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Abstract This study aims to create a 21‐year, high spatiotemporal resolution Global Satellite Mapping of Precipitation (GSMaP) rainfall product adjusted by rain gauge measurements over the Indian mainland and highlighted the importance of the Indian Meteorological Department (IMD) daily gridded rainfall to generate gauge adjusted GSMaP rainfall products over Indian landmass. The targeted resolutions of the GSMaP are hourly and 0.1° × 0.1°. The National Oceanic and Atmospheric Administration Climate Prediction Center daily gauge analysis (0.5° × 0.5°) and IMD daily gridded rainfall (0.25° × 0.25°) were utilized to generate long‐term rainfall products, GSMaP_CPC and GSMaP_IMD rainfall, respectively. After preliminary verification of the GSMaP_CPC and GSMaP_IMD rainfalls with IMD gauges, these rainfall products are evaluated for the Indian Summer Monsoon periods of 2000–2020 with comparisons of other gauge adjusted rainfall products such as the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement final‐run. The results suggest GSMaP_IMD has a smaller root‐mean‐square difference (RMSD) and higher correlation than GSMaP_CPC, evaluated against independent rainfall products. In the 3‐hour mean analysis with spaceborne precipitation radar data, it is found that the value of RMSD decreases in GSMaP_IMD with respect to GSMaP_CPC throughout the day. The statistics against the hourly dense gauge network suggests that the GSMaP_IMD is more effective in capturing large spatiotemporal rainfall variation. Thus, validation results with the independent sources suggest that GSMaP_IMD rainfall generally improved over GSMaP_CPC rainfall. These improvements are significant in orographic regions with high rainfall amounts, mainly the western Ghats and northeastern parts of India.

A feasibility study on wide swath observation assuming future spaceborne precipitation radar was demonstrated using data obtained from a wide swath observation experiment conducted in September 2017 by using the dual-frequency precipitation radar onboard the global precipitation measurement core observatory. In this experiment, scan angles of the Ku-band precipitation radar (KuPR) and Ka-band precipitation radar (KaPR) were experimentally changed to observe from nadir to about +34°. While the result showed that the precipitation echo could be obtained at wider scan angles, the occurrence of sidelobe and grating lobe clutters not seen in a normal scan were simultaneously observed. The mainlobe clutter height of KuPR increased linearly along the incident angle, and 0.5 mm/h precipitation may be masked up to 4 km over land at the angle of +34°. Besides, the grating lobe clutter clearly occurred in KuPR when the ground surface of the grating lobe direction was land or sea ice. The maximum scan angle without the grating lobe contamination was +23.43°, approximately corresponding to 355 km swath width with the orbit altitude of 407 km. For KaPR, the grating lobe contamination was not significant and the mainlobe clutter height peaked at the scan angle of around 26°, and then tended to decrease at larger scan angles. Therefore, precipitation at the lower altitude may have a chance to be detected even at larger scan angles, while weaker surface echoes at larger scan angles may need new techniques to estimate precipitation for the KaPR.

A need to monitor precipitation extremes from space is widely recognized, especially for regions where ground-based observations are limited or unavailable. This article examines the usefulness of precipitation extremes monitoring using the Global Satellite Mapping of Precipitation (GSMaP) near-real-time product in the East Asia and Western Pacific region-one of the world's most disaster-prone regions. With case studies and statistical analysis, heavy rainfall and drought detected using the GSMaP Near-real-time Gauge-adjusted Rainfall Product (GNRT6) were validated. Heavy rainfall for daily and weekly precipitation and short-term drought from one month up to three months were defined by a 90th percentile threshold or more and the Standardized Precipitation Index over periods from April 2000 to March 2019, respectively. The results of analyses suggested that the detectability varied depending on the region, such as good detection in dry areas and poor detection in rainy island nations. While the accuracy of GNRT6 is confirmed as being generally better than that of the satellite-only uncorrected product, low detectability can be caused by coarse resolutions of parameters used in the gauge-adjustment technique of the GNRT6 and is regarded as a future task.

This paper introduces the Method of Microwave Rainfall Normalization (MMN) for the Global Satellite Mapping of Precipitation (GSMaP) algorithm in its latest version (V05, algorithm version 8), released in December 2021. The method aims to mitigate the discrepancy of GSMaP rainfall estimates among passive microwave (PMW) imagers/sounders (MWIs/MWSs) due to differences in sensor specifications and retrieval algorithms. The basic idea of the MMN module is to calibrate target PMW sensors with reference sensors (the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI)) using the cumulative distribution function (CDF) of the rain rate. Differences between the CDF and normalization table for MWSs are greater than MWIs due to different rain retrieval algorithms. More (less) MWS rainfall is detected over the ocean (land) than GMI rainfall. Matchup rainfall data between GMI and a target PMW sensor are compared to evaluate MMN performance. The monthly mean rainfall and mean bias error were improved for almost all PMW sensors. This study leaves open the possibility for further inter-calibration and improvement of rain detection and heavy rainfall retrievals.

Abstract For the past two decades, precipitation radars (PR) onboard low‐orbiting satellites such as Tropical Rainfall Measuring Mission (TRMM) have provided invaluable insight into global precipitation variability and led to advancements in numerical weather prediction through data assimilation. Building upon this success, planning has begun on the next generation of satellite‐based PR instruments, with the consideration for a future geostationary‐based PR (GPR), bringing the advantage of higher observation frequency over previous and current PR satellites. Following the successful demonstration by a recent study to test the feasibility of a GPR to obtain three‐dimensional precipitation data, this study takes the first step to investigate the potential usefulness of GPR observations for numerical weather prediction by performing a perfect model observing system simulation experiment (OSSE) for a West Pacific tropical cyclone (TC). Data assimilation experiments are performed assimilating reflectivity observations obtained for a range of beam sampling spans, following a previous finding that oversampling improves observation quality. Results showed observations obtained with finer sampling spans of 5 km and 10 km were able to better capture key tropical cyclone features in analyses, including the eye, heavy rainfall associated with the eyewall, and outer convective rainbands. Results also showed that through increased moistening and upward velocity within the inner storm environment, assimilation of observations drove an intensification of the secondary circulation and deepening of the storm, leading to an improvement in TC intensity error. Intensity forecasts were found improved for assimilation of observations obtained with increasingly finer beam sampling span, suggesting an important benefit of oversampling.

This study proposes the use of satellite-based precipitation (SBP) products in combination with local rain gauges in Bolivia. Using this approach, the country was divided into three major hydrographic basins: the Altiplano, La Plata, and Amazon. The selected SBP products were Global Satellite Mapping of Precipitation (GSMaP) and Climate Hazards Group Infrared Precipitations with Stations (CHIRPS). The correlation coefficients of SBP were found to be from 0.94 to 0.98 at monthly temporal scale. The applied methodology iterates correction factors, taking advantage of surface measurements from the national rain gauge network; five iterations showed stability in the convergence. Once the improved SBP product was obtained, validation was performed by reducing ten percent the number of rain gauges randomly. After applying the correction factors, the combined products improved their correlation coefficient values by up to 0.99. The validation of the methodology showed that with a combination of products using 90% of the rain gauges, correlation coefficients ranged from 0.98 to 0.99. Among the three basins, the Amazon basin presented the poorest results; this fact may be related to low rain gauge density compared to the other two basins. The validation approach shows that the methodology has an acceptable performance. The database generated in this study, now open to the public, is ready to be used for different hydrological applications such as precipitation time-series analysis, water balance, and water assessment at the sub-basin scale within Bolivia.

Mongolian steppe is one of the largest and important ecosystems. The degradation of grassland and the expansion of desert are occurring due to drought and desertification processes. We attempted monitoring of the broad-scale vegetation in Mongolia by a space-borne precipitation radar, which may complement typical approaches of vegetation monitoring (such as NDVI). We utilized the Global Precipitation Mission’s (GPM) dual-frequency precipitation radar (DPR). We characterized backscatter (σ0) of GPM/DPR’s two microwave bands (Ku and Ka) with respect to the dominant vegetation zones (forest, grassland, desert). Both Ku and Ka radars’ σ0 values were investigated for incidence angle dependency and the seasonal variation. As a result, the use of multi-angle, multi-band observations of GPM/DPR could help to characterize the vegetation zones. Especially, the σ0 at incidence angles between 1° and 8° represented useful characteristics of vegetation. Based on it, by using unsupervised clustering, we produced annual maps describing vegetation zones from 2014 to 2018. The result indicated that Mongolia experienced extensive changes in grassland and desert areas during the study years.

The Global Precipitation Measurement (GPM) Core Observatory will carry a Dual-frequency Precipitation Radar (DPR) consisting of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). In this study, “at-launch” codes of DPR precipitation algorithms, which will be used in GPM ground systems at launch, were evaluated using synthetic data based upon the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data. Results from the codes (Version 4.20131010) of the KuPR-only, KaPR-only, and DPR algorithms were compared with “true values” calculated based upon drop size distributions assumed in the synthetic data and standard results from the TRMM algorithms at an altitude of 2 km over the ocean. The results indicate that the total precipitation amounts during April 2011 from the KuPR and DPR algorithms are similar to the true values, whereas the estimates from the KaPR data are underestimated. Moreover, the DPR estimates yielded smaller precipitation rates for rates less than about 10 mm/h and greater precipitation rates above 10 mm/h. Underestimation of the KaPR estimates was analyzed in terms of measured radar reflectivity (Zm) of the KaPR at an altitude of 2 km. The underestimation of the KaPR data was most pronounced during strong precipitation events of Zm m > 26 (moderate attenuation cases). The results suggest that the underestimation is caused by a problem in the attenuation correction method, which was verified by the improved codes.

This study focused on the total precipitable water (TPW) products of Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Global Change Observation Mission--Water (GCOM-W). The GCOM-W satellite has been flying in the Afternoon Constellation (A-train) orbit to synergize with other A-train satellites, such as Aqua. In this study, we compared two datasets of AMSR2 TPW from July 2012 to December 2020, independently produced by the Japan Aerospace Exploration Agency (JAXA) and Remote Sensing Systems (RSS). No significant differences were observed in the TPW anomaly trends between the two datasets. However, significant differences were observed in the absolute values of TPW in the northwest Pacific and northwest Atlantic Oceans during the boreal summer season. We investigated the meteorological conditions that caused these differences using reanalysis, in situ observation data, and visible and infrared data from the MODerate resolution Imaging Spectroradiometer (MODIS) on the Aqua. The results indicated that the lower atmosphere had an inversion layer with relative humidity close to 100 %, and very-low-altitude clouds (i.e., fog) were often distributed in the areas where the TPW differences between the JAXA and RSS are significant. The temperature profiles represented in the JAXA and RSS algorithms were approximated by a simple model. The influence of the inversion layer and fog on the JAXA and RSS TPW algorithms was also investigated using a radiative transfer model. Sensitivity experiments suggested that the inversion layer was associated with the underestimated TPW for the JAXA algorithm, whereas it was associated with the overestimated TPW for the RSS algorithm. [ABSTRACT FROM AUTHOR]

The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is a satellite mission implemented by the European Space Agency (ESA) in cooperation with the Japan Aerospace Exploration Agency (JAXA) to measure global profiles of aerosols, clouds and precipitation properties together with radiative fluxes and derived heating rates. The data will be used in particular to evaluate the representation of clouds, aerosols, precipitation and associated radiative fluxes in weather forecasting and climate models. The satellite scientific payload consists of four instruments, a lidar, a radar, an imager and a broad-band radiometer. The measurements of these instruments are processed in the ground segment, which produces and distributes the science data products. The EarthCARE observational requirements are addressed. An overview is given of the space segment with a detailed description of the four science instruments. Furthermore, the elements of the Space Segment and Ground Segment that are relevant for the science data users are described. [ABSTRACT FROM AUTHOR]

Pre-launch simulated data to be obtained from new sensors on a satellite is useful to develop retrieval algorithms and aid the rapid release of retrieval products after launch. Here we introduce Japanese Aerospace Exploration Agencies (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 for four types of EarthCARE sensor: 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 nadir. We discuss plans for updating JAXA EarthCARE synthetic data using a large eddy simulation and implementation of a three-dimensional radiation model. [ABSTRACT FROM AUTHOR]

This study aims to create a 21-year, high spatiotemporal resolution Global Satellite Mapping of Precipitation (GSMaP) rainfall product adjusted by rain gauge measurements over the Indian mainland and highlighted the importance of the Indian Meteorological Department (IMD) daily gridded rainfall to generate gauge adjusted GSMaP rainfall products over Indian landmass. The targeted resolutions of the GSMaP are hourly and 0.1° x 0.1°. The National Oceanic and Atmospheric Administration Climate Prediction Center daily gauge analysis (0.5° x 0.5°) and IMD daily gridded rainfall (0.25° x 0.25°) were utilized to generate long-term rainfall products, GSMaP_CPC and GSMaP_IMD rainfall, respectively. After preliminary verification of the GSMaP_CPC and GSMaP_IMD rainfalls with IMD gauges, these rainfall products are evaluated for the Indian Summer Monsoon periods of 2000--2020 with comparisons of other gauge adjusted rainfall products such as the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement final-run. The results suggest GSMaP_IMD has a smaller root-mean-square difference (RMSD) and higher correlation than GSMaP_CPC, evaluated against independent rainfall products. In the 3-hour mean analysis with spaceborne precipitation radar data, it is found that the value of RMSD decreases in GSMaP_IMD with respect to GSMaP_CPC throughout the day. The statistics against the hourly dense gauge network suggests that the GSMaP_IMD is more effective in capturing large spatiotemporal rainfall variation. Thus, validation results with the independent sources suggest that GSMaP_IMD rainfall generally improved over GSMaP_CPC rainfall. These improvements are significant in orographic regions with high rainfall amounts, mainly the western Ghats and northeastern parts of India. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) has operated in full scan (FS) mode in both the Ku-band and Ka-band since May 2018. This FS mode can enable about 245 km full swath dual-frequency processing for the first time, whereas previous algorithms enabled dual-frequency processing in the narrower inner swath, having a width of approximately 125 km. This paper describes the classification (CSF) module in newly developed DPR level 2 V06X experimental algorithms corresponding to the FS mode. The CSF module classifies precipitation as three major types, stratiform, convective, and other, and provides bright-band (BB) information. One-month statistics show that a significant jump occurs in the Ka-band precipitation type counts at the edges of the inner swath due to the different sensitivity of Ka-band radar in Ka-band Matched Scan (Ka-MS) mode compared with that in Ka-band High Sensitivity (Ka-HS) mode. However, precipitation type counts indicate that dual-frequency processing works well not only in the inner swath but also in the outer swath. Statistics on BB counts show a significant improvement in BB detection, especially in the outer swath, when dual-frequency processing is performed. In addition, the V06X Ku-band algorithm solves two problems related to the CSF module: (a) appearance of a very large near-surface precipitation rate of stratiform precipitation reclassified by the slope method and (b) a rare case of misjudging BB peak as an upper part of surface echo. The data structures of GPM DPR algorithms were drastically changed in V06X. The new data structures introduced in V06X will be used in V07A and later. In this regard, the V06X CSF module outlined in this paper is a prototype of future versions of each CSF algorithm. [ABSTRACT FROM AUTHOR]

Precipitation statistics from Global Precipitation Measurement Core Observatory Dual-frequency Precipitation Radar (GPM DPR) are underestimated due to systematic bias depending on the scanning angle. Over five years of GPM DPR KuPR Version 06A data, the precipitation anomaly is -7 % and -2 % over land and ocean, respectively. This study improves the estimation of low-level precipitation-rate profiles and the detection of shallow storms (with top heights of ≤ 2.5 km), using reference datasets of near-nadir measurements. First, the low-level precipitation profile (LPP) is updated using an a priori near-nadir database generated from structural-characteristics related variables of the precipitation and environmental parameters. The LPP correction increases precipitation over areas where downward-increasing precipitation profiles are dominant below 2 km, such as at high elevations and at middle and high latitudes. Globally, the LPP correction increases precipitation by 5 %. Second, the effect on precipitation data of missing shallow storms is estimated using the angle-bin difference in the detectability of storms with a top height of ≤ 2.5 km. The effect of the shallow-precipitation deficiency (SPD) is comparable in magnitude to that of the LPP correction. A priori lookup tables for the SPD correction, constrained by the clutter-free bottom level and spatially averaged shallow-precipitation fractions, are constructed so that the correction applies to gridded statistics at 0.1° and three-month scales. The SPD correction enhances precipitation by 50 % over specific low-rainfall oceans in the sub-tropics and at high latitudes, where shallow precipitation dominates. Based on these two corrections, precipitation increases by 8 % and 11 % over land and ocean, respectively. At latitudes between 60°N and 60°S, the difference in KuPR compared with satellite-gauge blended products is reduced from -17 % to -9 %, whereas with gauge-based products is reduced from -19 % to -15 % over land. [ABSTRACT FROM AUTHOR]

A non-Gaussian probability distribution function (PDF) and a new displacement correction method using PDF pseudo-regimes for precipitation were introduced to the dual-scale neighboring ensemble-based variational assimilation scheme (EnVar) to achieve improved assimilation of all-sky microwave imager (MWI) brightness temperatures (TBs) into a cloud-resolving model (CRM). We evaluated the fits of the precipitation forecast perturbations of various disturbances with the existing non-Gaussian PDF models and selected a mixed lognormal distribution for the precipitation PDF model. Then, we introduced rain-free and rainy PDF regimes to EnVar. We developed a new method for correcting precipitation displacement that introduces pseudo rain-free, rainy, and heavy-rain regimes and approximated their PDFs as regional averages of the PDFs around the target point. We estimated the horizontal scales of averaging based on the similarity of precipitation forecast perturbations. These methods improved the bias and normality of TB differences between observation and the first guess. We conducted assimilation experiments using all-sky MWI TB observations for Typhoon Etau (T1518). Results show that the precipitation analysis using the EnVar employed in this study was more similar to the global satellite map for precipitation (GSMaP) retrievals than those using a conventional EnVar. The introduction of a mixed lognormal PDF strengthened the precipitation analysis of heavy-rain areas around the typhoon and near a front. Using PDF pseudo-regimes considerably reduced the precipitation displacement error of the analysis. The EnVar employed in this study improved the CRM forecasts for precipitation distribution up to 12 h and the typhoon position and central surface pressure for > 24 h. The forecast analysis cycle of EnVar improved the CRM forecasts for heavy rain around the typhoon center up to 6 h and a heavy-rain band associated with the typhoon for > 24 h when compared with the EnVar using a single-time TB observation. [ABSTRACT FROM AUTHOR]

Reliability information of satellite precipitation products is required for various applications. This study describes and evaluates a reliability flag of the Global Satellite Mapping of Precipitation Near-Real-Time precipitation product (GSMaP_NRT). This flag was developed to characterize the reliability of GSMaP_NRT data simply and qualitatively by considering its algorithm characteristics. The reliability at each pixel is represented by any one of ten levels (10 being the best and 1 the worst) by considering three major factors: 1) "surface type reliability"--which takes into account that estimation of rainfall using passive microwave sensors is better over the oceans than over land and coastal areas; 2) "low-temperature reliability"--which takes into account the lower reliability due to surface snow cover in low-temperature conditions; and 3) "Moving Vector with Kalman Filter (MVK) propagation reliability"--which means that the reliability gets worse with the increase in time since the last overpass of the passive microwave sensor. To evaluate the utility of the reliability flag, statistical indices are calculated for each reliability level using gauge-calibrated ground radar data around Japan. It is found that the reliability flag represents the differences in GSMaP accuracy: the accuracy worsens as the reliability decreases. The GSMaP errors exhibit seasonal changes that are well represented by the ten levels of the reliability flag, indicating that the reliability flag can be used to catch seasonal variations in GSMaP accuracy due to changes in environmental factors. This study also raises the possibility of improving the reliability flag by using information related to heavy orographic rainfall. It is shown how the error features of heavy orographic rainfall differ from those of the total rainfall, and it is suggested that heavy orographic rainfall information can be utilized to further improve the reliability flag. [ABSTRACT FROM AUTHOR]

Accurate rainfall estimation during the Indian summer monsoon (ISM) is one of the most crucial activities in and around the Indian Sub-continent. Japan Aerospace Exploration Agency (JAXA) provides a couple of Global Satellite Mapping of Precipitation (GSMaP) rainfall products, namely, the GSMaP_MVK, which is a satellite-based product calculated with ancillary data including global objective analysis data, and the GSMaP_Gauge, which is adjusted by global rain gauges. In this study, the daily rainfall amount from the GSMaP rainfall product (version 7) is validated against a dense rain gauge network over Karnataka, one of the southwestern states of India, during ISM 2016-2018. Furthermore, as the primary objective of this study, these dense rain gauge observations are assimilated in the GSMaP rainfall product using a hybrid assimilation method to improve the final rainfall estimate. The hybrid assimilation method is a combination of the two-dimensional variational (2D-Var) method and the Kalman filter, in which the 2D-Var method is utilized to merge rain gauge observations and the Kalman filter is applied to update background error in the 2D-Var method. Preliminary verification results suggest that GSMaP_Gauge rainfall has sufficient skill over north interior Karnataka and south interior Karnataka regions, with large errors over the orographic heavy rainfall region of the Western Ghats. These errors are larger in the GSMaP_MVK rainfall product over orographic heavy rainfall regions. Hybrid assimilation results of randomly selected rain gauge observations improve the skill of GSMaP_Gauge and GSMaP_MVK rainfall products when compared with independent rain gauge observations. These improvements in daily rainfall are more prominent over orographic heavy rainfall regions. GSMaP_MVK rainfall product shows larger improvement due to the absence of the gauge adjustment in the JAXA operational processing. The superiority of the hybrid assimilation method against Cressman and optimal interpolation methods for impacts of utilized rain gauge numbers are also presented in the present study. [ABSTRACT FROM AUTHOR]

In this paper, precipitation rate retrieval algorithms for the Global Precipitation Measurement mission's Dualfrequency Precipitation Radar (DPR) are developed. The DPR consists of a Ku-band radar (KuPR; 13.6 GHz) and a Ka-band radar (KaPR; 35.5 GHz). For the KuPR, an algorithm similar to the Tropical Rainfall Measuring Mission's Precipitation Radar algorithm is developed, with the relation between precipitation rate R and massweighted mean diameter Dm (R-Dm relation) replacing the relation between the specific attenuation k and effective radar reflectivity factor Ze. The R-Dm relation can also be applied to the KaPR and dual-frequency algorithms. In both the single-frequency and dual-frequency algorithms, the forward retrieval method is applied with an assumed adjustment factor for the R-Dm relation (ε) and the results are evaluated to select the best value of ε. The advantages of the dual-frequency algorithm are the availability of the dual-frequency surface reference technique and the ZfKa method, which is a method to use the attenuation-corrected radar reflectivity factor Zf of KaPR, to select ε as well as the possibility to selectively use measurements from KuPR or KaPR. This paper also describes the derivation of the scattering table and the R-Dm relation as well as the procedure for non-uniform beam filling correction in detail. The outputs are then statistically analyzed to demonstrate algorithm performance. [ABSTRACT FROM AUTHOR]

Spaceborne precipitation radar such as Global Precipitation Measurement (GPM)/dual-frequency precipitation radar (DPR) provides valuable observations of precipitation systems in three dimensions. Assimilation of GPM/DPR data is becoming an important technique for improving the accuracy of forecasting to complement scarce ground-based observations. This study presents a new, one-dimensional maximum-likelihood estimation (1D-MLE) method developed by the authors that enables the estimation of relative humidity profiles according to a non-Gaussian probability density function. By assimilating the estimated relative humidity profiles using a four-dimensional variational (4D-Var) method, mesoscale precipitation forecasts by the Japan Meteorological Agency (JMA) have been considerably improved. The displacement of forecast precipitation during a severe weather event, in particular, is improved significantly. It was found that forecasting accuracy was maintained for a narrow GPM/DPR swath and low revisit frequency by repeating the assimilation–forecast cycle. Since the effectiveness was confirmed, the JMA began assimilating GPM/DPR data using the 1D-MLE approach from March 2016. [ABSTRACT FROM AUTHOR]

This study investigates the global drop size distribution (DSD) of rainfall and its relationship to large-scale precipitation characteristics using the Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) Core Observatory. This study focuses on seasonal variations in the dominant precipitation systems regarding variations in DSD. A mass-weighted mean diameter (Dm), which is estimated based on the dualfrequency information derived from the GPM/DPR, is statistically analyzed as a typical parameter of the DSD. Values of the annual mean Dm, in general, are larger over land than over the oceans, and the relationship between Dm and precipitation rate (R) is not a simple one-to-one relationship. Furthermore, Dm exhibits statistically significant seasonal variations, specifically over the northwest Pacific Ocean, whereas R shows insignificant variations, indicating the variations in R cannot explain the distinct seasonal changes in Dm. Focusing on the seasonal variation in Dm over the northwest Pacific Ocean, the results indicate that the variation in Dm is related to the seasonal change in the dominant precipitation systems. In the summer over the northwest Pacific Ocean, Dm is related to the organized precipitation systems associated with the Baiu front over the mid-latitudes and tropical disturbances over the subtropical region, with relatively higher precipitation top heights, composed of both stratiform and convective precipitations. Contrary to the summer, larger Dm over the mid-latitudes in winter is related to extra- tropical frontal systems with ice particles in the upper layers, which consists of more stratiform precipitation in the storm track region. The smaller Dm over the subtropical northwest Pacific Ocean in winter is associated with shallow convective precipitation systems with trade-wind cumulus clouds and cumulus congestus under the subtropical high. [ABSTRACT FROM AUTHOR]

This paper is the first publication presenting the predictability of the record-breaking rainfall in Japan in July 2018 (RJJ18), the severest flood-related disaster since 1982. Of the three successive precipitation stages in RJJ18, this study investigates synoptic-scale predictability of the third-stage precipitation using the near-realtime global atmospheric data assimilation system named NEXRA. With NEXRA, intense precipitation in western Japan on July 6 was well predicted 3 days in advance. Comparing forecasts at different initial times revealed that the predictability of the intense rains was tied to the generation of a low-pressure system in the middle of the frontal system over the Sea of Japan. Observation impact estimates showed that radiosondes in Kyusyu and off the east coast of China significantly reduced the forecast errors. Since the forecast errors grew more rapidly during RJJ18, data assimilation played a crucial role in improving the predictability. [ABSTRACT FROM AUTHOR]

Abstract. Spaceborne precipitation radars, such as the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) Core Observatory, have been important platforms to provide a direct measurement of three-dimensional precipitation structure globally. Building upon the success of TRMM and GPM Core Observatory, the Japan Aerospace Exploration Agency (JAXA) is currently surveying the feasibility of a potential satellite mission equipped with a precipitation radar on a geostationary orbit. The quasi-continuous observation realized by the geostationary satellite radar would offer a new insight into meteorology and would advance numerical weather prediction (NWP) through their effective use by data assimilation. Although the radar would be beneficial, the radar on the geostationary orbit measures precipitation obliquely at off-nadir points. Besides, the observing resolution will be several times larger than those onboard TRMM and GPM Core Observatory due to the limited antenna size that we could deliver. The tilted sampling volume and the coarse resolution would result in more contaminations from the surface clutters. To investigate the impact of these limitations and to explore the potential usefulness of the geostationary satellite radar, this study simulates the observation data for a typhoon case using an NWP model and a radar simulator. The results demonstrate that it would be possible to obtain three-dimensional precipitation data. However, the quality of the observation depends on the beam width, the beam sampling span, and the position of precipitation systems. With a wide beam width and a coarse beam span, the radar cannot observe weak precipitation at low altitudes due to the surface clutters. The limitation can be mitigated by oversampling (i.e., a wide beam width and a fine sampling span). With a narrow beam width and a fine beam sampling span, the surface clutter interference is confined to the surface level. When the precipitation system is located far from the nadir, the precipitation signal is obtained only for strong precipitation. [ABSTRACT FROM AUTHOR]

This study evaluated the impact of a future space-borne Doppler wind lidar (DWL) on a super-low-altitude orbit by using an observing system simulation experiment (OSSE) based on a sensitivity observing system experiment (SOSE) approach. Realistic atmospheric data, including wind and temperature, was provided as "pseudo-truth" (PT) to simulate DWL observations. Hourly aerosols and clouds that are consistent with PT winds were also created for the simulation. A full-scale lidar simulator, which is described in detail in the companion paper, simulated realistic line-of-sight wind measurements and observation quality information, such as signal-to-noise ratio (SNR) and measurement error. Quality control (QC) procedures in the data assimilation system were developed to select high-quality DWL observations on the basis of the averaged SNR from strong backscattering in the presence of aerosols or clouds. Furthermore, DWL observation errors used in the assimilation were calculated using the measurement error estimated by the lidar simulator. The forecast impacts of DWL onboard polar- and tropical-orbiting satellites were assessed using the operational global data assimilation system. Data assimilation experiments were conducted in January and August in 2010 to assess overall impact and seasonal dependence. It is found that DWL on either polar- or tropical-orbiting satellites is overall beneficial for wind and temperature forecasts, with greater impacts for the January experiments. The relative forecast error reduction reaches almost 2 % in the tropics. An exception is degradation in the southern hemisphere in August, thus suggesting a need to further refine observation error assignment and QC. A decisive conclusion cannot be drawn with regard to the superiority of polar- or tropical-orbiting satellites because of their mixed impacts. This is probably related to the characteristics of error growth in the tropics. The limitations and possible underestimation of the DWL impacts, for example, due to a simple observation error inflation setting, in the SOSE-OSSE are also discussed. [ABSTRACT FROM AUTHOR]

A feasibility study of tropospheric wind measurements using a coherent Doppler lidar aboard a super low altitude satellite is being conducted in Japan. The considered lidar uses a 2.05 µm laser light source of 3.75 W. In order to assess the measurement performances, simulations of wind measurements were conducted. The mission definition is presented in a companion paper (Part 1) while, in this paper, we describe the measurement simulator and characterize the errors on the retrieved line-of-sight (LOS) winds. Winds are retrieved from the Doppler-shift of the noisy backscattered signal with a horizontal resolution of 100 km along the orbit track and a vertical resolution between 0.5 and 2 km. Cloud and wind fields are the pseudo-truth of an Observing System Simulation Experiment while aerosol data are from the Model-of-Aerosol-Species-IN-the-Global-AtmospheRe (MASINGAR) constrained with the pseudo-truth wind. We present the results of the analysis of a full month of data in summer time for a near-polar orbiting satellite and a LOS nadir angle of 35°. Below ≈ 8 km, the ratio of good retrievals is 30 - 55 % and the median LOS wind error is better than 0.6 m s-1 (1.04 m s-1 for the horizontal wind). In the upper troposphere, the ratio is less than 15 % in the southern hemisphere and high-latitudes. However, the ratio is still 35 % in the northern Tropics and mid-latitudes where ice-clouds frequently occur. The upper-tropospheric median LOS-wind measurement error is between 1 - 2 m s-1 depending on the latitude (1.74 - 3.5 m s-1 for the horizontal wind). These errors are dominated by uncertainties induced by spatial atmospheric inhomogeneities. [ABSTRACT FROM AUTHOR]

A working group is studying the feasibility of a future Japanese space-borne coherent Doppler wind lidar (CDWL) for global wind profile observation. This study is composed of two companion papers: an instrumental overview of the space-borne CDWL for global wind profile observation (Part 1), and the wind measurement performance (error and bias) investigated using a full-fledged space-borne CDWL simulator (Part 2). This paper aims to describe the future space-borne CDWL in terms of technical points and observation user requirements. The future mission concept is designed to have two looks for vector wind measurement with vertical resolutions of 0.5 (lower troposphere: 0 - 3 km), 1 (middle troposphere: 3 - 8 km), and 2 km (upper troposphere: 8 - 20 km) and horizontal resolution of < 100 km along a satellite. The altitude and orbit of the satellite are discussed from a scientific viewpoint. The candidate altitude and orbit of the satellite are 220 km and an inclination angle of 96.4° (polar orbit) or 35.1° (low-inclination-angle orbit). The technical requirements of the space-borne CDWL are a single-frequency 2-μm pulse laser with an average laser power of 3.75 W, two effective 40-cm-diameter afocal telescopes, a wide-bandwidth (> 3.4 GHz) detector, a high-speed analog-to-digital converter, and a systematic lidar efficiency of 0.08. The space-borne CDWL looks at two locations at a nadir angle of 35° at two azimuth angles of 45° and 135° (225° and 315°) along the satellite track. The future space-borne CDWL wind profile observation will fill the gap of the current global wind observing systems and contribute to the improvement of the initial conditions for numerical weather prediction (NWP), the prediction of typhoons and heavy rain, and various meteorological studies. [ABSTRACT FROM AUTHOR]

This study evaluated the accuracy of gauge-adjusted Global Satellite Mapping of Precipitation (GSMaP_Gauge version V5.222.1, hereafter G_Gauge) data in Japan's Tone River basin during 2006-2009. Specifically, the accuracy of a gauge non-adjusted product, GSMaP Moving Vector with Kalman Filter (GSMaP_MVK, hereafter G_MVK), was also evaluated. Both products were also evaluated against ground observation data from rain gauge-radar combined product Radar-Automated Meteorological Data Acquisition System (Radar-AMeDAS) in terms of temporal and spatial variability. Temporal analyses showed that G_Gauge had better accuracy than G_MVK at sub-daily time scales (1, 3, 6, 9, 12, and 24 h) within any range of precipitation intensity and better detection capabilities of rainfall event. Linear regressions with Radar-AMeDAS showed better performance for G_Gauge than G_MVK at any time scales in terms of Pearson's correlation coefficient and the slope of regression. At an hourly scale, in particular, Pearson's correlation coefficient for G_Gauge (0.84) was higher than that for G_MVK (0.72) as well as the slope of linear regression (0.87 and 0.65, respectively). The probability of detection (POD) improved from 0.48 (G_MVK) to 0.70 (G_Gauge) when gauge-adjusted data were used. However, spatial analysis detected that G_Gauge still underestimated the precipitation intensity in high-elevation regions and slightly overestimated it in low elevation regions. The POD and false alarm ratio had a linear relationship with log-transformed elevation data, and the relationships were stronger in the winter seasons than in the summer seasons. At any spatial and temporal scale, the evaluation of these products should consider seasonal changes (especially in winter) and the topographic effects. For further improvements of G_Gauge, we suggest including higher resolution gauge-based network data than the Climate Prediction Center unified gauge-based analysis of global daily precipitation, which is used for G_Gauge. [ABSTRACT FROM AUTHOR]

Global wind profile observation is important to improve initial conditions for numerical weather prediction (NWP), general circulation model, and various other meteorological studies. A space-borne Doppler wind lidar (DWL) is one of promising remote sensing techniques for global wind measurement. We describe a study based on simulated satellite measurements for assessing the measurement performances of a Japanese coherent DWL. Global simulations are performed using pseudo-truth atmospheric model of an observing system simulation experiment (OSSE) conducted using the global NWP system of the Japan Meteorological Agency. Wind profile retrieval simulations have been performed for 1 month (August, 2010) and the results show that the percentage of good quality estimates is 40% below 8 km, and it decreases to 10% at 8-20 km in the southern hemisphere and is 20-50% in the northern hemisphere. Expected line-of-sight wind speed errors for good quality estimates are 0.5 m s-1 below 8 km and 1.1 m s-1 at 8-20 km. In the future, the simulated observations will be used in the OSSE to quantitatively infer the potential impacts on NWP accuracy. To illustrate such analysis, results are shown from an initial validation test using a simple wind measurement model instead of the realistic DWL simulations. [ABSTRACT FROM AUTHOR]