SWIFT. The Stratospheric Wind Interferometer for Transport Studies

Similar documents
High Resolution Sea Surface Roughness and Wind Speed with Space Lidar (CALIPSO)

Airborne Coherent Wind Lidar measurements of vertical and horizontal wind speeds for the investigation of gravity waves

Development of SAR-Derived Ocean Surface Winds at NOAA/NESDIS

EVALUATION OF ENVISAT ASAR WAVE MODE RETRIEVAL ALGORITHMS FOR SEA-STATE FORECASTING AND WAVE CLIMATE ASSESSMENT

Executive Summary of Accuracy for WINDCUBE 200S

Update of the COST726 Total Ozone Data Base up to December 31, 2008

High resolution wind retrieval for SeaWinds

Data modelling and interpretation

Short-period gravity waves over a high-latitude observation site: Rothera, Antarctica

Preparation of the ADM-Aeolus mission using 355nm high spectral resolution Doppler LIDAR and a Doppler cloud radar

Assessing the quality of Synthetic Aperture Radar (SAR) wind retrieval in coastal zones using multiple Lidars

Results for the stratosphere

CHANGE OF THE BRIGHTNESS TEMPERATURE IN THE MICROWAVE REGION DUE TO THE RELATIVE WIND DIRECTION

Strengthening of the tropopause inversion layer during the 2009 sudden stratospheric warming in the MERRA-2 analysis

HISUI Vicarious calibration and Cal/Val activities

WindProspector TM Lockheed Martin Corporation

Global observations of stratospheric gravity. comparisons with an atmospheric general circulation model

HIGH RESOLUTION WIND RETRIEVAL FOR SEAWINDS ON QUIKSCAT. Jeremy B. Luke. A thesis submitted to the faculty of. Brigham Young University

MOUNTAIN WAVES IN THE MIDDLE ATMOSPHERE: MICROWAVE LIMB SOUNDER OBSERVATIONS AND ANALYSES

Windcube FCR measurements

Studying cold pole problem in WACCM and comparisons to lidar temperature morphology

REMOTE SENSING APPLICATION in WIND ENERGY

Analysis on Turbulent Flows using Large-eddy Simulation on the Seaside Complex Terrain

Technical Note on: ORM Cal Val Analysis Part 2: detailed results. Draft. 22 October, 2003

OPERATIONAL AMV PRODUCTS DERIVED WITH METEOSAT-6 RAPID SCAN DATA. Arthur de Smet. EUMETSAT, Am Kavalleriesand 31, D Darmstadt, Germany ABSTRACT

ROSE-HULMAN INSTITUTE OF TECHNOLOGY Department of Mechanical Engineering. Mini-project 3 Tennis ball launcher

Gravity wave effects on the calibration uncertainty of hydrometric current meters

Extreme waves in the ECMWF operational wave forecasting system. Jean-Raymond Bidlot Peter Janssen Saleh Abdalla

The impact of ocean bottom morphology on the modelling of long gravity waves from tides and tsunami to climate

Constraining a global, eddying, ocean and sea ice model with scatterometer data

Assimilation of EOS Aura ozone data at the Global Modeling and Assimilation Office

Differences in trends and anomalies of upper-air observations from GPS RO, AMSU, and radiosondes

Vector Synoptic Maps at NSO. Luca Bertello

Modelling and Assessment of Marine Renewable Energy Resources. Andrew Cornett Canadian Hydraulics Centre National Research Council Canada May 2008

Characterization of Boundary-Layer Meteorology During DISCOVER-AQ

Kelvin waves as observed by Radiosondes and GPS measurements and their effects on the tropopause structure: Long-term variations

Air-Sea Interaction Spar Buoy Systems

Scanning Laser Vibrometry Assessment of Sports Equipment

Gravity wave driven descent of the stratopause following major stratospheric sudden warmings

MATRIX-MG Series. Innovation with Integrity. Automated High-Performance Gas Analyzers FT-IR

ERS WAVE MISSION REPROCESSING- QC SUPPORT ENVISAT MISSION EXTENSION SUPPORT

SIMON YUEH, WENQING TANG, ALEXANDER FORE, AND JULIAN CHAUBELL JPL-CALTECH, PASADENA, CA, USA GARY LAGERLOEF EARTH AND SPACE RESEARCH, SEATTLE, WA, US

Neal Butchart Steven Hardiman and Adam Scaife Met Office Hadley Centre March 2011, Honolulu, USA

ERS-1/2 Scatterometer new products: mission reprocessing and data quality improvement

The Case for Depth Imaging All 3D Data: Complex Thrust Belts to Low Relief Resource Plays

Reprocessed QuikSCAT (V04) Wind Vectors with Ku-2011 Geophysical Model Function

High-Resolution Measurement-Based Phase-Resolved Prediction of Ocean Wavefields

Tether-based Robot Locomotion Experiments in REX-J mission

Physical Science 1 Chapter 6 WAVES. A wave is a disturbance that is propagated through a system. Waves transfer energy.

ISOLATION OF NON-HYDROSTATIC REGIONS WITHIN A BASIN

PUV Wave Directional Spectra How PUV Wave Analysis Works

Physics 1520, Spring 2014 Quiz 1A, Form: A

Physics 1520, Spring 2014 Quiz 1B, Form: A

Axial Base (PN3A) Medium-Power (MP) J-box

THE QUALITY OF THE ASCAT 12.5 KM WIND PRODUCT

Feasibility of snow water equivalent retrieval by means of groundbased and spaceborne SAR interferometry

Measurement of Coastal & Littoral Toxic Material Tracer Dispersion. Dr. Robert E. Marshall T41 NSWCDD

Review of Equivalent Neutral Winds and Stress

University of Wisconsin SSEC Atmospheric SIPS and AHI/ABI Capabilities

Goal: Describe the principal features and characteristics of monsoons

Generalized Wave-Ray Approach for Propagation on a Sphere and Its Application to Swell Prediction

TRMM TMI and AMSR-E Microwave SSTs

P2.25 SUMMER-TIME THERMAL WINDS OVER ICELAND: IMPACT OF TOPOGRAPHY. Bergen

The Sea surface KInematics Multiscale (SKIM)

High Frequency Acoustical Propagation and Scattering in Coastal Waters

JCOMM Technical Workshop on Wave Measurements from Buoys

Prediction of Nearshore Waves and Currents: Model Sensitivity, Confidence and Assimilation

Airborne wind lidar campaigns for preparation of the Aeolus mission Oliver Reitebuch

7 th International Conference on Wind Turbine Noise Rotterdam 2 nd to 5 th May 2017

Geophysical Fluid Dynamics of the Earth. Jeffrey B. Weiss University of Colorado, Boulder

Oxygen sounding thermal profiler for ALMA

Offshore Wind Turbine Wake Characterization using Scanning Doppler Lidar

ENVISAT WIND AND WAVE PRODUCTS: MONITORING, VALIDATION AND ASSIMILATION

FULL-WAVEFORM INVERSION

using GPS radio occultation data

An algorithm for Sea Surface Wind Speed from MWR

Jackie May* Mark Bourassa. * Current affilitation: QinetiQ-NA

Status report on the current and future satellite systems by CMA. Presented to CGMS45-CMA-WP-01, Plenary session, agenda item D.1

SENSOR SYNERGY OF ACTIVE AND PASSIVE MICROWAVE INSTRUMENTS FOR OBSERVATIONS OF MARINE SURFACE WINDS

Coastal Scatterometer Winds Working Group

Section 1 Types of Waves. Distinguish between mechanical waves and electromagnetic waves.

M. Mikkonen.

An Engineering Approach to Precision Ammunition Development. Justin Pierce Design Engineer Government and International Contracts ATK Sporting Group

Toward a global view of extratropical UTLS tracer distributions. SPARC GA Sept Michaela I. Hegglin University of Toronto, Canada

3D Inversion in GM-SYS 3D Modelling

Boost Your Skills with On-Site Courses Tailored to Your Needs

Wind Flow Modeling: Are computationally intensive models more accurate?

ON THE USE OF DOPPLER SHIFT FOR SAR WIND RETRIEVAL

MOTUS Wave Buoys. Powered By the Aanderaa MOTUS Directional Wave Sensor

Crave the Wave, Feb 16, 2008 TEAM Mentor Invitational Score Rank

Application of Simulating WAves Nearshore (SWAN) model for wave simulation in Gulf of Thailand

J4.2 AUTOMATED DETECTION OF GAP WIND AND OCEAN UPWELLING EVENTS IN CENTRAL AMERICAN GULF REGIONS

The relationship between sea level and bottom pressure in an eddy permitting ocean model

MIKE 21 Toolbox. Global Tide Model Tidal prediction

Airborne Remote Sensing of Surface and Internal Wave Processes on the Inner Shelf

Super-parameterization of boundary layer roll vortices in tropical cyclone models

Wind tunnel acoustic testing of wind generated noise on building facade elements

Chapter 16 Waves and Sound

Pedestrian Dynamics: Models of Pedestrian Behaviour

Impact of the tides, wind and shelf circulation on the Gironde river plume dynamics

Transcription:

The Stratospheric Wind Interferometer for Transport Studies SWIFT I. McDade, C. Haley, J. Drummond, K. Strong, B. Solheim, T. Shepherd, Y. Rochon, and the SWIFT Team

ESA What is SWIFT?

SWIFT is the Stratospheric Wind Interferometer for Transport Studies it is a Canadian satellite instrument designed to make global stratospheric wind measurements between 15 and 55 km and provide simultaneous co-located ozone profiles. Very few satellite measurements of stratospheric winds exist, so this is something quite unique and of great interest to the international atmospheric science community SWIFT is just about to start Mission Phase B/C for implementation on a Canadian Space Agency Small Sat mission called Chinook scheduled for launch in late 2010

SCIENCE OBJECTIVES OF SWIFT To provide global maps of wind profiles in the stratosphere in order to study: Atmospheric dynamics and stratospheric circulation Ozone transport from SWIFT s co-located wind and ozone density measurements The potential of stratospheric wind measurements for improving medium range weather forecasts

Observational goals and required performance Obtain global vector winds to an accuracy of 3-5 m/s between 15 km and 55 km Simultaneously obtain ozone number densities to an accuracy of 5 % (15-30 km) Vertical resolution 1.5 km Horizontal sampling ~400 km along track Continuous near-global coverage

How does SWIFT work?

SWIFT is based on the Doppler Imaging Michelson concept already used by the WINDII instrument on UARS. WINDII measured Doppler shifts in the wavelengths of airglow emission lines in the visible region of the spectrum to determine winds in the upper mesosphere and thermosphere and made remarkable discoveries about atmospheric tides and mesosphere and thermosphere dynamics SWIFT will do the same thing but use a single thermal emission line from ozone in the mid IR region to push this technique down into the stratosphere

The Doppler Imaging Michelson concept as applied on SWIFT Using etalon filters, a single thermal emission line (an ozone rotation-vibration line near 9 µm) is isolated as shown in the left panel The wind produces a Doppler shift in the emission line A Michelson interferometer produces the Fourier transform (right) of the input line spectrum (left) The phase shift of a single fringe gives the Line of Sight (LOS) wind speed as illustrated on the next slide Intensity Line Spectrum δλ Wavelength, λ Intensity Interferogram 5 ~10 fringes δφ Path Difference, D

Phase measurement and the LOS wind speed The interferometer is phasestepped through four positions, yielding I1, I2, I3 and I4 From these the phase is computed, and from this the apparent LOS wind speed Fringe Intensity φ I 1 I 4 I 2 I3 A' I av This analysis is performed for each tangent height in the image field Path Difference I bkg I drk

SWIFT viewing geometry (side view) Image field 1X2 degree (~ 50 km x 100 km)

SWIFT will take pictures of bright line emission from ozone in the IR region

Sample simulated SWIFT images for phase steps 1,2,3 & 4 without noise φ Fringe Intensity I 1 I 4 I 2 I3 A' I av I bkg Path Difference I drk

SWIFT viewing geometry (top view) LIMB IMAGING GEOMETRY TOP VIEW 3800 km: ~ 8 min @ 7.5 km/s 45 deg Spacecraft Velocity Vector Tangent Point Track FOV 1 1900 km distance to tangent point FOV 2 FOV 2 FOV 1 horizontal resolution ~100 km across the field of view

For each tangent height in the limb image SWIFT obtains a LOS wind speed (after correcting for the satellite velocity and Earth rotation components) By observing at two orthogonal (or near orthogonal) directions as shown in the next slide, SWIFT can resolve the wind speed and direction i.e., measure the vector wind profiles

FOV 1 and 2 SWIFT viewing geometry SWIFT measures line of sight wind speeds in two orthogonal directions SWIFT on Chinook Image field 1 x 2 (50 km x 100 km) made up of 81x 162 pixels each 0.64 km high/wide. Stratospheric coverage from 15 km to 65 km Orthogonal FOVs resolve full horizontal wind vector Spacecraft velocity means ~8 minute delay between orthogonal components

SWIFT Retrieval algorithm Uses iterative Optimal Estimation with a forward model based on a SWIFT Instrument Simulator (SIS) and an atmospheric Radiative Transfer model, together with the Maximum a Posteriori (MAP) solver of Rodgers (2000), to find the FOV wind profile and ozone density profile most consistent with the observed phase-stepped images

SWIFT Illustrative retrieval noise standard deviations MAP+DR MAP Unconstrained Wind and ozone random error standard deviations (lines) and sample retrieval errors from a single Monte Carlo realization/simulation (points) with measurement noise

SWIFT Science Team Principal Investigator Ian McDade, York University, Toronto, Canada Assistant to P.I Craig Haley (York U.) Co.I. Co.I. Co.I. Co.I. Co.I Lead ID&C Lead GDR&SOC Lead GDV Lead GDA&M Lead ECUI&DA J.Drummond B. Solheim K. Strong T. Shepherd Y. Rochon (Dal. U. ) (York U.) (U. of T.) (U. of T.) (E. C.) Plus the other Co-Investigators and student members listed below: G. Shepherd, C. McLandress, W. Ward, D. Degenstein, R. Sica, W. Lahoz, C. Camy-Peyret, P. Rahnama, B. Quine, J. McConnell, E. Llewellyn, S. Turner, etc. ID&C = Instrument Development, Characterization and calibration GDR&SOC = Geophysical Data Retrieval and Science Operations Centre GDV = Geophysical Data Validation GDA&M = Geophysical Data Analysis and Modelling ECUI&DA = Environment Canada User Interface & Data Assimilation

SWIFT on Chinook in 2010

Extra slides

SWIFT Solid model SWIFT