Decomposition of Random Errors Inherent to HOAPS-3.2 Near-Surface Humidity Estimates Using Multiple Triple Collocation Analysis

Latent heat fluxes (LHF) play an essential role in the global energy budget and are thus important for understanding the climate system. Satellite-based remote sensing permits a large-scale determination of LHF, which, among others, are based on near-surface specific humidity q(a). However, the q(a) random retrieval error (E-tot) remains unknown. Here, a novel approach is presented to quantify the error contributions to pixel-level q(a) of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data, version 3.2 (HOAPS, version 3.2), dataset. The methodology makes use of multiple triple collocation (MTC) analysis between 1995 and 2008 over the global ice-free oceans. Apart from satellite records, these datasets include selected ship records extracted from the Seewetteramt Hamburg (SWA) archive and the International Comprehensive Ocean-Atmosphere Data Set (ICOADS), serving as the in situ ground reference. TheMTCapproach permits the derivation of E-tot as the sum of model uncertainty E-M and sensor noise E-N, while random uncertainties due to in situ measurement errors (E-ins) and collocation (E-C) are isolated concurrently. Results show an E-tot average of 1.1 +/- 0.3 g kg(-1), whereas the mean E-C (E-ins) is in the order of 0.5 +/- 0.1 g kg(-1) (0.5 +/- 0.3 g kg(-1)). Regional analyses indicate a maximum of E-tot exceeding 1.5 g kg(-1) within humidity regimes of 12-17 g kg(-1), associated with the single-parameter, multilinear q(a) retrieval applied in HOAPS. Multidimensional bias analysis reveals that global maxima are located off the Arabian Peninsula.