DOCSLIB.ORG

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

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
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. Simeonov (valentin.simeonov@epfl.ch) Methods and Methods and Received: 2 July 2012 – Published in Atmos. Meas. Tech. Discuss.: 20 September 2012 Data Systems Data Systems Revised: 19 April 2013 – Accepted: 22 April 2013 – Published: 22 May 2013 Discussions Open Access Open Access Geoscientific Geoscientific Abstract. A new Raman lidar for unattended, round-the- 1 Introduction Model Development Model Development clock measurement of vertical water vapor profiles for op- Discussions erational use by the MeteoSwiss has been developed dur- Water vapor plays a fundamental role in the radiative en- ing the past years by the Swiss Federal Institute of Tech- ergy transfer, hydrological cycle, and atmospheric chemistry Open Access Open Access nology, Lausanne. The lidar uses narrow field-of-view, nar- processes that determineHydrology weather and climate.and It influences Hydrology and rowband configuration, a UV laser, and four 30 cm in di- the radiative budget of the planet both directly and through ameter mirrors, fiber-coupled to a grating polychromator. coupling with clouds. BecauseEarth of System its strong absorption and Earth System The optical design allows water vapor retrieval from the in- emission bands, especially in theSciences infrared, and because of its Sciences complete overlap region without instrument-specific range- abundance, water vapor is the most significant greenhouse Discussions dependent corrections. The daytime vertical range covers the gas, and in this sense the most important in establishing the Open Access Open Access mid-troposphere, whereas the nighttime range extends to the Earth’s climate. tropopause. The near range coverage is extended down to Water vapor distribution is strongly affected by atmo- Ocean Science spheric dynamics, butOcean in turn it alsoScience influences atmospheric 100 m AGL by the use of an additional fiber in one of the Discussions telescopes. This paper describes the system layout and tech- circulation and temperature structure by condensation– nical realization. Day- and nighttime lidar profiles compared evaporation processes. The water evaporation–condensation ® cycle is an important mechanism for transferring heat energy Open Access to Vaisala RS92 and Snow White profiles and a six-day Open Access continuous observation are presented as an illustration of the from the Earth’s surface to its atmosphere and in moving heat lidar measurement capability. around the Earth. The large latent energy associated with the Solid Earth Solid Earth water phase changes significantly affects the meridional en- Discussions ergy balance and the vertical stability of the atmosphere as well as the structure and evolution of storm systems, not only in the boundary layer (Normand, 1938) but alsoOpen Access in the mid- Open Access dle and upper troposphere (Peppler, 1989; Sinha and Harries, 1995; Arnold, 2008).The Cryosphere The Cryosphere Discussions Published by Copernicus Publications on behalf of the European Geosciences Union. 1330 T. Dinoev et al.: RALMO – Part 1: Instrument description Because of its essential role in the atmospheric processes, The Raman lidar technique exploits Raman scattering the water vapor spatial distribution and its temporal evolution from water vapor and nitrogen molecules to derive a profile are two of the most important parameters in global and re- of water vapor mixing ratio (Cooney, 1970; Melfy, 1972). gional numerical weather prediction (NWP) models. To im- At present, the nighttime distance range can reach up to the prove near-surface weather predictions, and to better sim- lower stratosphere but at daytime the range is limited to the ulate the evolution of local severe weather events in com- middle troposphere because the weak Raman signals are de- plex terrains (Wulfmeyer et al., 2008), the time and space tected in the presence of intensive daylight background. A resolution of the NWP models, used by the meteorological Raman lidar requires calibration either against a reference services, is currently being increased (Calpini et al., 2011). instrument or by absolute radiometric calibration of the li- The vertical water vapor profiles assimilated by these mod- dar optics, provided accurate Raman cross sections are avail- els are mainly acquired by twice-a-day radiosonde observa- able. Contrary to a DIAL, a Raman lidar does not require tions which have insufficient time resolution to resolve fast tunable laser source with specific and highly stabilized wave- running meteorological phenomena. In addition, radiosonde lengths. Furthermore, the Raman data-treatment algorithm is measurements suffer from systematic errors that are dif- significantly simpler than the DIAL algorithm, and allows ficult to correct and from essential sonde-to-sonde varia- data retrieval from the incomplete overlap region. Because tions not only between instruments from different produc- of all these advantages and because of the higher reliability, ers and operational principles, but even between instruments Raman lidars are preferred for operational use in meteorol- of the same batch (Nash et al., 2011). Ground-based mi- ogy from ground-based stations (Goldsmith et al., 1998; En- crowave radiometers (Solheim, 1998 and references therein) gelbart et al., 2006; Reichardt et al., 2012; Simeonov et al., and Fourier transform infrared radiometers (Schneider and 2010; Appituley et al., 2009). Hase, 2009 and references therein) provide humidity profiles Raman lidars have been used for high resolution verti- with sufficient time resolution, but with rather low vertical cal profiling of water vapor within the troposphere since the resolution, to resolve the spatial variability of water vapor in early 1970s. Most of the measurements were performed for the low troposphere. Therefore, national meteorological ser- research purposes and at nighttime (Whiteman, 1992; Ans- vices need a new type of autonomous, continuously operated mann et al., 1992; Vaughan et al., 1988; Balin et al., 2004). (24-7-365) instruments for near real-time, high spatial reso- First daytime measurements were possible using laser wave- lution observations of the tropospheric water vapor field. length shorter than 300 nm because at these wavelengths the Lidar (light detection and ranging) is one of the techniques solar light is absorbed by stratospheric ozone (Renault et al., able to resolve the high temporal and spatial variability of 1980; Cooney et al., 1985). The vertical range of such li- tropospheric water vapor. The advances of the last decades in dars is, however, limited to about 2 km, mainly due to tropo- the field of laser technology and improved lidar design make spheric ozone absorption. The use of a narrow field-of-view lidars suitable for operational use in meteorological services. (NFOV), narrowband (NB) receiver allows operation at vis- Two lidar techniques, DIAL and Raman, are used for water ible and near UV wavelengths, resulting in the extension of vapor measurements. the operational range up to the mid troposphere (Goldsmith The DIAL principle exploits the difference in atmospheric et al., 1998). extinction due to water vapor absorption at two closely- The successful long-term operation of the first automated spaced, near-IR wavelengths. Provided accurate knowledge NFOV NB lidar – CART (Goldsmith et al., 1998) – motivated of water vapor absorption cross section is available, DIAL the German (Engelbart et al., 2006; Reichardt et al., 2012), does not need other external references for correct measure- the Swiss (Dinoev et al., 2006; Simeonov et al., 2010) and ments and in this sense is considered “self-calibrating”. A the Dutch (Appituley et al., 2009) meteorological services to DIAL system has the potential to perform daytime obser- establish programs
Load more

Recommended publications
  • 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 ................................................................
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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).
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • A Comparison of Lidar and Radiosonde Wind Measurements Valerie-M
    Available online at www.sciencedirect.com ScienceDirect Energy Procedia 53 ( 2014 ) 214 – 220 EERA DeepWind’2014, 11th Deep Sea Offshore Wind R&D Conference A comparison of LiDAR and radiosonde wind measurements Valerie-M. Kumera, Joachim Reudera, Birgitte R. Furevika,b aGeophysical Institute, University of Bergen, Allegaten 70, 5007 Bergen, Norway bNorwegian Meteorological Institute, Allegaten 70, 5007 Bergen, Norway Abstract Doppler LiDAR measurements are already well established in the wind energy research and their accuracy has been tested against met mast data up to 100 m above ground. However, the new generation of scanning LiDAR have a much higher range and thus it is not possible to verify measurements at higher altitudes. Therefore, the LiDAR Measurement Campaign Sola (LIMECS) was conducted at the airport of Stavanger from March to August 2013 to compare LiDAR and radiosonde winds. It was a collaborative test campaign between the University of Bergen, the Norwegian Meteorological Office (MET), Christian Michelsen Research (CMR) and Avinor. With the airports’ location at the Norwegian West Coast, additional motivations were the investigations in characteristics of coastal winds, as well as the validation of the LES turbulence forecast for the airport of Stavanger. We deployed two Windcubes v1 and a scanning Windcube 100S at two different sites in Sola, one next to the runway and the other one near to the autosonde from MET. The Windcube 100S scans several cross-sections of the ambient flow on hourly basis. In combination with wind profiles up to 200 m (Windcubes v1) and 3 km (Windcube 100S) and temporally more frequent radiosonde ascents, we collect a variety of wind information in the coastal atmospheric boundary layer.
    [Show full text]
  • 1 10.4 Comparative Analysis of Terminal Wind-Shear
    10.4 COMPARATIVE ANALYSIS OF TERMINAL WIND-SHEAR DETECTION SYSTEMS John Y. N. Cho, Robert G. Hallowell, and Mark E. Weber MIT Lincoln Laboratory, Lexington, Massachusetts 1. INTRODUCTION One of the key factors in estimating the benefits of a terminal wind-shear detection system is its performance. Low-level wind shear, especially a microburst, is very Thus, it is necessary to quantify the wind-shear detection hazardous to aircraft departing or approaching an airport. probability for each sensor, preferably on an airport-by- The danger became especially clear in a series of fatal airport basis. To consider sensors that are not yet commercial airliner accidents in the 1970s and 1980s at deployed, a model must be developed that takes into U.S. airports. In response, the Federal Aviation Agency account the various effects that factor into the detection (FAA) developed and deployed three ground-based low- probability. We have developed such a model. The altitude wind-shear detection systems: the Low Altitude focus of this paper is on this model and the results Wind Shear Alert System (LLWAS) (Wilson and Gram- obtained with it. zow 1991), the Terminal Doppler Weather Radar (TDWR) (Michelson et al. 1990), and the Airport Surveil- 2. SCOPE OF STUDY lance Radar Weather Systems Processor (ASR-9 WSP) (Weber and Stone 1995). Since the deployment of these In addition to the three FAA wind-shear detection sys- sensors, commercial aircraft wind-shear accidents have tems mentioned above, we included the Weather Sur- dropped to near zero in the U.S. This dramatic decrease veillance Radar 1988-Doppler (WSR-88D, commonly in accidents caused by wind shear appears to confirm known as NEXRAD) (Heiss et al.
    [Show full text]
  • 4.4 Comparison of Wind Measurements at the Howard University Beltsville Research Campus
    4.4 COMPARISON OF WIND MEASUREMENTS AT THE HOWARD UNIVERSITY BELTSVILLE RESEARCH CAMPUS Kevin Vermeesch1*, Bruce Gentry2, Grady Koch3, Matthieu Boquet4, Huailin Chen1, Upendra Singh3, Belay Demoz5, and Tulu Bacha5 1 Science Systems and Applications, Inc., Lanham, MD 2 NASA Goddard Space Flight Center, Greenbelt, MD 3 NASA Langley Research Center, Hampton, VA 4 LEOSPHERE, Orsay, France 5 Howard University, Washington D.C. 1. INTRODUCTION* been averaged using a moving average of 10 minutes in time and 120 meters in the vertical. The measurement of tropospheric wind is of great Wind lidars located on-site for the measurement importance to numeric weather prediction, air campaign include GLOW, VALIDAR, and a transportation, and wind-generated electricity. Wind LEOSPHERE WINDCUBE70, all of which are ground- lidar technology allows higher temporal measurement based and mobile. GLOW is a direct detection of wind profiles with greater spatial localization than Doppler wind lidar using a double-edge molecular the radiosonde. These technologies are compared technique (Korb et al. 1998). During normal usage, it during a wind measurement campaign in February can profile into the lower stratosphere (up to 35 km) and March of 2009 at the Howard University Beltsville (Gentry et al. 2001), however, due to an etalon Research Campus (HUBRC) in Beltsville, MD. The modulation problem discovered after the campaign instrumentation used in this campaign includes the (Chen 2011), GLOW was not able to produce usable Goddard Lidar Observatory for Winds (GLOW), data near clouds and in areas of heavy aerosol VALIDAR, a LEOSPHERE WINDCUBE70, a wind loading and there was a reduction in its vertical range.
    [Show full text]
  • Automated Surface Observing Systems (ASOS) IMPACTS
    Automated Surface Observing Systems (ASOS) IMPACTS Introduction The Automated Surface Observing Systems (ASOS) IMPACTS dataset consists of a variety of ground-based observations during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. This ASOS dataset consists of 176 stations within the IMPACTS domain. Each station provides observations of surface temperature, dew point, precipitation, wind direction, wind speed, wind gust, sea level pressure, and the observed weather code. The ASOS data are available from December 29, 2019 through February 29, 2020 in netCDF-4 format. Citation Brodzik, Stacy. 2020. Automated Surface Observing Systems (ASOS) IMPACTS [indicate subset used]. Dataset available online from the NASA Global Hydrology Resource Center DAAC, Huntsville, Alabama, U.S.A. doi: http://dx.doi.org/10.5067/IMPACTS/ASOS/DATA101 Keywords: NASA, GHRC, IMPACTS, ASOS, atmospheric temperature, sea level pressure, visibility, local winds, surface observations Campaign The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS), funded by NASA’s Earth Venture program, is the first comprehensive study of East Coast snowstorms in 30 years. IMPACTS will fly a complementary suite of remote sensing and in-situ instruments for three 6-week deployments (2020-2022) on NASA’s ER-2 high-altitude aircraft and P-3 cloud-sampling aircraft.
    [Show full text]
  • Lidar Remote Sensing
    LIDAR an Introduction and Overview Rooster Rock State Park & Crown Point. Oregon DOGAMI Lidar Project Presented by Keith Marcoe GEOG581, Fall 2007. Portland State University. What is Lidar? Light Detection And Ranging Active form of remote sensing: information is obtained from a signal which is sent from a transmitter, reflected by a target, and detected by a receiver back at the source. Airborne and Space Lidar Systems. Most are currently airborne. 3 types of information can be obtained: a) Range to target (Topographic Lidar, or Laser Altimetry) b) Chemical properties of target (Differential Absorption Lidar) c) Velocity of target (Doppler Lidar) Focus on Laser Altimetry. Most active area today. 1 Lidar History 60s and 70s - First laser remote sensing instruments (lunar laser ranging, satellite laser ranging, oceanographic and atmospheric research) 80s - First laser altimetry systems (NASA Atmospheric and Oceanographic Lidar (AOL) and Airborne Topographic Mapper (ATM)) 1995 - First commercial airborne Lidar systems developed. Last 10 years - Significant development of commercial and non-commercial systems 1994 - SHOALS (US Army Corps of Engineers) 1996 - Mars Orbiter Laser Altimer (NASA MOLA-2) 1997 - Shuttle Laser Altimeter (NASA SLA) Early 2000s - North Carolina achieves statewide Lidar coverage (used for updating FEMA flood insurance maps) Statewide and regional consortiums being developed for the management and distribution of large volumes of Lidar data Currently 2 predominate issues - Standardization of data formats Standardization of processing techniques to extract useful information Comparison of Lidar and Radar Lidar Radar Uses optical signals (Near IR, Uses microwave signals. Wavelengths ≈ 1 visible). Wavelengths ≈ 1 um cm. (Approx 100,000 times longer than Near IR) Shorter wavelengths allow Target size limited by longer wavelength detection of smaller objects (cloud particles, aerosols) Focused beam and high frequency Beam width and antenna length limit permit high spatial resolution spatial resolution (10s of meters).
    [Show full text]
  • Lidar for Turbine Control: March 1, 2005 – November 30, 2005 DE-AC36-99-GO10337
    A national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy National Renewable Energy Laboratory Innovation for Our Energy Future Lidar for Turbine Control Technical Report NREL/TP-500-39154 March 1, 2005 – November 30, 2005 January 2006 M. Harris QinetiQ Limited Malvern, Worcestershire, United Kingdom M. Hand, A. Wright National Renewable Energy Laboratory Golden, Colorado NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 Lidar for Turbine Control Technical Report NREL/TP-500-39154 March 1, 2005 – November 30, 2005 January 2006 M. Harris QinetiQ Limited Malvern, Worcestershire, United Kingdom M. Hand, A. Wright National Renewable Energy Laboratory Golden, Colorado NREL Technical Monitor: S. Schreck Prepared under Task No. WER6.0301 and Subcontract No. YAM-5-33200-11 National Renewable Energy Laboratory 1617 Cole Boulevard, Golden, Colorado 80401-3393 303-275-3000 • www.nrel.gov Operated for the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy by Midwest Research Institute • Battelle Contract No. DE-AC36-99-GO10337 NOTICE This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof.
    [Show full text]