Brightness temperature emitted by layered media with nonuniform temperature profile

1996 ◽  
Vol 17 (9) ◽  
pp. 1567-1571
Author(s):  
Ying Lu ◽  
Zuying Zhang ◽  
Wei Guo
Keyword(s):  
Brightness Temperature ◽  
Temperature Profile ◽  
Layered Media ◽  
Nonuniform Temperature

The range of flow structures fitting Jupiter's asymmetric gravity field

2020 ◽  
Author(s):  
Keren Duer ◽  
Eli Galanti ◽  
Yohai Kaspi
Keyword(s):  
Brightness Temperature ◽  
Temperature Profile ◽  
Gravity Field ◽  
Zonal Wind ◽  
Gravity Data ◽  
Flow Profile ◽  
Jet Streams ◽  
Uncertainty Range ◽  
Meridional Profile ◽  
Wind Profiles

<div>Jupiter's North-South asymmetric gravity field, measured by the Juno spacecraft, allowed estimating the depth of the zonal jets trough the relation between the measured density anomaly and the flow. This analysis was based on a combination of all four measured odd gravity harmonics, so the direct effect of each of them on the flow profile has not been investigated. Moreover, past calculations assumed that the cloud-level zonal wind maintains its meridional structure with depth; However, the Juno microwave radiometer measurements imply that a vertically dependent meridional profile might be more suitable, due to the reasonable relation between the Nadir brightness temperature profile and the zonal wind. In this study, we analyze in detail the possible range of structures of Jupiter’s deep jet-streams, fitting each of the Juno's measured asymmetric gravity harmonics. Specifically, we examine the possible vertical structure of Jupiter’s deep jet streams, different meridional structures of the cloud-level zonal wind and depth-dependent meridional profile compatible with the Nadir temperature tendency. We find that each odd gravity harmonic constrains the flow at a different depth, with J3 being the most dominant at depths below 3000 km, where the electrical conductivity becomes significant. J5 is the most restrictive harmonic overall, and J9 does not constrain the flow at all if the other odd harmonics are within the measurement range. Deep flow profiles constructed from perturbations to the cloud-level winds allow a more extensive range of solutions, yet when the patterns differ substantially from the cloud-level observed wind profile, the ability to match the gravity data reduces significantly. Random zonal wind profiles, unconnected to the cloud-level profile allow almost no solutions for the gravity data, and only 1% of the tested wind profiles yield any solution. Overall, we find that while interior wind profiles that diverge considerably from those at the cloud-level are possible, they are statistically unlikely. Finally, we find that meridional smoothing of the wind with depth, according to the Juno MWR brightness temperature profile, still allows fitting the measured gravity signal within the uncertainty range.</div>

scholarly journals Time evolution of the Luttinger model with nonuniform temperature profile

2017 ◽  
Vol 95 (23) ◽  
Author(s):  
Edwin Langmann ◽  
Joel L. Lebowitz ◽  
Vieri Mastropietro ◽  
Per Moosavi
Keyword(s):  
Temperature Profile ◽  
Time Evolution ◽  
Nonuniform Temperature ◽  
Luttinger Model

A Water Vapor Index from Satellite Measurements

1995 ◽  
Vol 34 (7) ◽  
pp. 1551-1558 ◽  
Author(s):  
Larry M. McMillin ◽  
David S. Crosby ◽  
Mitchell D. Goldberg
Keyword(s):  
Water Vapor ◽  
Brightness Temperature ◽  
Temperature Profile ◽  
Atmospheric Temperature ◽  
Alternative Methods ◽  
High Latitudes ◽  
Atmospheric Moisture ◽  
Satellite Measurements ◽  
The Difference ◽  
The Given

Abstract A method for deriving a water vapor index is presented. An important feature of the index is the fact that it does not rely on radiosondes. Thus, it is not influenced by problems associated with radiosondes and the extent to which the horizontal variability of moisture invalidates the extrapolations from radiosonde measurements to satellite measurements. The index is derived by using channels that are insensitive to changes in moisture to predict a brightness temperature for one of the moisture channels and then by subtracting this predicted value from the observation. The predicted value represents the moisture value expected for the given temperature profile, and the difference between the predicted and measured values is the index. The subtraction removes the variability due to changes in atmospheric temperature from the moisture signal. This separation greatly enhances the ability to monitor atmospheric moisture patterns, especially near the ground and at high latitudes where some alternative methods have difficulties. The ability of the indices to display moisture patterns at all levels and latitudes is demonstrated.

A Bayesian approach to infer ice sheet temperature in Antarctica from satellite observations

2021 ◽  
Author(s):  
Marion Leduc-Leballeur ◽  
Catherine Ritz ◽  
Giovanni Macelloni ◽  
Ghislain Picard
Keyword(s):  
Probability Distribution ◽  
Brightness Temperature ◽  
Temperature Profile ◽  
Bayesian Approach ◽  
Ice Sheet ◽  
Snow Accumulation ◽  
Microwave Emission ◽  
Temperature Profiles ◽  
Band Frequency ◽  
L Band

<p>The actual temperature profile is a determinant of ice rheology, which controls ice deformation and flow, and sliding over the underlying bedrock. Importantly, the ice flow in turn affects its temperature profile through strain heating, which makes observed temperature profiles a powerful input for ice sheet model validation.</p><p><span>Up to now temperature profile was available in few boreholes or from glaciological models. Recently, </span><span>Macelloni et al. (2016)</span><span> opened up new opportunities for probing ice </span><span>temperature</span><span> from space with the low-frequency passive sensors. </span><span>Indeed</span><span>, at L-band frequency, the very low absorption of ice and the low scattering by particles (grain size, bubbles in ice) allow a large penetration in the dry snow and ice (several hundreds of meters). Macelloni et al. (2019) performed the first retrieval of the ice sheet temperature in Antarctica by using the European Space Agency (ESA)’s Soil Moisture and Ocean Salinity (SMOS) L-band observations. The</span><span>y used </span><span>the</span><span> minimization of the difference between SMOS brightness temperature and microwave emission model simulations that include</span><span>s</span><span> a glaciological model.</span></p><p><span>Here, in the framework of the ESA 4D-Antarctica project, we propose a new method based on a Bayesian approach in order to improve the accuracy of the retrieved ice temperature and to provide an uncertainty estimation along the profiles. As a first step, a one-dimensional ice temperature profile model (Robin 1955) is used, which limits the retrieval to the Antarctic Plateau. Then, the new temperature emulator based on the three-dimensional glaciological GRISLI (Quiquet et al., 2018) will be used to enable retrievals over the entire continent (cf. Ritz’s presentation in this session for the GRISLI emulator </span><span>description</span><span>).</span></p><p><span>The Bayesian inference takes as free parameters: </span><span>ice thickness, </span><span>surface ice temperature, snow accumulation and geothermal heat flux (GHF). Their prior probability distribution is defined as normal, centered around a priori values taken from literature, and truncated to stay in a realistic range. The observed brightness temperature distribution is normal and a normal likelihood function is used to quantify the matching between the observed and simulated brightness temperature. The parameter space investigation is achieved through a Markov Chain Monte Carlo (MCMC) method. Here, the differential evolution adaptive Metropolis (DREAM) algorithm is used, which runs multiple different Markov chains in parallel and uses a discrete proposal distribution to evolve the sampler to the posterior distribution </span><span>(</span><span>Laloy and Vrugt, 2012).</span></p><p>For each SMOS brightness temperature observation, 1000 iterations are run on 5 parallel chains. The 2500 first iterations are discarded (aka. burn-in) and only the last 2500 are used for the final ice temperature profile estimation. The posterior probability distribution captures the most likely parameter set (i.e. a surface temperature, snow accumulation and GHF combination), and so, the most likely ice temperature profiles associated to this SMOS observation. It also provides the standard deviation which is an accurate estimate of the temperature uncertainty along the depth obtained with the method.</p>

Comparison of Large Eddy Simulation and Unsteady Reynolds-Averaged Navier–Stokes for Evaluation of Entropy Noise

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Eduard Ron ◽  
Kam Chana
Keyword(s):  
Large Eddy Simulation ◽  
Temperature Profile ◽  
Numerical Procedure ◽  
Navier Stokes ◽  
Two Dimensional ◽  
Nonuniform Temperature ◽  
Temperature Variations ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Reynolds Averaged Navier Stokes

This paper expands on the numerical simulation of entropy noise by performing a comparison of two commonly used models for resolving turbulent flow field: large eddy simulation (LES) and unsteady Reynolds-averaged Navier–Stokes (URANS). A brand new numerical procedure was developed allowing an accurate reproduction of two-dimensional spatial and temporal temperature variations of a nonuniform temperature profile. Experimental investigation was performed for the same nonuniform temperature profile, and comparison of the entropy noise level measured experimentally and evaluated numerically using the two models was performed. It was shown that large eddy simulation allows a better prediction of entropy noise within the developed numerical procedure than URANS.

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