NOAA-CoRP Symposium, Aug 15-18, 2006 Radiative Transfer Modeling for Simulating GOES-R imagery Manajit Sengupta 1 Contributions from: Louie Grasso 1, Jack.

Slides:

Advertisements

Similar presentations

Estimation of clouds in atmospheric models Tomislava Vukicevic CIRA/CSU and PAOS/CU.

Advertisements

The µm Band: A More Appropriate Window Band for the GOES-R ABI Than 11.2 µm? Daniel T. Lindsey, STAR/CoRP/RAMMB Wayne M. MacKenzie, Jr., Earth Resources.

Simulating cloud-microphysical processes in CRCM5 Ping Du, Éric Girard, Jean-Pierre Blanchet.

A short term rainfall prediction algorithm Nazario D. Ramirez and Joan Manuel Castro University of Puerto Rico NOAA Collaborator: Robert J. Kuligowski.

Severe Weather Applications with GOES-R: What will severe weather look like through the “eyes” of GOES-R? Dan Lindsey NOAA/NESDIS/STAR/RAMMB Fort Collins,

CoRP Symposium, 10-11August 2010, Fort Collins, CO 1 A daytime multispectral technique for detecting supercooled liquid water- topped mixed-phase clouds.

ARM Atmospheric Radiation Measurement Program. 2 Improve the performance of general circulation models (GCMs) used for climate research and prediction.

Numerical Simulations of Snowpack Augmentation for Drought Mitigation Studies in the Colorado Rocky Mountains William R. Cotton, Ray McAnelly, and Gustavo.

Synthetic satellite imagery Louie Grasso Cooperative Institute for Research in the Atmosphere NOAA/NESDIS CoRP August 2006.

MODIS CLOUD RETRIEVAL: A presentation based on this document.

32-bit 1) 1997: AIX operating system, 196 Mbytes of ram, few hundred Mbytes of disk space, RAMS-3b. 2) 1998: Linux operating system, 512 Mbytes of ram,

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.

GOES-R Synthetic Imagery over Alaska Dan Lindsey NOAA/NESDIS, SaTellite Applications and Research (STAR) Regional And Mesoscale Meteorology Branch (RAMMB)

Introduction and Methodology Daniel T. Lindsey*, NOAA/NESDIS/STAR/RAMMB Louie Grasso, Cooperative Institute for Research in the Atmosphere

Synthetic Satellite Imagery: A New Tool for GOES-R User Readiness and Cloud Forecast Visualization Dan Lindsey NOAA/NESDIS, SaTellite Applications and.

1 JCSDA Community Radiative Transfer Model (CRTM) Paul van Delst (CIMSS) Yong Han (NESDIS) Quanhua Liu (QSS) 5 th MURI Workshop 7-9 June 2005 Madison WI.

Mesoscale Modeling Review the tutorial at: –In class.

Rick Russotto Dept. of Atmospheric Sciences, Univ. of Washington With: Tom Ackerman Dale Durran ATTREX Science Team Meeting Boulder, CO October 21, 2014.

FIM Cloud Visualization for SOS and TerraViz Steve Albers.

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

In this work we present results of cloud electrification obtained with the RAMS model that includes the process of charge separation between ice particles.

An Example of the use of Synthetic 3.9 µm GOES-12 Imagery for Two- Moment Microphysical Evaluation Lewis D. Grasso (1) Cooperative Institute for Research.

Test simulation of Aerosol impact on solar radiation with WRF-CHEM DustDNI (w/o dust) – DNI (w/ dust) Positive value indicates the decreased DNI due to.

Progress in Community Radiative Transfer Model for Satellite Radiance Assimilation Quanhua (Mark) Liu 1,2, Paul van Delst 1,3, Ming Chen 1, David Groff.

The GOES-R Algorithm Working Group (AWG) program requests a high quality of proxy data for algorithm developments, testing and assessments. The central.

A Simple Model of the Mm-wave Scattering Parameters of Randomly Oriented Aggregates of Finite Cylindrical Ice Hydrometeors : An End-Run Around the Snow.

Bin resolved modeling of ice microphysics Wolfram Wobrock, Andrea Flossmann Workshop on Measurement Problems in Ice Clouds Zurich, Switzerland July 5-6,

GOES-R ABI New Product Development Donald W. Hillger NOAA/NESDIS, SaTellite Applications and Research (STAR) Regional And Mesoscale Meteorology Branch.

Jack Dostalek Louie Grasso Manajit Sengupta Mark DeMaria Synthetic GOES-R and NPOESS Imagery of Mesoscale Weather Events Overview This poster contains.

 Comparison of predicted radar reflectivity (Z) with observations, such as e.g. from the W-band ARM Cloud Radar (WACR), is an essential part of verifying.

Development of Severe Weather Products for the GOES-R Advanced Baseline Imager Introduction The Advanced Baseline Imager (ABI) aboard the GOES-R series.

GOES-R ABI Synthetic Imagery at 3.9 and 2.25 µm 24Feb2015 Poster 2 Louie Grasso, Yoo-Jeong Noh CIRA/Colorado State University, Fort Collins, CO

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

On the use of Synthetic Satellite Imagery to Evaluate Numerically Simulated Clouds Lewis D. Grasso (1) Cooperative Institute for Research in the Atmosphere,

Louie Grasso Daniel Lindsey A TECHNIQUE FOR COMPUTING HYDROMETEOR EFFECITVE RADIUS IN BINS OF A GAMMA DISTRIBUTION M anajit Sengupta INTRODUCTION As part.

Validation of HWRF forecasts with satellite observations Project team Tom Greenwald and James Davies CIMSS/UW Tomislava Vukicevic and Kathryn Sellwood.

P1.85 DEVELOPMENT OF SIMULATED GOES PRODUCTS FOR GFS AND NAM Hui-Ya Chuang and Brad Ferrier Environmental Modeling Center, NCEP, Washington DC Introduction.

Comparison of Evaporation and Cold Pool Development between Single- Moment (SM) and Multi-moment (MM) Bulk Microphysics Schemes In Idealized Simulations.

NOAA’s Next-Generation Polar and Geostationary Satellites – Hurricane Applications Ray Zehr, Mark DeMaria, John Knaff, Kimberly Mueller NOAA/NESDIS Office.

© Crown copyright Met Office Radiation scheme for Earth’s atmosphere …and what might not work for exoplanets James Manners 6/12/11.

Three real case simulations by Meso-NH validated against satellite observations J.-P. Chaboureau and J.-P. Pinty Laboratoire d’Aérologie, Toulouse 1.Elbe.

Improvement of Cold Season Land Precipitation Retrievals Through The Use of Field Campaign Data and High Frequency Microwave Radiative Transfer Model IPWG.

Cloud optical properties: modeling and sensitivity study Ping Yang Texas A&M University May 28,2003 Madison, Wisconsin.

Modeling GOES-R µm brightness temperature differences above cold thunderstorm tops Introduction As the time for the launch of GOES-R approaches,

A Statistical Assessment of Mesoscale Model Output using Computed Radiances and GOES Observations Manajit Sengupta 1, Louie Grasso 1, Daniel Lindsey 2.

What does radar measure? Hydrometeors: rain drops, ice particles Other objects: e.g. birds, insects.

Comparison Synthetic and Observed GOES-13 Imagery of hurricane Earl Lewis Grasso a Daniel T. Lindsey b Clark Evans c a Cooperative Institute for Research.

Dan Lindsey Louie Grasso Manajit Sengupta Mark DeMaria Synthetic GOES-R Imagery Development and Uses Introduction Synthetic GOES-R ABI imagery has been.

CIMSS Forward Model Capability to Support GOES-R Measurement Simulations Tom Greenwald, Hung-Lung (Allen) Huang, Dave Tobin, Ping Yang*, Leslie Moy, Erik.

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.

1 Atmospheric Radiation – Lecture 9 PHY Lecture 9 Infrared radiation in a cloudy atmosphere.

Real-time Display of Simulated GOES-R (ABI) Experimental Products Donald W. Hillger NOAA/NESDIS, SaTellite Applications and Research (STAR) Regional And.

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.

UCLA Vector Radiative Transfer Models for Application to Satellite Data Assimilation K. N. Liou, S. C. Ou, Y. Takano and Q. Yue Department of Atmospheric.

Jason Milbrandt Recherche en Prévision Numérique [RPN] Meteorological Research Division, Environment Canada GEM Workshop, June 12, 2007 Multi-Moment Cloud.

Overview of Community Radiative Transfer Model (CRTM) Fuzhong Weng, Yong Chen and Min-Jeong Kim NOAA/NESDIS/Center for Satellite Applications and Research.

GOES-R ABI AS A WARNING AID Louie Grasso, Renate Brummer, and Robert DeMaria CIRA, Fort Collins, CO Dan Lindsey, Don Hillger, NOAA/NESDIS/RAMMB, Fort Collins,

(A) Future of Radiation Parameterizations in CAM Bill Collins National Center for Atmospheric Research

Assimilating Cloudy Infrared Brightness Temperatures in High-Resolution Numerical Models Using Ensemble Data Assimilation Jason A. Otkin and Rebecca Cintineo.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Combining GOES Observations with Other Data to Improve Severe Weather Forecasts.

A. FY12-13 GIMPAP Project Proposal Title Page version 26 October 2011 Title: WRF Cloud and Moisture Verification with GOES Status: New GOES Utilization.

Generation of Simulated Atmospheric Datasets for Ingest into Radiative Transfer Models Jason A. Otkin, Derek J. Posselt, Erik R. Olson, and Raymond K.

Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes.

Developing Winter Precipitation Algorithm over Land from Satellite Microwave and C3VP Field Campaign Observations Fifth Workshop of the International Precipitation.

1 Detailed Microphysical Model Simulations of Freezing Drizzle Formation Istvan Geresdi Roy Rasmussen University of Pecs, Hungary NCAR Research funded.

Influences of Particle Bulk Density of Snow and Graupel in Microphysics-Consistent Microwave Brightness Temperature Simulations Research Group Meeting.

Synthetic GOES-R Imagery of Agricultural Burning and Forest Wildfires

Three-category ice scheme

Sensitivity of WRF microphysics to aerosol concentration

Comparison of Simulated Top of Atmosphere Radiance Datasets

Presentation transcript:

NOAA-CoRP Symposium, Aug 15-18, 2006 Radiative Transfer Modeling for Simulating GOES-R imagery Manajit Sengupta 1 Contributions from: Louie Grasso 1, Jack Dostalek 1, Dan Lindsey 2 and Mark DeMaria 2 1. CIRA/Colorado State University, 2. NOAA/NESDIS Fort Collins, CO

NOAA-CoRP Symposium, Aug 15-18, 2006 Methodology for simulating GOES-R imagery Mesoscale model output Inputs for radiative transfer Compute gaseous absorptionCompute cloud optical properties Compute GOES-R radiance or reflectance Forward Observational Operator

NOAA-CoRP Symposium, Aug 15-18, 2006 Mesoscale Model components for more accurate Radiative Transfer computations RAMS Numerical Cloud Model Non-hydrostatic cloud model developed at CSU Sophisticated two-moment cloud microphysics mass mixing ratio and number concentration are prognosed aggregates, graupel, hail, pristine ice, rain, snow, and cloud water Two-way interactive nested grids Four grids: 50 km, 10 km, 2 km (GOES-R) and 400 m (NPOESS). RAMS initial condition from NCEP ETA model analysis Run cloud model and use output as input to an observational operator to generate synthetic GOES-R satellite observations for different channels.

NOAA-CoRP Symposium, Aug 15-18, 2006 OPTRAN (part of JCSDA CRTM) used to calculate gaseous absorption GOES-R ABI coefficients obtained from JCSDA (3.9 – 13.3µm) Modified anomalous diffraction theory (MADT) for cloud optical properties assumes gamma distribution for the calculation of a mean diameter single scatter albedo, extinction coefficient, asymmetry factor for 7 hydrometeor types weighting by hydrometeor number concentration from RAMS for bulk optical properties Other possible methods for computing optical properties Mie theory for water drops Ice scattering tables from explicit computations. Observational Operator Highlights

NOAA-CoRP Symposium, Aug 15-18, 2006 Observational Operator Highlights Radiative transfer models Infrared Delta-Eddington scheme Visible and near-infrared Plane-parallel version of Spherical Harmonics Discrete Ordinate Method (SHDOMPP)

NOAA-CoRP Symposium, Aug 15-18, 2006 Case Studies May 8-9, 2003 :Severe thunderstorm outbreak over Oklahoma and Kansas. September 30-October 4, 2002: Hurricane Lili February 12-14, 2003: Lake effect snow

NOAA-CoRP Symposium, Aug 15-18, 2006 Synthetic 2 km GOES-R ABI Bands May Severe Weather Case 3.9 µm 6.2 µm 7.0 µm 7.3 µm 8.5 µm 9.6 µm µm 11.2 µm 12.3 µm 13.3 µm

NOAA-CoRP Symposium, Aug 15-18, 2006 Comparison of simulations from 3.9 µm with µm of GOES-R ABI 3.9 µm µm

NOAA-CoRP Symposium, Aug 15-18, 2006 Comparison of observations from Ch 3 (6.7 µm) of GOES-12 with Simulations ObservationsModeled May 8, 2003: thunderstorm outbreak

NOAA-CoRP Symposium, Aug 15-18, 2006 Comparison of observations from Ch 4 (10.7 µm) of GOES-12 with Simulations ObservationsModeled

NOAA-CoRP Symposium, Aug 15-18, 2006 Screened thunderstorm Brightness temperature <243 K observation model ObservationsModeled

NOAA-CoRP Symposium, Aug 15-18, 2006 Histograms of observed and modeled brightness temperatures ObservationsModel

NOAA-CoRP Symposium, Aug 15-18, 2006 Percentile scatter plot of 1 hour of observed versus modeled brightness temperatures Percentile Brightness temperature

NOAA-CoRP Symposium, Aug 15-18, 2006 Conclusions Mesoscale cloud model should contain 2 moment microphysics for cloud radiance/reflectance simulations. For simulation of all channels gaseous absorption coefficients should be available for OPTRAN. Optical properties for ice crystals and water droplets in clouds should be accurately computed. Radiative transfer modeling should be accurate but reasonably quick. Our current capabilities allow us to simulate different weather events.

NOAA-CoRP Symposium, Aug 15-18, 2006 Questions? Comments……….