Ice crystal number concentration estimates from lidar–radar satellite remote sensing – Part 1: Method and evaluation

Odran Sourdeval, Edward Gryspeerdt, Martina Krämer, Tom Goren, Julien Delanoë, Armin Afchine, Friederike Hemmer, Johannes Quaas
2018 Atmospheric Chemistry and Physics  
<p><strong>Abstract.</strong> 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 (<span class="inline-formula"><i>N</i><sub>i</sub></span>). This study investigates how combined lidar–radar measurements can be used to provide
more » ... estimates of <span class="inline-formula"><i>N</i><sub>i</sub></span>, 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 <span class="inline-formula"><math xmlns="" id="M3" display="inline" overflow="scroll" dspmath="mathml"><mrow><msub><mi>T</mi><mi mathvariant="normal">c</mi></msub><mo>&amp;lt;</mo><mo>-</mo><mn mathvariant="normal">30</mn></mrow></math><span><svg:svg xmlns:svg="" width="44pt" height="12pt" class="svg-formula" dspmath="mathimg" md5hash="9068ff82db5137b466baf58cb4f6024e"><svg:image xmlns:xlink="" xlink:href="acp-18-14327-2018-ie00001.svg" width="44pt" height="12pt" src="acp-18-14327-2018-ie00001.png"/></svg:svg></span></span><span class="thinspace"></span><span class="inline-formula"><sup>∘</sup></span>C.</p> <p>Theoretical considerations demonstrate the capability for accurate retrievals of <span class="inline-formula"><i>N</i><sub>i</sub></span>, apart from a possible bias in the concentration in small crystals when <span class="inline-formula"><i>T</i><sub>c</sub><i>≳</i>−50</span><span class="thinspace"></span><span class="inline-formula"><sup>∘</sup></span>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 <span class="inline-formula"><i>N</i><sub>i</sub></span>. Following these results, satellite estimates of <span class="inline-formula"><i>N</i><sub>i</sub></span> 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 <span class="inline-formula"><i>N</i><sub>i</sub></span> spatial distribution patterns. Notably, increases in <span class="inline-formula"><i>N</i><sub>i</sub></span> 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 <span class="inline-formula"><i>N</i><sub>i</sub></span>. Part 2 of this series uses this new dataset to examine the controls on <span class="inline-formula"><i>N</i><sub>i</sub></span>.</p>
doi:10.5194/acp-18-14327-2018 fatcat:3b5nerdrxjg43bgefkflxzkr3e