AIRS satellite data helped improve tropical cyclone forecasting

Our recent study indicated that using AIRS (Atmospheric Infrared Sounder) satellite data improved tropical cyclone forecasting significantly. AIRS, on board NASA's polar-orbiting spacecraft "Aqua", is an infrared sensor observing the Earth's radiation at different wavelengths and provides the horizontal and vertical structures of atmospheric temperature and moisture. The figure shows the observed track (black) of Typhoon Sinlaku (2008) and its forecasts initialized at 0600 UTC, September 12, 2008. Clearly, one of the forecasts shown by the red curve is closer to the black curve (observed). The red curve includes the AIRS data, while the blue curve does not.

It is widely known that satellite observations provide much information about atmospheric conditions. However, using satellite data effectively for weather forecasting is not a trivial task. Continuous efforts have been made to seek wiser use of satellite data for improving forecasts of significant weather. This study is a small step forward in this regard.

More details on this study can be found in our recent publication:
Miyoshi, T. and M. Kunii, 2012: Using AIRS retrievals in the WRF-LETKF system to improve regional numerical weather prediction. Tellus, 64A, 18408. doi:10.3402/tellusa.v64i0.18408

Thank you, wonderful summer visitors!

It is very fortunate to have these wonderful and very capable visitors in my lab this summer. Keiichi Kondo (left), a Ph.D. student in Tsukuba University, arrived on June 27 and has been working on a theoretical aspect of ensemble-based data assimilation. Dr. Juan Ruiz (2nd from left), a researcher at University of Buenos Aires, has been working on model parameter estimation in a case of tropical cyclone and is finishing his one-month stay. Tetsuro Miyachi (3rd from left), an M.S. student in Kyoto University, is visiting for a month for working with the NCEP operational global forecasting system (GFS). Prof. Shu-Chih Yang (2nd from right) is an alumna of our department, graduated in the same year as me, and kindly decided to spend about a month this summer in College Park. This picture also includes Prof. Eugenia Kalnay (3rd from right) and myself (right).

Visited the National Youth Science Camp (NYSC) for the first time in 16 years

The wonderful one-month summer experience in Blue Ridge mountains some 16 years ago is something unforgettable in my life. During July 8-12, I visited the National Youth Science Camp (NYSC), Pocahontas County, WV, for the first time in 16 years, as a guest lecturer this time. I gave a directed study on Weather Forecasting, and a lecture on numerical weather prediction, chaos and predictability.

What amazed me is that nothing has changed at the camp. The cabins, green, ping-pong tables, dining hall, rec hall, camp store, pay phones, and bathroom with full of bugs. The enthusiastic and energetic delegates. Some were running around, playing balls and frisbees, others were sitting or lying quietly on the green, reading books. I remember I liked to write journals on the green. Good that I lost the journal, so that I don't remember what stupid things I wrote. During the 4-day visit, details that I already forgot started to come back in my mind, very gradually. What amazed me was that everything I remembered looked the same 16 years ago, such as the face on the tree in front of the dining hall. First I could not remember whether or not the face was there, but gradually, I got the memory back.

This was a very interesting experience for me, visiting my first place in the US, where I had only vague memories in my youth. I was one of the energetic delegates 16 years ago, just as the ones there in front of me, being curious and enthusiastic about unknown science fields. I wonder how I grew from that time. I have more knowledge about the narrow field of my expertise, but I believe I have the same curiosity and enthusiasm about knowing what is unknown, exploring something never explored previously. I hope I entertained the delegates with my lecture, and was able to convey the joy of scientific exploration, at least to some extent.

Observation impacts have been estimated with an ensemble-based approach

It would be very useful if we could estimate the impact of each observation on the weather forecasts. Knowing the impacts of observations, we can optimize the cost-benefit balance of expensive observations and plan future observations wisely.

To estimate observation impacts, we usually perform data-denial experiments. Data-denial experiments are also known as Observing System Experiments (OSE), in which we perform additional experiments with/without our interested observation data. This way, we can estimate how much those observations help improve the weather forecasts, or possibly, degrade the forecasts.

Langland and Baker (2004) developed an adjoint-based approach to estimating observation impacts on forecasts without performing expensive data-denial experiments. Further, Liu and Kalnay (2008) proposed an equivalent approach without using an adjoint model. Instead, they proposed using an ensemble of model states. We call this the ensemble-based approach here.

Masaru Kunii, then visiting researcher in my lab, worked very hard on applying the ensemble-based approach to the real-case observations of Typhoon Sinlaku (2008). This is the first study showing the real observation impacts estimated by the ensemble-based approach. What is shown above is the impacts of atmospheric profile observations from so-called "dropsondes", weather instruments dropped from aircraft. This aircraft is operated by Taiwan, known as "DOTSTAR", which flies when strong typhoons approach Taiwan. The figure shows that those aircraft observations from DOTSTAR flights help improve the forecasts, although 5 out of 18 observations show negative impacts. We found that not using those negative-impact observations actually improved the forecasts. However, we need to be careful when interpreting the results from this method. The method estimates the impacts of observations in the given numerical weather prediction (NWP) system. These negative-impact observations may help improve the forecasts in other NWP systems. Nevertheless, we find a large potential in that we have this efficient method reasonably evaluate the value of observations in a given NWP system. Since our first trial of estimating real observation impacts was successful, we are excited about further studies to draw the full potential of the method.

These results have been published in the following literature. Please refer to the literature for more details.

Kunii, M., T. Miyoshi, and E. Kalnay, 2012: Estimating impact of real observations in regional numerical weather prediction using an ensemble Kalman filter. Mon. Wea. Rev., 140, 1975-1987. doi:10.1175/MWR-D-11-00205.1

Dr. Nobumasa Komori (JAMSTEC) visited UMD

On Monday, May 14, Dr. Nobumasa Komori from the Earth Simulator Center, JAMSTEC (Japan Agency for Marine-Earth Science and Technology) visited College Park and gave a talk at our regular Weather-Chaos meeting on the Coupled Atmosphere-Ocean General Circulation Model (CGCM) for the Earth Simulator, so-called "CFES". The Earth Simulator is a Japanese flagship supercomputer system located in Yokohama. Dr. Komori was visiting our area for attending a conference in Silver Spring in the previous week. Dr. Komori was a colleague of Professor Enomoto, who visited UMD in March, and is also my collaborator since 2005. We are currently collaborating very closely to apply the Local Ensemble Transform Kalman Filter (LETKF) to the CFES. Since Dr. Komori has been working on the modeling effort of CFES, he talked about CFES simulations and the reduced-resolution version known as "CFES-mini", that we use for CFES-LETKF. It was quite intriguing to hear about the cutting-edge simulation studies on the coupled atmosphere-ocean system at a relatively high resolution.

Goodbye Masaru!

In the end of March, Masaru Kunii finished his 2-year stay in my lab as a visiting researcher from the Meteorological Research Institute, Japan Meteorological Agency, and went back to Japan. He was a very dedicated researcher, kept working very hard and consistently, and accomplished a number of developments and original research. The most important achievements include the development of the WRF-LETKF system and the pioneering research on forecast sensitivity to observations with the ensemble approach of Liu and Kalnay (2008). I would like to highlight his scientific achievements in another blog entry near future.

For now, I would like to illuminate his achievement of finishing his first marathon in Baltimore 6 months ago. The picture above is what we took by ourselves with the Maryland Double medals/t-shirts and the corresponding Frederick Half-marathon and Baltimore Marathon finisher medals. I believe running was not his habit before he came to Maryland. I started running after I moved to Maryland, so did he. In 1 and a half years of his running career, he finished his first Marathon in Baltimore Running Festival in October 2011. This was my third marathon. I had a very nice company to be at the start line together in the early morning of the race day. I will do my third Maryland Double this year (what is shown in the picture is my second MD Double medal), but this time, alone again. I will miss Masaru on the race days.

Professor Takeshi Enomoto (Kyoto U) visited UMD and NCEP

On Monday, March 12, Professor Takeshi Enomoto from the Kyoto University visited College Park and gave a talk about the OREDA project (Observing System Research and Ensemble Data Assimilation Development Research) at our regular Weather-Chaos meeting. Professor Enomoto is my long-time collaborator since 2005 when we started applying the Local Ensemble Transform Kalman Filter (LETKF) to the Earth Simulator Global Model known as AFES (Atmospheric General Circulation Model for the Earth Simulator). He presented the revised version of AFES-LETKF Experimental Reanalysis (ALERA2) and showed the most recent results of global coupled ocean-atmosphere data assimilation using the LETKF.

On Tuesday, March 13, Professor Enomoto visited the NCEP in Camp Springs, and gave another talk about a completely different topic on his work on dynamical core developments. He talked about approaches to reduce numerical errors without much increase of computational time. The wide range of topics that he covered really impressed me.

On Wednesday, March 14, Professor Enomoto spent a whole day in College Park again, mainly meeting with our postdocs and students to discuss about research ideas. His 3-day visit to Maryland was very fruitful and constructive to us, and hopefully to him, too. I feel very lucky to have this close and long-time collaboration with him.

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