Probabilistic Flood Prediction in the Upper Huaihe Catchment Using TIGGE Data

Based on the precipitation and temperature data obtained from THORPEX （The Observing System Research and Predictability Experiment） Interactive Grand Global Ensemble （TIGGE）-China Meteorological Administration （CMA） archiving center and the raingauge data, the three-layer variable infiltration capacity （VIC-3L） land surface model was employed to carry out probabilistic hydrological forecast experiments over the upper Huaihe River catchment from 20 July to 3 August 2008. The results show that the performance of the ensemble probabilistic prediction from each ensemble prediction system （EPS） is better than that of the deterministic prediction. Especially, the 72-h prediction has been improved obviously. The ensemble spread goes widely with increasing lead time and more observed discharge is bracketed in the 5th-99th quantile. The accuracy of river discharge prediction driven by the European Centre （EC）-EPS is higher than that driven by the CMA-EPS and the US National Centers for Environmental Prediction （NCEP）-EPS, and the grand-ensemble prediction is the best for hydrological prediction using the VIC model. With regard to Wangjiaba station, all predictions made with a single EPS are close to the observation between the 25th and 75th quantile. The onset of the flood ascending and the river discharge thresholds are predicted well, and so is the second rising limb. Nevertheless, the flood recession is not well predicted.

A Comparison of De-noising Methods for Differential Phase Shift and Associated Rainfall Estimation

Measured differential phase shift ΦDP is known to be a noisy unstable polarimetric radar variable, such that the quality of ΦDP data has direct impact on specific differential phase shift KDP estimation, and subsequently, the KDP-based rainfall estimation. Over the past decades, many ΦDP de-noising methods have been developed; however, the de-noising effects in these methods and their impact on KDP-based rainfall estimation lack comprehensive comparative analysis. In this study, simulated noisy ΦDP data were generated and de-noised by using several methods such as finite-impulse response（FIR）, Kalman, wavelet,traditional mean, and median filters. The biases were compared between KDP from simulated and observedΦDP radial profiles after de-noising by these methods. The results suggest that the complicated FIR, Kalman,and wavelet methods have a better de-noising effect than the traditional methods. After ΦDP was de-noised,the accuracy of the KDP-based rainfall estimation increased significantly based on the analysis of three actual rainfall events. The improvement in estimation was more obvious when KDP was estimated with ΦDP de-noised by Kalman, FIR, and wavelet methods when the average rainfall was heavier than 5 mm h-1.However, the improved estimation was not significant when the precipitation intensity further increased to a rainfall rate beyond 10 mm h-1. The performance of wavelet analysis was found to be the most stable of these filters.

Characteristics of the water-soluble components of aerosol particles in Hefei,China

Size-classified daily aerosol mass concentrations and concentrations of water-soluble inorganic ions were measured in Hefei,China,in four representative months between September 2012 and August 2013.An annual average mass concentration of 169.09μg/m^3 for total suspended particulate（TSP）was measured using an Andersen Mark-II cascade impactor.The seasonal average mass concentration was highest in winter（234.73μg/m^3）and lowest in summer（91.71μg/m^3）.Water-soluble ions accounted for 59.49%,32.90%,48.62%and 37.08%of the aerosol mass concentration in winter,spring,summer,and fall,respectively,which indicated that ionic species were the primary constituents of the atmospheric aerosols.The four most abundant ions were NO3^-,SO4^2-,Ca^2＋ and NH4^＋.With the exception of Ca^2＋,the mass concentrations of water-soluble ions were in an intermediate range compared with the levels for other Chinese cities.Sulfate,nitrate,and ammonium were the dominant fine-particle species,which were bimodally distributed in spring,summer and fall;however,the size distribution became unimodal in winter,with a peak at 1.1–2.1μm.The Ca^2＋ peak occurred at approximately 4.7–5.8μm in all seasons.The cation to anion ratio was close to 1.4,which suggested that the aerosol particles were alkalescent in Hefei.The average NO3^-/SO4^2-mass ratio was 1.10 in Hefei,which indicated that mobile source emissions were predominant.Significant positive correlation coefficients between the concentrations of NH4^＋ and SO4^2-,NH4^＋ and NO3^-,SO4^2-and NO3^-,and Mg^2＋ and Ca^2＋ were also indicated,suggesting that aerosol particles may be present as（NH4）2SO4,NH4HSO4,and NH4NO3.

QpefBD:A Benchmark Dataset Applied to Machine Learning for Minute-Scale Quantitative Precipitation Estimation and Forecasting

Nowcasts of strong convective precipitation and radar-based quantitative precipitation estimations have always been hot yet challenging issues in meteorological sciences.Data-driven machine learning,especially deep learning,provides a new technical approach for the quantitative estimation and forecasting of precipitation.A high-quality,large-sample,and labeled training dataset is critical for the successful application of machine-learning technology to a specific field.The present study develops a benchmark dataset that can be applied to machine learning for minutescale quantitative precipitation estimation and forecasting(QpefBD),containing 231,978 samples of 3185 heavy precipitation events that occurred in 6 provinces of central and eastern China from April to October 2016-2018.Each individual sample consists of 8 products of weather radars at 6-min intervals within the time window of the corresponding event and products of 27 physical quantities at hourly intervals that describe the atmospheric dynamic and thermodynamic conditions.Two data labels,i.e.,ground precipitation intensity and areal coverage of heavy precipitation at 6-min intervals,are also included.The present study describes the basic components of the dataset and data processing and provides metrics for the evaluation of model performance on precipitation estimation and forecasting.Based on these evaluation metrics,some simple and commonly used methods are applied to evaluate precipitation estimates and forecasts.The results can serve as the benchmark reference for the performance evaluation of machine learning models using this dataset.This paper also gives some suggestions and scenarios of the QpefBD application.We believe that the application of this benchmark dataset will promote interdisciplinary collaboration between meteorological sciences and artificial intelligence sciences,providing a new way for the identification and forecast of heavy precipitation.