1 FWC 2006-10-24 IPWG MIT Lincoln Laboratory * This work was sponsored by the National Aeronautics and Space Administration under Contract NNG 04HZ53C,

Slides:



Advertisements
Similar presentations
Introduction to data assimilation in meteorology Pierre Brousseau, Ludovic Auger ATMO 08,Alghero, september 2008.
Advertisements

Enhancement of Satellite-based Precipitation Estimates using the Information from the Proposed Advanced Baseline Imager (ABI) Part II: Drizzle Detection.
Characterization of ATMS Bias Using GPSRO Observations Lin Lin 1,2, Fuzhong Weng 2 and Xiaolei Zou 3 1 Earth Resources Technology, Inc.
A Microwave Retrieval Algorithm of Above-Cloud Electric Fields Michael J. Peterson The University of Utah Chuntao Liu Texas A & M University – Corpus Christi.
A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.
Passive Measurements of Rain Rate in Hurricanes Ruba A.Amarin CFRSL December 10, 2005.
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.
Preparing for JPSS-1/ATMS Direct Readout Readiness Acknowledgments: This work was performed under contract NAS , sponsored by NASA Nikisa S. George.
Microwave Imagery and Tropical Cyclones Satellite remote sensing important resource for monitoring TCs, especially in data sparse regions Passive microwave.
Passive Microwave Rain Rate Remote Sensing Christopher D. Elvidge, Ph.D. NOAA-NESDIS National Geophysical Data Center E/GC2 325 Broadway, Boulder, Colorado.
16/06/20151 Validating the AVHRR Cloud Top Temperature and Height product using weather radar data COST 722 Expert Meeting Sauli Joro.
Atmospheric Emission.
Atmospheric structure from lidar and radar Jens Bösenberg 1.Motivation 2.Layer structure 3.Water vapour profiling 4.Turbulence structure 5.Cloud profiling.
Millimeter and sub-millimeter observations for Earth cloud hunting Catherine Prigent, LERMA, Observatoire de Paris.
NPP ATMS Snowfall Rate Product POES and MetOp AMSU/MHS and SNPP ATMS take passive microwave (MW) measurements at certain high frequencies (88.2~
MIT Lincoln Laboratory MIS IGARSS11-1 RVL 9/15/2010 An Atmospheric Algorithm Suite Based on Neural Networks for Microwave Imager/Sounders W. J. Blackwell,
Ben Kravitz October 29, 2009 Microwave Sounding. What is Microwave Sounding? Passive sensor in the microwave to measure temperature and water vapor Technique.
Diagnosing Climate Change from Satellite Sounding Measurements – From Filter Radiometers to Spectrometers William L. Smith Sr 1,2., Elisabeth Weisz 1,
Anthony DeAngelis. Abstract Estimation of precipitation provides useful climatological data for researchers; as well as invaluable guidance for forecasters.
SMOS+ STORM Evolution Kick-off Meeting, 2 April 2014 SOLab work description Zabolotskikh E., Kudryavtsev V.
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.
Evaluation and Improvement of AMSU Precipitation Retrievals Over Ocean Daniel Vila 1, Ralph R. Ferraro 1,2 1. CICS/ESSIC-NOAA, University of Maryland College.
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.
SeaWiFS Highlights February 2002 SeaWiFS Views Iceland’s Peaks Gene Feldman/SeaWiFS Project Office, Laboratory for Hydrospheric Processes, NASA Goddard.
SeaWiFS Highlights September 2002 SeaWiFS Views Development of Hurricane Isidore These two SeaWiFS images were collected ten days apart. The first was.
Faculty of Technology and Environment Prince of Songkla University 1 Chinnawat Surussavadee July 2011 Evaluation of High-Resolution.
CrIS Use or disclosure of data contained on this sheet is subject to NPOESS Program restrictions. ITT INDUSTRIES AER BOMEM BALL DRS EDR Algorithms for.
MIT REMOTE SENSING AND ESTIMATION GROUP 1 Geosynchronous Microwave Sounding of Precipitation Parameters at Convective Scales David.
25-28 October nd IPWG Monterey, CA The Status of the NOAA/NESDIS Operational AMSU Precipitation Algorithm Ralph Ferraro NOAA/NESDIS College Park,
University of Wisconsin - Madison Space Science and Engineering Center (SSEC) High Spectral Resolution IR Observing & Instruments Hank Revercomb (Part.
Water Vapour & Cloud from Satellite and the Earth's Radiation Balance
2 HS3 Science Team Meeting - BWI - October 19-20, 2010HAMSR/Lambrigtsen HAMSR Status Update (2015) Bjorn Lambrigtsen (HAMSR PI) Shannon Brown (HAMSR Task.
Evaluation of Passive Microwave Rainfall Estimates Using TRMM PR and Ground Measurements as References Xin Lin and Arthur Y. Hou NASA Goddard Space Flight.
Improvement of Cold Season Land Precipitation Retrievals Through The Use of Field Campaign Data and High Frequency Microwave Radiative Transfer Model IPWG.
Response of active and passive microwave sensors to precipitation at mid- and high altitudes Ralf Bennartz University of Wisconsin Atmospheric and Oceanic.
Science of the Aqua Mission By: Michael Banta ESS 5 th class Ms. Jakubowyc December 7, 2006.
Layered Water Vapor Quick Guide by NASA / SPoRT and CIRA Why is the Layered Water Vapor Product important? Water vapor is essential for creating clouds,
Challenges and Strategies for Combined Active/Passive Precipitation Retrievals S. Joseph Munchak 1, W. S. Olson 1,2, M. Grecu 1,3 1: NASA Goddard Space.
IPWG, 4 th Workshop, Beijing, October UPDATE ON THE STATUS OF PRECIPITATION PRODUCTS IN THE EUMETSAT SATELLITE APPLICATION FACILITY ON HYDROLOGY.
1 Recommendations from the 2 nd GOES-R Users’ Conference: Jim Gurka Tim Schmit NOAA/ NESDIS Dick Reynolds Short and Associates.
3 rd IPWG 2006: 1 RVL 2/14/2016 MIT Lincoln Laboratory Modeling Validation with NAST-M and a Cloud-Resolving Model at GHz This work was sponsored.
The Orbiting Carbon Observatory (OCO) Mission: Retrieval Characterisation and Error Analysis H. Bösch 1, B. Connor 2, B. Sen 1, G. C. Toon 1 1 Jet Propulsion.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
23-27 October 20063rd IPWG Melbourne, Australia The Status of the NOAA/NESDIS Operational AMSU/MHS Precipitation Algorithm Ralph Ferraro NOAA/NESDIS College.
An Overview of Satellite Rainfall Estimation for Flash Flood Monitoring Timothy Love NOAA Climate Prediction Center with USAID- FEWS-NET, MFEWS, AFN Presented.
A Combined Radar-Radiometer Approach to Estimate Rain Rate Profile and Underlying Surface Wind Speed over the Ocean Shannon Brown and Christopher Ruf University.
Retrieval of cloud parameters from the new sensor generation satellite multispectral measurement F. ROMANO and V. CUOMO ITSC-XII Lorne, Victoria, Australia.
Active and passive microwave remote sensing of precipitation at high latitudes R. Bennartz - M. Kulie - C. O’Dell (1) S. Pinori – A. Mugnai (2) (1) University.
Obs-sim[ECMWF] obs-sim[AIRS] Dashed curve = ECMWF curve shifted to AIRS curve at nadir This is our best estimate of scan bias Motivation: AIRS-retrieval.
Global vs mesoscale ATOVS assimilation at the Met Office Global Large obs error (4 K) NESDIS 1B radiances NOAA-15 & 16 HIRS and AMSU thinned to 154 km.
A Physically-based Rainfall Rate Algorithm for the Global Precipitation Mission Kevin Garrett 1, Leslie Moy 1, Flavio Iturbide-Sanchez 1, and Sid-Ahmed.
Developing Winter Precipitation Algorithm over Land from Satellite Microwave and C3VP Field Campaign Observations Fifth Workshop of the International Precipitation.
Evaluation and Improvement of the NPP CrIMSS Rain Flag Wenze Yang 1, Ralph Ferraro 2, Chris Barnet 2, and Murty Divakarla 2 1. UMD/ESSIC/CICS, College.
Passive Microwave Remote Sensing
Dec 12, 2008F. Iturbide-Sanchez Review of MiRS Rainfall Rate Performances F. Iturbide-Sanchez, K. Garrett, S.-A. Boukabara, and W. Chen.
Indirect impact of ozone assimilation using Gridpoint Statistical Interpolation (GSI) data assimilation system for regional applications Kathryn Newman1,2,
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course
Cassini Huygens EECS 823 DIVYA CHALLA.
Microwave Assimilation in Tropical Cyclones
SOLab work description
Precipitation Classification and Analysis from AMSU
NASA Aqua.
Requirements for microwave inter-calibration
Cezar Kongoli1, Huan Meng2, Jun Dong1, Ralph Ferraro2,
Geostationary Sounders
NPOESS Airborne Sounder Testbed (NAST)
Hyperspectral Wind Retrievals Dave Santek Chris Velden CIMSS Madison, Wisconsin 5th Workshop on Hyperspectral Science 8 June 2005.
Satellite Foundational Course for JPSS (SatFC-J)
Satellite Foundational Course for JPSS (SatFC-J)
Presentation transcript:

1 FWC IPWG MIT Lincoln Laboratory * This work was sponsored by the National Aeronautics and Space Administration under Contract NNG 04HZ53C, Grant NNG 04HZ51C, and Grant NAG , and the National Oceanic and Atmospheric Administration under Air Force Contract FA C Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the United States Government. Satellite-based Estimation of Precipitation Using Passive Opaque Microwave Radiometry* Frederick W. Chen, Laura J. Bickmeier, William J. Blackwell, R. Vincent Leslie MIT Lincoln Laboratory (Lexington, MA, USA) David H. Staelin, Chinnawat “Pop” Surussavadee MIT Research Laboratory of Electronics (Cambridge, MA, USA) 3 rd Workshop of the International Precipitation Working Group Melbourne, VIC, Australia 24 October 2006

MIT Lincoln Laboratory 2 FWC IPWG Outline Physical basis Algorithm development –AMSU (Advanced Microwave Sounding Unit) –ATMS (Advanced Technology Microwave Sounder) Future work Summary

MIT Lincoln Laboratory 3 FWC IPWG Physical Basis Transparent channels (or window channels) –Warm water vapor signatures over cold ocean –Scattering signatures due to ice particles over land Opaque channels –Varying atmospheric opacity –Sensitive primarily to specific layers of atmosphere OPAQUE BANDS TRANSPARENT BANDS

MIT Lincoln Laboratory 4 FWC IPWG 54-GHz and 183-GHz Weighting Functions 54-GHz 183-GHz

MIT Lincoln Laboratory 5 FWC IPWG Estimation of Precipitation Rate with Opaque  W Channels (54-GHz and 183-GHz) Precipitation rate ~  humidity × vertical wind velocity Absolute humidity –54-GHz band reveal temperature profile –54-GHz and 183-GHz bands reveal water vapor profile Vertical wind velocity –Stronger vertical wind → –Stronger vertical winds results in increased backscattering of cold space radiation –Perturbations (cold spots) in 54-GHz data reveal cloud-top altitude –Absolute albedos reveal hydrometeor abundance –Relative albedos (54 vs. 183-GHz) reveal hydrometeor size Greater hydrometeors size Greater hydrometeor abundance Higher cloud-top altitude

MIT Lincoln Laboratory 6 FWC IPWG Particle Sizes Revealed in NAST-M Data 54 GHz 118 GHz 183 GHz 425 GHz Visible Leslie & Staelin, IEEE TGRS, 10/2004 TBTB

MIT Lincoln Laboratory 7 FWC IPWG AMSU Radiometry Passive  W sounder AMSU-A –12 channels in opaque 54- GHz O 2 band –Window channels near 23.8, 31.4, and 89.0 GHz AMSU-B –3 channels in opaque GHz H 2 O band –Window channels near 89.0 and GHz AMSU-A Channel Frequencies (GHz) ± ± ± ± ± ± ± ± ± ± AMSU-B Channel Frequencies (GHz) ± ± ± 7

MIT Lincoln Laboratory 8 FWC IPWG General Structure of AMSU Algorithm (Chen and Staelin, IEEE TGRS, 2/2003) Signal processing –Regional Laplacian interpolation –Image sharpening –Principal component analysis Neural net –2-layer feedforward neural net –1 st layer: tanh transfer function –2 nd layer: linear transfer function

MIT Lincoln Laboratory 9 FWC IPWG Signal Processing Components Neural-net correction of angle-dependent variations in T B ’s Cloud-clearing via regional Laplacian interpolation –Temperature profile characterization –Cloud-top altitude characterization Principal component analysis for dimensionality reduction –Temperature profile PC’s –Window channel / H 2 O profile PC’s Image sharpening –AMSU-A data sharpened to AMSU-B resolution

MIT Lincoln Laboratory 10 FWC IPWG The Algorithm: Precipitation Masks & Precipitation-Induced Perturbations PRECIPITATION DETECTION IMAGE SHARPENING CORRUPT DATA DETECTION LIMB-&-SURFACE CORRECTION REGIONAL LAPLACIAN INTERPOLATION

MIT Lincoln Laboratory 11 FWC IPWG The Algorithm: Neural Net Trained to NEXRAD

MIT Lincoln Laboratory 12 FWC IPWG Final Output

MIT Lincoln Laboratory 13 FWC IPWG Example of Global Retrieval

MIT Lincoln Laboratory 14 FWC IPWG ATMS Similar to AMSU To be launched on NPP (2009) & NPOESS satellites –NPP = NPOESS Preparatory Project Improvements over AMSU –Additional channels in 54-GHz and 183-GHz bands –Better resolution in 54-GHz band –Better sampling Nyquist sampling of 54-GHz data Identical sampling of all channels

MIT Lincoln Laboratory 15 FWC IPWG Simulating ATMS T B ’s MM5 Atmospheric Circulation Model –Provides temperature profile, water vapor profile, hydrometeor profile, … –Used Goddard hydrometeor model (Tao & Simpson, 1993) Radiative Transfer –TBSCAT due to Rosenkranz (IEEE TGRS, 8/2002) Multi-stream, initial-value –Improved hydrometeor modeling due to Surussavadee & Staelin (IEEE TGRS, 10/2006) Filtering –Accurate matching of T B ’s on MM5 grid to ATMS resolution and geolocation using “satellite geometry” toolbox for MATLAB Computing angular offset of surface locations from boresight Computing satellite zenith angles from scan angle Computing geolocation from scan angle

MIT Lincoln Laboratory 16 FWC IPWG MM5 Rain Rate: Typhoon Pongsona, 2002/12/8

MIT Lincoln Laboratory 17 FWC IPWG AMSU vs. ATMS, 183±7 GHz Observed AMSUSimulated ATMS Simulated ATMS 183±7 GHz data shows reasonable agreement with AMSU-B Morphology difference between AMSU observations and MM5 predicted radiances is due to the inaccuracy of the NCEP analyses used to initialize the MM5 model

MIT Lincoln Laboratory 18 FWC IPWG AMSU vs. ATMS, 50.3 GHz Observed AMSUSimulated ATMS Simulated ATMS 50.3-GHz data with finer resolution and sampling shows finer features than AMSU-A Intense eyewall signature in simulated ATMS 50.3-GHz data due to NCEP initialization & limited 5-hr MM5 spinup time producing excess of large ice particles

MIT Lincoln Laboratory 19 FWC IPWG Future Developments Adapting Chen-Staelin algorithm (IEEE TGRS, 2/2003) for ATMS Exploiting Nyquist sampling in the 54-GHz band Using methods from window-channel-based algorithms Improving the signal processing of Chen-Staelin algorithm Improving neural net training –Representations of circular data

MIT Lincoln Laboratory 20 FWC IPWG Recently Launched & Future Instruments Similar to AMSU-A/B –AMSU/MHS on NOAA-18 (2005) –AMSU/MHS on NOAA-N’, METOP-1, METOP-2, METOP-3 ATMS –NPP (2009) –NPOESS  W instruments on geostationary satellites? –< 1 hr revisit times

MIT Lincoln Laboratory 21 FWC IPWG Summary Physical basis of precipitation estimation using opaque  W channels –Atmospheric sounding capabilities of opaque  W channels –Cloud shape and particle size distribution from NAST-M 54-, 118-, 183-, and 425-GHz data AMSU precipitation algorithm –Relies primarily on 54-GHz and 183-GHz opaque bands –Signal processing: regional Laplacian interpolation, principal component analysis, image sharpening ATMS precipitation algorithm development –Simulation system using MM5/TBSCAT

22 FWC IPWG MIT Lincoln Laboratory Backup Slides

MIT Lincoln Laboratory 23 FWC IPWG NAST-M NAST = NPOESS Aircraft Sounder Testbed –Risk-reduction effort by NPOESS Integrated Program Office –Cooperative effort of NASA, NOAA, & DoD Equipped with 54-, 118-, 183-, and 425-GHz radiometers Flown on high-altitude aircraft –ER-2 (NASA) –Proteus (Scaled Composites) ~2.5-km resolution near nadir

MIT Lincoln Laboratory 24 FWC IPWG Scattering in the 54-GHz and 183-GHz Bands 0.7 mm2.4 mm

MIT Lincoln Laboratory 25 FWC IPWG AMSU Geographical Coverage Aboard NOAA-15, NOAA-16, & NOAA-17 Nearly identical AMSU/HSB on Aqua

MIT Lincoln Laboratory 26 FWC IPWG AMSU-A/B Sampling & Resolution AMSU-A –3 1/3° sampling (~50 km near nadir) –3.3° resolution (~50 km near nadir) AMSU-B –1.1° resolution (~15 km near nadir) –1.1° sampling (~15-km near nadir) AMSU-A AMSU-B

MIT Lincoln Laboratory 27 FWC IPWG 15-km AMSU vs. NEXRAD Comparison

MIT Lincoln Laboratory 28 FWC IPWG RMS Discrepancies (mm/h) between AMSU and NEXRAD Range of NEXRAD rain rate 15-km ( km from radar) 15-km ( km from radar) 50-km ( km from radar) 50-km ( km from radar) < 0.5 mm/h – 1 mm/h – 2 mm/h – 4 mm/h – 8 mm/h – 16 mm/h – 32 mm/h > 32 mm/h

MIT Lincoln Laboratory 29 FWC IPWG Features of ATMS vs. AMSU Channel set –Similar to AMSU  Additional GHz channel  Additional ±4.5-GHz & ±1.8-GHz –165.5-GHz replaces 150-GHz on AMSU-B  No 89.0-GHz 15-km channel (available on AMSU-B) Resolution  54-GHz and 89-GHz: 2.2° vs. 3.33° on AMSU  and 31.4-GHz: 5.2° vs. 3.33° on AMSU Sampling –165.5-GHz, 183-GHz: Similar to AMSU-B  Other channels: ~3× finer than AMSU-A cross-track & along-track  All channels sampled at the same locations  Nyquist sampling of 54-GHz and 89-GHz Similar sensitivity

MIT Lincoln Laboratory 30 FWC IPWG ATMS & AMSU Footprints

MIT Lincoln Laboratory 31 FWC IPWG ATMS & AMSU Footprints (Near Nadir)

MIT Lincoln Laboratory 32 FWC IPWG ATMS Rain Rate Retrieval Algorithm Completely new algorithm Neural net Inputs –All 22 channels –sec(satellite zenith angle) Training, validation, and testing sets –MM5 data over Typhoon Pongsona –1 time step (1521 data points) each for training, validation, and testing

MIT Lincoln Laboratory 33 FWC IPWG ATMS vs. MM5, 1.1°

MIT Lincoln Laboratory 34 FWC IPWG ATMS vs. MM5, 5.2°

MIT Lincoln Laboratory 35 FWC IPWG Representations of Geolocation Rectangular (2-D) û Discontinuity across 180° E/W (Int’l Date Line) û Topological distortion around 90° N & 90° S (Geo. N & S Poles) Cylindrical (3-D) Continuity across 180° E/W û Topological distortion around 90° N & 90° S Spherical (3-D) Continuity across 180° E/W No topological distortion around 90° N & and 90° S

MIT Lincoln Laboratory 36 FWC IPWG Geolocation: Comparing the Representations Spherical representation produces the lowest RMS errors RMS error with 10 weights & biases Linear: 0.16 Cylindrical: 0.16 Spherical: 0.01 Weights & biases needed for RMS error < 1.5 × Rectangular: 23 Cylindrical: 18 Spherical: 6 RECTANGULAR CYLINDRICAL SPHERICAL