Investigating the impact of remotely sensed precipitation and hydrologic model uncertainties on the ensemble streamflow forecasting

In the past few years sequential data assimilation (SDA) methods have emerged as the best possible method at hand to properly treat all sources of error in hydrological modeling. However, very few studies have actually implemented SDA methods using realistic input error models for precipitation. In this study we use particle filtering as a SDA method to propagate input errors through a conceptual hydrologic model and quantify the state, parameter and streamflow uncertainties. Recent progress in satellite-based precipitation observation techniques offers an attractive option for considering spatiotemporal variation of precipitation. Therefore, we use the PERSIANN-CCS precipitation product to propagate input errors through our hydrologic model. Some uncertainty scenarios are set up to incorporate and investigate the impact of the individual uncertainty sources from precipitation, parameters and also combined error sources on the hydrologic response. Also probabilistic measure are used to quantify the quality of ensemble prediction. Copyright 2006 by the American Geophysical Union

Anisotropic superconducting properties of aligned Sm0.95_{0.95}La0.05_{0.05}FeAsO0.85_{0.85}F0.15_{0.15} microcrystalline powder

The Sm$_{0.95}$La$_{0.05}$FeAsO$_{0.85}$F$_{0.15}$ compound is a quasi-2D layered superconductor with a superconducting transition temperature T$_c$ = 52 K. Due to the Fe spin-orbital related anisotropic exchange coupling (antiferromagnetic or ferromagnetic fluctuation), the tetragonal microcrystalline powder can be aligned at room temperature using the field-rotation method where the tetragonal $\it{ab}$-plane is parallel to the aligned magnetic field B$_{a}$ and $\it{c}$-axis along the rotation axis. Anisotropic superconducting properties with anisotropic diamagnetic ratio $\chi_{c}/\chi_{ab}\sim$ 2.4 + 0.6 was observed from low field susceptibility $\chi$(T) and magnetization M(B$_{a}$). The anisotropic low-field phase diagram with the variation of lower critical field gives a zero-temperature penetration depth $\lambda_{c}$(0) = 280 nm and $\lambda_{ab}$(0) = 120 nm. The magnetic fluctuation used for powder alignment at 300 K may be related with the pairing mechanism of superconductivity at lower temperature.Comment: 4 pages, 6 figure

Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model

In general, there are no long-term meteorological or hydrological data available for karst river basins. The lack of rainfall data is a great challenge that hinders the development of hydrological models. Quantitative precipitation estimates (QPEs) based on weather satellites offer a potential method by which rainfall data in karst areas could be obtained. Furthermore, coupling QPEs with a distributed hydrological model has the potential to improve the precision of flood predictions in large karst watersheds. Estimating precipitation from remotely sensed information using an artificial neural network-cloud classification system (PERSIANN-CCS) is a type of QPE technology based on satellites that has achieved broad research results worldwide. However, only a few studies on PERSIANN-CCS QPEs have occurred in large karst basins, and the accuracy is generally poor in terms of practical applications. This paper studied the feasibility of coupling a fully physically based distributed hydrological model, i.e., the Liuxihe model, with PERSIANN-CCS QPEs for predicting floods in a large river basin, i.e., the Liujiang karst river basin, which has a watershed area of 58 270 km-2, in southern China. The model structure and function require further refinement to suit the karst basins. For instance, the sub-basins in this paper are divided into many karst hydrology response units (KHRUs) to ensure that the model structure is adequately refined for karst areas. In addition, the convergence of the underground runoff calculation method within the original Liuxihe model is changed to suit the karst water-bearing media, and the Muskingum routing method is used in the model to calculate the underground runoff in this study. Additionally, the epikarst zone, as a distinctive structure of the KHRU, is carefully considered in the model. The result of the QPEs shows that compared with the observed precipitation measured by a rain gauge, the distribution of precipitation predicted by the PERSIANN-CCS QPEs was very similar. However, the quantity of precipitation predicted by the PERSIANN-CCS QPEs was smaller. A post-processing method is proposed to revise the products of the PERSIANN-CCS QPEs. The karst flood simulation results show that coupling the post-processed PERSIANN-CCS QPEs with the Liuxihe model has a better performance relative to the result based on the initial PERSIANN-CCS QPEs. Moreover, the performance of the coupled model largely improves with parameter re-optimization via the post-processed PERSIANN-CCS QPEs. The average values of the six evaluation indices change as follows: the Nash-Sutcliffe coefficient increases by 14 %, the correlation coefficient increases by 15 %, the process relative error decreases by 8 %, the peak flow relative error decreases by 18 %, the water balance coefficient increases by 8 %, and the peak flow time error displays a 5 h decrease. Among these parameters, the peak flow relative error shows the greatest improvement; thus, these parameters are of page1506 the greatest concern for flood prediction. The rational flood simulation results from the coupled model provide a great practical application prospect for flood prediction in large karst river basins

Merging high-resolution satellite-based precipitation fields and point-scale rain gauge measurements-A case study in Chile

With high spatial-temporal resolution, Satellite-based Precipitation Estimates (SPE) are becoming valuable alternative rainfall data for hydrologic and climatic studies but are subject to considerable uncertainty. Effective merging of SPE and ground-based gauge measurements may help to improve precipitation estimation in both better resolution and accuracy. In this study, a framework for merging satellite and gauge precipitation data is developed based on three steps, including SPE bias adjustment, gauge observation gridding, and data merging, with the objective to produce high-quality precipitation estimates. An inverse-root-mean-square-error weighting approach is proposed to combine the satellite and gauge estimates that are in advance adjusted and gridded, respectively. The model is applied and tested with the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) estimates (daily, 0.04° × 0.04°) over Chile, for the 6 year period of 2009-2014. Daily observations from about 90% of collected gauges over the study area are used for model calibration; the rest of the gauged data are regarded as ground “truth” for validation. Evaluation results indicate high effectiveness of the model in producing high-resolution-precision precipitation data. Compared to reference data, the merged data (daily) show correlation coefficients, probabilities of detection, root-mean-square errors, and absolute mean biases that were consistently improved from the original PERSIANN-CCS estimates. The cross-validation evidences that the framework is effective in providing high-quality estimates even over nongauged satellite pixels. The same method can be applied globally and is expected to produce precipitation products in near real time by integrating gauge observations with satellite estimates

Diurnal variability of tropical rainfall retrieved from combined GOES and TRMM satellite information

Recent progress in satellite remote-sensing techniques for precipitation estimation, along with more accurate tropical rainfall measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) instruments, have made it possible to monitor tropical rainfall diurnal patterns and their intensities from satellite information. One year (August 1998-July 1999) of tropical rainfall estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) systems were used to produce monthly means of rainfall diurnal cycles at hourly and 1° × 1° scales over a domain (30°S-30°N, 80°E-10°W) from the Americas across the Pacific Ocean to Australia and eastern Asia. The results demonstrate pronounced diurnal variability of tropical rainfall intensity at synoptic and regional scales. Seasonal signals of diurnal rainfall are presented over the large domain of the tropical Pacific Ocean, especially over the ITCZ and South Pacific convergence zone (SPCZ) and neighboring continents. The regional patterns of tropical rainfall diurnal cycles are specified in the Amazon, Mexico, the Caribbean Sea, Calcutta, Bay of Bengal, Malaysia, and northern Australia. Limited validations for the results include comparisons of 1) the PERSIANN-derived diurnal cycle of rainfall at Rondonia, Brazil, with that derived from the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) radar data; 2) the PERSIANN diurnal cycle of rainfall over the western Pacific Ocean with that derived from the data of the optical rain gauges mounted on the TOGA-moored buoys: and 3) the monthly accumulations of rainfall samples from the orbital TMI and PR surface rainfall with the accumulations of concurrent PERSIANN estimates. These comparisons indicate that the PERSIANN-derived diurnal patterns at the selected resolutions produce estimates that are similar in magnitude and phase

Synergetic regulation of translational reading-frame switch by ligand-responsive RNAs in mammalian cells

Distinct translational initiation mechanisms between prokaryotes and eukaryotes limit the exploitation of prokaryotic riboswitch repertoire for regulatory RNA circuit construction in mammalian application. Here, we explored programmed ribosomal frameshifting (PRF) as the regulatory gene expression platform for engineered ligand-responsive RNA devices in higher eukaryotes. Regulation was enabled by designed ligand-dependent conformational rearrangements of the two cis-acting RNA motifs of opposite activity in -1 PRF. Particularly, RNA elements responsive to trans-acting ligands can be tailored to modify co-translational RNA refolding dynamics of a hairpin upstream of frameshifting site to achieve reversible and adjustable -1 PRF attenuating activity. Combined with a ligand-responsive stimulator, synthetic RNA devices for synergetic translational-elongation control of gene expression can be constructed. Due to the similarity between co-transcriptional RNA hairpin folding and co-translational RNA hairpin refolding, the RNA-responsive ligand repertoire provided in prokaryotic systems thus becomes accessible to gene-regulatory circuit construction for synthetic biology application in mammalian cells

Intrapore-Texturized Vanadia-Hydrate Supercapacitor with Ultrahigh Area-Normalized Capacitance

A pressing need for ultrahigh area‐normalized capacitance emerges from the migration to miniaturized composite supercapacitors. Herein, an advanced electric field‐assisted sol–gel synthesis protocol that allows to obtain ribbon‐like vanadium oxides that preferentially creep along the porous tunnels in a commercially available carbon host of low density is demonstrated. In particular, this design offers 1) to convert the original submicrometer‐sized pore network into hierarchically macroporous yet 3D‐interconnected bicontinuous composite frameworks and 2) to considerably add pseudo‐capacitive functionalities onto a highly conductive carbon cloth backbone. Both are demonstrated by an unprecedented area‐normalized capacitance exceeding 5 F cm−2. Moreover, the as‐designed symmetric supercapacitor is characterized by a maximum area‐normalized cell capacitance in the order of 1 F cm−2, a geometric energy density of 0.34 mW h cm−2, and a geometric power density of 28.3 mW cm−2. These features outperform commercial double‐layer supercapacitors as well as many state‐of‐the‐art composite pseudo‐capacitors and lithium‐ion microbatteries

Precise Pressure Dependence of the Superconducting Transition Temperature of FeSe: Resistivity and ^77Se--NMR Study

We report the precise pressure dependence of FeSe from a resistivity measurement up to 4.15 GPa. Superconducting transition temperature (T_c) increases sensitively under pressure, but shows a plateau between 0.5-1.5 GPa. The maximum T_c, which is determined by zero resistance, is 21 K at approximately 3.5 GPa. The onset value reaches ~37 K at 4.15 GPa. We also measure the nuclear spin-lattice relaxation rate 1/T_1 under pressure using 77Se--NMR measurement. 1/T_1 shows that bulk superconductivity is realized in the zero-resistance state. The pressure dependence of 1/T_1T just above T_c shows a plateau as well as the pressure dependence of T_c, which gives clear evidence of the close relationship between 1/T_1T and T_c. Spin fluctuations are suggested to contribute to the mechanism of superconductivity.Comment: 4pages, 6figures: to be published in J. Phys. Soc. Jpn. Vol.78 No.6 (2009