Automated Surface Observing Systems (ASOS) IMPACTS
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Geometric Characteristics of Clouds from Ceilometer Measurements and Radiosounding Methods
GEOMETRIC CHARACTERISTICS OF CLOUDS FROM CEILOMETER MEASUREMENTS AND RADIOSOUNDING METHODS Montserrat Costa Surós Dipòsit legal: Gi. 1888-2014 http://hdl.handle.net/10803/284084 http://creativecommons.org/licenses/by/4.0/deed.ca Aquesta obra està subjecta a una llicència Creative Commons Reconeixement Esta obra está bajo una licencia Creative Commons Reconocimiento This work is licensed under a Creative Commons Attribution licence GEOMETRIC CHARACTERISTICS OF CLOUDS FROM CEILOMETER MEASUREMENTS AND RADIOSOUNDING METHODS DOCTORAL THESIS Montserrat Costa Surós 2014 DOCTORAL THESIS GEOMETRIC CHARACTERISTICS OF CLOUDS FROM CEILOMETER MEASUREMENTS AND RADIOSOUNDING METHODS Montserrat Costa Surós 2014 Doctoral Programme in Experimental Sciences and Sustainability Supervisors: Josep Calbó Angrill José Abel González Gutiérrez Thesis submitted for the degree of Doctor of Philosophy by the University of Girona El Dr. Josep Calbó Angrill i el Dr. José Abel González Gutiérrez, professors titulars del Departament de Física de la Universitat de Girona, CERTIFIQUEN: Que aquest treball, titulat “Geometric characteristics of clouds from ceilometer measurements and radiosounding methods”, que presenta la Montserrat Costa Surós per a l’obtenció del títol de doctora, ha estat realitzat sota la seva direcció. I, perquè així consti i tingui els efectes oportuns, signen aquest document. Dr. Josep Calbó Angrill Dr. José Abel González Gutiérrez Girona, 29 de juliol de 2014. Un esforç total és una victòria completa M. Ghandi Acknowledgments First and the most important I would like to thank my supervisors Dr. Josep Calbó and Dr. Josep- Abel González for giving me the opportunity to begin my research career with them, which has led to this doctoral thesis, and for their guidance and support during these years. -
CHAPTER CONTENTS Page CHAPTER 5
CHAPTER CONTENTS Page CHAPTER 5. SPECIAL PROFILING TECHNIQUES FOR THE BOUNDARY LAYER AND THE TROPOSPHERE .............................................................. 640 5.1 General ................................................................... 640 5.2 Surface-based remote-sensing techniques ..................................... 640 5.2.1 Acoustic sounders (sodars) ........................................... 640 5.2.2 Wind profiler radars ................................................. 641 5.2.3 Radio acoustic sounding systems ...................................... 643 5.2.4 Microwave radiometers .............................................. 644 5.2.5 Laser radars (lidars) .................................................. 645 5.2.6 Global Navigation Satellite System ..................................... 646 5.2.6.1 Description of the Global Navigation Satellite System ............. 647 5.2.6.2 Tropospheric Global Navigation Satellite System signal ........... 648 5.2.6.3 Integrated water vapour. 648 5.2.6.4 Measurement uncertainties. 649 5.3 In situ measurements ....................................................... 649 5.3.1 Balloon tracking ..................................................... 649 5.3.2 Boundary layer radiosondes .......................................... 649 5.3.3 Instrumented towers and masts ....................................... 650 5.3.4 Instrumented tethered balloons ....................................... 651 ANNEX. GROUND-BASED REMOTE-SENSING OF WIND BY HETERODYNE PULSED DOPPLER LIDAR ................................................................ -
An LES-Based Airborne Doppler Lidar Simulator and Its Application to Wind Profiling in Inhomogeneous flow Conditions
Atmos. Meas. Tech., 13, 1609–1631, 2020 https://doi.org/10.5194/amt-13-1609-2020 © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. An LES-based airborne Doppler lidar simulator and its application to wind profiling in inhomogeneous flow conditions Philipp Gasch1, Andreas Wieser1, Julie K. Lundquist2,3, and Norbert Kalthoff1 1Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany 2Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO 80303, USA 3National Renewable Energy Laboratory, Golden, CO 80401, USA Correspondence: Philipp Gasch ([email protected]) Received: 26 March 2019 – Discussion started: 5 June 2019 Revised: 19 February 2020 – Accepted: 19 February 2020 – Published: 2 April 2020 Abstract. Wind profiling by Doppler lidar is common prac- profiling at low wind speeds (< 5ms−1) can be biased, if tice and highly useful in a wide range of applications. Air- conducted in regions of inhomogeneous flow conditions. borne Doppler lidar can provide additional insights relative to ground-based systems by allowing for spatially distributed and targeted measurements. Providing a link between the- ory and measurement, a first large eddy simulation (LES)- 1 Introduction based airborne Doppler lidar simulator (ADLS) has been de- veloped. Simulated measurements are conducted based on Doppler lidar has experienced rapidly growing importance LES wind fields, considering the coordinate and geometric and usage in remote sensing of atmospheric winds over transformations applicable to real-world measurements. The the past decades (Weitkamp et al., 2005). Sectors with ADLS provides added value as the input truth used to create widespread usage include boundary layer meteorology, wind the measurements is known exactly, which is nearly impos- energy and airport management. -
Clouds, Precipitation and Their Remote Sensing Intergovernmental
25.09.12 Clouds, Precipitation and their Remote Sensing Prof. Susanne Crewell AG Integrated Remote Sensing Institute for Geophysics and Meteorology University of Cologne Susanne Crewell, Kompaktkurs, Jülich24. 25 September September 2012 2012 Intergovernmental Panel on Climate Change (IPCC) www.ipcc.ch Nobel price 2007 IPCC Fourth Assessment Report (FAR), 2007: "Warming of the climate system is unequivocal", and "Most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations". Aerosols, clouds and their interaction with climate is still the most uncertain area of climate change and require multidisciplinary coordinated research efforts. SusanneSusS sanna ne Crewell,Crewewellelll,K, Kompaktkurs,Kompakta kurs, JülichJülJüü ichchc 252 SeptemberSSeeptetetembembber 201220121 1 25.09.12 Why are clouds so complex? Cloud microphysical processes occur on small spatial scales and need to be parametrized in atmospheric models Cloud microphysics is strongly connected to other sub-grid scale processes (turbulence, radiation) Cloud droplets 0.01 mm diameter 100-1000 per cm3 Condensation nuclei Drizzle droplets 0.001 mm diameter 0.1 mm diameter 1000 per cm3 1 per cm3 Rain drops ca. 1 mm diameter, 1 drops per liter Susannesa Crewell, Kompaktkurs, Jülich 25 September 2012 Why are clouds so complex? From hydrometeors to single clouds to Einzelwolken to the global and cloud fields system Susanne Crewell, Kompaktkurs, Jülich 25 September -
Rightsizing Project Nextgen IOC Sensor Assessment Summary AJP
RightSizing Project NextGen IOC Sensor Assessment Summary AJP-6830 1 of 64 December 1, 2009 RightSizing Project NextGen IOC Sensor Assessment Summary TABLE OF CONTENTS EXECUTIVE SUMMARY ............................................................................................................ 3 1 INTRODUCTION ................................................................................................................ 4 1.1 Context and Motivation .....................................................................................................4 1.1.1 NextGen ......................................................................................................................4 1.1.2 4D weather cube ........................................................................................................4 1.1.3 Weather observation and forecast requirements to meet NextGen goals .................5 1.2 RightSizing Project Goals ....................................................................................................6 1.2.1 Assessment of Sensor Network ..................................................................................7 1.2.2 Identification of gaps based on functional and performance requirements ..............8 1.2.3 Development of master plan to meet NextGen weather observation requirements .8 1.3 Scope of this Report (FY 2009) ...........................................................................................8 2 PROGRAM MANAGEMENT AND SCHEDULE ...................................................................... -
Raman Lidar for Meteorological Observations, RALMO – Part 1: Open Access Instrument Description Climate Climate of the Past 1 1 2 2 1,3 4 of The1 Past T
EGU Journal Logos (RGB) Open Access Open Access Open Access Advances in Annales Nonlinear Processes Geosciences Geophysicae in Geophysics Open Access Open Access Natural Hazards Natural Hazards and Earth System and Earth System Sciences Sciences Discussions Open Access Open Access Atmospheric Atmospheric Chemistry Chemistry and Physics and Physics Discussions Open Access Open Access Atmos. Meas. Tech., 6, 1329–1346, 2013 Atmospheric Atmospheric www.atmos-meas-tech.net/6/1329/2013/ doi:10.5194/amt-6-1329-2013 Measurement Measurement © Author(s) 2013. CC Attribution 3.0 License. Techniques Techniques Discussions Open Access Open Access Biogeosciences Biogeosciences Discussions Open Access Raman Lidar for Meteorological Observations, RALMO – Part 1: Open Access Instrument description Climate Climate of the Past 1 1 2 2 1,3 4 of the1 Past T. Dinoev , V. Simeonov , Y. Arshinov , S. Bobrovnikov , P. Ristori , B. Calpini , M. Parlange , and H. van den Discussions Bergh5 1 Open Access Laboratory of Environmental Fluid Mechanics and Hydrology (EFLUM), Ecole Polytechnique Fed´ erale´ de Lausanne Open Access EPFL-ENAC, Station 2, 1015 Lausanne, Switzerland Earth System 2 Earth System V.E. Zuev Institute of Atmospheric Optics SB RAS1, Academician Zuev Square, Tomsk, 634021, Russia Dynamics 3Division´ Lidar, CEILAP (UNIDEF-CITEDEF-MINDEF-CONICET), San Juan Bautista de La SalleDynamics 4397 (B1603ALO), Villa Martelli, Buenos Aires, Argentina Discussions 4Federal Office of Meteorology and Climatology MeteoSwiss, Atmospheric Data, P.O. Box 316, 1530 Payerne, Switzerland 5 Open Access Laboratory of Air and Soil Pollution, Ecole Polytechnique Fed´ erale´ de Lausanne EPFL, StationGeoscientific 6, Geoscientific Open Access 1015 Lausanne, Switzerland Instrumentation Instrumentation Correspondence to: V. B. -
Weather Observations
Operational Weather Analysis … www.wxonline.info Chapter 2 Weather Observations Weather observations are the basic ingredients of weather analysis. These observations define the current state of the atmosphere, serve as the basis for isoline patterns, and provide a means for determining the physical processes that occur in the atmosphere. A working knowledge of the observation process is an important part of weather analysis. Source-Based Observation Classification Weather parameters are determined directly by human observation, by instruments, or by a combination of both. Human-based Parameters : Traditionally the human eye has been the source of various weather parameters. For example, the amount of cloud that covers the sky, the type of precipitation, or horizontal visibility, has been based on human observation. Instrument-based Parameters : Numerous instruments have been developed over the years to sense a variety of weather parameters. Some of these instruments directly observe a particular weather parameter at the location of the instrument. The measurement of air temperature by a thermometer is an excellent example of a direct measurement. Other instruments observe data remotely. These instruments either passively sense radiation coming from a location or actively send radiation into an area and interpret the radiation returned to the instrument. Satellite data for visible and infrared imagery are examples of the former while weather radar is an example of the latter. Hybrid Parameters : Hybrid observations refer to weather parameters that are read by a human observer from an instrument. This approach to collecting weather data has been a big part of the weather observing process for many years. Proper sensing of atmospheric data requires proper siting of the sensors. -
The Benefits of Lidar for Meteorological Research: the Convective and Orographically-Induced Precipitation Study (Cops)
THE BENEFITS OF LIDAR FOR METEOROLOGICAL RESEARCH: THE CONVECTIVE AND OROGRAPHICALLY-INDUCED PRECIPITATION STUDY (COPS) Andreas Behrendt(1), Volker Wulfmeyer(1), Christoph Kottmeier(2), Ulrich Corsmeier(2) (1)Institute of Physics and Meteorology, University of Hohenheim, D-70593 Stuttgart, Germany, [email protected] (2) Institute for Meteorology and Climate Research (IMK), University of Karlsruhe/Forschungszentrum Karlsruhe, Karlsruhe, Germany ABSTRACT results of instrumental development within an intensive observations period. How can lidar serve to improve today's numerical Such a field experiment is the Convective and Oro- weather forecast? Which benefits does lidar offer com- graphically-induced Precipitation Study (COPS, pared with other techniques? Which measured parame- http://www.uni-hohenheim.de/spp-iop/) which takes ters, which resolution and accuracy, which platforms place in summer 2007 in a low-mountain range in and measurement strategies are needed? Using the next Southern-Western Germany and North-Eastern France. generation of high-resolution models, a refined model This area is characterized by high summer thunderstorm representation of atmospheric processes is currently in activity and particularly low skill of numerical weather development. Besides higher resolution, the refinements prediction models. COPS is part of the German Priority comprise improved parameterizations, new model phys- Program "Praecipitationis Quantitativae Predictio" ics – including, e.g., the effects of aerosols – and assimi- (PQP, http://www.meteo.uni-bonn.de/projekte/- lation of additional data. As example to discuss the role SPPMeteo/) and has been endorsed as World Weather lidar can play in this context we introduce the Convec- Research Program (WWRP) Research and Development tive and Orographically-induced Precipitation Study Project (Fig. -
Observations of Atmospheric Aerosol and Cloud Using a Polarized Micropulse Lidar in Xi’An, China
atmosphere Article Observations of Atmospheric Aerosol and Cloud Using a Polarized Micropulse Lidar in Xi’an, China Chao Chen 1,2,3,4, Xiaoquan Song 1,* , Zhangjun Wang 1,2,3,4,*, Wenyan Wang 5, Xiufen Wang 2,3,4, Quanfeng Zhuang 2,3,4, Xiaoyan Liu 1,2,3,4 , Hui Li 2,3,4, Kuntai Ma 1, Xianxin Li 2,3,4, Xin Pan 2,3,4, Feng Zhang 2,3,4, Boyang Xue 2,3,4 and Yang Yu 2,3,4 1 College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China; [email protected] (C.C.); [email protected] (X.L.); [email protected] (K.M.) 2 Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266100, China; [email protected] (X.W.); [email protected] (Q.Z.); [email protected] (H.L.); [email protected] (X.L.); [email protected] (X.P.); [email protected] (F.Z.); [email protected] (B.X.); [email protected] (Y.Y.) 3 Shandong Provincial Key Laboratory of Marine Monitoring Instrument Equipment Technology, Qingdao 266100, China 4 National Engineering and Technological Research Center of Marine Monitoring Equipment, Qingdao 266100, China 5 Xi’an Meteorological Bureau of Shanxi Province, Xi’an 710016, China; [email protected] * Correspondence: [email protected] (X.S.); [email protected] (Z.W.) Abstract: A polarized micropulse lidar (P-MPL) employing a pulsed laser at 532 nm was developed by the Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy Citation: Chen, C.; Song, X.; Wang, of Sciences). -
Evaluation of Long-Term Pavement Performance (LTTP) Climatic Data for May 2015 Use in Mechanistic-Empirical Pavement Design Guide(MEPDG) Calibration 6
Evaluation of LTPP Climatic Data for Use in Mechanistic-Empirical Pavement Design Guide Calibration and Other Pavement Analysis PUBLICATION NO. FHWA-HRT-15-019 MAY 2015 Research, Development, and Technology Turner-Fairbank Highway Research Center 6300 Georgetown Pike McLean, VA 22101-2296 FOREWORD This document presents the results of an evaluation of climate data from Modern-Era Retrospective Analysis for Research and Applications (MERRA) for use in the Long Term Pavement Performance (LTPP) Program and for other infrastructure applications. MERRA data were compared against the best available ground-based observations both statistically and in terms of effects on pavement performance as predicted using the Mechanistic-Empirical Pavement Design Guide (MEPDG). These analyses included a systematic quantitative evaluation of the sensitivity of MEPDG performance predictions to variations in fundamental climate parameters. A more extensive analysis of MERRA data included additional statistical analysis comparing operating weather station (OWS) and MERRA data, evaluation of the correctness of MEPDG surface shortwave radiation (SSR) calculations and comparison of MEPDG pavement performance predictions using OWS and MERRA climate data for more sections. The principal conclusion from these evaluations was that the MERRA climate data were as good as and in many cases substantially better than equivalent ground-based OWSs. MERRA is strongly recommended as the new future source for climate data in LTPP. Recommendations are provided for incorporating hourly MERRA data into the LTPP database. The LTPP program is an ongoing and active program. To obtain current information and access to other technical references, LTPP data users should visit the LTPP Web site at http://www.tfhrc.gov/pavement/ltpp/ltpp.htm. -
Bi-Directional Domain Adaptation for Sim2real Transfer of Embodied Navigation Agents
IEEE ROBOTICS AND AUTOMATION LETTERS. PREPRINT VERSION. ACCEPTED FEBRUARY, 2021 1 Bi-directional Domain Adaptation for Sim2Real Transfer of Embodied Navigation Agents Joanne Truong1 and Sonia Chernova1;2 and Dhruv Batra1;2 Abstract—Deep reinforcement learning models are notoriously This raises a fundamental question – How can we leverage data hungry, yet real-world data is expensive and time consuming imperfect but useful simulators to train robots while ensuring to obtain. The solution that many have turned to is to use that the learned skills generalize to reality? This question is simulation for training before deploying the robot in a real environment. Simulation offers the ability to train large numbers studied under the umbrella of ‘sim2real transfer’ and has been of robots in parallel, and offers an abundance of data. However, a topic of much interest in the community [4], [5], [6], [7], no simulation is perfect, and robots trained solely in simulation [8], [9], [10]. fail to generalize to the real-world, resulting in a “sim-vs-real In this work, we first reframe the sim2real transfer problem gap”. How can we overcome the trade-off between the abundance into the following question – given a cheap abundant low- of less accurate, artificial data from simulators and the scarcity of reliable, real-world data? In this paper, we propose Bi-directional fidelity data generator (a simulator) and an expensive scarce Domain Adaptation (BDA), a novel approach to bridge the sim- high-fidelity data source (reality), how should we best leverage vs-real gap in both directions– real2sim to bridge the visual the two to maximize performance of an agent in the expensive domain gap, and sim2real to bridge the dynamics domain gap. -
Conclusions Introduction Coherent Doppler LIDAR LIDAR Observation
Improvement of Long Range Doppler LIDARs of TM-SFW0603 Mitsubishi Electric Corporation (MELCO) Ikuya Kakimoto*1, Yutaka Kajiyama*1, Jong-Sung Ha*2, Hong-II Kim*2 *1 Mitsubishi Electric Corporation, 8-1-1 Tsukaguchi Honmachi, Amagasaki-city, 661-8661, Japan *2 Korea Aerospace research Institute, 169-84 Gwahangno, Yuseong-gu, Daejeon, 305-806, Korea Introduction LIDAR observation system linking with Radar Wind measurement is considered as one of the most important issues for Thunderstorm Forecasting System Wind shear detection system the prediction and elucidation of meteorological phenomena. As the formal instrument for wind measurement, ground-based anemometer, radiosonde, Doppler radar and Wind Profiler are utilized so far. However, it is quite difficult to scan the three dimensional (3D) wind field including zenith, and only Doppler radar can be utilized to measure 3D wind speed and direction in the case of rainfall. In recent years, Doppler LIDARs have been developed and it can scan 3-D wind field even in the case of fine weather and is proceeded to be utilized in a variety of fields. Therefore, it is possible to implement all-weather wind measurement with the set of Doppler LIDAR and Doppler radar and this set is much effective to Fig. 6 Wind shear detection system monitor the safety of air at the airport, launch complex and other fields. Fig. 5 Thunderstorm forecasting system Coherent Doppler LIDAR Radars can detect turbulences in The indication of torrential rain can be the rain and LIDAR can do that DIABREZZATM A Series detected 20 min. before by Ka-band. in fine weather.