Bayesian Sea Ice Detection With the ERS Scatterometer and Sea Ice Backscatter Model at C-Band

This paper describes the adaptation of a Bayesian sea ice detection algorithm for the scatterometer on-board the European Remote Sensing (ERS) satellites (ERS-1 and ERS-2). The algorithm is based on statistics of distances to ocean wind and sea ice geophysical model functions (GMFs) and its performance is validated against coincident active and passive microwave data. We furthermore propose a new model for sea ice backscatter at the C-band in vertical polarization based on the sea ice GMFs derived from ERS and advanced scatterometer data. The model characterizes the dependence of sea ice backscatter on the incidence angle and the sea ice type, allowing a more precise incidence angle correction than afforded by the usual linear transformation. The resulting agreement between the ERS, QuikSCAT, and special sensor microwave imager sea ice extents during the year 2000 is high during the fall and winter seasons, with an estimated ice edge accuracy of about 20 km, but shows persistent biases between scatterometer and radiometer extents during the melting period, with scatterometers being more sensitive to summer (lower concentration and rotten) sea ice types

Online approximations for wind-field models

We study online approximations to Gaussian process models for spatially distributed systems. We apply our method to the prediction of wind fields over the ocean surface from scatterometer data. Our approach combines a sequential update of a Gaussian approximation to the posterior with a sparse representation that allows to treat problems with a large number of observations

Characterization of precipitation product errors across the United States using multiplicative triple collocation

Validation of precipitation estimates from various products is a challenging problem, since the true precipitation is unknown. However, with the increased availability of precipitation estimates from a wide range of instruments (satellite, ground-based radar, and gauge), it is now possible to apply the triple collocation (TC) technique to characterize the uncertainties in each of the products. Classical TC takes advantage of three collocated data products of the same variable and estimates the mean squared error of each, without requiring knowledge of the truth. In this study, triplets among NEXRAD-IV, TRMM 3B42RT, GPCP 1DD, and GPI products are used to quantify the associated spatial error characteristics across a central part of the continental US. Data are aggregated to biweekly accumulations from January 2002 through April 2014 across a 2° × 2° spatial grid. This is the first study of its kind to explore precipitation estimation errors using TC across the US. A multiplicative (logarithmic) error model is incorporated in the original TC formulation to relate the precipitation estimates to the unknown truth. For precipitation application, this is more realistic than the additive error model used in the original TC derivations, which is generally appropriate for existing applications such as in the case of wind vector components and soil moisture comparisons. This study provides error estimates of the precipitation products that can be incorporated into hydrological and meteorological models, especially those used in data assimilation. Physical interpretations of the error fields (related to topography, climate, etc.) are explored. The methodology presented in this study could be used to quantify the uncertainties associated with precipitation estimates from each of the constellations of GPM satellites. Such quantification is prerequisite to optimally merging these estimates

Identifying coffee: development of a low-cost and robust barcoding assay for wild African Coffea species

With an estimated consumption of more than two billion cups a day, coffee is one of the most popular beverages in the world. Nearly all coffee is produced from the seeds of two species: Coffea arabica (Arabica coffee) and Coffea canephora (Robusta coffee). Both Arabica and Robusta coffee production is threatened by climate fluctuations and disease outbreaks, reducing yields and ravaging coffee plantations. To overcome these challenges, the potential of other wild Coffea species for the improvement of existing coffee varieties or for the development of new varieties has been studied. The Coffea genus consists of circa 130 described species that are mainly found in sub-Saharan Africa and Madagascar. Coffea species on the African continent are more closely related to Arabica and Robusta coffee. Nevertheless, the identification of African Coffea species at species level based on morphological traits can be challenging as several species seem to have overlapping trait characteristics. In this study, we developed a molecular barcoding assay consisting of eight nuclear markers between ca 200 and 800 base pairs long that can be sequenced using Sanger sequencing. Marker regions were selected based on the output of publicly available genotyping-by-sequencing data, ensuring that each Coffea species included in this dataset had a unique allele for at least two out of eight markers. The resulting barcoding assay is a cost-efficient and accessible tool for the molecular identification of wild African Coffea species, facilitating their conservation and their application for the improvement of coffee cultivation

A scatterometer record of sea ice extents and backscatter: 1992–2016

This paper presents the first long-term climate data record of sea ice extents and backscatter derived from intercalibrated satellite scatterometer missions (ERS, QuikSCAT and ASCAT) extending from 1992 to present date. This record provides a valuable independent account of the evolution of Arctic and Antarctic sea ice extents, one that is in excellent agreement with the passive microwave records during the fall and winter months but shows higher sensitivity to lower concentration and melting sea ice during the spring and summer months. The scatterometer record also provides a depiction of sea ice backscatter at C and Ku-band, allowing the separation of seasonal and perennial sea ice in the Arctic, and further differentiation between second year (SY) and older multiyear (MY) ice classes, revealing the emergence of SY ice as the dominant perennial ice type after the historical sea ice loss in 2007, and bearing new evidence on the loss of multiyear ice in the Arctic over the last 25 years. The relative good agreement between the backscatter-based sea ice (FY, SY and older MY) classes and the ice thickness record from Cryosat suggests its applicability as a reliable proxy in the historical reconstruction of sea ice thickness in the Arctic

ESA's wind Lidar mission ADM-AEOLUS; on-going scientific activities related to calibration, retrieval and instrument operation

The Earth Explorer Atmospheric Dynamics Mission (ADM-Aeolus) of ESA will be the first-ever satellite to provide global observations of wind profiles from space. Its single payload, namely the Atmospheric Laser Doppler Instrument (ALADIN) is a directdetection high spectral resolution Doppler Wind Lidar (DWL), operating at 355 nm, with a fringe-imaging receiver (analysing aerosol and cloud backscatter) and a double-edge receiver (analysing molecular backscatter). In order to meet the stringent mission requirements on wind retrieval, ESA is conducting various science support activities for the consolidation of the on-ground data processing, calibration and sampling strategies. Results from a recent laboratory experiment to study Rayleigh-Brillouin scattering and improve the characterisation of the molecular lidar backscatter signal detected by the ALADIN double-edge Fabry- Perot receiver will be presented in this paper. The experiment produced the most accurate ever-measured Rayleigh-Brillouin scattering profiles for a range of temperature, pressure and gases, representative of Earth’s atmosphere. The measurements were used to validate the Tenti S6 model, which is implemented in the ADM-Aeolus ground processor. First results from the on-going Vertical Aeolus Measurement Positioning (VAMP) study will be also reported. This second study aims at the optimisation of the ADM-Aeolus vertical sampling in order to maximise the information content of the retrieved winds, taking into account the atmospheric dynamical and optical heterogeneity. The impact of the Aeolus wind profiles on Numerical Weather Prediction (NWP) and stratospheric circulation modelling for the different vertical sampling strategies is also being estimated