Simulation of global temperature variations and signal detection studies using neural networks

The concept of neural network models (NNM) is a statistical strategy which can be used if a superposition of any forcing mechanisms leads to any effects and if a sufficient related observational data base is available. In comparison to multiple regression analysis (MRA), the main advantages are that NNM is an appropriate tool also in the case of non-linear cause-effect relations and that interactions of the forcing mechanisms are allowed. In comparison to more sophisticated methods like general circulation models (GCM), the main advantage is that details of the physical background like feedbacks can be unknown. Neural networks learn from observations which reflect feedbacks implicitly. The disadvantage, of course, is that the physical background is neglected. In addition, the results prove to be sensitively dependent from the network architecture like the number of hidden neurons or the initialisation of learning parameters. We used a supervised backpropagation network (BPN) with three neuron layers, an unsupervised Kohonen network (KHN) and a combination of both called counterpropagation network (CPN). These concepts are tested in respect to their ability to simulate the observed global as well as hemispheric mean surface air temperature annual variations 1874 - 1993 if parameter time series of the following forcing mechanisms are incorporated : equivalent CO2 concentrations, tropospheric sulfate aerosol concentrations (both anthropogenic), volcanism, solar activity, and ENSO (all natural). It arises that in this way up to 83% of the observed temperature variance can be explained, significantly more than by MRA. The implication of the North Atlantic Oscillation does not improve these results. On a global average, the greenhouse gas (GHG) signal so far is assessed to be 0.9 - 1.3 K (warming), the sulfate signal 0.2 - 0.4 K (cooling), results which are in close similarity to the GCM findings published in the recent IPCC Report. The related signals of the natural forcing mechanisms considered cover amplitudes of 0.1 - 0.3 K. Our best NNM estimate of the GHG doubling signal amounts to 2.1K, equilibrium, or 1.7 K, transient, respectively

The State-Of-The-Art in Short-Term Prediction of Wind Power, A Literature Overview, 2nd Edition. Joint deliverable report of ANEMOS.plus and SafeWind European projects.

This joint Deliverable of ANEMOS.plus and SafeWind projects presents the state of the art in wind power forecasting. More than 380 references of journal and conference papers have been reviewed.The ANEMOS.plus Project was funded by the European Commission under the 6th Framework Program. Grant Agreement N° 038692. Project title “Advanced Tools for the Management of Electricity Grids with LargeScale Wind Generation”.The SafeWind Project was funded by the European Commission under the 7th FrameWork Program. Grant Agreement N° 213740. Project title: “Multi-scale data assimilation, advanced wind modelling & forecasting with emphasis to extreme weather situations for a safe large-scale wind power integration”

High inter‐ and intraspecific niche overlap among three sympatrically breeding, closely related seabird species: Generalist foraging as an adaptation to a highly variable environment?

1. Ecological niche theory predicts sympatric species to show segregation in their spatio‐temporal habitat utilization or diet as a strategy to avoid competition. Similarly, within species individuals may specialize on specific dietary resources or foraging habitats. Such individual specialization seems to occur particularly in environments with predictable resource distribution and limited environmental variability. Still, little is known about how seasonal environmental variability affects segregation of resources within species and between closely related sympatric species. 2. The aim of the study was to investigate the foraging behaviour of three closely related and sympatrically breeding fulmarine petrels (Antarctic petrels Thalassoica antarctica, cape petrels Daption capense and southern fulmars Fulmarus glacialoides) in a seasonally highly variable environment (Prydz Bay, Antarctica) with the aim of assessing inter‐ and intraspecific overlap in utilized habitat, timing of foraging and diet and to identify foraging habitat preferences. 3. We used GPS loggers with wet/dry sensors to assess spatial habitat utilization over the entire breeding season. Trophic overlap was investigated using stable isotope analysis based on blood, feathers and egg membranes. Foraging locations were identified using wet/dry data recorded by the GPS loggers and expectationmaximization binary clustering. Foraging habitat preferences were modelled using generalized additive models and model cross‐validation. 4. During incubation and chick‐rearing, the utilization distribution of all three species overlapped significantly and species also overlapped in the timing of foraging during the day—partly during incubation and completely during chick‐rearing. Isotopic centroids showed no significant segregation between at least two species for feathers and egg membranes, and among all species during incubation (reflected by blood). Within species, there was no individual specialization in foraging sites or environmental space. Furthermore, no single environmental covariate predicted foraging activity along trip trajectories. Instead, best‐explanatory environmental covariates varied within and between individuals even across short temporal scales, reflecting a highly generalist behaviour of birds. 5. Our results may be explained by optimal foraging theory. In the highly productive but spatio‐temporally variable Antarctic environment, being a generalist may be key to finding mobile prey—even though this increases the potential for competition within and among sympatric species

Comparison of measured and forecasted water-vapor profilesduring COPS 2007

Measurements with the multiwavelength Raman pola-rization lidar BERTHA (Backscatter Extinction lidar Ratio Temperature Humidity profiling Apparatus) have been performed during COPS (Convective Orographi-cally induced Precipitation Study) in the Black Forest, Germany, from June to August 2007. Profiles of the water-vapor mixing ratio from BERTHA are compared with data from radiosonde and an air-borne DIAL (Differential Absorption Lidar) LEANDRE2 onboard the French Safire Falcon). The data of the two lidar systems are in better agreement (average difference of -0.03 g kg-1) than the comparisons of BERTHA and the radiosonde data (average difference smaller than 0.5 g kg-1). The discrepancy between lidar and radiosonde data is attributed to the drift of the radiosonde during its ascent. Raman lidar data are compared with short-range out-puts of the COSMO-DE model (Consortium for small-scale modeling; www.cosmo-model.org) of the Ger-man Weather Service. It is shown that the short-range forecast of water-vapor mixing ratio within the residual layer yields values that are on average 7.9% smaller than the measurement. In the free troposphere pre-dicted values are 9.7% smaller than the measurement

MAP D-PHASE Real-Time Demonstration of Weather Forecast Quality in the Alpine Region

International audienceDemonstration of probabilistic hydrological and atmospheric simulation of flood events in the Alpine region (D-PHASE) is made by the Forecast Demonstration Project in connection with the Mesoscale Alpine Programme (MAP). Its focus lies in the end-to-end flood forecasting in a mountainous region such as the Alps and surrounding lower ranges. Its scope ranges from radar observations and atmospheric and hydrological modeling to the decision making by the civil protection agents. More than 30 atmospheric high-resolution deterministic and probabilistic models coupled to some seven hydrological models in various combinations provided real-time online information. This information was available for many different catchments across the Alps over a demonstration period of 6 months in summer/ fall 2007. The Web-based exchange platform additionally contained nowcasting information from various operational services and feedback channels for the forecasters and end users. D-PHASE applications include objective model verification and intercomparison, the assessment of (subjective) end user feedback, and evaluation of the overall gain from the coupling of the various components in the end-to-end forecasting system