The cloud flag (CLFG) product from the second-generation global imager onboard the Global Change Observation Mission-Climate (GCOM-C) provides cloud/clear discrimination. It is generally difficult to distinguish clouds over surfaces covered with snow/ice. CLFG was updated in 2021 (Ver. 3) through the addition of a deep neural network (DNN) method to the previous Ver. 2 algorithm. We evaluated both versions against collocated CloudSat radar and CALIPSO lidar for October 2018-June 2019 at high latitudes in the Northern Hemisphere (over 600,000 matchup pairs, around 65–75 degrees North latitude). The results show that CLFG Ver. 3 achieved a cloud detection accuracy of 72.4%, a 2.0% improvement over Ver. 2. This improvement is attributed to the significant improvement of 8.1% for Land/Daytime scenes, where the DNN method successfully detects clouds over snow- or ice-covered surfaces which are missed by Ver. 2 algorithm. Limiting the samples to snow-/ice- covered surfaces yields an even greater improvement of 9.0% for Land/Day scenes and a net 3.1% increase across all scenes. Challenges remain in cloud detection at night when only thermal infrared channels are available (3.9% and 8.2% decrease in Overall Accuracy comparing daytime for Land and Ocean respectively). Cloud discrimination over Greenland is particularly challenging compared with other land regions, because the radiative contrast between clouds and the high-albedo surface is low. This study also examined the uncertainty information embedded in CLFG which enables user-tailored data extraction. We found that users can extract pixels with higher reliability in Ver. 3 by selecting only the most certain classes.

The NASA and the JAXA performed orbit boost maneuvers in November 2023 that raised the altitude of the Global Precipitation Measurement (GPM) Core Observatory from 407 km to 442 km to extend its lifetime. Effects of the orbit boost on the spaceborne precipitation radar have been investigated in the Tropical Rainfall Measuring Mission (TRMM). This study evaluates effects on Dual-frequency Precipitation Radar (DPR) observations due to the orbit boost. Spatial resolution at the nadir and swath width is changed at 5.04km×5.04km and 255.8 km at the satellite altitude of 407 km to 5.48km×5.48km and 277.9 km at the satellite altitude of 442 km, respectively. Observation data confirmed that changes in the sampling were larger in the cross-track direction (about 5 km to 5.5 km at the nadir). It was found that the DPR coverage tendency was changed by the GPM orbit boost. By analyzing measured radar reflectivity factor at storm top height over the ocean, the sensitivity degradation was found for about 0.8-0.9dB for KuPR, and about 0.7-0.9dB for KaPR.

Japan Aerospace Exploration Agency (JAXA) has developed and operated a series of passive microwave imager, called the Advanced Microwave Scanning Radiometer (AMSR) series, that to contribute to monitoring and projection of global water cycle variation and application in operational utilization. From the first generation of AMSR series, AMSR and AMSR-E, and the second generation, AMSR2, we successfully achieved to archive more than 20 years consistent and continuous data record. AMSR2 has been operating on the A-train orbit since July 2012 and it also joined to the Global Precipitation Measurement (GPM) constellations to provide valuable passive microwave observation in the afternoon orbit for producing global rainfall maps. JAXA is currently developing the third generation of AMSR series, AMSR3, with additional high-frequency channels in 166 and 183 GHz to be launched in Japanese Fiscal Year of 2024.

Recently, calibration methods of the ground-based weather radars with reference to spaceborne precipitation radars, such as the Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR), have attracted lot of attention, because of their cost effectiveness and near real-time inputs. This paper describes evaluations for radar reflectivity factors (Z-factors) of S-band and C-band ground-based radars over the Indian region with reference to the GPM/DPR observations. By statistical analyses, using a matching program between the ground-based radar and the GPM/DPR, the S-band ground-based radar measurements at Cherrapunji, Meghalaya, India tended to be significantly underestimated. In this case, a mean bias in the Z-factors was 13.203dBZ was computed. On the other hand, the C-band ground-based radar at Shar, Sriharikota, India was well-corresponded with the GPM/DPR and the mean bias tended to be small (0.688 dBZ).

To enhance precipitation monitoring, this study attempts integration among Global Precipitation Measurement (GPM) products such as the Dual-Frequency Precipitation Radar (DPR) and the merged satellite precipitation product, and ground-based radars. Here, radar reflectivity and rainfall composites are produced using three ground-based radars in Fiji. Calibration correction factors of the ground-based radars are calculated with reference to the GPM/DPR in the radar reflectivity composite. In the rainfall composite, two types of correction factors are calculated. One is a factor of the ground radars with the GPM/DPR, and the other is a factor of the merged satellite precipitation product with the ground-based radars. For making the composite, a weighting function is implemented with consideration of a beam height in the ground-based radar. These techniques can be helpful for the precipitation monitoring, because it allows seamless monitoring from the ground-based radar's observation range to satellite observation areas.

Beam matching accuracy between KuPR and KaPR was evaluated by external calibration using ARC. The averaged distance between the beam centers of KuPR and KaPR is 318 m for Rx and 269 m for Tx before the beam matching parameters were changed. After the change, the distance is 92 m for Rx and 85 m for Tx. The distance between the beam matched footprints of KuPR and KaPR during normal observation were confirmed using the geolocation. In the inner swath, the averaged distance before and after the scan pattern change is 140 m and 32 m, respectively. In the outer swath of KuPR angle-bin number 38 to 49 after the scan pattern change, the averaged distance is 48 m. However, the KuPR beams at angle-bin number 1 to 12 are slightly shifted from the corresponding KaHS beams in the along-track direction. The magnitude of misalignment is 301 m.

The Earth Clouds, Aerosol, and Radiation Explorer (EarthCARE) is a satellite mission jointly developed by the Japan Aerospace Exploration Agency (JAXA) and the European Space Agency (ESA). One challenging feature of this mission is the observation of Doppler velocity by the Cloud Profiling Radar (EC-CPR). The Doppler measurement accuracy is affected by random errors induced by Doppler broadening due to the finite beamwidth and Doppler folding caused by the finite pulse repetition frequency. We investigated the impact of horizontal (along-track) integration and unfolding methods on the reduction of Doppler errors, in order to improve Doppler data processing in the JAXA standard algorithm. We simulated EC-CPR-observed Doppler velocities from pulse-pair covariances with the latest EC-CPR specifications using the radar reflectivity factor and Doppler velocity fields simulated by a satellite data simulator and a global cloud system resolving simulation. Two representative cases of a cirrus cloud and precipitation were examined. In the cirrus cloud case, the standard deviation of random error was decreased to 0.5 m/s for −10 dB${Z} _{\mathbf {e}}$after 10-km horizontal integration. In the precipitation case, large falling speeds of precipitation caused Doppler folding errors due to larger Doppler velocities than that in the cirrus cloud case. When${Z} _{\mathbf {e}}$is larger than −15 dB${Z} _{\mathbf {e}}$, the standard deviations of random error were less than 1.0 m/s after 10-km horizontal integration and unfolding.

This article describes four-year calibration results of the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement (GPM) Core Observatory. The calibration method basically follows the method that was used to calibrate the precipitation radar (PR) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. However, both the hardware and data processing method for calibration are improved by taking advantage of the lessons learned from the PR’s calibration. Since the response of the radar receivers was found to depend on the waveform, the active calibrator was improved in such a way that the external calibration can be performed with both continuous and pulse waves. The methods for evaluating the calibration data were also improved. Instead of assuming a Gaussian antenna pattern, the effective beamwidths were determined by assuming an antenna pattern created by the Taylor distribution that was used to design the antennas. The results of the calibration including these improvements provide the new precise parameters of DPR’s calibration. The new parameters increased the Ku-band precipitation radar’s (KuPR’s) radar reflectivity factor ($Z$) by about 1.3 dB and that of the Ka-band precipitation radar (KaPR) by about 1.2 dB from the precalibrated$Z$values, and the minimum detectable radar reflectivities were 15.46, 19.18, and 13.71 dBZ for KuPR, matched beam of KaPR, and high-sensitivity beam of KaPR, respectively. After applying the new calibration methods to both DPR and PR, normalized radar cross sections ($\sigma ^{0}$) from the DPR and PR agree with each other.

An accurate estimation of precipitation with high temporal and spatial resolution on a global basis are useful for not only scientific research but also flood warning, hydrological applications, weather forecast and so on. GSMaP provides global precipitation with 0.1 degree and 1 hour resolution and has been used widely in more than thousands of organizations for operational use. This GSMaP system has several products including GSMaP_ MVK and GSMaP_Gauge and the GSMaP_Gauge is the best product among them in terms of accuracy though it has the latency of a few days. In this study, some algorithm modifications in the GSMaP_Gauge algorithm are reported, focusing on the precipitation over the desert in central Australia. In the current algorithm, sometimes precipitation over desert and an artificial precipitation pattern are detected. Precipitation distribution over desert in Australia from GSMaP _MVK, GSMaP_Gauge and ground based measurement are compared in this study, and some modifications in the GSMaP_Gauge algorithm are discussed. In the new version, it is shown that the modified results show more realistic pattern and it is compared with the ground rain gauge measurements.

We have improved a rainfall retrieval algorithm over coast for the Advanced Microwave Sounding Unit (AMSU) in order to upgrade to the Global Satellite Mapping of Precipitation (GSMaP) product version 8. As for the algorithm of product version 7, the zonal-mean monthly precipitation tends to overestimate low latitude coastal areas and to be a missing value the polar side of the winter hemisphere from 40 degrees latitude due to the low temperature screening. In order to improve these problems, we separated the coast into the seaward and landward coasts, and reconsidered Rain/No-Rain classification method, screening conditions and estimation method of each algorithm.

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

Dual-frequency Precipitation Radar (DPR) on the core satellite of Global Precipitation Measurement (GPM) mission has changed its scan pattern in May 2018. As the high-sensitivity measurement of KaPR has been moved to the outer swath from the inner swath, all pixels are measured by both KuPR and KaPR after the scan pattern change. Experimental product (DPR Version 06X; V6X in short) for the new scan pattern applies dual-frequency algorithm for the outer swath. In this study, precipitation rates and other outputs of V6X are analyzed. Dual-frequency algorithm of V6X shows strong incident angle dependence of precipitation rates in the outer swath.

Since sensor characteristics and retrieval algorithms are not identical, there are differences of rainfall estimates among passive microwave imagers/sounders (MWIs/MWSs) carried by the Global Precipitation Measurement (GPM) Core Observatory and constellation satellites. This study proposes a Method of Microwave rainfall Normalization (MMN) for the Global Satellite Mapping of Precipitation (GSMaP) to mitigate the discrepancy of GSMaP rainfall estimates among the MWIs/MWSs. A basic idea of MMN module is to calibrate all the MWIs/MWSs except for the reference sensor (TRM_TMI) using cumulative distribution function of rain rate. In order to validate the MMN module, cumulative distribution function and normalized table are investigated, and normalized monthly mean rainfall is compared to original one. The results reasonably show that rain amount of the MWI/MWSs increase (decrease) to mitigate the discrepancy.

Calibration consistency between Ku-band radars flown on the Tropical Rainfall Measuring Mission's (TRMM's) precipitation radar (PR) and the global precipitation measurement (GPM) mission's dual-frequency PR (DPR) can be attained by the use of the normalized radar cross section (NRCS) or σ0over the oceans. With the use of the sea surface echo (SSE) data obtained from the spaceborne PRs, this article aims to estimate the radar parameters of pulsewidth and beamwidth and to evaluate the bias in the NRCS estimates caused by the discrete range sampling. Since the SSE shape is closely related to the received pulsewidth and the two-way cross-track beamwidth, those parameters are individually estimated from the SSE shapes. The SSE shapes are also used to evaluate the impact of the discrete range sampling on the NRCS statistics. The pulsewidth and beamwidth estimated from the SSEs compare well with the level-1 values and accurately reflect changes in the configuration of the radars. The NRCS statistics in GPM version 06 show that the calibration consistency between GPM KuPR and TRMM PR is evaluated within the range of -0.39 to +0.03 dB (-0.48 to +0.11 dB) with (without) the peak correction.

This study aimed to characterize the microwave Ku-band and Ka-band backscattering over the Mongolian three different vegetation density regions, observed from the Global Precipitation Mission (GPM)/Dual-Frequency Precipitation Radar (DPR). The GPM/DPR's Ku-band and Ka-band signal's normalized radar cross section (NRCS or σ0) values were investigated for the analysis of incidence angle dependency and the seasonal variation. Around the nadir, σ0shows the highest values from all forest, grass and desert areas respectively. The seasonal variations of backscatter and Leaf Area Index (LAI) were similar to each other especially at the vicinity of nadir. For the incidence angle dependency, both Ku-band and Ka-band backscatter responses remained constant from dense canopy forest but decreased rapidly from the desert surface at higher than 2°. The GPM/DPR observation exhibits significant vegetation characteristics from the land surface at incidence angles within ~2° to ~8°. From the result, we proposed that Kuband radar is suitable for forest detection while Ka-band radar is suitable to distinguish the desert from the surface.

This study describes a usefulness of GSMaP Near-real-time Gauge-adjusted Rainfall Product (GSMaP_Gauge_NRT) provided by JAXA for monitoring drought from one-month to three-month and heavy rainfall from pentads up to a month. GSMaP_Gauge_NRT is processed 4 hours after observation and is the gauge-adjusted rainfall with spatial resolution of 0.1 degrees and temporal resolution 1 hour over the globe. In terms of latency and accuracy, it is the best product for a precipitation monitoring among the GSMaP products. Drought and heavy rainfall events are detected by the standardized precipitation index and percentiles over periods from April 2000 to March 2019, respectively. Case studies for drought and heavy rainfall in Asia-Pacific regions demonstrated feasibilities for the monitoring. These suggest monitoring of the precipitation extremes monitoring could be effective using the GSMaP_Gauge_NRT.

As one of the future precipitation observation missions discussed in Japan Aerospace Exploration Agency (JAXA), there is a concept of small spaceborne precipitation radar constellation in the Tropics. This can improve a quality of the multi-satellite precipitation product called Global Satellite Mapping of Precipitation (GSMaP) developed by the JAXA under the Global Precipitation Measurement (GPM) Mission if realized. In this study, an Observing System Simulation Experiment (OSSE) on accuracy of GSMaP caused by increases of spaceborne precipitation radar observation was evaluated over Japan area. It was found that the accuracy got better as the frequency of pseudo precipitation radar data increases. The morphed technique in the GSMaP was adopted also in well-known global precipitation map products such as NASA IMERG and NOAA CMORPH, and therefore, current results could be expected to be similar in experiments using those products.

The scan pattern of Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) core satellite was experimentally changed for about 1 day from 13UTC on September 26th, 2017 to observe from nadir to about +34° scan angle assuming future spaceborne precipitation radar with wider swath width. The height and strength of the surface echo clutter with larger incident angle were assessed statistically to examine the possibility of the rainfall retrieval with wide swath observation by DPR. The results indicate that even in the larger incident angle, relatively intense rainfall can be retrieved while shallow and weak rainfall may not be acceptable for retrieval because it should be masked by surface clutter.

The orbit of the tropical rainfall measuring mission (TRMM) satellite was boosted from 350 to 402.5 km in August 2001 to extend its lifetime by conserving fuel. Since the timing between transmission and reception by the precipitation radar (PR) onboard the TRMM satellite was a fixed constant for measurement from an original altitude of 350 km, the PR encountered a mismatch between the transmitting and receiving beams after the TRMM orbit boost. Although the PR algorithm in TRMM Version 7 employs a correction for the beam mismatch, its error remains as an underestimation of the precipitation estimates near surface in the second half of the swath. This paper aims to mitigate the beam-mismatch correction error in Version 7. The beam-mismatch correction needs to estimate the radar echo that would be measured in the virtual intermediate beam between the beam in question and the previous adjacent beam. The beam-mismatch correction developed in this paper assumes that the surface and precipitation echoes change linearly in the horizontal direction in parallel to the surface between the two beams. The new correction is tested with observational data, indicating that the method improves the accuracy of the correction at off-nadir angles. Statistics of the surface normalized radar cross sections and the bright band peak intensities at off-nadir angles are improved using the method. The asymmetric bias of the precipitation estimates with respect to the scan angle in Version 7 is mitigated by 95.9% over ocean and 72.5% over land with the new correction.

A rain-gauge-adjusted algorithm for global satellite mapping of precipitation (GSMaP) that estimates the surface precipitation rate with resolutions of 0.1° and 1 h over the globe is described herein. Precipitation is one of the most important parameters of the earth's system, and its global distribution and changes are essential data for modeling the water cycle, maintaining ecosystems, increasing agricultural production, improving weather forecasting precision, and implementing flood warning systems. In the Global Precipitation Measurement project, integrated products of high-resolution mapping of precipitation, obtained from microwave measurements made by a constellation satellite and infrared radiometers in geostationary orbit, are developed and supplied to the public. However, these high-resolution products, such as GSMaP_MVK, sometimes underestimate surface precipitation, introducing large errors into hydrological modeling. This paper combines the global gauge data set with GSMaP_MVK, using a new algorithm [gauge-adjusted GSMaP (GSMaP_Gauge)], described and evaluated herein using local radar and rain-gauge data sets. This algorithm outperforms other GSMaP products in all validation tests.

The Dual-frequency Precipitation Radar (DPR) installed on the Global Precipitation Measurement (GPM) core satellite was developed by the Japan Aerospace Exploration Agency (JAXA) and the National Institute of Information and Communications Technology (NICT). GPM core observatory was successfully launched by H-IIA launch vehicle on Feb 28, 2014. JAXA is continuing DPR trend monitoring, calibration and validation operations to confirm that DPR keeps its function and performance on orbit. The results of DPR trend monitoring, calibration and validation showed that DPR kept its function and performance on orbit during the 3 years and 2 months prime mission period. JAXA confirmed the prime mission results of GPM/DPR total system achieved the success criteria and the performance indicators. GPM/DPR moved to extended mission phase. JAXA conducted two types of scan pattern change test operations. These useful data will help feasibility studies of the proposed KaPR scan pattern for the next DPR product version up and the future spaceborne radar development.

The scan directions of the high sensitivity beams of the Ka-band channel (KaHS) of the Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) mission Core Satellite were experimentally changed from the original near-nadir swath to match with the Ku-band beams in the outer swaths for a day in September 2017. With this mode of operation, the dual-frequency ratio (DFR) between the Ka and Ku band reflectivity factors can be obtained over the entire swath. Furthermore, the DFR in the outer swaths has a better signal-to-noise ratio because KaHS's sensitivity is better than the KaMS that scans over the inner swath. This paper outlines results of this special experiment and some of the benefits expected in this mode of operation that will be implemented as the standard mode of operation in the spring of 2018.

Recently, contaminations related to the sidelobe clutter were found in the high sensitivity mode of the Ka-band Precipitation Radar (KaHS) onboard the GPM Core Observatory. When the sigmaΛ0 at the nadir was stronger, U -shaped sidelobe clutter was more distinct, while intensities were much weaker than those of the KuPR sidelobe clutter. The statistical method for reducing sidelobe clutter was constructed for the KaHS. By applied the method, contaminations in the KaHS were largely removed. This method will be installed in the coming KaPR level-2 algorithm.

The Dual-frequency Precipitation Radar (DPR) installed on the Global Precipitation Measurement (GPM) core satellite was developed by the Japan Aerospace Exploration Agency (JAXA) and the National Institute of Information and Communications Technology (NICT). This paper describes mission objectives, technical performance, resource allocation, ground test results, orbital check out results and orbital operation status of the DPR. The DPR system PFT has completed in February 2012. GPM core spacecraft satellite system test has completed at NASA Goddard Space Flight Center 2013. GPM core observatory was shipped to JAXA Tanegashima Space Center, JAPAN and GPM core observatory was successfully launched by H-IIA launch vehicle on Feb 28, 2014. DPR orbital check out was completed in May 2014. DPR products were released to the public on Sep. 2, 2014 and Normal Observation Operation period was started. The DPR Prime mission period was completed in May 2017. The prime mission results of the DPR are reported.

Wind is fundamental in many atmospheric phenomena. Global wind profile observation is important to improve numerical weather prediction (NWP) and various meteorological studies. Wind profile observations are measured mainly by radiosonde networks. Most of the weather stations are on land, while weather stations on oceans and remote land areas are sparsely distributed. The current global wind observing systems do not satisfy the globally homogeneous wind profile observation. A space-based Doppler wind lidar is one of the candidates for future global wind profile observations. In the paper, we present results from feasibility study of space-based Doppler Wind Lidar.

As one of Japanese Global Precipitation Measurement (GPM) products, Global Satellite Mapping of Precipitation (GSMaP) product has been provided by Japan Aerospace Exploration Agency (JAXA) to distribute hourly global precipitation map with 0.1×0.1 deg. lat/lon grid. The GSMaP products are composed of “standard product”, “near-real-time product”, “real-time product”, and “reanalysis product”. The standard products are processed 3 days after observation, and the near-real-time products are processed 4 hours after observation. In addition, the real-time version, GSMaP_NOW has been provided by the JAXA over the geostationary satellite “Himawari-8” region since Nov. 2015. Recently, the GSMaP algorithms were majorly updated on September 2014 and January 2017. The current paper describes overviews of the GSMaP products and recent progress of the GSMaP product.

Global Precipitation Measurement (GPM) mission's core satellite has been collecting precipitation data from space for more than a year. This paper reviews the performance of precipitation retrieval algorithm and evaluates the rainfall rates estimated with the Dual-frequency Precipitation Radar (DPR) onboard the GPM's core satellite. Differences between the rain estimates with TRMM's Precipitation Radar (PR) and those with GPM's DPR are examined.

The Tropical Rainfall Measuring Mission (TRMM) satellite was boosted from 350 km to 402.5 km to conserve its fuel keeping the altitude. As a result, the quality of the Precipitation Radar (PR) data slightly changed and, a mismatch of PR's beam between the transmission and reception antennas happened. Although latest version (V7) of PR's level-1 algorithm is corrected by a method, some systematic error of the beam-mismatch correction is found as the left-right asymmetric bias for PR's rain estimates sorted by its scan angle. The current study is conducted to advance the accuracy of the beam-mismatch correction and evaluate its improvement. The current method of the beam-mismatch correction works over the oceans and land, and its improvement is evaluated as about 96 (73) % mitigating the asymmetric bias for PR's rain estimates over the oceans (land) comparing to the V7 product.

This paper describes the rain gauge adjusted algorithm for the Global Satellite Mapping of Precipitation (GSMaP_Gauge_NRT) that estimates the surface rainfall rate with the resolution of 0.1 degree and 1 hour resolution over the globe in near realtime. Precipitation is one of the most important parameters on the earth system, and the global distribution of precipitation and its change are essential data for modeling the water cycle, maintaining the ecosystem environment, agricultural production, improvements of the weather forecast precision, flood warning and so on. In the GPM (Global Precipitation Measurement) project, the integrated products of high resolution mapping of precipitation from microwave measurements from constellation satellite and infrared radiometers from geo-stationary orbit are developed and supplied to the public. However, sometimes such high resolution product such as GSMaP_NRT underestimates the surface precipitateon and causes large error for hydrological modeling. In this paper, global gauge data set are combined with GSMaP_NRT. After describing and evaluating the GSMaP_Gauge which is a gauge adjusted product of the GSMaP_MVK, the methodology of a new combined algorithm (GSMaP_Gauge_NRT) is described.

Special experiments using the Precipitation Radar (PR) onboard the Tropical Rainfall Measuring Mission satellite during descent after the orbital maintenance fuel was depleted are reported in this paper. In these special observation experiments, a 90° yaw maneuver experiment, a wide swath width experiment, and the dense sampling experiments were carried out. The key operation of these experiments was a large change in the phase shifter settings of the transmitter and receiver, which was not tested before or after the launch. In addition, the 90° yaw experiment required the special operation of the satellite, whereas the other two experiments were implemented by changing only the PR operation. During the experiments, the PR was operated, and the expected data were obtained. The preliminary results suggest the possibility of future radar observation from space, and the results of these experiments will be utilized as basic data to improve the algorithm of the dual-frequency PR onboard the Global Precipitation Measurement mission core satellite and the design of future spaceborne PRs.

The Dual-frequency Precipitation Radar (DPR) installed on the Global Precipitation Measurement (GPM) core satellite was developed by JAXA and NICT. This paper describes mission objectives, technical performance, resource allocation, ground test results, orbital check out results and orbital operation status of the DPR. The DPR system PFT has completed in February 2012. GPM core spacecraft satellite system test has completed at NASA Goddard Space Flight Center 2013. GPM core observatory was shipped to JAXA Tanegashima Space Center, JAPAN and GPM core observatory was successfully launched by H-IIA launch vehicle on Feb 28, 2014. DPR orbital check out was completed in May 2014. DPR products released to the public on Sep. 2, 2014 and Normal Observation Operation period was started. The orbital operations status of DPR is reported in this paper.

Precipitation observation by the Tropical Rainfall Measuring Mission's (TRMM's) Precipitation Radar (PR) lasted for almost 17 years. Homogeneity of long-term PR data will be essential to study the water cycle change related to the interannual variability and the decadal change. In this study, we aim to develop a precipitation climate data from 17-year PR data. In this paper, PR data are adjusted to mitigate the discontinuity of the PR hardware (H/W) change. An obvious discontinuity of storm top height caused by the PR H/W change is mitigated creating the adjusted data. Semi-global (35S-35N) precipitation derived from the adjusted data is decreased by 0.98 % as compared with the original data.

After the launch of the Global Precipitation Measurement (GPM) Core Observatory on February 2014, significant efforts have been made in order to reduce the sidelobe clutter contamination in Ku-band precipitation radar (KuPR) of Dual-frequency Precipitation Radar (DPR). This work describes a development of a software routine to reduce the sidelobe clutter in the KuPR Level-2 algorithm for the product version V03B. The routine estimate sidelobe echoes based on the correlation between the surface echo and sidelobe clutter, and subtract sidelobe estimates from the received power at corresponding range bins. Performances of the routine were evaluated with reference to high-sensitivity beams of the KaPR.

This paper describes current status of the level 1 algorithm development and calibration for the Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM). JAXA has carried out the calibration for the DPR since the GPM core observatory was launched on February 28, 2014 (JST). As a result of the external calibration, it is detected that some biases seemed to exist. However, JAXA has not adjusted the calibration coefficient of the DPR level 1 algorithm because of two reasons. One is the errors of the external calibration. In the external calibration, the measurement error and estimation error for the power may occur. The other reason is the continuity of the surface backscattering cross section (denoted as σ0) from Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). Requirement of the continuity has been satisfied by using the current calibration coefficient.

The Global Satellite Mapping of Precipitation (GSMaP) product, which was developed toward the Global Precipitation Measurement (GPM), is operating and distributed in near-real-time basis at the Japan Aerospace Exploration Agency (JAXA). In this paper, we will introduce development status of the latest GSMaP algorithm, which will be distributed as one of JAXA GPM product.

The Global Precipitation Measurement (GPM) mission consists of the GPM core Observatory (satellite) and the constellation satellites. The GPM core Observatory launched at 3:37 on February 28, 2014 (JST) from Tanegashima Space Center in Japan. It carries the Dual-frequency Precipitation Radar (DPR) and the GPM Microwave Imager (GMI). The DPR consists of Ku-band precipitation radar (KuPR) and Ka-band precipitation radar (KaPR). These radars have developed by Japan Aerospace Exploration Agency (JAXA) and National Institute of Information and Communications Technology (NICT) [1] and GMI has developed by National Aeronautics and Space Administration (NASA). The DPR level 1 algorithm has been developed by the JAXA. The main roles of level 1 algorithm are limit check for fatal and caution incidents, orbital cut and calculation of geometric information and transformation for engineering value. JAXA also has the responsibility of calibration of the DPR. The calibration methods have two types. One is the internal calibration with onboard calibration system, and the other is the external calibration with Active Radar Calibrator (ARC). These calibrations will carry out densely by the public data release. This paper describes the concepts of the level 1 algorithm and the calibration methods.

The Precipitation Radar (PR) on the TRMM satellite has observed tropical rainfall for more than 15 years. The long-term rainfall data from TRMM PR have been very useful for scientific studies. However, several studies have shown that the amount of weak convective rainfall decreased in the PR product due to the TRMM orbit boost from 350 km to 402.5 km in August 2001. In this study, the impact of the sensitivity degradation on the rainfall amounts is estimated by adjusting the pre-boost data with a simulation of reduced sensitivity by the orbit boost. A PR precipitation product with reduced artificial discontinuity at the timing of the orbit boost is produced for climate studies. The result shows that the decrease of the global rainfall amount due to the sensitivity is about 2.1% on average. However, the decreases of rainfall amount due to the sensitivity degradation vary spatially and they are larger, in particular, in subtropical ocean.

The JAXA-NASA Joint Algorithm Team has developed the Level 2 (L2) algorithm for Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) core observatory, which provides estimated precipitation rate, radar reflectivity factor, and precipitation information. The synthetic DPR Level 1 (L1) data is necessary as a test bed of the DPR L2 algorithms. In this study, synthetic DPR L1 data estimated from the TRMM/PR data are produced in 7 orbits during 15th March 2007 and 32 orbits during 31st Mar. to 1st Apr. 2011. The at-launch codes of L2 Ku, L2 Ka, L2 DPR algorithms (Version 4.20130328) were applied to the L1B synthetic data. The Ku/Ka/DPR products are compared with the PR products. Precipitation rates at 2km altitudes of Ku-L2 estimates are similar to those of the PR 2A25 product. Correlation coefficients are 0.93 and 0.81 over ocean and land, respectively. On the other hand, underestimation in Ka-L2 estimates and overestimation in DPR-L2 estimates are found in precipitation rate more than 4mm/hr over ocean.

We develop the rain/no-rain classification (RNC) method for an over-land rainfall retrieval algorithm for the Advanced Microwave Sounding Unit (AMSU) based on the Global Satellite Mapping of Precipitation microwave radiometer algorithm. The current RNC method over land based on the window channel tends to underestimate warm rain areas due to high land surface emissivity. The sounder channels are less affected by surface emission. Therefore we developed a new RNC method using the sounder channels in order to detect the warm rain which is missed by the current method. The proposed methods are evaluated using the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data. The proposed method can detect the warm rain which is missed by the original method and about 84% of the AMSU-PR matched-up cases during Jan.-Dec. 2007 is improved.

The JAXA-NASA Joint Algorithm Team has developed the Level 2 algorithm for Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) core observatory, which will be launched in 2014. Correction method for attenuation by cloud liquid water (CLW) is one of future issues in the DPR algorithm. Recently, a 3.5km-mesh global simulation has been performed using a Nonhydrostatic ICosahedral Atmospheric Model (NICAM). The NICAM is a global cloud-system resolving model (GCRM), and explicitly calculates moist convection using a cloud microphysical scheme. In this work, vertical profiles of the CLW are classified with reference to temperatures and intensities of surface rain rate (SRR) using the NICAM data. Overall, the CLW amount tends to increase when the SRR increases. In the Tropics such as 15S-15N, clear peaks are found around 10-15 degrees Centigrade with small SRRs. This can be connected with shallow rainfall. In large SRRs, peaks are around the Freezing Level, which can be related to tall rainfall.

We have developed the GPM/DPR synthetic data from the TRMM/PR data for contribution to higher level algorithm development. The frequency of the PR is 13.8 GHz and it is necessary to estimate radar reflectivity factor (Z-factor) at the DPR frequencies. In this paper, Z-factors from rainfall signals are estimated at the DPR frequencies. The detectable rain range of the DPR is verified using the synthetic data from the PR observations on January and July 1998, which suggests that it is important to consider differences of the sensitivity between the KuPR-matched beams and the interlaced high-sensitivity beams of the KaPR. The estimation of the Z-factors at the DPR frequencies is applied to an experimental data on 15th March 2007 when the PR was operated according to the scanning geometry of the KaPR, and then the synthetic data are obtained with distinction of 2 kinds of KaPR beams.

This study demonstrated preliminary results in diagnosis of the numerical model with reference to the TRMM/PR, examples of the GPM/DPR synthetic data, and application of the synthetic data to the algorithm development in the nonuniform beamfilling correction method. These were performed using a satellite radar simulation algorithm by the National Institute of Information and Communications Technology (NICT) and the Japan Aerospace Exploration Agency (JAXA) named as the Integrated Satellite Observation Simulator for Radar (ISOSIM-Radar) and a cloud-resolving model by the Japan Meteorological Agency (JMA-NHM).

This study examines the trend in the 7-year monthly rainfall amounts (September 2001-August 2008) observed by the TRMM Precipitation Radar (PR). It is shown that the monthly rainfall amounts averaged in the range of 35°S to 35°N (PR observation range) tend to increase slightly over this period. This tendency can be considered to be affected by sampling errors due to the narrow observation swaths of the PR, in addition to natural variations. Therefore, this study developed the method of evaluating the sampling errors by a bootstrap method using the actual data observed by the PR. We examined the trend in the averaged rainfall amounts during 7 years considering the simulated sampling errors. As a result, the positive trend was significantly detected, even when sampling error was removed. From this, it can be concluded that this positive trend is highly likely to be due to natural variations.

We develop an over-ocean rainfall retrieval algorithm for the Advanced Microwave Sounding Unit (AMSU) based on the Global Satellite Mapping of Precipitation (GSMaP) microwave radiometer algorithm. This algorithm combines an emission-based estimate from brightness temperature (Tb) at 23 GHz and a scattering-based estimate from Tb at 89 GHz, depending on a scattering index (SI) computed from Tb at both 89 and 150 GHz. Precipitation inhomogeneities are also taken into account. The GSMaP-retrieved rainfall from the AMSU (GSMaP_AMSU) is compared with the National Oceanic and Atmospheric Administration (NOAA) standard algorithm (NOAA_AMSU)-retrieved data using Tropical Rainfall Measuring Mission (TRMM) data as a reference. Rain rates retrieved by GSMaP_AMSU have better agreement with TRMM estimates over midlatitudes during winter. Better estimates over multitudes over winter are given by the use of Tb at 23 GHz in the GSMaP_AMSU algorithm. It was also shown that GSMaP_AMSU has higher rain detection than NOAA_AMSU.

This paper documents the production and validation of retrieved rainfall data obtained from satellite-borne microwave radiometers by the Global Satellite Mapping of Precipitation (GSMaP) Project. Using various attributes of precipitation derived from Tropical Rainfall Measuring Mission (TRMM) satellite data, the GSMaP has implemented hydrometeor profiles derived from Precipitation Radar (PR), statistical rain/no-rain classification, and scattering algorithms using polarization-corrected temperatures (PCTs) at 85.5 and 37 GHz. Combined scattering-based surface rainfalls are computed depending on rainfall intensities. PCT85 is not used for stronger rainfalls, because strong depressions of PCT85 are related to tall precipitation-top heights. Therefore, for stronger rainfalls, PCT37 is used, with PCT85 used for weaker rainfalls. With the suspiciously strong rainfalls retrieved from PCT85 deleted, the combined rainfalls correspond well to the PR rain rates over land. The GSMaP algorithm for the TRMM Microwave Imager (TMI) is validated using the TRMM PR, ground radar [Kwajalein (KWAJ) radar and COBRA], and Radar Automated Meteorological Data Acquisition System (AMeDAS) precipitation analysis (RA). Monthly surface rainfalls retrieved from six microwave radiometers (GSMaP_MWR) are compared with the gauge-based dataset. Rain rates retrieved from the TMI (GSMaP_TMI) are in better agreement with the PR estimates over land everywhere except over tropical Africa in the boreal summer. Validation results of the KWAJ radar and COBRA show a good linear relationship for instantaneous rainfall rates, while validation around Japan using the RA shows a good relationship in the warm season. Poor results, connected to weak-precipitation cases, are found in the cold season around Japan.