•A dynamic Bayesian framework is proposed to merge 6-h multi-model soil moisture.•The dynamic Bayesian model averaging (BMA) method proves to be adaptive and robust.•Selecting a subset of soil ...moisture model products as the BMA members is optimal.•The dynamic BMA framework can be used for drought monitoring and prediction.
Accurate estimation of soil moisture (SM) from satellite products and model simulations at sub-daily timescale remains a challenge. This study proposes a general dynamic Bayesian model averaging (BMA) framework for merging sub-daily model products. Compared to the traditional BMA method, this study introduces adaptive weights (dynamically variant with time) for BMA members. Based on the previous evaluation work, a subset of model products is selected from eight model products as BMA members. The dynamic BMA experiment is performed for the surface SM (0–10 cm) model products at sub-daily (6-h) timescale in 2017 over the Yangtze-Huaihe river basin. The results are compared with the automatic SM observations (ASMOs) with unprecedented high spatial and temporal resolution (up to 7 stations within a 104 km2 pixel; hourly). Because weather pattern and model performance change over time, the determination of an optimal training period is critical to obtain adaptive BMA weights for rapid weather regime changes. The sensitivity of training length (days) is then examined, and the optimum data length used in the BMA training period proves to be about 80 days. With deterministic and probabilistic verification metrics, the dynamic BMA estimated SM is comprehensively evaluated against the ASMOs, eight global model products, and the CMA’s (China Meteorological Administration) regional Land Data Assimilation System (CLDAS) product. To better compare the probability distribution of different products, the cumulative distribution function (CDF) consistency histogram and a more objective metric consistency deviation (CD) are proposed to diagnose the consistency of two SM CDFs (e.g., the BMA estimated and the observed CDF). In terms of both the deterministic (the Kling-Gupta efficiency, correlation, system bias, and bias adjusted root-mean square error) and probabilistic verification methods (CD, QQ-plots, and reliability), the dynamic BMA estimated SM outperforms any BMA members and even the benchmark product CLDAS. This study demonstrates that the dynamic BMA framework provides a new solution for merging SM model products. The merged SM and the BMA combined probability distribution can be further used for drought monitoring and prediction.
Abstract
A feed-forward neural network is configured to calibrate the bias of a high-resolution probabilistic quantitative precipitation forecast (PQPF) produced by a 12-km version of the NCEP ...Regional Spectral Model (RSM) ensemble forecast system. Twice-daily forecasts during the 2002–2003 cool season (1 November–31 March, inclusive) are run over four U.S. Geological Survey (USGS) hydrologic unit regions of the southwest United States. Calibration is performed via a cross-validation procedure, where four months are used for training and the excluded month is used for testing. The PQPFs before and after the calibration over a hydrological unit region are evaluated by comparing the joint probability distribution of forecasts and observations. Verification is performed on the 4-km stage IV grid, which is used as “truth.” The calibration procedure improves the Brier score (BrS), conditional bias (reliability) and forecast skill, such as the Brier skill score (BrSS) and the ranked probability skill score (RPSS), relative to the sample frequency for all geographic regions and most precipitation thresholds. However, the procedure degrades the resolution of the PQPFs by systematically producing more forecasts with low nonzero forecast probabilities that drive the forecast distribution closer to the climatology of the training sample. The problem of degrading the resolution is most severe over the Colorado River basin and the Great Basin for relatively high precipitation thresholds where the sample of observed events is relatively small.
This paper introduces a unified approach for modeling high-frequency financial data that can accommodate both the continuous-time jump–diffusion and discrete-time realized GARCH model by embedding ...the discrete realized GARCH structure in the continuous instantaneous volatility process. The key feature of the proposed model is that the corresponding conditional daily integrated volatility adopts an autoregressive structure, where both integrated volatility and jump variation serve as innovations. We name it as the realized GARCH-Itô model. Given the autoregressive structure in the conditional daily integrated volatility, we propose a quasi-likelihood function for parameter estimation and establish its asymptotic properties. To improve the parameter estimation, we propose a joint quasi-likelihood function that is built on the marriage of daily integrated volatility estimated by high-frequency data and nonparametric volatility estimator obtained from option data. We conduct a simulation study to check the finite sample performance of the proposed methodologies and an empirical study with the S&P500 stock index and option data.
•Construct error correction ensembles of three satellite rainfall products.•Comprehensively assess the streamflow ensemble simulations at multiple stations.•The framework shows high potential for ...flood monitoring.
This study aims to investigate the hydrologic applicability of an error correction method – SREM2D (two-dimensional stochastic satellite rainfall error model) to three satellite precipitation products in streamflow simulations. Three satellite precipitation products, including the Tropical Rainfall Measuring Mission (TRMM) Multiple-Satellite Precipitation Analysis (TMPA) real-time 3B42 product (3B42RT), the Climate Prediction Centre (CPC) morphing technique (CMORPH) gauge merged product (CMORPH BLD), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network – Climate Data Record (PERSIANN CDR), are corrected using SREM2D. Over the upper Huaihe river basin, streamflow ensemble simulations are derived by forcing the distributed Variable Infiltration Capacity (VIC) model with the SREM2D-based rainfall ensemble.
After applying SREM2D to satellite precipitation products, the streamflow simulations forced by TMPA 3B42RT and PERSIANN CDR rainfall ensembles are capable to capture flood peaks. However, the streamflow simulations forced by CMORPH BLD rainfall ensemble show poor performance for the extreme events, but exhibit good accuracy in non-flood flow simulation. The calibration of the model over the headwater subbasin betters the streamflow simulation, especially for reproducing extreme events during the main flood cases. Overall, SREM2D provides great potential to facilitate the application of satellite precipitation products in water management and decision making over Chinese river basins.
•Generalized polynomial chaos expansion (PCE) is applied to quantify uncertainty of hydrologic parameters.•PCE offers an efficient way of sampling without running original model.•Computational cost ...using the PCE approach is significantly reduced by 71% (20%) with PCE-2 (PCE-3) when compared to MC methodology.
An integrated framework is proposed for parametric uncertainty analysis in hydrological modeling using a generalized polynomial chaos expansion (PCE) approach. PCE represents model output as a polynomial expression in terms of critical random variables that are determined by parameter uncertainties, thus offers an efficient way of sampling without running the original model, which is appealing to computationally expensive models. To demonstrate the applicability of generalized PCE approach, both second- and third-order PCEs (PCE-2 and PCE-3) are constructed for Xinanjiang hydrological model using three selected uncertain parameters. Uncertainties in streamflow predictions are assessed by sampling the random inputs. Results show that: (1) both PCE-2 and PCE-3 are capable of capturing the uncertainty information in hydrological predictions, generating consistent mean, variance, skewness and kurtosis estimates with the standard Monte Carlo (MC) methodology; (2) Using more collocation points and more polynomial terms, PCE-3 approximation slightly improves the model simulation and provides more matched distribution with that of MC compared to PCE-2; (3) the computational cost using the PCE approach is greatly reduced by 71% (20%) with PCE-2 (PCE-3). In general, PCE-2 is recommended to serve as a good surrogate model for Xinanjiang hydrological modelling in future with much higher computation speed, more efficient sampling, and compatible approximation results.
Enzymes and cell factories play essential roles in industrial biotechnology for the production of chemicals and fuels. The properties of natural enzymes and cells often cannot meet the requirements ...of different industrial processes in terms of cost-effectiveness and high durability. To rapidly improve their properties and performances, laboratory evolution equipped with high-throughput screening methods and facilities is commonly used to tailor the desired properties of enzymes and cell factories, addressing the challenges of achieving high titer and the yield of the target products at high/low temperatures or extreme pH, in unnatural environments or in the presence of unconventional media. Droplet microfluidic screening (DMFS) systems have demonstrated great potential for exploring vast genetic diversity in a high-throughput manner (>10
6
/h) for laboratory evolution and have been increasingly used in recent years, contributing to the identification of extraordinary mutants. This review highlights the recent advances in concepts and methods of DMFS for library screening, including the key factors in droplet generation and manipulation, signal sources for sensitive detection and sorting, and a comprehensive summary of success stories of DMFS implementation for engineering enzymes and cell factories during the past decade.
Ensemble forecasting plays an important role in studying the precipitation forecast errors of landfalling tropical cyclones. In this study, convective-scale ensemble forecasts based on the WRF model ...are conducted, focusing on the heavy precipitation of typhoon Lekima (2019) in Zhejiang, China. The perturbations of the initial/boundary conditions (ICs/BCs) and the microphysical schemes are constructed to represent the uncertainties of ensemble forecasts. The sensitivity of different perturbations on ensemble forecasts and the precipitation forecast errors are discussed. The results show the IC/BC perturbations have a greater impact on ensemble precipitation forecasts. An advantaged group and two disadvantaged groups are selected from 63 ensemble members based on the typhoon track errors, and the precipitation forecasts of the advantaged group have the highest forecast skill scores. The forecast errors are highly correlated with the intensity of the subtropical high (SH) and the steering flow of typhoon. The significantly weak SH in the ICs/BCs leads to a weaker SH in the forecasts, resulting in an easterly and slower typhoon motion and more accumulated precipitation. Also, the precipitation forecast errors are highly associated with the simulation errors of typhoon structure in different landfall stages, while the vertical wind shear may cause these errors. In addition, the orographic convergence and uplift promote the precipitation production, but the model tends to overestimate the orographic effects. About the simulation of hydrometeors, large snow content leads to increased precipitation in the Thompson scheme, while in the WDM6 scheme, the strong evaporation of low-level raindrops results in decreased precipitation.
•The initial and boundary conditions, especially the subtropical high, have great impacts on typhoon track and precipitation.•The inaccurate simulation of typhoon structure and overestimated orographic effects are significant precipitation error sources.•The different hydrometeor distributions in microphysical schemes may lead to the errors in precipitation magnitude.
A yellowish Ba3BP3O12:Mn2+ phosphor was fabricated by the simple solid state reaction under ambient atmosphere. With different Mn sources (KMnO4/MnO2/MnCO3), a broadband emission peaked at 576 nm was ...acquired for all the samples, which is ascribed to the 4T1(4G) → 6A1(6S) transition of Mn2+. The luminescence spectra of the samples prepared in air similar to those prepared in reducing atmosphere demonstrated that the high valence state Mn ions were spontaneously reduced to Mn2+ in Ba3BP3O12. The corresponding self-reduction mechanism was investigated by XRD Rietveld refinement, photoluminescence spectroscopy (PL), and electron paramagnetic resonance (EPR), revealing that interstitial oxygen ions induced by non-equivalent substitution of high valence state Mn at two Ba sites is critical to such process. The obtained results not only provide a better understanding on the self-reduction of Mn2+, but also propose a significant way to develop novel Mn2+ activated phosphor applied in white LEDs.
•A new kind of Mn2+ activated Ba3BP3O12 phosphor prepared in air with different starting Mn source has been proposed.•A yellowish broadband luminescence covering 500 nm–750 nm that can be applied in white pc-LEDs is acquired in Ba3BP3O12:Mn2+ phosphor.•The self-reduction mechanism of Mn2+ in Ba3BP3O12 is related to the interstitial oxygen ions.
High-resolution (3 km) time-lagged (initialized every 3 h) multimodel ensembles were produced in support of the Hydrometeorological Testbed (HMT)-West-2006 campaign in northern California, covering ...the American River basin (ARB). Multiple mesoscale models were used, including the Weather Research and Forecasting (WRF) model, Regional Atmospheric Modeling System (RAMS), and fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). Short-range (6 h) quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) were compared to the 4-km NCEP stage IV precipitation analyses for archived intensive operation periods (IOPs). The two sets of ensemble runs (operational and rerun forecasts) were examined to evaluate the quality of high-resolution QPFs produced by time-lagged multimodel ensembles and to investigate the impacts of ensemble configurations on forecast skill. Uncertainties in precipitation forecasts were associated with different models, model physics, and initial and boundary conditions. The diabatic initialization by the Local Analysis and Prediction System (LAPS) helped precipitation forecasts, while the selection of microphysics was critical in ensemble design. Probability biases in the ensemble products were addressed by calibrating PQPFs. Using artificial neural network (ANN) and linear regression (LR) methods, the bias correction of PQPFs and a cross-validation procedure were applied to three operational IOPs and four rerun IOPs. Both the ANN and LR methods effectively improved PQPFs, especially for lower thresholds. The LR method outperformed the ANN method in bias correction, in particular for a smaller training data size. More training data (e.g., one-season forecasts) are desirable to test the robustness of both calibration methods.
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