Retrieval of an ice water path over the ocean from ISMAR and MARSS millimeter and submillimeter brightness temperatures
Artikel i vetenskaplig tidskrift, 2018

A neural-network-based retrieval method to determine the snow ice water path (SIWP), liquid water path (LWP), and integrated water vapor (IWV) from millimeter and submillimeter brightness temperatures, measured by using airborne radiometers (ISMAR and MARSS), is presented. The neural networks were trained by using atmospheric profiles from the ICON numerical weather prediction (NWP) model and by radiative transfer simulations using the Atmospheric Radiative Transfer Simulator (ARTS). The basic performance of the retrieval method was analyzed in terms of offset (bias) and the median fractional error (MFE), and the benefit of using submillimeter channels was studied in comparison to pure microwave retrievals. The retrieval is offset-free for SIWP > 0.01kgm -2 , LWP > 0:1kgm -2 , and IWV > 3kgm -2 . The MFE of SIWP decreases from 100% at SIWPD 0.01kgm -2 to 20% at SIWPD 1kgm -2 and the MFE of LWP from 100% at LWP D 0.05kgm -2 to 30% at LWPD 1kgm -2 . The MFE of IWV for IWV > 3kgm -2 is 5 to 8%. The SIWP retrieval strongly benefits from submillimeter channels, which reduce the MFE by a factor of 2, compared to pure microwave retrievals. The IWV and the LWP retrievals also benefit from submillimeter channels, albeit to a lesser degree. The retrieval was applied to ISMAR and MARSS brightness temperatures from FAAM flight B897 on 18 March 2015 of a precipitating frontal system west of the coast of Iceland. Considering the given uncertainties, the retrieval is in reasonable agreement with the SIWP, LWP, and IWV values simulated by the ICON NWP model for that flight. A comparison of the retrieved IWV with IWV from 12 dropsonde measurements shows an offset of 0:5kgm -2 and an RMS difference of 0:8kgm -2 , showing that the retrieval of IWV is highly effective even under cloudy conditions.


brightness temperature

climate prediction

airborne survey


radiative transfer

atmospheric modeling

measurement method

water vapor

ice retreat

artificial neural network


M. Brath

Universität Hamburg

Patrick Eriksson

Chalmers, Rymd-, geo- och miljövetenskap, Mikrovågs- och optisk fjärranalys

R. Chawn Harlow

Met Office

Martin Burgdorf

Universität Hamburg

S.A. Buehler

Universität Hamburg

Atmospheric Measurement Techniques

1867-1381 (ISSN) 1867-8548 (eISSN)

Vol. 11 1 611-632


Meteorologi och atmosfärforskning


Oceanografi, hydrologi, vattenresurser



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