Abstract :
At the same time that the Kalman filter (KF) was being developed, the United States produced its first operationally useful numerical weather prediction in 1958 using a single-layer barotropic model. Over the next 40 years, incredible increases in computing power have allowed numerical weather prediction (NWP) models to become far larger and more comprehensive so that modern models have tens of millions of state variables. Throughout this period, operational NWP models always taxed the capabilities of the largest computer systems available.
Keywords :
Kalman filters; Monte Carlo methods; covariance matrices; data assimilation; geophysics computing; mathematics computing; weather forecasting; Monte Carlo methods; covariance matrices; ensemble Kalman filters; geophysical data assimilation; large geophysical applications; numerical geophysical modeling; numerical weather prediction models; Application software; Atmospheric modeling; Calculus; Cost function; Geophysics computing; Kalman filters; Monte Carlo methods; Power system modeling; Predictive models; Weather forecasting;
