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Michiya Hayashi and Masahiro Watanabe

Abstract

Coupled dynamics between westerly wind events (WWEs) and the El Niño–Southern Oscillation (ENSO) is examined using an atmosphere–ocean coupled model with intermediate complexity. The model incorporates state-dependent stochastic noise that mimics observed WWEs, which occur at the edge of the Pacific warm pool when the Niño-4 sea surface temperature (SST) anomaly increases positively. The model parameter that controls the efficiency of the thermocline feedback, γ, is perturbed to elaborate the sensitivity of the results to the system’s stability. Without the noise (experiment NO), the model produces an ENSO-like regular oscillation with a 6-yr period, the variance of which increases with γ. When additive noise is introduced over the western Pacific (experiment AD), the oscillations become irregular with a dominant period of 4–6 years and the increase in the variance relative to the NO experiment depends on γ. When state-dependent noise is included (experiment SD), the oscillatory solution is also irregular, and its variance and asymmetry are increased irrespective of the value of γ. Both the additive and state-dependent noise contribute to the occurrence of two types of variability, corresponding to the eastern Pacific (EP) and central Pacific (CP) El Niños. In SD, the state dependence of the stochastic noise guarantees the existence of CP El Niño regardless of γ since the increased likelihood of WWE occurrence with Niño-4 SSTs results in a positive feedback in the central Pacific. The above results suggest that the state dependence of WWEs plays a crucial role in the asymmetry and diversity of ENSO.

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Michiko Otsuka, Hiromu Seko, Masahiro Hayashi, and Ko Koizumi

Abstract

Himawari-8 optimal cloud analysis (OCA), which employs all 16 channels of the Advanced Himawari Imager, provides cloud properties such as cloud phase, top pressure, optical thickness, effective radius, and water path. By using OCA, the water vapor distribution can be inferred with high spatiotemporal resolution and with a wide coverage, including over the ocean, which can be useful for improving initial states for prediction of the torrential rainfalls that occur frequently in Japan. OCA products were first evaluated by comparing them with different kinds of datasets (surface, sonde, and ceilometer observations) and with model outputs, to determine their data characteristics. Overall, OCA data were consistent with observations of water clouds with moderate optical thicknesses at low to midlevels. Next, pseudorelative humidity data were derived from the OCA products, and utilized in assimilation experiments of a few heavy rainfall cases, conducted with the Japan Meteorological Agency’s nonhydrostatic model–based Variational Data Assimilation System. Assimilation of OCA pseudorelative humidities caused there to be significant differences in the initial conditions of water vapor fields compared to the control, especially where OCA clouds were detected, and their influence lasted relatively long in terms of forecast hours. Impacts of assimilation on other variables, such as wind speed, were also seen. When the OCA data successfully represented low-level inflows from over the ocean, they positively impacted precipitation forecasts at extended forecast times.

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Michiko Otsuka, Hiromu Seko, Masahiro Hayashi, and Ko Koizumi

Abstract

Himawari-8 optimal cloud analysis (OCA), which employs all 16 channels of the Advanced Himawari Imager, provides cloud properties such as cloud phase, top pressure, optical thickness, effective radius, and water path. By using OCA, the water vapor distribution can be inferred with high spatiotemporal resolution and with a wide coverage, including over the ocean, which can be useful for improving initial states for prediction of the torrential rainfalls that occur frequently in Japan. OCA products were first evaluated by comparing them with different kinds of data sets (surface, sonde, and ceilometer observations) and with model outputs, to determine their data characteristics. Overall, OCA data were consistent with observations of water clouds with moderate optical thicknesses at low to mid levels. Next, pseudo-relative humidity data were derived from the OCA products, and utilized in assimilation experiments of a few heavy rainfall cases, conducted with the Japan Meteorological Agency’s nonhydrostatic model-based Variational Data Assimilation System. Assimilation of OCA pseudo-relative humidities caused there to be significant differences in the initial conditions of water vapor fields compared to the control, especially where OCA clouds were detected, and their influence lasted relatively long in terms of forecast hours. Impacts of assimilation on other variables, such as wind speed, were also seen. When the OCA data successfully represented low-level inflows from over the ocean, they positively impacted precipitation forecasts at extended forecast times.

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