Using Scatterometers and Radiometers to Estimate Ocean Wind Speeds and Latent Heat Flux Presented by: Brad Matichak April 30, 2008 Based on an article.

Slides:



Advertisements
Similar presentations
ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) The Global Observing System Overview of data sources Data coverage Data.
Advertisements

Global trends in air-sea CO 2 fluxes based on in situ and satellite products Rik Wanninkhof, NOAA/AOML ACE Ocean Productivity and Carbon Cycle (OPCC) Workshop.
A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.
1 st Joint GOSUD/SAMOS Workshop The Florida State University 1 Sensitivity of Surface Turbulent Fluxes to Observational Errors  or.
Atmospheric Influences Physical oceanography Instructor: Dr. Cheng-Chien LiuCheng-Chien Liu Department of Earth Sciences National Cheng Kung University.
TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.
CM SAF Event Week HOAPS Thematic Climate Data Records Karsten Fennig, Axel Andersson, Marc Schröder Satellite Application Facility.
R. A. Brown 2003 U. ConcepciÓn. UW; Patoux, ‘03 R. A. Brown 2003 U. Concepci Ó n.
State Oceanic Administration (SOA) and HY-2 Outline HY-1B(SeaWifs type) launched and is functioning HY-2(Jason type) SOA’s new ocean data assimilation.
Passive Microwave Rain Rate Remote Sensing Christopher D. Elvidge, Ph.D. NOAA-NESDIS National Geophysical Data Center E/GC2 325 Broadway, Boulder, Colorado.
ATS 351 Lecture 8 Satellites
Comparison and Evaluation of Scatterometer (SCR) observed wind data with buoy wind data Xinzhong Zhang Remote Sensing December 8 th, 2009.
Satellite Drifter Technology Dr. Sergey Motyzhev.
PORSEC 2010 Taiwan Tutorial Surface Wind Fields from Satellite Radar and Radiometer Measurements Abderrahim Bentamy Laboratoire d’Océanographie Spatiale.
Data assimilation of polar orbiting satellites at ECMWF
Why We Care or Why We Go to Sea.
ATMS 373C.C. Hennon, UNC Asheville Observing the Tropics.
OC3522Summer 2001 OC Remote Sensing of the Atmosphere and Ocean - Summer 2001 A Brief History of Environmental Satellite Systems A Brief History.
Experiments with the microwave emissivity model concerning the brightness temperature observation error & SSM/I evaluation Henning Wilker, MIUB Matthias.
EECS 823 MACHARIA.  Four-frequency, linearly-polarized, passive microwave radiometric system which measures atmospheric, ocean and terrain microwave.
Evaporative heat flux (Q e ) 51% of the heat input into the ocean is used for evaporation. Evaporation starts when the air over the ocean is unsaturated.
OC3522Summer 2001 OC Remote Sensing of the Atmosphere and Ocean - Summer 2001 Active Microwave Radar.
Problems and Future Directions in Remote Sensing of the Ocean and Troposphere Dahai Jeong AMP.
Retrieving Snowpack Properties From Land Surface Microwave Emissivities Based on Artificial Neural Network Techniques Narges Shahroudi William Rossow NOAA-CREST.
Automated Weather Observations from Ships and Buoys: A Future Resource for Climatologists Shawn R. Smith Center for Ocean-Atmospheric Prediction Studies.
Satellite-derived Sea Surface Temperatures Corey Farley Remote Sensing May 8, 2002.
Also known as CMIS R. A. Brown 2005 LIDAR Sedona.
WIND STRESS OVER INDIAN OCEAN Abhijit Sarkar, K Satheesan, Anant Parekh Ocean Sciences Division Space Applications Centre, INDIA ISRO-CNES Joint Programme.
Why We Care or Why We Go to Sea.
OC3522Summer 2001 OC Remote Sensing of the Atmosphere and Ocean - Summer 2001 Microwave Applications.
Applications of Satellite Derived
Downscaling tropical cyclones from global re-analysis and scenarios: Statistics of multi-decadal variability of TC activity in E Asia Hans von Storch,
Sources of Surface Wind Fields for Climate Studies From Surface Measurements –Ships –Buoys From Models –GCM (with K-theory PBLs) –UW Similarity Model.
R. A. Brown 2003 U. Concepci Ó n Nov 9 ‘96 18Z Gulf of Alaska rab.
A New Inter-Comparison of Three Global Monthly SSM/I Precipitation Datasets Matt Sapiano, Phil Arkin and Tom Smith Earth Systems Science Interdisciplinary.
An evaluation of satellite derived air-sea fluxes through use in ocean general circulation model Vijay K Agarwal, Rashmi Sharma, Neeraj Agarwal Meteorology.
Graduate Course: Advanced Remote Sensing Data Analysis and Application A COMPARISON OF LATENT HEAT FLUXES OVER GLOBAL OCEANS FOR FOUR FLUX PRODUCTS Shu-Hsien.
Some Background I’m in the wind business --- –My thesis dealt with the mathematical solution for PBL winds –I’ve written two texts on flow equations; in.
National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Soil Moisture Active and.
Graduate Course: Advanced Remote Sensing Data Analysis and Application RETRIEVAL OF SURFACE AIR HUMIDITY FROM SSM/I Shu-Hsien Chou Dept. of Atmospheric.
Ocean Surface heat fluxes Lisan Yu and Robert Weller
Evaluation of the Real-Time Ocean Forecast System in Florida Atlantic Coastal Waters June 3 to 8, 2007 Matthew D. Grossi Department of Marine & Environmental.
Ocean Surface heat fluxes
Science of the Aqua Mission By: Michael Banta ESS 5 th class Ms. Jakubowyc December 7, 2006.
Ocean Vector Wind Workshops and the Role of Cal/Val in Preparing for Future Satellite Wind Sensors Dudley Chelton Cooperative Institute for Oceanographic.
Satellite Oceanography Modified from a Presentation at STAO 2003 By Dr. Michael J. Passow.
NCEP Data Reanalysis 陳漢卿. Data analyses Use the data available for the original operational NCEP analyses. (available from 1962) Add other datasets to.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
Infrared and Microwave Remote Sensing of Sea Surface Temperature Gary A. Wick NOAA Environmental Technology Laboratory January 14, 2004.
NOAA Environmental Technology Laboratory Gary A. Wick Observed Differences Between Infrared and Microwave Products Detailed comparisons between infrared.
Evaluation of Satellite-Derived Air-Sea Flux Products Using Dropsonde Data Gary A. Wick 1 and Darren L. Jackson 2 1 NOAA ESRL, Physical Sciences Division.
Satellite Derived Ocean Surface Vector Winds Joe Sienkiewicz, NOAA/NWS Ocean Prediction Center Zorana Jelenak, UCAR/NOAA NESDIS.
NASA, CGMS-44, 7 June 2016 Coordination Group for Meteorological Satellites - CGMS SURFACE PRESSURE MEASUREMENTS FROM THE ORBITING CARBON OBSERVATORY-2.
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.
Remote Sensing of the Hydrosphere. The Hydrologic Cycle 70% of Earth is covered by oceans and surface freshwater Residence time varies from seconds to.
In order to accurately estimate polar air/sea fluxes, sea ice drift and then ocean circulation, global ocean models should make use of ice edge, sea ice.
Understanding and Improving Marine Air Temperatures David I. Berry and Elizabeth C. Kent National Oceanography Centre, Southampton
Passive Microwave Remote Sensing
Presented by Beth Caissie
Surface Pressure Measurements from the NASA Orbiting Carbon Observatory-2 (OCO-2) Presented to CGMS-43 Working Group II, agenda item WGII/10 David Crisp.
A Fear-Inspiring Intercomparison of Monthly Averaged Surface Forcing
NASA Aqua.
M. Goldberg NOAA/NESDIS Z. Cheng (QSS)
Satellite Oceanography
Chapter 4 Atmospheric Influences
The HOAPS-3 climatology
OC Remote Sensing of the Atmosphere and Ocean - Summer 2001
Ocean Winds.
Chapter 4 Atmospheric Influences
NASA Jet Propulsion Laboratory, California Institute of Technology
Presentation transcript:

Using Scatterometers and Radiometers to Estimate Ocean Wind Speeds and Latent Heat Flux Presented by: Brad Matichak April 30, 2008 Based on an article from the Journal of Climate: Sattelite Estimates of Wind Speed and Latent Heat Flux over the Global Oceans by Abderrahim Bentamy, Kristina B. Katsaros, Alberto M. Mestas-Nunez, William M. Drennan, Evan B. Forde, and Herve Roquet

Objectives The satellites being used The satellites being used European Remote Sensing Satellite Scatterometer (ERS-2) European Remote Sensing Satellite Scatterometer (ERS-2) NASA Scatterometer (NSCAT) NASA Scatterometer (NSCAT) Defense Meteorological Satellite Program (DMSP) Radiometers Defense Meteorological Satellite Program (DMSP) Radiometers Alternative methods of measuring wind speeds and latent heat flux Alternative methods of measuring wind speeds and latent heat flux Collecting the data Collecting the data Results/Conclusions Results/Conclusions

European Remote Sensing Scatterometer (ERS-2) Launched on April 21, 1995 Launched on April 21, 1995 Sun-synchronous polar orbit Sun-synchronous polar orbit Height of 780km Height of 780km Inclination of 98.5° Inclination of 98.5° Gives it a visibility of all areas of the Earth Gives it a visibility of all areas of the Earth Period of 100 minutes Period of 100 minutes 35 day repeat cycle 35 day repeat cycle Wind Scatterometer Wind Scatterometer Observes wind speed and direction at the sea surface for climatological datasets and models Observes wind speed and direction at the sea surface for climatological datasets and models

Nasa Scatterometer (NSCAT) Launched on August 16, 1996 abord the Advanced Earth Observing Satellite (ADEOS) Launched on August 16, 1996 abord the Advanced Earth Observing Satellite (ADEOS) Near-polar Sun-synchronous orbit Near-polar Sun-synchronous orbit Height of 800km Height of 800km Inclination of 98.6° Inclination of 98.6° Period of 101 minutes Period of 101 minutes 41 day repeat cycle 41 day repeat cycle Study runs from October 1996-June 1997 while NSCAT was operational Study runs from October 1996-June 1997 while NSCAT was operational

Defense Meteorological Satellite Program Designs, builds, launches, and maintains satellites that monitor meteorological and oceanographic environments Designs, builds, launches, and maintains satellites that monitor meteorological and oceanographic environments Each satellite has a near-polar Sun-synchronous orbit Each satellite has a near-polar Sun-synchronous orbit Height of 830km Height of 830km Period of 101 minutes Period of 101 minutes Study focuses on Special Sensor Microwave Imagers (SSM/I) aboard the F10-F14 satellites Study focuses on Special Sensor Microwave Imagers (SSM/I) aboard the F10-F14 satellites

Real time observations and calculations to compare satellite estimates to Buoy Networks Buoy Networks Observe wind speed and direction, air and sea surface temperatures, and on some, relative humidity Observe wind speed and direction, air and sea surface temperatures, and on some, relative humidity Data collected from three networks Data collected from three networks National Data Buoy Center (NDBC) National Data Buoy Center (NDBC) European Offshore Data Acquisition System (ODAS) European Offshore Data Acquisition System (ODAS) Tropical Atmosphere Ocean (TAO) Tropical Atmosphere Ocean (TAO) Ships Ships Comprehensive Ocean-Atmosphere Data Set (COADS) Comprehensive Ocean-Atmosphere Data Set (COADS) COADS based on quality-controlled marine surface observations from ships COADS based on quality-controlled marine surface observations from ships Also from moored environmental buoys and near-surface measurements from oceanographic profiles Also from moored environmental buoys and near-surface measurements from oceanographic profiles

Observations/Calculations, continued Atmospheric analyses Atmospheric analyses European Centre for Medium-Range Weather Forecasts (ECMWF) European Centre for Medium-Range Weather Forecasts (ECMWF) Analyses of 10-m wind vectors and surface latent heat fluxes Analyses of 10-m wind vectors and surface latent heat fluxes Calculates sea surface values using the SST analysis released daily by NOAA/NCEP Calculates sea surface values using the SST analysis released daily by NOAA/NCEP National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) Used 4 times daily surface wind speed fields and daily averaged latent heat fluxes calculated and given on the NOAA Climate Diagnostic Center Web site Used 4 times daily surface wind speed fields and daily averaged latent heat fluxes calculated and given on the NOAA Climate Diagnostic Center Web site Weekly averages of the ECMWF and NCEP-NCAR wind speeds and latent heat flux fields compiled for comparison with satellite estimates Weekly averages of the ECMWF and NCEP-NCAR wind speeds and latent heat flux fields compiled for comparison with satellite estimates

Collecting Satellite Estimates (Wind Speeds) ERS-2 Scatterometer ERS-2 Scatterometer Measured backscatter from sea surface of a 5.3-GHz elecromagnetic signal emitted at different incidence angles Measured backscatter from sea surface of a 5.3-GHz elecromagnetic signal emitted at different incidence angles 10-m wind speeds derived over one swath 500km wide using ERS-2 backscatter coefficients based on the The Center for Satellite Exploitation and Research (CERSAT) wind algorithm 10-m wind speeds derived over one swath 500km wide using ERS-2 backscatter coefficients based on the The Center for Satellite Exploitation and Research (CERSAT) wind algorithm NSCAT Scatterometer NSCAT Scatterometer Measured backscatter using a 14.1-GHz signal Measured backscatter using a 14.1-GHz signal Wind speed values taken over two swaths 600km wide from the Jet Propulsion Laboratory (JPL) Wind speed values taken over two swaths 600km wide from the Jet Propulsion Laboratory (JPL) SSM/I radiometers on the DMSP F10-F14 Satellites SSM/I radiometers on the DMSP F10-F14 Satellites Measured surface brightness temperatures at frequencies of 19, 22, 37, and 85 GHz respectively Measured surface brightness temperatures at frequencies of 19, 22, 37, and 85 GHz respectively Wind speeds taken over swaths of 1394km width using a modified algorithm that includes a water vapor content correction Wind speeds taken over swaths of 1394km width using a modified algorithm that includes a water vapor content correction All satellite wind fields merged into a single weekly gridded wind field All satellite wind fields merged into a single weekly gridded wind field

Collecting Satellite Estimations (Latent Heat Flux) Uses the following equation: Uses the following equation: Q E = - l ρ C E (Ū a – Ū s )(q a – q s ) Q E = latent heat flux Q E = latent heat flux l = latent heat of evaporation l = latent heat of evaporation ρ = air density ρ = air density C E = bulk transfer coefficient for water vapor (Dalton Number) C E = bulk transfer coefficient for water vapor (Dalton Number) Ū a = surface wind speed at height of 10m Ū a = surface wind speed at height of 10m Ū s = ocean surface speed (set to 0) Ū s = ocean surface speed (set to 0) q a = near-surface air specific humidity q a = near-surface air specific humidity q s = air specific humidity q s = air specific humidity

Latent Heat Flux, continued Errors can occur in wind speed, exchange coefficient, SST, and specific air humidity due to instrumental errors, boundary layer models, sampling schemes, and aliasing problems Errors can occur in wind speed, exchange coefficient, SST, and specific air humidity due to instrumental errors, boundary layer models, sampling schemes, and aliasing problems Assumptions made for calculation: Assumptions made for calculation: 1. SST at a grid point is constant over a day 2. Surface pressure is constant at hPa 3. Air temperature at 10m is 1.25K less than at sea surface As with wind speeds, estimations are collected into weekly averages for comparisons with other observations and calculations As with wind speeds, estimations are collected into weekly averages for comparisons with other observations and calculations

Comparisons/Results

Results, continued Results show a generally good agreement between satellite estimates, buoy and ship data, and atmospheric analyses Results show a generally good agreement between satellite estimates, buoy and ship data, and atmospheric analyses Differences in satellite estimates and ECMWF and NCEP-NCAR calculations suggest satellites may be becoming more accurate than atmospheric analyses Differences in satellite estimates and ECMWF and NCEP-NCAR calculations suggest satellites may be becoming more accurate than atmospheric analyses One error not corrected estimating specific air humidity to neutral stratification One error not corrected estimating specific air humidity to neutral stratification Value of error not expected to be large since winds are neutral and most of the ocean is in near-neutral stratification during the averaging period Value of error not expected to be large since winds are neutral and most of the ocean is in near-neutral stratification during the averaging period

Suggestions for Future Work The source of the error between satellite estimations and the ODAS network needs further research The source of the error between satellite estimations and the ODAS network needs further research Include the stratification correction for specific air humidity to further test satellite accuracies Include the stratification correction for specific air humidity to further test satellite accuracies Including stress, divergence, and curl from the wind field into the estimations of the flux fields Including stress, divergence, and curl from the wind field into the estimations of the flux fields Satellites to be used include QuikSCAT and ADEOS-2 Satellites to be used include QuikSCAT and ADEOS-2

Questions/Comments?