Pre-launch simulated satellite data are useful to develop retrieval algorithms and to facilitate the rapid release of retrieval products after launch. Here we introduce the Japanese Aerospace Exploration Agency's (JAXA) EarthCARE synthetic data based on simulations using a 3.5 km
horizontal-mesh global storm-resolving model. Global aerosol transport
simulation results are added for aerosol retrieval developers. Synthetic
data were produced corresponding to the four EarthCARE instrument 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). JAXA EarthCARE synthetic data include a standard product
with data for two orbits and a research product with shorter frames and more
detailed instrument settings. In the research products, random errors in the
CPR are considered based on the observation window, and noise in ATLID
signals are added using a noise simulator. We consider the spectral
misalignment effect of the visible and near-infrared MSI channels based on
response functions depending on the angle from the nadir. We introduce plans
for updating the JAXA EarthCARE synthetic data using large eddy simulation
model data and the implementation of a three-dimensional radiation model.
The JAXA EarthCARE synthetic data are available publicly.
Ministry of Land, Infrastructure, Transport and TourismNAIntroduction
The Earth Clouds, Aerosol, and Radiation Explorer (EarthCARE) satellite is a
joint mission of the Japanese Aerospace Exploration Agency (JAXA) and the
European Space Agency (ESA) (Illingworth et al., 2015; Wehr et al., 2023). The satellite will carry four instruments: a 94 GHz cloud-profiling radar (CPR), a 355 nm atmospheric lidar (ATLID), a seven-channel multispectral imager (MSI), and a broadband radiometer (BBR). These instruments are aboard a single platform and are expected to provide synergistic retrieval products.
Nominal level 1 (L1) data are observed directly by the instruments. There
are plans to produce retrieval products (L2) for clouds, aerosol, and
radiative properties using L1 data from single or multiple instruments. For
the development and validation of L2 data, pre-launch simulated L1 data are
required. The JAXA EarthCARE-like L1 synthetic data (JAXA L1 data) were
developed using a global storm-resolving model (GSRM; Satoh et al., 2019;
Stevens et al., 2019) and a satellite simulator developed by JAXA and the
University of Tokyo, Japan.
The simulation scenes for JAXA L1 data were constructed using numerically
simulated GSRM data, which resolve cloud and precipitation systems without
convective parametrization by enhancing horizontal resolution above that of
a typical global circulation model (GCM). One of the merits of GSRMs is that
they do not heavily rely on ambiguous assumptions of cloud fractions at
subgrid scales, in contrast to GCMs. The Nonhydrostatic ICosahedral
Atmospheric Model (NICAM; Tomita and Satoh, 2004; Satoh et al., 2008; Satoh et al., 2014) is one of the pioneering
GSRMs. It has been evaluated and improved using various satellite data
(e.g., Masunaga et al., 2008; Roh and Satoh, 2014, 2018; Roh et al., 2017,
2020). Evaluations have included global precipitation and cloud systems in
various locations.
A satellite simulator is a collection of radiative transfer models used to
simulate satellite-like signals based on outputs of atmospheric models such
as GSRMs and GCMs (e.g., Bodas-Salcedo et al., 2011; Hashino et al., 2013,
2016; Matsui et al., 2014; Saunders et al., 2018). Simulators have been
developed to evaluate, improve, and compare numerical models using satellite
observation data. Here, the Joint-Simulator for satellite sensors (Hashino
et al., 2013, 2016; Satoh et al., 2016) was used as a satellite simulator to
produce EarthCARE synthetic data before the launch of the satellite.
JAXA L1 data have been used in several studies to evaluate the performances
of CPR and MSI. Hagihara et al. (2021) investigated expected Doppler errors
based on the instrument settings related to the top height of the
observation (observation window). In testing different observation windows
of CPR, it has been found that the unfolding correction and increased
horizontal sampling reduced Doppler errors. The latitude variation of
Doppler errors has also been investigated using JAXA L1 data (Hagihara et
al., 2022), and Wang et al. (2022) investigated the SMILE (spectral
misalignment effect) of MSI data on the cloud retrieval algorithm. JAXA L1
data can also be used as a testbed to check retrieval algorithm performance,
and it is possible to directly compare original cloud data and precipitation
simulated by NICAM with data retrieved from retrieval algorithms for each
sensor.
JAXA L1 data are of two types, namely the standard product and the research
product, the latter of which includes noise and more detailed information
about instrument settings. The former comprises two sets of orbit data
covering two full global circles, whereas the latter has shorter frames with
more detailed instrument settings for retrieval algorithm developers.
Here we introduce the JAXA simulated L1 EarthCARE data set. Detailed
information concerning input data, the orbit and scan simulator, and satellite
simulators are described in Sect. 2. Data for each sensor are also described with instrument settings and output data. Recent and planned developments are discussed in Sect. 4, including the use of large eddy simulation mode data and the implementation of a three-dimensional (3D)
radiation model.
Data and model descriptionsGlobal storm-resolving simulations
The JAXA L1 simulation data are based on input data for meteorological
conditions, distributions, and characteristics of clouds, precipitation, and
aerosols related to signals from satellite sensors. We used NICAM data to
drive the instrument simulations. NICAM was configured with a horizontal
resolution of about 3.5 km, and the vertical grid had 40 levels (Table 1 in
Satoh et al., 2010). The simulation commenced at 00:00Z on 15 June 2008 and
was initialized using a 0.5∘× 0.5∘ ECMWF
(European Centre for Medium-Range Weather Forecasts) year of tropical
convection analysis (Waliser et al., 2012); data for 00:00Z on 19 June 2008 were used here. A bulk single-moment cloud microphysics scheme with
six water categories (NSW6; Tomita, 2008) and the MYNN2 (Nakanishi and Niino,
2009) boundary-layer scheme were applied. See previous works (Hashino et al., 2013; Yamada et al., 2016; Nasuno et al., 2016) for the details of the
simulation data. The data have been analyzed also in several papers (Hashino
et al., 2016; Matsui et al., 2016; Roh et al., 2017; Kubota et al., 2020).
Aerosol data were simulated using the NICAM Spectral Radiation–Transport
Model for Aerosol Species (NICAM–SPRINTARS; Takemura et al., 2000), which
was implemented using a global 3D aerosol transport–radiation model. The
horizontal resolution was ∼240 km, and the vertical resolution was the same as that used in the 3.5 km mesh simulation. Aerosol data simulated by NICAM–SPRINTARS include carbonaceous aerosols (black carbon and organic matter), sulfate, soil dust, sea salt, and the precursor gases of sulfate (sulfur dioxide and dimethylsulfide (DMS)). Aerosol data were used with the ATLID, MSI, and BBR simulations.
The relationship between orbits and cloud distribution in the NICAM
simulation is shown in Fig. 1, where simulated 11 µm brightness
temperatures (representing cloud top temperatures) indicate high clouds. The
lines indicate the expected EarthCARE orbits corresponding to the
simulations presented in this paper.
Simulated tracks and a swath of the EarthCARE satellite. The black/white contour is the 11 µm brightness temperature (K). Colors
indicate the time from the starting point (00:00Z) in seconds.
Joint-Simulator for satellite sensors
The Joint-Simulator for satellite sensors (Hashino et al., 2013, 2016) was
used to simulate JAXA L1 data from NICAM data. The Joint-Simulator was
developed as part of the JAXA EarthCARE mission (Satoh et al., 2016) and has
simulators for a visible/infrared imager, radar, lidar, and broadband
radiometer corresponding to MSI, CPR, ATLID, and BBR EarthCARE sensors. It
also has a microwave radiometer simulator. The basic structure was inherited
from the satellite data simulator unit (SDSU; Masunaga et al., 2010) and the
NASA Goddard SDSU (Matsui et al., 2014); several simulators with these SDSUs
were shared. The Joint-Simulator has a history of evaluations and
improvements of NICAM (Hashino et al., 2013, 2016; Roh et al., 2020). The
settings and descriptions of the simulators are described for each sensor in
Sect. 3.
Orbit and scan simulators
Orbit and scan simulators produce orbit and swath data based on EarthCARE and
NICAM data; the orbit simulator determines the satellite location, and the
scan simulator determines sampling intervals and the maximum sample number
per scan. The simulators are described in Matsui (2013).
The orbit and scan simulator assumes a Kepler satellite orbit, and six Keplerian
elements are needed to calculate satellite position including inclination,
an argument of perigee, and the right ascension of the ascending node. The
satellite is in an elliptical orbit, and eccentricity, the semi-major angle,
and orbit inclination angle define the shape and size of the orbit.
The orbit was designed as the EarthCARE passed the Equator at 14:00 local
time in descending node. For this, we set up a semi-major axis of 6771.28 km, eccentricity of 0.001283, and an orbit inclination angle of
97.05∘, together with initial values of mean anomalies of
270∘, an argument of perigee of 270∘, and a right
ascension of the ascending node of 297.5∘.
The along- and cross-track sensor sampling intervals were 500 m, 285 m, 500 m, and 10 km for CPR, ATLID, MSI, and BBR, respectively. There were 384
samples per scan for the MSI, with 102 nadir pixels. The ATLID was
considered with 3∘ of off-nadir angle, as for CALIPSO. The
Joint-Simulator applies vertical interpolation on the NICAM data to obtain
the samples on the vertical grid defined for each sensor.
There are eight frames for an EarthCARE single orbit, A–H, divided at
latitudes of 22.5 and 62.5∘ N/S. For example, frame
A spans from 22.5∘ S to 22.5∘ N in ascending mode.
Simulation of EarthCARE signals
A flowchart describing the production of JAXA L1 data is shown in Fig. 2.
Input data for the Joint-Simulator were provided by the orbit and scan simulators based on numerical data of NICAM and NICAM–SPRINTARS. Input data
were provided for each instrument with the same horizontal resolution and
frames. There are two versions of the products, like the standard product and
the research product (Table 1).
Flowchart for production of JAXA L1 data.
The differences between the standard product and the research
product.
Additional data in the research productCPRRandom errors of Doppler velocity based on the observation window, surface clutterATLIDRandom noiseMSIConsideration of the response function depending on the pixel numberCPR
The CPR is a 94 GHz cloud profiling radar that can detect radar reflectivity
and Doppler velocities. The minimum radar reflectivity is -36 dBZ, which
is a higher sensitivity than that of CloudSat because of the larger antenna
and lower orbit than CloudSat.
Radar reflectivity and Doppler velocity were simulated by the EarthCARE
Active Sensor Simulator (EASE; Okamoto et al., 2007, 2008; Nishizawa et al., 2008). The EASE simulator takes into account the attenuation of radar
related to water vapor and hydrometeors. Doppler velocity is calculated
using the terminal velocity of hydrometeors weighted by the radar
reflectivity and air motion. We set the vertical resolution of CPR at
99.9308 m. The lowest altitude is 50 m, and the top of the observation
window is 19936.23 m. We added a total extinction coefficient of 94 GHz in
the simulated data product.
The CPR data of the JAXA L1 data are shown in Fig. 3, crossing over the
African continent with frame A. Convective clouds are located near the
Equator, and a high fraction of cirrus clouds is present in this frame. The
94 GHz radar reflectivity is sensitive to both cloud and precipitation
particles. However, the attention by liquid hydrometeors is higher than that
of the precipitation radar of the global precipitation measurement (GPM)
system. Doppler velocity is not affected by the attenuation.
CPR data of the JAXA L1 data for frame A over the African
continent (a), with radar reflectivities (b) and Doppler velocities (c). The units of contours are dBZ (b) and m s-1(c).
Surface clutter was considered as being based on the pulse response function
of CPR in the research product. The EarthCARE CPR has less surface clutter
than CloudSat; an example of expected surface clutter over the ocean is shown
in Fig. 4. It is possible to detect low clouds higher than 600 m. Expected
surface clutter was calculated to indicate the limitation of low-cloud
height and to provide realistic JAXA L1 data. Here, the normalized radar
cross-section of the surface was set to 10 dB over the ocean and 0 dB over
the land. An example of surface clutter over the ocean in the JAXA L1 data
is shown in Fig. 5. The normalized radar cross-section over oceans depends
on surface winds and sea surface temperature; that over land is more
complicated that over oceans and will be updated after the EarthCARE satellite launch.
Radar reflectivities due to surface clutter of CPR compared for
EarthCARE and CloudSat over the ocean.
Radar reflectivity of the standard data (a) and with (b) surface clutter over the ocean, based on the response function of CPR. The unit of contours is dBZ.
The observed Doppler velocity from the EarthCARE CPR would be expected to
have random errors because of the slight vibration of the instrument on the
satellite. The maximum Doppler velocity and its random errors were
determined by setting the pulse repetition frequency (PRF) of the CPR. CPR
has two modes of observation window in operation: low mode (-1 to 16 km)
at latitudes of 60–90∘ and high mode (-1 to 20 km)
at latitudes of 0–60∘ (Hagihara et al., 2022). The other alternative observation window is the middle mode between -1 and 18 km. The PRF changes in a range of 6100–7500 Hz with latitude and observation window because the PRF is determined by the satellite altitude (Fig. 1 of Hagihara et al., 2021). The observation window setting is based on that of Hagihara et al. (2021), who investigated random errors of Doppler velocity of CPR in different modes.
The Doppler velocity of an example of the JAXA L1 data is shown in Fig. 6
for observation windows of 16, 18, and 20 km. The 20 km window mode
reproduced the noisy Doppler velocity for ice and rain (Fig. 6b) relative to
the original L1 data. This mode has a small range of Doppler folding
velocity, and it is particularly difficult to retrieve the terminal velocity
of ice particles in cirrus clouds. The 16 km observation window yields
better performance of Doppler velocity for ice and rain than the other two
windows (Fig. 6d), and the top of cirrus clouds is located near an altitude
of 15 km. However, it is still possible to neglect high clouds above 16 km
over tropical regions. The uncertainty in Doppler velocity can be derived
from the observation window using the Joint-Simulator.
Examples of Doppler velocity for the standard L1 data using a 20 km observation window (a), high mode (b), 18 km observation window and middle mode (c), and 16 km observation window and low mode (d). The unit of contours is m s-1.
ATLID
ATLID is the 355 nm high-spectral-resolution lidar, which can observe 355 nm backscatter from Mie and Rayleigh scattering. The Mie scattering channel
with co-polarization is related to cloud and aerosol particles, and the
Rayleigh scattering channel with co-polarization is related to atmospheric
molecules. The total attenuated backscatter channel with cross-polarization
is related to the shapes of hydrometeors and aerosols.
EASE simulates the lidar signals of ATLID by considering the scattering and
attenuation of molecules, hydrometeors, and aerosols. The outputs of ATLID
are 355 nm total attenuated backscatters from Mie or Rayleigh scattering.
The effect of multiple scattering by liquid hydrometeors on lidar signals
was considered using a correction factor parameterized using Monte Carlo
simulation (Ishimoto and Masuda, 2002). We provided CALIPSO lidar signals of
532 nm with a depolarization ratio of only 532 nm. The parameterization of
the depolarization ratio for 532 nm signals is described by Roh et al. (2020).
Signals of ATLID data over the African continent for frame A (Fig. 3a) are
shown in Fig. 7. The attenuation by water clouds is more pronounced than
that for CPR below 5 km height. The Rayleigh channels show the backscatter
from atmospheric molecules (Fig. 7b), and the Mie channels show cloud and
aerosol distributions (Fig. 7c, d). Saharan dust is located within
10 and 20∘ N (Fig. 7c, d). For the validation of retrieval algorithms, 355 nm extinction coefficients are provided for liquid and ice clouds, dust, sulfate, sea salt, and black carbon/organic carbon, as well as the molecular extinction coefficient.
Examples of ATLID L1 data for frame A (Fig. 3a), showing combined
Rayleigh (Ray) and Mie channels with cross-polarization (CR) (a), Rayleigh channels with co-polarization (CO) (b), and Mie channels with CR (c) and CO (d). Contours are shown on a base-10 log scale of the backscattering coefficients (m-1 sr-1).
For realistic ATLID L1 data, random noise was also considered in the
research product, with noise data provided by ESA. The noise model was based
on a Gaussian random noise from shot noise, dark count rate, and solar
background counts of ATLID.
Examples of ATLID signals with and without noise are shown in Fig. 8, with
two cloud layers related to cirrus clouds above 10 km and water clouds below
10 km. There is strong attenuation in the water clouds, under which values
are undefined (Fig. 8a). The undefined values are filled with random noise in the simulation, and it is possible to misclassify the area under the cloud. Using this random noise, the retrieval algorithm developer can consider the expected random noise when retrieving physical variables related to aerosols and clouds.
Examples of 355 nm total attenuated backscattering coefficients of
ATLID of the standard product (a) and with random noise (b). The contour is a base-10 log scale of backscattering coefficients (m-1 sr-1).
MSI
The MSI is a passive sensor used to observe infrared and reflected solar
radiances, with seven channels at 0.67, 0.865, 1.65, 2.21, 8.80, 10.8, and
12.0 µm. The total pixel number is 384 in the direction orthogonal to the satellite orbit. The approximated nadir location is in the 102nd pixel. MSI signals were calculated by RSTAR (System for Transfer of
Atmospheric Radiation; Nakajima and Tanaka, 1986, 1988) as the sensor
simulator. RSTAR (Nakajima and Tanaka, 1986) derives the solution of the
discrete-ordinate method using Eigenspace transformations of symmetric
matrices. RSTAR is a general package for simulating radiation fields in the
atmosphere–land–ocean system at wavelengths of 0.17–1000 µm.
Monochromatic intensity and intensity with a finite range of wavelengths can
be calculated, as required for channels with significant gas absorption.
Three streams were set in each hemisphere in the Joint-Simulator (i.e., the
six-stream method).
We used a fixed wavelength for each channel as the default setting in the
JAXA L1 data. The units of channels are radiances (Wm-2sr-1µm-1) for 0.67, 0.865, 1.65, and 2.21 µm. The unit for the other channels is brightness
temperature (K). Optical depths of clouds and aerosols were calculated to
validate the algorithms. Examples of MSI L1 data for seven channels over the
ocean are shown in Fig. 9 for frame F. High clouds are located near latitude
33∘ S, with strong reflection in the 0.67 µm channel and low brightness temperatures in the 8.80, 10.8, and 12.0 µm channels.
Examples of MSI data over the ocean for frame F for seven channels
at 0.67 (a), 0.865 (b), 1.65(c), 2.21 (d), 8.80 (e), 10.8 (f), and 12.0 µm(g). The contours of the upper panels are radiance (Wm-2sr-1µm-1) and those in the
bottom panels are temperatures (K).
The MSI has a spectral distortion termed the “SMILE effect”, which can be
considered using the shifted response function in the spectral domain,
depending on the across-track pixel in the swath. MSI is known to be
affected by the SMILE effect in the 0.67, 0.865, 1.65, and 2.21 µm
channels. The shift of wavelength in the 0.67 and 1.65 µm channels is
more obvious than that in the 0.865 and 2.21 µm channels.
We introduced the response function of MSI to reproduce the SMILE effect in
the Joint-Simulator and simulated two channels of MSI in the research
product. We investigated its effect on the radiance of the 0.67 and 1.65 µm channels. Two sets of the shifted response functions for the SMILE effect for these channels are shown in Fig. 10. These channels are used to retrieve cloud properties; the 0.67 µm channel is primarily sensitive to cloud optical thickness and the 1.65 µm channel to cloud effective radius (e.g., Platnick et al., 2017).
Response functions of MSI (considered the SMILE effect) for the
0.67 µm channel (band 1) (a) and the 1.65 µm channel (band 3) (b). Colors indicate MSI pixel numbers.
Differences between the response function on the nadir and that with the
SMILE effect for the descending scene of the satellite are shown in Fig. 11.
For the 0.67 µm channel, there are large differences on the left side of the satellite direction, with signals with the SMILE effect being
underestimated relative to the simulation using the fixed response function.
The difference is greatest at the edge of the swath with spectral
distortion, where the maximum difference is 5.76 radiances (Wm-2sr-1µm-1). The difference in the right half of the swath is not greater than that on the other side.
Examples of differences between MSI signal with the response function on the nadir and with the SMILE effect for the 0.67 µm (band 1) (a) and 1.65 µm (band 3) (b) channels over the ocean, for the descending mode where the satellite is moving southwestward. The base is the MSI signal with the response function on the nadir. The solid line is the location of the nadir. The units of contours are radiances (Wm-2sr-1µm-1).
For the 1.65 µm channel, the differences in radiance are smaller than those of the 0.67 µm channel. The SMILE effect causes a positive bias on the right and a negative bias on the left of satellite direction in the 1.65 µm channel, with a different pattern to the 0.67 µm channel. Wang et al. (2022) investigated the SMILE effect for the cloud retrieval algorithm for water and ice clouds over the ocean using these MSI data.
BBR
BBR has two channels of 0.25 and 50 µm for estimating the total
radiative flux at the top of the atmosphere (TOA) and 0.25 and 4 µm for the short-wave radiative flux at TOA with 10 km horizontal sampling. The long-wave flux at TOA is obtained by subtracting the short-wave flux from
the total flux. There are three view modes of nadir, forward, and backward
in the BBR. Only the nadir mode was calculated for the JAXA L1 data.
Radiative fluxes were simulated by MSTRN-X (Sekiguchi and Nakajima, 2008).
MSTRN-X uses two-stream approximation and the correlated k-distribution
(CKD) methods to model gas absorption using quadrature points and weights.
MSTRN-X considers 28 species compiled in HITRAN2004, and the radiative
transfer solver uses a two-stream approximation. MSTRN-X is also used as the
radiation scheme for NICAM simulations (Satoh et al., 2014). We did not
consider the radiative effect of aerosols in the NICAM simulation, so we
calculated aerosol transport using NICAM–SPRINTARS with coarse resolution.
BBR data are produced using simulated data with aerosols.
BBR data comprise short-wave and long-wave fluxes with downward and upward
directions at TOA and the surface; these data are to be used for validation.
We also added vertical profiles of short-wave/long-wave heating rates with
500 m vertical resolution and the optical depth at 532 nm.
BBR data are intended to be used to evaluate the radiative transfer
calculation from retrieved products of vertical profiles of clouds and
aerosols. Examples of BBR data and their relation to the CPR signals in JAXA L1 data are shown in Fig. 12. Multi-layer clouds were located in the
southern part of the orbit between 32 and 30∘ S (Fig. 12a, b), where the outward short-wave fluxes at TOA are large due to the multi-layer clouds having stronger reflectance than cirrus clouds. Short-wave heating in upper cloud layers and long-wave heating/cooling near the cloud layers are reasonably simulated (Fig. 12c, d). Note that the
horizontal resolution of CPR is 500 m and differs from the resolution of the
BBR signals, where the short-wave/long-wave heating rates are calculated
with a 10 km resolution.
Examples of BBR simulations (a) and corresponding CPR signals (b). Short-wave (c) and long-wave (d) radiative heating rates are also provided as JAXA L1 data for use in validation. The units of contours are dBZ (b) and K d-1(c, d).
Future improvements
We have introduced the JAXA simulated EartCARE L1 data that are currently
distributed to L2 algorithm developers. Although these data are useful, we
have plans for improvement, as discussed below.
High-resolution experiments
The EarthCARE CPR has a larger sampling volume than the ground observation
and a fast movement. The inhomogeneous distribution of hydrometeors within
the instantaneous field of view (IFOV) caused significant Doppler velocity
biases (Schutgens, 2008; Kollias et al., 2018). The effect of the
inhomogeneous distribution in the IFOV on the Doppler velocity is different
in the along-track direction and is referred to as the non-uniform beam
filling effect (NUBFs). The impact of NUBFs on the Doppler velocity
accuracy should be investigated for its improvement.
The NICAM simulation was undertaken using a 3.5 km horizontal resolution
over the global domain for JAXA L1 data. However, simulation data with
higher resolution than the horizontal CPR sample are desirable for L2
algorithm developers and not necessarily for the global domain. Therefore,
we undertook a regional high-resolution simulation using ASUCA (A System
based on a Unified Concept for the Atmosphere; Ishida et al., 2022). ASUCA
is a regional operational model of the Japan Meteorological Agency (JMA). We
conducted a simulation using 100 m horizontal resolution with ASUCA over the
Kanto area within the ULTIMATE (ULTra-sIte for Measuring Atmosphere of Tokyo
metropolitan Environment) research project (Satoh et al., 2022). We prepared
three cases in September 2019, covering an intensive observation period by
cloud radar from ground level.
Horizontal distributions of precipitation between ground radar observations
and the ASUCA simulation are shown in Fig. 13 for Typhoon Faxai. ASUCA
reproduced detailed structures of rain bands similar to observations. The
Joint-Simulator simulated the cross-section of radar reflectivity and
Doppler velocity of EarthCARE (Fig. 13c, d). The higher-resolution
experiment reproduced a more detailed structure of the eyewall system of the
cyclone, but the domain size was limited by computational resources. These
data were used in the production of new JAXA L1 data to investigate NUBFs of
CPR as the research product. We evaluated the ASUCA simulations using
intensive ground observations, data from which are also helpful in
validation of the EarthCARE satellite after launch.
Example of the ASUCA simulation for Typhoon Faxai, with horizontal distributions of observed precipitation (a), the ASUCA simulation (b), cross-sections of radar reflectivity (c), and Doppler velocity (d). Panels (c) and (d) pertain to the red line in panel (b). The units of contours are
mm h-1(a, b), dBZ (c), and m s-1(d).
3D radiation
In the EarthCARE research product, 3D cloud fields will be constructed using
the three sensors, CPR, ATLID, and MSI. Currently, JAXA L1 data are based on
a 1D radiation calculation. For study of the effect of 3D fields, 3D
radiation calculations are needed. Okata et al. (2017) developed the 3D
radiation model (MCstar) using a Monte Carlo method and investigated the 3D
radiation effect of 3D cloud fields constructed by CPR of CloudSat and
Moderate Resolution Imaging Spectroradiometer/AQUA data on the A-train. We
plan to implement MCstar in the Joint-Simulator and produce new MSI and BBR L1 data based on the 3D radiation calculation with high-resolution
simulation data. Input data of MCstar will be the ASUCA simulation with 100 m horizontal resolution.
Summary
JAXA EarthCARE synthetic data (JAXA L1 data) were compiled using the global storm-resolving model (GSRM) NICAM simulation with 3.5 km horizontal resolution, and the Joint-Simulator. JAXA L1 data are intended to support the development of JAXA retrieval algorithms for the EarthCARE sensor before launch of the satellite. The expected orbit of EarthCARE and horizontal sampling of each sensor were used to simulate the signals. EarthCARE has four instruments: 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 EarthCARE CPR is the first atmospheric radar in space with Doppler
capability. It has better radar sensitivity with a larger antenna than the
previous CPR aboard CloudSat. JAXA L1 data are considered using the same
vertical and horizontal sampling as CPR. Surface clutter over both the ocean
and land was added based on the response function of CPR in the research
product. Expected random errors in Doppler velocity were considered based on
three observation windows (Hagihara et al., 2021).
ATLID is the 355 nm high-spectral-resolution lidar with three channels: the
Mie channel with co-polarization, the Rayleigh channel with co-polarization,
and the total channel (Mie and Rayleigh channels) with cross-polarization.
JAXA L1 data include these three data channels related to clouds, aerosols,
and atmospheric molecules. The Mie channel with co-polarization is related
to clouds and aerosols and the Rayleigh channel to scattering from
atmospheric molecules. For validations, extinction coefficients were
separated between clouds and aerosols. The 534 nm backscatter was also
simulated for comparison with CALIPSO. ATLID data were considered with
random noise based on instrument settings for the research product.
MSI is the multi-spectral imager for the observation of emitted infrared and
reflected solar radiances and is used to construct 3D cloud scenes using two
active sensors. MSI data are calculated using fixed-wavelength data as
default data. We investigated the SMILE effect using the research product
for 0.67 and 1.65 µm wavelengths, with data based on the response
function depending on pixel number.
The BBR is a multi-angle broadband radiometer with three telescopes with
nadir, forward, and backward modes. MSI test data have only the nadir mode
and are simulated by the same radiation code as that of NICAM, with 10 km
horizontal sampling. Optical depth and vertical profiles of the heating rate for
short-wave and long-wave radiation were added.
We have plans for improvement of the JAXA L1 data for the investigation of
NUBFs on the accuracy of Doppler velocity. Higher-resolution data are
required for the distribution of Doppler velocities with <500 m
horizontal sampling size. We undertook regional simulations with 100 m
horizontal resolution over the Kanto region of Japan to produce new JAXA L1
data. To investigate the 3D radiation effect, we will add a 3D radiation
model to the Joint-Simulator and introduce MSI and BBR data for 3D radiation
to the research product.
After the launch of EarthCARE, its data will provide new insights for the
evaluation and improvement of GSRMs. According to the first GSRM
intercomparison study, vertical profiles of cloud ice and water vary among
models, although the horizontal distribution of OLR is consistent (Roh et
al., 2021). EarthCARE can provide more detailed information on the vertical
distribution of hydrometeors, with two active sensors of CPR and ATLID for
the validation of GSRMS.
The production of JAXA L1 data is related to the development of the
Joint-Simulator, which has been updated with detailed settings of instrument
information for EarthCARE. These updates will improve our understanding of
uncertainties in observations and retrieved values. Roh et al. (2023)
compared two microphysics schemes using a CPR on the ground and the expected
CPR of EarthCARE using the Joint-Simulator and found the expected Doppler
velocity of EarthCARE from the low-window mode would be better for
evaluating the characteristics of cloud microphysics schemes consistent with
ground observation data.
Satellite remote sensing data have been fruitful in understanding clouds and
aerosols. However, it is difficult to interpret the radiances or signals
from the sensors. Most of the modeling community uses the retrieved product
from the satellite data such as precipitation. This study implies how to
understand the directly observed signals and their uncertainty from
simulations of the specific instruments of the EarthCARE satellite. This
research would be helpful for the modeling community to improve and
constrain the physical parameters in the model using satellite observations.
Data availability
The standard products of the JAXA EarthCARE synthetic data are available from 10.5281/zenodo.7835229 (Roh et al., 2023). The CPR data with random errors of Doppler velocity in operation and surface clutters are also added on the same Zenodo site. The Joint-Simulator is available from
https://www.eorc.jaxa.jp/theme/Joint-Simulator/userform/js_userform.html (JAXA EORC, 2023).
Author contributions
WR drafted the paper and produced the JAXA L1 data. MS checked the
paper and helped with the production of JAXA L1 data. HT developed the
Joint-Simulator and supported JAXA L1 data production. SM did the ASUCA
simulations. TN did a global storm-resolving data simulation using NICAM. TK
led the Joint-Simulator development and provided feedback on the paper
draft.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “EarthCARE Level 2 algorithms and data products”. It is not associated with a conference.
Acknowledgements
The authors thank members of the JAXA EarthCARE science team and the
Joint-Simulator project. The authors also thank to ESA for providing the
measured value of response functions of EarthCARE/MSI. The authors thank
Toshi Matsui for providing the orbit and scan simulator. Computational resources
were partly provided by the National Institute for Environmental Studies.
Financial support
This work was supported by the EarthCARE satellite study commissioned by the Japan Aerospace Exploration Agency. Masaki Satoh and Woosub Roh were supported by a Grant-in-Aid for Scientific Research B (grant no. 20H01967) and the Program for
Promoting Technological Development of Transportation of the Ministry of
Land, Infrastructure, Transport, and Tourism (MLIT).
Review statement
This paper was edited by Robin Hogan and reviewed by two anonymous referees.
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