Cloud-Top Properties of Growing Cumulus prior to Convective Initiation as Measured by Meteosat Second Generation. Part I: Infrared Fields

Infrared (IR) data from the Meteosat Second Generation (MSG) satellite are used to understand cloud-top signatures for growing cumulus clouds prior to known convective initiation (CI) events, or the first occurrence of a $35-dBZ echo from a new convective cloud. In the process, this study proposes how MSG IR fields may be used to infer three physical attributes of growing cumuli, cloud depth, cloud-top glaciation, and updraft strength, with limited information redundancy. These three aspects are observed as unique signatures within MSG IR data, for which this study seeks to relate to previous research, as well as develop a new understanding on which subset of IR information best identifies these attributes. Data from 123 subjectively identified CI events observed during the 2007 Convection and Orograpically Induced Precipitation Study (COPS) field experiment conducted over southern Germany and northeastern France are processed, per convective cell, to meet this study’s objectives. A total of 67 IR ‘‘interest fields’’ are initially assessed for growing cumulus clouds, with correlation and principal component analyses used to highlight the top 21 fields that are considered the best candidates for describing the three attributes. Using between 6 and 8 fields per category, a method is then proposed on how growing convective clouds may be quantified per 3-km 2 pixel (or per cumulus cloud object) toward inferring each attribute. No independent CI-nowcasting analysis is performed, which instead is the subject of ongoing research.

[1]  Bryan A. Baum,et al.  Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS: 1. Data and models , 2000 .

[2]  J. Sadler,et al.  Deriving Surface Winds from Satellite Observations of Low-Level Cloud Motions , 1985 .

[3]  Kristopher M. Bedka,et al.  Convective cloud identification and classification in daytime satellite imagery using standard deviation limited adaptive clustering , 2008 .

[4]  Terri Betancourt,et al.  NCAR Auto-Nowcast System , 2003 .

[5]  Alfred J Prata,et al.  Observations of volcanic ash clouds in the 10-12 μm window using AVHRR/2 data , 1989 .

[6]  Steven A. Ackerman,et al.  High-Spectral- and High-Temporal-Resolution Infrared Measurements from Geostationary Orbit , 2009 .

[7]  Maurice Herman,et al.  Cloud thermodynamical phase classification from the POLDER spaceborne instrument , 2000 .

[8]  Johannes Schmetz,et al.  Monitoring deep convection and convective overshooting with METEOSAT , 1997 .

[9]  Kwo-Sen Kuo,et al.  A comparison of paired histogram, maximum likelihood, class elimination, and neural network approaches for daylight global cloud classification using AVHRR imagery , 1999 .

[10]  W. Petersen,et al.  The Tropical Convective Spectrum. Part I: Archetypal Vertical Structures , 2005 .

[11]  R. Welch,et al.  Automated Cloud Classification of Global AVHRR Data Using a Fuzzy Logic Approach , 1997 .

[12]  Kwo-Sen Kuo,et al.  Clustering, randomness, and regularity in cloud fields: 5. The nature of regular cumulus cloud fields , 1998 .

[13]  Gary P. Ellrod,et al.  Impact on volcanic ash detection caused by the loss of the 12.0 μm “Split Window” band on GOES Imagers , 2004 .

[14]  R. A. Rozumalski.  A Quantitative Assessment of the NESDIS Auto-Estimator , 2000 .

[15]  Bryan A. Baum,et al.  Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS: 3. Cloud Overlap , 2000 .

[16]  R. Scofield.  The NESDIS Operational Convective Precipitation- Estimation Technique , 1987 .

[17]  David A. Clark,et al.  Advanced Aviation Weather Forecasts , 2006 .

[18]  D. Starr,et al.  The Relationship between Anvil Clouds and Convective Cells: A Case Study in South Florida during CRYSTAL-FACE , 2008 .

[19]  Gary P. Ellrod,et al.  Advances in the Detection and Analysis of Fog at Night Using GOES Multispectral Infrared Imagery , 1995 .

[20]  D. McCann.  The Enhanced-V: A Satellite Observable Severe Storm Signature , 1983 .

[21]  Steven J. Nieman,et al.  Upper-Tropospheric Winds Derived from Geostationary Satellite Water Vapor Observations , 1997 .

[22]  Toshiro Inoue,et al.  A cloud type classification with NOAA 7 split‐window measurements , 1987 .

[23]  W. Paul Menzel,et al.  Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS: 2. Cloud thermodynamic phase , 2000 .

[24]  David B. Parsons,et al.  A Review of Convection Initiation and Motivation for IHOP_2002 , 2006 .

[25]  Michael J. Pavolonis,et al.  A Daytime Complement to the Reverse Absorption Technique for Improved Automated Detection of Volcanic Ash , 2006 .

[26]  W. Paul Menzel,et al.  Cloud Properties inferred from 812-µm Data , 1994 .

[27]  Steven J. Goodman,et al.  Three Years of TRMM Precipitation Features. Part I: Radar, Radiometric, and Lightning Characteristics , 2005 .

[28]  John R. Mecikalski,et al.  Application of Meteosat second generation data towards improving the nowcasting of convective initiation , 2010 .

[29]  Itamar M. Lensky,et al.  Clouds-Aerosols-Precipitation Satellite Analysis Tool (CAPSAT) , 2008 .

[30]  Fuqing Zhang,et al.  Mesoscale predictability of an extreme warm-season precipitation event , 2006 .

[31]  Steven A. Ackerman,et al.  Global Satellite Observations of Negative Brightness Temperature Differences between 11 and 6.7 µm , 1996 .

[32]  Kristopher M. Bedka,et al.  A Statistical Evaluation of GOES Cloud-Top Properties for Nowcasting Convective Initiation , 2008 .

[33]  J. Mecikalski,et al.  Forecasting Convective Initiation by Monitoring the Evolution of Moving Cumulus in Daytime GOES Imagery , 2004 .

[34]  Steven A. Ackerman,et al.  Radiation budget studies using collocated observations from advanced very high resolution radiometer, high‐resolution infrared sounder/2, and Earth Radiation Budget Experiment instruments , 1992 .

[35]  Robert F. Adler,et al.  Thunderstorm Intensity as Determined from Satellite Data , 1979 .

[36]  A. J. M. Jacobs,et al.  Numerical Guidance Methods for Decision Support in Aviation Meteorological Forecasting , 2005 .

[37]  Charles A. Doswell,et al.  Satellite observations of convective storm tops in the 1.6, 3.7 and 3.9 μm spectral bands , 2003 .

[38]  Volker Wulfmeyer,et al.  RESEARCH CAMPAIGN: The Convective and Orographically Induced Precipitation Study , 2008 .

[39]  Steven A. Rutledge,et al.  Nowcasting Storm Initiation and Growth Using GOES-8 and WSR-88D Data , 2003 .

[40]  Patrick Minnis,et al.  Aviation Applications for Satellite-Based Observations of Cloud Properties, Convection Initiation, In-Flight Icing, Turbulence, and Volcanic Ash , 2007 .

[41]  Dennis J. Boccippio,et al.  Thermodynamic Conditions Favorable to Superlative Thunderstorm Updraft, Mixed Phase Microphysics and Lightning Flash Rate , 2005 .

[42]  G. S. Wade,et al.  Application of GOES-8/9 Soundings to Weather Forecasting and Nowcasting. , 1998 .

[43]  Cynthia K. Mueller,et al.  The Utility of Sounding and Mesonet Data to Nowcast Thunderstorm Initiation , 1993 .

[44]  Lei Han,et al.  3D Convective Storm Identification, Tracking, and Forecasting—An Enhanced TITAN Algorithm , 2009 .

[45]  Milan Salek,et al.  Tornadoes within the Czech Republic: from early medieval chronicles to the "internet society" , 2003 .

[46]  R. Pielke,et al.  Convective Initiation at the Dryline: A Modeling Study , 1997 .

[47]  Thermodynamic, cloud microphysics, and rainfall responses to initial moisture perturbations in the tropical deep convective regime , 2006 .

[48]  J. Schmetz,et al.  AN INTRODUCTION TO METEOSAT SECOND GENERATION (MSG) , 2002 .

[49]  Toshiro Inoue,et al.  An Instantaneous Delineation of Convective Rainfall Areas Using Split Window Data of NOAH-7 AVHRR , 1987 .

[50]  R. Adler,et al.  Detection of Severe Midwest Thunderstorms Using Geosynchronous Satellite Data , 1985 .

[51]  Robert M. Rabin,et al.  A Quantitative Analysis of the Enhanced-V Feature in Relation to Severe Weather , 2007 .

[52]  Robert O. Knuteson,et al.  An Improvement to the High-Spectral-Resolution CO2-Slicing Cloud-Top Altitude Retrieval , 2006 .

[53]  Elizabeth E. Ebert,et al.  Sydney 2000 Forecast Demonstration Project: Convective Storm Nowcasting , 2004 .

[54]  Vincenzo Levizzani,et al.  Multispectral, high-resolution satellite observations of plumes on top of convective storms , 1996 .

[55]  Juanzhen Sun,et al.  Nowcasting Thunderstorms: A Status Report , 1998 .

[56]  V. Balaji,et al.  Scale selection in locally forced convective fields and the initiation of deep cumulus , 1988 .

[57]  Keith A. Browning,et al.  Initiation of Precipitation in Vigorous Convective Clouds , 1965 .

[58]  Arnold Tafferner,et al.  Cb-TRAM: Tracking and monitoring severe convection from onset over rapid development to mature phase using multi-channel Meteosat-8 SEVIRI data , 2008 .

[59]  GOES Climatology and Analysis of Thunderstorms with Enhanced 3.9-μm Reflectivity , 2006 .

[60]  Guy Kelman,et al.  Satellite detection of severe convective storms by their retrieved vertical profiles of cloud particle effective radius and thermodynamic phase , 2008 .

[61]  James F. W. Purdom,et al.  Some Uses of High-Resolution GOES Imagery in the Mesoscale Forecasting of Convection and Its Behavior , 1976 .