ALBERT

Description: 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.

Description: The Fundamental Climate Data Record (FCDR) of Microwave Imager Radiances from the Satellite Application Facility on Climate Monitoring (CM SAF) comprises inter-calibrated and homogenised brightness temperatures from the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder SSMIS radiometers. It covers the time period from October 1978 to December 2015 including all available data from the SMMR radiometer aboard Nimbus-7 and all SSM/I and SSMIS radiometers aboard the Defence Meteorological Satellite Program (DMSP) platforms. SMMR, SSM/I and SSMIS data are used for a variety of applications, such as analyses of the hydrological cycle, remote sensing of sea ice or as input into reanalysis projects. The improved homogenisation and inter-calibration procedure ensures the long term stability of the FCDR for climate related applications. All available raw data records from different sources have been reprocessed to a common standard, starting with the calibration of the raw Earth counts, to ensure a completely homogenised data record. The data processing accounts for several known issues with the instruments and corrects calibration anomalies due to along-scan inhomogeneity, moonlight intrusions, sunlight intrusions, and emissive reflector. Corrections for SMMR are limited because the SMMR raw data records were not available. Furthermore, the inter-calibration model incorporates a scene dependent inter-satellite bias correction and a non-linearity correction to the instrument calibration. The data files contain all available original sensor data (SMMR: Pathfinder Level 1b) and meta-data to provide a completely traceable climate data record. Inter-calibration and Earth incidence angle normalisation offsets are available as additional layers within the data files in order to keep this information transparent to the users. The data record is complemented with noise equivalent temperatures (NeΔT), quality flags, surface types, and Earth incidence angles. The FCDR together with its full documentation, including evaluation results, is freely available at: https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V003 (Fennig et al., 2017).

Description: The Fundamental Climate Data Record (FCDR) of Microwave Imager Radiances from the Satellite Application Facility on Climate Monitoring (CM SAF) comprises inter-calibrated and homogenized brightness temperatures from the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder SSMIS radiometers. It covers the time period from October 1978 to December 2015 including all available data from the SMMR radiometer aboard Nimbus-7 and all SSM/I and SSMIS radiometers aboard the Defense Meteorological Satellite Program (DMSP) platforms. SMMR, SSM/I, and SSMIS data are used for a variety of applications, such as analyses of the hydrological cycle, remote sensing of sea ice, or as input into reanalysis projects. The improved homogenization and inter-calibration procedure ensures the long-term stability of the FCDR for climate-related applications. All available raw data records from different sources have been reprocessed to a common standard, starting with the calibration of the raw Earth counts, to ensure a completely homogenized data record. The data processing accounts for several known issues with the instruments and corrects calibration anomalies due to along-scan inhomogeneity, moonlight intrusions, sunlight intrusions, and emissive reflector. Corrections for SMMR are limited because the SMMR raw data records were not available. Furthermore, the inter-calibration model incorporates a scene dependent inter-satellite bias correction and a non-linearity correction in the instrument calibration. The data files contain all available original sensor data (SMMR: Pathfinder level 1b) and metadata to provide a completely traceable climate data record. Inter-calibration and Earth incidence angle normalization offsets are available as additional layers within the data files in order to keep this information transparent to the users. The data record is complemented with noise-equivalent temperatures (NeΔT), quality flags, surface types, and Earth incidence angles. The FCDR together with its full documentation, including evaluation results, is freely available at: https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V003 (Fennig et al., 2017).

Description: Latent heat fluxes (LHF) are one of the main contributors to the global energy budget. As the density of 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 include estimates of systematic, random retrieval, and sampling uncertainties, all of which are essential for assessing their quality. Here, this challenge is taken on by applying regionally independent multi-dimensional bias analyses to LHF-related parameters (wind speed U, near-surface specific humidity qa, and sea surface saturation specific humidity qs) of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) climatology. In connection with multiple triple collocation analyses, it is demonstrated how both instantaneous (gridded) uncertainty measures may be assigned to each pixel (grid box). A high-quality in situ data archive including buoys and selected ships serves as the ground reference. Results show that systematic LHF uncertainties range between 15–50 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 by qa (U) to the overall LHF uncertainty are in 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. Time series analyses show footprints of climate events, such as the strong El Niño during 1997/98. Regional and seasonal analyses suggest that largest total (i.e., systematic + instantaneous random) 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. First intercomparisons to other LHF climatologies (in situ, satellite) reveal overall resemblance with few, yet distinct exceptions.