Environment Canada s Regional Ensemble Kalman Filter
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1 Environment Canada s Regional Ensemble Kalman Filter May 19, 2014 Seung-Jong Baek, Luc Fillion, Kao-Shen Chung, and Peter Houtekamer Meteorological Research Division, Environment Canada, Dorval, Quebec Atmospheric & Oceanic Sciences, McGill University, Montreal, Quebec
2 Current Operational System ensemble! (GEPS) ensemble! (REPS) 4DVar deterministic! (GDPS) 4DVar deterministic! (RDPS) Global system Regional system
3 2014 implementation of the NWP suites ensemble! (GEPS) ensemble! (REPS) Background! error! covariances EnVar deterministic! (GDPS) EnVar deterministic! (RDPS) Global system Regional system
4 implementation of the NWP suites ensemble! (GEPS) ensemble! (REPS) Background! error! covariances Background! error! covariances EnVar deterministic! (GDPS) EnVar deterministic! (RDPS) Global system Regional system
5 Monte Carlo methods in the ensemble 192 sets of perturbed observation 192 9h forecasts with the same model configurations Ensemble Kalman filter Add random model error Use all 192 analyses for data assimilation cycle Select 20 members for 72 hour forecasts Generation of products for users Perform hour regional forecasts using stochastic parameterizations Add random model error
6 Autonomous Regional Driven by the Global Global {x b } (06Z,18Z) {x a } GEM! (66km) {x b } Subsample (00Z,12Z) {x a } GEM! (66km) 16 day global! forecast {x b } obs Model error driving obs Model error driving {x b } {x a } GEM-LAM! (15km) {x b } {x a } GEM-LAM! (15km) {x b } Regional Subsample 72h regional! forecast Regional starts from global ensemble analyses and then runs autonomously. Global Environmental Multi-scale (GEM) model is used for the forecast.
7 Sequential ensemble Kalman filter 192 first-guess fields first-guess covariance from the ensemble 192 second-guess fields first set of observations second-guess covariance from the ensemble Kalman filter: observations can be grouped into independent batches with the same final result. 192 third-guess fields 192 analyses second set of observations Stochastic with localization: the final result depends on the order of the observations.
8 Assimilation window 6 H background 6 H analysis observation Localization Due to the small ensemble size, the impact of an observation needs to be localized using Schur product of an ensemble based covariances. Localization causes imbalance in the analysis. Performance of regional is more sensitive to the localization distance than performance of the global.
9 GEM Settings for the Regional 15 km resolution. 622 X 657 grid points. (~ 10000km X 10000km) 65 staggered hybrid vertical levels. Model top around 13hPa.
10 Comparison between Regional and Global Regional has to perform as good as the global or better. Make regional autonomous as long as possible. Test shows that regional is not as good as global with the same parameters as the global. Localization distance and the amount of the model error are the main parameters to improve the performance.
11 Correlation Distance for Localization 0 Horizontal correlation 5 Localization distance model level ln(p) Zonal wind Temperature km global regional km
12 Ensemble Spread 0.01 Trial spread of zonal wind global in regional domain regional 0.01 Trial spread of temperature global in regional domain regional ln(p) 0.1 ln(p) m/s C
13 Radiosonde Verification: R.vs. G U V U V GZ T GZ T
14 Power Spectrum Regional Global
15 Low Pass Filtering (1000km) Regional Global
16 Radiosonde Verification with Low Pass Filtering (1000km) U V U V GZ T GZ T Maintain the large scale quality as the global.
17 Low Pass Filtering (500km) Regional Global
18 Radiosonde Verification with Low Pass Filtering (500km) U V U V GZ T GZ T
19 Summary and Future Work Regional is more sensitive to the localization structure than global. Fully autonomous regional is possible. Regional performs as good as the global in large scale. Regional is better to capture mesoscale circulation. Beneficial to a high resolution (2.5km). Assimilate more observations due to higher resolution. Obtain perturbations from the regional covariances. Currently, they are from global covariances. Model parameter perturbation. Multi-parameter model. Stochastic parameterization.
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