Accurate short-term precipitation prediction

2021 ◽  
Author(s):  
Fernando Chirigati
Keyword(s):  
Short Term ◽  
Precipitation Prediction

scholarly journals Impact of Lightning Data Assimilation on Forecasts of a Leeward Slope Precipitation Event in the Western Margin of the Junggar Basin

2021 ◽  
Vol 13 (18) ◽  
pp. 3584
Author(s):  
Peng Liu ◽  
Yi Yang ◽  
Yu Xin ◽  
Chenghai Wang
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Three Dimensional ◽  
Junggar Basin ◽  
Precipitation Event ◽  
Short Term ◽  
Western Margin ◽  
Precipitation Prediction ◽  
Analysis Experiment ◽  
Short Term Forecasting

A moderate precipitation event occurring in northern Xinjiang, a region with a continental climate with little rainfall, and in leeward slope areas influenced by topography is important but rarely studied. In this study, the performance of lightning data assimilation is evaluated in the short-term forecasting of a moderate precipitation event along the western margin of the Junggar Basin and eastern Jayer Mountain. Pseudo-water vapor observations driven by lightning data are assimilated in both single and cycling analysis experiments of the Weather Research and Forecast (WRF) three-dimensional variational (3DVAR) system. Lightning data assimilation yields a larger increment in the relative humidity in the analysis field at the observed lightning locations, and the largest increment is obtained in the cycling analysis experiment. Due to the increase in water vapor content in the analysis field, more suitable thermal and dynamic conditions for moderate precipitation are obtained on the leeward slope, and the ice-phase and raindrop particle contents increase in the forecast field. Lightning data assimilation significantly improves the short-term leeward slope moderate precipitation prediction along the western margin of the Junggar Basin and provides the best forecast skill in cycling analysis experiments.

scholarly journals Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 977 ◽  
Author(s):  
Qinghua Miao ◽  
Baoxiang Pan ◽  
Hao Wang ◽  
Kuolin Hsu ◽  
Soroosh Sorooshian
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Precipitation Forecast ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Precipitation Prediction ◽  
Downscaling Method ◽  
Forecast Lead Time ◽  
Long Short Term Memory

Precipitation downscaling is widely employed for enhancing the resolution and accuracy of precipitation products from general circulation models (GCMs). In this study, we propose a novel statistical downscaling method to foster GCMs’ precipitation prediction resolution and accuracy for the monsoon region. We develop a deep neural network composed of a convolution and Long Short Term Memory (LSTM) recurrent module to estimate precipitation based on well-resolved atmospheric dynamical fields. The proposed model is compared against the GCM precipitation product and classical downscaling methods in the Xiangjiang River Basin in South China. Results show considerable improvement compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)-Interim reanalysis precipitation. Also, the model outperforms benchmark downscaling approaches, including (1) quantile mapping, (2) the support vector machine, and (3) the convolutional neural network. To test the robustness of the model and its applicability in practical forecasting, we apply the trained network for precipitation prediction forced by retrospective forecasts from the ECMWF model. Compared to the ECMWF precipitation forecast, our model makes better use of the resolved dynamical field for more accurate precipitation prediction at lead times from 1 day up to 2 weeks. This superiority decreases with the forecast lead time, as the GCM’s skill in predicting atmospheric dynamics is diminished by the chaotic effect. Finally, we build a distributed hydrological model and force it with different sources of precipitation inputs. Hydrological simulation forced with the neural network precipitation estimation shows significant advantage over simulation forced with the original ERA-Interim precipitation (with NSE value increases from 0.06 to 0.64), and the performance is only slightly worse than the observed precipitation forced simulation (NSE = 0.82). This further proves the value of the proposed downscaling method, and suggests its potential for hydrological forecasts.

scholarly journals Precipitation forecast based on CEEMD-LSTM coupled model

2021 ◽  
Author(s):  
Xianqi Zhang ◽  
Xilong Wu ◽  
Shaoyu He ◽  
Dong Zhao
Keyword(s):  
Network Model ◽  
Short Term Memory ◽  
Back Propagation ◽  
Precipitation Forecast ◽  
Precipitation Process ◽  
Monthly Precipitation ◽  
Efficiency Coefficient ◽  
Short Term ◽  
Term Memory ◽  
Precipitation Prediction

Abstract Precipitation forecasting is an important guide to the prevention and control of regional droughts and floods, the rational use of water resources and the ecological protection. The precipitation process is extremely complex and is influenced by the intersection of many variables, with significant randomness, uncertainty and non-linearity. Based on the advantages that Complementary ensemble empirical modal decomposition (CEEMD) can effectively overcome modal aliasing, white noise interference, and the ability of Long Short-Term Memory (LSTM) networks to handle problems such as gradient disappearance. A CEEMD-LSTM coupled long & short-term memory network model was developed and adopted for monthly precipitation prediction of Zhengzhou city. The performance shows that the CEEMD-LSTM model has a mean absolute error of 0.056, a root mean square error of 0.153, a mean relative error of 2.73% and a Nash efficiency coefficient of 0.95, which is better than the CEEMD- Back Propagation (BP) neural network model, the LSTM model and the BP model in terms of prediction accuracies. This demonstrates its powerful non-linear and complex process learning capability in hydrological factor simulation for regional precipitation prediction.

Conceptual short-term memory (CSTM) supports core claims of Christiansen and Chater

2016 ◽  
Vol 39 ◽  
Author(s):  
Mary C. Potter
Keyword(s):  
Working Memory ◽  
Rapid Serial Visual Presentation ◽  
Language Comprehension ◽  
Short Term Memory ◽  
Visual Presentation ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Use Of Knowledge

AbstractRapid serial visual presentation (RSVP) of words or pictured scenes provides evidence for a large-capacity conceptual short-term memory (CSTM) that momentarily provides rich associated material from long-term memory, permitting rapid chunking (Potter 1993; 2009; 2012). In perception of scenes as well as language comprehension, we make use of knowledge that briefly exceeds the supposed limits of working memory.

Ultrastructural Changes of Canine Kidneys During 24-Hour Perfusion on a LI-400 Preservation System

1971 ◽  
Vol 29 ◽  
pp. 316-317
Author(s):  
M. O. Magnusson ◽  
D. G. Osborne ◽  
T. Shimoji ◽  
W. S. Kiser ◽  
W. A. Hawk
Keyword(s):  
Electron Microscopy ◽  
Electrolyte Solution ◽  
Ultrastructural Changes ◽  
Midline Incision ◽  
Short Term ◽  
Pentobarbital Anesthesia ◽  
Pulsatile Perfusion

Short term experimental and clinical preservation of kidneys is presently best accomplished by hypothermic continuous pulsatile perfusion with cryoprecipitated and millipore filtered plasma. This study was undertaken to observe ultrastructural changes occurring during 24-hour preservation using the above mentioned method.A kidney was removed through a midline incision from healthy mongrel dogs under pentobarbital anesthesia. The kidneys were flushed immediately after removal with chilled electrolyte solution and placed on a LI-400 preservation system and perfused at 8-10°C. Serial kidney biopsies were obtained at 0-½-1-2-4-8-16 and 24 hours of preservation. All biopsies were prepared for electron microscopy. At the end of the preservation period the kidneys were autografted.

Polychlorinated biphenyl induced hepatocyte alterations

1987 ◽  
Vol 45 ◽  
pp. 716-717
Author(s):  
D.N. Collins ◽  
J.N. Turner ◽  
K.O. Brosch ◽  
R.F. Seegal
Keyword(s):  
Lipid Droplets ◽  
Wistar Rats ◽  
Homovanillic Acid ◽  
Polychlorinated Biphenyl ◽  
Corn Oil ◽  
Environmental Pollutants ◽  
Short Term ◽  
Pcb Congeners ◽  
Urinary Levels ◽  
The Brain

Polychlorinated biphenyls (PCBs) are a ubiquitous class of environmental pollutants with toxic and hepatocellular effects, including accumulation of fat, proliferated smooth endoplasmic recticulum (SER), and concentric membrane arrays (CMAs) (1-3). The CMAs appear to be a membrane storage and degeneration organelle composed of a large number of concentric membrane layers usually surrounding one or more lipid droplets often with internalized membrane fragments (3). The present study documents liver alteration after a short term single dose exposure to PCBs with high chlorine content, and correlates them with reported animal weights and central nervous system (CNS) measures. In the brain PCB congeners were concentrated in particular regions (4) while catecholamine concentrations were decreased (4-6). Urinary levels of homovanillic acid a dopamine metabolite were evaluated (7).Wistar rats were gavaged with corn oil (6 controls), or with a 1:1 mixture of Aroclor 1254 and 1260 in corn oil at 500 or 1000 mg total PCB/kg (6 at each level).

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