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Hong-Bo Liu, Jing Yang, Da-Lin Zhang, and Bin Wang

Abstract

During the mei-yu season of the summer of 2003, the Yangtze and Huai River basin (YHRB) encountered anomalously heavy rainfall, and the northern YHRB (nYHRB) suffered a severe flood because of five continuous extreme rainfall events. A spectral analysis of daily rainfall data over YHRB reveals two dominant frequency modes: one peak on day 14 and the other on day 4 (i.e., the quasi-biweekly and synoptic-scale mode, respectively). Results indicate that the two scales of disturbances contributed southwesterly and northeasterly anomalies, respectively, to the mei-yu frontal convergence over the southern YHRB (sYHRB) at the peak wet phase. An analysis of bandpass-filtered circulations shows that the lower and upper regions of the troposphere were fully coupled at the quasi-biweekly scale, and a lower-level cyclonic anomaly over sYHRB was phase locked with an anticyclonic anomaly over the Philippines. At the synoptic scale, the strong northeasterly components of an anticyclonic anomaly with a deep cold and dry layer helped generate the heavy rainfall over sYHRB. Results also indicate the passages of five synoptic-scale disturbances during the nYHRB rainfall. Like the sYHRB rainfall, these disturbances originated from the periodical generations of cyclonic and anticyclonic anomalies at the downstream of the Tibetan Plateau. The nYHRB rainfalls were generated as these disturbances moved northeastward under the influence of monsoonal flows and higher-latitude eastward-propagating Rossby wave trains. It is concluded that the sYHRB heavy rainfall resulted from the superposition of quasi-biweekly and synoptic-scale disturbances, whereas the intermittent passages of five synoptic-scale disturbances led to the flooding rainfall over nYHRB.

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Hong Lin, Kevin J. Noone, Johan Ström, and Andrew J. Heymsfield

Abstract

An air parcel model has been used to study dynamic influences on cirrus cloud microphysical processes. Representative data selected from a measurement campaign carried out over southern Germany during March 1994 were used for a base-case model run where a modeled air parcel moved in a wave trajectory with a period similar to the measured Brunt–Väisälä frequency and an amplitude of about 30 m. Six case studies were performed for this paper. In each case, ice crystal nucleation processes were examined as an air parcel moved with trajectories having different wave forms. A random walk trajectory simulating turbulence with turbulent structure was also considered. The relationships between the parameters in the air parcel trajectories and crystal microphysical properties are discussed. Simulation results show that after two wave cycles, the model-produced crystal spectra are usually narrower than typical measurement data;however, broader spectra can be produced for certain types of trajectories. The broadness of crystal spectra is closely related to the air parcel’s initial position in the wave trajectory. It is not necessary to invoke entrainment to produce a broad crystal spectrum.

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Hong Lin, Kevin J. Noone, Johan Ström, and Andrew J. Heymsfield

Abstract

An air parcel model including homogeneous freezing nucleation of ice crystals has been used to study the formation and development of cirrus clouds. In situ measurements taken during March 1994 over southern Germany were used for comparison with model predictions. Typical experimental data were chosen for a base-case model run. Using measured aerosol properties as input values, the model predicts the measured ice crystal size distribution. In particular, both measurements and model results show the presence of numerous small ice crystals (diameter between 1 and 20 μm). Both measurements and model results also show that small aerosol particles (below 0.1 μm diameter) are active in forming cirrus cloud particles. The modeled microphysical properties including ice crystal size distribution, number concentration, and the residual particle size distribution are in good agreement with the experimental data. Based on the measured parameter values, a model sensitivity study considering air parcel updraft velocity, initial temperature, relative humidity, aerosol size distribution, number concentration, and air parcel vertical displacement is presented.

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Yang Hong, Kuo-Lin Hsu, Soroosh Sorooshian, and Xiaogang Gao

Abstract

A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described. This algorithm extracts local and regional cloud features from infrared (10.7 μm) geostationary satellite imagery in estimating finescale (0.04° × 0.04° every 30 min) rainfall distribution. This algorithm processes satellite cloud images into pixel rain rates by 1) separating cloud images into distinctive cloud patches; 2) extracting cloud features, including coldness, geometry, and texture; 3) clustering cloud patches into well-organized subgroups; and 4) calibrating cloud-top temperature and rainfall (T bR) relationships for the classified cloud groups using gauge-corrected radar hourly rainfall data. Several cloud-patch categories with unique cloud-patch features and T bR curves were identified and explained. Radar and gauge rainfall measurements were both used to evaluate the PERSIANN CCS rainfall estimates at a range of temporal (hourly and daily) and spatial (0.04°, 0.12°, and 0.25°) scales. Hourly evaluation shows that the correlation coefficient (CC) is 0.45 (0.59) at a 0.04° (0.25°) grid scale. The averaged CC of daily rainfall is 0.57 (0.63) for the winter (summer) season.

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Ming-Yang He, Hong-Bo Liu, Bin Wang, and Da-Lin Zhang

Abstract

In this study, the three-dimensional structures and diurnal evolution of a typical low-level jet (LLJ) with a maximum speed of 24 m s−1 occurring in the 850–800-hPa layer are examined using both large-scale analysis and a high-resolution model simulation. The LLJ occurred on the eastern foothills of the Yun-Gui Plateau in south China from 1400 LST 29 June to 1400 LST 30 June 2003. The effects of surface radiative heating, topography, and latent heat release on the development of the LLJ case are also studied. Results show that a western Pacific Ocean subtropical high and a low pressure system on the respective southeast and northwest sides of the LLJ provide a favorable large-scale mean pressure pattern for the LLJ development. The LLJ reaches its peak intensity at 850 hPa near 0200 LST with wind directions veering from southerly before sunset to southwesterly at midnight. A hodograph at the LLJ core shows a complete diurnal cycle of the horizontal wind with a radius of 5.5 m s−1. It is found that in an LLJ coordinates system the along-LLJ geostrophic component regulates the distribution and 65% of the intensity of LLJ, whereas the ageostrophic component contributes to the clockwise rotation, thus leading to the formation and weakening of the LLJ during night- and daytime, respectively. Numerical sensitivity experiments confirm the surface radiative heating as the key factor in determining the formation of the nocturnal LLJ. The existence of the Yun-Gui Plateau, and the downstream condensational heating along the mei-yu front play secondary roles in the LLJ formation.

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Yu Wang, Hong-Qing Wang, Lei Han, Yin-Jing Lin, and Yan Zhang

Abstract

This study was designed to provide basic information for the improvement of storm nowcasting. According to the mean direction deviation of storm movement, storms were classified into three types: 1) steady storms (S storms, extrapolated efficiently), 2) unsteady storms (U storms, extrapolated poorly), and 3) transitional storms (T storms). The U storms do not fit the linear extrapolation processes because of their unsteady movements. A 6-yr warm-season radar observation dataset was used to highlight and analyze the differences between U storms and S storms. The analysis included geometric features, dynamic factors, and environmental parameters. The results showed that storms with the following characteristics changed movement direction most easily in the Beijing–Tianjin region: 1) smaller storm area, 2) lower thickness (echo-top height minus base height), 3) lower movement speed, 4) weaker updrafts and the maximum value located in the mid- and upper troposphere, 5) storm-relative vertical wind profiles dominated by directional shear instead of speed shear, 6) lower relative humidity in the mid- and upper troposphere, and 7) higher surface evaporation and ground roughness.

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Qiong Wu, Hong-Qing Wang, Yin-Jing Lin, Yi-Zhou Zhuang, and Yan Zhang

Abstract

An optical flow algorithm based on polynomial expansion (OFAPE) was used to derive atmospheric motion vectors (AMVs) from geostationary satellite images. In OFAPE, there are two parameters that can affect the AMV results: the sizes of the expansion window and optimization window. They should be determined according to the temporal interval and spatial resolution of satellite images. A helpful experiment was conducted for selecting those sizes. The limitations of window sizes can cause loss of strong wind speed, and an image-pyramid scheme was used to overcome this problem. Determining the heights of AMVs for semitransparent cloud pixels (STCPs) is challenging work in AMV derivation. In this study, two-dimensional histograms (H2Ds) between infrared brightness temperatures (6.7- and 10.8-μm channels) formed from a long time series of cloud images were used to identify the STCPs and to estimate their actual temperatures/heights. The results obtained from H2Ds were contrasted with CloudSat radar reflectivity and CALIPSO cloud-feature mask data. Finally, in order to verify the algorithm adaptability, three-month AMVs (JJA 2013) were calculated and compared with the wind fields of ERA data and the NOAA/ESRL radiosonde observations in three aspects: speed, direction, and vector difference.

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Yan Zhang, Hong-Hai Zhang, Gui-Peng Yang, and Qiu-Lin Liu

Abstract

The total suspended particulate (TSP) samples over the Bohai Sea and the Yellow Sea were collected during two cruises in spring and autumn in 2012. Concentrations of water-soluble ions {Na+, K+, NH4 +, Mg2+, Ca2+, Cl, NO3 , SO4 2−, and CH3SO3 [methanesulfonic acid (MSA)]} and trace metals (Al, Pb, Zn, Cd, Cu, and V) were measured. Mass concentrations of TSP samples ranged from 65.2 to 136 μg m−3 in spring and from 15.9 to 70.3 μg m−3 in autumn, with average values of 100 ± 22.4 and 40.2 ± 17.8 μg m−3, respectively. The aerosol was acidic throughout the sampling periods according to calculation of equivalent concentrations of the cations (NH4 +, nss-Ca2+, and nss-K+) and anions (nss-SO4 2− and NO3 ). Correlation analysis and enrichment factors revealed that the aerosol composition in the coastal marine atmosphere had a feature of a mixture of air masses: that is, crustal, marine, and anthropogenic emissions. Trace metals were enriched by a wide range of 1–103, and enrichment factors for crustal source (EFc) were relatively higher in spring. Species like Cd, Zn, and Pb had an overwhelming contribution from anthropogenic sources. In addition, the concentrations of MSA varied from 0.0075 to 0.17 and from 0.0019 to 0.018 μg m−3 during the spring and autumn cruises, respectively, with means of 0.061 and 0.012 μg m−3, respectively. Based on the observed MSA and nss-SO4 2− concentrations in spring and autumn, the relative biogenic sulfur contributions to nss-SO4 2− were estimated to be 8.0% and 3.5% on average, respectively, implying that anthropogenic sources had a dominant contribution to the sulfur budget over the observational area.

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Fumin Ren, Chenchen Ding, Da-Lin Zhang, Deliang Chen, Hong-li Ren, and Wenyu Qiu

Abstract

Combining dynamical models with statistical algorithms is an important way to improve weather and climate prediction. In this study, a concept of a perfect model, whose solutions are from observations, is introduced, and a dynamical-statistical-analog ensemble forecast (DSAEF) model is developed as an initial-value problem of the perfect model. This new analog-based forecast model consists of the following three steps: (i) construct generalized initial value (GIV), (ii) identify analogs from historical observations, and (iii) produce an ensemble of predictands. The first step includes all appropriate variables, not only at an instant state but also during their temporal evolution, that play an important role in determining the accuracy of each predictand. An application of the DSAEF model is illustrated through the prediction of accumulated rainfall associated with 21 landfalling typhoons occurring over South China during the years of 2012–16. Assuming a reliable forecast of landfalling typhoon track, two different experiments are conducted, in which the GIV is constructed by including (i) typhoon track only; and (ii) both typhoon track and landfall season. Results show overall better performance of the second experiment than the first one in predicting heavy accumulated rainfall in the training sample tests. In addition, the forecast performance of both experiments is comparable to the operational numerical weather prediction models currently used in China, the United States, and Europe. Some limitations and future improvements as well as comparisons with some existing analog ensemble models are also discussed.

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Juanzhen Sun, Ying Zhang, Junmei Ban, Jing-Shan Hong, and Chung-Yi Lin

Abstract

Radar and surface rainfall observations are two sources of operational data crucial for heavy rainfall prediction. Their individual values on improving convective forecasting through data assimilation have been examined in the past using convection-permitting numerical models. However, the benefit of their simultaneous assimilations has not yet been evaluated. The objective of this study is to demonstrate that, using a 4D-Var data assimilation system with a microphysical scheme, these two data sources can be assimilated simultaneously and the combined assimilation of radar data and estimated rainfall data from radar reflectivity and surface network can lead to improved short-term heavy rainfall prediction. In our study, a combined data assimilation experiment is compared with a rainfall-only and a radar-only (with or without reflectivity) experiments for a heavy rainfall event occurring in Taiwan during the passage of a mei-yu system. These experiments are conducted by applying the Weather Research and Forecasting (WRF) 4D-Var data assimilation system with a 20-min time window aiming to improve 6-h convective heavy rainfall prediction. Our results indicate that the rainfall data assimilation contributes significantly to the analyses of humidity and temperature whereas the radar data assimilation plays a crucial role in wind analysis, and further, combining the two data sources results in reasonable analyses of all three fields by eliminating large, unphysical analysis increments from the experiments of assimilating individual data only. The results also show that the combined assimilation improves forecasts of heavy rainfall location and intensity of 6-h accumulated rainfall for the case studied.

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