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

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Atmos. Meas. Tech., 11, 1793-1815, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
29 Mar 2018
Uncertainty characterization of HOAPS 3.3 latent heat-flux-related parameters
Julian Liman1, Marc Schröder1, Karsten Fennig1, Axel Andersson2, and Rainer Hollmann1 1Satellite-Based Climate Monitoring, Deutscher Wetterdienst, Frankfurter Straße 135, 63067 Offenbach, Germany
2Marine Data Centre, Deutscher Wetterdienst, Bernhard-Nocht-Straße 76, 20359 Hamburg, Germany
Abstract. Latent heat flux (LHF) is one of the main contributors to the global energy budget. As the density of in situ LHF measurements over the global oceans is generally poor, the potential of remotely sensed LHF for meteorological applications is enormous. However, to date none of the available satellite products have included estimates of systematic, random, and sampling uncertainties, all of which are essential for assessing their quality. Here, the challenge is taken on by matching LHF-related pixel-level data of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) climatology (version 3.3) to in situ measurements originating from a high-quality data archive of buoys and selected ships. Assuming the ground reference to be bias-free, this allows for deriving instantaneous systematic uncertainties as a function of four atmospheric predictor variables. The approach is regionally independent and therefore overcomes the issue of sparse in situ data densities over large oceanic areas. Likewise, random uncertainties are derived, which include not only a retrieval component but also contributions from in situ measurement noise and the collocation procedure. A recently published random uncertainty decomposition approach is applied to isolate the random retrieval uncertainty of all LHF-related HOAPS parameters. It makes use of two combinations of independent data triplets of both satellite and in situ data, which are analysed in terms of their pairwise variances of differences. Instantaneous uncertainties are finally aggregated, allowing for uncertainty characterizations on monthly to multi-annual timescales. Results show that systematic LHF uncertainties range between 15 and 50 W m−2 with a global mean of 25 W m−2. Local maxima are mainly found over the subtropical ocean basins as well as along the western boundary currents. Investigations indicate that contributions from qa (U) to the overall LHF uncertainty are on the order of 60 % (25 %). From an instantaneous point of view, random retrieval uncertainties are specifically large over the subtropics with a global average of 37 W m−2. In a climatological sense, their magnitudes become negligible, as do respective sampling uncertainties. Regional and seasonal analyses suggest that largest total LHF uncertainties are seen over the Gulf Stream and the Indian monsoon region during boreal winter. In light of the uncertainty measures, the observed continuous global mean LHF increase up to 2009 needs to be treated with caution. The demonstrated approach can easily be transferred to other satellite retrievals, which increases the significance of the present work.
Citation: Liman, J., Schröder, M., Fennig, K., Andersson, A., and Hollmann, R.: Uncertainty characterization of HOAPS 3.3 latent heat-flux-related parameters, Atmos. Meas. Tech., 11, 1793-1815,, 2018.
Publications Copernicus
Short summary
Latent heat fluxes (LHF) play a major role in the climate system. Over open ocean, they are increasingly observed by satellite instruments. To access their quality, this research focuses on thorough uncertainty analysis of all LHF-related variables of the HOAPS satellite climatology, in parts making use of novel analysis approaches. Results indicate climatological LHF uncertainies up to 50 W m−2, whereby underlying specific humidities tend to be more uncertain than contributing wind speeds.
Latent heat fluxes (LHF) play a major role in the climate system. Over open ocean, they are...