Satoh et al.
Progress in Earth and Planetary Science(2022) 9:51
https://doi.org/10.1186/s40645-022-00511-5

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open Access

Evaluation of cloud and precipitation
processes in regional and global models
with ULTIMATE (ULTra‑sIte for Measuring
Atmosphere of Tokyo metropolitan
Environment): a case study using
the dual‑polarization Doppler weather radars
Masaki Satoh1* , Shuhei Matsugishi1, Woosub Roh1, Yasutaka Ikuta2, Naomi Kuba1, Tatsuya Seiki3,
Tempei Hashino4 and Hajime Okamoto5

Abstract
We describe a collaborative analysis study involving numerical models and observation data for the Tokyo metropolitan area called the ULTra-sIte for Measuring Atmosphere of Tokyo Metropolitan Environment (ULTIMATE) project.
It evaluates cloud microphysics schemes of numerical models using extensive observation data for the Tokyo area.
We have access to various remote sensing and in situ data for the Tokyo area for operational and research purposes,
particularly by enhancing observations for ground validation of the EarthCARE satellite, which is set to launch in2023. This study focuses on using the dual-polarization Doppler weather radar, operated by the Japan Meteorological
Agency. In terms of numerical models, we use and compare multi-models with single-moment (SM) and doublemoment (DM) cloud microphysics schemes; the global non-hydrostatic model, Non-hydrostatic ICosahedral Atmospheric Model (NICAM) and the two regional models with A System based on a Unified Concept for Atmosphere(ASUCA) and Scalable Computing for Advanced Library and Environment (SCALE) are used. In particular, because
NICAM can be used as both a global and a regional model, we can immediately test the improved scheme on a
global scale for its effect on climatology and the evaluation of climate sensitivity. This paper introduces the methodology for evaluating numerical models by the dual-polarization radar using the observation simulator and comparesnumerical model results with observations. In particular, we evaluate the simulated rain in the lower level near the
ground and the large ice particles just above the melting level. The simulation with NICAM-DM reproduces the comparable polarimetric radar characteristics of rain as the observation. However, the simulations with NICAM-SM and
ASUCA-SM show larger raindrop sizes in stronger rain areas compared to the observation. For the larger ice particles
just above the melting level around 4 km, NICAM-DM and ASUCA-SM overestimate particle sizes of graupel or snow,
while NICAM-SM has a similar size of the ice particles. In future studies, we will use the present results to improve thecloud microphysics scheme, which will be tested on a global model.

*Correspondence: satoh@aori.u-tokyo.ac.jp
1

Atmosphere and Ocean Research Institute, The University of Tokyo, 5‑1‑5Kashiwanoha, Kashiwa, Chiba 277‑8564, Japan
Full list of author information is available at the end of the article© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

Page 2 of 28

Keywords: ULTIMATE, Cloud microphysics, EarthCARE, Polarimetric radar, Regional and global models

1 Introduction
Atmospheric numerical models are used for weather
forecasts and climate projections, such as daily weather
forecasting and future climate change. Precipitation andthe atmospheric energy balance in these models depend
heavily on representing cloud microphysical processes.
To improve the prediction accuracy of extreme atmospheric events such as heavy rainfalls and typhoons, it isnecessary to accurately predict convective clouds by representing the formation of clouds and precipitations. The
projection of future temperature change in climate models (e.g., climate sensitivity; Sherwood et al. 2020) is also
greatly affected by the representation of cloud microphysical processes. However, cloud schemes in theseweather and climate models are highly uncertain andmust be validated by various observational data.
Many studies have validated numerical models using
satellite data, particularly from the satellite simulator
approach (Masunaga et al. 2010; Bodas-Salcedo et al.
2011). The atmospheric general circulation models (hereafter referred to as GCMs) used in the Cloud Feedback
Model Intercomparison Project (CFMIP; Webb et al.
2017) were evaluated by using the CFMIP Observation
Simulator Package (COSP; Bodas-Salcedo et al. 2011)
to contribute to the Coupled Model Intercomparison
Project Phase 6 (CMIP6; Eyring et al. 2016). Recently,
higher-resolution global storm-resolving models (hereafter referred to GSRMs), in which cloud microphysics
schemes are used to simulate convective clouds explicitly
without using cumulus parameterization, have been used
more widely for weather and climate simulations (Satohet al. 2019). A dedicated satellite simulator package has
been used to evaluate GSRMs to consider more consistency among sensor simulators (Hashino et al. 2013; Satohet al. 2016). Roh et al. (2021) compared the vertical cloudprofiles from the simulated data of the first inter-comparison experiment for GSRMs, called the Dynamics of
the Atmospheric general circulation Modeled On Nonhydrostatic Domains (DYAMOND; Stevens et al. 2019),
and found a wide diversity of profiles among the models.
Therefore, to improve the cloud microphysics schemes
used in GSRMs, simulated clouds and precipitation
should be more carefully evaluated using observation.
Some types of GSRMs can be used as seamless models with a common dynamical core and physics schemes
for regional and global configurations; examples of such
GSRMSs are the Non-hydrostatic ICosahedral Atmospheric Model (NICAM; Tomita and Satoh 2004; Satohet al. 2008, 2014), ICOsahedral Non-hydrostatic model(ICON; Zängl et al. 2014), Model for Prediction AcrossScales (MPAS; Skamarock et al. 2012), System for
High‐resolution prediction on Earth‐to‐Local Domains(SHiELD; Harris et al. 2020), and System for Atmospheric
Modeling (SAM; Khairoutdinov et al. 2022). NICAM cansimulate a global atmosphere using a quasi-uniform-gridsystem (Tomita et al. 2001) and simulate local regions
at high resolution using a stretched grid system (hereafter referred to as stretch-NICAM; Tomita 2008; Uchida
et al. 2016; 2017). Several studies have evaluated clouds
simulated by NICAM by comparing them with satellite data using satellite simulators (Masunaga et al. 2008;
Satoh et al. 2010; Kodama et al. 2012; Hashino et al. 2013,2016; Roh and Satoh 2014; Roh et al. 2017, 2018, 2020;
Matsui et al. 2016). In particular, Roh and Satoh (2014)
simulated cumulus clusters with stretch-NICAM focusedon the tropics. They compared the simulated results withobservations from the Tropical Rainfall Measuring Mission (TRMM) satellite to improve the cloud microphysics scheme. Hashino et al. (2013) evaluated model results
using a satellite simulator, called the Joint Simulator for
Satellite Sensors, to calculate satellite signal values fromnumerical model results and compare them directly withthe satellite observations by CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations(CALIPSO). In the High Resolution Model Intercomparison Project (HighResMIP; Haarsma et al. 2016) experiment of CMIP6 using NICAM (Kodama et al. 2021), thecloud microphysics scheme developed by Roh and Satoh(2014) was applied to the global simulation.
Furthermore, Roh et al. (2020) evaluated simulated
lower clouds in the Southern Ocean using stretchNICAM with lidar observation data by the Cloud Aerosol LIdar with Orthogonal Polarization (CALIOP)
onboard CALIPSO. Based on these results, Seiki andRoh (2020) improved the cloud microphysics scheme of
NICAM to reproduce supercooled water in the Southern Ocean more realistically. Their improvement of thecloud microphysics scheme is essential for future climate
experiments using NICAM for the global domain (Nodaet al. 2021).
Previous studies evaluating and improving cloud
microphysics schemes of global atmospheric models have mainly used satellite observation data. However, there are more types and volumes of remotesensing data in ground-based observations than in satellite observations. Therefore, we expect that the cloud
microphysics schemes can be further evaluated andimproved by investigating clouds and precipitation using

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

ground-based observation data. Ground-based observation has been used mainly to evaluate regional numerical models. Recently, however, it has become possible toevaluate both regional and global models using groundbased observations due to developing a seamless model
approach for GSRMs that use a common cloud microphysics scheme for regional and global models.
To validate and improve regional and global numericalmodels using various observational data in a local area,
we developed a research framework called ULTIMATE(ULTra-sIte for Measuring Atmosphere of Tokyo metropolitan Environment), which uses intensive observation
data in the Tokyo metropolitan region for model evaluations. The goal of ULTIMATE is to improve the accuracyof numerical models through validation and improvement of their cloud microphysics to achieve better
regional and global simulations. Our research framework
ULTIMATE aims to assess cloud microphysical processes
in a unified approach in more detail by using various
types of ground-based observation data, which have notbeen fully exploited in previous studies of GSRMs.
This paper introduces ULTIMATE as a research framework for validating and improving a numerical modelthat seamlessly computes regional and global clouds andpresents the first result of ULTIMATE. This project is apart of the ground-based observation evaluation program planned for the EarthCARE satellite (Illingworth
et al. 2015), scheduled for launch in 2023. In ULTIMATE,
numerical models can be verified and improved by comparing simulated results with ground-based and satellite
observations. Here, we describe our specific method forevaluating numerical models using observations from thedual-polarization Doppler weather radar installed by theJapan Meteorological Agency (JMA).

2 Methods/experimental
2.1 Observations at the Kanto region ultra‑site

In the Kanto region, various remote sensing observation networks have been established by operationaland research organizations. Thus, the Kanto region has
the characteristics of an observational “ultra-site” that
surpasses so-called supersites. Previous research projects in the Kanto region have combined observations
and numerical models. The Tokyo Metropolitan Area
Convection Study for Extreme Weather Resilient Cities (TOMACS) has been conducted since 2010 to elucidate the mechanisms behind local high-impact weather
in urban areas, improve nowcasting and forecasting
techniques, and share high-resolution weather information to end-users through social experiments (Misumi
2018; Misumi et al. 2019). In the summers of 2020 and
2021, a RIKEN team conducted a real-time demonstration of ultra-fast precipitation forecasting for the Kanto

Page 3 of 28

region using the Multi-Parameter Phased Array Weather
Radar (MP-PAWR) installed in Saitama Prefecture, Japan(Miyoshi et al. 2020). Following these previous studies, ULTIMATE also targets the Kanto region. In ULTIMATE, we focus on evaluating cloud and precipitation
processes, the most uncertain components in numericalmodels.
ULTIMATE utilizes a series of observational data prepared as ground validations for the satellite mission
EarthCARE (Illingworth et al. 2015). The observation
data used for ULTIMATE are introduced in “Appendix
1.” Figure 1 shows an overview of these observation data
networks. These observation datasets make it possible
to collectively perform a comprehensive spatiotemporal
analysis of cloud precipitation systems, such as severe
convective systems in the Kanto region. We can use thesedatasets for the validation and improvement of numericalmodels.
Among various observation data, this study uses the
JMA’s C-band dual-polarization Doppler radar for airport weather at the Haneda and Narita airports. The
basic specifications of the Haneda and Narita radars aredescribed in Table 1 and are referred to Tsukamoto et al.
(2016) and Umehara et al. (2021).
Similar intensive projects with observation-model collaborations, including polarimetric radars, are underway
in other countries. The German Priority Programme,called Polarimetric Radar Observations meet Atmospheric Modelling (PROM), which started in 2019, aims
to synergize polarimetric radar observations and atmospheric models to understand cloud and precipitation
processes better and to improve their representation innumerical models (Trömel et al. 2021). In the USA, theBiLateral Operational Storm-Scale Observation andModeling (BLOSSOM) Project has been proposed (Matsui et al. 2019a).
Using observation simulators for evaluating numericalmodels using data from polarimetric radars is becoming a standard approach (Matsui et al. 2019b, 2020;
Shrestha et al. 2022). Intensive research has been conducted on collaborating polarimetric radar observations
and numerical modeling in regional model studies. Jung
et al. (2010) developed a polarimetric radar simulator
and applied it to the simulation data of a supercell storm.
They suggest that two- or higher-moment microphysics schemes can adequately describe certain important
microphysical processes comparable to the observed
polarimetric parameters. Snyder et al. (2017a, 2017b)
further applied the simulator to supercell experimental
results with a three-moment microphysics scheme anddescribed evolutions of the polarimetric parameters.
Ryzhkov et al. (2011) applied the simulator to a moredetailed spectral bin model result. Putnam et al. (2017)

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

Page 4 of 28

Fig. 1 Map of the ULTIMATE observation network in the Kanto region. Observation instruments are located at the National Institute of Information
and Communications Technology (NICT; Koganei City), the National Institute for Environmental Studies (NIES), the Meteorological Research Institute(MRI), the National Research Institute for Earth Science and Disaster Resilience (NIED) (all in Tsukuba City), and the Atmosphere and Ocean ResearchInstitute (AORI; Kashiwa City) of The University of Tokyo. “Polarimetric radar” indicates the Haneda and Narita radars and the Tokyo radar at Kashiwa(the Japan Meteorological Agency)

Table 1 Basic specifications of the Haneda and Narita radars (Tsukamoto et al. 2016; Umehara et al. 2021)
Transmitting frequency

C-band (5330 MHz at the Haneda radar and 5335 MHz at the Narita radar)Beam width< 0.7° (7-m-diameter parabolic antenna)

Pulse width

1 µs (range < 12 km), 64 µs (range ≥ 12 km)

Azimuth spacing

0.7°

Range gate spacing

150 m

Observable range radius

120 kmObservation frequency

A volume scan (including 15 PPIs and an RHI) updates every 5 min

Elevation angles of PPI

0.7°, 17.0°, 12.5°, 0.7°, 9.2°, 6.9°, 5.1°, 0.7°, 3.8°, 2.8°, 2.1°, 0.7°, 1.5°, 1.1°, 0.7°

Azimuth angles of RHI

Haneda: 329.8° to 149.8° and 239.8° to 59.8°
Narita: 149.5° to 329.5° and 239.5° to 59.5°
The two orthogonal planes are 5 min alternatively scanned

Transmission mode

Simultaneous transmit and receive (STAR) modesystematically compared various cloud microphysics
schemes by applying the simulator to simulations for amesoscale convective system (MCS) and supercell cases.
These studies are based on regional models and applications to specific weather phenomena such as MCS andsupercell cases. These studies highlight the polarimetric radar characteristics in MCS and supercell cases andthe importance of a multi-moment scheme to represent
polarimetric parameters such as by a size-sorting effectof hydrometeors. Bringi et al. (2020) proposed how to
improve rain and riming processes of cloud microphysics schemes using polarimetric radar observations of ahurricane. Multi-frequency Doppler radar observations
are also used to improve snow aggregation representation in a cloud microphysics scheme (Karrer et al. 2021).
These ground-observation-modeling studies are uniquely
important because of the characterization of weather
systems in each area, even though thermodynamic

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

Table 2 Overview of all simulations
Model

Mesh size

Initial condition

Cloud
microphysics
schemeASUCA​

1 km

Meso-scale Analysis

MP2003

NICAM-NSW6

1.4 km*ERA5 reanalysis

NSW6

NICAM-NDW6

1.4 km*ERA5 reanalysis

NDW6

SCALE-NSW6

1 km

Meso-scale Analysis

NSW6

SCALE-NDW6

1 km

Meso-scale Analysis

NDW6*For NICAM-NSW6 and NICAM-NDW6, the minimum mesh size of the stretched
grid is provided

conditions primarily determine cloud microphysics processes (Hashino et al. 2013). Among the weather systems observed in the Kanto area, we focus in this study
on severe convective systems associated with an extratropical cyclone. As a companion study, Ikuta et al. (2022)
analyzed the typhoon FAXAI (2019) as part of ULTIMATE. Such severe convective systems are frequently
observed in the Kanto region, indicating the uniquenessof the observation of ULTIMATE.
2.2 Numerical models

We use three numerical models in this study, as shownin Table 2. First, we use NICAM, with which we canseamlessly simulate both domain-scale and global experiments in the same model (Satoh et al. 2014). NICAM
can directly resolve MCSs over the globe with a quasiuniform horizontal grid spacing (of a few kilometers).
In addition, NICAM can be used as a regional model torepresent local mesoscale weather systems in detail byusing a stretched grid model (stretch-NICAM) in which

Page 5 of 28

the mesh is concentrated in a target region such as the
Kanto region. NICAM uses the same dynamical andphysical schemes and common program code for global
and regional simulations. It is possible to switch between
the global and regional models by changing the model
configuration. Thus, the cloud microphysics schemes
developed for global cloud-resolving simulations with
NICAM (Satoh et al. 2014) can be tested for both global
and regional systems. In the present study, we conduct
regional simulations using stretch-NICAM and evaluate the results by comparing them with ground-based
observation data. These results can be used in the global
version of NICAM to study global cloud precipitation
processes and climatology and clarify their impact on climate sensitivity and global cloud distribution.
In this study, we use two types of bulk cloud microphysics schemes in stretch-NICAM simulations:
NICAM single-moment bulk cloud microphysics with
six water categories (NSW6) and NICAM doublemoment bulk cloud microphysics with six water categories (NDW6; Seiki and Nakajima 2014; Seiki et al.
2014; 2015). NSW6 was proposed by Tomita (2008) and
was improved by Roh and Satoh (2014) by comparing
it with satellite observation data. Clouds simulated by
NICAM with NSW6 have been evaluated by Roh et al.
(2017) and Kodama et al. (2012, 2021). NDW6 wasintroduced by Seiki and Nakajima (2014) and was evaluated by Seiki et al. (2014, 2015). The development of
the two schemes is summarized in Satoh et al. (2018).
We assess the results of the regional experiment bystretch-NICAM using the observation data from the
Kanto ultra-site to improve the cloud microphysics
schemes. As for the horizontal resolution, we obtain

Fig. 2 a Schematic image of the grid structure of stretch-NICAM. The grid structure for g-level 4 is shown for readability. The actual grid used forthe simulation is at g-level 9, whose grid can be obtained by subdividing the grid of g-level 4. Navy contours show the areas of the grid interval of
g-level 9 at 1.5, 2, 3, 4, and 5 km. b The calculation domains for ASUCA for the 5 km mesh model (green) and the 1 km mesh model (red)Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

the minimum grid spacing of 1.4 km by setting the grid
partition to level 9 (g-level 9) and the stretch factor to
100 in stretch-NICAM (Fig. 2, left). Initial values forthe simulations were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF)
Reanalysis v5 (ERA5; Hersbach et al., 2020).
It is possible to perform numerical simulations using
other high-resolution models for an area limited to the
Kanto region. We use the JMA’s regional domain model,
ASUCA (A System based on a Unified Concept for
Atmosphere; Japan Meteorological Agency 2019) and
SCALE (Scalable Computing for Advanced Library andEnvironment) developed by RIKEN (Nishizawa et al.
2015; Sato et al. 2015) for comparison. For ASUCA, we
use the cloud microphysics scheme MP2003, describedin Ikuta et al. (2021). For SCALE, the same schemes as
in NICAM are used for this study: NSW6 and NDW6. In
principle, comparing experiments with multi-models canreveal the differences and uncertainties of cloud microphysics schemes in these numerical models. We should
also note that the differences in other factors, including
the dynamical cores, affect representations of clouds andprecipitation; this is revealed by comparing results fromNICAM and SCALE. For ASUCA and SCALE, we conduct experiments with a grid spacing of 1 km. The simulation domain of the two models is the same and covers
the whole area of the Kanto region (Fig. 2, right). For
ASUCA, we first simulate the Japan region at a resolutionof 5 km, using the same settings as the Meso-Scale Model(MSM) of JMA (Japan Meteorological Agency 2019).
The 5 km initial value is from the Meso-scale Analysis of JMA, and the boundary values are from the JMA
global model forecast. For SCALE, we used the resultsof ASUCA with a resolution of 5 km as the initial andboundary values. We integrated the Kanto region over an
area almost identical to that used in ASUCA, with a resolution of 1 km. The initial value for all models was set to
00:00 on April 18, 2020, and a 12-h time evolution was
simulated in the models.

Page 6 of 28

differential reflectivity Zdr [dB]; specific differential phase
Kdp [° ­km−1]; and copolar correlation coefficient ρhv).
These values are directly compared to observed values
from the dual-polarization Doppler weather radar. iPOLARRIS is based on the Colorado State University (CSU)
radar Hydrometeor Identification (HID) (e.g., Dolan
et al. 2013; Dolan and Rutledge 2009). Ten categories areallowed in the HID: Drizzle, Rain, Ice Crystals, Aggregates, Wet Snow, Vertical Ice, Low‐Density Graupel (LDGraupel), High‐Density Graupel (HD Graupel), Hail, andBig Drops.
The parameters of axis ratio and orientation angle
distributions of ice particles used for POLARRIS arereferred to in Table 1 of Matsui et al. (2019a, b). We use
the setting of “MA18” following Matsui et al. (2019a, b).
In simulations, the cloud microphysics schemes assume
their characteristics from the shapes of ice particles. For
example, the cloud ice of NDW6 is assumed to be nonspherical. However, snow and graupel are assumed to be
spherical in all the simulations used in this study. We notethat the parameters used for the NICAM simulations are
not necessarily consistent with those in J-Sim; we can test
more detailed assumptions such as shape information ofice particles in J-Sim (Roh and Satoh 2018). Matsui et al.
(2020) stated that the polarimetric parameters above themelting layer comprise significant uncertainties due tothe assumption in POLARRIS-f. In addition, the brightband is not well represented by POLARRIS-f because
the polarization parameters cannot be calculated for
melted particles which should be diagnosed by a bright2.3 Analysis method

We use the observation simulator, POLARRIS (POLArimetric Radar Retrieval and Instrument Simulator) (Matsui et al. 2019b, 2020) to evaluate experimental resultsby the data from the dual-polarization Doppler weather
radar. POLARRIS is implemented in the Joint Simulator for Satellite Sensors (hereafter referred to as J-Sim;
Hashino et al. 2013; Satoh et al. 2016), and the experimental results are processed with POLARRIS. POLARRIS comprises the forward simulator (POLARRIS‐f ) andthe inverse retrieval and diagnostic components (iPOLARRIS). POLARRIS-f calculates the polarimetric radar
parameters (horizontally polarized reflectivity Zhh [dBZ];

Fig. 3 Weather chart for Japan and surrounding areas at 0:00 UTC on
April 18, 2020 (source: the Japan Meteorological Agency)

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

Page 7 of 28

Fig. 4 Comparison of numerical simulations of rainfall distributions at a 04:00 UTC (top 6 panels) and b 06:00 UTC (bottom 6 panels) on April 18,
2020. In each group of 6 panels; Top row, from left to right: ASUCA 1-km grid, NICAM-NSW6 1.4-km grid, NICAM-NDW6 1.4 km grid; Bottom row,from left to right: radar observation rainfall rate, SCALE-NSW6 1 km grid, and SCALE-NDW6 1 km gridSatoh et al. Progress in Earth and Planetary Science(2022) 9:51

Page 8 of 28

Fig. 5 a Distance circles with a radius of 60 km (dashed circle) and 120 km (solid circle) from the Haneda (red) and Narita (blue) radars. The dottedlines indicate the RHI (Range–Height Indicator) observation lines. Green shading shows the area used to compare rainfall and mass concentrations.
b Temporal variation in 1-h rainfall amount from 00:00 to 12:00 UTC on April 18, 2020, in ASUCA (blue), NICAM-NSW6 (solid orange), NICAM-NDW6(dotted orange), SCALE-NSW6 (solid green), SCALE-NDW6 (dotted green), and observed rainfall amount (red)Fig. 6
Radar observation by the Narita radar at 04:00 UTC, April 18, 2020. Top row: a horizontal distribution of radar reflectivity [dBZ] (elevation
angle 0.7°), vertical cross sections of b radar reflectivity Zhh [dBZ] and c differential reflectivity Zdr [dB]. Bottom row: vertical cross sections of d cloudtypes, e specific differential phase Kdp [° k­ m−1], and f copolar correlation coefficient ρhv. The vertical cross section is located along the straight line in
the upper left figure (azimuth angle 239.5°)

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

Page 9 of 28

Fig. 7 The same as Fig. 6 but for the results of the NICAM-NSW6 simulation. The cross sections are shown for the east–west direction designated by
the red line in the top-left panel. The contours in the cross section show the temperature [°C]band model. Thus, we must be careful about interpreting
the results of the simulated signals, particularly for solid
particles.
To visualize the dual-polarization Doppler weather
radar data, we use Py-ART (The Python ARM Radar
Toolkit) (Helmus and Collis 2016).

3 Results
3.1 Overview

We evaluate the cloud and precipitation characteristics ofthe models by comparing a case study of a weather event.
In general, cloud and precipitation statistics are equilibrated on a daily scale (Matsui et al. 2015). We chose acase of a severe convective storm associated with an
extratropical cyclone that passed over the Kanto region
on April 18, 2020. We assume that a single event captures
representative statistics of clouds and precipitation andgives plenty of data for model evaluation. A bias foundin one case generally emerges in other cases because
cloud microphysics depends primarily on thermodynamic conditions. However, other weather events may
have different statistical properties. One may need to collectively conduct evaluations for various weather events
to improve cloud microphysics schemes further. Further
evaluation will be conducted in subsequent studies using
the methodology proposed in this study.
Figure 3 shows a weather chart at 00:00 UTC on April
18, 2020. At this time, a synoptic-scale extratropical
cyclone was developing around Japan, and the central
area of the low-pressure system was approaching the
Kanto region. Figure 4 shows the rainfall distribution at
04:00 and 06:00 UTC on April 18, 2020. Around this time,the mesoscale convective systems around the central
area of the low-pressure system were active in the Kanto
region, and quasi-linear rainfall distribution from thenorth-northeast (NNE) to the south-southwest (SSW)Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

Page 10 of 28

Fig. 8 The same as Fig. 6, but for the results of the NICAM-NDW6 simulation

formed a frontal structure (06:00 UTC). Figure 4 compares the results of numerical simulations by NICAM,ASUCA, and SCALE with the observation. The initialvalue for all the simulations is set at 00:00 UTC on April
18. Each simulation reproduces the overall convective
system embedded in the central area of the low-pressure
system over the Kanto region but at a relatively coarser
resolution than the observation. The observed finer-scale
rainband system is broken into several rain areas in thesimulations. This result suggests that higher resolution
is required to reproduce such a linear rainband system
realistically.
Figure 5 (right) shows the time sequence of the 1-haccumulated rainfall amount averaged over the rectangular area shown in Fig. 5 (left), which also shows
the horizontal view of the ranges of the two radars at
Haneda and Narita. The observed rainfall amount is the
Radar-raingauge Analyzed Precipitation provided by theJapan Meteorological Agency. The simulations captured
the overall evolution of 1-h rainfall amount between

00:00–01:00 and 11:00–12:00 UTC on April 18. This
comparison indicates that all the models capture convective systems embedded in the extratropical cyclone,though the detailed structure of mesoscale convective
systems differs from the observation. For the present
purpose of evaluating cloud and precipitation characteristics, we use these datasets as they represent typical convective systems in the simulations.
3.2 Cross sections

Figure 6 shows the horizontal distribution and vertical cross section of the radar parameters at the Narita
radar just before 04:00 UTC. Precipitation spread overthe southern Kanto region in the vertical cross section obtained in Range–Height Indicator (RHI) mode.
The cross section of radar reflectivity Zhh shows thatthe intensity is more prominent in the lower layers anddecreases rapidly above an altitude of 3 km. This contrast
is because liquid water with a strong reflectivity spreads

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

Page 11 of 28

Fig. 9 The same as Fig. 6, but for the results of the ASUCA simulation

below an altitude of 3 km, and the solid phase prevails
above 3 km. A melting layer exists between these layers,
showing an increase in radar reflectivity around an altitude of 3 km. The melting layer is visible in the polarization parameters: i.e., differential reflectivity Zdr, specificdifferential phase Kdp, and copolar correlation coefficientρhv. There is a significant decrease in ρhv, and Zdr shows
various positive and negative values. This signal of themelting layer is due to the presence of water substancesin the mixed phases around the melting layer, including
water, ice with complex shapes, and ice covered by melting water. In Hydrometeor Identification (HID) (Fig. 6d),
Wet Snow and HD/LD Graupel are found near the melting layer, indicating that ice particles and rain in various
phases are mixed. Focusing on the rain in the lower layer,
Zdr and Kdp tend to be large where the reflection intensity
is high (e.g., at around x = –80 km). These larger valuesof Zdr and Kdp indicate that larger hydrometeor particles

exist in the case of strong precipitation. However, thecontrasts in Zdr and Kdp do not correspond exactly, indicating the difference in the particle shape distribution. In
general, Zdr is more sensitive to larger particles and canbe noisier for moderate size particles.
Figures 7, 8 and 9 show the corresponding simulated
polarimetric radar signals using J-Sim with POLARRIS; Figs. 7 and 8 are for the NICAM simulations withthe two cloud microphysics schemes NSW6 and NDW6,
respectively, and Fig. 9 is for the ASUCA simulation. The
vertical cross sections are shown in the zonal direction
indicated by the black arrow in Figs. 7a, 8a and 9a, andtemperatures are indicated by solid and dotted curves.
Qualitatively, the vertical distribution of the radar reflectivity Zhh is well reproduced, although the cloud area
west of Narita is relatively small in all the simulations.
Zhh in NICAM-NDW6 in the rain layers below about
3 km is weaker than NICAM-NSW6 and is closer to theSatoh et al. Progress in Earth and Planetary Science(2022) 9:51

Page 12 of 28

Fig. 10 a The same as Fig. 6a, showing the horizontal distribution of radar reflectivity (elevation angle 0.7°) for the NICAM-NSW6 simulation. Verticalcross sections of specific density of b cloud water, c rain, d cloud ice, e snow, and f graupel. The contours in the cross section show the temperature[°C]. The hatched area indicates vertical velocity stronger than 1 m ­s−1

observation (Figs. 6b, 7b, 8b). The melting level signals
are not simulated in Figs. 7, 8 and 9 because polarization parameters cannot be calculated for melting particles in POLARRIS-f. The contrast in Zdr and Kdp in therain layers of all the simulations is much larger than in
the observations (Figs. 6c, e; 7c, e; 8c, e; 9c, e). HID showsthat the vertical extent of the graupel categories (HD/LD
Graupel) is simulated differently above the melting level(Figs. 6d, 7d, 8d, 9d). Rain and Drizzle are simulated forrain below the melting level (lower than an altitude of
3 km), while only Rain is identified in the observation.
To understand the difference in the simulated polarimetric radar signals, we compared the distributions ofeach hydrometeor category for the three simulations in
Figs. 10, 11 and 12, showing the vertical cross sections ofmass concentrations of cloud water, rain, cloud ice, snow,
and graupel along the zonal direction shown by the redline in the horizontal view (Figs. 10a, 11a, 12a). For thesynthesis view, Fig. 13 shows the vertical distribution ofmass concentrations of these five hydrometeor classes
averaged over the domain (Fig. 5, green square) by comparing all the simulation results. The vertical cross section and the vertical distribution of mass concentrationof rain are not so different between NICAM-NSW6
and NICAM-NDW6 (Figs. 10c, 11c; Fig. 13), while theradar reflectivity Zhh is larger in NICAM-NSW6 than in
NICAM-NDW6 (Figs. 7c, 8c), suggesting that raindrop
size in strong rainfall areas is larger in NICAM-NSW6
than in NICAM-NDW6. Cloud water in ASUCA extends
to the upper level at around 8 km in the convective areas(Fig. 12b), similar to NICAM-NDW6. For the ice phase,the vertical distributions of snow and graupel are very
different among all the simulations (Figs. 10d–f, 11d–f,
12d–f; Fig. 13); mass concentrations of snow and graupel are larger in NICAM-NSW6 than in NICAM-NDW6(Figs. 10e, f; 11e, f ). Cloud ice spreads above the melting level in NICAM-NDW6, while it is confined to theupper layers above 7 km in NICAM-NSW6. Figure 12

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

Fig. 11 The same as Fig. 10, but for the NICAM-NDW6 simulation

Fig. 12 The same as Fig. 10, but for the ASUCA simulation

Page 13 of 28

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

Page 14 of 28

Fig. 13 Domain-averaged vertical distribution of hydrometeors at 4:00 UTC on April 18, 2020, for the region shown by the green area in Fig. 3. a
ASUCA, b NICAM-NSW6, c NICAM-NDW6, d SCALE-NSW6, and e SCALE-NDW6. The vertical axis is altitude [km], and the horizontal axis is the massconcentration [g ­kg−1]. Hydrometeors include cloud water (blue), rain (orange), cloud ice (red), snow (purple), and graupel (brown)shows that cloud ice in ASUCA also exists in the upper
layers, while snow and graupel prevail lower than cloudice (Fig. 12d–f ). Figures 10, 11 and 12 also indicate areasof the vertical velocity stronger than 1 m ­s−1 (hatched);
they show that all hydrometeor categories become moreabundant in regions of stronger upward motion.
A comparison of all the models in Fig. 13 shows thediversity in simulating the ice categories of cloud ice,
snow, and graupel; the vertical distribution of thesehydrometeors strongly depends on the model. Notabledifferences are seen in the height of the peak of snow
mass concentration and the relative magnitudes of thepeak values between snow and graupel. Cloud ice prevails down to the melting level of around 3 km for thetwo models with NDW6, while it exists only above 5 km
for the models with single-moment schemes. Mass concentrations of rain are much closer between the models;

ASUCA shows a larger mass concentration of rain andmore vertical distributions above the melting layer up to
4 km (Fig. 13a).
Figure 13 shows that the difference between NSW6
and NDW6 is similar between NICAM and SCALE.
The altitude of maximum mass concentration of snow
is higher for NDW6 than for NSW6, and the relative
magnitudes of the peak values of snow versus graupel
are smaller for NDW6 than for NSW6. The altitudes
where cloud ice and cloud water prevail are deeper for
NDW6 than for NSW6; cloud ice exists above an altitude of 3 km for NDW6 compared to above 5 km for
NSW6, while cloud water reaches up to 8 km for NDW6
compared to 6 km for NSW6. That is, in NDW6, there
are altitudes where cloud ice and cloud water coexist
between 3 and 8 km, while there are few overlapping

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

Page 15 of 28

Fig. 14 The statistical vertical distributions of the polarimetric parameters for the observation by the Narita radar at 04:00 UTC, April 18, 2020. a
the horizontal distribution of radar reflectivity Zhh at an altitude of 2.25 km; the same as Fig. 4a. CFADs of b radar reflectivity Zhh [dBZ], c differentialreflectivity Zdr [dB], d HID, e specific differential phase Kdp [° k­ m−1], and f copular correlation coefficient ρhv. The solid curve is the median, and thedashed curves are the 25 and 75 percentiles of the observed PDF at each altitudealtitudes of the two categories in NSW6. In moredetail, we can see the differences between NICAM and
SCALE in Fig. 13, even for the same cloud microphysics scheme. Among the differences, the height and thevalue of the maximum mass concentration of snow arevisible. These differences suggest that other factors (e.g.,
the dynamical cores, resolutions, or initial conditions)affect the distributions of hydrometeors. By conducting other case studies, we will reveal the robustness ofthe difference between NICAM and SCALE. Hereafter,
we mainly analyze the results for NICAM and ASUCA,
and details of the results for SCALE are summarized in“Appendix 2.”

3.3 Contoured frequency diagrams

Figures 14, 15, 16 and 17 compare the contoured frequency by altitude diagrams (CFADs) of the polarimetricradar signals and cloud types, statistically analyzed in thegreen domain shown in Fig. 5 for the observation and thethree simulations. These CFADs can be used for quantitative and statistical evaluation of cloud microphysics.
For NICAM-NSW6 (Fig. 15), Zhh in the lower layer isstronger than in the observation (Fig. 14), and the maximum of Zhh decreases rapidly around the melting layer
and closer to the observation. Between altitudes of 0 and
2 km, ­Zdr has a probability peak between 1.5 and 2 dB
and is larger than the observation. For NICAM-NDW6(Fig. 16), ­Zhh is consistent with the observation between

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

Page 16 of 28

Fig. 15 The same as Fig. 14, but for the NICAM-NSW6 simulation. The black solid curve is the median, the dashed curves are the 25 and 75
percentiles of the observed PDF at each altitude, and the gray curves are those for the NICAM-NSW6 simulation

the altitudes of 0 and 2 km, but it is stronger above themelting layer than in the observation. The magnitude of
Zdr near the surface between the altitudes 0 and 1 km isconsistent with the observation. For ASUCA (Fig. 17),
as in NICAM-NSW6, Zhh in the lower layer is overestimated compared to the observation. For all the simulations, ρhv has smaller variability than the observation in
the lower layer below the melting layer and is close to 1
above the melting level due to the smaller dispersion of
habits of ice particles in the simulations. In the lower
layer, the size of rain particles affects the variability of ρhv;
NICAM-NDW6 shows ρhv is closer to the observation,
while NICAM-NSW6 and ASUCA represent smaller
values of ρhv near the surface because the size is directly
related to the mass concentrations of rain.
In the lower layers below an altitude of 2 km, the twopolarimetric parameters Zdr and Kdp in NICAM-NSW6
are generally larger than the observations. In contrast,

these parameters in NICAM-NDW6 are closer to theobservations.
Ice particles dominate the layer above the melting level(3–4 km). Because the shapes of ice particles affect polarimetric parameters, it is generally difficult to evaluate
numerical model results in which the shapes of ice particles
are not considered. In general, however, radar reflectivity
Zhh can be used to evaluate ice particles because it reflects
the size of ice particles. In the layer just above the melting
level (3–4 km), the maximum value of Zhh in NICAMNSW6 is smaller than the observation. In contrast, themaximum values of Zhh in NICAM-NDW6 and ASUCA
are larger than the observation. The polarimetric parameters, Zdr, Kdp, and ρhv, all show a small spread above themelting level; Zdr and Kdp are close to zero, and ρhv is almost
1. These small spreads are due to the assumptions on ice
particles used for POLARRIS (Matsui et al. 2019b), whichmight inhibit a larger spread of these radar parameters.

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

Page 17 of 28

Fig. 16 The same as Fig. 15, but for the NICAM-NDW6 simulation

3.4 Hydrometeor Identification

We now compare the distributions of cloud particle categories using HID between the observation and the models. The overall differences can be viewed in the CFAD of
HID in Figs. 14d, 15d, 16d and 17d. We should note thatthe HID of the simulations does not correspond to thehydrometeors of the cloud microphysics schemes usedin the numerical models. HID is derived solely from thepolarimetric parameters, and the characteristics of HID
distribution must be viewed with these parameters. HID
diagnosed for the observed signals is also affected by theuncertainties of the observation, such as the precipitation
attenuation and other noises. We compare the HID of
the observation with that of the three simulations in thelayers below, near, and above the melting level, locatedaround an altitude of 3–4 km.
The observation shows a mixture of Rain, Drizzle, and HD Graupel for the layer below the melting
level (Fig. 14d). For NICAM-NSW6 (Fig. 15d), the frequency of Drizzle is similar to the observation. For

NICAM-NDW6 and ASUCA (Figs. 16d, 17d), Drizzle
is more often diagnosed than the observed HID. Thissuggests that the size of the rain categories in NICAMNSW6 is larger than in NICAM-NDW6 and ASUCA. As
for HD Graupel, all the simulations show a smaller frequency than the observation; this might be a bias in thesimulations.
Near the melting level (3–4 km), Wet Snow is mostfrequent around an altitude of 2 km and HD Graupel isobserved above and below the melting level (Fig. 14d).
HD Graupel is mixed with Wet Snow just around themelting level (Fig. 6d; x ~ 50–100 km), which indicatesthat HD Graupel and Wet Snow coexist in the precipitating regions of the ice phase particles near themelting level. Wet Snow is not diagnosed in all the simulations. Above the melting level up to an altitude ofaround 5–6 km, LD Graupel is diagnosed in the simulations instead of HD Graupel. This result implies thatthe comparison with the observation detects a bias inSatoh et al. Progress in Earth and Planetary Science(2022) 9:51

Page 18 of 28

Fig. 17 The same as Fig. 15, but for the ASUCA simulation

the size of graupel in the numerical models. This aspect
will be discussed in the next section.
Above the melting level (3–4 km), HID is barely reliable up to an altitude of about 10 km because of thelimitation of the assumptions of HID (Matsui et al.
2020). In the observation, Aggregates, Ice Crystals, andVertical Ice exist at altitudes of 4–10 km, with Aggregates being the most frequent occurrence (Fig. 14d).
Only Ice Crystals and Aggregates exist in all three simulations without Vertical Ice (Figs. 15d, 16d, 17d). The
cloud microphysics schemes that were used do notinclude assumptions of the shape of ice particles, so
we speculate that these HIDs of ice particles are based
solely on particle size. The probability of Ice Crystals
is larger than the observation for all the simulations.
This difference between the observation and the simulations is related to the difference in the probability of
Zhh at these altitudes (Figs. 14b, 15b, 16b, 17b); nearan altitude of 8 km, the highest probability of Zhh isobserved at larger values of Zhh compared to thoseof the simulations: about 10 dBZ in the observation(Fig. 14b) vs. < 5 dBZ in NICAM-NSW6 and ASUCA(Figs. 15b, 17b). For NICAM-NDW6 (Fig. 16d), theprobability of Ice Crystals is the closest to the observation. However, the radar reflectivity at the higher levelis affected by the attenuation and other noises, andsmaller signals are not always captured accurately, as
inferred from Fig. 6b.
The two hydrometeor categories of Rain and Drizzle are classified in the lower layer. Although we do
not distinguish between rain and drizzle in the cloud
microphysics schemes, the simulated radar signals distinguish these two cloud types. The size distributionof rain particles is used in CSU hydrometeor classification. The difference in HID between Rain and Drizzle suggests inconsistency in the particle size of therain categories used in the simulations. It will also benecessary to investigate how the parameters related tonon-sphericity, which is not explicitly defined in thecloud microphysics scheme, can be included in J-Sim/Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

Page 19 of 28

Fig. 18 Joint probability distributions between Zhh [dBZ] and Zdr [dB] at an altitude of 1.75 km at 04:00 UTC, April 18, 2020. a The Narita radar; b
ASUCA; c NICAM-NSW6; and d NICAM-NDW6

POLARRIS. We note from the above results that Rainis detected up to 2 km, which is higher than the observation in which Wet Snow is diagnosed. In addition,
the vertical extent of simulated probability of HD/LD
Graupel is deeper than the observation, extending toaltitudes 4–6 km above the melting level. In contrast,
the observation shows that they are categorized as HD
Graupel and are confined below an altitude of 4 km.

4 Discussion
By comparing the observation and the simulations, wefound that the polarimetric parameters are useful for
identifying the model bias of the rain processes in thelower layer below the melting level (around an altitude
of about 3 km). In contrast, for the ice particles above
the melting level, the cloud microphysics assumptionsof the numerical models used in this study do not fully

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

reproduce realistic polarimetric parameters comparable with the observations. In particular, we do not takeaccount of the shapes of hydrometeors for the cloud ice,
snow, and graupel categories in the models. The shapes
and the spatial orientation of the different hydrometeors are assumed in the observation simulators (Matsui
et al. 2019a, b). That is why the polarimetric parameters
have smaller dispersions, such that Zdr and Kdp are close
to zero, and ρhv is close to 1 above the melting level for
all the simulations (Figs. 15, 16, 17). Thus, with the current complexity of the cloud microphysics assumptions,the polarimetric parameters do not help detect the biasof the models. This limitation was pointed out by Matsui
et al. (2020). For Zhh, however, the difference among themodels and between the models and the observation canbe identified because Zhh reflects the particle size. In particular, the maximum values of Zhh in the layer just above
the melting level are different between the observationand the models (Figs. 14b, 15b, 16b, 17b), which we thinkcomes from the assumption of the size distribution in the
graupel category in the model. Here, we summarize thecomparisons of the following two aspects: Zhh and Zdr at
an altitude of 1.75 km, which is in the lower layer below
the melting level for validation of rain particles, and Zhh
at the altitude of 4 km, which is just above the melting
level, for validation of graupel particles.
Figure 18 shows the joint probability distributions between Zhh and Zdr at an altitude of 1.75 km.
For the simulations using the single-moment bulk
schemes (NICAM-NSW6, ASUCA), Zdr is uniquely
related to Zhh. Both results show overestimations of
Zdr: Fig. 14 (observation) vs. Figs. 15 and 17 (NICAMNSW6, ASUCA). That is, NICAM-NSW6 and ASUCA

Page 20 of 28

overestimate the particle size of rain. The simulation withthe double-momentum scheme, NICAM-NDW6, showsa closer resemblance of the probability to the observation (Figs. 14 and 16). A similar analysis to Fig. 18 waspresented by Putnam et al. (2017) and Bringi et al. (2020)
and is used for evaluating the size sorting of hydrometeors, including rain.
The snow and graupel simulations just above the melting level between altitudes of 4 and 6 km differ from theobservation. Figure 19 compares the probability of Zhh at
an altitude of 4 km at two snapshots, 4:00 and 6:00 UTC.
The observation and NICAM-NSW6 show a clear cutoff of Zhh around 30 dBZ, indicating the limitation of the
maximum size of ice particles. Both NICAM-NDW6 and
ASUCA show larger probabilities between 30 and 40 dBZ,
and much larger ice particles exist at about 50 dBZ. The
similarity between the observation and NICAM-NSW6 is
specifically noted because the assumption for the graupel
category in NICAM-NSW6 is based on Roh and Satoh(2014), in which the tropical condition is assumed.
The comparison between the observation and thesimulations can be summarized as follows. The simulation with the double-moment scheme, NICAM-NDW6,
reproduced the comparable polarimetric radar characteristics as the observation in the Zhh-Zdr space. However, the simulations with the single-moment schemes,
NICAM-NSW6 and ASUCA, show larger raindrop
sizes in stronger rain areas compared to the observation. Because Zhh uniquely determines Z­ dr in the singlemoment schemes, the stronger precipitation is associatedwith the larger raindrop size, unlike the observation.
For the larger ice particles just above the melting levelaround 4 km, NICAM-NDW6 and ASUCA produced

Fig. 19 Comparison of the probability distributions of Zhh at an altitude of 4.25 km. a at 04:00 UTC, b at 06:00 UTC, April 18, 2020. The observationof the Narita radar (red), ASUCA (blue), NICAM-NSW6 (orange), and NICAM-NDW6 (green)

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

larger particles of graupel or snow, while NICAM-NSW6
has a similar cutoff of the high values of Zhh as in theobservation.

5 Conclusions
This paper introduces ULTIMATE, a framework for collaborative research between observation and numericalmodeling using various observation data in the Kanto
region. An example of validating numerical models using
dual-polarization Doppler weather radars in the Kanto
region is presented. ULTIMATE is intended to validate
not only ordinary regional models but also global stormresolving models (GSRMs) that can seamlessly be used
as local and global models with common dynamical coreand physics schemes.
We use multiple models for comparison: NICAM,SCALE, and ASUCA. Two cloud microphysics schemes,
NSW6 and NDW6, are used for NICAM and SCALE.
We propose utilizing the observation simulator of a dualpolarization Doppler weather radar, called POLARRIS,
which is implemented in the Joint Simulator for Satellite Sensors (J-Sim). The vertical cross sections of theobserved polarimetric radar parameters in the Range–
Height Indicator (RHI) mode are compared with the simulated counterparts. The contoured frequency by altitude
diagrams (CFADs) are used for quantitative and statistical comparisons between the observation and the simulations. The multiple model approach revealed the common
bias of the polarimetric parameters in the single-moment
scheme and the advantages of the two-moment scheme.
Comparison between NICAM and SCALE, which use thesame cloud microphysics schemes, shows similar overall
results, although many factors other than cloud microphysics are different. This result indicates that the present
assessment may be applicable as representative even if asingle meteorological event is used.
Among various aspects of the comparisons, we pointed
out the difference in the rain layer below the melting level(Fig. 18) and the characteristics associated with larger
ice particles just above the melting layer (Fig. 19). The
simulation with the double-moment scheme, NICAMNDW6, reproduced the comparable polarimetric radar
characteristics as the observation. However, the simulations with the single-moment schemes, NICAM-NSW6
and ASUCA, show relatively large raindrop sizes instronger rain areas compared to the observation. For thelarger ice particles just above the melting level around
4 km, NICAM-NDW6 and ASUCA produced larger
particles of graupel or snow, while NICAM-NSW6
has a similar size of the ice particles to the observation.
This result should be remarked on because NSW6 wasPage 21 of 28

improved by comparing the TRMM observation in tropical regions (Roh and Satoh 2014) and evaluated over the
global scale (Roh et al. 2017). NICAM-NDW6 has also
been developed mainly by comparing satellite observation over the global area (Satoh et al., 2018).
In addition to the above point, the unique aspect of
this study shows a path of the use of the local observation to a GSRM with a seamless approach. NICAM has
been developed for a global model, and the global satellite observation data have been used to evaluate the current schemes NSW6 and NDW6 as described above. In
the forthcoming studies, we will investigate cloud microphysics and other physical processes to improve polarimetric radar characteristics.
Under ULTIMATE, we will continue verifying andimproving the cloud microphysics processes of numericalmodels using various ground-based observation data. The
Kanto region, with about a (100 km)2 square domain, is thetarget of a regional model with a horizontal mesh intervalof about O(km). It can also be covered by higher-resolution models with a mesh interval of O(100 m), called largeeddy simulations (LES), to resolve deep convective clouds(Ikuta et al. 2022). Roh et al. (personal communication)found that NICAM-NSW6 underestimates signals related
to larger ice particles when observing intense deep convection in a tropical cyclone. Suppose we improve cloud
microphysics schemes by validating numerical results in
the Kanto region using NICAM simulations; in that case,the improved scheme can be readily tested in the global
domain because NICAM can be used as both a global
model and a regional model by utilizing a stretched grid.
Furthermore, by comparing and validating the global
model with satellite observations, it will be possible toconstrain the numerical model using satellite and groundobservations. Based on such comparisons and verifications, it is possible to perform climate calculations using
the improved global model. Using ground-based observations limited to the Kanto region, we can establish a pathto use the global weather and climate simulation model; it
may improve the accuracy of numerical weather prediction and reduce the uncertainty of climate prediction andclimate sensitivity.

Appendix 1: Observation data for ULTIMATEThe observation data used for the ULTIMATE framework include the multi-frequency lidar (355 nm, 10 ch),the high spectrally resolved lidar (355 nm), high spectral
resolution lidar (355 nm), Doppler lidar (355 nm), coherent Doppler lidar, HG-Spider 94 GHz Doppler radar,Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

and wind profiler at NICT, and Mie polarization lidar(532 nm polarization, 1064 nm), multi-view angle multiple-scattering polarization lidar (532 nm), Multi-view
Angle Multiple Scatter Polarization LIDAR (532 nm),and High Spectral Resolution LIDAR (532 nm). These
are deployed by the EarthCARE satellite validation teamof Kyushu University and the National Institute for Environmental Studies (NIES). The National Research Institute for Earth Science and Disaster Prevention (NIED)
observes cloud and rain particle size distributions using
the Fog Monitor and Laser Precipitation Monitor atTokyo Sky Tree for ground validation. Existing X-band
and Ka-Band radar data in NIED are available. In addition, routine observation data from JMA are available.
In March 2020, the first JMA’s C-band dual-polarization
Doppler radar for disaster prevention was deployed in
Kashiwa City. The same series of the other radars aresequentially replaced by dual-polarization radar. In addition, data from the dual-polarization Doppler radars for
airport weather already in operation at Haneda and Narita airports can be used, as in this study. The JMA’s windprofiler radar observations are made at the Katsuura,
Kumagaya, Mito, and Kawaguchiko observational sites in
the Kanto region.

Fig. 20 The same as Fig. 7, but for the SCALE-NSW6 simulation

Page 22 of 28

Appendix 2: Analysis of the experimentswith SCALE
Figures 20, 21, 22, 23, 24, and 25 show the analysis of
the experiments with SCALE. The cross sections of the
distribution of polarimetric parameters and hydrometeors of the SCALE-NSW6 experiment are shown in
Figs. 20, 21, and those of the SCALE-NDW6 experiment are shown in Figs. 22, 23. The CFADs of the polarimetric parameters of the SCALE-NSW6 experimentare shown in Fig. 24, and those of the SCALE-NDW6
experiment are shown in Fig. 25.
One of the major differences between NICAM and
SCALE is that of the storm top height: Figs. 10, 11 vs.
Figs. 21, 23. For example, SCALE shows the snow heightis higher than NICAM. We found that this difference insnow comes from the upward motions. The frequency
distribution of upwelling is stronger for SCALE than for
NICAM in the middle of the troposphere (not shown),
although the CFADs for ­Zhh are not significantly different(Figs. 15b, 16b vs. Figs. 24b, 25b). Overall, we interpretthese results as there is not much difference in microphysics reproducibility if we consider the difference
between the dynamical core and the initial condition.

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

Fig. 21 The same as Fig. 10, but for the SCALE-NSW6 simulation

Fig. 22 The same as Fig. 7, but for the SCALE-NDW6 simulation

Page 23 of 28

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

Fig. 23 The same as Fig. 10, but for the SCALE-NDW6 simulation

Fig. 24 The same as Fig. 15, but for the SCALE-NSW6 simulation

Page 24 of 28

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

Page 25 of 28

Fig. 25 The same as Fig. 15, but for the SCALE-NDW6 simulation

Abbreviations
ASUCA​: A System based on a Unified Concept for Atmosphere; DM: Double
moment; DYAMOND: Dyanamics of the Atmospheric general circulation Modeled on Non-hydrostatic Domains; GCM: General circulation model; GSRM:
Global storm-resolving model; HID: Hydrometeor Identification; JMA: Japan
Meteorological Agency; J-Sim: Joint Simulator for Satellite Sensors; MCS: Mesoscale convective system; NICAM: Non-hydrostatic ICosahedral Atmospheric
Model; NDW6: NICAM double-moment bulk cloud microphysics with six water
categories; NSW6: NICAM single-moment bulk cloud microphysics with six
water categories; POLARRIS: POLArimetric Radar Retrieval and Instrument
Simulator; PPI: Plan position indicator; RHI: Range–Height Indicator; SCALE:
Scalable Computing for Advanced Library and Environment; SM: Single
moment; ULTIMATE: ULTra-sIte for Measuring Atmosphere of Tokyo metropolitan Environment.
Acknowledgements
The Meteorological Research Institute provided the data of the dual-polarization Doppler radar for airport weather. The cooperation of the Japan Meteorological Agency made it possible for the authors to use the regional model
ASUCA. The regional model SCALE was obtained from RIKEN (https://​scale.​riken.​jp/​ja/), and CSU Radar Tools is available at https://​github.​com/​CSU-​Radar​
met/​CSU_​Radar​Tools. JAXA supports the scientific research of the EarthCARE
mission. This work was supported by the Japan Society for the Promotion of
Science, KAKENHI, Grant-in-Aid for Scientific Research B (20H01967) for ULTrasIte for Measuring Atmosphere of Tokyo Metropolitan Environment and Collaboration Studies with High-Resolution Atmospheric Models (ULTIMATE), andby the Program for Promoting Technological Development of Transportation(Ministry of Land, Infrastructure, Transport and Tourism of Japan). The computational resources were provided by the National Institute for Environmental
Studies. This work was partly supported by the joint research program of theInstitute for Space-Earth Environmental Research (ISEE), Nagoya University.
The authors appreciate Dr. Akihito Umehara for giving information on thedescription of the dual-polarization Doppler radar for airport weather and for
improving the manuscript.
Author contributions
MS proposed the topic and led ULTIMATE. SM conducted all the numericalsimulations. WR and YI investigated the cloud microphysics characteristics of
NICAM and ASUCA, respectively. NK contributed to summarizing the results.
TS contributed to the analysis of cloud microphysics schemes, particularly
NDW6. TH developed the Joint Simulator for Satellite Sensors and implemented POLARRIS. HO contributed to the observation of the ground validation of EarthCARE. All authors read and approved the final manuscript.
Funding
This research is part of the EarthCARE satellite study commissioned by theJapan Aerospace Exploration Agency, “Enhancement of the Joint Simulator for
Satellite Sensors,” and the Grant-in-Aid for Scientific Research B “Promotion ofCollaborative Research on High-Resolution Numerical Models through Comprehensive Use of Ultrasite Observations in the Kanto Region” (20H01967).
Program for Promoting Technological Development of Transportation of the
Ministry of Land, Infrastructure, Transport and Tourism (MLIT) supported theSatoh et al. Progress in Earth and Planetary Science(2022) 9:51

research “Advancement of Extreme Weather Prediction for Efficient Operationof Aircraft in the Kanto Region.” This work was carried out as part of the joint
research program of the Institute for Space-Earth Environmental Research(ISEE), Nagoya University. We also acknowledge the National Institute for Environmental Studies (NIES) granting us permission to use their supercomputer
system (NEC SX-ACE/128M16, NEC SX-Aurora TSUBASA A511-64).
Availability of data and materials
The numerical model SCALE is available at https://​scale.​riken.​jp/; NICAM isavailable on request, as described at http://​nicam.​jp/​hiki/?​Resea​rch+​Colla​borat​ions. Those interested in the availability of the numerical model “ASUCA”
should contact the first author (MS). The Joint Simulator for Satellite Sensors
is described at https://​www.​eorc.​jaxa.​jp/​theme/​Joint-​Simul​ator/​userf​orm/​js_​
userf​orm.​html. The numerical data from the present study are available on
request from the first author (MS).

Declarations
Competing interests
The authors declare that they have no competing interests.
Author details
1
Atmosphere and Ocean Research Institute, The University of Tokyo, 5‑1‑5Kashiwanoha, Kashiwa, Chiba 277‑8564, Japan. 2 Meteorological ResearchInstitute, Japan Meteorological Agency, Tsukuba, Japan. 3 Japan Agency
for Marine-Earth Science and Technology, Yokohama, Japan. 4 Kochi Universityof Technology, Kochi, Japan. 5 Research Institute for Applied Mechanics,
Kyushu University, Fukuoka, Japan.
Received: 25 March 2022 Accepted: 26 September 2022

References
Bodas-Salcedo A, Webb MJ, Bony S, Chepfer H, Dufresne JL, Klein SA et al(2011) COSP: Satellite simulation software for model assessment. Bull Am
Meteorol Soc 92:1023–1043. https://​doi.​org/​10.​1175/​2011B​AMS28​56.1
Bringi V, Seifert A, Wu W, Thurai M, Huang GJ, Siewert C (2020) Hurricane
Dorian outer rain band observations and 1d particle model simulations: a case study. Atmosphere (basel) 11:879. https://​doi.​org/​10.​3390/​
ATMOS​11080​879
Dolan B, Rutledge SA (2009) A theory-based hydrometeor identification
algorithm for X-band polarimetric radars. J Atmos Ocean Technol
26:2071–2088. https://​doi.​org/​10.​1175/​2009J​TECHA​1208.1
Dolan B, Rutledge SA, Lim S, Chandrasekar V, Thurai M (2013) A robust
C-band hydrometeor identification algorithm and application toa long-term polarimetric radar dataset. J Appl Meteorol Climatol
52:2162–2186. https://​doi.​org/​10.​1175/​JAMC-D-​12-​0275.1
Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ et al (2016)
Overview of the Coupled Model Intercomparison Project Phase 6(CMIP6) experimental design and organization. Geosci Model Dev 9:9.
https://​doi.​org/​10.​5194/​gmd-9-​1937-​2016
Haarsma RJ, Roberts M, Vidale PL, Senior C, Bellucci A, Corti S, Fuckar N, Guemas V, von Hardenberg J, Hazeleger W, Kodama C, Koenigk T, Leung
R, Lu J, Luo J-J, Mao J, Mizielinsky M, Mizuta R, Nobre P, Satoh M, Scoccimarro E, Semmler T, Small J, von Storch J-S (2016) High resolution
model intercomparison project (HighResMIP v1.0) for CMIP6. Geosci
Model Dev 9:4185–4208. https://​doi.​org/​10.​5194/​gmd-​2016-​66
Harris L, Zhou L, Lin S-J, Chen J-H, Chen X, Gao K et al (2020) GFDL SHiELD: aunified system for weather-to-seasonal prediction. J. Adv. Model. Earth
Syst. 12:e2020MS002223. https://​doi.​org/​10.​1029/​2020M​S0022​23
Hashino T, Satoh M, Hagihara Y, Kubota T, Matsui T, Nasuno T, Okamoto H(2013) Evaluating cloud microphysics from the NICAM against CloudSat and CALIPSO. J Geophys Res 118:7273–7293. https://​doi.​org/​10.​
1002/​jgrd.​50564
Hashino T, Satoh M, Hagihara Y, Kato S, Kubota T, Matsui T, Nasuno T,
Okamoto H, Sekiguchi M (2016) Evaluating cloud radiative effects

Page 26 of 28

simulated by NICAM with A-train. J Geophys Res 121:7041–7063.
https://​doi.​org/​10.​1002/​2016J​D0247​75
Helmus JJ, Collis SM (2016) The Python ARM Radar Toolkit (Py-ART), a library forworking with weather radar data in the python programming language. J
Open Res Software 4:e25. https://​doi.​org/​10.​5334/​jors.​119
Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J et al(2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146:1999–2049.
https://​doi.​org/​10.​1002/​QJ.​3803
Ikuta Y, Satoh M, Sawada M, Kusabiraki H, Kubota T (2021) Improvement of
the cloud microphysics scheme of the mesoscale model at the Japan
Meteorological Agency using space-borne radar and microwave Imagerof the Global Precipitation Measurement as reference. Mon Wea Rev
149:3803–3819. https://​doi.​org/​10.​1175/​MWR-D-​21-​0066.1
Ikuta Y, Sawada M, Satoh M (2022) Determining the impact of boundary layer
schemes on the secondary circulation of Typhoon FAXAI using radarobservations in the gray zone. J Atmos Sci (submitted)
Illingworth A, Barker H, Beljaars A, Ceccaldi M, Chepfer H, Delanoe J, Domenech C, Donovan D, Fukuda S, Hirakata M, Hogan R, Huenerbein A, Kollias
P, Kubota T, Nakajima T, Nakajima T, Nishizawa T, Ohno Y, Okamoto H, Oki
R, Sato K, Satoh M, Wandinger U, Wehr T (2015) The EARTHCARE satellite:
the next step forward in global measurements of clouds, aerosols, precipitation and radiation. Bull Am Meteorol Soc 96:1311–1332
Japan Meteorological Agency (2019) Outline of the operational numerical
weather prediction at the Japan Meteorological Agency, Appendix to
WMO technical progress report on the global data-processing and forecasting system and numerical weather prediction. 229 pp. https://​www.​jma.​go.​jp/​jma/​jma-​eng/​jma-​center/​nwp/​outli​ne2019-​nwp/​index.​htm
Jung Y, Xue M, Zhang G (2010) Simulations of polarimetric radar signatures ofa supercell storm using a two-moment bulk microphysics scheme. J Appl
Meteorol Climatol 49:146–163. https://​doi.​org/​10.​1175/​2009J​AMC21​78.1
Karrer M, Seifert A, Ori D, Kneifel S (2021) Improving the representation of
aggregation in a two-moment microphysical scheme with statistics
of multi-frequency Doppler radar observations. Atmos Chem Phys
21:17133–17166. https://​doi.​org/​10.​5194/​acp-​21-​17133-​2021
Khairoutdinov MF, Blossey PN, Bretherton CS (2022) Global system for atmospheric modeling: model description and preliminary results. J Adv Model
Earth Syst 14:e2021MS002968. https://​doi.​org/​10.​1029/​2021M​S0029​68
Kodama C, Noda AT, Satoh M (2012) An assessment of the cloud signals simulated by NICAM using ISCCP, CALIPSO, and CloudSat satellite simulators. J
Geophys Res 117:D12210. https://​doi.​org/​10.​1029/​2011J​D0173​17
Kodama C, Ohno T, Seiki T, Yashiro H, Noda AT, Nakano M, Yamada Y, Roh W,
Satoh M, Nitta T, Goto D, Miura H, Nasuno T, Miyakawa T, Chen Y-W, Sugi
M (2021) The non-hydrostatic global atmospheric model for CMIP6 HighResMIP simulations (NICAM16-S): experimental design, model description, and sensitivity experiments. Geosci Model Dev 14:795–820
Masunaga H, Satoh M, Miura H (2008) A joint satellite and global CRM analysisof an MJO event: model diagnosis. J Geophys Res 113:D17210. https://​doi.​org/​10.​1029/​2008J​D0099​86
Masunaga H, Matsui T, Tao WK, Hou AY, Kummerow CD, Nakajima T et al (2010)
Satellite data simulator unit: a multisensor, multispectral satellite simulator package. Bull Am Meteorol Soc 91:1625–1632. https://​doi.​org/​10.​
1175/​2010B​AMS28​09.1
Matsui T, Tao WK, Munchak SJ, Grecu M, Huffman GJ (2015) Satellite view ofquasi-equilibrium states in tropical convection and precipitation microphysics. Geophys Res Lett 42:1959–1968. https://​doi.​org/​10.​1002/​2015G​
L0632​61
Matsui T, Tao W-K, Chern J, Lang S, Satoh M, Hashino T, Kubota T (2016) On theland-ocean contrast of tropical convection and microphysics statistics
derived from TRMM satellite signals and global storm-resolving models. J
Hydrometeor 17:1425–1445. https://​doi.​org/​10.​1175/​JHM-D-​15-​0111.1
Matsui T, Wolff DB, Mohr KI, Lang SE, Zhang M (2019a) BiLateral operational
storm-scale observation and modeling: BLOSSOM. American Geophysical Union, Fall Meeting 2019a, abstract #H31P-1980, December 2019,2019aAGUFM.H31P1980M.
Matsui T, Dolan B, Rutledge SA, Tao WK, Iguchi T, Barnum J, Lang SE (2019b)
POLARRIS: A POLArimetric radar retrieval and instrument simulator. J
Geophys Res Atmos 124:4634–4657
Matsui T, Dolan B, Iguchi T, Rutledge SA, Tao W-K, Lang S (2020) Polarimetricradar characteristics of simulated and observed intense convective

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

cores for a midlatitude continental and tropical maritime environment.
J Hydrometeorol 21:501–517
Misumi R (2018) Preface. Special Issue on Tokyo Metropolitan Area Convection Study for Extreme Weather Resilient Cities (TOMACS). J Meteor Soc
Japan 96A:1–2
Misumi R, Shoji Y, Saito K, Seko H, Seino N, Suzuki S et al (2019) Results of
the Tokyo Metropolitan Area Convection Study for Extreme Weather
Resilient Cities (TOMACS). Bull Am Meteorol Soc 100:2027–2041.
https://​doi.​org/​10.​1175/​BAMS-D-​18-​0316.1
Miyoshi T, Sato S, Ushio T, Koike K, Park T, Nakajima K (2020) Guerrilla rainfall
forecast updated every 30 seconds—Real-time demonstration in theTokyo metropolitan area. https://​www.​riken.​jp/​pr/​news/​2020/​20200​
821_1/​index.​html. Accessed April 17, 2021
Nishizawa S, Yashiro H, Sato Y, Miyamoto Y, Tomita H (2015) Influence of grid
aspect ratio on planetary boundary layer turbulence in large-eddy
simulations. Geosci Model Dev 8:3393–3419
Noda AT, Seiki T, Roh W, Satoh M, Ohno T (2021) Improved representationof low-level mixed-phase clouds in a global cloud-system-resolving
simulation. J Geophys Res Atmosphere 126:e2021JD035223. https://​doi.​org/​10.​1029/​2021J​D0352​23
Putnam BJ, Xue M, Jung Y, Zhang G, Kong F (2017) Simulation of polarimetric radar variables from 2013 CAPS spring experiment storm-scaleensemble forecasts and evaluation of microphysics schemes. Mon Wea
Rev 145:49–73. https://​doi.​org/​10.​1175/​MWR-D-​15-​0415.1
Roh W, Satoh M (2014) Evaluation of precipitating hydrometeor parameterizations in a single-moment bulk microphysics scheme for deep convective
systems over the tropical open ocean. J Atmos Sci 71:2654–2673
Roh W, Satoh M (2018) Extension of a multisensor satellite radiance-based
evaluation for cloud system resolving models. J Meteor Soc Japan
96:55–63
Roh W, Satoh M, Nasuno T (2017) Improvement of a cloud microphysics
scheme for a global nonhydrostatic model using TRMM and a satellite
simulator. J Atmos Sci 74:167–184
Roh W, Satoh M, Hashino T, Okamoto H, Seiki T (2020) Evaluations of the thermodynamic phases of clouds in a cloud system-resolving model using
CALIPSO and a satellite simulator over the Southern Ocean. J Atmos Sci
77:3781–3801
Roh W, Satoh M, Hohenegger C (2021) Intercomparison of cloud properties
in DYAMOND simulations over the Atlantic Ocean. J Meteor Soc Japan
99:1439–1451
Ryzhkov A, Pinsky M, Pokrovsky A, Khain A (2011) Polarimetric radar observation operator for a cloud model with spectral microphysics. J Appl Meteorol Climatol 50:873–894. https://​doi.​org/​10.​1175/​2010J​AMC23​63.1
Sato Y, Nishizawa S, Yashiro H, Miyamoto Y, Kajikawa Y, Tomita H (2015) Impactsof cloud microphysics on trade wind cumulus: which cloud microphysics processes contribute to the diversity in a large eddy simulation. Prog
Earth Planet Sci 2:23
Satoh M, Matsuno T, Tomita H, Miura H, Nasuno T, Iga S (2008) Nonhydrostatic
Icosahedral Atmospheric Model (NICAM) for global cloud resolving
simulations. J Comp Phys 227:3486–3514. https://​doi.​org/​10.​1016/j.​jcp.​
2007.​02.​006
Satoh M, Inoue T, Miura H (2010) Evaluations of cloud properties of global andlocal cloud system resolving models using CALIPSO/CloudSat simulators.
J Geophys Res 115:D00H14. https://​doi.​org/​10.​1029/​2009J​D0122​47
Satoh M, Tomita H, Yashiro H, Miura H, Kodama C, Seiki T, Noda AT, Yamada Y,
Goto D, Sawada M, Miyoshi T, Niwa Y, Hara M, Ohno Y, Iga S, Arakawa T,
Inoue T, Kubokawa H (2014) The non-hydrostatic icosahedral atmospheric model: description and development. Prog Earth Planet Sci 1:18
Satoh M, Roh W, Hashino T (2016) Evaluations of clouds and precipitations in
NICAM using the Joint Simulator for Satellite Sensors. Cger’s Supercomputer Monograph Report 22:110
Satoh M, Noda AT, Seiki T, Chen Y-W, Kodama C, Yamada Y, Kuba N, Sato Y(2018) Toward reduction of the uncertainties in climate sensitivity due tocloud processes using a global non-hydrostatic atmospheric model. Prog
Earth Planet Sci 5:67. https://​doi.​org/​10.​1186/​s40645-​018-​0226-1
Satoh M, Stevens B, Judt F, Khairoutdinov M, Lin S, Putman WM (2019) Global
cloud-resolving models. Curr Clim Chang Rep 5:172–184. https://​doi.​org/​
10.​1007/​s40641-​019-​00131-0
Seiki T, Nakajima T (2014) Aerosol effects of the condensation process on aconvective cloud simulation. J Atmos Sci 71:833–853

Page 27 of 28

Seiki T, Roh W (2020) Improvements in supercooled liquid water simulations of
low-level mixed-phase clouds over the Southern Ocean using a singlecolumn model. J Atmos Sci 77:3803–3819
Seiki T, Satoh M, Tomita H, Nakajima T (2014) Simultaneous evaluation of ice
cloud microphysics and non-sphericity of the cloud optical properties
using hydrometeor video sonde and radiometer sonde in-situ observations. J Geophys Res Atmos 119:6681–6701
Seiki T, Kodama C, Satoh M, Hashino T, Hagihara Y, Okamoto H (2015) Verticalgrid spacing necessary for simulating tropical cirrus clouds with a highresolution atmospheric general circulation model. Geophys Res Lett
42:4150–4157
Sherwood SC, Webb MJ, Annan JD, Armour KC, Forster PM, Hargreaves JC et al(2020) An assessment of earth’s climate sensitivity using multiple lines ofevidence. Rev Geophys 58:1–92
Shrestha P, Mendrok J, Pejcic V, Trömel S, Blahak U, Carlin JT (2022) Evaluation of the COSMO model (v5.1) in polarimetric radar space—impact of
uncertainties in model microphysics, retrievals and forward operators.
Geosci Model Dev 15:291–313. https://​doi.​org/​10.​5194/​gmd-​15-​291-​2022
Skamarock WC, Klemp JB, Duda MG, Fowler LD, Park SH, Ringler TD (2012) A
multiscale nonhydrostatic atmospheric model using centroidal Voronoi
tesselations and C-grid staggering. Mon Weather Rev 140:3090–3105.
https://​doi.​org/​10.​1175/​MWR-D-​11-​00215.1
Snyder JC, Bluestein HB, Dawson DT, Jung Y (2017a) Simulations of polarimetric, X-band radar signatures in supercells. Part I: description of experimentand simulated ρhv rings. J Appl Meteorol Climatol 56:1977–1999. https://​doi.​org/​10.​1175/​JAMC-D-​16-​0138.1
Snyder JC, Bluestein HB, Dawson DT, Jung Y (2017b) Simulations of polarimetric, X-band radar signatures in supercells. Part II: Z­ DR columns and ringsand ­KDP columns. J Appl Meteorol Climatol 56:2001–2026. https://​doi.​
org/​10.​1175/​JAMC-D-​16-​0139.1
Stevens B, Satoh M, Auger L, Biercamp J, Bretherton C, Chen X, Duben P, Judt
F, Khairoutdinov M, Klocke D, Kodama C, Kornblueh L, Lin S-L, Putman
W, Shibuya R, Neumann P, Rober N, Vannier B, Vidale P-L, Wedi N, Zhou L(2019) DYAMOND: the dynamics of the atmospheric general circulation
modeled on non-hydrostatic domains. Prog Earth Planet Sci 6:61. https://​doi.​org/​10.​1186/​s40645-​019-​0304-z
Tomita H (2008) A stretched grid on a sphere by new grid transformation. J
Meteor Soc Japan 86A:107–119
Tomita H, Tsugawa M, Satoh M, Goto K (2001) Shallow water model on a modified icosahedral geodesic grid by using spring dynamics. J Comput Phys
174:579–613. https://​doi.​org/​10.​1006/​jcph.​2001.​6897
Tomita H, Satoh M (2004) A new dynamical framework of nonhydrostatic
global model using the icosahedral grid. Fluid Dyn Res 34:357–400
Trömel S, Simmer C, Blahak U, Blanke A, Doktorowski S, Ewald F et al (2021)
Overview: fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation
processes. Atmos Chem Phys 21:17291–17314. https://​doi.​org/​10.​5194/​
acp-​21-​17291-​2021
Tsukamoto N, Yamauchi H, Okumura H, Umehara A, Kajiwara Y (2016) JMA’s
C-band dual-polarization Doppler weather radars with SSPAs. WMO
Technical Conference on Meteorological and Environmental Instruments
and Methods of Observation 2016, Madrid, Spain, 27–29 September
2016. Available online at: https://​www.​wmoci​mo.​net/​event​papers/​sessi​
on2/​poste​rs2/​P2(68)%​20Tsu​kamoto_​JMA-​radars.​pdf. accessed 04 August2022
Uchida J, Mori M, Nakamura H, Satoh M, Suzuki K, Nakajima T (2016) Error andenergy budget analysis of a nonhydrostatic stretched-grid global atmospheric model. Mon Wea Rev 144:1423–1447
Uchida J, Mori M, Hara M, Satoh M, Goto D, Kataoka T, Suzuki K, Nakajima T(2017) Impact of lateral boundary errors on the simulation of clouds witha non-hydrostatic regional climate model. Mon Wea Rev 145:5059–5082
Umehara A, Adachi T, Mashiko W, Yamauchi H (2021) Analysis of the tornadic
debris signatures of the Ichihara Tornado in a typhoon environment
using two operational C-band dual-polarization weather radars. SOLA
17:196–201. https://​doi.​org/​10.​2151/​SOLA.​2021-​034
Webb MJ, Andrews T, Bodas-Salcedo A, Bony S, Bretherton CS, Chadwick R
et al (2017) The cloud feedback model intercomparison project (CFMIP)
contribution to CMIP6. Geosci Model Dev 10:359–384. https://​doi.​org/​10.​
5194/​gmd-​10-​359-​2017

Satoh et al. Progress in Earth and Planetary Science(2022) 9:51

Zängl G, Reinert D, Rípodas P, Baldauf M (2014) The ICON (ICOsahedral Nonhydrostatic) modelling framework of DWD and MPI-M: Description of the
non-hydrostatic dynamical core. Q J R Meteorol Soc 141:563–579. https://​doi.​org/​10.​1002/​qj.​2378

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Page 28 of 28