著者

TERASAKI Koji MIYOSHI Takemasa

出版者

(公社)日本気象学会

雑誌

気象集誌. 第2輯 (ISSN:00261165)

巻号頁・発行日

pp.2017-028, (Released:2017-09-15)

被引用文献数

18

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An observation operator to assimilate satellite radiances with the Non-hydrostatic Icosahedral Atmospheric Model (NICAM)-based Local Ensemble Transform Kalman Filter (LETKF) is newly developed using the radiative transfer model RTTOV (Radiative Transfer for the TOVS (TIROS Operational Vertical Sounder)) version 11.1. Here we assimilate the Advanced Microwave Sounding Unit-A (AMSU-A) brightness temperature observations which are known to bring a large improvement to global numerical weather prediction. We apply the online estimation of bias correction for both airmass and scan biases, or the biases originating from the atmospheric state and scan position. Comparing the two experiments with and without the AMSU-A radiances, we find that the adaptive bias correction methods work appropriately, and that the analysis is significantly improved by assimilating the AMSU-A radiances. This is an important step toward assimilating different types of satellite radiances with NICAM-LETKF.

著者

LIANG Jianyu TERASAKI Koji MIYOSHI Takemasa

出版者

公益社団法人 日本気象学会

雑誌

気象集誌. 第2輯 (ISSN:00261165)

巻号頁・発行日

pp.2023-005, (Released:2022-11-01)

被引用文献数

3

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The observation operator (OO) is essential in data assimilation (DA) to derive the model equivalent of observations from the model variables. In the satellite DA, the OO for satellite microwave brightness temperature (BT) is usually based on the radiative transfer model (RTM) with a bias correction procedure. To explore the possibility to obtain OO without using physically based RTM, this study applied machine learning (ML) as OO (ML-OO) to assimilate BT from Advanced Microwave Sounding Unit-A (AMSU-A) channels 6 and 7 over oceans and channel 8 over both land and oceans under clear-sky conditions. We used a reference system, consisting of the nonhydrostatic icosahedral atmospheric model (NICAM) and the local ensemble transform Kalman filter (LETKF). The radiative transfer for TOVS (RTTOV) was implemented in the system as OO, combined with a separate bias correction procedure (RTTOV-OO). The DA experiment was performed for one month to assimilate conventional observations and BT using the reference system. Model forecasts from the experiment were paired with observations for training the ML models to obtain ML-OO. In addition, three DA experiments were conducted, which revealed that DA of the conventional observations and BT using ML-OO was slightly inferior, compared to that of RTTOV-OO, but it was better than the assimilation based on only conventional observations. Moreover, ML-OO treated bias internally, thereby simplifying the overall system framework. The proposed ML-OO has limitations due to (1) the inability to treat bias realistically when a significant change is present in the satellite characteristics, (2) inapplicability for many channels, (3) deteriorated performance, compared with that of RTTOV-OO in terms of accuracy and computational speed, and (4) physically based RTM is still used to train the ML-OO. Future studies can alleviate these drawbacks, thereby improving the proposed ML-OO.

著者

Žagar Nedjeljka Terasaki Koji Tanaka Hiroshi L.

出版者

American Meteorological Society

雑誌

Monthly weather review (ISSN:00270644)

巻号頁・発行日

vol.140, no.7, pp.2297-2307, 2012-07

被引用文献数

9

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This paper deals with the large-scale inertio-gravity (IG) wave energy in the operational ECMWF analyses in July 2007. Energy percentages of the IG waves obtained from the standard-pressure-level data are compared to those derived from various discretizations of the model-level data. The results show a small albeit systematic increase of the IG energy percentage as the vertical level density increases from the standard-pressure levels toward the model-level density; the small relative change is explained by the sufficient vertical resolution to resolve the large-scale IG waves in the tropics that make the majority of the global IG energy on large scales. A relatively larger increase of the IG energy is obtained when the mesospheric model levels are included; however, the analyses at these levels in July 2007 are less reliable. Furthermore, two numerical methods for the normal-mode function (NMF) decomposition are shown to provide similar results. The decomposition of atmospheric analyses into the NMF series is proposed as a tool to analyze the spatial and temporal variations of the large-scale equatorial waves and their role in global energetics.