1,410 research outputs found

    The use of spatial constraints in the derivation of mesoscale sea surface current fields from multi-sensor satellite data

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    An evaluation of novel remotely sensed data to improve and verify ocean- atmosphere forecasting.

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    The aim of this study is to evaluate the use of novel remote observations and spatial data analysis to improve the skill of an ocean forecasting system for the central Mediterranean Sea. A high-resolution (0.042 by 0.042ๆ ocean forecasting system was setup consisting of an atmosphere model (NCEP Eta model) that was coupled to an ocean model (Princeton Ocean Model). This coupling consisted of the provision of surface atmospheric fluxes predicted at 3-hourly intervals to drive forward the ocean model. This research study dealt with a variety of aspects to improve this forecasting system using an inter-disciplinary approach. The main aspect of this thesis is an evaluation of novel, remotely- sensed data acquired by an orbiting passive microwave sensor as a tool to assess and improve ocean forecasting. Thus, SST derived by the Tropical Microwave Imager onboard the TRMM satellite was evaluated for its potential to define one of the lower boundary conditions of the Eta model. The impact was positive, and resulted in an average improvement of the skill of the model to predict lower surface marine winds by approximately 10%. TMI-data proved extremely useful to derive instantaneous turbulent heat fluxes and other surface geophysical fields that were needed to diagnose and fine-tune the skill of the Eta model to forecast these fields. The TMI SST product also proved to be a valuable data source for data assimilation by the ocean model. An optimised data assimilation scheme was derived resulting in a bias of just -0.05 С after a 15-day model integration run. This thesis shows how spatial data analysis can provide more detailed information about the high-resolution forecasts and their quality in addition to standard verification tools. Routines that explore the spatial data of the forecasts, observations and their relationship were developed and applied. Geostatistical analysis was used to model the spatial structure of the residual fields of the predictions and observations, and to translate the degree of spatial correlation in numerical and graphical terms

    Comparison of sea-ice freeboard distributions from aircraft data and cryosat-2

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    The only remote sensing technique capable of obtain- ing sea-ice thickness on basin-scale are satellite altime- ter missions, such as the 2010 launched CryoSat-2. It is equipped with a Ku-Band radar altimeter, which mea- sures the height of the ice surface above the sea level. This method requires highly accurate range measure- ments. During the CryoSat Validation Experiment (Cry- oVEx) 2011 in the Lincoln Sea, Cryosat-2 underpasses were accomplished with two aircraft, which carried an airborne laser-scanner, a radar altimeter and an electro- magnetic induction device for direct sea-ice thickness re- trieval. Both aircraft flew in close formation at the same time of a CryoSat-2 overpass. This is a study about the comparison of the sea-ice freeboard and thickness dis- tribution of airborne validation and CryoSat-2 measure- ments within the multi-year sea-ice region of the Lincoln Sea in spring, with respect to the penetration of the Ku- Band signal into the snow

    Precipitation and latent heating distributions from satellite passive microwave radiometry. Part I: improved method and uncertainties

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    A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5°-resolution range from approximately 50% at 1 mm h−1 to 20% at 14 mm h−1. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%–80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5° resolution is relatively small (less than 6% at 5 mm day−1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%–35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%–15% at 5 mm day−1, with proportionate reductions in latent heating sampling errors

    Bio-Optical Sensors on Argo Floats

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    The general objective of the IOCCG BIO-Argo working group is to elaborate recommendations for establishing a framework for the future development of a cost-effective, bio-optical float network corresponding to the needs and expectations of the scientific community. In this context, our recommendations will necessarily be broad; they range from the identification of key bio-optical measurements to be implemented on floats, to the real-time management of the data flux resulting from the deployment of a "fleet of floats". Each chapter of this report is dedicated to an essential brick leading towards the goal of implementing a bio-optical profiling float network. The following topics are discussed in the Chapters listed below: - Chapter 2 reviews the scientific objectives that could be tackled through the development of such networks, by allowing some of the gaps in the present spatio-temporal resolution of bio-optical variables to be progressively filled. - Chapter 3 identifies the optical and bio-optical properties that are now amenable to remote and autonomous measurement through the use of optical sensors mounted on floats. - Chapter 4 addresses the question of sensor requirements, in particular with respect to measurements performed from floats. - Chapter 5 proposes and argues for the development of dedicated float missions corresponding to specific scientific objectives and relying on specific optical sensor suites, as well as on specific modes of float operation. - Chapter 6 identifies technological issues that need to be addressed for the various bio-optical float missions to become even more cost-effective. - Chapter 7 covers all aspects of data treatment ranging from the development of various quality control procedures (from real-time to delayed mode) to the architecture required for favoring easy access to data. - Chapter 8 reviews the necessary steps and experience required before the operational implementation of different types of float networks can become a reality.JRC.H.5-Land Resources Managemen

    Automated in situ observations of upper ocean biogeochemistry, bio-optics, and physics and their potential use for global studies

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    The processes controlling the flux of carbon in the upper ocean have dynamic ranges in space and time of at least nine orders of magnitude. These processes depend on a broad suite of inter-related biogeochemical, bio-optical, and physical variables. These variables should be sampled on scales matching the relevant phenomena. Traditional ship-based sampling, while critical for detailed and more comprehensive observations, can span only limited portions of these ranges because of logistical and financial constraints. Further, remote observations from satellite platforms enable broad horizontal coverage which is restricted to the upper few meters of the ocean. For these main reasons, automated subsurface measurement systems are important for the fulfillment of research goals related to the regional and global estimation and modeling of time varying biogeochemical fluxes. Within the past few years, new sensors and systems capable of autonomously measuring several of the critical variables have been developed. The platforms for deploying these systems now include moorings and drifters and it is likely that autonomous underwater vehicles (AUV's) will become available for use in the future. Each of these platforms satisfies particular sampling needs and can be used to complement both shipboard and satellite observations. In the present review, (1) sampling considerations will be summarized, (2) examples of data obtained from some of the existing automated in situ sampling systems will be highlighted, (3) future sensors and systems will be discussed, (4) data management issues for present and future automated systems will be considered, and (5) the status of near real-time data telemetry will be outlined. Finally, we wish to make it clear at the outset that the perspectives presented here are those of the authors and are not intended to represent those of the United States JGOFS program, the International JGOFS program, NOAA's C&GC program, or other global ocean programs
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