Active Projects for FY04 Clouds from AVHRR – technical support for OSDPD transition to operations, validation of products and development of better algorithms.

Slides:



Advertisements
Similar presentations
NOAA National Geophysical Data Center
Advertisements

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 A Cloud Object Based Volcanic.
SPoRT Products in Support of the GOES-R Proving Ground and NWS Forecast Operations Andrew Molthan NASA Short-term Prediction Research and Transition (SPoRT)
Title: Applications of the AWG Cloud Height Algorithm (ACHA) Authors and AffiliationsAndrew Heidinger, NOAA/NESDIS/STAR Steve Wanzong, UW/CIMSS Topics:
SNPP VIIRS green vegetation fraction products and application in numerical weather prediction Zhangyan Jiang 1,2, Weizhong Zheng 3,4, Junchang Ju 1,2,
1 1. FY09 GOES-R3 Project Proposal Title Page Title: Trace Gas and Aerosol Emissions from GOES-R ABI Project Type: GOES-R algorithm development project.
Pathfinder –> MODIS -> VIIRS Evolution of a CDR Robert Evans, Peter Minnett, Guillermo Podesta Kay Kilpatrick (retired), Sue Walsh, Vicki Halliwell, Liz.
Upgrades to the MODIS near-IR Water Vapor Algorithm and Cirrus Reflectance Algorithm For Collection 6 Bo-Cai Gao & Rong-Rong Li Remote Sensing Division,
1 GOES Users’ Conference October 1, 2002 GOES Users’ Conference October 1, 2002 John (Jack) J. Kelly, Jr. National Weather Service Infusion of Satellite.
NRL09/21/2004_Davis.1 GOES-R HES-CW Atmospheric Correction Curtiss O. Davis Code 7203 Naval Research Laboratory Washington, DC 20375
Green Vegetation Fraction (GVF) derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard the SNPP satellite Zhangyan Jiang 1,2,
VIIRS Cloud Products Andrew Heidinger, Michael Pavolonis Corey Calvert
Polar Atmospheric Composition: Some Measurements and Products A Report on Action item STG3-A11 Jeff Key NOAA/NESDIS.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Correction of Vegetation Time Series for Long Term Monitoring Marco Vargas¹.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 NOAA Operational Geostationary Sea Surface Temperature Products from NOAA.
Cooperative Institute for Meteorological Satellite Studies University of Wisconsin - Madison Steve Ackerman Director, Cooperative Institute for Meteorological.
Motivation Many GOES products are not directly used in NWP but may help in diagnosing problems in forecasted fields. One example is the GOES cloud classification.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 CLOUD MASK AND QUALITY CONTROL FOR SST WITHIN THE ADVANCED CLEAR SKY PROCESSOR.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 STAR and NPOESS Presented.
Earth Observation Science Recent and potential scientific achievements of the (A)ATSR Series - and some possibilities for synergy with MERIS David Llewellyn-Jones.
Toward a Stable Real-Time Green Vegetation Fraction Le Jiang, Dan Tarpley, Felix Kogan, Wei Guo and Kenneth Mitchell JCSDA Science Workshop May 31 – June.
Remote sensing of aerosol from the GOES-R Advanced Baseline Imager (ABI) Istvan Laszlo 1, Pubu Ciren 2, Hongqing Liu 2, Shobha Kondragunta 1, Xuepeng Zhao.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 GOES Solar Radiation Products in Support of Renewable Energy Istvan Laszlo.
A43D-0138 Towards a New AVHRR High Cloud Climatology from PATMOS-x Andrew K Heidinger, Michael J Pavolonis, Aleksandar Jelenak* and William Straka III.
GOES Users’ Conference III May 10-13, 2004 Broomfield, CO Prepared by Integrated Work Strategies, LLC GOES USERS’ CONFERENCE III: Discussion Highlights.
1 Center for S a t ellite A pplications and R esearch (STAR) Applicability of GOES-R AWG Cloud Algorithms for JPSS/VIIRS AMS Annual Meeting Future Operational.
Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental Satellite (GOES)-R platform. The sensor.
Initial Trends in Cloud Amount from the AVHRR Pathfinder Atmospheres Extended (PATMOS-x) Data Set Andrew K Heidinger, Michael J Pavolonis**, Aleksandar.
Towards Operational Satellite-based Detection and Short Term Nowcasting of Volcanic Ash* *There are research applications as well. Michael Pavolonis*,
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 In Situ SST for Satellite Cal/Val and Quality Control Alexander Ignatov.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Infrared Temperature and.
Near-Real-Time Simulated ABI Imagery for User Readiness, Retrieval Algorithm Evaluation and Model Verification Tom Greenwald, Brad Pierce*, Jason Otkin,
Andrew Heidinger and Michael Pavolonis
Hyperspectral Infrared Alone Cloudy Sounding Algorithm Development Objective and Summary To prepare for the synergistic use of data from the high-temporal.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 The Influences of Changes.
AIRS Radiance and Geophysical Products: Methodology and Validation Mitch Goldberg, Larry McMillin NOAA/NESDIS Walter Wolf, Lihang Zhou, Yanni Qu and M.
As components of the GOES-R ABI Air Quality products, a multi-channel algorithm similar to MODIS/VIIRS for NOAA’s next generation geostationary satellite.
R. T. Pinker, H. Wang, R. Hollmann, and H. Gadhavi Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland Use of.
ISCCP Calibration 25 th Anniversary Symposium July 23, 2008 NASA GISS Christopher L. Bishop Columbia University New York, New York.
Early Detection & Monitoring North America Drought from Space
Use of Solar Reflectance Hyperspectral Data for Cloud Base Retrieval Andrew Heidinger, NOAA/NESDIS/ORA Washington D.C, USA Outline " Physical basis for.
Satellite based instability indices for very short range forecasting of convection Estelle de Coning South African Weather Service Contributions from Marianne.
Eileen Maturi 1, Jo Murray 2, Andy Harris 3, Paul Fieguth 4, John Sapper 1 1 NOAA/NESDIS, U.S.A., 2 Rutherford Appleton Laboratory, U.K., 3 University.
Cloud Products and Applications: moving from POES to NPOESS (A VIIRS/NOAA-biased perspective) Andrew Heidinger, Fuzhong Weng NOAA/NESDIS Office of Research.
Prepared by Ken Knapp, NOAA/NCDC 3/19/2015.  ISCCP Background  Activities in 2014  Plans for 2015  Potential interactions with other SCOPE-CM projects.
Satellite Precipitation Estimation and Nowcasting Plans for the GOES-R Era Robert J. Kuligowski NOAA/NESDIS Center for Satellite Applications and Research.
1 Recommendations from the 2 nd GOES-R Users’ Conference: Jim Gurka Tim Schmit NOAA/ NESDIS Dick Reynolds Short and Associates.
Retrieval Algorithms The derivations for each satellite consist of two steps: 1) cloud detection using a Bayesian Probabilistic Cloud Mask; and 2) application.
Blended Sea Surface Temperature EnhancementsPolar Winds Blended Hydrometeorological Products Blended Total Ozone Products are derived by tracking cloud.
Visible optical depth,  Optically thicker clouds correlate with colder tops Ship tracks Note, retrievals done on cloudy pixels which are spatially uniform.
AVHRR Visible Band Calibration / Intercalibration (for Climate Studies) Andrew Heidinger and Michael Pavolonis* Changyong Cao, Aleksandar Jelenak, Jerry.
AVHRR Stewardship Project Pathfinder Atmospheres – Extended (PATMOS-x) Andrew Heidinger, Aleksandar Jelenak, Michael Pavolonis NOAA/NESDIS/ORA.
McIDAS-X Software Development and Demonstration Dave Santek and Jay Heinzelman 2 June 2009 PDA Animated Weather (PAW) Status by Russ Dengel.
(Towards) A New AVHRR Cloud Climatology Andrew Heidinger, Mitch Goldberg, Dan Tarpley NOAA/NESDIS Office of Research and Applications Michael Pavolonis.
Preliminary results from the new AVHRR Pathfinder Atmospheres Extended (PATMOS-x) Data Set Andrew Heidinger a, Michael Pavolonis b and Mitch Goldberg a.
Cooperative Institute for Meteorological Satellite Studies.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 STAR Enterprise Synthesis.
Global Multi-layer Cloud Distribution from AVHRR
CLAVR-x in CSPP Andrew Heidinger, NOAA/NESDIS/STAR, Madison WI
NOAA Report on Ocean Parameters - SST Presented to CGMS-43 Working Group 2 session, agenda item 9 Author: Sasha Ignatov.
PATMOS-x Reflectance Calibration and Reflectance Time-Series
AVHRR operational cloud masks intercomparison
Global Multi-layer Cloud Distribution from AVHRR
Building-in a Validation cycle for GSICS Products
AVHRR Pathfinder Atmospheres Extended (PATMOS-x)
Generation of Cloud Products from NOAA’s Operational Satellite Imagers
Andrew Heidinger and Michael Pavolonis
GSICS Data and Products Servers
Andrew Heidinger JPSS Cloud Team Lead
Calibration of AVHRR Reflectance Channels
Presentation transcript:

Active Projects for FY04 Clouds from AVHRR – technical support for OSDPD transition to operations, validation of products and development of better algorithms. VIIRS Cloud Work – Improving the VIIRS cloud algorithms based on NOAA expertise. AVHRR Data Stewardship - Improving the AVHRR data (calibration and navigation) and generating climate records. Microwave Radiance Assimilation (co-I) – development of a fast radiative transfer model for moderately scattering atmospheres. GOES Surface and Insolation Fulldisk – extension of the GSIP CONUS system to the full disk and higher resolution.

Clouds from AVHRR – FY04 Highlights CLAVR-x is similar in scope to MSPPS except limited to the AVHRR Last Year of the CLAVR-x project (that involves ORA directly) The OSDPD CLAVR-x code continues to run on the IBM SP (the new platform). Operational transition set for December. Also runs within ORA in real-time. This year ORA completing the transition to HDF output which has greatly facilitated the use of data by outsiders. HDF is the also the format for NPOESS and EOS. CLAVR-x cloud mask now being used in NESDIS operational products (radiation budget) and plans exist for its use in new SST, GVI. CLAVR-x SST data used for GOES/POES high resolution SST project (RAL) New development this year included a pixel level cloud top temperature algorithm that is consistent for all orbits and all times of day. This will potentially benefit IASI sounding as MODIS cloud products benefit AIRS soundings.

Example CLAVR-x products available in real-time Cloud Mask + Surface Temp. Cloud Top Temperature The suite of CLAVR-x includes a full range of cloud products in addition to the standard cloud mask.

Pixel Level SST (SST – This is what is used by RAL for POES/GOES SST) (All of this information is available in the SST file and this analysis can be done automatically) Unmasked pixel level SST Cloud Masked SSTbackground SST field Histogram of SST – background SST

Example of the type of gridded information produced by CLAVR-x. All are made available through OSDPD/ORA. Real-time orbital Single Satellite Daily Asc/Des fields Multi-satellite Synoptic Fields

Current Uses of CLAVR-x Other NESDIS Applications NCEP is grabbing CLAVR-x gridded cloud fields. Potential of clear radiances for assimilation being considered. Two JCSDA projects are using CLAVR-x output CLAVR-x algorithms are used in GASP, GSIP and for some aviation safety program (ASAP) here at CIMSS. Parts used by NGST for VIIRS Basis of PATMOS-x which serves the climate community

VIIRS Cloud EDR Risk Reduction FY04 Highlights Continue as VIIRS OAT co-chair (with P. Menzel) Developed an extension of the CLAVR-x cloud type to the VIIRS channels – demonstrably better than the VIIRS baseline approach. (This will also work for ABI) Successfully worked with NGST and helped them adopt the CLAVR-x cloud typing approach for VIIRS. Worked with NGST to adopt the CLAVR-x spatial uniformity tests into the VIIRS cloud mask to improve VIIRS SST. Helping NGST adopt NDVI-dependent reflectance thresholds to improve land cloud mask performance. Researching the continuity in the POES/NPOESS climate record using MODIS as a surrogate for VIIRS.

Example Pixel Level Result of the Cloud Typing with Multi-layer Detection using the AVHRR Algorithm: Typhoon Inoue in Indian Ocean observed from MODIS Idealized cross section of a hurricane showing expected areas of multi-layer cloud (taken from NOAA)

Same as before but using the “VIIRS” Algorithm Idealized cross section of a hurricane showing expected areas of multi-layer cloud (taken from NOAA)

AVHRR Data Stewardship / AVHRR Pathfinder Atmospheres ORA funded a pilot AVHRR Data Stewardship Initiative in FY03 & FY04. The goal of these projects is to demonstrate that ORA can use its expertise in all areas of AVHRR processing to improve the AVHRR data record and perform climate analyses. (ORA Participants: Goldberg, Tarpley, Heidinger, Harris, Key, Kogan, Wu, Sullivan, Cao, Maturi) ORA “archive” now contains all AVHRR GAC data from Data moving from the SAA as fast as possible. So far we have developed the navigation correction, the GVI reprocessing software and the PATMOS-x software. Currently working on reflectance calibration improvements. PATMOS-x is a climate data-set produced from AVHRR with a 50 km resolution and contains cloud, surface and aerosol products (all developed within ORA).

Initial Results from PATMOS-x: Improvements in the physics with the cloud mask have removed the large satellite to satellite jumps observed in the original PATMOS time series. Note, the differences in PATMOS-x (black) are much less than PATMOS (blue) on either side of the satellite transitions (vertical lines)

Initial Results from PATMOS-x: Our time series of cloud amount seems stable compared to that from ISCCP. The downward trend seen in ISCCP is contentious – we do not see that trend and are one of a few projects that produce an independent global cloud climatology.

GOES Surface and Insolation Project Highlights The new system that will replace the CONUS GSIP is running within ORA. (It is essentially CLAVR-x applied to GOES imager with the Laszlo insolation algorithm). We were funded by Ground System and currently NCEP is able to get the data from us for testing. Once this testing is complete and NCEP is satisfied with the new product, we will try to transition to OSDPD. Currently, we felt it unwise to transition during the OSDPD reorganization. Microwave Radiance Assimilation Highlights My contribution was the development of an extension of the traditional method of successive order of scattering that is superior for moderately scattering atmospheres (successive order of interaction). The “winning” model will be decided in a JCSDA contest this fall. Collaborators include Ralf Bennartz, Tom Greenwald and Chris Odell.

Issues Should we actively pursue the transition of NOAA methods for NPOESS? What is the ideal relationship between NGST and STAR. Should we accept offers to co-authors on NGST led publications? Tom Schott explained to me that PSDI is going away and being replaced by an NDE funding line. The NDE list of NOAA unique products is currently very modest. There is no Polar GIMPAP counterpart. Are we getting out of the business of product development from polar orbiters?