Cloud Fraction from Cloud Mask vs Total Sky Imager Comparison of 1 and 4 km Data The native resolution of the vis channel on GOES Imager is roughly 1 km.

Slides:

Advertisements

Similar presentations

Title: Applications of the AWG Cloud Height Algorithm (ACHA) Authors and AffiliationsAndrew Heidinger, NOAA/NESDIS/STAR Steve Wanzong, UW/CIMSS Topics:

Advertisements

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.

A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.

1 1. FY08 GOES-R3 Project Proposal Title Page  Title: Investigation of Daytime-Nighttime Inconsistencies in Cloud Optical Parameters  Project Type: Product.

Xiaolei Niu and R. T. Pinker Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland Radiative Flux Estimates from.

GIST 24/10/06 Jeff Settle, ESSC Reconciling Point and Area Data for RADAGAST GIST 24 October 2006.

GOES-R AWG 2 nd Validation Workshop Hye-Yun Kim (IMSG), Istvan Laszlo (NOAA) and Hongqing Liu (IMSG) GOES-R AWG 2 nd Validation Workshop, Jan , 2014.

The Radiative Budget of an Atmospheric Column in Tropical Western Pacific Zheng Liu Department of Atmospheric Science University of Washington.

Initial testing of longwave parameterizations for broken water cloud fields - accounting for transmission Ezra E. Takara and Robert G. Ellingson Department.

GOES-R AEROSOL PRODUCTS AND AND APPLICATIONS APPLICATIONS Ana I. Prados, S. Kondragunta, P. Ciren R. Hoff, K. McCann.

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.

1 Andrew Heidinger, Michael Pavolonis NOAA/NESDIS Steve Wanzong, Andi Walther, Pat Heck, William Straka, Marek Rogal UW/CIMSS Patrick Minnis NASA/LaRC.

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.

Aircraft spiral on July 20, 2011 at 14 UTC Validation of GOES-R ABI Surface PM2.5 Concentrations using AIRNOW and Aircraft Data Shobha Kondragunta (NOAA),

Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.

1 GOES-R AWG Product Validation Tool Development Cloud Products Andrew Heidinger (STAR) Michael Pavolonis (STAR) Andi Walther (CIMSS) Pat Heck and Pat.

Use of satellite observations in wind and solar forecasting and in offshore wind resource assessments Melinda Marquis Renewable Energy Program Manager.

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.

1 CIMSS Participation in the Development of a GOES-R Proving Ground Timothy J. Schmit NOAA/NESDIS/Satellite Applications and Research Advanced Satellite.

GOES-R ABI PROXY DATA SET GENERATION AT CIMSS Mathew M. Gunshor, Justin Sieglaff, Erik Olson, Thomas Greenwald, Jason Otkin, and Allen Huang Cooperative.

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.

PLANS FOR THE GOES-R SERIES AND COMPARING THE ADVANCED BASELINE IMAGER (ABI) TO METEOSAT-8 UW-Madison James J Gurka, Gerald J Dittberner NOAA/NESDIS/OSD.

AGU 2002 Fall Meeting NASA Langley Research Center / Atmospheric Sciences Validation of GOES-8 Derived Cloud Properties Over the Southeastern Pacific J.

Towards Operational Satellite-based Detection and Short Term Nowcasting of Volcanic Ash* *There are research applications as well. Michael Pavolonis*,

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.

GOES and GOES-R ABI Aerosol Optical Depth (AOD) Validation Shobha Kondragunta and Istvan Laszlo (NOAA/NESDIS/STAR), Chuanyu Xu (IMSG), Pubu Ciren (Riverside.

Estimation of fractional cloud cover from all-sky digital images at sea  Motivation and background  MORE cruises: all-sky digital images of cloud  Testing.

Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental Satellite (GOES)-R platform. The sensor.

Algorithm and Software Development of Atmospheric Motion Vector Products for the GOES-R ABI Jaime M. Daniels 1, Wayne Bresky 2, Chris Velden 3, Iliana.

Initial Trends in Cloud Amount from the AVHRR Pathfinder Atmospheres Extended (PATMOS-x) Data Set Andrew K Heidinger, Michael J Pavolonis**, Aleksandar.

1. GOES-R AWG Land Team The advanced baseline imager (ABI), which will be on board the GOES-R satellites with the first launch being approximately in 2014,

Towards Operational Satellite-based Detection and Short Term Nowcasting of Volcanic Ash* *There are research applications as well. Michael Pavolonis*,

2015 AMS Annual Meeting Non Export-Controlled Information Progress in Development and Integration of the Product Generation Capabilities of the GOES- R.

Himawari‐8 Operational Data Processing and Access

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 Using CALIPSO to Explore the Sensitivity to Cirrus Height in the Infrared.

High impact weather studies with advanced IR sounder data Jun Li Cooperative Institute for Meteorological Satellite Studies (CIMSS),

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.

INFRARED-DERIVED ATMOSPHERIC PROPERTY VALIDATION W. Feltz, T. Schmit, J. Nelson, S. Wetzel-Seeman, J. Mecikalski and J. Hawkinson 3 rd Annual MURI Workshop.

R. T. Pinker, H. Wang, R. Hollmann, and H. Gadhavi Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland Use of.

Studies of Advanced Baseline Sounder (ABS) for Future GOES Jun Li + Timothy J. Allen Huang+ W. +CIMSS, UW-Madison.

Developing Assimilation Techniques for Atmospheric Motion Vectors Derived via a New Nested Tracking Algorithm Derived for the GOES-R Advanced Baseline.

Consistency of reflected moonlight based nighttime precipitation product with its daytime equivalent. Andi Walther 1, Steven Miller 3, Denis Botambekov.

Hands-on exercise showcasing ABI’s 16 channels with improved spatial resolution and temporal refresh rate (plus Weighting Functions and RGB ABI examples)

Retrieval Algorithms The derivations for each satellite consist of two steps: 1) cloud detection using a Bayesian Probabilistic Cloud Mask; and 2) application.

Estimation of Potential Evapotranspiration from Merged CERES and MODIS Observations Anand Inamdar & A. French Arid Land Agricultural Research Center (ALARC/ARS/USDA)

Visible optical depth,  Optically thicker clouds correlate with colder tops Ship tracks Note, retrievals done on cloudy pixels which are spatially uniform.

4 th JCSDA Science Workshop, 31 May – 1 June 2006 Broadband Satellite-Like (Infrared) Cloud Products from NCEP Models and Preliminary Cloud Verification.

Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.

A. FY12-13 GIMPAP Project Proposal Title Page version 04 August 2011 Title: Fusing Goes Observations and RUC/RR Model Output for Improved Cloud Remote.

Satellite Data Assimilation Activities at CIMSS for FY2003 Robert M. Aune Advanced Satellite Products Team NOAA/NESDIS/ORA/ARAD Cooperative Institute for.

May 15, 2002MURI Hyperspectral Workshop1 Cloud and Aerosol Products From GIFTS/IOMI Gary Jedlovec and Sundar Christopher NASA Global Hydrology and Climate.

Preliminary results from the new AVHRR Pathfinder Atmospheres Extended (PATMOS-x) Data Set Andrew Heidinger a, Michael Pavolonis b and Mitch Goldberg a.

Data Distribution/dissemination Method

Roger A. Stocker 1 Jason E. Nachamkin 2 An overview of operational FNMOC mesoscale cloud forecast support 1 FNMOC: Fleet Numerical Meteorology & Oceanography.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Development of a visibility retrieval for the GOES-R Advanced Baseline.

CLAVR-x in CSPP Andrew Heidinger, NOAA/NESDIS/STAR, Madison WI

PATMOS-x Reflectance Calibration and Reflectance Time-Series

USING GOES-R TO HELP MONITOR UPPER LEVEL SO2

William Straka III and Christine Molling

PATMOS-x AVHRR Calibration Update

PATMOS-x AVHRR Solar Reflectance Calibration Update

Generation of Cloud Products from NOAA’s Operational Satellite Imagers

Andrew Heidinger and Michael Pavolonis

First Global 1km Operational Cloud Products from NESDIS

Andrew Heidinger JPSS Cloud Team Lead

Presentation transcript:

Cloud Fraction from Cloud Mask vs Total Sky Imager Comparison of 1 and 4 km Data The native resolution of the vis channel on GOES Imager is roughly 1 km and resolution of the thermal channels is 4 km. PATMOS-x processes both 1 and 4km GOES imager data. When processing 4km data, the vis channel is under-sampled. When processing 1 km data, the thermal channels are over-sampled. The images below show example of 1 and 4 km cloud optical thickness over OH/PA/WV on Day 88 of 2013 at 17:32Z. The vis channel is most relevant for solar energy and the benefits outweigh the errors introduced by the thermal channel sampling. We are assuming the closer we approach the spatial and temporal scales that impact solar energy plants, the better. High Resolution Cloud Products from GOES to Support the Solar Power Industry Andrew K Heidinger 1, Christine C Molling 2 1. STAR, NOAA/NESDIS, Madison, WI; 2. CIMSS/SSEC, University of Wisconsin, Madison, WI Introduction The Department of Energy’s SunShot Initiative seeks to make un- subsidized solar power as affordable as that generated by fossil fuels. One barrier to affordable solar power is the inability for large power generation facilities to forecast short-term power ramps caused by transient clouds, and the inability to accurately forecast power generation capacity 24 hours in advance. NOAA and the UW CIMSS are supporting the SunShot effort by providing high resolution cloud products derived from GOES imagery. While the standard NOAA operation products (GSIP) are generated hourly from 4 km data, the resolution of the data provided for SunShot will be 1 km (spatial) and 15 minute (temporal). GOAL IS TO AID THE DEVELOPMENT OF SHORT-TERM SOLAR ENERGY FORECATS MADE BY THE SUNSHOT TEAMS (NCAR & IBM). Methodology The PATMOS-x cloud algorithms are used in the NESDIS operational AVHRR and GOES cloud processing systems and based on the GOES- R AWG approaches. PATMOS-x will be run at UW/CIMSS using data from the SSEC Data Center. Relevant products are cloud optical depth, solar transmission/albedo, phase and height. PATMOS-x can also generate SASRAB solar fluxes incoordination with the SASRAB developer (Dr. Istvan Laszlo) Goal to generate products within 5 minutes using domains from GOES/East and GOES/West over the CONUS region. SURFRAD Comparison The validation of the PATMOS-x cloud properties for solar energy applications is accomplished via comparisons to the SURface RADiation Network (SURFRAD) run by NOAA/OAR/ESRL. SURFRAD is also a part of the NOAA SunShot Effort. SURFRAD provides downwelling/upwelling solar and thermal fluxes at the surface and other meteorological parameters. We derived a cloud transmission using the SURFRAD fluxes and a simple model of the clear-sky solar fluxes. Conclusions NOAA and UW/CIMSS are supporting an important DOE/NOAA solar energy initiative (SunShot). NOAA will provide GOES cloud products leveraging GOES-R AWG research to aid in the short-term forecasting of cloudiness over solar energy sites. The new 1 km results are shown to agree well with SURFRAD estimates of cloud transmission. Correlation with SURFRAD is higher using 1 km than 4 km results but more advanced comparison techniques are needed to better quantify this. Impact of Spatial Resolution on Performance These images show 3 days of cloud transmission comparisons from 1-km PATMOS-x and SURFRAD. SURFRAD sites are PSU, GWN and BON. 15-minute window for SURFRAD 10-km window for PATMOS-x Results are not-conclusive but correlations are higher with 1km vs. 4 km data. More advanced comparisons (use wind to adjust window) are being developed. Ramp Potential = Max – Min Transmission. RAMPS are large deviations in solar energy that represent a large forecasting issue for solar energy. 1 km 4 km Computation of Cloud Solar Transmission from SURFRAD Cloud transmission is a product from the PATMOS-x DCOMP algorithm. Converting SURFRAD fluxes into a cloud transmission allows for a direct comparison to the GOES-derived cloud transmission. This process is highlighted below. The TOA downward solar flux and the atmospheric transmission are modeled (blue lines). The Cloud Transmission = the Total Trans / The Non-Cloud Trans. Computation of Cloud Solar Transmission from SURFRAD Cloud transmission is a product from the PATMOS-x DCOMP algorithm. Converting SURFRAD fluxes into a cloud transmission allows for a direct comparison to the GOES-derived cloud transmission. This process is highlighted below. The TOA downward solar flux and the atmospheric transmission are modeled (blue lines). The Cloud Transmission = the Total Trans / The Non-Cloud Trans. 1km product resolves more cloud breaks. 1km (n=53) & 4km (n=4) are averaged over 5km box. * PATMOS-x products are more accurate than TSI at low sun angles. * Ohio West Virginia Pennsylvania Ohio West Virginia Pennsylvania