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Overview These data are the Japanese Aerospace Exploration Agencies (JAXA) EarthCARE synthetic data based on simulations using a 3.5 km horizontal-mesh global storm-resolving model. Synthetic data were produced corresponding to the four EarthCARE instruments sensors, namely a 94 GHz cloud-profiling radar (CPR), a 355 nm atmospheric lidar (ATLID), a seven-channel multispectral imager (MSI), and a broadband radiometer (BBR). The data include a standard product with data for a single orbit, which is described in a Special Issue of the journal Atmospheric Modelling Techniques (AMT). Directory structure and file format There are four files for each instrument. For CPR, there is a subdirectory for a CPR data with more detailed information like a random error of Doppler velocity and surface clutters of radar reflectivities (JAXA_L1). For BBR, there are two subdirectories for the heating rates and the optical depth of 532 nm. The files are in the NetCDF4 format. The files have the following name format. nicamXXX_TIME_Y.ZZZ.nc XXX: the sensor name TIME: The start time of the orbit, year-month-date-minute-second Y: frame number depending on the latitude ZZZ: the name of the sensor simulator/sensor name/output name in the Joint Simulator The data was supported by the Japan Aerospace Exploration Agency. The data would be expanded and updated. If you have any questions or want additional data, please contact the email below Contacts: Woosub Roh (ws-roh@aori.u-tokyo.ac.jp), {"references":["Roh, W., Satoh, M., Hashino, T., Matsugishi, S., Nasuno, T., and Kubota, T.: Introduction to EarthCARE synthetic data using a global storm-resolving simulation, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2023-18, in review, 2023."]}

The Nonhydrostatic ICosahedral Atmospheric Model (NICAM), a global model with an icosahedral grid system, has been under development for nearly two decades. This paper describes NICAM16-S, the latest stable version of NICAM (NICAM.16), modified for the Coupled Model Intercomparison Project Phase 6, High Resolution Model Intercomparison Project (HighResMIP). Major updates of NICAM.12, a previous version used for climate simulations, included updates of the cloud microphysics scheme and land surface model, introduction of natural and anthropogenic aerosols and a subgrid-scale orographic gravity wave drag scheme, and improvement of the coupling between the cloud microphysics and the radiation schemes. External forcings were updated to follow the protocol of the HighResMIP. A series of short-term sensitivity experiments were performed to determine and understand the impacts of these various model updates on the simulated mean states. The NICAM16-S simulations demonstrated improvements in the ice water content, high cloud amount, surface air temperature over the Arctic region, location and strength of zonal mean subtropical jet, and shortwave radiation over Africa and South Asia. Some long-standing biases, such as the double intertropical convergence zone and smaller low cloud amount, still exist or are even worse in some cases, suggesting further necessity for understanding their mechanisms, upgrading schemes and parameter settings, and enhancing horizontal and vertical resolutions.

High-performance computing resources allow us to conduct numerical simulations with a horizontal grid spacing that is sufficiently high to resolve cloud systems on a global scale, and high-resolution models (HRMs) generally provide better simulation performance than low-resolution models (LRMs). In this study, we execute a next-generation model that is capable of simulating global aerosols using version 16 of the Nonhydrostatic Icosahedral Atmospheric Model (NICAM.16). The simulated aerosol distributions are obtained for 3 years with an HRM using a global 14 km grid spacing, an unprecedentedly high horizontal resolution and long integration period. For comparison, a NICAM with a 56 km grid spacing is also run as an LRM, although this horizontal resolution is still high among current global aerosol climate models. The comparison elucidated that the differences in the various variables of meteorological fields, including the wind speed, precipitation, clouds, radiation fluxes and total aerosols, are generally within 10 % of their annual averages, but most of the variables related to aerosols simulated by the HRM are slightly closer to the observations than are those simulated by the LRM. Upon investigating the aerosol components, the differences in the water-insoluble black carbon and sulfate concentrations between the HRM and LRM are large (up to 32 %), even in the annual averages. This finding is attributed to the differences in the aerosol wet deposition flux, which is determined by the conversion rate of cloud to precipitation, and the difference between the HRM and LRM is approximately 20 %. Additionally, the differences in the simulated aerosol concentrations at polluted sites during polluted months between the HRM and LRM are estimated with normalized mean biases of −19 % for black carbon (BC), −5 % for sulfate and −3 % for the aerosol optical thickness (AOT). These findings indicate that the impacts of higher horizontal grid spacings on model performance for secondary products such as sulfate, and complex products such as the AOT, are weaker than those for primary products, such as BC. On a global scale, the subgrid variabilities in the simulated AOT and cloud optical thickness (COT) in the 1∘×1∘ domain using 6-hourly data are estimated to be 28.5 % and 80.0 %, respectively, in the HRM, whereas the corresponding differences are 16.6 % and 22.9 % in the LRM. Over the Arctic, both the HRM and the LRM generally reproduce the observed aerosols, but the largest difference in the surface BC mass concentrations between the HRM and LRM reaches 30 % in spring (the HRM-simulated results are closer to the observations). The vertical distributions of the HRM- and LRM-simulated aerosols are generally close to the measurements, but the differences between the HRM and LRM results are large above a height of approximately 3 km, mainly due to differences in the wet deposition of aerosols. The global annual averages of the effective radiative forcings due to aerosol–radiation and aerosol–cloud interactions (ERFari and ERFaci) attributed to anthropogenic aerosols in the HRM are estimated to be -0.293±0.001 and -0.919±0.004 W m−2, respectively, whereas those in the LRM are -0.239±0.002 and -1.101±0.013 W m−2. The differences in the ERFari between the HRM and LRM are primarily caused by those in the aerosol burden, whereas the differences in the ERFaci are primarily caused by those in the cloud expression and performance, which are attributed to the grid spacing. The analysis of interannual variability revealed that the difference in reproducibility of both sulfate and carbonaceous aerosols at different horizontal resolution is greater than their interannual variability over 3 years, but those of dust and sea salt AOT and possibly clouds were the opposite. Because at least 10 times the computer resources are required for the HRM (14 km grid) compared to the LRM (56 km grid), these findings in this study help modelers decide whether the objectives can be achieved using such higher resolution or not under the limitation of available computational resources.

The Weather Research and Forecasting (WRF) Model and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) are forced with the Global Forecast System (GFS) data and run over the United Arab Emirates (UAE) for two 4-day periods: one in the cold season (16–18 December 2017) and another in the warm season (13–15 April 2018). The models’ performance is evaluated against four observational datasets: weather station observations, eddy-covariance flux measurements at Al Ain, microwave radiometer–derived temperature profile, and twice-daily radiosonde measurements at Abu Dhabi. An overestimation of the daily mean air temperature by 1°–3°C is noticed for both models and periods. This warm bias is attributed to the reduced cloud cover and resulting increased surface downward shortwave radiation flux. A comparison with the eddy-covariance data suggested that both models also underestimate the observed albedo. However, when the models predict heavier amounts of precipitation, they tend to be colder than observations, typically by 2°–3°C. NICAM and WRF overpredict the strength of the near-surface wind speed at all weather stations by roughly 1–3 m s−1, which has been attributed to a poor representation of its subgrid-scale fluctuations and surface drag parameterization. WRF tends to be wetter and NICAM drier than the station observations, possibly because of differences in the cloud microphysics schemes. While the performance of both models for the near-surface fields is comparable, NICAM outperforms WRF in the simulation of vertical profiles of temperature, relative humidity, and wind speed, being able to partially correct some of the biases in the GFS data.

This study newly applies measurements from two geostationary satellites, the Advanced Himawari Imager (AHI) onboard the geostationary satellite Himawari-8 and the Geostationary Ocean Color imager (GOCI) onboard the geostationary satellite COMS, to evaluate a unique regional aerosol-transport model coupled to a non-hydrostatic icosahedral atmospheric model (NICAM) at a high resolution without any nesting technique and boundary conditions of the aerosols. Taking advantage of the unique capability of these geostationary satellites to measure aerosols with unprecedentedly high temporal resolution, we focus on a target area (115°E-155°E, 20°N-50°N) in East Asia in May 2016, which featured the periodic transport of industrial aerosols and a very heavy aerosol plume from Siberian wildfires. The aerosol optical thickness (AOT) fields are compared among the AHI, GOCI, MODIS, AERONET and NICAM data. The results show that both AHI- and GOCI-retrieved AOTs were generally comparable to the AERONET-retrieved ones, with high correlation coefficients of approximately 0.7 in May 2016. They also show that NICAM successfully captured the detailed horizontal distribution of AOT transported from Siberia to Japan on the most polluted day (18 May 2016). The monthly statistical metrics, including correlation between the model and either AHI or GOCI, are estimated to be >0.4 in 42–49% of the target area. With the aid of sensitivity model experiments with and without Siberian wildfires, it was found that a long-range transport of aerosols from Siberian wildfires (from as far as 3000 km) to Japan influenced the monthly mean aerosol levels, accounting for 7–35% of the AOT, 26–49% of the surface PM2.5 concentrations, and 25–66% of the aerosol extinction above 3 km in height over Japan. Therefore, the air pollutants from Siberian wildfire cannot be ignored for the spring over Japan.

The Non-hydrostatic ICosahedral Atmospheric Model (NICAM), a global model with an icosahedral grid system, has been under development for nearly two decades. Here, we present its recent updates for the Coupled Model Intercomparison Project Phase 6, High Resolution Model Intercomparison Project (HighResMIP) and their impact on the simulated mean states using 56-14km mesh model. Major updates include updates of the cloud microphysics scheme and land surface model, introduction of natural and anthropogenic aerosols and a subgrid-scale orographic gravity wave drag scheme, and improvement of the coupling between the cloud microphysics and the radiation schemes. A short-term sensitivity experiments demonstrate improvements in the ice water content, high cloud amount, surface air temperature over the Arctic region, location and strength of zonal mean subtropical jet, and shortwave radiation over Africa and South Asia. The decadal climate simulations further reveal an improvement in the genesis and structure of the tropical cyclones compared with those with the previous model. Finally, we will address outlook toward the cloud-resolving climate simulation based on a fresh benchmark result on supercomputer Fugaku, a flagship supercomputer in Japan.

The stable water isotopes (SWIs) (δ18O and δD) are used as an indicator of the intensity of the atmospheric hydrological cycle due to their large variability in time and space. Although data about vapor isotope ratio with high frequency and high resolution are now available by satellite observations and spectroscopic analyses, there is some room for discussion on the variability of isotope ratios in vapor and precipitation related to cloud microphysical processes.Here, we incorporated SWI tracer into the latest version of a global cloud system resolving model (the Nonhydrostatic Icosahedral Atmospheric Model (NICAM)), iso-NICAM, and investigated the contribution of cloud microphysical processes to the variability of isotope ratios in precipitation and vapor. One of the merits used NICAM is that its physical process can cover from low spatial resolution to high spatial resolution. We conducted two mode simulations (GCM and CRM). The GCM mode simulation is based on the Arakawa-Schubert scheme as convective parameterization and a large-scale condensation scheme as the cloud physical process. In contrast, the CRM mode simulation is based on the a single-moment bulk cloud microphysics scheme with 6 water categories as cloud microphysical scheme, convective parameterization scheme was not used. These simulations are set to about 223 km of horizontal mesh resolution and 78 vertical layers. We conducted an AMIP-type climate experiment for one year from 1979.The simulated precipitation δ18O showed the latitude effect pattern (high δ18O in low latitude region, low δ18O in high latitude region), but those values in the CRM mode was slightly lower than that in the GCM mode . The simulated precipitation δ18O in the CRM mode was lower in high altitude or inland regions compared with those in the GCM mode . Besides, the precipitation d-excess in the CRM mode shows large spatial variability compared with the GCM mode. Although the low spatial resolution was set in this study, these simulations indicated cloud microphysical processes are important for understanding the variability of isotope physics. We will conduct these simulations with finer spatial resolution and a more extended simulation period.

In parallel with the new Japanese flagship supercomputer, Fugaku, we have continued improving a nonhydrostatic icosahedral atmospheric model (NICAM). Here, we introduce the results of our system-application co-design since 2014. Fugaku's CPU (A64FX) is based on the Arm instruction-set architecture. This 48-core many-core CPU is equipped with 32GB of HBM2 memory, showing data transfer performance comparable to GPUs. We have implemented kernel-level optimizations to take advantage of Fugaku's high memory performance. Among them, we recognized trade-offs related to ensuring memory locality and parallelism, and register allocation. We improved the application's average arithmetic intensity through detailed loop-by-loop performance measurements and reduced memory pressure by actively using single-precision operations. We also redesigned the data layout and the file I/O component of the ensemble data assimilation (DA) system and achieved good scalability in the atmospheric simulation and DA. We performed a global 3.5km mesh, 1024-member ensemble simulation, and DA using 82% of the Fugaku system (131,072 nodes, 6,291,456 cores). In this world's most massive ensemble DA benchmark experiment, the simulation and the DA achieved 29 PFLOPS and 79 PFLOPS of effective performance.

This paper describes a cloud liquid water (CLW) database derived from a global cloud-system resolving model NICAM for precipitation retrievals of Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) core observatory. Furthermore, impacts of the CLW assumptions were evaluated by experiments using the operational DPR Level-2 algorithm that provides estimated precipitation rates. The CLW database was calculated using the 3.5km-mesh global atmospheric simulation data as a function of surface precipitation rate (SPR), precipitation type (convective or stratiform), temperature, latitude, and land surface type. By comparing the 1-month experiment of the CLW amount with 0 mg/m3, impacts of the current CLW assumptions to surface precipitation estimations were 1.8% for the Ku, 14.0% for the Ka, 8.0% for the Dual-frequency algorithms in global averages. The CLW database was calculated using the 3.5km-mesh global atmospheric simulation data as a function of surface precipitation rate (SPR), precipitation type (convective or stratiform), temperature, latitude, and land surface type. By comparing the 1-month experiment of the CLW amount with 0 mg/m3, impacts of the current CLW assumptions to surface precipitation estimations were 1.8% for the Ku, 14.0% for the Ka, 8.0% for the Dual-frequency algorithms in global averages. By comparing the 1-month experiment of the CLW amount with 0 mg/m3, impacts of the current CLW assumptions to surface precipitation estimations were 1.8% for the Ku, 14.0% for the Ka, 8.0% for the Dual-frequency algorithms in global averages.

The vertical profiles of temperature and lapse rate in the troposphere are studied in the latitudinal direction with time and zonal mean data of NICAM aqua-planet results and five-year averaged JRA-55 analysis. It is found that（１）latitudinal variations of the vertical profiles of temperature are small, but（２）vertical variations are large; small （large）in the lower（higher）layer of troposphere. Physical reasoning of（１）small horizontal variations of lapse rates and（２）large vertical variation and its impact is discussed. It is suggested that horizontal propagation of neutral waves appeared in the atmosphere is responsible for the problem（１）．

In this study, simulations of the annual mean aerosol budget, aerosol optical properties, and surface mass concentration in 2006 in China are performed with two aerosol interactive global atmosphere models, namely, the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) coupled with the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) and the Beijing Climate Center Atmospheric General Circulation Model (BCC_AGCM) coupled with the Canadian Aerosol Module (CAM) online. The observed and simulated aerosol optical depths (AODs) exhibit similar horizontal distributions with large values over eastern and central China, and sulfate aerosols contribute the main differences between the AODs simulated by NICAM and BCC_AGCM. The simulated sulfate and dust surface concentrations are more consistent with observations compared with the simulated carbonaceous surface concentrations, and both models can reproduce the decreasing tendency of the sulfate surface concentration from urban sites to rural sites. However, the dust emission and deposition levels in China simulated by BCC_AGCM are three times as high as those simulated by NICAM, and the major sink processes of the anthropogenic sulfate, black carbon (BC), and organic carbon (OC) aerosols over China are very different between the two models. The emission and deposition results, which are closely related to the model-assumed aerosol particle size distribution, indicate that the current aerosol size distribution used in the two models should be further improved. The differences in dust emission parameterizations also lead significant discrepancies in aerosol cycles and the dust emission scheme is an important factor determining the magnitudes of global and regional dust emission fluxes.

NICAM, a nonhydrostatic global atmospheric model with an icosahedral grid system, has been developed for nearly two decades. This paper describes NICAM16-S, the latest stable version of NICAM (NICAM.16) modified for Coupled Model Intercomparison Project Phase 6 (CMIP6). Major updates from NICAM.12, a previous version used for climate simulations, include updates of a cloud microphysics scheme and a land model, an introduction of natural and anthropogenic aerosols and a subgrid-scale orographic gravity wave drag, and improvement of coupling between cloud microphysics and radiation schemes. External forcings were updated to follow a protocol of CMIP6 High Resolution Model Intercomparison Project (HighResMIP). A series of short-term sensitivity experiments were performed to check and understand impacts of the model updates on the simulated mean states. Improvements in the ice water content, the high cloud amounts, the surface air temperature over the Arctic region, the location and the strength of zonal mean subtropical jet, and shortwave radiation over the Africa and the South Asia were found in the NICAM16-S simulations. Some long-standing biases such as the double intertropical convergence zone and smaller low cloud amounts still exist or even worsen in some cases, suggesting further necessity for understanding their mechanisms and upgrading schemes and/or their parameter settings as well as for enhancing horizontal and vertical resolutions.

In this study, we present simulations of the global dust cycle for present day conditions using a new dust-atmosphere model based on the Non-hydrostatic Icosahedral Atmospheric Model (NICAM) coupled with the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS). We focus on evaluations of the dust simulation with respect to emissions, depositions, surface concentrations, aerosol optical depths (AODs), and the dust-aerosol direct radiative effects (DREs). The sensitivities of the dust simulation to the meteorological fields are also investigated through with and without meteorological nudging. NICAM without meteorology nudging tends to systemically overestimate the 10 m wind speeds by approximately 30%–40%, whereas the daily magnitudes and variations in the 10 m wind speeds are both significantly improved with meteorological nudging, especially over the Sahara Desert. The estimated annual global mean dust emission flux, dust AOD, and dust-aerosol shortwave DRE at the top of the atmosphere with meteorological nudging are 1463 Tg yr−1, 0.033, and −1.3 Wm-2, respectively. Due to the approximately 30%–40% overestimations of the 10 m wind speeds over the two major desert regions, the estimated annual global mean dust emission flux, AOD, and DRE without meteorological nudging are significantly greater than those with meteorological nudging. The overestimations of 10 m wind speeds and the associated dust emissions are mainly caused by the positive biases of wind speeds especially from surface to approximately 2 km and slightly affected by the temperature fields. The monthly variations in the dust depositions over the Atlantic and the surface dust concentrations over the Pacific are all better simulated with meteorological nudging. Compared to both the AERONET (Aerosol Robotics Network)- and MODIS (Moderate-Resolution Imaging Spectroradiometer)- retrieved AODs, the simulated daily AOD variations are significantly improved with meteorological nudging, especially over the dust-aerosol dominated regions. The global and annual mean dust lifetime and size distribution, which are two critical factors for estimating dust radiative effects, are quite similar between the dynamic and nudged NICAMs. We therefore can use the dynamic model to understand climate-dust interactions in a global and annual scale. Furthermore, we can improve the model performances for some applications in regional and seasonal scales by meteorological nudging which probably cannot be achieved by just tuning the dust emission.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, 資料番号: PA1810045000

A four-dimensional variational method (4D-Var) is a popular technique for source/sink inversions of atmospheric constituents, but it is not without problems. Using an icosahedral grid transport model and the 4D-Var method, a new atmospheric greenhouse gas (GHG) inversion system has been developed. The system combines offline forward and adjoint models with a quasi-Newton optimization scheme. The new approach is then used to conduct identical twin experiments to investigate optimal system settings for an atmospheric CO2 inversion problem, and to demonstrate the validity of the new inversion system. In this paper, the inversion problem is simplified by assuming the prior flux errors to be reasonably well known and by designing the prior error correlations with a simple function as a first step. It is found that a system of forward and adjoint models with smaller model errors but with nonlinearity has comparable optimization performance to that of another system that conserves linearity with an exact adjoint relationship. Furthermore, the effectiveness of the prior error correlations is demonstrated, as the global error is reduced by about 15 % by adding prior error correlations that are simply designed when 65 weekly flask sampling observations at ground-based stations are used. With the optimal setting, the new inversion system successfully reproduces the spatiotemporal variations of the surface fluxes, from regional (such as biomass burning) to global scales. The optimization algorithm introduced in the new system does not require decomposition of a matrix that establishes the correlation among the prior flux errors. This enables us to design the prior error covariance matrix more freely.

Covariance inflation plays an important role in the ensemble Kalman filter because the ensemble-based error variance is usually underestimated due to various factors such as the limited ensemble size and model imperfections. Manual tuning of the inflation parameters by trial and error is computationally expensive; therefore, several studies have proposed approaches to adaptive estimation of the inflation parameters. Among others, this study focuses on the covariance relaxation method which realizes spatially dependent inflation with a spatially homogeneous relaxation parameter. This study performs a series of experiments with the non-hydrostatic icosahedral atmospheric model (NICAM) and the local ensemble transform Kalman filter (LETKF) assimilating the real-world conventional observations and satellite radiances. Two adaptive covariance relaxation methods are implemented: relaxation to prior spread based on Ying and Zhang (adaptive-RTPS), and relaxation to prior perturbation (adaptive-RTPP). Both adaptive-RTPS and adaptive-RTPP generally improve the analysis compared to a baseline control experiment with an adaptive multiplicative inflation method. However, the adaptive-RTPS and adaptive-RTPP methods lead to an over-dispersive (under-dispersive) ensemble in the sparsely (densely) observed regions compared with the adaptive multiplicative inflation method. We find that the adaptive-RTPS and adaptive-RTPP methods are robust to a sudden change in the observing networks and observation error settings.

A four-dimensional variational (4D-Var) method is a popular algorithm for inverting atmospheric greenhouse gas (GHG) measurements. In order to meet the computationally intense 4D-Var iterative calculation, offline forward and adjoint transport models are developed based on the Nonhydrostatic ICosahedral Atmospheric Model (NICAM). By introducing flexibility into the temporal resolution of the input meteorological data, the forward model developed in this study is not only computationally efficient, it is also found to nearly match the transport performance of the online model. In a transport simulation of atmospheric carbon dioxide (CO2), the data-thinning error (error resulting from reduction in the time resolution of the meteorological data used to drive the offline transport model) is minimized by employing high temporal resolution data of the vertical diffusion coefficient; with a low 6-hourly temporal resolution, significant concentration biases near the surface are introduced. The new adjoint model can be run in discrete or continuous adjoint mode for the advection process. The discrete adjoint is characterized by perfect adjoint relationship with the forward model that switches off the flux limiter, while the continuous adjoint is characterized by an imperfect but reasonable adjoint relationship with its corresponding forward model. In the latter case, both the forward and adjoint models use the flux limiter to ensure the monotonicity of tracer concentrations and sensitivities. Trajectory analysis for high CO2 concentration events are performed to test adjoint sensitivities. We also demonstrate the potential usefulness of our adjoint model for diagnosing tracer transport. Both the offline forward and adjoint models have computational efficiency about 10 times higher than the online model. A description of our new 4D-Var system that includes an optimization method, along with its application in an atmospheric CO2 inversion and the effects of using either the discrete or continuous adjoint method, is presented in an accompanying paper Niwa et al.(2016).

In this chapter, a long-term simulation using the high-top non-hydrostatic general circulation model is carried out to examine mesospheric gravity waves observed by the PANSY radar in five months from April to August 2016. Successive runs lasting 7 days are made from the MERRA reanalysis data with an overlap of two days between each run. The data for analyses are prepared by extracting the last five days of each simulation. The simulated wind fields are closely compared to the PANSY radar observations and the MERRA reanalysis data. Moreover, statistical characteristics of the mesospheric disturbances simulated by NICAM such as ω spectra of each variable, kinetic and potential energies, momentum and energy fluxes of gravity waves are examined.

This chapter describes the authors’ effort on ensemble data assimilation of satellite precipitation measurements. The Local Ensemble Transform Kalman Filter (LETKF) was implemented with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM), and JAXA’s GSMaP (Global Satellite Mapping of Precipitation) data were assimilated at 112-km resolution.

Aerosol vertical information is critical to quantify the influences of aerosol on the climate and environment; however, large uncertainties still persist in model simulations. In this study, the vertical aerosol extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are assimilated to optimize the hourly aerosol fields of the Non-hydrostatic ICosahedral Atmospheric Model (NICAM) online coupled with the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) using a four-dimensional local ensemble transform Kalman filter (4-D LETKF). A parallel assimilation experiment using bias-corrected aerosol optical thicknesses (AOTs) from the Moderate Resolution Imaging Spectroradiometer (MODIS) is conducted to investigate the effects of assimilating the observations (and whether to include vertical information) on the model performances. Additionally, an experiment simultaneously assimilating both CALIOP and MODIS observations is conducted. The assimilation experiments are successfully performed for 1 month, making it possible to evaluate the results in a statistical sense. The hourly analyses are validated via both the CALIOP-observed aerosol vertical extinction coefficients and the AOT observations from MODIS and the AErosol RObotic NETwork (AERONET). Our results reveal that both the CALIOP and MODIS assimilations can improve the model simulations. The CALIOP assimilation is superior to the MODIS assimilation in modifying the incorrect aerosol vertical distributions and reproducing the real magnitudes and variations, and the joint CALIOP and MODIS assimilation can further improve the simulated aerosol vertical distribution. However, the MODIS assimilation can better reproduce the AOT distributions than the CALIOP assimilation, and the inclusion of the CALIOP observations has an insignificant impact on the AOT analysis. This is probably due to the nadir-viewing CALIOP having much sparser coverage than MODIS. The assimilation efficiencies of CALIOP decrease with increasing distances of the overpass time, indicating that more aerosol vertical observation platforms are required to fill the sensor-specific observation gaps and hence improve the aerosol vertical data assimilation.

This study aims to propose two new approaches to improve precipitation forecasts from numerical weather prediction (NWP) models through effective data assimilation of satellite-derived precipitation. The assimilation of precipitation data is known to be very difficult mainly because of highly non-Gaussian statistics of precipitation variables. Following Lien et al., this study addresses the non-Gaussianity issue by applying the Gaussian transformation (GT) based on the empirical cumulative distribution function (CDF) of precipitation. We propose a method that constructs the CDF with only recent 1 month samples, without using a long period of samples needed previously. We also propose a method to use the inverse GT, with which we can obtain realistic precipitation fields from biased NWP model outputs. We assimilate the Japan Aerospace eXploration Agency's Global Satellite Mapping of Precipitation (GSMaP) data into the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) at 112 km horizontal resolution. Assimilating the GSMaP data results in improved weather forecasts compared to the control experiment assimilating only rawinsonde data. We find that horizontal observation thinning is necessary, probably due to the horizontal observation-error correlations in the GSMaP data. We also obtained precipitation fields similar to GSMaP from the NICAM precipitation forecasts by using the inverse GT, leading to an improved precipitation forecast.

Study of long range transport of radioactive gases and aerosols is necessary to estimate radiological impact to the members of public and environment during nuclear reactor accidents. In the present work, an attempt has been made to employ an atmospheric circulation model that predicts meteorological parameters at the regional and global scales, and a transport model that utilizes these meteorological parameters for the radioactive aerosol dispersion. Non-hydrostatic Icosahedral Atmospheric Model (NICAM) coupled with Spectral Radiation-Transport Model for Aerosol Species (SPRINTARS) is adapted to fulfil this objective, which is used for simulating effects of conventional aerosols on atmospheric pollution and climate system. As an illustrative case study, global simulation is carried out for a horizontal resolution of ~110 km to model the dispersion of radioactive aerosol (35S, 131I and 137Cs) releases from the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident. The results obtained from this case study are compared with the available literature data and other simulation results. The statistical analyses show that the comparisons are better for locations far away (> 150 km) from the emission location, and the results are further discussed. Continuous run of this system will help in predicting the activity concentration in forecast mode, and it may be used for decision support, particularly in the case of long range transport (> 100 km).

The cloud and precipitation simulated by a global nonhydrostatic model with a 3.5-km horizontal resolution, the Nonhydrostatic Icosahedral Atmospheric Model (NICAM), are evaluated using the Tropical Rainfall Measuring Mission (TRMM) and a satellite simulator. A previous study by Roh and Satoh evaluated the single-moment bulk microphysics and established the modified microphysics scheme for the specific tropical open ocean using a regional version of NICAM. In this study, the authors expanded the evaluation over the entire tropics and parts of the midlatitude areas (20°–36°S, 20°–36°N) using a joint histogram of the cloud-top temperature and precipitation echo-top heights and contoured frequency by altitude diagrams of the deep convective systems. The modified microphysics simulation improves the joint probability density functions of the cloud-top temperatures and precipitation cloud-top heights over not only the tropical ocean but also the land and midlatitude areas. Compared with the default microphysics simulation, the modified microphysics simulation shows a clearer distinction between the land and ocean in the tropics, which is related to the contrast between the shallow and the deep clouds. In addition, the two microphysics simulation methods were also compared over the tropics using joint histograms of the cloud-top and precipitation cloud-top heights on the basis of CloudSat measurements. It was found that the microphysics scheme that was modified for the tropical ocean displayed general cloud and precipitation improvements in the global domain over the tropics.

Evaluation of cloud radiative effects (CREs) in global atmospheric models is of vital importance to reduce uncertainties in weather forecasting and future climate projection. In this paper, we describe an effective way to evaluate CREs from a 3.5 km mesh global nonhydrostatic model by comparing it against A-train satellite data. The model is the Nonhydrostatic Icosahedral Atmospheric Model (NICAM), and its output is run through a satellite-sensor simulator (Joint Simulator for satellite sensors) to produce the equivalent CloudSat radar, CALIPSO lidar, and Aqua Clouds and the Earth's Radiant Energy System (CERES) data. These simulated observations are then compared to real observations from the satellites. We focus on the Arctic, which is a region experiencing rapid climate change over various surface types. The NICAM simulation significantly overestimates the shortwave CREs at top of atmosphere and surface as large as 24 W m−2 for the month of June. The CREs were decomposed into cloud fractions and footprint CREs of cloud types that are defined based on the CloudSat-CALIPSO cloud top temperature and maximum radar reflectivity. It turned out that the simulation underestimates the cloud fraction and optical thickness of mixed-phase clouds due to predicting too little supercooled liquid and predicting overly large snow particles with too little mass content. This bias was partially offset by predicting too many optically thin high clouds. Offline sensitivity experiments, where cloud microphysical parameters, surface albedo, and single scattering parameters are varied, support the diagnosis. Aerosol radiative effects and nonspherical single scattering of ice particles should be introduced into the NICAM broadband calculation for further improvement.

A 14-yr climatology of Tropical Rainfall Measuring Mission (TRMM) collocated multisensor signal statistics reveals a distinct land-ocean contrast as well as geographical variability of precipitation type, intensity, and microphysics. Microphysics information inferred from the TRMM Precipitation Radar and Microwave Imager show a large land-ocean contrast for the deep category, suggesting continental convective vigor. Over land, TRMM shows higher echo-top heights and larger maximum echoes, suggesting taller storms and more intense precipitation, as well as larger microwave scattering, suggesting the presence of more/larger frozen convective hydrometeors. This strong land-ocean contrast in deep convection is invariant over seasonal and multiyear time scales. Consequently, relatively short-term simulations from two global storm-resolving models can be evaluated in terms of their land-ocean statistics using the TRMM Triple-Sensor Three-Step Evaluation Framework via a satellite simulator. The models evaluated are the NASA Multiscale Modeling Framework (MMF) and the Nonhydrostatic Icosahedral Cloud Atmospheric Model (NICAM). While both simulations can represent convective land-ocean contrasts in warm precipitation to some extent, near-surface conditions over land are relatively moister in NICAM than MMF, which appears to be the key driver in the divergent warm precipitation results between the two models. Both the MMF and NICAM produced similar frequencies of large CAPE between land and ocean. The dry MMF boundary layer enhanced microwave scattering signals over land, but only NICAM had an enhanced deep convection frequency over land. Neither model could reproduce a realistic land-ocean contrast in deep convective precipitation microphysics. A realistic contrast between land and ocean remains an issue in global storm-resolving modeling., 形態: カラー図版あり, Physical characteristics: Original contains color illustrations, 資料番号: PA1610019000

A nonhydrostatic stretched-grid (SG) model is used to analyze the large-scale errors generated by stretching horizontal grids and their influence on a region of interest. Simulations by a fully compressible, nonhydrostatic global atmospheric model, the Nonhydrostatic Icosahedral Atmospheric Model (NICAM), and its SG regional model, stretched-NICAM, were performed for the months of March, April, and May of 2011 using various resolutions and stretching factors. A comparison of week-long accumulative precipitation amounts between the Tropical Rainfall Measuring Mission (TRMM) satellite data and the quasi-uniform and SG simulations showed that a stretched run better represents storms and associated precipitation because the errors generated in the outer regions with coarser grid spacing do not significantly affect the inner domain centered at the focal point. For season-long simulations, in one particular set of stretched runs with the focal point located in the eastern United States, the artificial suppression of baroclinic development of midlatitude eddies in the Southern Hemisphere weakened the eddy-driven polar-front jet (PFJ), which yielded a cold bias at mid- to high latitudes. However, in the Northern Hemisphere, in contrast, the aforementioned changes are less apparent. Therefore, for the SG runs, the mean temperature was maintained at the region of interest, and an increased amount of moderate to heavy precipitation, which is also frequently found in the TRMM data, was observed; thus, the benefits of increased resolution were realized. However, careful attention must be given when applying the SG model because a regional climate response to the change in the large-scale circulations may not be fully accounted for.

In our previous study (Li et al., 2019a), using observational data, we found some evidences of Taklimakan dust effects on the convective clouds over the Tibetan Plateau (TP) and then causing heavy rainfall in the downstream region. Here, a simulation study is performed to quantify the effects of Taklimakan dust on the cloud properties over the TP and downstream rainfall using the Spectral Radiation-Transport Model for Aerosol Species (SPRINTARS) coupled to the Nonhydrostatic Icosahedral Atmospheric Model (NICAM). The results indicate that the dusts in Taklimakan desert can be lifted to the northern slope of the TP. The dust aerosols transported to the TP can diminish the cloud effective radius (CER), but they can increase the cloud optical depth (COD), liquid water path (LWP) and ice water path (IWP) over the TP. The maximum impact of the dusts on the CER lags behind the peak of aerosol optical depth (AOD). Meanwhile, the dusts show a more profound impact on the ice-cloud optical depth (ICOD) than liquid-cloud optical depth (LCOD), which implies a more significant impact on the ice particles than liquid drops in convective clouds over the TP. Overall, due to the indirect effects of Taklimakan dust aerosols, the clouds are developed further over the TP. The eastward movement of the clouds polluted by Taklimakan dust can delay the occurrence of heavy rainfall for 12 h but intensify the rainfall over the northern area. This modeling study verified and quantified the indirect effect of Taklimakan dusts on the cloud properties over the TP and further impacts on the rainfall over the northern area.

In this paper, we propose the design and implementation of an ensemble data assimilation (DA) framework for weather prediction at a high resolution and with a large ensemble size. We consider the deployment of this framework on the data throughput of file input/output (I/O) and multi-node communication. As an instance of the application of the proposed framework, a local ensemble transform Kalman filter (LETKF) was used with a Non-hydrostatic Icosahedral Atmospheric Model (NICAM) for the DA system. Benchmark tests were performed using the K computer, a massive parallel supercomputer with distributed file systems. The results showed an improvement in total time required for the workflow as well as satisfactory scalability of up to 10 K nodes (80 K cores). With regard to high-performance computing systems, where data throughput performance increases at a slower rate than computational performance, our new framework for ensemble DA systems promises drastic reduction of total execution time.

Sulfate aerosols simulated by an aerosol module coupled to the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) at a spatial resolution (220 km) widely used by global climate models were evaluated by a comparison with in situ observations and the same aerosol module coupled to the Model for Interdisciplinary Research on Climate (MIROC) over East Asia for January, April, July, and October 2006. The results indicated that a horizontal gradient of sulfate from the source over China to the outflow over Korea-Japan was present in both the simulations and the observations. At the observation sites, the correlation coefficients of the sulfate concentrations between the simulations and the observations were high (NICAM: 0.49–0.89, MIROC: 0.61–0.77), whereas the simulated sulfate concentrations were lower than those obtained by the observation with the normalized mean bias of NICAM being −68 to −54% (all), −77 to −63% (source), and −67 to −30% (outflow) and that of MIROC being −61 to −28% (all), −77 to −63% (source), and −60 to +2% (outflow). Both NICAM and MIROC strongly underpredict surface SO2 over China source regions and Korea-Japan outflow regions, but the MIROC SO2 is much higher than NICAM SO2 over both regions. These differences between the models were mainly explained by differences in the sulfate formation within clouds and the dry deposition of SO2. These results indicated that the uncertainty of the meteorological and cloud fields as well as the vertical transport patterns between the different host climate models has a substantial impact on the simulated sulfate distribution.

Characteristically strong vertical wind disturbances (VWDs) with magnitudes larger than 1 m s−1 were observed in the Antarctic troposphere using a new mesosphere–stratosphere–troposphere (MST) radar called the Program of the Antarctic Syowa MST/incoherent scatter (IS) Radar (PANSY) during 15–19 June 2012 at Syowa Station (69.0°S, 39.6°E). In the same period, two synoptic-scale cyclones approached Syowa Station and caused a strong wind event (SWE) at the surface. The VWDs observed during the SWE at Syowa Station had a nearly standing (i.e., no phase tilt with height) phase structure up to the tropopause and a power spectrum proportional to the − power of frequency. On the other hand, the observed VWDs were not associated with systematic horizontal momentum fluxes. Meteorological fields around Syowa Station during the SWE were successfully simulated using the Nonhydrostatic Icosahedral Atmospheric Model (NICAM). A strong VWD was also simulated at the model grid of 70.0°S, 40.0°E in NICAM, which had a standing phase structure similar to the observed ones. An analysis based on the Froude number showed that the simulated VWD was likely due to a hydraulic jump leeward of the coastal mountain ridge. The Scorer parameter analysis indicated that the observed VWDs at Syowa Station during 16–17 June 2012 were likely due to the hydraulic jump similar to that in NICAM. On the other hand, a possibility of lee waves was also suggested for the VWD observed on 18 June 2012.