Merging multiple precipitation sources for flash flood forecasting

We investigated the effectiveness of combining gauge observations and satellite-derived precipitation on flood forecasting. Two data merging processes were proposed: the first one assumes that the individual precipitation measurement is non-bias, while the second process assumes that each precipitation source is biased and both weighting factor and bias parameters are to be calculated. Best weighting factors as well as the bias parameters were calculated by minimizing the error of hourly runoff prediction over Wu-Tu watershed in Taiwan. To simulate the hydrologic response from various sources of rainfall sequences, in our experiment, a recurrent neural network (RNN) model was used. The results demonstrate that the merged method used in this study can efficiently combine the information from both rainfall sources to improve the accuracy of flood forecasting during typhoon periods. The contribution of satellite-based rainfall, being represented by the weighting factor, to the merging product, however, is highly related to the effectiveness of ground-based rainfall observation provided gauged. As the number of gauge observations in the basin is increased, the effectiveness of satellite-based observation to the merged rainfall is reduced. This is because the gauge measurements provide sufficient information for flood forecasting; as a result the improvements added on satellite-based rainfall are limited. This study provides a potential advantage for extending satellite-derived precipitation to those watersheds where gauge observations are limited. © 2007 Elsevier B.V. All rights reserved

Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information

The complex temporal heterogeneity of rainfall coupled with mountainous physiographic context makes a great challenge in the development of accurate short-term rainfall forecasts. This study aims to explore the effectiveness of multiple rainfall sources (gauge measurement, and radar and satellite products) for assimilation-based multi-sensor precipitation estimates and make multi-step-ahead rainfall forecasts based on the assimilated precipitation. Bias correction procedures for both radar and satellite precipitation products were first built, and the radar and satellite precipitation products were generated through the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), respectively. Next, the synthesized assimilated precipitation was obtained by merging three precipitation sources (gauges, radars and satellites) according to their individual weighting factors optimized by nonlinear search methods. Finally, the multi-step-ahead rainfall forecasting was carried out by using the adaptive network-based fuzzy inference system (ANFIS). The Shihmen Reservoir watershed in northern Taiwan was the study area, where 641 hourly data sets of thirteen historical typhoon events were collected. Results revealed that the bias adjustments in QPESUMS and PERSIANN-CCS products did improve the accuracy of these precipitation products (in particular, 30-60% improvement rates for the QPESUMS, in terms of RMSE), and the adjusted PERSIANN-CCS and QPESUMS individually provided about 10% and 24% contribution accordingly to the assimilated precipitation. As far as rainfall forecasting is concerned, the results demonstrated that the ANFIS fed with the assimilated precipitation provided reliable and stable forecasts with the correlation coefficients higher than 0.85 and 0.72 for one- and two-hour-ahead rainfall forecasting, respectively. The obtained forecasting results are very valuable information for the flood warning in the study watershed during typhoon periods. © 2013 Elsevier B.V

Collisional angular momentum depolarization of OH(A) and NO(A) by Ar: A comparison of mechanisms

This paper discusses the contrasting mechanisms of collisional angular momentum depolarization of OH(A(2)Σ(+)) and NO(A(2)Σ(+)) by Ar. New experimental results are presented for the collisional depolarization of OH(A) + Ar under both thermal and superthermal collision conditions, including cross sections for loss of both angular momentum orientation and alignment. Previous work on the two systems is summarized. It is shown that NO(A) + Ar depolarization is dominated by impulsive events in which the projection of the angular momentum, j, along the kinematic apse, a, is nearly conserved, and in which the majority of the trajectories can be described as "nearside." By contrast, at the relatively low collision energies sampled at 300 K, OH(A) + Ar depolarization is dominated by attractive collisions, which show a preponderance of "farside" trajectories. There is also evidence for very long-lived, complex type trajectories in which OH(A) and Ar orbit each other for several rotational periods prior to separation. Nevertheless, there is still a clear preference for conservation of the projection of j along the kinematic apse for both elastic and inelastic collisions. Experimental and theoretical results reveal that, as the collision energy is raised, the depolarization of OH(A) by Ar becomes more impulsive-like in nature

A new potential energy surface for OH(A 2Σ+)–Kr: The van der Waals complex and inelastic scattering

New ab initio studies of the OH(A(2)Σ(+))-Kr system reveal significantly deeper potential energy wells than previously believed, particularly for the linear configuration in which Kr is bound to the oxygen atom side of OH(A(2)Σ(+)). In spite of this difference with previous work, bound state calculations based on a new RCCSD(T) potential energy surface yield an energy level structure in reasonable accord with previous studies. However, the new calculations suggest the need for a reassignment of the vibrational levels of the electronically excited complex. Quantum mechanical and quasi-classical trajectory scattering calculations are also performed on the new potential energy surface. New experimental measurements of rotational inelastic scattering cross sections are reported, obtained using Zeeman quantum beat spectroscopy. The values of the rotational energy transfer cross sections measured experimentally are in good agreement with those derived from the dynamical calculations on the new adiabatic potential energy surface

Collisional depolarization of NO(A) by He and Ar studied by quantum beat spectroscopy

Zeeman and hyperfine quantum beat spectroscopies have been used to measure the total elastic plus inelastic angular momentum depolarization rate constants at 300 K for NO (A 2 σ+) in the presence of He and Ar. In the case of Zeeman quantum beats it is shown how the applied magnetic field can be used to allow measurement of depolarization rates for both angular momentum orientation and alignment. For the systems studied here, collisional loss of alignment is more efficient than loss of orientation. In the case of NO (A) with He, and to a lesser extent NO (A) with Ar, collisional depolarization is found to be a relatively minor process compared to rotational energy transfer, reflecting the very weak long-range forces in these systems. Detailed comparisons are made with quantum mechanical and quasiclassical trajectory calculations performed on recently developed potential energy surfaces. For both systems, the agreement between the calculated depolarization cross sections and the present measurements is found to be very good, suggesting that it is reasonable to consider the NO (A) bond as frozen during these angular momentum transferring collisions. A combination of kinematic effects and differences in the potential energy surfaces are shown to be responsible for the differences observed in depolarization cross section with He and Ar as a collider. © 2009 American Institute of Physics

A single sub-km Kuiper Belt object from a stellar Occultation in archival data

The Kuiper belt is a remnant of the primordial Solar System. Measurements of its size distribution constrain its accretion and collisional history, and the importance of material strength of Kuiper belt objects (KBOs). Small, sub-km sized, KBOs elude direct detection, but the signature of their occultations of background stars should be detectable. Observations at both optical and X-ray wavelengths claim to have detected such occultations, but their implied KBO abundances are inconsistent with each other and far exceed theoretical expectations. Here, we report an analysis of archival data that reveals an occultation by a body with a 500 m radius at a distance of 45 AU. The probability of this event to occur due to random statistical fluctuations within our data set is about 2%. Our survey yields a surface density of KBOs with radii larger than 250 m of 2.1^{+4.8}_{-1.7} x 10^7 deg^{-2}, ruling out inferred surface densities from previous claimed detections by more than 5 sigma. The fact that we detected only one event, firmly shows a deficit of sub-km sized KBOs compared to a population extrapolated from objects with r>50 km. This implies that sub-km sized KBOs are undergoing collisional erosion, just like debris disks observed around other stars.Comment: To appear in Nature on December 17, 2009. Under press embargo until 1800 hours London time on 16 December. 19 pages; 7 figure

Electrochemical polymerisation of phenol in aqueous solution on a Ta/PbO2 anode

This paper deals with the treatment of aqueous phenol solutions using an electrochemical technique. Phenol can be partly eliminated from aqueous solution by electrochemically initiated polymerisation. Galvanostatic electrolyses of phenol solutions at concentration up to 0.1 mol dm−3 were carried out on a Ta/PbO2 anode. The polymers formed are insoluble in acidic medium but soluble in alkaline. These polymers were filtered and then dissolved in aqueous solution of sodium hydroxide (1 mol dm−3). The polymers formed were quantified by total organic carbon (TOC) measurement. It was found that the conversion of phenol into polymers increases as a function of initial concentration, anodic current density, temperature, and solution pH. The percentage of phenol polymerised can reach 15%

The collisional depolarization of OH(A 2Σ+) and NO(A 2Σ+) with Kr

Quantum beat spectroscopy has been used to measure rate coefficients at 300 K for collisional depolarization for NO(A 2Σ+) and OH(A 2Σ+) with krypton. Elastic depolarization rate coefficients have also been determined for OH(A) + Kr, and shown to make a much more significant contribution to the total depolarization rate than for NO(A) + Kr. While the experimental data for NO(A) + Kr are in excellent agreement with single surface quasiclassical trajectory (QCT) calculations carried out on the upper 2A ′ potential energy surface, the equivalent QCT and quantum mechanical calculations cannot account for the experimental results for OH(A) + Kr collisions, particularly at low N. This disagreement is due to the presence of competing electronic quenching at low N, which requires a multi-surface, non-adiabatic treatment. Somewhat improved agreement with experiment is obtained by means of trajectory surface hopping calculations that include non-adiabatic coupling between the ground 1A ′ and excited 2A ′ states of OH(X/A) + Kr, although the theoretical depolarization cross sections still significantly overestimate those obtained experimentally

Prediction-Coherent LSTM-based Recurrent Neural Network for Safer Glucose Predictions in Diabetic People

In the context of time-series forecasting, we propose a LSTM-based recurrent neural network architecture and loss function that enhance the stability of the predictions. In particular, the loss function penalizes the model, not only on the prediction error (mean-squared error), but also on the predicted variation error. We apply this idea to the prediction of future glucose values in diabetes, which is a delicate task as unstable predictions can leave the patient in doubt and make him/her take the wrong action, threatening his/her life. The study is conducted on type 1 and type 2 diabetic people, with a focus on predictions made 30-minutes ahead of time. First, we confirm the superiority, in the context of glucose prediction, of the LSTM model by comparing it to other state-of-the-art models (Extreme Learning Machine, Gaussian Process regressor, Support Vector Regressor). Then, we show the importance of making stable predictions by smoothing the predictions made by the models, resulting in an overall improvement of the clinical acceptability of the models at the cost in a slight loss in prediction accuracy. Finally, we show that the proposed approach, outperforms all baseline results. More precisely, it trades a loss of 4.3\% in the prediction accuracy for an improvement of the clinical acceptability of 27.1\%. When compared to the moving average post-processing method, we show that the trade-off is more efficient with our approach