 123 Summary 124 Part III: Applying Data Modeling with Hadoop 125 Chapter 7: Machine Learning with Hadoop 127 Overview of Machine Learning 127 Terminology 128 Task Types in Machine Learning 129 Big Data and Machine Learning 130 Tools for Machine Learning 131 The Future of Machine Learning and Artificial Intelligence 132 Summary 132 Chapter 8: Predictive Modeling 133 Overview of Predictive Modeling 133 Classification Versus Regression 134 Evaluating Predictive Models 136 Supervised Learning Algorithms 140 Building Big Data Predictive Model Solutions 141 Example: Sentiment Analysis 145 Summary 150 Chapter 9: Clustering 151 Overview of Clustering 151 Uses of Clustering 152 Designing a Similarity Measure 153 Clustering Algorithms 154 Example: Clustering Algorithms 155 Evaluating the Clusters and Choosing the Number of Clusters 157 Building Big Data Clustering Solutions 158 Example: Topic Modeling with Latent Dirichlet Allocation 160 Summary 163 Chapter 10: Anomaly Detection with Hadoop 165 Overview 165 Uses of Anomaly Detection 166 Types of Anomalies in Data 166 Approaches to Anomaly Detection 167 Tuning Anomaly Detection Systems 170 Building a Big Data Anomaly Detection Solution with Hadoop 171 Example: Detecting Network Intrusions 172 Summary 179 Chapter 11: Natural Language Processing 181 Natural Language Processing 181 Tooling for NLP in Hadoop 184 Textual Representations 187 Sentiment Analysis Example 189 Summary 193 Chapter 12: Data Science with Hadoop-The Next Frontier 195 Automated Data Discovery 195 Deep Learning 197 Summary 199 Appendix A: Book Web Page and Code Download 201 Appendix B: HDFS Quick Start 203 Quick Command Dereference 204 Appendix C: Additional Background on Data Science and Apache Hadoop and Spark 209 General Hadoop/Spark Information 209 Hadoop/Spark Installation Recipes 210 HDFS 210 MapReduce 211 Spark 211 Essential Tools 211 Machine Learning 212 Index 213

Anmerkung: Introduction.- Spectral Analysis of Deterministic Process.- Structural Dynamics.- Spectral Analysis of Stationary Stochastic Process.- Stochastic Structural Dynamics.- Ambient Data Analysis and Simulation.- Bayesian Inference.- Classical Statistical Inference.- Bayesian OMA Framework.- Single Mode Problem.- Multi-Mode Problem.- Multi-Setup Problem.- Managing identification uncertainty.- Theory of Uncertainty Laws.

Anmerkung: Optical fibre technology -- Principles of optical time domain reflectometry (OTDR) for distributed sensing -- Raman-based distributed temperature sensors (DTS) -- Brillouin based distributed temperature and strain sensors -- Rayleigh backscatter: distributed vibration sensors and static measurements -- Applications of distributed temperature sensors -- Distributed strain sensors: practical issues, solutions and applications -- Applications of distributed vibration sensors (DVS) -- Other types of distributed sensors and their applications

Anmerkung: Contents 1. Introduction 2. Postglacial uplift rates 3. Climatic sea level rise 4. Exponential effect 5. Mean sea level effect 6. Calculation of historical shore level and its uncertainty 7. Applications to the Aland Island, Gamla Uppsala and Birka 8. A note to the near future References

Anmerkung: Contents 1. Introduction 2. Gravity measurements 3. Gravity values 4. Gravity errors 5. Conclusions References

Anmerkung: Front Cover --- Introduction to Satellite Remote Sensing --- Introduction to Satellite Remote Sensing: Atmosphere, Ocean, Land and Cryosphere Applications --- Copyright --- Dedication --- Contents --- 1 - THE HISTORY OF SATELLITE REMOTE SENSING --- 1.1 THE DEFINITION OF REMOTE SENSING --- 1.2 THE HISTORY OF SATELLITE REMOTE SENSING --- 1.2.1 THE NATURE OF LIGHT AND THE DEVELOPMENT OF AERIAL PHOTOGRAPHY --- 1.2.2 THE BIRTH OF EARTH-ORBITING SATELLITES --- 1.2.3 THE FUTURE OF POLAR-ORBITING SATELLITES --- 1.2.3.1 The Cross-Track Infrared Sounder --- 1.2.4 OTHER HISTORICAL SATELLITE PROGRAMS --- 1.2.4.1 The NIMBUS Program --- 1.2.4.2 The Landsat Program --- 1.2.4.3 The Defense Meteorological Satellite Program --- 1.2.4.4 Geostationary Weather Satellites --- 1.2.4.4.1 GOES-R --- 1.3 STUDY QUESTIONS --- 2 - BASIC ELECTROMAGNETIC CONCEPTS AND APPLICATIONS TO OPTICAL SENSORS --- 2.1 MAXWELL'S EQUATIONS --- 2.2 THE BASICS OF ELECTROMAGNETIC RADIATION --- 2.3 THE REMOTE SENSING PROCESS --- 2.4 THE CHARACTER OF ELECTROMAGNETIC WAVES --- 2.4.1 DEFINITION OF RADIOMETRIC TERMS --- 2.4.2 POLARIZATION AND THE STOKES VECTOR --- 2.4.3 REFLECTION AND REFRACTION AT THE INTERFACE OF TWO FLAT MEDIA --- 2.4.4 BREWSTER'S ANGLE --- 2.4.5 CRITICAL ANGLE --- 2.4.6 ALBEDO VERSUS REFLECTANCE --- 2.5 ELECTROMAGNETIC SPECTRUM: DISTRIBUTION OF RADIANT ENERGIES --- 2.5.1 GAMMA, X-RAY, AND ULTRAVIOLET PORTIONS OF THE ELECTROMAGNETIC SPECTRUM --- 2.5.2 VISIBLE SPECTRUM --- 2.5.3 THERMAL INFRARED SPECTRUM --- 2.5.4 MICROWAVE SPECTRUM --- 2.6 ATMOSPHERIC TRANSMISSION --- 2.6.1 SPECTRAL WINDOWS --- 2.6.2 ATMOSPHERIC EFFECTS --- 2.6.2.1 Beer-Lambert Absorption Law --- 2.6.2.2 Beer-Lambert Absorption Law: Opacity --- 2.6.2.3 Atmospheric Scattering --- 2.7 SENSORS TO MEASURE PARAMETERS OF THE EARTH'S SURFACE --- 2.8 INCOMING SOLAR RADIATION --- 2.9 INFRARED EMISSIONS --- 2.10 SURFACE REFLECTANCE: LAND TARGETS --- 2.10.1 LAND SURFACE MIXTURES --- 2.11 STUDY QUESTIONS --- 3 - OPTICAL IMAGING SYSTEMS --- 3.1 PHYSICAL MEASUREMENT PRINCIPLES --- 3.2 BASIC OPTICAL SYSTEMS --- 3.2.1 PRISMS --- 3.2.2 FILTER-WHEEL RADIOMETERS --- 3.2.2.1 An Example: The Cloud Absorption Radiometer --- 3.2.2.2 Filters --- 3.2.3 GRATING SPECTROMETER --- 3.2.4 INTERFEROMETER --- 3.3 SPECTRAL RESOLVING POWER --- THE RAYLEIGH CRITERION --- 3.4 DETECTING THE SIGNAL --- 3.5 VIGNETTING --- 3.6 SCAN GEOMETRIES --- 3.7 FIELD OF VIEW --- 3.8 OPTICAL SENSOR CALIBRATION --- 3.8.1 VISIBLE WAVELENGTHS CALIBRATION --- 3.8.2 POLARIZATION FILTERS --- 3.9 LIGHT DETECTION AND RANGING --- 3.9.1 PHYSICS OF THE MEASUREMENT --- 3.9.2 OPTICAL AND TECHNOLOGICAL CONSIDERATIONS --- 3.9.3 APPLICATIONS OF LIDAR SYSTEMS --- 3.9.4 WIND LIDAR --- 3.9.4.1 Vector Wind Velocity Determination --- 3.9.4.1.1 Velocity Azimuth Display LIDAR Vector Wind Method --- 3.9.4.1.2 Doppler Beam Swinging LIDAR Vector Wind Method --- 3.9.4.2 Direct Detection Doppler Wind LIDAR --- 3.9.4.3 LIDAR Wind Summary --- 3.10 STUDY QUESTIONS --- 4 - Microwave Radiometry --- 4.1 Basic Concepts on Microwave Radiometry --- 4.1.1 Blackbody Radiation --- 4.1.2 Gray-body Radiation: Brightness Temperature and Emissivity --- 4.1.3 General Expressions for the Emissivity --- 4.1.3.1 Simple Emissivity Models: Emission From a Perfect Specular Surface --- 4.1.3.2 Simple Emissivity Models: Emission From a Lambertian Surface --- 4.1.3.1 Simple Emissivity Models: Emission From a Perfect Specular Surface --- 4.1.3.2 Simple Emissivity Models: Emission From a Lambertian Surface --- 4.1.4 Power Collected by an Antenna Surrounded by a Blackbody --- 4.1.5 Power Collected by an Antenna Surrounded by a Gray body: Apparent Temperature and Antenna Temperature --- 4.2 The Radiative Transfer Equation --- 4.2.1 The Complete Polarimetric Radiative Transfer Equation --- 4.2.2 Usual Approximations to the Radiative Transfer Equation --- 4.3 Emission Behavior of Natural Surfaces --- 4.3.1 The Atmosphere --- 4.3.1.1 Attenuation by Atmospheric Gases --- 4.3.1.2 Attenuation by Rain --- 4.3.1.3 Attenuation by Clouds and Fog --- 4.3.2 The Ionosphere --- 4.3.2.1 Faraday Rotation --- 4.3.2.2 Ionospheric Losses: Absorption and Emission --- 4.3.3 Land Emission --- 4.3.3.1 Soil Dielectric Constant Models --- 4.3.3.2 Bare Soil Emission --- 4.3.3.3 Vegetated Soil Emission --- 4.3.3.4 Snow-Covered Soil Emission --- 4.3.3.5 Topography Effects --- 4.3.4 Ocean Emission --- 4.3.4.1 Water Dielectric Constant Behavior --- 4.3.4.2 Calm Ocean Emission --- 4.3.4.2.1 Influence of the Salinity --- 4.3.4.2.2 Influence of Frequency --- 4.3.4.2.3 Influence of the Water Temperature --- 4.3.4.3 Influence of the Sea State --- 4.3.4.3.1 Influence of the Look Angle --- 4.3.4.4 Emissivity of the Sea Surface Covered With Oil --- 4.3.4.5 Emissivity of the Sea Ice Surface --- 4.4 Understanding Microwave Radiometry Imagery --- 4.5 Applications of Microwave Radiometry --- 4.6 Sensors --- 4.6.1 Historical Review of Microwave Radiometers and Frequency Bands Used --- 4.6.2 Microwave Radiometers: Basic Performance --- 4.6.2.1 Spatial Resolution --- 4.6.2.1.1 Real Aperture Radiometers --- 4.6.2.1.2 Synthetic Aperture Radiometers --- 4.6.2.2 Radiometric Resolution --- 4.6.2.2.1 Real Aperture Radiometers --- 4.6.2.2.2 Synthetic Aperture Radiometers --- 4.6.2.3 Trade-off Between Spatial Resolution and Radiometric Precision --- 4.6.3 Real Aperture Radiometers --- 4.6.3.1 Instrument Considerations --- 4.6.3.1.1 Antenna Considerations --- 4.6.3.1.2 Receiver Considerations --- 4.6.3.1.3 Sampling Considerations --- 4.6.3.2 Types of Real Aperture Radiometers --- 4.6.3.3 Radiometer Calibration --- 4.6.3.3.1 External Calibration --- 4.6.3.3.1.1 Using Hot and Cold Targets --- 4.6.3.3.1.2 Fully Polarimetric Radiometer Calibration Using External Targets --- 4.6.3.3.1.3 Tip Curves --- 4.6.3.3.1.4 Earth Targets: Vicarious Calibration --- 4.6.3.3.2 Internal Calibration --- 4.6.3.3.3 Radiometer Linearity --- 4.6.3.4 Radio Frequency Interference Detection and Mitigation --- 4.6.3.5 Example: Special Sensor Microwave Imager Radiometric and Geometric Corrections --- 4.6.4 Synthetic Aperture Radiometers --- 4.6.4.1 Types of Synthetic Aperture Radiometers --- 4.6.4.1.1 Mills Cross --- 4.6.4.1.2 Synthetic Aperture Radiometers using Matched Filtering --- 4.6.4.1.3 Synthetic Aperture Radiometers using Fourier Synthesis --- 4.6.4.1.3.1 1D Synthetic Aperture Radiometers: Array Thinning --- 4.6.4.1.3.2 2D Synthetic Aperture Radiometers: Array Topologies --- 4.6.4.1.3.3 Other Synthetic Aperture Radiometer Concepts --- 4.6.4.2 Radiometer Calibration --- 4.6.4.2.1 Internal Calibration --- 4.6.4.2.2 External Calibration --- 4.6.4.3 Image Reconstruction --- 4.6.4.4 ESA's SMOS Mission and the MIRAS Instrument --- 4.6.5 Future Trends in Microwave Radiometers --- 4.7 Study Questions --- 5 - RADAR --- 5.1 A COMPACT INTRODUCTION TO RADAR THEORY --- 5.1.1 REMOTE RANGING --- 5.1.2 DOPPLER ANALYSIS --- 5.2 RADAR SCATTERING --- 5.2.1 RADAR FREQUENCY BANDS --- 5.2.2 NORMALIZATIONS OF THE RADAR REFLECTIVITY --- 5.2.3 POINT VERSUS DISTRIBUTED SCATTERERS --- 5.2.4 SPECKLE, MULTILOOK, AND RADIOMETRIC RESOLUTION --- 5.2.5 RADAR EQUATION --- 5.2.6 RADAR WAVES AT AN INTERFACE --- 5.2.7 MULTIPLE REFLECTIONS: DOUBLE BOUNCE, TRIPLE BOUNCE, AND URBAN AREAS --- 5.2.8 BACKSCATTERING OF SURFACES --- 5.2.9 PERIODIC SCATTERING: THE BRAGG MODEL --- 5.2.10 BACKSCATTERING OF VOLUMES --- 5.2.11 OVERALL SUMMARY OF RADAR BACKSCATTER --- 5.2.12 DEPOLARIZATION OF RADAR WAVES --- 5.3 RADAR SYSTEMS --- 5.3.1 RANGE-DOPPLER RADARS --- 5.3.2 OPTIMAL RECEIVER FOR A SINGLE ECHO: THE MATCHED FILTER --- 5.3.3 MATCHED FILTER VERSUS INVERSE FILTER --- 5.3.4 OPTIMAL RECEIVER FOR RANGE-DOPPLER RADAR ECHOES: THE BACKPROJECTION OPERATOR --- 5.3.5 RADAR WAVEFORMS --- 5.3.6 A PARADIGMATIC EXAMPLE: LINEAR FREQUENCY MODULATED PULSES (CHIRPS) --- 5.3.7 GEOMET

Anmerkung: Text auf Englisch, Vorwort auf Deutsch, Französisch und Englisch, Zusammenfassung auf Englisch.

Anmerkung: Text auf Englisch, Vorwort auf Deutsch, Französisch und Englisch, Zusammenfassung auf Englisch und Deutsch.

Anmerkung: Text auf Englisch, Vorwort auf Deutsch, Französisch und Englisch, Zusammenfassung auf Deutsch, Französisch und Englisch.

Anmerkung: 1. Introduction 2. Overview of Linear Algebra 3. Univariate Distribution Theory 4. Multivariate Distribution Theory 5. Introduction to Calculus of Variation 6. Introduction to Control Theory 7. Optimal Control Theory 8. Numerical Solutions to Initial Value Problems 9. Numerical Solutions to Boundary Problems 10. Introduction to Semi-Langrangian Advection Methods 11. Introduction to Finite Element Modeling 12. Numerical Modeling of the Sphere 13. Tangent Linear Modeling and Adjoints 14. Observations 15. Non-variational Sequential Data Assimilation Methods 16. Variational Data Assimilation 17. Subcomponents of Variational Data Assimilation 18. Observation of Space Variation Data Assimilation Methods 19. Kalman Filter and Smoother 20. Ensemble-Based Data Asssimilation 21. Non-Gaussian Variational Data Assimilation 22. Markov Chain Monte Carlo and Particle Filter Methods 23. Applications of Data Asssimilation in the Geosciences 24. Solutions to Select Exercise Bibliography Index

Anmerkung: Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2017 , Contents 1 Introduction 1.1 Motivation, research questions and overview 1.1.1 Rainfall estimation at high spatial and temporal resolution 1.1.2 Precipitation estimation with cars 1.1.3 Motion estimation from in-situ sensor data 1.2 Outline 2 Basics 2.1 Precipitation 2.1.1 Resolution, accuracy and precision of precipitation measurements 2.1.2 In-situ point measurements of precipitation by rain gauges 2.1.3 Weather radar 2.2 Wireless Sensor Networks 2.2.1 Modeling sensor networks 2.2.2 Sensor network algorithms and protocols 2.3 Statistics 2.3.1 Basics and notation 2.3.2 Regression 2.3.3 Stochastic processes 2.3.4 Stochastic filtering and the Kalman filter 2.3.5 Geostatistics 2.4 Interpolation methods 2.4.1 Inverse-Distance-Weighted 2.4.2 Ordinary kriging 2.4.3 Regression kriging 2.4.4 Cross-validation for performance assessment 2.5 Optical flow 2.5.1 Optical flow intensity conservation 2.5.2 Gradient-based optical flow 2.5.3 Probabilistic optical flow 3 Related Work 3.1 Quantitative precipitation estimation from rain gauges, weather radar and other data sources 3.1.1 Precipitation estimation with weather radar 3.1.2 Precipitation estimation by interpolation of rain gauges measurements 3.1.3 Geostatistical merging of radar and rain gauge data 3.1.4 Motion-based methods used in nowcasting 3.1.5 New data sources for precipitation estimation 3.2 Decentralized estimation with geosensor networks 3.2.1 Estimation of spatio-temporal field properties with GSN 3.2.2 Object-tracking with GSN 4 Methodology for precipitation intensity estimation at 1-min resolution 4.1 Time-window approach for estimation 4.1.1 Estimation of field motion 4.1.2 Weather radar upsampling 4.1.3 Variogram estimation 4.2 Estimation methods 4.2.1 Spatial rain gauge interpolation methods 4.2.2 Space-time symmetric rain gauge interpolation method 4.2.3 Space-time asymmetric rain gauge interpolation methods 4.2.4 Radar-rain gauge merging methods 4.2.5 Estimation methods solely based on radar 4.3 Summary 5 Methodology for precipitation intensity estimation with car sensors 5.1 Car sensors 5.1.1 Wiper Frequency Sensor 5.1.2 Xanonex optical sensor 5.1.3 Other sensors investigated 5.1.4 Experimental setup and preprocessing 5.2 Theoretical considerations for the calibration of the W-R relationship in the field . 5.3 Dependency between car speed, windscreen angle and sensor readings 5.3.1 Manually-operated windscreen wipers 5.3.2 Automatically-operated windscreen wipers 5.3.3 Xanonex optical sensor 5.4 Summary 6 Methodology for motion estimation with a geosensor network 6.1 Algorithm overview 6.2 Network and field model 6.3 Gradient constraint estimation in the network 6.3.1 Gradient constraint estimation from irregular data 6.3.2 Requirements on node stationarity and sampling synchronicity 6.3.3 Estimation of partial derivative error 6.3.4 Gradient constraint selection and derivation of gradient constraint error 6.4 Temporal coherence: Kalman filter for recursive motion estimation 6.4.1 Estimation of process noise Q 6.4.2 Estimation of measurement noise R 6.4.3 Difference to common Kalman filtering problems 6.5 Algorithm protocol 6.6 Algorithm complexity 6.6.1 Communication complexity 6.6.2 Load balance 6.6.3 Computational complexity of partial derivative estimation 6.6.4 Computational complexity of motion estimation 6.7 Summary 7 Results 7.1 Precipitation intensity estimation at 1-min resolution 7.1.1 Study area and data basis 7.1.2 Performance assessment via cross-validation 7.1.3 Exploratory and visual data analysis 7.1.4 Radar estimation and rain gauge cross-validation results 7.1.5 Summary 7.2 Precipitation intensity estimation with cars 7.2.1 Study area and data basis 7.2.2 Selection of the reference method 7.2.3 Manually-operated windscreen wipers 7.2.4 Automatically-operated windscreen wipers 7.2.5 Xanonex optical rain sensor 7.2.6 Results of experiments on the VW rain track 7.2.7 Summary 7.3 Motion estimation with a geosensor network 7.3.1 Study Area, sensor network and deployment strategies 7.3.2 Error measures 7.3.3 Setting the filter parameters 7.3.4 Results - simulated field 7.3.5 Results - radar field 7.3.6 Summary 8 Summary and discussion of the research hypotheses 8.1 Discussion of research hypotheses 1 and 2: 1-min precipitation intensity estimation 8.2 Discussion of research hypothesis 3: precipitation estimation with cars 8.3 Discussion of research hypothesis 4: decentralized motion estimation 8.4 Outlook 9 Appendix 9.1 Discussion on the 'frozen field' distance function 9.2 Executable Kalman filter equations for the motion estimation algorithm 9.3 Controllability and Observability of the Kalman filter for motion estimation List of Figures List of Tables References