The Bear Brook Watershed in Maine: Multi-decadal whole-watershed experimental acidification

The Bear Brook Watershed in Maine (BBWM) is a long-term research site established to study the response of forest ecosystem function to environmental disturbances of chronic acidic deposition and ecosystem nitrogen enrichment. Starting in 1989, the West Bear (treated) watershed received bimonthly applications of ammonium sulfate [(NH₄)₂SO₄] fertilizer from above the canopy, whereas East Bear (reference) received ambient deposition. The treatments were stopped in 2016, marking the beginning of the recovery phase. Research at the site has focused on soils, streams, and vegetation. Here, we describe data collected over three decades at the BBWM—input and stream output nutrient fluxes, quantitative soil pits and soil chemistry, and soil temperature and moisture.     

Impact of the sampling duration on the uncertainty of averaged velocity measurements with acoustic instruments

Average velocity in streams is a key variable for the analysis and modelling of hydrological and hydraulic processes underpinning water resources science and practice. The present study evaluates the impact of the sampling duration on the quality of average velocity measurements acquired with contemporary instruments such as Acoustic Doppler Velocimeters (ADV) an Acoustic Doppler Current Profilers (ADCP). The evaluation combines considerations on turbulent flows and principles and configurations of acoustic instruments with practical experience in conducting customized analysis for uncertainty analysis purposes. The study sheds new insights on the spatial and temporal variability of the uncertainty in the measurement of average velocities due to variable sampling durations acting in isolation from other sources of uncertainties. Sampling durations of 90 and 150 s are found sufficient for ADV and ADCP, respectively, to obtain reliable average velocities in a flow affected only by natural turbulence and instrument noise. Larger sampling durations are needed for measurements in most of the natural streams exposed to additional sources of data variability.     

Subsurface flow measurements using passive flux meters in variably-saturated cold-regions landscapes

To date, passive flux meters have predominantly been applied in temperate environments for tracking the movement of contaminants in groundwater. This study applies these instruments to reduce uncertainty in (typically instantaneous) flux measurements made in a low-gradient, wetland dominated, discontinuous permafrost environment. This method supports improved estimation of unsaturated and over-winter subsurface flows which are very difficult to quantify using hydraulic gradient-based approaches. Improved subsurface flow estimates can play a key role in understanding the water budget of this landscape.     

Gamma-Hadron Separation using Čerenkov Photon Density Fluctuations

In the atmospheric Čerenkov technique γ-rays are detected against the abundant background produced by hadronic showers. In order to improve the signal to noise ratio of theexperiment, it is necessary to reject a significant fraction of hadronic showers. Traditional background rejection methods based on image shape parameters have been extensively used for the data from imaging telescopes. However, non-imaging Čerenkov telescopes have to develop very different means of statistically identifying and removing cosmic ray events. Some of the parameters, which could be potentially important for non-imaging arrays, are the temporal and spectral differences, the lateral distributions and density fluctuations of Čerenkov photons generated by γ-ray and hadron primaries. Here we study the differences in fluctuations of Čerenkov photon density in the light pool at the observation level from showers initiated by photons and those initiated by protons or heavier nuclei. The database of simulated events for the PACT array has been used to evaluate the efficiency of the new technique. Various types of density fluctuations like the short range and medium range fluctuations as well as flatness parameter are studied. The estimated quality factors reflect the efficiencies with which the hadrons can be rejected from the data. Since some of these parameters are independent, the cuts may be applied in tandem and we demonstrate that the proton rejection efficiency of ∼90% can be achieved. Use of density fluctuations is particularly suited for wavefront sampling observations and it seems to be a good technique to improve the signal to noise ratio. This revised version was published online in July 2006 with corrections to the Cover Date.     

The effects of topographically-controlled thermal tides in the martian upper atmosphere as seen by the MGS accelerometer

Mars Global Surveyor accelerometer observations of the martian upper atmosphere revealed large variations in density with longitude during northern hemisphere spring at altitudes of 130-160 km, all latitudes, and mid-afternoon local solar times (LSTs). This zonal structure is due to tides from the surface. The zonal structure is stable on timescales of weeks, decays with increasing altitude above 130 km, and is dominated by wave-3 (average amplitude 22% of mean density) and wave-2 (18%) harmonics. The phases of these harmonics are constant with both altitude and latitude, though their amplitudes change significantly with latitude. Near the South Pole, the phase of the wave-2 harmonic changes by 90° with a change of half a martian solar day while the wave-3 phase stays constant, suggesting diurnal and semidiurnal behaviour, respectively. We use a simple application of classical tidal theory to identify the dominant tidal modes and obtain results consistent with those of General Circulation Models. Our method is less rigorous, but simpler, than the General Circulation Models and hence complements them. Topography has a strong influence on the zonal structure.     

Single star scidar: atmospheric parameters profiling using the simulated annealing algorithm

In this paper, we present a new method to estimate, for each turbulent layer labelled i , the horizontal wind speed   v ( h _i )  , the standard deviation of the horizontal wind speed fluctuations  σ _v ( h_i )  and the integrated value of   C ² _n   over the thickness  Δ h_i   of the turbulent layer   C ² _n ( h_i )Δ h_i   , where   h_i   is the altitude of the turbulent layer. These parameters are extracted from single star scintillation spatiotemporal cross-correlation functions of atmospheric speckles obtained within the generalized mode. This method is based on the simulated annealing algorithm to find the optimal solution required to solve the problem. Astrophysics parameters for adaptive optics are also calculated using   C ² _n ( h_i )  and   v ( h_i )  values. The results of other techniques support this new method.     

Nutrient transports in a Swedish estuary

Himmerfjärd is a Swedish estuary bordering on the Baltic. The estuary lacks astronomical tides and its circulation is driven by winds and freshwater runoff. Because of a tertiary sewage treatment plant located at its inner end, the estuary is becoming increasingly eutrophic. A field study was carried out for a 78-day period in late summer and early fall of 1977 to determine rates of nutrient transport and to construct nutrient budgets. Since physical parameters (current velocity, temperature, salinity, winds and water level changes) were measured more frequently than nutrients (phosphate, nitrate, nitrite and ammonium) it was necessary to develop a suitable method to calculate nutrient flux time series and net nutrient fluxes. Over the study period, Himmerfjärd imported phosphorus and exported nitrogen. Direction of nutrient fluxes and changes in flux direction were consistent with the structure of the baroclinic currents.     

Correcting systematic effects in a large set of photometric light curves

We suggest a new algorithm to remove systematic effects in a large set of light curves obtained by a photometric survey. The algorithm can remove systematic effects, such as those associated with atmospheric extinction, detector efficiency, or point spread function changes over the detector. The algorithm works without any prior knowledge of the effects, as long as they linearly appear in many stars of the sample. The approach, which was originally developed to remove atmospheric extinction effects, is based on a lower rank approximation of matrices, an approach which has already been suggested and used in chemometrics, for example. The proposed algorithm is especially useful in cases where the uncertainties of the measurements are unequal. For equal uncertainties, the algorithm reduces to the Principal Component Analysis (PCA) algorithm. We present a simulation to demonstrate the effectiveness of the proposed algorithm and we point out its potential, in the search for transit candidates in particular.     

Atmospheric radiative transfer simulation for atmospheric correction of remote sensing data

IntroductionThe radiance leaving the earth-atmosphere sys-tem which can be sensed by a satellite borne ra-diometer is the sum of radiation emission fromtheearth surface and each atmospheric level that aretransmittedtothe top of the atmosphere.The radia-tion emissionfromthe earthsurface andthe radianceof each atmospheric level can be separated fromtheradiance at the top the atmospheric level a satellitemeasured.Thus,the earth surface parameters willbe retrieved from the surface radiance after a…     

Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.