Issue 10 Volume 41 May 16, 2003

20030034728 NASA Goddard Space Flight Center, Greenbelt, MD, USA
The Retrospective Iterated Analysis Scheme for Nonlinear Chaotic Dynamics
Todling, Ricardo; July 11, 2002; 12 pp.; In English; Original contains color illustrations; No Copyright; Avail: CASI; A03,
Hardcopy
Atmospheric data assimilation is the name scientists give to the techniques of blending atmospheric observations with
atmospheric model results to obtain an accurate idea of what the atmosphere looks like at any given time. Because two pieces
of information are used, observations and model results, the outcomes of data assimilation procedure should be better than
what one would get by using one of these two pieces of information alone. There is a number of different mathematical
techniques that fall under the data assimilation jargon. In theory most these techniques accomplish about the same thing. In
practice, however, slight differences in the approaches amount to faster algorithms in some cases, more economical algorithms
in other cases, and even give better overall results in yet some other cases because of practical uncertainties not accounted for
by theory. Therefore, the key is to find the most adequate data assimilation procedure for the problem in hand. In our Data
Assimilation group we have been doing extensive research to try and find just such data assimilation procedure. One promising
possibility is what we call retrospective iterated analysis (RIA) scheme. This procedure has recently been implemented and
studied in the context of a very large data assimilation system built to help predict and study weather and climate. Although
the results from that study suggest that the RIA scheme produces quite reasonable results, a complete evaluation of the scheme
is very difficult due to the complexity of that problem. The present work steps back a little bit and studies the behavior of the
RIA scheme in the context of a small problem. The problem is small enough to allow full assessment of the quality of the RIA
scheme, but it still has some of the complexity found in nature, namely, its chaotic-type behavior. We find that the RIA
performs very well for this small but still complex problem which is a result that seconds the results of our early studies.
Author
Atmospheric Models; Atmospheric Physics; Climate; Numerical Analysis; Data Processing; Data Reduction; Iterative
Solution
20030036980 California Energy Commission, Sacramento, ICF, Inc., Washington, DC
Inventory of California Greenhouse Gas Emissions and Sinks: 1990-1999
Birkinshaw, K.; Nov. 2002; In English
Report No.(s): PB2003-102668; PUB-600-02-001F; No Copyright; Avail: National Technical Information Service (NTIS)
In September 2000, the California Legislature passed Senate Bill 1771, Senator Sher (Chapter 1018, Statutes of 2000),
requiring the California Energy Commission (Commission), in consultation with other state agencies, to update California’s
inventory of greenhouse gas emissions in January 2002 and every five years thereafter. The inventory update is to include all
emission sources in the State that were identified in the Commission’s 1998 report, Historical and Forecasted Greenhouse Gas
Emissions Inventories for California. This report, Inventory of California Greenhouse Gas Emissions and Sinks: 1990-1999,
presents the Commission’s preliminary estimates of emissions and carbon sinks from 1990 to 1999. As Senate Bill 1771
requires, the report includes emissions of greenhouse gases and compares California’s emissions with the emissions from other
states and nations. Limited information was available to allow a complete and thorough analysis and discussion of the impact
of air quality and energy policies and programs on greenhouse gas emissions.
NTIS
Exhaust Emission; Exhaust Gases; Greenhouse Effect; Sinks; California; Inventory Management; Energy Technology;
Climatology
20030036985 Department of Energy, Washington, DC
Report on Broadband Solar Radiometer Inconsistencies at the Atmospheric Radiation Measurement (ARM) Southern
Great Plains (SGP) Central Facility During the ARM Enhanced Shortwave Experiment (ARESE)
Long, C. N.; 1996; 20 pp.
Report No.(s): PB2003-102674; ARM-TR-003; No Copyright; Avail: CASI; A03, Hardcopy
Broadband solar radiometer data collected at the U.S. Department of Energy’s Atmospheric Radiation Measurement
(ARM) Southern Great Plains (SGP) Central Facility during the ARM Enhanced Shortwave Experiment (ARESE) exhibits
inconsistencies and inter-calibration offsets. This report examines these problems, and in some cases, suggests error sources
and possible solutions. The data discussed here covers the period from September 28, 1995, through October 30, 1995. Prior
to that, the Baseline Surface Radiation Network (BSRN) radiometer data were not being logged for about 2 and one half
weeks. This problem was not rectified until we inquired about the data for September 27, 1995, in support of an ARESE
analysis. Most of the following discussion is based on 5-minute averages of the data. Site reports state that on October 13,
1995, the BSRN total shortwave (SW) pyranometer was changed to a newly calibrated one; therefore, much of the analysis
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