Overview of Satellite-Derived Cirrus Properties During SPARTICUS and MACPEX P. Minnis, L. Nguyen NASA Langley Research Center, Hampton, VA R. Palikonda,

Slides:

Advertisements

Similar presentations

Robin Hogan Julien Delanoe University of Reading Remote sensing of ice clouds from space.

Advertisements

Retrieving Cloud Properties for Multilayered Clouds Using Simulated GOES-R Data Fu-Lung Chang 1, Patrick Minnis 2, Bing Lin 2, Rabindra Palikonda 3, Mandana.

Improved Automated Cloud Classification and Cloud Property Continuity Studies for the Visible/Infrared Imager/Radiometer Suite (VIIRS) Michael J. Pavolonis.

1 Satellite Imagery Interpretation. 2 The SKY Biggest lab in the world. Available to everyone. We view from below. Satellite views from above.

GOES-R Fog/Low Stratus (FLS) IFR Probability Product.

TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.

1 An initial CALIPSO cloud climatology ISCCP Anniversary, July 2008, New York Dave Winker NASA LaRC.

The SMHI AVHRR & Cloud Type dataset for CNN-I/II & BBC Adam Dybbroe The AVHRR dataset Navigation, Coverage, Re-mapping, Resolution The Cloud Type product.

1 A First Look at Mid-Level Clouds Using CloudSat, CALIPSO, and MODIS Data Stanley Q. Kidder, J. Adam Kankiewicz, Thomas H. Vonder Haar Cooperative Institute.

Satellite Rainfall Estimation Robert J. Kuligowski NOAA/NESDIS/STAR 3 October 2011 Workshop on Regional Flash Flood Guidance System—South America Santiago.

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

VIIRS Cloud Products Andrew Heidinger, Michael Pavolonis Corey Calvert

Remote Sensing of Mesoscale Vortices in Hurricane Eyewalls Presented by: Chris Castellano Brian Cerruti Stephen Garbarino.

Spatial & Temporal Distribution of Clouds as Observed by MODIS onboard the Terra and Aqua Satellites  MODIS atmosphere products –Examples from Aqua Cloud.

Profiling Clouds with Satellite Imager Data and Potential Applications William L. Smith Jr. 1, Douglas A. Spangenberg 2, Cecilia Fleeger 2, Patrick Minnis.

Fire Products Training Workshop in Partnership with BAAQMD Santa Clara, CA September 10 – 12, 2013 Applied Remote SEnsing Training (ARSET) – Air Quality.

Visible Satellite Imagery Spring 2015 ARSET - AQ Applied Remote Sensing Education and Training – Air Quality A project of NASA Applied Sciences Week –

Ten Years of Cloud Optical/Microphysical Measurements from MODIS M. D. King 1, S. Platnick 2, and the entire MOD06 Team 1 Laboratory for Atmospheric and.

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.

Possibility of stratospheric hydration by overshooting analyzed with space-borne sensors Suginori Iwasaki (National Defense Academy, Japan) T. Shibata.

Remote Sensing Allie Marquardt Collow Met Analysis – December 3, 2012.

Metr 415/715 Monday May Today’s Agenda 1.Basics of LIDAR - Ground based LIDAR (pointing up) - Air borne LIDAR (pointing down) - Space borne LIDAR.

Douglas Spangenberg, SSAI, Hampton, VA Rabi Palikonda, SSAI, Hampton, VA Louis Nguyen, NASA LaRC, Hampton VA MUG Meeting Sep

Testing of V1. GPM algorithm of rainfall retrieval from microwave brightness temperatures - preliminary results using TRMM observations Chuntao Liu Department.

Satellite Cloud Properties for CalWater 2 P. Minnis NASA LaRC.

UNCLASSIFIED Navy Applications of GOES-R Richard Crout, PhD Naval Meteorology and Oceanography Command Satellite Programs Presented to 3rd GOES-R Conference.

Overshooting Convective Cloud Top Detection A GOES-R Future Capability Product GOES-East (-8/-12/-13) OT Detections at Full Spatial and Temporal.

Orbit Characteristics and View Angle Effects on the Global Cloud Field

DoD Center for Geosciences/Atmospheric Research at Colorado State University Annual Review April 17-19, Development of Satellite Products for the.

16 UTC PIREPS Determination of Aircraft Icing Threat from Satellite William L. Smith, Jr. 1, Patrick Minnis 1, Stephanie Houser 2, Douglas A. Spangenberg.

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

Régis Borde Polar Winds EUMETRAIN Polar satellite week 2012 Régis Borde

Using Meteosat-8 SEVIRI as a Surrogate for Developing GOES-R Cloud Algorithms P. Minnis, W. L. Smith Jr., L. Nguyen, J. K. Ayers, D. R. Doelling, R. Palikonda,

CloudSat/VIIRS CBH Validation Activities at CIRA Curtis Seaman, Yoo-Jeong Noh, Steve Miller, Dan Lindsey 10 September 2012.

An Estimate of Contrail Coverage over the Contiguous United States David Duda, Konstantin Khlopenkov, Thad Chee SSAI, Hampton, VA Patrick Minnis NASA LaRC,

MODIS OCEAN QA Browse Imagery (MQABI Browse Tool) NASA Goddard Space Flight Center Sept 4, 2003

STAR JPSS 2015 Annual Science Team Meeting

The WMO Space Programme

Validation of Satellite-Retrieved Cloud Properties Using SEAC 4 RS Data P. Minnis, W. L. Smith, Jr., K. M. Bedka NASA Langley Research Center, Hampton,

LASE Measurements During IHOP Edward V. Browell, Syed Ismail, Richard A. Ferrare, Susan A Kooi, Anthony Notari, and Carolyn F. Butler NASA Langley Research.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Satellite Wind Products Presented.

Access to Real-time and Historical Satellite Products from a Mobile Application Thad Chee 1, Matthew Kelly 1, Louis Nguyen 2, Patrick Minnis 2, Rabindra.

A CERES-Consistent Cloud Property And Surface Temperature Climate Data Record Using AVHRR Data Patrick Minnis 1, Kristopher Bedka 2, Rabindra Palikonda.

CBH statistics for the Provisional Review Curtis Seaman, Yoo-Jeong Noh, Steve Miller and Dan Lindsey CIRA/Colorado State University 12/27/2013.

Yuying Zhang, Jim Boyle, and Steve Klein Program for Climate Model Diagnosis and Intercomparison Lawrence Livermore National Laboratory Jay Mace University.

Cloud Mask: Results, Frequency, Bit Mapping, and Validation UW Cloud Mask Working Group.

Preparing for GOES-R: old tools with new perspectives Bernadette Connell, CIRA CSU, Fort Collins, Colorado, USA ABSTRACT Creating.

Satellite based instability indices for very short range forecasting of convection Estelle de Coning South African Weather Service Contributions from Marianne.

Summary of User Feedback on GOES-R Air Quality Proving Ground Summer 2011 Experiment Shobha Kondragunta, Amy Huff, and Raymond Hoff Proving Ground All-Hands.

ASAP In-Flight Icing Research at NCAR J. Haggerty, F. McDonough, J. Black, S. Landolt, C. Wolff, and S. Mueller In collaboration with: P. Minnis and W.

Students as Ground Observers for Satellite Cloud Retrieval Validation 13th Conference on Satellite Meteorology & Oceanography Norfolk, VA Sept Lin.

MACPEX Planning Meeting, Houston, TX, 2-3 February 2011 NASA LaRC Satellite Cloud Remote Sensing & Field Mission/Satellite Support Patrick Minnis, Louis.

Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.

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

An Overview of Satellite Rainfall Estimation for Flash Flood Monitoring Timothy Love NOAA Climate Prediction Center with USAID- FEWS-NET, MFEWS, AFN Presented.

Comparison between aircraft and A-Train observations of midlevel, mixed-phase clouds from CLEX-10/C3VP Curtis Seaman, Yoo-Jeong Noh, Thomas Vonder Haar.

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

Methodology n Step 1: Identify MOG (EDR ≥ 0.25) observations at cruising altitude (≥ FL250). n Step 2: Account for ascending/descending flights by filtering.

Shaima Nasiri University of Wisconsin-Madison Bryan Baum NASA - Langley Research Center Detection of Overlapping Clouds with MODIS: TX-2002 MODIS Atmospheres.

NASA Langley Research Center / Atmospheric Sciences CERES Instantaneous Clear-sky and Monthly Averaged Radiance and Flux Product Overview David Young NASA.

Phil Arkin, ESSIC University of Maryland With thanks to: Pingping Xie, John Janowiak, and Bob Joyce Climate Prediction Center/NOAA Describing the Diurnal.

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

The study of cloud and aerosol properties during CalNex using newly developed spectral methods Patrick J. McBride, Samuel LeBlanc, K. Sebastian Schmidt,

W. Smith, D. Spangenberg, S. Sun-Mack, P.Minnis

Maritime Continent Convection

Cloud Property Retrievals over the Arctic from the NASA A-Train Satellites Aqua, CloudSat and CALIPSO Douglas Spangenberg1, Patrick Minnis2, Michele L.

Who We Are SSEC (Space Science and Engineering Center) is part of the Graduate School of the University of Wisconsin-Madison (UW). SSEC hosts CIMSS (Cooperative.

Generation of Cloud Products from NOAA’s Operational Satellite Imagers

Andrew Heidinger and Michael Pavolonis

Presentation transcript:

Overview of Satellite-Derived Cirrus Properties During SPARTICUS and MACPEX P. Minnis, L. Nguyen NASA Langley Research Center, Hampton, VA R. Palikonda, K. Bedka, T. Chee, D. A. Spangenberg, J. K. Ayers SSAI, Hampton, VA SEAC4RS Leadership Meeting Palmdale, CA, 31 January – 1 February 2012

OBJECTIVES Provide satellite complement to in situ, ground, and modeling studies Provide high temporal resolution imagery for experiments Provide consistent retrievals of cloud properties for experiments Provide real-time support in field Real-time imagery and interactive analysis - mission planning & execution Near-real time cloud retrievals

MTSAT 30 min (on the half hour), 4-km resolution , 3.9, 6.7, 10.8, 12.0 µm FY-2E 1 hr (on the hour), 4-km resolution (calib?) , 3.9, 6.7, 10.8, 12.0 µm Meteosat-7 30 min, 4-km resolution - GOES-13 (east): 0.73, 6.9, 11.5, µm MODIS, twice daily (Terra & Aqua), 1-km, multispectral NPP VIIRS, 1-km multispectral 1330 LT NOAA-15/16/18/19 AVHRR, later TRMM VIRS, TMI later Data

High-Cloud Statistics, SEAC4RS Domain 0.25°, MTSAT, Aug-Sept Frequency of OccurrenceFractional Coverage Good location for studying ice clouds Greatest amounts & frequencies over - Thailand, Cambodia, Bangladesh, & NE India - Gulf of Thailand, Bay of Bengal, Andaman Sea, E. South China Sea

High-Cloud Diurnal Cycle, SEAC4RS Domain 0.25°, MTSAT, Aug-Sept UTC = 0700 LT1200 UTC = 1900 UTC

Overshooting Tops, Example IR channel used to detect OT, Bedka method 1-km VIS MTSATIR Detections correspond to turrets in 1-km VIS imagery

Overshooting Tops, SEAC4RS Domain 0.25°, MTSAT, Aug-Sept Total OccurrencesDiurnal Behavior Most common over Thailand & Cambodia, S. China Sea, NE Indian coast Daytime peak over land Night peaks over coastal waters Mixed times over open waters

Overshooting Tops, SEAC4RS Domain 0.25°, MTSAT, Aug-Sept Occurrences, 9 AM – 9 PMOccurrences, 9 PM – 9AM 9 AM - 9 PM: most over land, W. Bay of Bengal, S. China Sea 9 PM – 9 AM: most over coastal water, S. China Sea

Overshooting Tops, SEAC4RS Domain 0.25°, MTSAT, Aug-Sept

Convective Cycle Summary Over land - Peak convection occurs at ~0900 UTC (~4 PM) near coasts - Peak convection an hour or two later deep inland - Peak convection at ~ 2 AM over eastern Thai lowlands & Nan River Basin - Minimal deep convection before 0730 UTC (~2:30 PM) Over water - Peak convection occurs at ~1400 UTC (~9 PM) near coasts - Peak convection moves seaward at later times - Peak convection during morning (6-10 AM) over N Bay of Bengal

Langley SEAC4RS Web Page angler.larc.nasa.gov angler.larc.nasa.gov Click on “SEAC4RS” on sidebar or from main SEAC4RS page Under construction, will add climatological info soon + MODIS, AVHRR, VIRS

Near-real time imagery from 3 GEOSats, 26 Jan 2012 FY-2, 0801 UTCMTSAT, 0832 UTC Met-7 IR, 1900 UMet-7 WV, 1900 U

MTSAT-2 Retrieval Domains, SEAC4RS 0832 UTC, 26 Jan 2012 Domain (4-km)Large Domain (8-km)

New Satellite Prediction Tool angler.larc.nasa.gov/gsp angler.larc.nasa.gov Will complement the old system

Example of Convective Object Tracking FY2-E, Sept, 2010 IRage TminOT vectorsstrong conv

Summary Capabilities on site will be much enhanced compared to online data - change resolution and domain as desired - provide exact locations of objects as needed - variety of loops and model overlays as needed - flight tracks in near real time (given the nav data) Add additional satellites to web page - MODIS, NPP, AVHRR, CALIPSO, CloudSat, TRMM Matching of data with flights and ground sites as it comes in - cloud products & radiances New algorithms to retrieve thin cirrus and ML clouds will be employed Add features as requested, if possible

August September Cloud Cover: 88% Multi-layer: 52% Above 15 km all cirrus are tenuous – IWC < g/m Km Most Cirrus are tenuous - IWC < 0.01 g/m3 At 10 km, most cirrus volumes have IWC > 0.05 g/m3 SEAC4RS A-Train Cloud Climatology ( ) 20 degree box centered on Bangkok.

August September Base Height- Layer Thickness Histograms 15% TTL all < 3 km thick 35% Cirrus 6-14 km > 3 km 10% Cirrus 6-10 km > 6 km Deep Echoes ~ 5% cover 20% Boundary Layer Cloud more or less evenly distributed in thickness Tendency for Cirrus to be lower and thicker in September