Water Budget Closure in the Upper Chao Phraya River Basin, Thailand Using Multisource Data

Accurate quantification of the terrestrial water cycle relies on combinations of multisource datasets. This analysis uses data from remotely sensed, in-situ, and reanalysis records to quantify the terrestrial water budget/balance and component uncertainties in the upper Chao Phraya River Basin from May 2002 to April 2020. Three closure techniques are applied to merge independent records of water budget components, creating up to 72 probabilistic realizations of the monthly water budget for the upper Chao Phraya River Basin. An artificial neural network (ANN) model is used to gap-fill data in and between GRACE and GRACE-FO-based terrestrial water storage anomalies. The ANN model performed well with r ≥ 0.95, NRMSE = 0.24 − 0.37, and NSE ≥ 0.89 during the calibration and validation phases. The cumulative residual error in the water budget ensemble mean accounts for ~15% of the ensemble mean for both the precipitation and evapotranspiration. An increasing trend of 0.03 mm month−1 in the residual errors may be partially attributable to increases in human activity and the relative redistribution of biases among other water budget variables. All three closure techniques show similar directions of constraints (i.e., wet or dry bias) in water budget variables with slightly different magnitudes. Our quantification of water budget residual errors may help benchmark regional hydroclimate models for understanding the past, present, and future status of water budget components and effectively manage regional water resources, especially during hydroclimate extremes.

Numerical approach of riemann-liouville fractional derivative operator

<p>This article introduces some new straightforward and yet powerful formulas in the form of series solutions together with their residual errors for approximating the Riemann-Liouville fractional derivative operator. These formulas are derived by utilizing some of forthright computations, and by utilizing the so-called weighted mean value theorem (WMVT). Undoubtedly, such formulas will be extremely useful in establishing new approaches for several solutions of both linear and nonlinear fractionalorder differential equations. This assertion is confirmed by addressing several linear and nonlinear problems that illustrate the effectiveness and the practicability of the gained findings.</p>

Performance of GPS Positioning in the Presence of Irregularities in the Auroral and Polar Ionospheres during EISCAT UHF/ESR Measurements

Irregularities in the spatial distribution of ionospheric electron density introduce temporal fluctuations in the intensity and phase of radio signals received from Global Navigation Satellite Systems (GNSS). The impact of phase fluctuations originating from irregularities in the auroral and polar ionospheres on GPS positioning was investigated on three days in March 2018 in the presence of quiet-to-moderately disturbed magnetic conditions by combining measurements from GPS and EISCAT UHF/ESR incoherent scatter radars. Two different positioning solutions were analysed: broadcast kinematic (BK) and precise static (PS). The results show that the propagation through irregularities induced residual errors on the observables leading to an increase in the positioning error, in its variability, and in the occurrence of gaps. An important aspect emerging from this study is that the variability of the 3-D positioning error was reduced, and the presence of gaps disappeared when the positioning solutions were evaluated at a 1 s rate rather than at a 30 s rate. This is due to the transient nature of residual errors that are more significant over 30 s time intervals in the presence of irregularities with scale size between few kilometres in the E region to few tens of kilometres in the F region.

Polypolish: short-read polishing of long-read bacterial genome assemblies

Long-read-only bacterial genome assemblies usually contain residual errors, most commonly homopolymer-length errors. Short-read polishing tools can use short reads to fix these errors, but most rely on short-read alignment which is unreliable in repeat regions. Errors in such regions are therefore challenging to fix and often remain after short-read polishing. Here we introduce Polypolish, a new short-read polisher which uses all-per-read alignments to repair errors in repeat sequences that other polishers cannot. In benchmarking tests using both simulated and real reads, we find that Polypolish performs well, and the best results are achieved by using Polypolish in combination with other short-read polishers.

Automated Optimization of Residual Reduction Algorithm Parameters in OpenSim

The residual reduction algorithm (RRA) in OpenSim improves dynamic consistency of movement simulations of musculoskeletal models. RRA requires the user to select numerous tracking weights for the joint kinematics to reduce residual errors. Selection is often performed manually, which can be time-consuming and is unlikely to yield optimal tracking weights. A multi-heuristic optimization algorithm was employed to expedite tracking weight decision making to reduce residual errors. This method produced more rigorous results than manual iterations and although the total computation time was not significantly reduced, this method does not require the user to monitor the algorithm's progress to find a solution, thereby reducing manual tuning. Supporting documentation and code to implement this optimization is freely provided to assist the community with developing movement simulations.

Comparison of methods for sensitivity correction in Talbot–Lau computed tomography

Purpose In Talbot–Lau X-ray phase contrast imaging, the measured phase value depends on the position of the object in the measurement setup. When imaging large objects, this may lead to inhomogeneous phase contributions within the object. These inhomogeneities introduce artifacts in tomographic reconstructions of the object. Methods In this work, we compare recently proposed approaches to correct such reconstruction artifacts. We compare an iterative reconstruction algorithm, a known operator network and a U-net. The methods are qualitatively and quantitatively compared on the Shepp–Logan phantom and on the anatomy of a human abdomen. We also perform a dedicated experiment on the noise behavior of the methods. Results All methods were able to reduce the specific artifacts in the reconstructions for the simulated and virtual real anatomy data. The results show method-specific residual errors that are indicative for the inherently different correction approaches. While all methods were able to correct the artifacts, we report a different noise behavior. Conclusion The iterative reconstruction performs very well, but at the cost of a high runtime. The known operator network shows consistently a very competitive performance. The U-net performs slightly worse, but has the benefit that it is a general-purpose network that does not require special application knowledge.

Global Residue Harmonic Balance Method for a System of Strongly Nonlinear Oscillator

In this paper, the global residue harmonic balance method is applied to obtain the approximate periodic solution and frequency for a well-known system of strongly nonlinear oscillator in engineering. This method can improve accuracy by considering all the residual errors in deriving each order approximation. With this procedure, the expressions of the higher-order approximate solution and corresponding frequency for the considered system can be determined easily. The comparison of the obtained results with previously existing and corresponding exact solutions shows the high accuracy and efficiency of the method.

Mitigating baryonic effects with a theoretical error covariance

One of the primary sources of uncertainties in modeling the cosmic-shear power spectrum on small scales is the effect of baryonic physics. Accurate cosmology for Stage-IV surveys requires knowledge of the matter power spectrum deep in the nonlinear regime at the percent level. Therefore, it is important to develop reliable mitigation techniques to take into account baryonic uncertainties if information from small scales is to be considered in the cosmological analysis. In this work, we develop a new mitigation method for dealing with baryonic physics for the case of the shear angular power spectrum. The method is based on an augmented covariance matrix that incorporates baryonic uncertainties informed by hydrodynamical simulations. We use the results from 13 hydrodynamical simulations and the residual errors arising from a fit to a ΛCDM model using the extended halo model code HMCode to account for baryonic physics. These residual errors are used to model a so-called theoretical error covariance matrix that is added to the original covariance matrix. In order to assess the performance of the method, we use the 2D tomographic shear from four hydrodynamical simulations that have different extremes of baryonic parameters as mock data and run a likelihood analysis comparing the residual bias on Ωm and σ8 of our method and the HMCode for an LSST-like survey. We use different modelling of the theoretical error covariance matrix to test the robustness of the method. We show that it is possible to reduce the bias in the determination of the tested cosmological parameters at the price of a modest decrease in the precision.

Clarity from Chaos

Experimentalists interpret their observations as a combination of effects arising from their models of understanding and residual errors. Successive refinements of theory can reduce the observed discrepancies in a systematic way. All experimental findings are provisional, and the associated uncertainty can be quantified. Correlation does not imply causation, but causation is what we really seek: how can it be established? The process of establishing cigarette smoking as a leading cause of lung cancer is taken as a case study. General approaches to understanding causation are described.