Keiya Yumimoto

Abstract Himawari‐8, a next‐generation geostationary meteorological satellite, was launched on 7 October 2014 and became operational on 7 July 2015. The advanced imager on board Himawari‐8 is equipped with 16 observational bands (including three visible and three near‐infrared bands) that enable retrieval of full‐disk aerosol optical properties at 10 min intervals from geostationary (GEO) orbit. Here we show the first application of aerosol optical properties (AOPs) derived from Himawari‐8 data

Abstract. We introduce a Multi-mOdel Multi-cOnstituent Chemical data assimilation (MOMO-Chem) framework that directly accounts for model error in transport and chemistry, and we integrate a portfolio of data assimilation analyses obtained using multiple forward chemical transport models in a state-of-the-art ensemble Kalman filter data assimilation system. The data assimilation simultaneously optimizes both concentrations and emissions of multiple species through ingestion of a suite of measurem

Abstract. A global aerosol reanalysis product named the Japanese Reanalysis for Aerosol (JRAero) was constructed by the Meteorological Research Institute (MRI) of the Japan Meteorological Agency. The reanalysis employs a global aerosol transport model developed by MRI and a two-dimensional variational data assimilation method. It assimilates maps of aerosol optical depth (AOD) from MODIS onboard the Terra and Aqua satellites every 6 h and has a TL159 horizontal resolution (approximately 1.1° × 1

A four‐dimensional variational (4DVAR) data assimilation system was developed for a regional dust model. This report presents results of the first adjoint inversion of Asian dust emissions over East Asia using NIES LIDAR observations, targeting the extreme dust phenomenon on 30 April 2005. Optimized dust emissions mitigated underestimation of dust concentrations and brought the structure of the elevated dust layer (both onset timing and extinction coefficient intensity) into better agreement wit

Abstract. Acidifying species in precipitation can have severe impacts on ecosystems. The chemical composition of precipitation is directly related to the amount of precipitation; accordingly, it is difficult to identify long-term variation in chemical concentrations. The ratio of the nitrate (NO3−) to non-sea-salt sulfate (nss-SO42−) concentration in precipitation on an equivalent basis (hereinafter, Ratio) is a useful index to investigate the relative contributions of these acidifying species.

Abstract Inverse modeling of Asian dust over the 8 year period 2005–2012 was performed with the Spectral Radiation‐Transport Model for Aerosol Species/four‐dimensional variational (SPRINTARS/4D‐Var) data assimilation system and satellite‐measured aerosol optical thickness over the ocean. We validated the inversion results with independent measurements provided by ground‐based and space‐based lidar and various in situ measurements. The inversion results were used to analyze interannual variations

In mid‐August 2009, ground‐based lidar networks on both sides of the Pacific Basin detected an elevated dust layer. A combined analysis by ground‐based lidars, space‐borne lidar CALIOP, and numerical models revealed that dust particles emitted in the Taklimakan Desert were transported across the Pacific Ocean in 12 to 13 days. This was the first evidence of summertime trans‐Pacific transport of Asian dust from the Taklimakan Desert. A large‐scale dust storm occurred in the Taklimakan Desert duri

[1] We developed a new ensemble-based data-assimilation system based on a global aerosol climate model and performed a 1-month assimilation experiment using satellite optical measurements from MODIS onboard TERRA and AQUA to estimate the direct radiative effect (DRE) of aerosols. Using the assimilated data field, monthly averaged optical thickness (AOT) was estimated to be 0.15 ± 0.030 (a 52.0% increase over a priori), and the root mean-square difference (RMSD) between modeled values and MODIS m

The Japan Meteorological Agency (JMA) launched a next-generation geostationary meteorological satellite (GMS), Himawari-8, on October 7, 2014, which began its operation on July 7, 2015. The Advanced Himawari Imager (AHI) onboard Himawari-8 has 16 observational bands that enable the retrieval of full-disk maps of aerosol optical properties (AOPs), including aerosol optical thickness (AOT) and the Ångström exponent (AE), with unprecedented spatial and temporal resolutions. In this study, we combin

Abstract. We developed a new aerosol satellite retrieval algorithm combining a numerical aerosol forecast. In the retrieval algorithm, the short-term forecast from an aerosol data assimilation system was used as an a priori estimate instead of spatially and temporally constant values. This method was demonstrated using observation of the Advanced Himawari Imager onboard the Japan Meteorological Agency's geostationary satellite Himawari-8. Overall, the retrieval results incorporated strengths of

Abstract. We present an aerosol data assimilation system based on a global aerosol climate model (SPRINTARS – Spectral Radiation-Transport Model for Aerosol Species) and a four-dimensional variational data assimilation method (4D-Var). Its main purposes are to optimize emission estimates, improve composites, and obtain the best estimate of the radiative effects of aerosols in conjunction with observations. To reduce the huge computational cost caused by the iterative integrations in the models,