Inversion Of Temperature Profiles Research Articles

Inversion of the Full-Depth Temperature Profile Based on Few Depth-Fixed Temperatures

Seawater temperature plays a key role in underwater acoustics and marine fishery, etc. In oceanographic surveys, it is often desirable to detect the temperature profile and obtain its spatio-temporal variation. The present study shows that the temperatures at the depths which are the three extreme points of the first two empirical orthogonal function (EOF) modes, contain the largest amount of information. Based on the back propagation (BP) neural network, a model for reconstructing the full-depth temperature profile using a few temperatures at fixed depth is established. The experimental result shows that the root mean square error (RMSE) of the temperature profile inversion in the test set is mostly less than 0.2 °C, and the three-dimensional temperature field obtained in this study is relatively reliable.

Review of temperature profile inversion of satellite-borne infrared hyperspectral sensors

Review of temperature profile inversion of satellite-borne infrared hyperspectral sensors

Atmospheric wind and temperature profiles inversion using infrasound: An ensemble model context.

This paper presents an inversion methodology where acoustic observations of infrasound waves are used to update an atmospheric model. This paper sought a flexible parameterization that permits to incorporate physical and numerical constraints without the need to reformulate the inversion. On the other hand, the optimization conveys an explicit search over the solution space, making the solver computationally expensive. Nevertheless, through a parallel implementation and the use of tight constraints, this study demonstrates that the methodology is computationally tractable. Constraints to the solution space are derived from the spread (variance) of ERA5 ensemble reanalysis members, which summarize the best current knowledge of the atmosphere from assimilated measurements and physical models. Similarly, the initial model temperature and winds for the inversion are chosen to be the average of these parameters in the ensemble members. The performance of the inversion is demonstrated with the application to infrasound observations from an explosion generated by the destruction of ammunition at Hukkakero, Finland. The acoustic signals are recorded at an array station located at 178 km range, which is within the classical shadow zone distance. The observed returns are assumed to come from stratospheric reflections. Thus, the reflection altitude is also an inverted parameter.

Neural network approximation of Bayesian models for the inference of ion and electron temperature profiles at W7-X

In this paper, we describe a method for training a neural network (NN) to approximate the full model Bayesian inference of plasma profiles from x-ray imaging diagnostic measurements. The modeling is carried out within the Minerva Bayesian modeling framework where models are defined as a set of assumptions, prior beliefs on parameter values and physics knowledge. The goal is to use NNs for fast ion and electron temperature profile inversion from measured image data. The NN is trained solely on artificial data generated by sampling from the joint distribution of the free parameters and model predictions. The training is carried out in such a way that the mapping learned by the network constitutes an approximation of the full model Bayesian inference. The analysis is carried out on images constituted of 20 × 195 pixels corresponding to binned lines of sight and spectral channels, respectively. Through the full model inference, it is possible to infer electron and ion temperature profiles as well as impurity density profiles. When the network is used for the inference of the temperature profiles, the analysis time can be reduced down to a few tens of microseconds for a single time point, which is a drastic improvement if compared to the ≈4 h long Bayesian inference. The procedure developed for the generation of the training set does not rely on diagnostic-specific features, and therefore it is in principle applicable to any other model developed within the Minerva framework. The trained NN has been tested on data collected during the first operational campaign at W7-X, and compared to the full model Bayesian inference results.

Temperature Profile Inversion from Carbon-Dioxide Spectral Intensities Through Tikhonov Regularization

In this study, an inverse calculation model based on the Levenberg–Marquardt optimization method with Tikhonov regularization to reconstruct temperature profiles and average concentrations from measured line-of-sight spectral intensity data is presented. The measured spectra were synthesized through calculations from high-temperature molecular spectroscopic database 2010, the high-temperature spectral database, for the and bands. Although it has been shown that Tikhonov regularization is suitable for ill-posed inverse problems, it is difficult to select an appropriate regularization parameter, especially for nonlinear problems. A new regularization selection method based on the combination of the L-curve criterion, and the discrepancy principle is proposed and shows good generality for different temperature profile inversions.

Saturation correction near the ground for pure rotational Raman lidar

Saturation correction for photon counting near the Earth’s surface can improve the accuracy of temperature measurement in the troposphere by pure rotational Raman lidar (PRRL). The PRRL system uses a 532-nm band as a light source, and the echo signals of high-J and low-J channels separated by a double grating monochromator are used for temperature profile inversion. The pulse-height distribution and the discriminator level selection in the system, which comprise a photomultiplier and a photon counter, saturate photon counting near the ground and affect the echo signals from the higher layer for calibration. Given its nonlinear effect on the photomultiplier tubes, the correction method is applied to the echo signals of the calibration interval in troposphere temperature measurements. The method not only reduces the error in temperature measurement (the root-mean-square error of temperature reaches 1.5 K), but also accurately indicates the distribution of the inversion layer within the boundary layer. It can be applied to deal with the saturation phenomenon of a photomultiplier tube.

Passive temperature tomography experiments to characterize transmissivity and connectivity of preferential flow paths in fractured media

The detection of preferential flow paths and the characterization of their hydraulic properties are major challenges in fractured rock hydrology. In this study, we propose to use temperature as a passive tracer to characterize fracture connectivity and hydraulic properties. In particular, we propose a new temperature tomography field method in which borehole temperature profiles are measured under different pumping conditions by changing successively the pumping and observation boreholes. To interpret these temperature- depth profiles, we propose a three step inversion-based framework. We consider first an inverse model that allows for automatic permeable fracture detection from borehole temperature profiles under pumping conditions. Then we apply a borehole-scale flow and temperature model to produce flowmeter profiles by inversion of temperature profiles. This second step uses inversion to characterize the relationship between temperature variations with depth and borehole flow velocities (Klepikova et al., 2011). The third inverse step, which exploits cross-borehole flowmeter tests, is aimed at inferring inter-borehole fracture connectivity and transmissivities. This multi-step inverse framework provides a means of including temperature profiles to image fracture hydraulic properties and connectivity. We test the proposed approach with field data obtained from the Ploemeur (N.W. France) fractured rock aquifer, where the full temperature tomography experiment was carried out between three 100 m depth boreholes 10 m apart. We identified several transmissive fractures and their connectivity which correspond to known fractures and corroborate well with independent information, including available borehole flowmeter tests and geophysical data. Hence, although indirect, temperature tomography appears to be a promising approach for characterizing connectivity patterns and transmissivities of the main flow paths in fractured rock.

Non-local heat transport, rotation reversals and up/down impurity density asymmetries in Alcator C-Mod ohmic L-mode plasmas

Several seemingly unrelated effects in Alcator C-Mod ohmic L-mode plasmas are shown to be closely connected: non-local heat transport, core toroidal rotation reversals, energy confinement saturation and up/down impurity density asymmetries. These phenomena all abruptly transform at a critical value of the collisionality. At low densities in the linear ohmic confinement regime, with collisionality ν* ⩽ 0.35 (evaluated inside of the q = 3/2 surface), heat transport exhibits non-local behaviour, core toroidal rotation is directed co-current, edge impurity density profiles are up/down symmetric and a turbulent feature in core density fluctuations with kθ up to 15 cm−1 (kθρs ∼ 1) is present. At high density/collisionality with saturated ohmic confinement, electron thermal transport is diffusive, core rotation is in the counter-current direction, edge impurity density profiles are up/down asymmetric and the high kθ turbulent feature is absent. The rotation reversal stagnation point (just inside of the q = 3/2 surface) coincides with the non-local electron temperature profile inversion radius. All of these observations suggest a possible unification in a model with trapped electron mode prevalence at low collisionality and ion temperature gradient mode domination at high collisionality.

Multisensor analysis of integrated atmospheric water vapor over Canada and Alaska

Atmospheric water vapor is a key parameter for the analysis of climatic systems (greenhouse gas effect), in particular over high latitudes where water vapor displays an important seasonal variability. The sparse spatial and temporal sampling of atmospheric water vapor observations across Canada needs to be improved. A series of instruments and methods including a 940‐nm solar absorption band radiometer (R) and radiosonde (S) analysis from a numerical weather prediction model and a ground‐based bi‐frequency Global Positioning System (GPS) were used to evaluate the integrated atmospheric water vapor (IWV) at various sites in Canada and Alaska from a multiyear database. The IWV‐R measurements were collected within the framework of the North American Sun Radiometry network (AERONET/AEROCAN). Intercomparisons between [IWV‐GPS and IWV‐S], [IWV‐R and IWV‐GPS], and [IWV‐R and IWV‐S] show root mean square (RMS) differences of 1.8, 1.9, and 2.2 kg m−2, respectively. GPS meteorology appears to be the easiest approach to calibrate the solar radiometer water vapor band owing to its flexibility, and it allows us to overcome the Sun radiometry limitation in high‐latitude areas like the Arctic. The sensitivity of the GPS retrieval to various parameters like GPS satellite constellation and meteorological data are discussed. The classical linear relationship between the surface temperature and the integrated weighted mean temperature profile needed for IWV‐GPS retrieval may be significantly different for Arctic air masses compared with midlatitude air masses in the case of tropospheric temperature profile inversion. An ever‐expanding multiyear (1994–2001) North American summer water vapor climatology, derived from AERONET/Canadian Sun Radiometer Network, is presented and analyzed, showing a mean value of 19.8 ± 6.1 kg m−2 and variations from 17 kg m−2 in Alaska to 23 kg m−2 in southeastern Canada. The results in Bonanza Creek, Alaska, show significant interannual variations with a peak in 1997, which may be linked to an El Niño event that occurred in the same year. Such a database may also be useful for climate model validation as shown for the Canadian Global Environmental Model (RMS difference of 3.4 kg m−2). In the end we show that, even if data are selected only for cloud‐free atmospheres, there are no significant differences as compared with radiosonde climatology at Canadian Northwestern sites (≤3% relatively to Bonanza Creek summer mean value).