Cloudsat-CALIPSO Launch
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Estimating Gale to Hurricane Force Winds Using the Satellite Altimeter
VOLUME 28 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY APRIL 2011 Estimating Gale to Hurricane Force Winds Using the Satellite Altimeter YVES QUILFEN Space Oceanography Laboratory, IFREMER, Plouzane´, France DOUG VANDEMARK Ocean Process Analysis Laboratory, University of New Hampshire, Durham, New Hampshire BERTRAND CHAPRON Space Oceanography Laboratory, IFREMER, Plouzane´, France HUI FENG Ocean Process Analysis Laboratory, University of New Hampshire, Durham, New Hampshire JOE SIENKIEWICZ Ocean Prediction Center, NCEP/NOAA, Camp Springs, Maryland (Manuscript received 21 September 2010, in final form 29 November 2010) ABSTRACT A new model is provided for estimating maritime near-surface wind speeds (U10) from satellite altimeter backscatter data during high wind conditions. The model is built using coincident satellite scatterometer and altimeter observations obtained from QuikSCAT and Jason satellite orbit crossovers in 2008 and 2009. The new wind measurements are linear with inverse radar backscatter levels, a result close to the earlier altimeter high wind speed model of Young (1993). By design, the model only applies for wind speeds above 18 m s21. Above this level, standard altimeter wind speed algorithms are not reliable and typically underestimate the true value. Simple rules for applying the new model to the present-day suite of satellite altimeters (Jason-1, Jason-2, and Envisat RA-2) are provided, with a key objective being provision of enhanced data for near-real- time forecast and warning applications surrounding gale to hurricane force wind events. Model limitations and strengths are discussed and highlight the valuable 5-km spatial resolution sea state and wind speed al- timeter information that can complement other data sources included in forecast guidance and air–sea in- teraction studies. -
Remote Sensing (Test)
Scioly Summer Study Session 2017 Remote Sensing (Test) Topic: Climate Change Pro c esses* ___ By user whythelongface (merge) Name(s): _________________________________________ Test format: This test is worth 150 points. There are four sections: 1. Remote Sensing Technology and techniques (50 points) ___ /50 2. Data Use and Manipulation (20 points) ___ /20 3. Image Interpretation (30 points) ___ /30 4. Weather and Climate Processes (50 points) ___ /50 Total: ___ /150 As of the 2016-2017 season, each person is allowed one double-sided 8.5 × 11” notesheet. Each partnership is allowed a protractor, ruler, writing implements, and a scientific calculator. Graphing calculators are not allowed. The author wishes you best of luck on this test and in the 2017-2018 Science Olympiad season. *The topic for the 2017-2018 season is still unknown at the time this test is being written, so it will focus on the same topic as that of the 2016-2017 season. Part 1: Remote Sensing Technology Multiple Choice (1 point each) -
High-Temporal-Resolution Water Level and Storage Change Data Sets for Lakes on the Tibetan Plateau During 2000–2017 Using Mult
Earth Syst. Sci. Data, 11, 1603–1627, 2019 https://doi.org/10.5194/essd-11-1603-2019 © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. High-temporal-resolution water level and storage change data sets for lakes on the Tibetan Plateau during 2000–2017 using multiple altimetric missions and Landsat-derived lake shoreline positions Xingdong Li1, Di Long1, Qi Huang1, Pengfei Han1, Fanyu Zhao1, and Yoshihide Wada2 1State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China 2International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria Correspondence: Di Long ([email protected]) Received: 21 February 2019 – Discussion started: 15 March 2019 Revised: 4 September 2019 – Accepted: 22 September 2019 – Published: 28 October 2019 Abstract. The Tibetan Plateau (TP), known as Asia’s water tower, is quite sensitive to climate change, which is reflected by changes in hydrologic state variables such as lake water storage. Given the extremely limited ground observations on the TP due to the harsh environment and complex terrain, we exploited multiple altimetric mis- sions and Landsat satellite data to create high-temporal-resolution lake water level and storage change time series at weekly to monthly timescales for 52 large lakes (50 lakes larger than 150 km2 and 2 lakes larger than 100 km2) on the TP during 2000–2017. The data sets are available online at https://doi.org/10.1594/PANGAEA.898411 (Li et al., 2019). With Landsat archives and altimetry data, we developed water levels from lake shoreline posi- tions (i.e., Landsat-derived water levels) that cover the study period and serve as an ideal reference for merging multisource lake water levels with systematic biases being removed. -
Improved Quality Control for Quikscat Near Real-Time Data
JP4.6 Improved Quality Control for QuikSCAT Near Real-time Data S. Mark Leidner, Ross N. Hoffman, and Mark C. Cerniglia Atmospheric and Environmental Research Inc., Lexington, Massachusetts Abstract errors. We will illustrate the types of errors that occur due to rain contamination and ambiguity removal. SeaWinds on QuikSCAT, launched in June 1999, We will also give examples of how the quality of the provides a new source of surface wind information retrieved winds varies across the satellite track, and over the world’s oceans. This new window on global varies with wind speed. surface vector winds has been a great aid to real- SeaWinds is an active, Ku-band microwave radar time operational users, especially in remote areas operating near ¢¤£¦¥¨§ © and is sensitive to centimeter- of the world. As with in situ observations, the qual- scale or capillary waves on the ocean surface. ity of remotely-sensed geophysical data is closely These waves are usually in equilibrium with the wind. tied to the characteristics of the instrument. But Each radar backscatter observation samples a patch remotely-sensed scatterometer winds also have a of ocean about . The vector wind is re- whole range of additional quality control concerns trieved by combining several backscatter observa- different from those of in situ observation systems. tions made from multiple viewing geometries as the The retrieval of geophysical information from the raw scatterometer passes overhead. The resolution of satellite measurements introduces uncertainties but the retrieved winds is . also produces diagnostics about the reliability of the Backscatter from capillary waves on the ocean retrieved quantities. -
Watching the Winds Where Sea Meets Sky 14 August 2014, by Rosalie Murphy
Watching the winds where sea meets sky 14 August 2014, by Rosalie Murphy the speed and direction of wind at the ocean's surface. "Before scatterometers, we could only measure ocean winds on ships, and sampling from ships is very limited," said Timothy Liu of NASA's Jet Propulsion Laboratory in Pasadena, California, who led the science team for NASA's QuikScat mission. Scatterometry began to emerge during World War II, when scientists realized wind disturbing the ocean's surface caused noise in their radar signals. NASA included an experimental scatterometer in its The SeaWinds scatterometer on NASA's QuikScat first space station in 1973 and again when it satellite stares into the eye of 1999's Hurricane Floyd as launched its SeaSat satellite in 1978. During its it hits the U.S. coast. The arrows indicate wind direction, three-month life, SeaSat's scatterometer provided while the colors represent wind speed, with orange and scientists with more individual wind observations yellow being the fastest. Credit: NASA/JPL-Caltech than ships had collected in the previous century. The ocean covers 71 percent of Earth's surface and affects weather over the entire globe. Hurricanes and storms that begin far out over the ocean affect people on land and interfere with shipping at sea. And the ocean stores carbon and heat, which are transported from the ocean to the air and back, allowing for photosynthesis and affecting Earth's climate. To understand all these processes, scientists need information about winds A JPL team then designed a mission called near the ocean's surface. -
Highlights in Space 2010
International Astronautical Federation Committee on Space Research International Institute of Space Law 94 bis, Avenue de Suffren c/o CNES 94 bis, Avenue de Suffren UNITED NATIONS 75015 Paris, France 2 place Maurice Quentin 75015 Paris, France Tel: +33 1 45 67 42 60 Fax: +33 1 42 73 21 20 Tel. + 33 1 44 76 75 10 E-mail: : [email protected] E-mail: [email protected] Fax. + 33 1 44 76 74 37 URL: www.iislweb.com OFFICE FOR OUTER SPACE AFFAIRS URL: www.iafastro.com E-mail: [email protected] URL : http://cosparhq.cnes.fr Highlights in Space 2010 Prepared in cooperation with the International Astronautical Federation, the Committee on Space Research and the International Institute of Space Law The United Nations Office for Outer Space Affairs is responsible for promoting international cooperation in the peaceful uses of outer space and assisting developing countries in using space science and technology. United Nations Office for Outer Space Affairs P. O. Box 500, 1400 Vienna, Austria Tel: (+43-1) 26060-4950 Fax: (+43-1) 26060-5830 E-mail: [email protected] URL: www.unoosa.org United Nations publication Printed in Austria USD 15 Sales No. E.11.I.3 ISBN 978-92-1-101236-1 ST/SPACE/57 *1180239* V.11-80239—January 2011—775 UNITED NATIONS OFFICE FOR OUTER SPACE AFFAIRS UNITED NATIONS OFFICE AT VIENNA Highlights in Space 2010 Prepared in cooperation with the International Astronautical Federation, the Committee on Space Research and the International Institute of Space Law Progress in space science, technology and applications, international cooperation and space law UNITED NATIONS New York, 2011 UniTEd NationS PUblication Sales no. -
Cloudsat CALIPSO
www.nasa.gov andSpaceAdministration National Aeronautics & CloudSat CALIPSO Clean air is important to everyone’s health and well-being. Clean air is vital to life on Earth. An average adult breathes more than 3000 gallons of air every day. In some places, the air we breathe is polluted. Human activities such as driving cars and trucks, burning coal and oil, and manufacturing chemicals release gases and small particles known as aerosols into the atmosphere. Natural processes such as for- est fires and wind-blown desert dust also produce large amounts of aerosols, but roughly half of the to- tal aerosols worldwide results from human activities. Aerosol particles are so small they can remain sus- pended in the air for days or weeks. Smaller aerosols can be breathed into the lungs. In high enough con- centrations, pollution aerosols can threaten human health. Aerosols can also impact our environment. Aerosols reflect sunlight back to space, cooling the Earth’s surface and some types of aerosols also absorb sunlight—heating the atmosphere. Because clouds form on aerosol particles, changes in aerosols can change clouds and even precipitation. These effects can change atmospheric circulation patterns, and, over time, even the Earth’s climate. The Air We Breathe The Air We We need better information, on a global scale from satellites, on where aerosols are produced and where they go. Aerosols can be carried through the atmosphere-traveling hundreds or thousands of miles from their sources. We need this satellite infor- mation to improve daily forecasts of air quality and long-term forecasts of climate change. -
Summary of the Key Issues in Space-Based Measurements
Summary of the Key Issues in Space-based Measurements: Identification of Future Needs and Opportunities Jean-Christopher LAMBERT Belgian Institute for Space Aeronomy (IASB-BIRA) Brussels, Belgium with contributions by 8ORM Participants, CEOS, and NDACC Satellite WG 8th ORM, WMO/UNEP, Geneva, CH, May 2-4, 2011 Summary of the Key Issues in Space-based Measurements: Identification of Future Needs and Opportunities 1. Satellite missions 2. Follow-up of 7ORM issues 3. Data quality strategy 4. Suggestions and recommendations 8th ORM, WMO/UNEP, Geneva, CH, May 2-4, 2011 Catalogues and details on satellite missions NDACC Satellite WG Web Site http://www.oma.be/NDSC_SatWG/Home.html Committee on Earth Observation Satellites http://ceos.org WMO Satellite & Requirements Database http://192.91.247.60/sat/index.htm 8th ORM, WMO/UNEP, Geneva, CH, May 2-4, 2011 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 I 1999 UV/VIS/NIR 8-2020) VIS/IR 1998 multi-sensor 1997 1996 1995 UV IR MW 1994 I 1993 I 1992 1991 I I 1990 I 1989 Spectral range: I I 1988 I I 1987 I I 1986 I I 1985 I I 1984 I I 1983 1982 Sun/Moon occultation stellar occultation 1981 multi-target I 1980 1979 1978 I nadir limb nadir/limb I Nimbus 7 METEOR 3 ADEOS 1 Earth Probe Nimbus 7 NOAA-9 NOAA-11 NOAA-14 NOAA-16 NOAA-17 NOAA-N/18 NOAA-N1/19 STS STS 87 & 107 NPP Sounding strategy: NPOESS SATELLITE MISSIONS FOR ATMOSPHERIC COMPOSITIONFeng-Yun-3A (197 Feng-Yun-3B Feng-Yun-3x SOUNDER MISSION Nimbus 7 AEM-B TOMS ERBS METOR 3M STS-64 CALIPSO -
NASA Earth Science Research Missions NASA Observing System INNOVATIONS
NASA’s Earth Science Division Research Flight Applied Sciences Technology NASA Earth Science Division Overview AMS Washington Forum 2 Mayl 4, 2017 FY18 President’s Budget Blueprint 3/2017 (Pre)FormulationFormulation FY17 Program of Record (Pre)FormulationFormulation Implementation MAIA (~2021) Implementation MAIA (~2021) Landsat 9 Landsat 9 Primary Ops Primary Ops TROPICS (~2021) (2020) TROPICS (~2021) (2020) Extended Ops PACE (2022) Extended Ops XXPACE (2022) geoCARB (~2021) NISAR (2022) geoCARB (~2021) NISAR (2022) SWOT (2021) SWOT (2021) TEMPO (2018) TEMPO (2018) JPSS-2 (NOAA) JPSS-2 (NOAA) InVEST/Cubesats InVEST/Cubesats Sentinel-6A/B (2020, 2025) RBI, OMPS-Limb (2018) Sentinel-6A/B (2020, 2025) RBI, OMPS-Limb (2018) GRACE-FO (2) (2018) GRACE-FO (2) (2018) MiRaTA (2017) MiRaTA (2017) Earth Science Instruments on ISS: ICESat-2 (2018) Earth Science Instruments on ISS: ICESat-2 (2018) CATS, (2020) RAVAN (2016) CATS, (2020) RAVAN (2016) CYGNSS (>2018) CYGNSS (>2018) LIS, (2020) IceCube (2017) LIS, (2020) IceCube (2017) SAGE III, (2020) ISS HARP (2017) SAGE III, (2020) ISS HARP (2017) SORCE, (2017)NISTAR, EPIC (2019) TEMPEST-D (2018) SORCE, (2017)NISTAR, EPIC (2019) TEMPEST-D (2018) TSIS-1, (2018) TSIS-1, (2018) TCTE (NOAA) (NOAA’S DSCOVR) TCTE (NOAA) (NOAA’SXX DSCOVR) ECOSTRESS, (2017) ECOSTRESS, (2017) QuikSCAT (2017) RainCube (2018*) QuikSCAT (2017) RainCube (2018*) GEDI, (2018) CubeRRT (2018*) GEDI, (2018) CubeRRT (2018*) OCO-3, (2018) CIRiS (2018*) OCOXX-3, (2018) CIRiS (2018*) CLARREO-PF, (2020) EOXX-1 CLARREOXX XX-PF, (2020) EOXX-1 -
Evaluation of Quikscat Data for Monitoring Vegetation Phenology
City University of New York (CUNY) CUNY Academic Works Dissertations and Theses City College of New York 2011 Evaluation of QuikSCAT data for Monitoring Vegetation Phenology Doralee Pellot CUNY City College How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/cc_etds_theses/27 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] Evaluation of QuikSCAT data for Monitoring Vegetation Phenology Thesis Submitted in partial fulfillment of the requirements for the degree Master of Engineering (Civil) at The City College of New York of The City University of New York by Doralee Pellot May 2012 ____________________________________________ Professor Reza Khanbilvardi Dr. Tarendra Lakhankar Department of Civil Engineering Table of Contents List of Figures ................................................................................................................................. 1 Abstract ........................................................................................................................................... 3 1 Introduction ............................................................................................................................. 4 2 Data Sources ........................................................................................................................... 6 2.1 Radar Backscatter from QuikSCAT ................................................................................ -
Algorithm Theoretical Basis Document (ATBD) for Land
ICE, CLOUD, and Land Elevation Satellite (ICESat-2) Algorithm Theoretical Basis Document (ATBD) for Land - Vegetation Along-track products (ATL08) Contributions by Land/VEG SDT Team Members and ICESAt-2 Project Science Office (Amy Neuenschwander, Sorin Popescu, Ross Nelson, David Harding, Katherine Pitts, John Robbins, Dylan Pederson, and Ryan Sheridan) ATBD Document prepared by Amy Neuenschwander June 2018 Content reviewed: technical approach, assumptions, scientific soundness, maturity, scientific utility of the data product 21 Contents 1 INTRODUCTION 8 1.1. Background 9 1.2 Photon Counting Lidar 11 1.3 The ICESat-2 concept 12 1.4 Height Retrieval from ATLAS 16 1.5 Accuracy Expected from ATLAS 17 1.6 Additional Potential Height Errors from ATLAS 19 1.7 Dense Canopy Cases 20 1.8 Sparse Canopy Cases 20 2. ATL08: DATA PRODUCT 21 2.1 Subgroup: Land Parameters 23 2.1.1 Georeferenced_segment_number_beg 24 2.1.2 Georeferenced_segment_number_end 25 2.1.3 Segment_terrain_height_mean 25 2.1.4 Segment_terrain_height_med 25 2.1.5 Segment_terrain_height_min 25 2.1.6 Segment_terrain_height_max 26 2.1.7 Segment_terrain_height_mode 26 2.1.8 Segment_terrain_height_skew 26 2.1.9 Segment_number_terrain_photons 26 2.1.10 Segment height_interp 27 2.1.11 Segment h_te_std 27 2.1.12 Segment_terrain_height_uncertainty 27 2.1.13 Segment_terrain_slope 27 21 2.1.14 Segment number_of_photons 27 2.1.15 Segment_terrain_height_best_fit 28 2.2 Subgroup: Vegetation Parameters 28 2.2.1 Georeferenced_segment_number_beg 31 2.2.2 Georeferenced_segment_number_end 31 2.2.3 -
GLAS HDF Standard Data Product Specification Revision - November 01, 2012
ICE, CLOUD, and Land Elevation Satellite (ICESat) Project GLAS_HDF Standard Data Product Specification Revision - November 01, 2012 SGT/Jeffrey Lee Cryospheric Sciences Laboratory Hydrospheric and Biospheric Processes NASA Goddard Space Flight Center Goddard Space Flight Center Greenbelt, Maryland National Aeronautics and Space Administration GLAS_HDF Standard Data Product Specification Revision - Table of Contents Table of Contents ............................................................................................... 1-1 List of Figures ..................................................................................................... 1-3 List of Tables ...................................................................................................... 1-3 1.0 Introduction ................................................................................................ 1-1 1.1 Identification of Document ...................................................................... 1-1 1.2 Scope ..................................................................................................... 1-1 1.3 Purpose and Objectives ......................................................................... 1-1 1.4 Acknowledgements ................................................................................ 1-1 1.5 Document Status and Schedule ............................................................. 1-2 1.6 Document Change History ..................................................................... 1-2 2.0 Related Documentation ............................................................................