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Kozo Okamoto and John C. Derber

Abstract

A technique for the assimilation of Special Sensor Microwave Imager (SSM/I) data in the National Centers for Environmental Prediction (NCEP) global data assimilation and forecast system is described. Because the radiative transfer model used does not yet allow for cloud/rain effects, it is crucial to properly identify and exclude (or correct) cloud/rain-contaminated radiances using quality control (QC) and bias correction procedures. The assimilation technique is unique in that both procedures take into account the effect of the liquid cloud on the difference between observed and simulated brightness temperature for each SSM/I channel. The estimate of the total column cloud liquid water from observed radiances is used in a frequency-dependent cloud detection component of the QC and as a predictor in the bias correction algorithm. Also, a microwave emissivity Jacobian model with respect to wind speed is developed for oceanic radiances. It was found that the surface wind information in the radiance data can be extracted through the emissivity model Jacobian rather than producing and including a separate SSM/I wind speed retrieval.

A two-month-long data assimilation experiment from July to August 2004 using NCEP’s Gridpoint Statistical Interpolation analysis system and the NCEP operational forecast model was performed. In general, the assimilation of SSM/I radiance has a significant positive impact on the analyses and forecasts. Moisture is added in the Northern Hemisphere and Tropics and is slightly reduced in the Southern Hemisphere. The moisture added appears to be slightly excessive in the Tropics verified against rawinsonde observations. Nevertheless, the assimilation of SSM/I radiance data reduces model spinup of precipitation and substantially improves the dynamic fields, especially in measures of the vector wind error at 200 hPa in the Tropics. In terms of hurricane tracks, SSM/I radiance assimilation produces more cases with smaller errors and reduces the average error. No disruption of the Hadley circulation is found from the introduction of the SSM/I radiance data.

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Kazumasa Aonashi, Kozo Okamoto, Tomoko Tashima, Takuji Kubota, and Kosuke Ito

Abstract

In ensemble-based assimilation schemes for cloud-resolving models (CRMs), the precipitation-related variables have serious sampling errors. The purpose of the present study is to examine the sampling error properties and the forecast error characteristics of the operational CRM of the Japan Meteorological Agency (JMANHM) and to develop a sampling error damping method based on the CRM forecast error characteristics.

The CRM forecast error was analyzed for meteorological disturbance cases using 100-member ensemble forecasts of the JMANHM. The ensemble forecast perturbation correlation had a significant noise associated with the precipitation-related variables, because of sampling errors. The precipitation-related variables were likely to suffer this sampling error in most precipitating areas. An examination of the forecast error characteristics revealed that the CRM forecast error satisfied the assumption of the spectral localization, while the spatial localization with constant scales, or variable localization, were not applicable to the CRM.

A neighboring ensemble (NE) method was developed, which was based on the spectral localization that estimated the forecast error correlation at the target grid point, using ensemble members for neighboring grid points. To introduce this method into an ensemble-based variational assimilation scheme, the present study horizontally divided the NE forecast error into large-scale portions and deviations. As single observation assimilation experiments showed, this “dual-scale NE” method was more successful in damping the sampling error and generating plausible, deep vertical profile of precipitation analysis increments, compared to a simple spatial localization method or a variable localization method.

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Kozo Okamoto, Kazumasa Aonashi, Takuji Kubota, and Tomoko Tashima

Abstract

Space-based precipitation radar data have been underused in data assimilation studies and operations despite their valuable information on vertically resolved hydrometeor profiles around the globe. The authors developed direct assimilation of reflectivities (Ze) from the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) Core Observatory to improve mesoscale predictions. Based on comparisons with Ze observations, this cloud resolving model mostly reproduced Ze but produced overestimations of Ze induced by excessive snow with large diameter particles. With an ensemble-based variational scheme and preprocessing steps to properly treat reflectivity observations including conservative quality control and superobbing procedures, the authors assimilated DPR Ze and/or rain-affected radiances of GPM Microwave Imager (GMI) for the case of Typhoon Halong in July 2014. With the vertically resolving capability of DPR, the authors effectively selected Ze observations most suited to data assimilation, for example, by removing Ze above the melting layer to avoid contamination due to model bias. While the GMI radiance had large impacts on various control variables, the DPR made a fine delicate analysis of the rain mixing ratio and updraft. This difference arose from the observation characteristics (coverage width and spatial resolution), sensitivities represented in the observation operators, and structures of the background error covariance. Because the DPR assimilation corrected excessive increases in rain and clouds due to the radiance assimilation, the combined use of DPR and GMI generated more accurate analysis and forecast than separate use of them with respect to the agreement of observations and tropical cyclone position errors.

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Takumi Honda, Takemasa Miyoshi, Guo-Yuan Lien, Seiya Nishizawa, Ryuji Yoshida, Sachiho A. Adachi, Koji Terasaki, Kozo Okamoto, Hirofumi Tomita, and Kotaro Bessho

Abstract

Japan’s new geostationary satellite Himawari-8, the first of a series of the third-generation geostationary meteorological satellites including GOES-16, has been operational since July 2015. Himawari-8 produces high-resolution observations with 16 frequency bands every 10 min for full disk, and every 2.5 min for local regions. This study aims to assimilate all-sky every-10-min infrared (IR) radiances from Himawari-8 with a regional numerical weather prediction model and to investigate its impact on real-world tropical cyclone (TC) analyses and forecasts for the first time. The results show that the assimilation of Himawari-8 IR radiances improves the analyzed TC structure in both inner-core and outer-rainband regions. The TC intensity forecasts are also improved due to Himawari-8 data because of the improved TC structure analysis.

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