Remote cloud ceiling assessment using data-mining methods

Abstract Data-mining methods are applied to numerical weather prediction (NWP) output and satellite data to develop automated algorithms for the diagnosis of cloud ceiling height in regions where no local observations are available at analysis time. A database of hourly records that include Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) output, satellite data, and ground truth observations [aviation routine weather reports (METAR)] has been created. Data were collected over a 2.5-yr period for specific locations in California. Data-mining techniques have been applied to the database to determine relationships in the collected physical parameters that best estimate cloud ceiling conditions, with an emphasis on low ceiling heights. Algorithm development resulted in a three-step approach: 1) determine if a cloud ceiling exists, 2) if a cloud ceiling is determined to exist, determine if the ceiling is high or low (below 1 000 m), and 3) if the cloud ceiling is determined to be low, compute ceil...

[1]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[2]  S. Djorgovski,et al.  Automated Star/Galaxy Classification for Digitized Poss-II , 1995 .

[3]  R. Hodur.  Development and Testing of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) , 1993 .

[4]  Valery J. Dagostaro,et al.  New NGM-Based MOS Guidance for Maximum/Minimum Temperature, Probability of Precipitation, Cloud Amount, and Surface Wind , 1990 .

[5]  Padhraic Smyth,et al.  Multiple Regimes in Northern Hemisphere Height Fields via MixtureModel Clustering* , 1999, Journal of the Atmospheric Sciences.

[6]  Thomas T. Warner,et al.  Nonhydrostatic, Mesobeta-Scale Model Simulations of Cloud Ceiling and Visibility for an East Coast Winter Precipitation Event , 1999 .

[7]  J. Louis.  A parametric model of vertical eddy fluxes in the atmosphere , 1979 .

[8]  Roger Davies,et al.  A fast radiation parameterization for atmospheric circulation models , 1987 .

[9]  R. Hodur.  The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) , 1997 .

[10]  Vipin Kumar,et al.  Discovery of climate indices using clustering , 2003, KDD '03.

[11]  F. Joseph Turk,et al.  Stratus and Fog Products Using GOES-8–9 3.9-μm Data , 1997 .

[12]  Robert L. Vislocky,et al.  An Automated, Observations-Based System for Short-Term Prediction of Ceiling and Visibility , 1997 .

[13]  S. Rutledge,et al.  The Mesoscale and Microscale Structure and Organization of Clouds and Precipitation in Midlatitude Cyclones. VIII: A Model for the “Seeder-Feeder” Process in Warm-Frontal Rainbands , 1983 .

[14]  G. Mellor,et al.  Development of a turbulence closure model for geophysical fluid problems , 1982 .

[15]  Hisashi Nakamura,et al.  Mining Geophysical Data for Knowledge , 1996, IEEE Expert.

[16]  Pietro Perona,et al.  Automating the hunt for volcanoes on Venus , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Donald L. Reinke,et al.  Cloud-Base Height Estimates Using a Combination of Meteorological Satellite Imagery and Surface Reports , 2000 .

[18]  Melanie A. Wetzel,et al.  Satellite‐observed patterns in stratus microphysics, aerosol optical thickness, and shortwave radiative forcing , 1999 .

[19]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[20]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[21]  D. Norquist.  Cloud Predictions Diagnosed from Mesoscale Weather Model Forecasts , 1999 .

[22]  Sholom M. Weiss,et al.  Predictive data mining - a practical guide , 1997 .

[23]  Tim R. Colvin,et al.  A unified lunar control network: The near side , 1987 .

[24]  C. Marzban.  Scalar measures of performance in rare-event situations , 1998 .

[25]  David Haussler,et al.  Mining scientific data , 1996, CACM.

[26]  Joe Turk.  Capabilities and Characteristics of Rainfall Estimates from Geostationary- and Geostationary+ Microwave-Based Satellite Techniques , 2001 .

[27]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[28]  Tetsuji Yamada,et al.  Simulations of Nocturnal Drainage Flows by a q2l Turbulence Closure Model , 1983 .