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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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  03 May 2017

03 May 2017

Review status: this preprint was under review for the journal AMT but the revision was not accepted.

On the link between precipitation and the ice water path over tropical and mid-latitude regimes as derived from satellite observations

Yaniv Tubul, Ilan Koren, Orit Altaratz, and Reuven H. Heiblum Yaniv Tubul et al.
  • Department of Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot, Israel

Abstract. In this study, we explored the link between clouds’ integrated water content and surface rain rate (RR), focusing on deep convective clouds with iced tops. We used a 3-year (2006–2008) global dataset of cloud properties and precipitation rates retrieved from the MODerate-Resolution Imaging Spectroradiometer (MODIS) on Aqua and the Tropical Rain Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA). Previous studies focusing on marine stratocumulus clouds showed a robust link between liquid water path and drizzle rates, in the form of a power law. Consistent with this, we show a quantified link between ice water path (IWP) and surface RR. To reduce the problem to one non-dimensional variable (the power law exponent, β), the IWP and RR were normalized by their local means. We examined the geographical variability of β, and found its mean value to be 1.03 ± 0.13 (1.04 ± 0.12) in the tropics and 1.19 ±  0.19 (1.26 ± 0.20) over the mid-latitudes, during June–August (December–February). The results over the tropical belt showed the best correlation (R2 > 0.9) and lowest standard deviation values, thus the estimations of RR based on IWP measurements for this area are expected to be the most reliable. Such a method offers an estimation of RR using IWP information measured by passive polar-orbiting sensors (such as MODIS). Moreover, it can aid in parameterizing rain properties in regional and global climate models. To enable use of this method, we provide global maps (for June–August) of the required parameters to calculate RR using IWP data.

Yaniv Tubul et al.

Yaniv Tubul et al.

Yaniv Tubul et al.


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