MISR Cloud Motion Vectors, ERA-I Reanalysis Winds, and Stereo Heights Dong L Wu NASA Goddard Space Flight Center Greenbelt, Maryland Acknowledgment: MISR.

Slides:



Advertisements
Similar presentations
ESTO Advanced Component Technology 11/17/03 Laser Sounder for Remotely Measuring Atmospheric CO 2 Concentrations GSFC CO 2 Science and Sounder.
Advertisements

1 6th GOES Users' Conference, Madison, Wisconsin, Nov 3-5 WMO Activities and Plans for Geostationary and Highly Elliptical Orbit Satellites Jérôme Lafeuille.
Weather Dynamics in Earth’s Atmosphere. An atmosphere is a blanket of a gases surrounding a planet. Earth’s atmosphere has distinct layers defined by.
Atmospheric Iron Flux and Surface Chlorophyll at South Atlantic Ocean: A case study Near Patagonia J. Hernandez*, D. J. Erickson III*, P. Ginoux†, W. Gregg‡,
Requirements for monitoring the global tropopause Bill Randel Atmospheric Chemistry Division NCAR.
Moisture flux estimates using AIRS version 6 data in the Arctic Boisvert, L. N., D. L. Wu, T. Vihma, and J. Susskind (2014), Verification of air / surface.
Mars’ North and South Polar Hood Clouds Jennifer L. Benson Jet Propulsion Laboratory, California Institute of Technology July 22, 2010 Copyright 2010 California.
Global Winds Michael J. Garay
How Does Air Move Around the Globe?
Menglin Jin Department of Atmospheric & Oceanic Science University of Maryland, College park Observed Land Impacts on Clouds, Water Vapor, and Rainfall.
AOSC 200 Lesson 14. Fig Subtropical and Polar jet streams in relation to the three cells.
Satellite Imagery Meteorology 101 Lab 9 December 1, 2009.
(a)(b)(c) Simulation of upper troposphere CO 2 from two-dimensional and three-dimensional models Xun Jiang 1, Runlie Shia 2, Qinbin Li 1, Moustafa T Chahine.
Use of Humidity data from MT and other platforms for Science projects on Monsoon Cloud systems KUSUMA G RAO Space Sciences Indian Space Research Organization.
Use of GPS RO in Operations at NCEP
Using GPS data to study the tropical tropopause Bill Randel National Center for Atmospheric Research Boulder, Colorado “You can observe a lot by just watching”
Stereoscopic cloud imaging for future 3-D wind constellation Dong L. Wu Climate and Radiation Laboratory (613) NASA Goddard Space Flight Center Working.
Orbit Characteristics and View Angle Effects on the Global Cloud Field
June, 2003EUMETSAT GRAS SAF 2nd User Workshop. 2 The EPS/METOP Satellite.
Maria Val Martin and J. Logan (Harvard Univ., USA) D. Nelson, C. Ichoku, R. Kahn and D. Diner (NASA, USA) S. Freitas (INPE, Brazil) F.-Y. Leung (Washington.
ESS 111 – Climate & Global Change
Régis Borde Polar Winds EUMETRAIN Polar satellite week 2012 Régis Borde
Also known as CMIS R. A. Brown 2005 LIDAR Sedona.
2010 AMS Effect of changes in GCM resolution on the connection between summertime precipitation, moisture flux, and the position of the Bermuda High Laura.
On the Use of Geostationary Satellites for Remote Sensing in the High Latitudes Yinghui Liu 1, Jeffrey R. Key 2, Xuanji Wang 1, Tim Schmit 2, and Jun Li.
LASE Measurements During IHOP Edward V. Browell, Syed Ismail, Richard A. Ferrare, Susan A Kooi, Anthony Notari, and Carolyn F. Butler NASA Langley Research.
How Does Air Move Around the Globe?
Assimilation and Evaluation of MISR Cloud Tracked Winds with GEOS-5 Operational Data Assimilation System Junjie Liu 1 and Kevin Mueller 1 1. Jet Propulsion.
Real-time Generation of Winds and Sea Ice Motion from MODIS Jeff Key 1, Dave Santek 2, Chris Velden 2 1 Office of Research and Applications, NOAA/NESDIS,
Preliminary results from assimilation of GPS radio occultation data in WRF using an ensemble filter H. Liu, J. Anderson, B. Kuo, C. Snyder, A. Caya IMAGe.
Developing the configuration trade space for space-based DWLs: vertical and horizontal coverage G. D. Emmitt, S. A. Wood and S. Greco Simpson Weather Associates.
DEPARTMENT OF GEOMATIC ENGINEERING Validation of MODIS Cloud products through an inter- comparison with MISR, GOES and ground-based radar/lidar Jan-Peter.
Doppler Lidar Winds & Tropical Cyclones Frank D. Marks AOML/Hurricane Research Division 7 February 2007.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
Lecture 12 Rossby waves, propagation, breaking, climatic effects
(a)(b)(c) Simulation of upper troposphere CO 2 from two-dimensional and three-dimensional models Xun Jiang 1, Runlie Shia 2, Qinbin Li 1, Moustafa T Chahine.
Global Ice Coverage Claire L. Parkinson NASA Goddard Space Flight Center Presentation to the Earth Ambassador program, meeting at NASA Goddard Space Flight.
Day Meridional Propagation of Global Circulation Anomalies ( A Global Convection Circulation Paradigm for the Annular Mode) Ming Cai 1 and R-C.
A comparison of AMSR-E and AATSR SST time-series A preliminary investigation into the effects of using cloud-cleared SST data as opposed to all-sky SST.
Assimilation of GPM satellite radiance in improving hurricane forecasting Zhaoxia Pu and ChauLam (Chris) Yu Department of Atmospheric Sciences University.
OMI Nitrogen Dioxide Workshop Ellen Brinksma, Folkert Boersma.
Magdalena D. Anguelova Michael H. Bettenhausen Michael H. Bettenhausen William F. Johnston William F. Johnston Peter W. Gaiser Peter W. Gaiser Whitecap.
Slide 1 Investigations on alternative interpretations of AMVs Kirsti Salonen and Niels Bormann 12 th International Winds Workshop, 19 th June 2014.
W. Smith, D. Spangenberg, S. Sun-Mack, P.Minnis
NASA/US Ocean Satellite Missions
Dynamics in Earth’s Atmosphere
Chairs: James Cotton and Niels Bormann
Welcome to IWW12 What are AMVs?
Clouds, shear and the simulation of hybrid wind lidar
University Allied Workshop (1-3 July, 2008)
GEO-CAPE to TEMPO GEO-CAPE mission defined in 2007 Earth Science Decadal Survey Provide high temporal & spatial resolution observations from geostationary.
R2971 Seq0100 Scn003 Hohenpeissenberg (48N, 11W)
Mark A. Bourassa and Qi Shi
Hyperspectral Wind Retrievals Dave Santek Chris Velden CIMSS Madison, Wisconsin 5th Workshop on Hyperspectral Science 8 June 2005.
Reprocessing of Atmospheric Motion Vector for JRA-3Q at JMA/MSC
Use of satellite winds at Deutscher Wetterdienst (DWD)
Investigating AMV and NWP model errors using the NWP SAF monitoring
Assimilation of GOES-R Atmospheric Motion Vectors
Stéphane Laroche Judy St-James Iriola Mati Réal Sarrazin
UPDATE ON SATELLITE-DERIVED amv RESEARCH AND DEVELOPMENTS
Aura Science Team meeting
Dynamics in Earth’s Atmosphere
Matthew A. Lazzara1, Linda M
Evaluation of the MERRA-2 Assimilated Ozone Product
Application of Aeolus Winds
Comparability and Reproducibility of RO Data
Exploring the ionosphere of Mars
VALIDATION OF DUAL-MODE METOP AMVs
Thomas Smith1 Phillip A. Arkin2 George J. Huffman3 John J. Bates1
Presentation transcript:

MISR Cloud Motion Vectors, ERA-I Reanalysis Winds, and Stereo Heights Dong L Wu NASA Goddard Space Flight Center Greenbelt, Maryland Acknowledgment: MISR science and engineering teams 1

Outline MISR CMV Data –Sampling and coverage –Geo-registration MISR and ERA-Interim Comparison –Monthly mean –Effects of orographic clouds Stereo CMV Techniques from Space –Dual-GEO approach –Dual-LEO stereoscopic approach 2

MISR CMV Data Level 1B (9 Views in ~7 min., Red band, 275 m pixel, ~350 km swath) 17.6-km CTH CMV 1.1-km CTH CMV_cross_trac k 1.1-km CTH0 (zero wind) CMV_cross_track Level 2 TCSP CTH = Cloud Top Height, CMV = Cloud Motion Vector GP_GMP (aka MIB2GEOP) Latitude Longitude Elevation Surface type

MISR Monthly Data Statistics 4 Number of Samples

MISR 17.6 km CMV (courtesy of K. Mueller) MISR CMV: 70.4 km 17.6 km 4.4 km PastPresentFuture CMV height assignment Z u Z u IR Wind MISR Wind Z u ERA-I Wind 0.75° x 0.75° 17.6 km

6 PBL Dynamics and Processes Resolution: 1.1 km Precision: height: ~100 m wind: ~0.3-1 m/s Subtropical Cold Pool

Terra/MISR Geo-Registration Orbit-to-orbit variations of a few pixel size DA camera (70° aft) worst 1 pixel (275m) ≈ 6 m/s 7 Jovanovic et al. [2007] MISR ATBD [JPL D-11532, Jovanovic et al., 1999] 46 s

Terra/MISR Geo-Registration (2) Occasional large geo-registration error Entire orbit of MISR meridional winds affected Evident in MISR- GRL analysis V- wind differences 8 MISR v-wind innovation – MISR v-wind Difference (m/s) Courtesy of Nancy Baker (Presentation in the 2 nd JCSDA Symposium)

Large Offsets Likely due to Geo-Registration Error 9 Recommendations: Near-real time O-B check for rejecting the bad MISR orbits. Investigation and development of more robust MISR geo-registration. V-winds at 0-2 km

MISR CMV Data –Sampling and coverage –Geo-registration MISR and ERA-Interim Comparison –Monthly mean –Effects of orographic clouds Stereo CMV Techniques from Space –Dual-GEO approach –Dual-LEO stereoscopic approach 10

Zonal Mean Differences Slower MISR winds in the poleward side of jets 11 January ( ) s s s MISR ERA-I Diff Zonal (U) s s s Stronger poleward MISR winds in the upper trop Slightly southward bias in the lower trop Meridional (V) ERA-I winds are too zonal at the tropical jets

Zonal Mean Differences (2) Slower MISR winds in the poleward side of jets 12 July ( ) F S MISR ERA-I Diff Zonal (U) Stronger poleward MISR winds in the upper trop Slightly southward bias in the lower trop Meridional (V) F S S S ERA-I winds are too zonal at the tropical jets

Lower Tropospheric Winds 13 January, z=0-5km U V

Lower Tropospheric Winds (2) 14 July, z=0-5km U V

Arctic Region Lower Troposphere (MISR-ERA): MISR U wind slower over the Arctic Ocean. MISR V wind less poleward over landmasses. MISR weaker Greenland gyro. 15 July winds at 0-5km U V

Antarctic Region Lower Troposphere (MISR-ERA): MISR-ERA biases correlated strongly to topography MISR v-wind more poleward 16 January winds at 0-5km U V

Effects of Topography Kerguelen Islands ~80 x 90 km January U at <5km

Effects of Topography (2) 18 Argentine PatagoniaJanuary mean winds at 0-5km U V

MISR CMV Data –Sampling and coverage –Geo-registration MISR and ERA-Interim Comparison –Monthly mean –Effects of orographic clouds Stereo CMV Techniques from Space –Dual-GEO approach –Dual-LEO stereoscopic approach 19

Lidar/Radar Winds, AMVs and Stereo Height 1 CGMS-41 NASA whitepaper (NASA WP-5) [D. Wu and M. Kavaya] 20 Radar (2D Wind) LIDAR (2D Wind) Single Satellite (3D Wind) Dual and Multi Satellites (3D Wind) LEOGEOLEOGEO Stereo LEO Stereo NASA/NScatESA/Aeolus (2015/16) MISR 1 GOESMetOp A/BGOES-E/WAATSR/A TSR-2 MetOp- SG Proposals NASA/QScat ESA/AScat ISRO/OScat CMA/HY1-2 … NASA/RScat NASA/CYG NSS NASA/3D- WIND 1 (2025+) AASTRMeteoSat 7-10 MTSAT FY2 … MODIS/VII RS N18-20 … Meteo-7/10 FY2-FY2 GEO/LEO … SwathCurtainSwathDiskSwathPartial DiskSwath Sea surf windWind profilesCloud-top, thick-aerosol, water vapor winds  x =12-50 km  u <2 m/s  z =1-3 km  y =50 km?  u =1-3 m/s  z =0.5-1 km  x =1-20 km  u =1-3 m/s  z =1-3 km  x =10-40 km  u =1-3 m/s ???  z =200 m  x = 1-5 km  u < 1 m/s  w = ? m/s

Hasler (1981) Black (1982) Fujita and Dodge (1982) Shenk (1971) Apollo-6 60° 2-GOES Concept: pointing knowledge and time-sync are critical

Mack et al. (1983) IR vs Stereo Height: 2-GOES Measurements Importance of Vertical Motion w = 4-8 m/s 30 min.

IR vs. Stereo Height: A Pattern Height 23 Baja Peninsula (July 12, 1998) Mahani et al., (2000) TEFLUN-B radiosonde  h = km 8 K/km

Pattern Matching: Spatial vs Temporal Variabilities Holling etal [1992] Westley et al. [2002] GOES MISR

Spaceborne Atmospheric Boundary Layer Explorer (SABLE) Earth Venture-2 mission proposal (PI Rob Wood) Pre-decisional – for Planning and Discussion Purposes Only

Summary MISR zonal winds are slower than ERA-I in the January upper trop (UT) where the latitudinal gradient of polar jets is large. MISR meridional winds show a stronger poleward flow in the UT, compared to ERA-I MISR CMVs have occasionally large bias due to geo-registration problems, mainly affecting the meridional wind. Topography seems to produce slower lee-side wind speed in MISR than in ERA-I, but is small compared to other factors. Lidar/Radar winds and CMVs are complementary. Overlapped samples provide valuable cross-calibration globally. The CMV technique from space, providing a much wider swath (or coverage), is 5x-10x cheaper than wind-lidars (still technically challenging from space). 26