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Editorial Type:
Article
Article Type:
Research Article

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

Julian Kinzel1, Karsten Fennig1, Marc Schröder1, Axel Andersson1, Karl Bumke2, and Rainer Hollmann3
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  • 1 Satellite-based Climate Monitoring, Deutscher Wetterdienst, Offenbach Germany
  • | 2 GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
  • | 3 Satellite-based Climate Monitoring, Deutscher Wetterdienst, Offenbach, Germany
Print Publication:
01 Jul 2016
DOI:
https://doi.org/10.1175/JTECH-D-15-0122.1
Page(s):
1455–1471
Restricted access

Abstract

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 . However, the random retrieval error () remains unknown. Here, a novel approach is presented to quantify the error contributions to pixel-level 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. The MTC approach permits the derivation of as the sum of model uncertainty and sensor noise , while random uncertainties due to in situ measurement errors () and collocation () are isolated concurrently. Results show an average of 1.1 ± 0.3 g kg−1, whereas the mean () 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 exceeding 1.5 g kg−1 within humidity regimes of 12–17 g kg−1, associated with the single-parameter, multilinear retrieval applied in HOAPS. Multidimensional bias analysis reveals that global maxima are located off the Arabian Peninsula.

Corresponding author address: Julian Kinzel, Deutscher Wetterdienst, Frankfurter Strasse 135, 63067 Offenbach, Germany. E-mail: julian.kinzel@dwd.de

Abstract

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 . However, the random retrieval error () remains unknown. Here, a novel approach is presented to quantify the error contributions to pixel-level 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. The MTC approach permits the derivation of as the sum of model uncertainty and sensor noise , while random uncertainties due to in situ measurement errors () and collocation () are isolated concurrently. Results show an average of 1.1 ± 0.3 g kg−1, whereas the mean () 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 exceeding 1.5 g kg−1 within humidity regimes of 12–17 g kg−1, associated with the single-parameter, multilinear retrieval applied in HOAPS. Multidimensional bias analysis reveals that global maxima are located off the Arabian Peninsula.

Corresponding author address: Julian Kinzel, Deutscher Wetterdienst, Frankfurter Strasse 135, 63067 Offenbach, Germany. E-mail: julian.kinzel@dwd.de
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