A paper on WRF-LETKF has been published

Miyoshi, T. and M. Kunii, 2012: The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real Observations. Pure and Appl. Geophys., 169, 321-333. doi:10.1007/s00024-011-0373-4

Dr. Kosuke Ito (National Taiwan U) visited NCEP and UMD

On Monday, February 27, Dr. Kosuke Ito from the National Taiwan University visited UMD and presented his work on Tropical Cyclones at our regular Weather-Chaos meeting. He also visited NCEP and gave the EMC seminar on February 28.

His research focused on including surface exchange coefficients Cd/Ck in the control variables of 4-dimensional variational data assimilation. In this way, he could estimate these crucial model parameters for Tropical Cyclone evolution, and improved Tropical Cyclone analyses both in an idealized experiment and in a real Typhoon case. Estimating key model parameters is one of the frontier research topics, and it was very interesting to see his pioneering work.

A paper on observation error correlations has been accepted

Miyoshi, T., E. Kalnay, and H. Li, 2012: Estimating and including observation error correlations in data assimilation. Inv. Prob. Sci. Eng., in press.

A paper on lateral boundary perturbations has been published

Saito, K., H. Seko, M. Kunii, and T. Miyoshi, 2012: Effect of lateral boundary perturbations on the breeding method and the local ensemble transform Kalman filter for mesoscale ensemble prediction. Tellus, 64A, 11594. doi:10.3402/tellusa.v64i0.11594

A new 2-way nested WRF-LETKF system has been developed

Higher resolutions are tempting.

A while ago, we have developed the Local Ensemble Transform Kalman Filter (LETKF) data assimilation system with the Weather Research and Forecasting (WRF) model (Miyoshi and Kunii 2011). We have been using 60-km resolution WRF, which produced good analyses and forecasts of Typhoon Sinlaku (2008) as shown in the left panel of the above figure. Several papers were published or are currently under review using this 60-km system. However, it is difficult to fight with the desire for a higher resolution.

Very recently, we have developed a new two-way nested WRF-LETKF system. The LETKF system was modified so that it can consider multiple resolutions simultaneously. Localization scales can be specified at each grid point separately. The enhanced LETKF and 60/20-km two-way nested WRF model enabled to produce a higher-resolution analysis as shown in the right panel. We start to see filaments of spiral rain bands of the tropical cyclone (color shades showing mm/6h rain rate). The eye is clearer, and the deep structure is more evident. Resolving capability is essential for these smaller-scale phenomena.

It is simply exciting as a scientist to have this better resolving capability. Now that we can see more details, we are going to investigate even more to improve tropical cyclone analyses and forecasts.

Professor Watanabe and Dr. Chikamoto (U of Tokyo) visited NCEP and UMD

On Wednesday, November 16, Professor Watanabe and Dr. Chikamoto of the Atmospheric and Oceanic Research Institute (AORI), University of Tokyo visited the NCEP and UMD.

They gave a seminar at NCEP in the morning on their work on decadal climate prediction using their own global air-sea coupled earth system model MIROC. Professor Watanabe provided an overview of the latest development of MIROC version 5, showing much improvement mainly due to the newly developed physics packages. Dr. Chikamoto talked about decadal ensemble prediction using MIROC, particularly with a new idea of "Anomaly Assimilation" in which the systematic differences between model climatology and observation data are considered to deal with the initial drift problem.

After enjoying conversation over lunch with NCEP folks, they visited my office in College Park and had a very nice Japanese conversation (picture).

A paper on observation impact estimates has been accepted

Kunii, M., T. Miyoshi, and E. Kalnay, 2011: Estimating impact of real observations in regional numerical weather prediction using an ensemble Kalman filter. Mon. Wea. Rev., in press.