Comparison of linear, Kalman filter and neural network downscaling of wind speeds from numerical weather prediction

2011 ◽  
Author(s):  
Christophe Sibuet Watters ◽  
Paul Leahy
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Wind Speeds

scholarly journals Reduced non-Gaussianity by 30-second rapid update inconvective-scale numerical weather prediction

2021 ◽  
Author(s):  
Juan Ruiz ◽  
Guo-Yuan Lien ◽  
Keiichi Kondo ◽  
Shigenori Otsuka ◽  
Takemasa Miyoshi
Keyword(s):  
Kalman Filter ◽  
Numerical Weather Prediction ◽  
Forecast Error ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Convective Clouds ◽  
Environment Model ◽  
Error Distributions ◽  
Ensemble Transform Kalman Filter ◽  
Non Gaussian

Abstract. Non-Gaussian forecast error is a challenge for ensemble-based data assimilation (DA), particularly for more nonlinear convective dynamics. In this study, we investigate the degree of non-Gaussianity of forecast error distributions at 1-km resolution using a 1000-member ensemble Kalman filter, and how it is affected by the DA update frequency and observation number. Regional numerical weather prediction experiments are performed with the SCALE (Scalable Computing for Advanced Library and Environment) model and the LETKF (Local Ensemble Transform Kalman Filter) assimilating every-30-second phased array radar observations. The results show that non-Gaussianity develops rapidly within convective clouds and is sensitive to the DA frequency and the number of assimilated observations. The non-Gaussianity is reduced by up to 40 % when the assimilation window is shortened from 5 minutes to 30 seconds, particularly for vertical velocity and radar reflectivity.

scholarly journals Experience of using the Kalman filter to correct numerical forecasts of surface air temperature

2020 ◽  
Vol 4 ◽  
pp. 28-42
Author(s):  
Yu.V. Alferov . ◽  
◽  
E.G. Klimova ◽  
Keyword(s):  
Kalman Filter ◽  
Air Temperature ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Surface Air Temperature ◽  
One Dimensional ◽  
Numerical Weather ◽  
Statistical Correction ◽  
The One

A possibility of using the one-dimensional Kalman filter to improve the forecast of surface air temperature at an irregular grid of point is studied. This mechanism is tested using the forecasts obtained from different configurations of two different numerical weather prediction models. An algorithm for the statistical correction of numerical forecasts of surface air temperature based on the one-dimensional Kalman filter is constructed. Two methods are proposed for estimating the bias noise dispersion. The series of experiments demonstrated the effectiveness of the algorithm for the bias compensation.The most significantresults are achieved for the models with large bias or for long-range forecasts. At the same time, the use of the algorithm has little effect on the root-meansquare error of the forecast. Keywords: hydrodynamic model of the atmosphere, numerical weather prediction, statistical correction of numerical forecasts, Kalman filter

scholarly journals IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK FOR MONTHLY RAIN ATTACKS IN SOUTH KALIMANTAN

2019 ◽  
Vol 4 (1) ◽  
pp. 35-42
Author(s):  
Nur Arminarahmah ◽  
Miftahul Munir
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Numerical Weather Prediction ◽  
Empirical Orthogonal Function ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Orthogonal Function ◽  
Artificial Neural

Prakiraan hujan bulanan bisa digunakan untuk antisipasi banjir dan manajemen sumber daya air, keselamatan jiwa dan harta benda, serta keberlangsungan aktivitas ekonomi. Penggunaan Jaringan Syaraf Tiruan sebagai bagian dari Machine Learning adalah teknik yang sering digunakan selain numerical weather prediction dan metode statistik. Menggunakan peru-bah data bulan dan data empirical orthogonal function anomali suhu muka laut bulanan pada 12 lokasi menghasilkan nilai korelasi yang baik saat pembuatan model, tetapi hasil verifikasi menunjukkan akurasi yang baik didapatkan saat periode musim kemarau dan skill terjelek saat peralihan musim kemarau ke musim hujan.

Scatterometer-Based Assessment of 10-m Wind Analyses from the Operational ECMWF and NCEP Numerical Weather Prediction Models

2005 ◽  
Vol 133 (2) ◽  
pp. 409-429 ◽  
Author(s):  
Dudley B. Chelton ◽  
Michael H. Freilich
Keyword(s):  
Wind Stress ◽  
Numerical Weather Prediction ◽  
Measurement Errors ◽  
Prediction Models ◽  
Weather Prediction ◽  
Systematic Errors ◽  
Random Component ◽  
Numerical Weather ◽  
Wind Speeds ◽  
Time Periods

Abstract Wind measurements by the National Aeronautics and Space Administration (NASA) scatterometer (NSCAT) and the SeaWinds scatterometer on the NASA QuikSCAT satellite are compared with buoy observations to establish that the accuracies of both scatterometers are essentially the same. The scatterometer measurement errors are best characterized in terms of random component errors, which are about 0.75 and 1.5 m s−1 for the along-wind and crosswind components, respectively. The NSCAT and QuikSCAT datasets provide a consistent baseline from which recent changes in the accuracies of 10-m wind analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the U.S. National Centers for Environmental Prediction (NCEP) operational numerical weather prediction (NWP) models are assessed from consideration of three time periods: September 1996–June 1997, August 1999–July 2000, and February 2002–January 2003. These correspond, respectively, to the 9.5-month duration of the NSCAT mission, the first 12 months of the QuikSCAT mission, and the first year after both ECMWF and NCEP began assimilating QuikSCAT observations. There were large improvements in the accuracies of both NWP models between the 1997 and 2000 time periods. Though modest in comparison, there were further improvements in 2002, at least partly attributable to the assimilation of QuikSCAT observations in both models. There is no evidence of bias in the 10-m wind speeds in the NCEP model. The 10-m wind speeds in the ECMWF model, however, are shown to be biased low by about 0.4 m s−1. While it is difficult to eliminate systematic errors this small, a bias of 0.4 m s−1 corresponds to a typical wind stress bias of more than 10%. This wind stress bias increases to nearly 20% if atmospheric stability effects are not taken into account. Biases of these magnitudes will result in significant systematic errors in ocean general circulation models that are forced by ECMWF winds.

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