Assessing the impact of different liquid water permittivity models on the fit between model and observations

• Main Authors: K. Lonitz, A. J. Geer
• Format: Article
• Language: English
• Published: Copernicus Publications 2019-01-01
• Series: Atmospheric Measurement Techniques
• Online Access: https://www.atmos-meas-tech.net/12/405/2019/amt-12-405-2019.pdf

<p>Permittivity models for microwave frequencies of liquid water below 0&thinsp;<span class="inline-formula">^∘</span>C (supercooled liquid water) are poorly constrained due to limited laboratory experiments and observations, especially for high microwave frequencies. This uncertainty translates directly into errors in retrieved liquid water paths of up to 80&thinsp;%. This study investigates the effect of different liquid water permittivity models on simulated brightness temperatures by using the all-sky assimilation framework of the Integrated Forecast System. Here, a model configuration with an improved representation of supercooled liquid water has been used. The comparison of five different permittivity models with the current one shows a small mean reduction in simulated brightness temperatures of at most 0.15&thinsp;K at 92&thinsp;GHz on a global monthly scale. During austral winter, differences occur more prominently in the storm tracks of the Southern Hemisphere and in the intertropical convergence zone with values of around 0.5 to 1.5&thinsp;K. Compared to the default <span class="cit" id="xref_text.1"><a href="#bib1.bibx21">Liebe</a> (<a href="#bib1.bibx21">1989</a>)</span> approach, the permittivity models of <span class="cit" id="xref_text.2"><a href="#bib1.bibx26">Stogryn et al.</a> (<a href="#bib1.bibx26">1995</a>)</span>, <span class="cit" id="xref_text.3"><a href="#bib1.bibx24">Rosenkranz</a> (<a href="#bib1.bibx24">2015</a>)</span> and <span class="cit" id="xref_text.4"><a href="#bib1.bibx27">Turner et al.</a> (<a href="#bib1.bibx27">2016</a>)</span> all improve fits between observations and all-sky brightness temperatures simulated by the Integrated Forecast System. In cycling data assimilation these newer models also give small improvements in short-range humidity forecasts when measured against independent observations. Of the three best-performing models, the <span class="cit" id="xref_text.5"><a href="#bib1.bibx26">Stogryn et al.</a> (<a href="#bib1.bibx26">1995</a>)</span> model is not quite as beneficial as the other two, except at 183&thinsp;GHz. At this frequency, <span class="cit" id="xref_text.6"><a href="#bib1.bibx24">Rosenkranz</a> (<a href="#bib1.bibx24">2015</a>)</span> and <span class="cit" id="xref_text.7"><a href="#bib1.bibx27">Turner et al.</a> (<a href="#bib1.bibx27">2016</a>)</span> look worse because they expose a scattering-related forward model bias in frontal regions. Overall, <span class="cit" id="xref_text.8"><a href="#bib1.bibx24">Rosenkranz</a> (<a href="#bib1.bibx24">2015</a>)</span> is favoured due to its validity up to 1&thinsp;THz, which will support future submillimetre missions.</p>