Brigham Young University DGL Dec 03 Rain/Wind Backscatter Model  Model for measured backscatter  Radar signal scattered by falling droplets  Surface.

Slides:



Advertisements
Similar presentations
The time-dependent two-stream method for lidar and radar multiple scattering Robin Hogan (University of Reading) Alessandro Battaglia (University of Bonn)
Advertisements

Cloud Radar in Space: CloudSat While TRMM has been a successful precipitation radar, its dBZ minimum detectable signal does not allow views of light.
Quantification of Spatially Distributed Errors of Precipitation Rates and Types from the TRMM Precipitation Radar 2A25 (the latest successive V6 and V7)
Atelier Moment Cinetique Paris 26 Novembre 2012 Les Vents de Surface Diffusiométriques ERS-1 ERS-2 ADEOS-1 QuikSCAT.
Microwave Remote Sensing of Hurricanes & Tropical Meteorology Tyler Adams and Megan Leigh.
All-Weather Wind Vector Measurements from Intercalibrated Active and Passive Microwave Satellite Sensors Thomas Meissner Lucrezia Ricciardulli Frank Wentz.
TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.
Combined Active & Passive Rain Retrieval for QuikSCAT Satellite Khalil A. Ahmad Central Florida Remote Sensing Laboratory University of Central Florida.
Global Tropical Cyclone Winds from the QuikSCAT and OceanSAT-2 Scatterometers Bryan W. Stiles 1, Rick Danielson 2, W. Lee Poulsen 1, Alexander Fore 1,
R. A. Brown 2003 U. ConcepciÓn. UW; Patoux, ‘03 R. A. Brown 2003 U. Concepci Ó n.
Comparison and Evaluation of Scatterometer (SCR) observed wind data with buoy wind data Xinzhong Zhang Remote Sensing December 8 th, 2009.
Andrew Burton Bureau of Meteorology, Perth, Australia Use of Scatterometer Winds in TC Forecasting Tropical Cyclone Warning Centre Perth.
Tropical Cyclone Analysis With Satellite Radars SOES 6026 – Radar Remote Sensing Ray Bell.
Scatterometer winds Manager NWP SAF at KNMI Manager OSI SAF at KNMI PI European OSCAT Cal/Val project Leader KNMI Satellite Winds.
ATMS 373C.C. Hennon, UNC Asheville Observing the Tropics.
Initial Results on the Cross- Calibration of QuikSCAT and Oceansat-2 Scatterometers David G. Long Department of Electrical and Computer Engineering Brigham.
March 27, 2007 Using H*Wind to Improve Satellite-Based Wind Measurements in Tropical Cyclones John Allard Faculty Advisor: Dr. Christopher C. Hennon Introduction.
MWR Roughness Correction Algorithm for the Aquarius SSS Retrieval W. Linwood Jones, Yazan Hejazin, Salem Al-Nimri Central Florida Remote Sensing Lab University.
SeaWinds Empirical Rain Correction Using AMSR January 17, 2005 Bryan Stiles, Svetla Hristova-Veleva, and Scott Dunbar.
Precipitation and altimeter missions Jean Tournadre Laboratoire d’Océanographie Spatiale IFREMER Plouzane France.
SMOS+ STORM Evolution Kick-off Meeting, 2 April 2014 SOLab work description Zabolotskikh E., Kudryavtsev V.
Evaluation of Microwave Scatterometers and Radiometers as Satellite Anemometers Frank J. Wentz, Thomas Meissner, and Deborah Smith Presented at: NOAA/NASA.
A Combined Radar/Radiometer Retrieval for Precipitation IGARSS – Session 1.1 Vancouver, Canada 26 July, 2011 Christian Kummerow 1, S. Joseph Munchak 1,2.
Weather and Climate Is there a Difference?. Another I Love Science All Rights Reserved
Usefulness of vertical velocity measurements in clouds with a 1290 MHz profiler Henk Klein Baltink Atmospheric Research Section.
Also known as CMIS R. A. Brown 2005 LIDAR Sedona.
Corrections to Scatterometer Wind Vectors from the Effects of Rain, Using High Resolution NEXRAD Radar Collocations David E. Weissman Hofstra University.
A Novel Ocean Vector Winds Retrieval Technique for Tropical Cyclones Peth Laupattarakasem 1, Suleiman Alsweiss1, W. Linwood Jones 1, and Christopher C.
Ocean Surface Winds Research Summary: Meteorological Applications Mark DeMaria, NOAA/NESDIS, Fort Collins, CO Ocean Surface Winds Workshop NCEP/Tropical.
Impacts of surface currents on derived scatterometer wind at Ku and C band Amanda Plagge and Doug Vandemark (UNH) James Edson (UConn) Bertrand Chapron.
Applications of Satellite Derived
A New Inter-Comparison of Three Global Monthly SSM/I Precipitation Datasets Matt Sapiano, Phil Arkin and Tom Smith Earth Systems Science Interdisciplinary.
1 Airborne Measurements of Ocean Backscatter Work In Progress by D. Esteban, Z. Jelenak, T. Mavor, P. Chang, NOAA/NESDIS/ORA D. Esteban, Z. Jelenak, T.
2007 IHC – New Orleans 5 – 9 March 2007 JHT Project: Operational SFMR- NAWIPS Airborne Processing and Data Distribution Products OUTLINE 2006 Hurricane.
Wind Stress Data Products for Model Comparison 2012 ECCO Meeting California Institute of Technology David Moroni 10/31/12.
Measuring Surface Stress in Tropical Cyclones with Scatterometers W. Timothy Liu, Wenqing Tang, & Xiaosu Xie Jet Propulsion Laboratory Air-sea interaction.
Simulator Wish-List Gary Lagerloef Aquarius Principal Investigator Cal/Val/Algorithm Workshop March GSFC.
High Quality Wind Retrievals for Hurricanes Using the SeaWinds Scatterometer W. Linwood Jones and Ian Adams Central Florida Remote Sensing Lab Univ. of.
Multi-Satellite Observations of Tropical Convection and associated Environmental Regimes Greg Elsaesser CMMAP Graduate Student Colloquium July 2008.
Improved Aquarius Salinity Retrievals using Auxiliary Products from the CONAE Microwave Radiometer (MWR) W. Linwood Jones Central Florida Remote Sensing.
Graduate Course: Advanced Remote Sensing Data Analysis and Application RETRIEVAL OF SURFACE AIR HUMIDITY FROM SSM/I Shu-Hsien Chou Dept. of Atmospheric.
A Global Rainfall Validation Strategy Wesley Berg, Christian Kummerow, and Tristan L’Ecuyer Colorado State University.
IOVWST Meeting May 2011 Maryland Calibration and Validation of Multi-Satellite scatterometer winds Topics  Estimation of homogeneous long time.
Comparison of Oceanic Warm Rain from AMSR-E and CloudSat Matt Lebsock Chris Kummerow.
TRMM TMI Rainfall Retrieval Algorithm C. Kummerow Colorado State University 2nd IPWG Meeting Monterey, CA. 25 Oct Towards a parametric algorithm.
Ocean and Land Surface Characterization in the GPM Radar-Radiometer Combined Algorithm S. Joseph Munchak 1,2 *, William S. Olson 1,3, Mircea Grecu 1,4,
A Novel Hurricane OVW Retrieval Technique for QuikSCAT W. Linwood Jones 1, Peth Laupattarakasem 1, Suleiman Alsweiss 1, Christopher C. Hennon 2, and Svetla.
Geophysical Ocean Products from AMSR-E & WindSAT Chelle L. Gentemann, Frank Wentz, Thomas Meissner, Kyle Hilburn, Deborah Smith, and Marty Brewer
Liang Liao Goddard Earth Sciences & Technology Research Morgan State University Greenbelt, MD Robert Meneghini NASA/Goddard Space Flight Center Greenbelt,
Scatterometer wind retrieval over Lake Baikal Alexey Pan’kov (Irkutsk State University)
9 Feb 2005, Miami 1 An Introduction to SeaWinds Near-Real Time Data Ross Hoffman Mark Leidner Atmospheric and Environmental Research, Inc. Lexington, MA.
Satellite Derived Ocean Surface Vector Winds Joe Sienkiewicz, NOAA/NWS Ocean Prediction Center Zorana Jelenak, UCAR/NOAA NESDIS.
Ocean Vector Wind Experience Joe Sienkiewicz NOAA Ocean Prediction Center.
Remote Sensing of the Hydrosphere. The Hydrologic Cycle 70% of Earth is covered by oceans and surface freshwater Residence time varies from seconds to.
Passive Microwave Remote Sensing
1 XOVWM User Impact Study Rita Simulations 9/21/05:15:30 – 09/22/05:15:30 Contacts:
THE WEATHER QUIZ GAME.
SOLab work description
Retrieving Extreme wind speeds using C-band instruments
A very stormy day In the ocean
Connecting Observations With Theory
Hurricanes Hurricanes are big storms that destroy everything nocking things down. When there is really big water and it creates a storm. Mexico is usually.
Severe Weather S6E4 b. Relate unequal heating of land and water surfaces to form large global wind systems and weather events such as tornados and thunderstorms.
SeaWinds AMSR-derived Impact Table
Roughness Correction for Aquarius (AQ) Sea Surface Salinity (SSS) Algorithm using MicroWave Radiometer (MWR) W. Linwood Jones, Yazan Hejazin Central FL.
Matt Lebsock Chris Kummerow Graeme Stephens Tristan L’Ecuyer
Validation of CYGNSS winds using microwave scatterometers/radiometers
Alejandro López Ontavilla
Severe Weather S6E4 b. Relate unequal heating of land and water surfaces to form large global wind systems and weather events such as tornados and thunderstorms.
Shuyi S. Chen1 Wei Zhao1, Ralph Foster2, W. Timothy Liu3
Presentation transcript:

Brigham Young University DGL Dec 03 Rain/Wind Backscatter Model  Model for measured backscatter  Radar signal scattered by falling droplets  Surface signal attenuated by atmospheric rain  Surface wind-induced backscatter perturbed by rain striking the water  Model derived from colocated TRMM PR and QuikSCAT data Rain affects ~4% of SeaWinds data

Brigham Young University DGL Dec 03 Simultaneous Wind/Rain Retrieval

Brigham Young University DGL Dec 03 Wind Rain Regimes  Regime 1: rain dominates wind backscatter – poor quality wind estimates (10% of rain cases*)  Regime 2: both wind and rain important – can retrieve wind and rain rate (34% of rain cases*)  Regime 3: rain effects insignificant – wind estimates unaffected by rain(56% of rain cases*)  Note: globally, only about 4% of all QuikSCAT data effected by rain * From collocated TRMM PR and QuikSCAT data in tropics  incorporates surface rain perturbation, atmospheric rain scattering, and attenuation - Empirical function of rain rate derived from collocated QuikSCAT and TRMM PR

Brigham Young University DGL Dec 03 Wind/Rain Validation

Brigham Young University DGL Dec 03 Scatterometer Rain Retrieval Validation Bias can be estimated and corrected for

Brigham Young University DGL Dec 03 Rain Estimate Validation  QuikSCAT-derived rain rates vs TMI-derived rain rates

Brigham Young University DGL Dec 03 Storm Example

Brigham Young University DGL Dec 03 Floyd (rev 1201, 11 Sept 1999)

Brigham Young University DGL Dec 03 Typhoon (rev 1222, 12 Sept 1999)