Diurnal variation of tropical precipitation using five years TRMM data

The tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) data are used in this study to reveal diurnal variations of precipitation over the Tropics (30◦S − 30◦N) from January, 1998, to December 2002. The TMI data were used for the regions over oceans and islands and the PR data was used over continents. The observations are sorted regionally to examine the difference in diurnal cycle of rainfall over ocean, island, and continental regions. The rain rate is averaged over individual two hour intervals of local time in each region to include more observations in order to reduce the sampling error. F-test is used to determine those regions whose diurnal cycle is detected at the 95% confidence level. In most oceanic regions there is a maximum at 0400 LST - 0700 LST. The amplitude of diurnal variation over ocean regions with small total rain is a little higher than that of the ocean regions with heavy total rain. The diurnal cycle peaks at 0700 LST - 0800 LST over islands with rainfall variation similar to surrounding oceanic regions. A maximum at 1400 LST - 1500 LST was found in areas over continents with heavy total rain, while the maximum occured at 1900 LST - 2100 LST over continents with lesser total rain. The amplitudes of variation over continents with heavy total rain and with small total rain do not show significant differences. The diurnal cycle in in JJA (June, July, August) and DJF (December, January, February) varies with latitude over continents. A seasonal cycle of diurnal cycle can also be found in some oceanic regions. The diurnal cycle annual change is not evident over continents, while the diurnal cycle annual change over oceans exists in some regions. Island regions in this paper exhibit no evident seasonal and annual diurnal change

Observational and numerical study of Atlantic tropical instability waves

This study uses high resolution satellite measurements from the Tropical Rainfall Measuring Mission (TRMM), Quick Scatterometer (QuikSCAT) and Special Sensor Mi¬crowave Imager (SSM/I) to investigate the variability of sea surface temperature (SST), surface wind velocity, water vapor, cloud liquid water and precipitation associated with westward moving tropical instability waves (TIWs) in the Atlantic Ocean from 1998 to 2005. On interannual scales, TIWs in the Pacific Ocean are strongest during the cold phase of El Ni¨ no Southern Oscillation (ENSO), when the cold tongue is most pronounced. The waves are weak during the warm phase of ENSO. A low-frequency Atlantic air-sea cou-pled mode influences the TIW activity in the Atlantic Ocean as ENSO does in the Pacific Ocean. The characteristics of TIWs are largely associated with the background oceano-graphic states. Coherent ocean-atmosphere patterns are shown in the Atlantic Ocean during eight years. Southeasterly trades strengthen and water vapor increases over warm SST anomalies associated with TIWs. The opposite is true over cold TIW SST anomalies. The cloud liquid water and rain response to the SST follows a very similar pattern, appearing to be roughly in phase with wind convergence and divergence in the central tropical Atlantic. The atmospheric response to the TIW SST anomalies extends north of the TIW active region, suggesting a remote response to the TIWs. The atmospheric response to the Atlantic TIWs shows interannual variability. In 1999, the rainfall response to the TIW SST anomalies is much larger than in other years, which is due to the southward movement of Atlantic ITCZ (Intertropical Convergence Zone). When the Atlantic ITCZ moves south, it is more susceptible to TIW influence. One regional climate model and one global climate model are applied to study the mechanism of atmospheric response to the Atlantic TIWs with daily TMI satellite SST forcing. Both models successfully simulated the wind velocity, wind convergence and pre-cipitation as observed. While the satellite observations support the vertical mixing mech-anism for the surface wind response to TIWs, both models show the pressure gradient mechanism is dominant in the Atlantic

Ensemble forecast of Indo- Pacific SST based on IPCC twentieth- century climate simulations. Geophys

[1] A set of Markov models is developed based on a statistical linearization of 5 coupled ocean-atmosphere general circulation models used in the Intergovernmental Panel on Climate Changes Fourth Assessment Report (IPCC AR4), and is applied to ensemble prediction of the tropical IndoPacific sea surface temperature variations. By taking advantage of the long data records of IPCC simulations, the linear model is statistically robust, and exhibits a level of ENSO prediction skill comparable to other forecast models. More importantly, the model shows much higher skill in the western Pacific and the tropical Indian Ocean than previously achieved, thus providing new insight and optimism for the predictability of the short-term climate change in the whole tropical Indo-Pacific region. Citation: Wu, Q., and D. Chen (2010), Ensemble forecast of Indo-Pacific SST based on IPCC twentieth-century climate simulations, Geophys. Res. Lett., 37, L16702

A Quantum Federated Learning Framework for Classical Clients

Quantum Federated Learning (QFL) enables collaborative training of a Quantum Machine Learning (QML) model among multiple clients possessing quantum computing capabilities, without the need to share their respective local data. However, the limited availability of quantum computing resources poses a challenge for each client to acquire quantum computing capabilities. This raises a natural question: Can quantum computing capabilities be deployed on the server instead? In this paper, we propose a QFL framework specifically designed for classical clients, referred to as CC-QFL, in response to this question. In each iteration, the collaborative training of the QML model is assisted by the shadow tomography technique, eliminating the need for quantum computing capabilities of clients. Specifically, the server constructs a classical representation of the QML model and transmits it to the clients. The clients encode their local data onto observables and use this classical representation to calculate local gradients. These local gradients are then utilized to update the parameters of the QML model. We evaluate the effectiveness of our framework through extensive numerical simulations using handwritten digit images from the MNIST dataset. Our framework provides valuable insights into QFL, particularly in scenarios where quantum computing resources are scarce

Trainability Analysis of Quantum Optimization Algorithms from a Bayesian Lens

The Quantum Approximate Optimization Algorithm (QAOA) is an extensively studied variational quantum algorithm utilized for solving optimization problems on near-term quantum devices. A significant focus is placed on determining the effectiveness of training the $n$-qubit QAOA circuit, i.e., whether the optimization error can converge to a constant level as the number of optimization iterations scales polynomially with the number of qubits. In realistic scenarios, the landscape of the corresponding QAOA objective function is generally non-convex and contains numerous local optima. In this work, motivated by the favorable performance of Bayesian optimization in handling non-convex functions, we theoretically investigate the trainability of the QAOA circuit through the lens of the Bayesian approach. This lens considers the corresponding QAOA objective function as a sample drawn from a specific Gaussian process. Specifically, we focus on two scenarios: the noiseless QAOA circuit and the noisy QAOA circuit subjected to local Pauli channels. Our first result demonstrates that the noiseless QAOA circuit with a depth of $\tilde{\mathcal{O}}\left(\sqrt{\log n}\right)$ can be trained efficiently, based on the widely accepted assumption that either the left or right slice of each block in the circuit forms a local 1-design. Furthermore, we show that if each quantum gate is affected by a $q$-strength local Pauli channel with the noise strength range of $1/{\rm poly} (n)$ to 0.1, the noisy QAOA circuit with a depth of $\mathcal{O}\left(\log n/\log(1/q)\right)$ can also be trained efficiently. Our results offer valuable insights into the theoretical performance of quantum optimization algorithms in the noisy intermediate-scale quantum era

Inhibitory effect of small interfering RNA on dengue virus replication in mosquito cells

<p>Abstract</p> <p>Background</p> <p>Dengue viruses (DENs) are the wildest transmitted mosquito-borne pathogens throughout tropical and sub-tropical regions worldwide. Infection with DENs can cause severe flu-like illness and potentially fatal hemorrhagic fever. Although RNA interference triggered by long-length dsRNA was considered a potent antiviral pathway in the mosquito, only limited studies of the value of small interfering RNA (siRNA) have been conducted.</p> <p>Results</p> <p>A 21 nt siRNA targeting the membrane glycoprotein precursor gene of DEN-1 was synthesized and transfected into mosquito C6/36 cells followed by challenge with DEN. The stability of the siRNA in cells was monitored by flow cytometry. The antiviral effect of siRNA was evaluated by measurement of cell survival rate using the MTT method and viral RNA was quantitated with real-time RT-PCR. The presence of cells containing siRNA at 0.25, 1, 3, 5, 7 days after transfection were 66.0%, 52.1%, 32.0%, 13.5% and 8.9%, respectively. After 7 days incubation with DEN, there was reduced cytopathic effect, increased cell survival rate (76.9 ± 4.5% <it>vs </it>23.6 ± 14.6%) and reduced viral RNA copies (Ct value 19.91 ± 0.63 <it>vs </it>14.56 ± 0.39) detected in transfected C6/36 cells.</p> <p>Conclusions</p> <p>Our data showed that synthetic siRNA against the DEN-1 membrane glycoprotein precursor gene effectively inhibited DEN-1 viral RNA replication and increased C6/36 cell survival rate. siRNA may offer a potential new strategy for prevention and treatment of DEN infection.</p

Achieving large super-elasticity through changing relative easiness of deformation modes in Ti-Nb-Mo alloy by ultra-grain refinement

Large super-elasticity approaching its theoretically expected value was achieved in Ti-13.3Nb-4.6Mo alloy having an ultrafine-grained β-phase. In-situ synchrotron radiation X-ray diffraction analysis revealed that the dominant yielding mechanism changed from dislocation slip to martensitic transformation by decreasing the β-grain size down to sub-micrometer. Different grain size dependence of the critical stress to initiate dislocation slip and martensitic transformation, which was reflected by the transition of yielding behavior, was considered to be the main reason for the large super-elasticity in the ultrafine-grained specimen. The present study clarified that ultra-grain refinement down to sub-mirometer scale made dislocation slips more difficult than martensitic transformation, leading to an excellent super-elasticity close to the theoretical limit in the β-Ti alloy

Rotatable precipitates change the scale-free to scale dependent statistics in compressed Ti nano-pillars.

Compressed nano-pillars crackle from moving dislocations, which reduces plastic stability. Crackling noise is characterized by stress drops or strain bursts, which scale over a large region of sizes leading to power law statistics. Here we report that this "classic" behaviour is not valid in Ti-based nanopillars for a counterintuitive reason: we tailor precipitates inside the nano-pillar, which "regulate" the flux of dislocations. It is not because the nano-pillars become too small to sustain large dislocation movements, the effect is hence independent of size. Our precipitates act as "rotors": local stress initiates the rotation of inclusions, which reduces the stress amplitudes dramatically. The size distribution of stress drops simultaneously changes from power law to exponential. Rotors act like revolving doors limiting the number of passing dislocations. Hence each collapse becomes weak. We present experimental evidence for Ti-based nano-pillars (diameters between 300 nm and 2 μm) with power law distributions of crackling noise P(s) ∼ s-τ with τ ∼ 2 in the defect free or non-rotatable precipitate states. Rotors change the size distribution to P(s) ∼ exp(-s/s0). Rotors are inclusions of ω-phase that aligns under stress along slip planes and limit dislocation glide to small distances with high nucleation rates. This opens new ways to make nano-pillars more stable