29th IOAS-AOLS Poster Session I

Monday, 13 January 2025: 3:00 PM-4:30 PM
New Orleans Ernest N. Morial Convention Center
Host: 29th Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS)
Chair:
Zhaoxia Pu, University of Utah, Department of Atmospheric Sciences, Salt Lake City, UT
CoChair:
Robert M. Atlas, AOML, Miami, FL

Papers:
75
Release of Version 2 of an all in-situ high-resolution Northeast Pacific Regional Ocean Climatology (NEPv2)
Courtney Bouchard, NOAA, Stennis Space Center, MS; and A. Mishonov, O. Baranova, T. Boyer, J. Webster, S. Cross, and E. Nyadjro

76
Data Assimilation Tools to Advance Earth System Science Research Across Scales: The Newest Developments in NSF NCAR's Data Assimilation Research Testbed
Kevin Raeder, NCAR, Boulder, CO; and D. Amrhein, J. Anderson, B. Gaubert, M. Gharamti, I. Grooms, B. K. Johnson, H. Kershaw, C. Merchant, A. Norcio, N. Pedatella, A. Q. Pham, B. Raczka, S. shekhar, and M. Smith

77
A Regime-Based Assessment of GNSS Radio Occultation Retrievals in the Planetary Boundary Layer using ARM Radiosondes and Ceilometer Data
Kevin J. Nelson, Jet Propulsion Laboratory, Pasadena, CA; and K. N. Wang and C. O. Ao

78
Characterizing Near-Surface Moisture in Marine Planetary Boundary Layer Using GNSS-RO Grazing Reflection Observations
Kuo-Nung Wang, JPL, Pasadena, CA

79
Comparing Alternative Base Glider Distributions for Collecting Observations Under Gulf of Mexico Hurricanes
Ashton William Lawrence, NRL, Covington, LA

80
An Observation Validation System for Verification and Diagnosis Data Assimilation of the KIM Model.
Min-Woo Choi, Korea Institute of Atmospheric prediction systems, Seoul, South korea

81
The Effectiveness of Machine Learning Methods in The Nonlinear Coupled Data Assimilation
Ziying Xuan, IAP, Beijing, China

82
Ocean Multi-Scale Data Assimilation Using Scale-Dependent Vertical Covariances
Andrew J Iversen, Peraton, Reston, VA; and J. M. D'Addezio, G. A. Jacobs, R. W. Helber, and M. J. Carrier

83
Development of a new 4DVAR Global Ocean Data Assimilation System (MOVE/MRI.COM-G4) and a new bias correction scheme in the Japan Meteorological Agency
Masashi Sumitomo, JMA, Tsukuba, 08, Japan; and T. Yoshida and Y. Fujii

Handout (732.1 kB)

84
Geostationary Satellite-measured Land Surface Temperature Measurement Statistical Comparisons with in situ Soil Temperature Observations for Land Data Assimilation
John B. Eylander, Cold Regions Research and Engineering Lab, Hanover, NH; and W. J. Capehart, T. Letcher, and S. V. Kumar, PhD

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