Passive Microwave Data at NCDC John J. Bates and Hilawe Semunegus NOAA’s National Climatic Data Center Asheville, NC AMSR-E Group Meeting June 28, 2011.

Slides:



Advertisements
Similar presentations
Extension and application of an AMSR global land parameter data record for ecosystem studies Jinyang Du, John S. Kimball, Lucas A. Jones, Youngwook Kim,
Advertisements

Maintaining and Improving the AMSR-E and WindSat Ocean Products Frank J. Wentz Remote Sensing Systems, Santa Rosa CA AMSR TIM Agenda 4-5 September 2013.
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
Using Scatterometers and Radiometers to Estimate Ocean Wind Speeds and Latent Heat Flux Presented by: Brad Matichak April 30, 2008 Based on an article.
Monitoring the Arctic and Antarctic By: Amanda Kamenitz.
MWR Algorithms (Wentz): Provide and validate wind, rain and sea ice [TBD] retrieval algorithms for MWR data Between now and launch (April 2011) 1. In-orbit.
Recent activities on utilization of microwave imager data in the JMA NWP system - Preparation for AMSR2 data assimilation - Masahiro Kazumori Japan Meteorological.
Joint Polar Satellite System Harry Cikanek Director, Joint Polar Satellite System March 18, Science Week.
EECS 823 MACHARIA.  Four-frequency, linearly-polarized, passive microwave radiometric system which measures atmospheric, ocean and terrain microwave.
Presented at the LANCE User Working Group Meeting April 29, 2015 LANCE AMSR2 UPDATE Sherry Harrison
ISCCP at 30, April 2013 Concurrent Study of a) 22 – year reanalysis and extension of global water vapor over both land and ocean (NVAP–M) and b) the matching.
Yimin Ji - Page 1 October 5, 2010 Global Precipitation Measurement (GPM) mission Precipitation Processing System (PPS) Yimin Ji NASA/GSFC,
Videos and Questions. Aqua phttp://aqua.nasa.gov/reference/visualizations.ph p Launch separation sequence,
September 4 -5, 2013Dawn Conway, AMSR-E / AMSR2 TLSCF Lead Software Engineer AMSR-E / AMSR2 Team Leader Science Computing Facility Current Science Software.
Princeton University Development of Improved Forward Models for Retrievals of Snow Properties Eric. F. Wood, Princeton University Dennis. P. Lettenmaier,
Retrieving Snowpack Properties From Land Surface Microwave Emissivities Based on Artificial Neural Network Techniques Narges Shahroudi William Rossow NOAA-CREST.
Calibration and Validation Studies for Aquarius Salinity Retrieval PI: Shannon Brown Co-Is: Shailen Desai and Anthony Scodary Jet Propulsion Laboratory,
Precipitation Retrievals Over Land Using SSMIS Nai-Yu Wang 1 and Ralph R. Ferraro 2 1 University of Maryland/ESSIC/CICS 2 NOAA/NESDIS/STAR.
Passive Microwave Remote Sensing
Development of AMSU-A Fundamental CDR’s Huan Meng 1, Wenze Yang 2, Ralph Ferraro 1 1 NOAA/NESDIS/STAR/CoRP/Satellite Climate Studies Branch 2 NOAA Corporate.
25 June 2009 Dawn Conway, AMSR-E TLSCF Lead Software Engineer AMSR-E Team Leader Science Computing Facility.
SSMIS Unified Preprocessor for Use With Climate and Precipitation (UPP-CP) Overview Hilawe Semunegus NOAA’s National Climatic Data Center Asheville, NC.
HDF-EOS at NOAA/NESDIS NOAA / NESDIS / ORA orbit-net.nesdis.noaa.gov/arad2/MSPPS Huan Meng, Doug Moore, Limin Zhao, Ralph Ferraro NOAA / NESDIS.
OC3522Summer 2001 OC Remote Sensing of the Atmosphere and Ocean - Summer 2001 Microwave Applications.
An Intercalibrated Microwave Radiance Product for Use in Rainfall Estimation Level 1C Christian Kummerow, Wes Berg, G. Elsaesser Dept. of Atmospheric Science.
Remote Sensing Systems Climate Satellite Program Frank J. Wentz and Carl Mears Remote Sensing Systems, Santa Rosa, CA Supported in part by : NASA’s Earth.
National Polar-orbiting Operational Satellite System (NPOESS) Microwave Imager/Sounder (MIS) Capabilities Pacific METSAT Working Group Apr 09 Rebecca Hamilton,
NASA Snow and Ice Products NASA Remote Sensing Training Geo Latin America and Caribbean Water Cycle capacity Building Workshop Colombia, November 28-December.
ISCCP at 30, April 2013 Backup Slides. ISCCP at 30, April 2013 NVAP-M Climate Monthly Average TPW Animation Less data before 1993.
PASSIVE MICROWAVES Figure 5-2 Sensitivity of brightness temperature to geophysical parameters over ocean surface.
NPOESS Conical Scanning Microwave Imager/ Sounder (CMIS) Overview
Evaluation of Passive Microwave Rainfall Estimates Using TRMM PR and Ground Measurements as References Xin Lin and Arthur Y. Hou NASA Goddard Space Flight.
SeaWiFS Views Hurricane Fabian Gathering Strength 970.2/Gene Feldman, Laboratory for Hydrospheric Processes, SeaWiFS SIMBIOS Project Office
AN OVERVIEW OF THE CURRENT NASA OPERATIONAL AMSR-E/AMSR2 SNOW SCIENCE TEAM ACTIVITIES M. Tedesco*, J. Jeyaratnam, M. Sartori The Cryospheric Processes.
The Variability of Sea Ice from Aqua’s AMSR-E Instrument: A Quantitative Comparison of the Team and Bootstrap Algorithms By Lorraine M. Beane Dr. Claire.
AMSR-E Ocean Rainfall Algorithm Status AMSR-E Science Team Meeting Asheville, NC June, 2011 C. Kummerow Colorado State University.
IPWG, 4 th Workshop, Beijing, October UPDATE ON THE STATUS OF PRECIPITATION PRODUCTS IN THE EUMETSAT SATELLITE APPLICATION FACILITY ON HYDROLOGY.
Early Results from AIRS and Risk Reduction Benefits for other Advanced Infrared Sounders Mitchell D. Goldberg NOAA/NESDIS Center for Satellite Applications.
DIRECT READOUT APPLICATIONS USING ATOVS ANTHONY L. REALE NOAA/NESDIS OFFICE OF RESEARCH AND APPLICATIONS.
The Inter-Calibration of AMSR-E with WindSat, F13 SSM/I, and F17 SSM/IS Frank J. Wentz Remote Sensing Systems 1 Presented to the AMSR-E Science Team June.
AMSR Team Meeting September 16, 2015 AMSR2 Rainfall Algorithm Update Christian Kummerow Colorado State University.
NGAS ATMS Cal/Val Activities and Findings Degui Gu, Alex Foo and Chunming Wang Jan 13, 2012.
CIOSS Ocean Optics Aug 2005 Ocean Optics, Cal/Val Plans, CDR Records for Ocean Color Ricardo M Letelier Oregon State University Outline - Defining Ocean.
Recent SeaWiFS view of the forest fires over Alaska Gene Feldman, NASA GSFC, Laboratory for Hydrospheric Processes, Office for Global Carbon Studies
“Land Surface Study” Scenario – Ted Strub’s Effort Search on “soil moisture brightness temperature” At the bottom of the first page of the results was.
AMSR-E and WindSAT Version 7 Microwave SSTs C. Gentemann, F. Wentz, T. Meissner, & L.Riccardulli Remote Sensing Systems NASA SST ST October.
PoDAG XXV, 26 Oct SSM/I Update (with some AMSR-E) Walt Meier.
SCM x330 Ocean Discovery through Technology Area F GE.
Development of passive microwave cryospheric climate data records - and a possible alternative for GHRSST W. Meier, F. Fetterer, R. Duerr, J. Stroeve Presented.
Radiance Simulation System for OSSE  Objectives  To evaluate the impact of observing system data under the context of numerical weather analysis and.
Modifications to the SSMIS Unified Preprocessor for Use With Climate and Precipitation (UPP-CP) Joe Turk Jet Propulsion Laboratory, California Institute.
Passive Microwave Remote Sensing
SSMIS and the Unified Pre-Processor (UPP)
NSIDC’s Passive Microwave Sensor Transition for Polar Data
GSICS Microwave Sub Group Meeting
NOAA/NESDIS/Center for Satellite Applications and Research
Report to 8th GSICS Exec Panel
Passive Microwave Systems & Products
NSIDC DAAC UWG Meeting August 9-10 Boulder, CO
NOAA-20 and Suomi NPP ATMS On-orbit Performance
An Update on the Activities of the Precipitation Measurement Missions (i.e. TRMM/GPM) XCAL Team PMM XCAL Team Wesley Berg, Rachael Kroodsma, Faisal Alquaeid,
The HOAPS-3 climatology
The SSMI/SSMIS Global Hydrological Gridded Products
AMSR-E Ocean Rainfall Algorithm Status
GRWG MW-SubGroup Candidate GSICS products – Window Channels
Satellite Foundational Course for JPSS (SatFC-J)
Improved Forward Models for Retrievals of Snow Properties
Get final-look Atlas/Ardizzone wind product.
A Hydrologically-Consistent Multi-Satellite Climatology of Evaporation, Precipitation, and Water Vapor Transport Over the Oceans Project team: Frank Wentz.
Presentation transcript:

Passive Microwave Data at NCDC John J. Bates and Hilawe Semunegus NOAA’s National Climatic Data Center Asheville, NC AMSR-E Group Meeting June 28, 2011 Asheville, NC NOAA’s National Climatic Data Center

 Passive microwave data archive at NCDC  Recent work to address SSMIS data quality issues using the Unified Preprocessor (UPP) software developed by NRL and the UK Met Office (NWP SAF)  Collaborators include Steve Swadley (NRL), Bill Bell (NWP SAF), Joe Turk (NASA JPL), Wesley Berg/Mathew Sapiano (Kummerow’s group at CSU)  Modification of UPP for Climate and Precipitation (CP) applications  Latest on the GCOM-W1 (AMSR2) instrument Outline

 DMSP SSM/T1 and SSM/T2 (POR: ); Fundamental Climate Data Record (FCDR) in progress; Luo et al  DMSP SSM/I and SSMIS (POR: 1987-present); FCDR in progress; Kummerow et al.  AMSU-A and AMSU-B (POR: 1998-present); FCDR in progress; Ferraro et al. and John et al.  Several products based on these passive microwave datasets are archived at NCDC (e.g. MIRS, SSMI-SSMIS Hydrological Products).  GCOM-W1 (AMSR2); pre-launch planning stage (will be archived at NCDC) NCDC Passive Microwave Data Archive and CDR development

Passive Microwave Time Coverage Since 1987

Sustained Climate Information Flow

Scan Non-Uniformity Correction (all satellites) Channel dependent multiplicative coefficient applied to each beam position from static scan non-uniformity files Radiometer Gain Correction (F16 all year, F17 at high solar elevation angles in spring and summer) Corrects for solar intrusions into warm load resulting in short duration positive Gain anomalies Radiometer sees warm load tines “warmer” than recorded warm load temperature, resulting in a brightness temperature depression of ~ K Short lived Gain anomalies are filtered using the Northrup Grumann (NG) developed algorithm and available in the operationally produced gain files (one file for each TDR file) Reflector Emission and Thermistor Location (F16 and F17, but applied for all) Requires knowledge of reflector temperature T refl and frequency-dependent emissivity ε F-16 required an emprically developed T refl using the reflector rim temperature F-17 - F-20 have the thermistor moved to the back of the graphite reflector shell Corrections Needed for SSMIS TDR Data

Scan non-uniformity correction Apply scan uniformity to IMA, ENV, LAS Gain anomaly correction Apply gain ratio to each channel set ProcessLASTDR_v2 Remap to LAS and apply TB corrections UPP (original NWP application): Averages all channels to LAS resolution (N/3 X 60), then calls routines to apply corrections For UPP-CP: Apply corrections at native resolution and output data in unique binary format IMA (6 chans) ENV (5) LAS (8) Native: UPP to UPP-Climate & Precipitation (CP) Applications Reflector emission correction

ENV GHz UAS 60 +/- GHz IMA GHz LAS GHz IMA GHz Reflector Emission (~1-2K) 1) SSMIS Cal/Val team determined reflector’s layered SiOx/AL VDA coating process resulted in emissive F16 and F17 reflectors. F18 fixed this problem:  Thick white coating used over graphite epoxy reflector  Reflector surface slightly roughened using abrasive material Reflector Emission (1-2 K) and Thermistor Location 2) Depending upon the time of year and orbital parameters the sensor enters Earth shadow and/or solar Array shadowing and the reflector cools by ≈ 80K, then rapidly warms to near 300K upon exiting shadow  F16: thermistor located on reflector rim, T refl based on “lagged- derivative” approach; over hundred scans discarded per orbit F17-F20: thermistor moved to back to reflector, gives better estimate of T refl ; only few scans discarded per orbit. F18 does not have this issue

T_rflct does not keep up with the actual reflector face temperature immediately after emerging from shadow F-16 Reflector Temperature Issue

Y-axis: 1-(Gain_Original/ Gain_Filtered), Range of to X-axis: scanline, range Solar intrusions into the warm load occur 2-4 times per orbit, as evidenced by the Gain ratio or Gain_Original/ Gain_Filtered F-16 SSMIS Solar Intrusions (-0.5 to -1K) June 1, 2010: Rev (one orbit)

Northrup Grumman (NG) developed a solar intrusion correction for operational use that is applied in UPP (and will be applied in UPP-CP) – NG produces ancillary “gain files” which are required to apply correction – Do not know how these are produced Currently evaluating suitability of this correction technique for climate but may be sufficient for ICDR – At this point we have more questions than answers... Example from Gain file red line: actual gain; blue line: filtered gain Solar Intrusion Corrections

Scan Dependence

 Publicly available SSMIS SDR data (from FNMOC; archived at NCDC) uses spillover and cross- polarization coefficients (APC components)  FNMOC APC derived based on surface-dependent inter-calibration to SSM/I  Not applicable for climate and precipitation applications  UPP-CP needs new APC coefficients, currently being developed by CSU (Kummerow’s group)  US Navy (FNMOC) plans to run an operational version after UPP-CP group resolves APC coefficient issues. Antenna Pattern Corrections (APC)

Colorado State University (CSU) producing an FCDR of SSM/I and SSMIS under NCDC CDR program  FCDR will include QC, improved geolocation, intercalibratrion; code is open source and freely available All three of the previously listed corrections are implemented for CSU SSMIS  Now beginning testing to understand effect on calibration of brightness temperatures produced  Will apply 4 independent inter-calibration techniques (already applied to SSM/I) to check for agreement between SSMIS and SSM/I  In addition, CSU will apply new scan non-uniformity corrections and will supply inter-calibration numbers SSM/I and SSMIS Fundamental Climate Data Record

GCOM-W1 (AMSR-2) Update Expected instrument launch is Feb (delayed by 3 months because of Japanese earthquake) NCDC/CLASS will receive JAXA GCOM-W1 (AMSR-2) RDRs in HDF5 format via the IDPS (very similar to NPP RDR functionality in terms of packaging and delivery) Expected volume is ~975 MB/day of GCOM-W1 AMSR-2 RDRs, which represent 15 orbits per day; ~65 MB per file. AMSR-2 RDR will have science, telemetry, housekeeping and diagnostic data types AMSR-2 RDRs will be restricted to users specified by GCOM- W1 Working Group (POC: Jennifer Clapp at NESDIS IIA). Continuity of existing observations (Level 2 and Level 3) for sea surface temperatures, sea ice and snow cover extent, vegetation index, soil moisture, ocean surface wind speed, water vapor, precipitation rates and ocean color. Products slated for 2013.

BACKUP

SSMIS Channel Sets