Precipitation Data Assimilation System Based on a Neural Network and Case-Based Reasoning System

Information (Switzerland) - Tập 9 Số 5 - Trang 106
Jing Lu1,2, Wei Hu2, Xiakun Zhang3
1Department of Computer Science, Oklahoma State University, Stillwater, OK 74075, USA
2Shanxi Meteorological Administration, Taiyuan 030006, China
3National Meteorological Center, Nanjing 210044, China

Tóm tắt

There are several methods to forecast precipitation, but none of them is accurate enough since predicting precipitation is very complicated and influenced by many factors. Data assimilation systems (DAS) aim to increase the prediction result by processing data from different sources in a general way, such as a weighted average, but have not been used for precipitation prediction until now. A DAS that makes use of mathematical tools is complex and hard to carry out. In our paper, machine learning techniques are introduced into a precipitation data assimilation system. After summarizing the theoretical construction of this method, we take some practical weather forecasting experiments and the results show that the new system is effective and promising.

Từ khóa


Tài liệu tham khảo

Carton, 2008, A Reanalysis of Ocean Climate Using Simple Ocean Data Assimilation (SODA), Mon. Weather Rev., 136, 2999, 10.1175/2007MWR1978.1

Williams, 2010, An improved analysis of forest carbon dynamics using data assimilation, Glob. Chang. Biol., 11, 89, 10.1111/j.1365-2486.2004.00891.x

Fossum, 2014, Parameter sampling capabilities of sequential and simultaneous data assimilation: II. Statistical analysis of numerical results, Inverse Probl., 30, 114003, 10.1088/0266-5611/30/11/114003

Dee, 2011, The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc., 137, 553, 10.1002/qj.828

Makarynskyy, 2005, Filling gaps in wave records with artificial neural networks, Maritime Transportation and Exploitation of Ocean and Coastal Resources, Volume 2, 1085

Butunoiu, D., and Rusu, E. (2014, January 27–28). Wave modeling with data assimilation to support the navigation in the Black Sea close to the Romanian Ports. Proceedings of the Second International Conference on Traffic and Transport Engineering (ICTTE), Belgrade, Serbia.

Butunoiu, D., and Rusu, E. (2015, January 18–21). A data assimilation scheme to improve the Wave Predictions in the Black Sea. Proceedings of the OCEANS 2015, Genoa, Italy.

Rusu, 2016, A multi-parameter data-assimilation approach for wave prediction in coastal areas, J. Oper. Oceanogr., 9, 13

Leith, 1975, Numerical weather prediction, Rev. Geophys., 13, 681, 10.1029/RG013i003p00681

Buizza, 2010, Computation of optimal unstable structures for a numerical weather prediction model, Tellus, 45, 388, 10.1034/j.1600-0870.1993.t01-4-00005.x

Rusu, 2015, Impact of assimilating altimeter data on wave predictions in the western Iberian coast, Ocean Model., 96, 126, 10.1016/j.ocemod.2015.07.016

Lorenz, 2010, Energy and Numerical Weather Prediction, Tellus, 12, 364, 10.3402/tellusa.v12i4.9420

Rodwell, 2010, Using numerical weather prediction to assess climate models, Q. J. R. Meteorol. Soc., 133, 129, 10.1002/qj.23

Kug, 2010, Systematic Error Correction of Dynamical Seasonal Prediction of Sea Surface Temperature Using a Stepwise Pattern Project Method, Mon. Weather Rev., 136, 3501, 10.1175/2008MWR2272.1

Ghil, 1991, Data Assimilation in Meteorology and Oceanography, Adv. Geophys., 33, 141, 10.1016/S0065-2687(08)60442-2

Tombette, 2009, PM10 data assimilation over Europe with the optimal interpolation method, Atmos. Chem. Phys., 9, 57, 10.5194/acp-9-57-2009

Lee, 2013, PM10 data assimilation over south Korea to Asian dust forecasting model with the optimal interpolation method, Asia-Pac. J. Atmos. Sci., 49, 73, 10.1007/s13143-013-0009-y

Piccolo, 2011, Adaptive mesh method in the Met Office variational data assimilation system, Q. J. R. Meteorol. Soc., 137, 631, 10.1002/qj.801

Krysta, 2011, A Consistent Hybrid Variational-Smoothing Data Assimilation Method: Application to a Simple Shallow-Water Model of the Turbulent Midlatitude Ocean, Mon. Weather Rev., 139, 3333, 10.1175/2011MWR3150.1

Wu, 2010, Assimilation of Tropical Cyclone Track and Structure Based on the Ensemble Kalman Filter (EnKF), J. Atmos. Sci., 67, 3806, 10.1175/2010JAS3444.1

Wu, 2012, Concentric Eyewall Formation in Typhoon Sinlaku (2008). Part I: Assimilation of T-PARC Data Based on the Ensemble Kalman Filter (EnKF), Mon. Weather Rev., 140, 506, 10.1175/MWR-D-11-00057.1

Almeida, S., Rusu, L., and Guedes Soares, C. (2015). Application of the Ensemble Kalman Filter to a high-resolution wave forecasting model for wave height forecast in coastal areas. Maritime Technology and Engineering, Taylor & Francis Group.

Torn, 2010, Performance of a Mesoscale Ensemble Kalman Filter (EnKF) during the NOAA. High-Resolution Hurricane Test, Mon. Weather Rev., 138, 4375, 10.1175/2010MWR3361.1

Skachko, 2014, Comparison of the ensemble Kalman filter and 4D-Var assimilation methods using a stratospheric tracer transport model, Geosci. Model Dev., 7, 1451, 10.5194/gmd-7-1451-2014

Tong, 2013, Application of a data assimilation method via an ensemble Kalman filter to reactive urea hydrolysis transport modeling, Stoch. Environ. Res. Risk Assess., 28, 729, 10.1007/s00477-013-0786-y

Rempel, 2008, Neural networks in auroral data assimilation, J. Atmos. Sol.-Terr. Phys., 70, 1243, 10.1016/j.jastp.2008.03.018

Cintra, R.S., and Haroldo, F.C.V. (2018). Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model. Advanced Applications for Artificial Neural Networks, InTech.

Pereira, 2010, Multilayer perceptron neural network in a data assimilation scenario, Eng. Appl. Comput. Fluid Mech., 4, 237

Santhosh, 2010, An efficient weather forecasting system using artificial neural network, Int. J. Environ. Sci. Dev., 1, 321

Rosangela, C., Haroldo, C.V., and Steven, C. (2016, January 24–29). Tracking the model: Data assimilation by artificial neural network. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.