著者

OTSUKA Shigenori KOTSUKI Shunji OHHIGASHI Marimo MIYOSHI Takemasa

出版者

Meteorological Society of Japan

雑誌

気象集誌. 第2輯 (ISSN:00261165)

巻号頁・発行日

pp.2019-061, (Released:2019-09-03)

被引用文献数

7

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Since January 2016, RIKEN has been running an extrapolation-based nowcasting system of global precipitation in real time. Although our previous paper reported its advantage of the use of data assimilation in a limited verification period, long-term stability of its forecast accuracy through different seasons has not been investigated. In addition, the algorithm was updated seven times between January 2016 and March 2018. Therefore, this paper aims to present how motion vectors can be derived more accurately, and how data assimilation can constrain an advection-diffusion model for extrapolation stably for the long-term operation. The Japan Aerospace Exploration Agency's Global Satellite Mapping of Precipitation (GSMaP) Near-Real-Time product is the only input to the nowcasting system. Motion vectors of precipitation areas are computed by a cross-correlation method, and the Local Ensemble Transform Kalman Filter generates a smooth, complete set of motion vectors. Precipitation areas are moved by the motion vectors up to 12 hours, and the product, called “GSMaP RIKEN Nowcast”, is disseminated on a webpage in real time. Most of the algorithmic updates were related to better estimating motion vectors, and the forecast accuracy was gradually and consistently improved by these updates. Particularly, the threat scores increased the most around 40°S and 40°N. A performance drop in the northern hemisphere winter was also reduced by reducing noise in advection. The time series of ensemble spread showed that an increase in the number of available motion vectors by a system update led to a decrease in the ensemble spread, and vice versa.

著者

AWAZU Taeka OTSUKA Shigenori MIYOSHI Takemasa

出版者

Meteorological Society of Japan

雑誌

気象集誌. 第2輯 (ISSN:00261165)

巻号頁・発行日

pp.2019-066, (Released:2019-09-22)

被引用文献数

1

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This paper proposes a new verification metric that can evaluate location errors and shapes of rainfall areas simultaneously: the Pattern Similarity Index (PSI). Pixel-by-pixel verification methods such as the threat score and root mean squared error have difficulties in evaluating location errors and shapes of rainfall areas, and in evaluating small rainfall areas. To address these difficulties, various object-based methods have been developed. However, object-based methods tend to be complicated and computationally expensive. Therefore, PSI adopts a simpler, computationally more efficient algorithm as follows. First, bounding rectangles of individual rainfall areas are computed, and neighboring rectangles are combined so that they are treated as a single precipitation system to mimic the human recognition. Next, shape parameters are computed for each integrated bounding rectangle. For each pair of the observed and forecasted rainfall areas, the location error weighted by the differences of the shape parameters is used as the verification score. If no observed rainfall area with a similar size exists near a forecasted rainfall area, this distance- based score of the forecasted area is set to a large value. The integration method of the bounding rectangle and the precipitation threshold are the only tunable parameters in this method, and we repeat computing the verification score by varying these parameters. The best value is used as the final verification score. Idealized cases showed the ability of PSI to evaluate location errors and differences in the shape parameters. A real case with global precipitation nowcasting showed that the proposed evaluation value increased almost linearly with the forecast time, whereas the threat score and root mean squared error tended to saturate as the forecast time increases, showing a potential advantage of PSI. Comparison with another object-based method revealed the advantage of PSI in its computational efficiency while providing similar verification scores.

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

MAEJIMA Yasumitsu MIYOSHI Takemasa KUNII Masaru SEKO Hiromu SATO Kae

出版者

Meteorological Society of Japan

雑誌

気象集誌. 第2輯 (ISSN:00261165)

巻号頁・発行日

pp.2019-014, (Released:2018-11-16)

被引用文献数

7

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This study aims to investigate the potential impact of surface observations with a high spatial and temporal density on a local heavy rainstorm prediction. A series of Observing System Simulation Experiments (OSSEs) are performed using the Local Ensemble Transform Kalman Filter with the Japan Meteorological Agency non-hydrostatic model at 1-km resolution and with 1-minute update cycles. For the nature run of the OSSEs, a 100-m-resolution simulation is performed for the heavy rainstorm case that caused 5 fatalities in Kobe, Japan on July 28, 2008. Synthetic radar observation data, both reflectivity and Doppler velocity, are generated at 1-km resolution every minute from the 100-m-resolution nature run within a 60-km range, simulating the phased array weather radar (PAWR) at Osaka University. The control experiment assimilates only the radar data, and two sensitivity experiments are performed to investigate the impact of additional surface observations obtained every minute at 8 and 167 stations in Kobe. The results show that the dense and frequent surface observations have a significant positive impact on the analyses and forecasts of the local heavy rainstorm, although the number of assimilated observations is three orders of magnitude less than the PAWR data. Equivalent potential temperature and convergence at the low levels are improved, contributing to intensified convective cells and local heavy rainfalls.

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.