DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, 2010 1 Statistical Extrapolation of Vertically.

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.
Aerosol-Precipitation Responses Deduced from Ship Tracks as Observed by CloudSat Matthew W. Christensen 1 and Graeme L. Stephens 2 Department of Atmospheric.
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 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.
Matthew Shupe Ola Persson Paul Johnston Cassie Wheeler Michael Tjernstrom Surface-Based Remote-Sensing of Clouds during ASCOS Univ of Colorado, NOAA and.
ATS 351 Lecture 8 Satellites
CoRP Symposium, 10-11August 2010, Fort Collins, CO 1 A daytime multispectral technique for detecting supercooled liquid water- topped mixed-phase clouds.
Lee Smith Anthony Illingworth
National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 1 Joao Teixeira, Brian.
Initial 3D isotropic fractal field An initial fractal cloud-like field can be generated by essentially performing an inverse 3D Fourier Transform on the.
MODIS Regional and Global Cloud Variability Brent C. Maddux 1,2 Steve Platnick 3, Steven A. Ackerman 1,2, Paul Menzel 1, Kathy Strabala 1, Richard Frey.
Profiling Clouds with Satellite Imager Data and Potential Applications William L. Smith Jr. 1, Douglas A. Spangenberg 2, Cecilia Fleeger 2, Patrick Minnis.
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
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.
MDSS Challenges, Research, and Managing User Expectations - Weather Issues - Bill Mahoney & Kevin Petty National Center for Atmospheric Research (NCAR)
Introduction and Methodology Daniel T. Lindsey*, NOAA/NESDIS/STAR/RAMMB Louie Grasso, Cooperative Institute for Research in the Atmosphere
Cyclone composites in the real world and ACCESS Pallavi Govekar, Christian Jakob, Michael Reeder and Jennifer Catto.
Possibility of stratospheric hydration by overshooting analyzed with space-borne sensors Suginori Iwasaki (National Defense Academy, Japan) T. Shibata.
Data Integration: Assessing the Value and Significance of New Observations and Products John Williams, NCAR Haig Iskenderian, MIT LL NASA Applied Sciences.
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
Evaluation of the VIIRS Cloud Base Height (CBH) EDR Using CloudSat
Diagnosing Climate Change from Satellite Sounding Measurements – From Filter Radiometers to Spectrometers William L. Smith Sr 1,2., Elisabeth Weisz 1,
Orbit Characteristics and View Angle Effects on the Global Cloud Field
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
New Products from combined MODIS/AIRS Jun Li, Chian-Yi Liu, Allen Huang, Xuebao Wu, and Liam Gumley Cooperative Institute for Meteorological Satellite.
National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 1 J. Teixeira(1), C. A.
Estimation of Cloud and Precipitation From Warm Clouds in Support of the ABI: A Pre-launch Study with A-Train Zhanqing Li, R. Chen, R. Kuligowski, R. Ferraro,
COSMIC GPS Radio Occultation Temperature Profiles in Clouds L. LIN AND X. ZOU The Florida State University, Tallahassee, Florida R. ANTHES University Corporation.
Hurricane Intensity Estimation from GOES-R Hyperspectral Environmental Suite Eye Sounding Fourth GOES-R Users’ Conference Mark DeMaria NESDIS/ORA-STAR,
1 of 26 Characterization of Atmospheric Aerosols using Integrated Multi-Sensor Earth Observations Presented by Ratish Menon (Roll Number ) PhD.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Improving Hurricane Intensity.
Towards Operational Satellite-based Detection and Short Term Nowcasting of Volcanic Ash* *There are research applications as well. Michael Pavolonis*,
STAR JPSS 2015 Annual Science Team Meeting
Andrew Heidinger and Michael Pavolonis
Water Vapour & Cloud from Satellite and the Earth's Radiation Balance
Vertical Structure of the Atmosphere within Clouds Revealed by COSMIC Data Xiaolei Zou, Li Lin Florida State University Rick Anthes, Bill Kuo, UCAR Fourth.
Matthew Shupe Ola Persson Paul Johnston Duane Hazen Clouds during ASCOS U. of Colorado and NOAA.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Using CALIPSO to Explore the Sensitivity to Cirrus Height in the Infrared.
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.
DoD Center for Geosciences/Atmospheric Research at Colorado State University Annual Review March 8-9, Mixed-phase clouds and icing research. Part.
Preparing for GOES-R: old tools with new perspectives Bernadette Connell, CIRA CSU, Fort Collins, Colorado, USA ABSTRACT Creating.
Use of Solar Reflectance Hyperspectral Data for Cloud Base Retrieval Andrew Heidinger, NOAA/NESDIS/ORA Washington D.C, USA Outline " Physical basis for.
Towards a Characterization of Arctic Mixed-Phase Clouds Matthew D. Shupe a, Pavlos Kollias b, Ed Luke b a Cooperative Institute for Research in Environmental.
Overview of Satellite-Derived Cirrus Properties During SPARTICUS and MACPEX P. Minnis, L. Nguyen NASA Langley Research Center, Hampton, VA R. Palikonda,
Initial Analysis of the Pixel-Level Uncertainties in Global MODIS Cloud Optical Thickness and Effective Particle Size Retrievals Steven Platnick 1, Robert.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
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.
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.
Understanding Cirrus Cloud Behavior using A-Train and Geostationary Satellite and NCEP/NCAR Reanalysis Data Betsy Dupont and Jay Mace, University of Utah.
Preliminary results from the new AVHRR Pathfinder Atmospheres Extended (PATMOS-x) Data Set Andrew Heidinger a, Michael Pavolonis b and Mitch Goldberg a.
1 GLIMPSING THE FIRST PRODUCTS FROM VIIRS Dr. Wayne Esaias NASA GSFC Thomas F. Lee Jeffrey Hawkins Arunas Kuciauskas Kim Richardson Jeremy Solbrig Naval.
MODIS, AIRS, and Midlevel Cloud Phase Shaima Nasiri CIMSS/SSEC, UW-Madison Brian Kahn Jet Propulsion Laboratory MURI Hyperspectral Workshop 7-9 June, 2005.
Shaima Nasiri University of Wisconsin-Madison Bryan Baum NASA - Langley Research Center Detection of Overlapping Clouds with MODIS: TX-2002 MODIS Atmospheres.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Combining GOES Observations with Other Data to Improve Severe Weather Forecasts.
NASA, CGMS-44, 7 June 2016 Coordination Group for Meteorological Satellites - CGMS LIMB CORRECTION OF POLAR- ORBITING IMAGERY FOR THE IMPROVED INTERPRETATION.
Roger A. Stocker 1 Jason E. Nachamkin 2 An overview of operational FNMOC mesoscale cloud forecast support 1 FNMOC: Fleet Numerical Meteorology & Oceanography.
Applications of the NPOESS Imagers Thomas F. Lee, Jeffrey Hawkins, F. Joseph Turk, Peter Gaiser, Mike Bettenhausen Naval Research Laboratory Monterey CA.
W. Smith, D. Spangenberg, S. Sun-Mack, P.Minnis
Matthew Christensen and Graeme Stephens
GOES-R Risk Reduction Research on Satellite-Derived Overshooting Tops
Relationships inferred from AIRS-CALIPSO synergy
Cloud Property Retrievals over the Arctic from the NASA A-Train Satellites Aqua, CloudSat and CALIPSO Douglas Spangenberg1, Patrick Minnis2, Michele L.
Winds in the Polar Regions from MODIS: Atmospheric Considerations
Hyperspectral Wind Retrievals Dave Santek Chris Velden CIMSS Madison, Wisconsin 5th Workshop on Hyperspectral Science 8 June 2005.
Andrew Heidinger and Michael Pavolonis
Mesoscale Convective Systems Observed by CloudSat
Presentation transcript:

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Statistical Extrapolation of Vertically Resolved Cloud Information from CloudSat/CALIPSO Observations to Regional Swaths John Forsythe, Steven D. Miller, Phil Partain and Tom Vonder Haar Cooperative Institute for Research in the Atmosphere (CIRA) Colorado State University Fort Collins, CO with Rich Bankert and Jeff Hawkins Naval Research Laboratory Monterey, CA CIRA

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Research Questions To what extent do limited (but detailed) observations of cloud vertical structure (water content) and geometric boundaries (top/base) relate to surrounding clouds? To what extent do limited (but detailed) observations of cloud vertical structure (water content) and geometric boundaries (top/base) relate to surrounding clouds? Can this concept be applied to ‘vertical-slice’ observations from active sensors (radar/lidar) to augment the information provided from passive sensor 2-D imagery? Can we use sparse cloud observations to create local 3-D cloud scenes? Can this concept be applied to ‘vertical-slice’ observations from active sensors (radar/lidar) to augment the information provided from passive sensor 2-D imagery? Can we use sparse cloud observations to create local 3-D cloud scenes? CIRA Hypothesis Certain kinds of clouds form under characteristic environmental conditions of temperature, moisture, stability, etc., that occur over regional/synoptic scales. Certain kinds of clouds form under characteristic environmental conditions of temperature, moisture, stability, etc., that occur over regional/synoptic scales. Local observations of clouds occurring within that regional/synoptic-scale environment may provide useful information about the surrounding cloud field. Local observations of clouds occurring within that regional/synoptic-scale environment may provide useful information about the surrounding cloud field. CloudSat (cloud radar) and CALIPSO (cloud lidar) have been providing vertical cloud profiles at ~ 1.1 km horizontal and 240 m vertical resolution since 2006.

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Relevance Cloud base estimation in data void regions UAV operations (visibility / icing) Model evaluation (e.g. scene contributions to the NCAR Model Evaluation Tool) Representativeness of data near taken CloudSat/CALIPSO track (like in a field experiment) CIRA

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Opaque Ice cloud Water cloud Target is under a Cirrus cloud, use these observations 30000’ 3000’ 10000’ Current Dilemma: Given sparse ceiling observations, how to determine the ceiling over a data-void region? x x x 1. Average the observations? 2. Be cautious and use the lowest value? 3. Use the nearest observation? 30000’ x 100 km (notional)

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, GSIP (GOES Surface and Insolation Product) Cloud Classification (applied to MODIS channels used here):   A 5-channel (0.65, 3.9, 6.7, 11, µm) physically- based pixel-level cloud classification with heritage from AVHRR and GOES (Pavolonis and Heidinger, 2005). These are the baseline channels for most contemporary geostationary meteorological satellites.   Additional published spectral tests using MODIS 1.38 and1.61 µm channels added. Visible / Infrared Cloud Typing to Provide Spatial Context CIRA

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, CIRA CloudSat Track 5 channel MODIS GSIP (“GOES Imager-like”) 5 channel MODIS GSIP µm and synthesized 1.61 µm MODIS-derived cloud type provides spatial context. The GSIP classifier (Pavolonis and Heidinger, 2005)) shown for Typhoon Choi-wan case, September 15, 2009.

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Typhoon Choi-wan MODIS Visible (view from east) with CloudSat / CALIPSO curtain overlaid in colors of MODIS cloud type. 10 km 0 km September 15, 2009 Question: What is the cloud vertical structure away from the curtain?

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Brief Review of Method to Gather Cloud Type Spatial Statistics

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, ) Form Geometric “Traces” Cirrus Top Base Cumulus Top Base Used Cloud Scenario Classification to compute departures in base/top height for contiguous cloud layers of a given cloud type, traced from a reference point. Orbital sub-segment example CIRA

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, ) Composite these Traces > 3 E+08 cloud layer samples CIRA

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, ) Compute Statistics on the Composites of Traces Cloud Top Cloud Base Distance (km) CIRA Statistics also collected by region, season and surface type

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, The Application Concept CIRRUS STRATOCUMULUS CUMULUS Cloud Type & Top Height: Retrieved Liquid Water Path (LWP): Retrieved 1) Estimate the cloud base height: 2) Distribute LWP between cloud Top and Base according to cloud type: CIRA

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Taking the Next Steps Towards 3-D Cloud Fields – Behavior of the GSIP Classifier

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Good news, supposed to be multilayer Deep clouds From MODIS cloud mask Where Modis Cloud Mask says cloudy, a cloud layer is almost always present (i.e. Number of Layers > 0) # of cloud layers (GEOPROF_LIDAR) by GSIP MODIS class. January 20, Daylight granules equatorward of 50°

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, channel classification # of cloud layers (GEOPROF_LIDAR) by GSIP MODIS class. January 20, Daylight granules equatorward of 50° 5 channel and 1.61 µm tests More Overlap Generated

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Aggregated cloud thickness (m) by GSIP MODIS class. January 20, Daylight granules equatorward of 50° Missed clouds are thin Almost always > 5 km thick “true” cirrus Lower cloud masked Very similar to Glaciated class Perhaps the most variable class clear Partly Cloudy LiquidSupercooled Opaque Ice CirrusOverlap 5-channel classification

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Summary A technique for providing vertically-resolved cloud information for the top-most layer(s) of passive imager swath data, based on cloud type dependent statistics from CloudSat/CALIPSO, has shown some skill. Early results show skill in prediction of cloud ceilings when applying the correlative approach and constraining cloud type. As the active datasets continue to grow, statistics for the stratified datasets will become increasingly robust. CIRA

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Backup Slides

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, In-class vs Out-of-class Data Denial Experiment Design Distance (km) along CloudSat track (1.1 km spacing between samples) Truth point: Class = Opaque Ice Opaque Ice Statistics for different class (dotted line) Statistics for same class (solid line)

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Predictions shown for lowest cloud base Percentage of lowest cloud bases within 1 km for members vs nonmembers of 5 GSIP cloud classes as a function of distance from predicted location. January 20, 2009 (14 CloudSat granules). Different Class: WaterSupercooledOpaque Ice Overlap Cirrus Distance between solid and dotted lines is justification for our hypothesis Same Class as Truth: KEY: Predictions Using

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Standard Deviation Fits Deep Convection Tops CIRA

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, How Far to Extend?  Computed cumulative density functions for cloud top/base ‘climatologies’ from CloudSat as F(zone,season,sfc_type). Approximate Gaussian distribution to obtain standard deviations. CIRA At some point we can’t beat climatology…so join it.

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Climatology-Based Limits Some variability with season & land/ocean found, but main variance tracks with latitude (  depth of troposphere) CIRA

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Example 1: Application Along the CloudSat Groundtrack

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Jan. 20, UTC East Pacific Case Study * * * * * * * * * * * * ** CIRA

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Altitude (AMSL, km) REF Altitude (AMSL, km) Distance Along Ground Track (km) Cloud Top Cloud Base Predicted Base/Top Predicted Base/Top Predicted by Withholding Observations Within 1km of Reference Point Observed base/top colored by cloud class

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Altitude (AMSL, km) Altitude (AMSL, km) Distance Along Ground Track (km) Cloud Top Cloud Base Predicted Base/Top Predicted Base/Top Predicted by Withholding Observations Within 100 km of Reference Point Observed base/top colored by cloud class REF

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Altitude (AMSL, km) Altitude (AMSL, km) Distance Along Ground Track (km) Cloud Top Cloud Base Predicted Base/Top Predicted Base/Top Predicted by Withholding Observations Within 200 km of Reference Point Observed base/top colored by cloud class REF

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Example 2: Toward “Off-CloudSat Groundtrack” Applications

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, CloudSat 2B GEOPROF-LIDAR profile overlaid on MODIS 11 µm image of atmospheric river. CloudSat track shown as purple line. GSIP class for each profile shown by color. Jan 20, 2009, 2150 UTC. 10 km 0 km

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Clear Cirrus Altostratus Altocumulus Stratocumulus Cumulus Nimbostratus Deep Convection Overlap Cirrus Opaque ice Supercooled/Mixed Liquid water CloudSat 2B-CLDCLASS Type at Top of Cloud GSIP MODIS Class January 20, All daylight granules equatorward of 50° latitude. Only cloudy cases from MODIS Cloud Mask shown. % (of clouds in each GSIP class)

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Cloud vertical occurrence by GSIP MODIS class. January 20, Daylight granules equatorward of 50° Only cases where MCM says clear, but has cloud layer Expected to be bimodal Missed cirrus

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Aggregated cloud thickness (m) by GSIP MODIS class. January 20, Daylight granules equatorward of 50° Missed clouds are thin Almost always > 5 km thick “true” cirrus Lower cloud masked Very similar to Glaciated class Perhaps the most variable class

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Good news, supposed to be multilayer Deep clouds From MODIS cloud mask Where Modis Cloud Mask says cloudy, a cloud layer is almost always present (i.e. Number of Layers > 0) # of cloud layers (GEOPROF_LIDAR) by GSIP MODIS class. January 20, Daylight granules equatorward of 50°

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Predictions shown for base of topmost layer Percentage of highest cloud bases within 1 km for members vs non-members of 5 GSIP cloud classes as a function of distance from predicted location. January 20, 2009 (14 CloudSat granules). Same class: Any class: Different class: WaterSupercooled Opaque Ice OverlapCirrus Distance between solid and dotted lines is justification for using GSIP classes Less skill for cirrus and overlap classes, requires more work Solid: “How well we potentially could do” Dashed: “How well a forecaster can do now” KEY: Predictions Using

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Next Steps Encode the range-dependent limits of application (based on climatology analyses). Introduce refined statistics based on zones, season, etc. Refine the GSIP software to incorporate more information from MODIS (e.g., 1.38 micrometer band) to improve cloud classification. Conduct reanalysis when the combined CloudSat + CALIPSO cloud classifier becomes available. CIRA

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, TARGET Obs. class: opaque ice. Predicted ceiling: 19750’ Predicted ice profile also supplied. Obs. class = opaque ice Obs. ceiling = 20000’ 21000’ ceiling over carrier; class = opaque ice Obs. class = opaque ice Obs. ceiling = 19500’ ! ! Forecast models have difficulties predicting cloud cover (horizontal and vertical extent) and water content. CONCEPT: Take cloud observations in friendly areas, extend them into data-denied areas. AFGHANISTAN x x x

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, CIRA Water Content Vertical Structure Image Courtesy of UCAR * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Using CloudSat Level 2 Cloud Water Content (2B-CWC; R03) and Cloud Scenario Classification (2B-CLDCLASS) products, we computed liquid/ice water content (g/m 3 ) profiles for each cloud type e+06 samples Water Content (Normalized) Height (Normalized) Top Base

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Observed base/top colored by cloud class Cloud Tops 1 km Exclusion Observed (km) 100 km Exclusion 200 km Exclusion Observed (km)

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Observed base/top colored by cloud class 1 km Exclusion 100 km Exclusion 200 km Exclusion Observed (km) Lowest Cloud Base

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Landscape of CG/AR Extended Cloud Statistics Work Create truth datasets for cloud forecast model verification. Aligns with AWFA ACAPS effort via scene contributions to the NCAR Model Evaluation Tool (MET). Three year NASA project underway to use A-Train datasets for model evaluation. Current CG/AR work: Create methodology to nowcast (0-3 hr) cloud base in data-denied areas from sparse observations. Many applications such as WRE-N and UAV routing tool. Improve science understanding of cloud vertical occurrence and remote sensing techniques. Connects with NRL Monterey work on cloud classification (Miller, Bankert, Mitrescu et al.) CIRA

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, Vertical Structure Statistics Vertical structure consistent with expected LWC profiles of convective/stratiform types - - Cirrus: growth of IWC in fall streaks prior to sublimation - - Cumulus: growth of droplets in ascending air CIRA

DoD Center for Geosciences/Atmospheric Research at Colorado State University CoRP Symposium August 10-11, ) Stratify Composites CirrusCumulus Distance (km) Tops Bases 90  75  45  15  -15  -45  -75  -90  NHEM1 NHEM2 NHEM3 SHEM1 SHEM2 SHEM3 TROP CIRA