Processing of Ice Cloud In-Situ Data Collected by Bulk Water, Scattering, and Imaging Probes: Fundamentals, Uncertainties and Efforts towards Consistency

Processing of Ice Cloud In-Situ Data Collected by Bulk Water, Scattering, and Imaging Probes:... AbstractIn-situ observations of cloud properties made by airborne probes play a critical role in ice cloud research through their role in process studies, parameterization development, and evaluation of simulations and remote sensing retrievals. To determine how cloud properties vary with environmental conditions, in-situ data collected during different field projects processed by different groups must be used. However, due to the diverse algorithms and codes that are used to process measurements, it can be challenging to compare the results. Therefore it is vital to understand both the limitations of specific probes and uncertainties introduced by processing algorithms. Since there is currently no universally accepted framework regarding how in-situ measurements should be processed, there is a need for a general reference that describes the most commonly applied algorithms along with their strengths and weaknesses.Methods used to process data from bulk water probes, single particle light scattering spectrometers and cloud imaging probes are reviewed herein, with emphasis on measurements of the ice phase. Particular attention is paid to how uncertainties, caveats and assumptions in processing algorithms affect derived products since there is currently no consensus on the optimal way of analyzing data. Recommendations for improving the analysis and interpretation of in-situ data include the following: establishment of a common reference library of individual processing algorithms; better documentation of assumptions used in these algorithms; development and maintenance of sustainable community software for processing in-situ observations; and more studies that compare different algorithms with the same benchmark data sets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Meteorological Monographs American Meteorological Society

Loading next page...
 
/lp/ams/processing-of-ice-cloud-in-situ-data-collected-by-bulk-water-uJ8kGPppQw
Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
0065-9401
D.O.I.
10.1175/AMSMONOGRAPHS-D-16-0007.1
Publisher site
See Article on Publisher Site

Abstract

AbstractIn-situ observations of cloud properties made by airborne probes play a critical role in ice cloud research through their role in process studies, parameterization development, and evaluation of simulations and remote sensing retrievals. To determine how cloud properties vary with environmental conditions, in-situ data collected during different field projects processed by different groups must be used. However, due to the diverse algorithms and codes that are used to process measurements, it can be challenging to compare the results. Therefore it is vital to understand both the limitations of specific probes and uncertainties introduced by processing algorithms. Since there is currently no universally accepted framework regarding how in-situ measurements should be processed, there is a need for a general reference that describes the most commonly applied algorithms along with their strengths and weaknesses.Methods used to process data from bulk water probes, single particle light scattering spectrometers and cloud imaging probes are reviewed herein, with emphasis on measurements of the ice phase. Particular attention is paid to how uncertainties, caveats and assumptions in processing algorithms affect derived products since there is currently no consensus on the optimal way of analyzing data. Recommendations for improving the analysis and interpretation of in-situ data include the following: establishment of a common reference library of individual processing algorithms; better documentation of assumptions used in these algorithms; development and maintenance of sustainable community software for processing in-situ observations; and more studies that compare different algorithms with the same benchmark data sets.

Journal

Meteorological MonographsAmerican Meteorological Society

Published: Aug 3, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off