Uncertainty characterization of HOAPS 3.3 latent heat-flux-related parameters

Abstract

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.

GEWEX Water Vapor Assessment: Validation of AIRS Tropospheric Humidity Profiles With Characterized Radiosonde Soundings

“…A new approach to estimate the collocation uncertainty was introduced by Kinzel et al () and refined in Liman et al (). They estimate the collocation uncertainty as a function of TCWV, near‐surface specific humidity, near‐surface wind speed, and sea surface temperature.…”

Air-Sea Fluxes With a Focus on Heat and Momentum

“…ICOADS is also the main in situ data source for the construction of gridded analyses of surface marine ECVs and EOVs including those used as surface boundary conditions for atmospheric reanalyses (e.g., HadSST3: Kennedy et al, 2011;ERSSTv5: Huang et al, 2017;COBE-SST2: Hirahara et al, 2014;HadNMAT2: Kent et al, 2013). Additionally, ICOADS data are used for satellite data calibration and evaluation (e.g., Jackson and Wick, 2010;Liman et al, 2018;Tomita et al, 2018), as well as long-term regional reconstructions of surface fluxes (Gulev et al, 2013). ICOADS is an archive for observations but requires access to expertly managed data from each different network type.…”

Ship-Based Contributions to Global Ocean, Weather, and Climate Observing Systems

“…Satellitebased estimates of chlorophyll concentrations rely on in situ measurements of surface water optical properties to refine and calibrate retrieval algorithms (McClain, 2009;Brewin et al, 2016). In addition, in situ ship measurements are used to validate and evaluate products developed from one or more remote sensing systems (e.g., Bourassa et al, 2003;Liman et al, 2018). The remote sensing community is seeking observations taken at high temporal sampling rates (1 min or less) to support precise collocations with satellite measurements and from a wide range of ocean environments (e.g., tropics to polar regions and light winds to storm conditions) to help constrain the retrieval algorithms and ensure the measurements are accurate across all oceanic and atmospheric conditions.…”