Estimating model parameters with ensemble-based data assimilation: A review
Weather forecast and earth system models usually have a number of parameters, which are
often optimized manually by trial and error. Several studies have proposed objective …
often optimized manually by trial and error. Several studies have proposed objective …
Correcting weather and climate models by machine learning nudged historical simulations
Due to limited resolution and inaccurate physical parameterizations, weather and climate
models consistently develop biases compared to the observed atmosphere. Using the …
models consistently develop biases compared to the observed atmosphere. Using the …
Kalman filter and analog schemes to postprocess numerical weather predictions
Two new postprocessing methods are proposed to reduce numerical weather prediction's
systematic and random errors. The first method consists of running a postprocessing …
systematic and random errors. The first method consists of running a postprocessing …
Reanalyses suitable for characterizing long-term trends
PW Thorne, RS Vose - Bulletin of the American Meteorological …, 2010 - journals.ametsoc.org
Reanalyses are, by a substantial margin, the most utilized climate data products, and they
are applied in a myriad of different contexts. Despite their popularity, there are substantial …
are applied in a myriad of different contexts. Despite their popularity, there are substantial …
4-D-Var or ensemble Kalman filter?
We consider the relative advantages of two advanced data assimilation systems, 4-D-Var
and ensemble Kalman filter (EnKF), currently in use or under consideration for operational …
and ensemble Kalman filter (EnKF), currently in use or under consideration for operational …
Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter
Covariance inflation plays an important role within the ensemble Kalman filter (EnKF) in
preventing filter divergence and handling model errors. However the inflation factor needs to …
preventing filter divergence and handling model errors. However the inflation factor needs to …
[HTML][HTML] Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution
T Miyoshi, S Yamane - Monthly Weather Review, 2007 - journals.ametsoc.org
Local Ensemble Transform Kalman Filtering with an AGCM at a T159/L48 Resolution in:
Monthly Weather Review Volume 135 Issue 11 (2007) Jump to Content Jump to Main …
Monthly Weather Review Volume 135 Issue 11 (2007) Jump to Content Jump to Main …
Recent progress of data assimilation methods in meteorology
T Tsuyuki, T Miyoshi - 気象集誌. 第2 輯, 2007 - jlc.jst.go.jp
Data assimilation is a methodology for estimating accurately the state of a time-evolving
complex system like the atmosphere from observational data and a numerical model of the …
complex system like the atmosphere from observational data and a numerical model of the …
Wind speed forecast correction models using polynomial neural networks
L Zjavka - Renewable Energy, 2015 - Elsevier
Accurate short-term wind speed forecasting is important for the planning of a renewable
energy power generation and utilization, especially in grid systems. In meteorology it is …
energy power generation and utilization, especially in grid systems. In meteorology it is …
GFDL's SPEAR seasonal prediction system: Initialization and ocean tendency adjustment (OTA) for coupled model predictions
The next‐generation seasonal prediction system is built as part of the Seamless System for
Prediction and EArth System Research (SPEAR) at the Geophysical Fluid Dynamics …
Prediction and EArth System Research (SPEAR) at the Geophysical Fluid Dynamics …