ADEOS-II and NASDA future programs

Five programs, i.e. TRMM, AMSR-E, ASTER, GOSAT and GCOM-W1 are going on in Japanese Earth Observation programs. ASTER has lost its short wave infrared channels. AMSR-E stopped its operation, but it started its operation from Sep. 2012. GCOM-W1 was launched on 18, May, 2012 and is operating well as well as TRMM and GOSAT. ALOS (Advanced Land Observing Satellite) was successfully launched on 24th Jan. 2006. ALOS carries three instruments, i.e., PRISM (Panchromatic Remote Sensing Instrument for Stereo Mapping), AVNIR-2 (Advanced Visible and Near Infrared Radiometer), and PALSAR (Phased Array L band Synthetic Aperture Radar). Unfortunately, ALOS has stopped its operation on 22nd, April, 2011 by power loss. GOSAT (Greenhouse Gas Observation Satellite) was successfully launched on 29, January, 2009. GOSAT carries 2 instruments, i.e. a green house gas sensor (TANSOFTS) and a cloud/aerosol imager (TANSO-CAI). The main sensor is a Fourier transform spectrometer (FTS) and covers 0.76 to 15 μm region with 0.2 to 0.5 cm^-1 resolution. SMILES (Super-conducting Millimeter wave Emission Spectrometer) was launched on September 2009 to ISS and started the observation, but stopped its operation on April 2010. After the unfortunate accident of ADEOS2, JAXA still have plans of Earth observation programs. Next generation satellites will be launched in 2012-2015 timeframe. They are, GCOM-C (ADEOS-2 follow on), and GPM (Global Precipitation Mission) core satellite. GPM is a joint project with NASA and will carry two instruments. JAXA will develop DPR (Dual frequency Precipitation Radar) which is a follow on of PR on TRMM. Another project is EarthCare. It is a joint project with ESA and JAXA is going to provide CPR (Cloud Profiling Radar). GCOM-C1 will be launched on fiscal 2016, GPM core satellite will be launched on 2014 and EarthCare will be launched on 2015. ALOS F/O satellites are divided into two satellites, i.e. SAR and optical satellites. The first one of ALOS F/O is called ALOS 2 and will carry L-band SAR. It will be launched on fiscal 2013. JAXA is planning to launch follow on of optical sensors on ALOS. GOSAT2 project is going to start and will be launched on 2018.

The Japan Aerospace Exploration Agency (JAXA) is pressing forward with Global Change Observation Mission (GCOM) for long-term monitoring of earth environment. GCOM consists of two series of medium-size satellites, GCOM-W (Water) and GCOM-C (Climate). The first satellite, GCOM-W with Advance Microwave Radiometer -2 (AMSR-2), was already launched in 2012 and has been observing continuously. GCOM-C which carries the optical radiometer, Second Generation Global Imager (SGLI), was launched on December 23, 2017. SGLI observation data will produce more than 20 scientific products such as cloud, aerosol, ocean color, vegetation, ice field and so on, and will contribute to improve the understanding of the global mechanism of carbon cycle and radiation budget. SGLI includes two radiometer units of Visible and Near Infrared Radiometer (VNR) and Infrared Scanning Radiometer (IRS), which perform a wide-spectral (380 nm-12 μm) optical observation with relatively high (250 m) spatial resolution. After the launch, the satellite and SGLI instruments were initialized and commissioned for routine operations during a threemonth period called in-orbit checkout. SGLI-VNR performance tests included internal light calibration, solar diffuser calibration, dark signal calibration and electrical calibration. In addition to that, solar angle correction maneuvers were performed within L+2 weeks and lunar calibration maneuvers were performed each synodic period. 90-deg. yaw maneuvers were also performed. In this paper, the in-orbit commissioning activities of SGLI-VNR will be described. Especially we focus on the internal light calibration and solar diffuser calibration results during a commissioning phase.

Three programs, i.e. TRMM, ADEOS2 and ASTER, are going on in Japanese Earth Observation programs. TRMM and ASTER are operating well, though TRMM operation after June 2005 is still unclear. ADEOS2 was failed, but AMSR-E on Aqua is operating. After the unfortunate accident of ADEOS2, JAXA still have plans of Earth observation programs. The first satellite which will be launched is ALOS (Advanced Land Observing Satellite). The tentative launch date is 1st, Sep. 2005. ALOS will carry three instruments, i.e., PRISM (Panchromatic Remote Sensing Instrument for Stereo Mapping), AVNIR-2 (Advanced Visible and Near Infrared Radiometer), and PALSAR (Phased Array L band Synthetic Aperture Radar). PRISM is a 3 line panchromatic push broom scanner with 2.5m IFOV. AVNIR-2 is a 4 channel multi spectral scanner with 10m IFOV. PALSAR is a full polarimetric active phased array SAR. PALSAR has many observation modes including full polarimetric mode and scan SAR mode. Next generation satellites will be launched in 2007-2009 timeframe. They are GOSAT (Greenhouse Gas Observation Satellite), GCOM-W and GCOM-C (ADEOS-2 follow on), and GPM (Global Precipitation Mission) core satellite. GOSAT will carry 2 instruments, i.e. a green house gas sensor and a cloud/aerosol imager. The main sensor is a Fourier transform spectrometer (FTS) and covers 0.76 to 15 μm region with 0.1 to 0.2 cm-1 resolution. GPM is a joint project with NASA and will carry two instruments. JAXA will develop DPR (Dual frequency Precipitation Radar) which is a follow on of PR on TRMM. Another project is EarthCare. It is a joint project with ESA and JAXA is going to provide CPR (Cloud Profiling Radar). Discussions on future Earth Observation programs have been started including discussions on ALOS F/O.

The Second-generation Global Imager (SGLI) onboard the Global Change Observation Mission – Climate (GCOM-C) satellite launched on December 23, 2017, observes various geophysical parameters with the aim of a better understanding of the global climate system. As part of that aim, SGLI has great potential to unravel several uncertainties related to clouds by providing new cloud products along with several other atmospheric products related to cloud climatology, including aerosol products from polarization channels. However, a very little is known about the quality of the SGLI cloud products. This study uses data about clouds and global irradiances observed from the Earth’s surface using a sky radiometer and a pyranometer, respectively, to understand the quality of the two most fundamental cloud properties—cloud optical depth (COD) and cloud-particle effective radius (CER)—of both water and ice clouds. The SGLI-observed COD agrees well with values observed from the surface, although it agrees better for water clouds than for ice clouds, while the SGLI-observed CER exhibits poorer agreement than does the COD, with the SGLI values being generally higher than the sky radiometer values. These comparisons between the SGLI and sky radiometer cloud properties are found to differ for different cloud types of both the water and ice cloud phases and different solar and satellite viewing angles by agreeing better for relatively uniform and flat cloud type and for relatively low solar zenith angle. Analyses of SGLI-observed reflectance functions and values calculated by assuming plane-parallel cloud layers suggest that SGLI-retrieved cloud properties can have biases on the solar and satellite viewing angles, similar to other satellite sensors including the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, it is found that the SGLI-observed cloud properties reproduce global irradiances quite satisfactorily for both water and ice clouds by resembling several important features of the COD comparison, such as the better agreement for water clouds than for ice clouds and the tendency to underestimate (resp. overestimate) the COD in SGLI observations for optically thick (resp. thin) clouds.

Abstract. The Second-generation Global Imager (SGLI) onboard the Global Change Observation Mission – Climate (GCOM-C) satellite, launched on 23 December 2017, observes various geophysical parameters with the aim of better understanding the global climate system. As part of that aim, SGLI has great potential to unravel several uncertainties related to clouds by providing new cloud products along with several other atmospheric products related to cloud climatology, including aerosol products from polarization channels. However, very little is known about the quality of the SGLI cloud products. This study uses data about clouds and global irradiances observed from the Earth's surface using a sky radiometer and a pyranometer, respectively, to understand the quality of the two most fundamental cloud properties – cloud optical depth (COD) and cloud-particle effective radius (CER) – of both water and ice clouds. The SGLI-observed COD agrees well with values observed from the surface, although it agrees better for water clouds than for ice clouds, while the SGLI-observed CER exhibits poorer agreement than does the COD, with SGLI values being generally higher than the sky radiometer values. These comparisons between the SGLI and sky radiometer cloud properties are found to differ for different cloud types of both the water and ice cloud phases and different solar and satellite viewing angles by agreeing better for relatively uniform and flat cloud type and for relatively low solar zenith angle. Analyses of SGLI-observed reflectance functions and values calculated by assuming plane-parallel cloud layers suggest that SGLI-retrieved cloud properties can have biases in the solar and satellite viewing angles, similar to other satellite sensors including the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, it is found that the SGLI-observed cloud properties reproduce global irradiances quite satisfactorily for both water and ice clouds by resembling several important features of the COD comparison, such as better agreement for water clouds than for ice clouds and the tendency to underestimate (resp. overestimate) the COD in SGLI observations for optically thick (resp. thin) clouds.

The Second-generation Global Imager (SGLI) onboard the Global Change Observation Mission – Climate (GCOM-C) satellite launched on December 23, 2017, observes various geophysical parameters with the aim of a better understanding of the global climate system. As part of that aim, SGLI has great potential to unravel several uncertainties related to clouds by providing new cloud products along with several other atmospheric products related to cloud climatology, including aerosol products from polarization channels. However, a very little is known about the quality of the SGLI cloud products. This study uses data about clouds and global irradiances observed from the Earth’s surface using a sky radiometer and a pyranometer, respectively, to understand the quality of the two most fundamental cloud properties—cloud optical depth (COD) and cloud-particle effective radius (CER)—of both water and ice clouds. The SGLI-observed COD agrees well with values observed from the surface, although it agrees better for water clouds than for ice clouds, while the SGLI-observed CER exhibits poorer agreement than does the COD, with the SGLI values being generally higher than the sky radiometer values. These comparisons between the SGLI and sky radiometer cloud properties are found to differ for different cloud types of both the water and ice cloud phases and different solar and satellite viewing angles by agreeing better for relatively uniform and flat cloud type and for relatively low solar zenith angle. Analyses of SGLI-observed reflectance functions and values calculated by assuming plane-parallel cloud layers suggest that SGLI-retrieved cloud properties can have biases on the solar and satellite viewing angles, similar to other satellite sensors including the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, it is found that the SGLI-observed cloud properties reproduce global irradiances quite satisfactorily for both water and ice clouds by resembling several important features of the COD comparison, such as the better agreement for water clouds than for ice clouds and the tendency to underestimate (resp. overestimate) the COD in SGLI observations for optically thick (resp. thin) clouds.

The concept study of new multi spectral imager has been showed. Multi spectral imager is the basic sensor for Earth remote sensing. The result of Global Imager (GLI) of Advanced Earth Observation Satellite -2 (ADEOS-2) showed us the various change of climate system. However, according to the progress of climate system change study, the reconsideration of observation channels is necessary. First, we studied the requirements of the next generation imager on the basis of GLI design considering the less observed parameters of the climate change. Then we found the necessity of the parameters related the human activities. Several parameters of each area, i.e., atmosphere, ocean, land, cryosphere, are selected. For the atmosphere, aerosol over land is emphasized. For the ocean, coastal change observation is proposed. For the land, advanced vegetation index is proposed. For the cryosphere, impurity of snow is emphasized. For the observation purposes, the proposed imager requirements are including the multi-angle polarization observation. And the resolution requirements of most channels are changed from 1 km to 250m. As the result of preliminary engineering study, the imager consists of a push broom imager for Visible and Near Infrared (VNIR) region, and a whisk broom imager for Shortwave Infrared (SWIR) and Thermal Infrared (TIR) region. Among the all channels, two channels, center wavelength 678 nm and 865 nm, are defined as multi-angle polarization observation channels. Each channel has fore, nadir, aft angles, also three polarization angles. The satellite orbit is about 800 km height sun-synchronous polar orbit to cover global area. The new sensor was named Second generation of Global Imager (SGLI). We are proposing to launch SGLI around 2010 on a satellite of Global Change Observation Mission (GCOM).

Abstract. Using observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) day–night band (DNB), we examined the feasibility of developing a gridded nighttime aerosol optical thickness (AOT) data set based on the spatial derivative of measured top-of-atmosphere attenuated upwelling artificial lights at night (ALAN) over the US, Middle East, and Indian Subcontinent regions for 2017. We also studied the potential of using NASA's standard operational Black Marble nighttime lights product suite (VNP46) for estimating the spatial derivatives of surface artificial-light emissions, which is one of the key lower boundary conditions for the retrieval process. The sensitivity of nighttime aerosol retrievals to observing conditions and different methods of estimating the spatial derivative of surface artificial-light emissions were also explored. Root-mean-square errors (RMSEs) of ∼ 0.15 and ∼ 0.18 and correlations of ∼ 0.8 and ∼ 0.6 were found between VIIRS nighttime AOT and Aerosol Robotic Network (AERONET) nighttime and daytime data, respectively, suggesting that the proposed gridded nighttime AOT retrievals have reasonable skill levels for potential data assimilation, air quality, and climate studies of significant events. We also found that NASA Black Marble products can be used to estimate the spatial derivative of surface artificial-light emissions for nighttime AOT retrievals over regions that are not frequently contaminated by aerosol plumes, such as the USA. This study demonstrated the feasibility of constructing a gridded nighttime AOT data, using artificial lights, for monitoring of nighttime aerosol events over large spatial and temporal domains. Given the deployment of VIIRS instruments (currently in orbit and forthcoming) aboard the NOAA Joint Polar Satellite System (JPSS) series satellites, this study can be viewed as a precursor for gridded nighttime AOT retrievals at both regional and global scales in the future. We also show that the use of the NASA Black Marble products, which would greatly save the processing time of this method, is challenging over regions with frequent aerosol pollution, such as the Indian Subcontinent, and further exploration is required.

The Atmospheric Infrared Sounder (AIRS) was launched in 2002, along with two companion microwave sounders. This AIRS sounding suite is the most advanced atmospheric sounding system to date, with measurement accuracies far surpassing those of current weather satellites. From its Sun synchronous polar orbit, the AIRS system provides more than 300,000 all-weather soundings covering more than 90% of the globe every 24 hours. Much of the post-launch period has been devoted to optimizing the retrieval system used to derive atmospheric and other parameters from the observations and to validate those parameters. The geophysical parameters have been produced since the beginning of 2003 - the first data were released to the public in mid-2003, and future improved versions be released periodically. The ongoing calibration/validation effort has confirmed that the system is very accurate and stable. There are a number of applications for the AIRS products, ranging from numerical weather prediction - where positive impact on forecast accuracy has already been demonstrated, to atmospheric research - where the AIRS water vapor products near the surface and in the mid and upper troposphere promise to make it possible to characterize and model phenomena that are key for atmospheric processes, from weather patterns to long-term phenomena, such as interannual variability and climate change.

Current spectrometers provide high-quality absorption spectra from both ground-based direct sun measurements and spaceborne backscatter measurements. Accurate retrievals of atmospheric CO2 concentrations from these measured spectra are fundamental for modelling large-scale atmosphere-surface exchange fluxes. When retrieving CO2 concentrations from measured spectra, high-quality spectroscopic reference data are essential to drive radiative transfer simulations and to enable accurate retrievals. Here, we investigate how various modern molecular absorption cross-section datasets affect CO2 retrievals in the 1.6 μm and 2 μm wavelength regions. This includes recent parameter sets derived from laboratory measurements at the German Aerospace Center (DLR e.V.) for line-mixing parameterizations (Birk et al., 2024) with separate continuum data. We compare these new data to those from HITRAN 2020 (Gordon et al., 2022) with and without speed-dependent Voigt profile extension as well as to the ABSCO tables (Benner et al., 2016), (Devi et al., 2016).To evaluate the quality of the spectroscopic databases, we submit high-resolution direct-sun spectra collected by the TCCON (Total Carbon Column Observing Network) spectrometer at Karlsruhe to our RemoTeC retrieval algorithm under variation of the driving spectroscopic parameters. We evaluate systematic spectral residuals as well as spurious dependencies of the retrieved CO2 columns on slant airmass. We further retrieve CO2 separately from the P- and R-branches within a spectral window to assess potential mismatches. In addition, we use one year of GOSAT satellite measurements to investigate whether and how differences in CO2 concentrations retrieved under variation of the spectroscopic parameters show dependencies on geophysical parameters such as latitude, season or surface type. Our analyses show that the DLR cross-sections lead to noticeable improvements in spectral line modelling in the strong 2 µm band, which in turn reduces airmass dependencies. Including the CO2 continuum from the DLR dataset further reduces airmass-dependent biases, although this improvement is not reflected in the spectral residuals. Using the ABSCO tables results in residuals comparable to those using the HITRAN 2020 cross-sections. However, the airmass bias is low and comparable to the DLR cross-sections. In the weaker 1.6 µm bands, fit quality is comparable across all datasets, with small differences in airmass dependence. For the 2 μm band, the comparison of P- and R-branch retrievals reveals differences of up to 0.2 % and a pronounced airmass-dependent bias in the HITRAN 2020 R-branch. This inter-branch difference vanishes in the 1.6 μm bands when using DLR cross-sections but persists for HITRAN. Furthermore, retrievals with DLR cross-sections show significantly improved agreement between the 2 µm and 1.6 μm bands compared to HITRAN. In the GOSAT analysis, in addition to airmass-dependent effects leading to latitudinal and seasonal biases, we found surface albedo to strongly correlate with differences in retrieved CO2 concentrations.Benner et al., 2016: https://doi.org/10.1016/j.jms.2016.02.012Devi et al., 2016: https://doi.org/10.1016/j.jqsrt.2015.12.020Birk et al., 2024: https://elib.dlr.de/208834/Gordon et al., 2022: https://doi.org/10.1016/j.jqsrt.2021.107949

NOTE: This dataset has been superseded by GPCP Version 2.3, which is available in RDA dataset ds728.4 [https://rda.ucar.edu/datasets/ds728.4/]. Users are advised to transition to this updated dataset.\n\n This dataset contains Version 2.2 of the Global Precipitation Climatology Project (GPCP) combined satellite-gauge precipitation estimate and combined satellite-gauge error estimate. The data are monthly analyses defined on a global 2.5 degree by 2.5 degree longitude/latitude grid and cover the period January 1979 to (delayed) present. A monthly climatology (1979-2011) is also available.\n\n Please note that the original binary data were written using the big endian representation of unformatted binary words. Users reading this data on little endian platforms, therefore, will need to byte swap the data.\n\n The GPCP was established by the World Climate Research Program (WCRP) and subsequently attached to the Global Energy and Water Exchange program (GEWEX) to address the problem of quantifying the distribution of precipitation around the globe over many years. The general approach is to combine the precipitation information available from each of several sources into a final merged product, taking advantage of the strengths of each data type. The passive microwave estimates are based on Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) data from the series of Defense Meteorological Satellite Program (DMSP, United States) satellites that fly in sun-synchronous low-earth orbits at 6am / 6pm. The infrared precipitation estimates are computed primarily from geostationary satellites (United States, Europe, Japan), and secondarily from NOAA series polar-orbiting satellites (United States). Additional low-Earth orbit estimates include Atmospheric Infrared Sounder (AIRS) data from the NASA Aqua, and Television Infrared Observation Satellite Program (TIROS) Operational Vertical Sounder (TOVS) and Outgoing Longwave Radiation Precipitation Index (OPI) data from the NOAA series satellites. The precipitation gauge data are assembled and analyzed by the Global Precipitation Climatology Centre (GPCC) of the Deutscher Wetterdienst.\n\n The Version 2.2 Data Set contains data from the following contributing centers: \n * GPCP Polar Satellite Precipitation Data Centre - Emission (SSM/I and SSMIS emission estimates)\n * GPCP Polar Satellite Precipitation Data Centre - Scattering (SSM/I and SSMIS scattering estimates)\n * GPCP Geostationary Satellite Precipitation Data Centre (GPI and OPI estimates)\n * NASA/GSFC Sounder Research Team (TOVS and AIRS estimates)\n * GPCP Global Precipitation Climatology Centre (precipitation gauge analyses)\n\nRequest to users from the data authors: The GPCP datasets are developed and maintained with international cooperation and are used by the worldwide scientific community. To better understand the evolving requirements across the GPCP user community and to increase the utility of the GPCP product suite, the dataset authors request that a citation be provided for each publication that uses the GPCP products. Please email the citation to george.j.huffman@nasa.gov or david.t.bolvin@nasa.gov. Your help and cooperation will provide valuable information for making future enhancements to the GPCP product suite.

The constellation of spaceborne passive microwave (MW) sensors, coordinated under the framework of the Precipitation Measurement Missions international agreement, continuously produces observations of clouds and precipitation all over the globe. The Goddard profiling algorithm (GPROF) is designed to infer the instantaneous surface precipitation rate from the measured MW radiances. The last version of the algorithm (GPROF-2014)—the product of more than 20 years of algorithmic development, validation, and improvement—is currently used to estimate precipitation rates from the microwave imager GMI on board the GPM core satellite. The previous version of the algorithm (GPROF-2010) was used with the microwave imager TMI on board TRMM. In this paper, TMI-GPROF-2010 estimates and GMI-GPROF-2014 estimates are compared with coincident active measurements from the Precipitation Radar on board TRMM and the Dual-Frequency Precipitation Radar on board GPM, considered as reference products. The objective is to assess the improvement of the GPM-era microwave estimates relative to the TRMM-era estimates and diagnose regions where continuous improvement is needed. The assessment is oriented toward estimating the “effective resolution” of the MW estimates, that is, the finest scale at which the retrieval is able to accurately reproduce the spatial variability of precipitation. A wavelet-based multiscale decomposition of the radar and passive microwave precipitation fields is used to formally define and assess the effective resolution. It is found that the GPM-era MW retrieval can resolve finer-scale spatial variability over oceans than the TRMM-era retrieval. Over land, significant challenges exist, and this analysis provides useful diagnostics and a benchmark against which future retrieval algorithm improvement can be assessed.

The GPM core satellite will be ready to launch in February 2014, less than 7 months after the end of IGARSS2013 symposium. In the pre-launch era, several international validation experiments such as LPVEX (Light Precipitation Validation Experiment), MC3E (Midlatitude Continental Convective Clouds Experiment), and IFloodS (Iowa Flood Studies) have already generated a substantial set of measurements that continue to contribute to the development and test of pre-launch GPM algorithms. Following launch, it is expected that GPM ground validation will focus on evaluating precipitation data products, generated by a constellation of GPM satellites as well as assumptions made in algorithms. This paper presents simple concepts of dual-polarization radar observation strategies and products that can be generated for the post launch era of the GPM program, especially when there are satellite overpasses. The use of microphysical retrievals from ground-based radars and in-situ observations for validating the retrievals from space based observations is the main focus of this paper.

The technology of Synthetic Aperture Radars (SAR) is growing at a rapid pace in the last decade. The advancements in SAR opens doors to explore more scientific problems which were considered impactical in the past. The study of meteorological targets using spaceborne SAR platforms is an emerging field of study. In the past, spaceborne monostatic SAR for observing precipitation echoes was studied. In this paper, a bistatic architecture of the SAR system is considered for studying the precipitation target using signal simulations. The precipitation radar parameters on-board the GPM core satellite is considered for the simulation study. The architecture of the bistatic SAR considered is introduced and the various factors considered for a bistatic SAR is studied in detail. This simulation work currently being researched on shows the initial results. The results presented will aid in understanding the precipitation echoes observed from spaceborne SAR platforms.

This paper focuses on the inter-comparison of dual-frequency Precipitation radar(DPR) on board GPM core satellite and ground-based radar observations during the extreme precipitation estimation in southern China. Time and location matching technology is used to match DPR data and radar data. A Gauss function is applied compute sample weights to obtain a more accurate result. By distinguishing convective rainfall and stratiform rainfall, different Z-R relationships are adopted to estimate precipitation. The results are as follows: 1) DPR_HS, DPR_MS and DPR_NS show low correlation coefficient so that they are poorly correlated with radar for instantaneous precipitation rates; 2) DPR_HS, DPR_MS and DPR_NS show much more occurrences of light rainfall rates than radar; 3) DPR_MS occurrences of rainfall rates are similar with DPR_NS.