Ice crystal number concentration estimates from lidar–radar satellite remote sensing – Part 1: Method and evaluation
<p>The number concentration of cloud particles is a key quantity for understanding aerosol–cloud interactions and describing clouds in climate and numerical weather prediction models. In contrast with recent advances for liquid clouds, few observational constraints exist regarding the ice...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-10-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/18/14327/2018/acp-18-14327-2018.pdf |
| Summary: | <p>The number concentration of cloud particles is a
key quantity for understanding aerosol–cloud interactions and describing
clouds in climate and numerical weather prediction models. In contrast with
recent advances for liquid clouds, few observational constraints exist
regarding the ice crystal number concentration (<i>N</i><sub>i</sub>). This study
investigates how combined lidar–radar measurements can be used to provide
satellite estimates of <i>N</i><sub>i</sub>, using a methodology that constrains
moments of a parameterized particle size distribution (PSD). The operational
liDAR–raDAR (DARDAR) product serves as an existing base for this method,
which focuses on ice clouds with temperatures <i>T</i><sub>c</sub> < −30 °C.</p><p>Theoretical considerations demonstrate the capability for accurate retrievals
of <i>N</i><sub>i</sub>, apart from a possible bias in the concentration in small
crystals when <i>T</i><sub>c</sub><i>≳</i> − 50 °C, due to the assumption of
a monomodal PSD shape in the current method. This is verified via a
comparison of satellite estimates to coincident in situ measurements, which additionally
demonstrates the sufficient sensitivity of lidar–radar observations to
<i>N</i><sub>i</sub>. Following these results, satellite estimates of
<i>N</i><sub>i</sub> are evaluated in the context of a case study and a preliminary
climatological analysis based on 10 years of global data. Despite a lack
of other large-scale references, this evaluation shows a reasonable physical
consistency in <i>N</i><sub>i</sub> spatial distribution patterns. Notably,
increases in <i>N</i><sub>i</sub> are found towards cold temperatures and, more
significantly, in the presence of strong updrafts, such as those related to
convective or orographic uplifts. Further evaluation and improvement of this
method are necessary, although these results already constitute a first encouraging
step towards large-scale observational constraints for <i>N</i><sub>i</sub>. Part 2
of this series uses this new dataset to examine the controls on
<i>N</i><sub>i</sub>.</p> |
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| ISSN: | 1680-7316 1680-7324 |