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Shigenori Otsuka and Shigeo Yoden

Abstract

The temporal–spatial distribution of thin moist layers in the midtroposphere over the tropical eastern Pacific is studied by data analyses of radiosonde soundings and downscaling numerical experiments with a regional model. Radiosonde soundings at San Cristóbal, Galápagos, show frequent existence of thin moist layers between 2 and 10 km in altitude, with a local minimum at 7–8 km. The downscaling experiments with global objective analyses are completed for 2005–06, September and December of 1999–2004, and March of 2000–04. The vertical distribution of thin moist layers has three local maxima at 5, 10, and 16 km, where bimodality of the frequency distribution of water vapor is evident. Between 4 and 7 km, an annual variation is dominant in the occurrence ratio of thin moist layers, which tend to appear in nonconvective regions. In boreal winter, the layers appear to the north of the intertropical convergence zone (ITCZ), whereas in boreal summer the layers appear in the equator-side of the ITCZ. Interannual variations of the appearance of thin moist layers are also studied in 1999–2006, based on the experiments for particular months (March, September, and December). The occurrence ratio is generally high in December and March and low in September. In La Niña years, the annual variation is smaller than that in El Niño years; the occurrence ratio is higher in boreal summer to the south of the ITCZ.

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Shigenori Otsuka and Takemasa Miyoshi

Abstract

Multimodel ensemble data assimilation may account for uncertainties of numerical models due to different dynamical cores and physics parameterizations. In the previous studies, the ensemble sizes for each model are prescribed subjectively, for example, uniformly distributed to each model. In this study, a Bayesian filter approach to a multimodel ensemble Kalman filter is adopted to objectively estimate the optimal combination of ensemble sizes for each model. An effective inflation method to make the discrete Bayesian filter work without converging to a single imperfect model was developed.

As a first step, the proposed approach was tested with the 40-variable Lorenz-96 model. Different values of the model parameter F are used to mimic the multimodel ensemble. The true F is first chosen to be , and the observations are generated by adding independent Gaussian noise to the true time series. When the multimodel ensemble consists of , 7, 8, 9, and 10, the Bayesian filter finds the true model and converges to quickly. When , 7, 9, and 10, the closest two models, and F = 9, are selected. When the true F has a periodic variation about with a time scale much longer than the observation frequency, the proposed system follows the temporal change, and the error becomes less than that of the time-invariant optimal combination. Sensitivities to several parameters in the proposed system were also investigated, and the system was found to show improvements in a wide range of parameters.

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Nurjanna J. Trilaksono, Shigenori Otsuka, and Shigeo Yoden

Abstract

A numerical experiment using a regional nonhydrostatic model is performed to investigate the synoptic condition related to the heavy precipitation event that occurred at Jakarta in West Java, Indonesia, in January–February 2007. A time-lagged ensemble forecast method is employed with nine ensemble members. The ensemble mean well reproduces the temporal modulation of the spatial distributions of precipitation obtained from the Tropical Rainfall Measuring Mission data.

During the simulated two months, several monsoon surges are observed, but only the surge event during which the Jakarta flood event occurred is associated with a cold anomaly. The top of the cold northerly is about 1.5 km. The cold surge event is preceded by the so-called Borneo vortex event, which is dominated by a cyclonic vortex around Borneo, Indonesia, with a horizontal scale of 1000 km and a vertical scale of 3 km.

An analysis of cumulative distribution functions in a pentad time scale shows the modulation of the probability of rainfall rate. In pentad 7 (31 January–4 February), which includes the heavy rainfall event, the fraction of the area with precipitation is the highest and the contribution of heavy rainfall to the total amount is one of the highest in the two-month period. The diurnal cycle of occurrence of heavy rainfall is also modulated; in pentad 7, semidiurnal variation becomes dominant, and the largest peak appears in the early morning.

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Shigenori Otsuka, Shunji Kotsuki, and Takemasa Miyoshi

Abstract

Space–time extrapolation is a key technique in precipitation nowcasting. Motions of patterns are estimated using two or more consecutive images, and the patterns are extrapolated in space and time to obtain their future patterns. Applying space–time extrapolation to satellite-based global precipitation data will provide valuable information for regions where ground-based precipitation nowcasts are not available. However, this technique is sensitive to the accuracy of the motion vectors, and over the past few decades, previous studies have investigated methods for obtaining reliable motion vectors such as variational techniques. In this paper, an alternative approach applying data assimilation to precipitation nowcasting is proposed. A prototype extrapolation system is implemented with the local ensemble transform Kalman filter and is tested with the Japan Aerospace Exploration Agency’s Global Satellite Mapping of Precipitation (GSMaP) product. Data assimilation successfully improved the global precipitation nowcasting with the real-case GSMaP data.

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Shunji Kotsuki, Kenta Kurosawa, Shigenori Otsuka, Koji Terasaki, and Takemasa Miyoshi

Abstract

Over the past few decades, precipitation forecasts by numerical weather prediction (NWP) models have been remarkably improved. Yet, precipitation nowcasting based on spatiotemporal extrapolation tends to provide a better precipitation forecast at shorter lead times with much less computation. Therefore, merging the precipitation forecasts from the NWP and extrapolation systems would be a viable approach to quantitative precipitation forecast (QPF). Although the optimal weights between the NWP and extrapolation systems are usually defined as a global constant, the weights would vary in space, particularly for global QPF. This study proposes a method to find the optimal weights at each location using the local threat score (LTS), a spatially localized version of the threat score. We test the locally optimal weighting with a global NWP system composed of the local ensemble transform Kalman filter and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM-LETKF). For the extrapolation system, the RIKEN’s global precipitation nowcasting system called GSMaP_RNC is used. GSMaP_RNC extrapolates precipitation patterns from the Japan Aerospace Exploration Agency (JAXA)’s Global Satellite Mapping of Precipitation (GSMaP). The benefit of merging in global precipitation forecast lasts longer compared to regional precipitation forecast. The results show that the locally optimal weighting is beneficial.

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Shigenori Otsuka, Gulanbaier Tuerhong, Ryota Kikuchi, Yoshikazu Kitano, Yusuke Taniguchi, Juan Jose Ruiz, Shinsuke Satoh, Tomoo Ushio, and Takemasa Miyoshi

Abstract

The phased-array weather radar (PAWR) is a new-generation weather radar that can make a 100-m-resolution three-dimensional (3D) volume scan every 30 s for 100 vertical levels, producing ~100 times more data than the conventional parabolic-antenna radar with a volume scan typically made every 5 min for 15 scan levels. This study takes advantage of orders of magnitude more rapid and dense observations by PAWR and explores high-precision nowcasting of 3D evolution at 1–10-km scales up to several minutes, which are compared with conventional horizontal two-dimensional (2D) nowcasting typically at O(100) km scales up to 1–6 h. A new 3D precipitation extrapolation system was designed to enhance a conventional algorithm for dense and rapid PAWR volume scans. Experiments show that the 3D extrapolation successfully captured vertical motions of convective precipitation cores and outperformed 2D nowcasting with both simulated and real PAWR data.

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