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Due to the importance of a reference atmospheric radiative transfer model for both planning and calibrating ground-based observations at millimetre and submillimetre wavelengths, we have undertaken a validation campaign consisting of acquiring atmospheric spectra under different weather conditions, in different diurnal moments and seasons, with the Atacama Pathfinder EXperiment (APEX), due to the excellent stability of its receivers and the very high frequency resolution of its backends. As a result, a data set consisting of 56 spectra within the 157.3-742.1 GHz frequency range, at kHz resolution (smoothed to $\sim$2-10 MHz for analysis), and spanning one order of magnitude ($\sim$ 0.35-3.5 mm) in precipitable water vapour columns, has been gathered from October 2020 to September 2022. These data are unique for their quality and completeness and, due to the proximity of APEX to the Atacama Large Millimetre/Submillimetre Array (ALMA), they provide an excellent opportunity to validate the atmospheric radiative transfer model currently installed in the ALMA software. The main issues addressed in the study are possible missing lines in the model, line shapes, vertical profiles of atmospheric physical parameters and molecular abundances, seasonal and diurnal variations and collision induced absorption (CIA), to which this paper is devoted, in its N$_2$-N$_2$ + N$_2$-O$_2$ + O$_2$-O$_2$ (dry), and N$_2$-H$_2$O + O$_2$-H$_2$O (``foreign'' wet) mechanisms. All these CIA terms should remain unchanged in the above mentioned ALMA atmospheric model as a result of this work., Comment: 10 pages, 5 figures. Accepted for publication in A&A

The effect of ion bombardment on MnAs single crystalline thin films is studied. The role of elastic collisions between ions and atoms of the material is singled-out as the main process responsible for modifying the properties of the material. Thermal hysteresis suppression, and the loss of sharpness of the magneto-structural phase transition are studied as a function of different irradiation conditions. While the latter is shown to be associated with the ion induced disorder at the scale of the transition correlation length, the former is related to the coupling between disorder and the large-scale elastic field associated with the phase coexistence pattern.

Magnetic refrigeration based on the magnetocaloric effect at room temperature is one of the most attractive alternative to the current gas compression/expansion method routinely employed. Nevertheless, in giant magnetocaloric materials, optimal refrigeration is restricted to the narrow temperature window of the phase transition (Tc). In this work, we present the possibility of varying this transition temperature into a same giant magnetocaloric material by ion irradiation. We demonstrate that the transition temperature of iron rhodium thin films can be tuned by the bombardment of ions of Ne 5+ with varying fluences up to 10 14 ions cm --2 , leading to optimal refrigeration over a large 270--380 K temperature window. The Tc modification is found to be due to the ion-induced disorder and to the density of new point-like defects. The variation of the phase transition temperature with the number of incident ions opens new perspectives in the conception of devices using giant magnetocaloric materials.

Investigations of the complex behavior of the magnetization of manganese arsenide thin films due to defects induced by irradiation of slow heavy ions are presented. In addition to the thermal hysteresis suppression already highlighted in M. Trassinelli et al., Appl. Phys. Lett. 104, 081906 (2014), we report here on new local magnetic features recorded by a magnetic force microscope at different temperatures close to the characteristic sample phase transition. Complementary measurements of the global magnetization measurements in different conditions (applied magnetic field and temperatures) enable to complete the film characterization. The obtained results suggest that the ion bombardment produces regions where the local mechanical constraints are significantly different from the average, promoting the local presence of magneto-structural phases far from the equilibrium. These regions could be responsible for the thermal hysteresis suppression previously reported, irradiation-induced defects acting as seeds in the phase transition.

In a recent experiment we demonstrated the possibility to suppress the thermal hysteresis of the phase transition in giant magnetocaloric MnAs thin film by interaction with slow highly charged ions (Ne 9+ at 90 keV) [1]. This phenomenon has a major impact for possible applications in magnetic refrigeration and thus its reproducibility and robustness are of prime importance. Here we present some new investigations about the origin and the nature of the irradiation-induced defects responsible for the thermal hysteresis suppression. Considering in particular two samples that receive different ion fluences (two order of magnitude of difference), we investigate the reliability of this process. The stability of the irradiation-induced defects with respect to a soft annealing is studied by X-ray diffraction and magnetometry measurements, which provide some new insights on the mechanisms involved.

Absolute Ar XK (3 keV) and Xe XL (4 keV) photon yields are measured as a function of laser peak intensity, for large (104 - 105 atoms) argon and xenon clusters submitted to intense laser pulses. A clear threshold behavior is observed. The intensity threshold is found as low as 2 1014 Wcm-2, a value matching the production of singly-charged argon or doubly-charged xenon by optical field ionization. Possible mechanisms involved in the inner-shell ionization are discussed. The outcome on the optimization of keV X-ray production is underlined., Comment: 1 table, 4 figures. submitted to Physical Review Letters

We investigate the heating of the quasi-free electrons in large rare gas clusters (N exceeding 10^5 atoms) by short laser pulses at moderate intensities (I~10^{15} Wcm^{-2}). We identify elastic large-angle backscattering of electrons at ionic cores in the presence of a laser field as an efficient heating mechanism resembling the Fermi shuttle. Its efficiency as well as the effect of collective electron motion, electron-impact ionization and cluster charging, are studied employing a mean-field classical transport simulation. Results for the absolute x-ray yields are in surprisingly good quantitative agreement with recent experimental results., Comment: 5 pages,4 figures, submitted to Phys. Rev. Lett

We developed a remote sensing approach based on multi-satellite observations, which provides an unprecedented estimate of monthly distribution and area of land-surface open water over the whole globe. Results for 1993 to 2007 exhibit a large seasonal and inter-annual variability of the inundation extent with an overall decline in global average maximum inundated area of 6% during the fifteen-year period, primarily in tropical and subtropical South America and South Asia. The largest declines of open water are found where large increases in population have occurred over the last two decades, suggesting a global scale effect of human activities on continental surface freshwater: denser population can impact local hydrology by reducing freshwater extent, by draining marshes and wetlands, and by increasing water withdrawals. Citation: Prigent, C., F. Papa, F. Aires, C. Jimenez, W. B. Rossow, and E. Matthews (2012), Changes in land surface water dynamics since the 1990s and relation to population pressure, in section 4, insisting on the potential applications of the wetland dataset.

A global intercomparison of 12 monthly mean land surface heat flux products for the period 1993-1995 is presented. The intercomparison includes some of the first emerging global satellite-based products (developed at Paris Observatory, Max Planck Institute for Biogeochemistry, University of California Berkeley, University of Maryland, and Princeton University) and examples of fluxes produced by reanalyses (ERA-Interim, MERRA, NCEP-DOE) and off-line land surface models (GSWP-2, GLDAS CLM/ Mosaic/Noah). An intercomparison of the global latent heat flux (Q(sub le)) annual means shows a spread of approx 20 W/sq m (all-product global average of approx 45 W/sq m). A similar spread is observed for the sensible (Q(sub h)) and net radiative (R(sub n)) fluxes. In general, the products correlate well with each other, helped by the large seasonal variability and common forcing data for some of the products. Expected spatial distributions related to the major climatic regimes and geographical features are reproduced by all products. Nevertheless, large Q(sub le)and Q(sub h) absolute differences are also observed. The fluxes were spatially averaged for 10 vegetation classes. The larger Q(sub le) differences were observed for the rain forest but, when normalized by mean fluxes, the differences were comparable to other classes. In general, the correlations between Q(sub le) and R(sub n) were higher for the satellite-based products compared with the reanalyses and off-line models. The fluxes were also averaged for 10 selected basins. The seasonality was generally well captured by all products, but large differences in the flux partitioning were observed for some products and basins.

Neural network (NN) techniques have proved successful for many regression problems, in particular for remote sensing; however, uncertainty estimates are rarely provided. In this article, a Bayesian technique to evaluate uncertainties of the NN parameters (i.e., synaptic weights) is first presented. In contrast to more traditional approaches based on point estimation of the NN weights, we assess uncertainties on such estimates to monitor the robustness of the NN model. These theoretical developments are illustrated by applying them to the problem of retrieving surface skin temperature, microwave surface emissivities, and integrated water vapor content from a combined analysis of satellite microwave and infrared observations over land. The weight uncertainty estimates are then used to compute analytically the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of an NN model. The uncertainties on the NN Jacobians are then considered in the third part of this article. Used for regression fitting, NN models can be used effectively to represent highly nonlinear, multivariate functions. In this situation, most emphasis is put on estimating the output errors, but almost no attention has been given to errors associated with the internal structure of the regression model. The complex structure of dependency inside the NN is the essence of the model, and assessing its quality, coherency, and physical character makes all the difference between a blackbox model with small output errors and a reliable, robust, and physically coherent model. Such dependency structures are described to the first order by the NN Jacobians: they indicate the sensitivity of one output with respect to the inputs of the model for given input data. We use a Monte Carlo integration procedure to estimate the robustness of the NN Jacobians. A regularization strategy based on principal component analysis is proposed to suppress the multicollinearities in order to make these Jacobians robust and physically meaningful.

The analysis of microwave observations over land to determine atmospheric and surface parameters is still limited due to the complexity of the inverse problem. Neural network techniques have already proved successful as the basis of efficient retrieval methods for non-linear cases, however, first-guess estimates, which are used in variational methods to avoid problems of solution non-uniqueness or other forms of solution irregularity, have up to now not been used with neural network methods. In this study, a neural network approach is developed that uses a first-guess. Conceptual bridges are established between the neural network and variational methods. The new neural method retrieves the surface skin temperature, the integrated water vapor content, the cloud liquid water path and the microwave surface emissivities between 19 and 85 GHz over land from SSM/I observations. The retrieval, in parallel, of all these quantities improves the results for consistency reasons. A data base to train the neural network is calculated with a radiative transfer model and a a global collection of coincident surface and atmospheric parameters extracted from the National Center for Environmental Prediction reanalysis, from the International Satellite Cloud Climatology Project data and from microwave emissivity atlases previously calculated. The results of the neural network inversion are very encouraging. The r.m.s. error of the surface temperature retrieval over the globe is 1.3 K in clear sky conditions and 1.6 K in cloudy scenes. Water vapor is retrieved with a r.m.s. error of 3.8 kg/sq m in clear conditions and 4.9 kg/sq m in cloudy situations. The r.m.s. error in cloud liquid water path is 0.08 kg/sq m . The surface emissivities are retrieved with an accuracy of better than 0.008 in clear conditions and 0.010 in cloudy conditions. Microwave land surface temperature retrieval presents a very attractive complement to the infrared estimates in cloudy areas: time record of land surface temperature will be produced.