Accelerating data acquisition in magnetic resonance imaging (MRI) has been of perennial interest due to its prohibitively slow data acquisition process. Recent trends in accelerating MRI employ data-centric deep learning frameworks due to its fast inference time and 'one-parameter-fit-all' principle unlike in traditional model-based acceleration techniques. Unrolled deep learning framework that combines the deep priors and model knowledge are robust compared to naive deep learning based framework. In this paper, we propose a novel multi-scale unrolled deep learning framework which learns deep image priors through multi-scale CNN and is combined with unrolled framework to enforce data-consistency and model knowledge. Essentially, this framework combines the best of both learning paradigmsmodel-based and data-centric learning paradigms. Proposed method is verified using several experiments on numerous data sets.This study investigates the feedbacks between an interactive sea surface temperature (SST) and the self-aggregation of deep convective clouds, using a cloud-resolving model in nonrotating radiative-convective equilibrium. The ocean is modeled as one layer slab with a temporally fixed mean but spatially varying temperature. We find that the interactive SST decelerates the aggregation and that the deceleration is larger with a shallower slab, consistent with earlier studies. The surface temperature anomaly in dry regions is positive at first, thus opposing the diverging shallow circulation known to favor self-aggregation, consistent with the slower aggregation. But surprisingly, the driest columns then have a negative SST anomaly, thus strengthening the diverging shallow circulation and favoring aggregation. This diverging circulation out of dry regions is found to be well correlated with the aggregation speed. It can be linked to a positive surface pressure anomaly (PSFC), itself the consequence of SST anomalies and boundary layer radiative cooling. The latter cools and dries the boundary layer, thus increasing PSFC anomalies through virtual effects and hydrostasy. Sensitivity experiments confirm the key role played by boundary layer radiative cooling in determining PSFC anomalies in dry regions, and thus the shallow diverging circulation and the aggregation speed.The need for high-precision calculations with 64-bit or 32-bit floating-point arithmetic for weather and climate models is questioned. Lower-precision numbers can accelerate simulations and are increasingly supported by modern computing hardware. This paper investigates the potential of 16-bit arithmetic when applied within a shallow water model that serves as a medium complexity weather or climate application. There are several 16-bit number formats that can potentially be used (IEEE half precision, BFloat16, posits, integer, and fixed-point). It is evident that a simple change to 16-bit arithmetic will not be possible for complex weather and climate applications as it will degrade model results by intolerable rounding errors that cause a stalling of model dynamics or model instabilities. However, if the posit number format is used as an alternative to the standard floating-point numbers, the model degradation can be significantly reduced. Furthermore, mitigation methods, such as rescaling, reordering, and mixed precision, are available to make model simulations resilient against a precision reduction. If mitigation methods are applied, 16-bit floating-point arithmetic can be used successfully within the shallow water model.  https://www.selleckchem.com/products/YM155.html  The results show the potential of 16-bit formats for at least parts of complex weather and climate models where rounding errors would be entirely masked by initial condition, model, or discretization error.The thermal component of oceanic eddy available potential energy (EPE) generation due to air-sea interaction is proportional to the product of anomalous sea surface temperature (SST) and net air-sea heat flux (SHF). In this study we assess EPE generation and its timescale and space-scale dependence from observations and a high-resolution coupled climate model. A dichotomy exists in the literature with respect to the sign of this term, that is, whether it is a source or a sink of EPE. We resolve this dichotomy by partitioning the SST and net heat flux into climatological mean, climatological seasonal cycle, and remaining transient contributions, thereby separating the mesoscale eddy variability from the forced seasonal cycle. In this decomposition the mesoscale air-sea SST-SHF feedbacks act as a 0.1 TW global sink of EPE. In regions of the ocean with a large seasonal cycle, for example, midlatitudes of the Northern Hemisphere, the EPE generation by the forced seasonal cycle exceeds the mesoscale variability sink, such that the global generation by seasonal plus eddy variability acts as a 0.8 TW source. EPE destruction is largest in the midlatitude western boundary currents due to mesoscale air-sea interaction and in the tropical Pacific where SST variability is due mainly to the El Niño-Southern Oscillation. The EPE sink in western boundary currents is spatially aligned with SST gradients and offset to the poleward side of currents, while the mean and seasonal generation are aligned with the warm core of the current. By successively smoothing the data in space and time we find that half of the EPE sink is confined to timescales less than annual and length scales less than 2°, within the oceanic mesoscale band.Understanding the past, present, and future evolution of methane remains a grand challenge. Here we have used a hierarchy of models, ranging from simple box models to a chemistry-climate model (CCM), UM-UKCA, to assess the contemporary and possible future atmospheric methane burden. We assess two emission data sets for the year 2000 deployed in UM-UKCA against key observational constraints. We explore the impact of the treatment of model boundary conditions for methane and show that, depending on other factors, such as CO emissions, satisfactory agreement may be obtained with either of the CH4 emission data sets, highlighting the difficulty in unambiguous choice of model emissions in a coupled chemistry model with strong feedbacks. The feedbacks in the CH4-CO-OH system, and their uncertainties, play a critical role in the projection of possible futures. In a future driven by large increases in greenhouse gas forcing, increases in tropospheric temperature drive, an increase in water vapor, and, hence, [OH]. In the absence of methane emission changes this leads to a significant decrease in methane compared to the year 2000.