Abstract

Space-based precipitation radar data have been underused in data assimilation studies and operations despite their valuable information on vertically resolved hydrometeor profiles around the globe. The authors developed direct assimilation of reflectivities (Ze) from the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) Core Observatory to improve mesoscale predictions. Based on comparisons with Ze observations, this cloud resolving model mostly reproduced Ze but produced overestimations of Ze induced by excessive snow with large diameter particles. With an ensemble-based variational scheme and preprocessing steps to properly treat reflectivity observations including conservative quality control and superobbing procedures, the authors assimilated DPR Ze and/or rain-affected radiances of GPM Microwave Imager (GMI) for the case of Typhoon Halong in July 2014. With the vertically resolving capability of DPR, the authors effectively selected Ze observations most suited to data assimilation, for example, by removing Ze above the melting layer to avoid contamination due to model bias. While the GMI radiance had large impacts on various control variables, the DPR made a fine delicate analysis of the rain mixing ratio and updraft. This difference arose from the observation characteristics (coverage width and spatial resolution), sensitivities represented in the observation operators, and structures of the background error covariance. Because the DPR assimilation corrected excessive increases in rain and clouds due to the radiance assimilation, the combined use of DPR and GMI generated more accurate analysis and forecast than separate use of them with respect to the agreement of observations and tropical cyclone position errors.

Abstract

In ensemble-based assimilation schemes for cloud-resolving models (CRMs), the precipitation-related variables have serious sampling errors. The purpose of the present study is to examine the sampling error properties and the forecast error characteristics of the operational CRM of the Japan Meteorological Agency (JMANHM) and to develop a sampling error damping method based on the CRM forecast error characteristics.

The CRM forecast error was analyzed for meteorological disturbance cases using 100-member ensemble forecasts of the JMANHM. The ensemble forecast perturbation correlation had a significant noise associated with the precipitation-related variables, because of sampling errors. The precipitation-related variables were likely to suffer this sampling error in most precipitating areas. An examination of the forecast error characteristics revealed that the CRM forecast error satisfied the assumption of the spectral localization, while the spatial localization with constant scales, or variable localization, were not applicable to the CRM.

A neighboring ensemble (NE) method was developed, which was based on the spectral localization that estimated the forecast error correlation at the target grid point, using ensemble members for neighboring grid points. To introduce this method into an ensemble-based variational assimilation scheme, the present study horizontally divided the NE forecast error into large-scale portions and deviations. As single observation assimilation experiments showed, this “dual-scale NE” method was more successful in damping the sampling error and generating plausible, deep vertical profile of precipitation analysis increments, compared to a simple spatial localization method or a variable localization method.

Abstract

Heavy rainfall associated with shallow orographic rainfall systems has been underestimated by passive microwave radiometer algorithms owing to weak ice scattering signatures. The authors improve the performance of estimates made using a passive microwave radiometer algorithm, the Global Satellite Mapping of Precipitation (GSMaP) algorithm, from data obtained by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) for orographic heavy rainfall. An orographic/nonorographic rainfall classification scheme is developed on the basis of orographically forced upward vertical motion and the convergence of surface moisture flux estimated from ancillary data. Lookup tables derived from orographic precipitation profiles are used to estimate rainfall for an orographic rainfall pixel, whereas those derived from original precipitation profiles are used to estimate rainfall for a nonorographic rainfall pixel. Rainfall estimates made using the revised GSMaP algorithm are in better agreement with estimates from data obtained by the radar on the TRMM satellite and by gauge-calibrated ground radars than are estimates made using the original GSMaP algorithm.

Abstract

Seto et al. developed rain/no-rain classification (RNC) methods over land for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). In this study, the methods are modified for application to other microwave radiometers. The previous methods match TMI observations with TRMM precipitation radar (PR) observations, classify the TMI pixels into rain pixels and no-rain pixels, and then statistically summarize the observed brightness temperature at the no-rain pixels into a land surface brightness temperature database. In the modified methods, the probability distribution of brightness temperature under no-rain conditions is derived from unclassified TMI pixels without the use of PR. A test with the TMI shows that the modified (PR independent) methods are better than the RNC method developed for the Goddard profiling algorithm (GPROF; the standard algorithm for the TMI) while they are slightly poorer than corresponding previous (PR dependent) methods. M2d, one of the PR-independent methods, is applied to observations from the Advanced Microwave Scanning Radiometer for Earth Observing Satellite (AMSR-E), is evaluated for a matchup case with PR, and is evaluated for 1 yr with a rain gauge dataset in Japan. M2d is incorporated into a retrieval algorithm developed by the Global Satellite Mapping of Precipitation project to be applied for the AMSR-E. In latitudes above 30°N, the rain-rate retrieval is compared with a rain gauge dataset by the Global Precipitation Climatology Center. Without a snow mask, a large amount of false rainfall due to snow contamination occurs. Therefore, a simple snow mask using the 23.8-GHz channel is applied and the threshold of the mask is optimized. Between 30° and 60°N, the optimized snow mask forces the miss of an estimated 10% of the total rainfall.

Abstract

In this study, the single-moment 6-class bulk cloud microphysics scheme used in the operational numerical weather prediction system at the Japan Meteorological Agency was improved using the observations of the Global Precipitation Measurement (GPM) core satellite as reference values. The original cloud microphysics scheme has the following biases: underestimation of cloud ice compared to the brightness temperature of the GPM Microwave Imager (GMI) and underestimation of the lower-troposphere rain compared to the reflectivity of GPM Dual-frequency Precipitation Radar (DPR). Furthermore, validation of the dual-frequency rate of reflectivity revealed that the dominant particles in the solid phase of simulation were graupel and deviated from DPR observation. The causes of these issues were investigated using a single-column kinematic model. The underestimation of cloud ice was caused by a high ice-to-snow conversion rate, and the underestimation of precipitation in the lower layers was caused by an excessive number of small-diameter rain particles. The parameterization of microphysics scheme is improved to eliminate the biases in the single-column model. In the forecast obtained using the improved scheme, the underestimation of cloud ice and rain is reduced. Consequently, the prediction errors of hydrometeors were reduced against the GPM satellite observations, and the atmospheric profiles and precipitation forecasts were improved.

Abstract

The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) algorithms consist of modules. This paper describes version 4 (V4) of GPM DPR level 2 (L2) classification (CSF) modules, which consist of two single-frequency (SF) modules—that is, Ku-only and Ka-only modules—and a dual-frequency (DF) module. Each CSF module detects bright band (BB) and classifies rain into three major types, that is, stratiform, convective, and other. The Ku-only and Ka-only CSF modules use algorithms that are similar to the Tropical Rainfall Measuring Mission (TRMM) rain type classification algorithm 2A23. The DF CSF module uses a new method called the measured dual-frequency ratio (DFRm) method for the rain type classification and the detection of BB. It is shown that the Ku-only CSF module and the DF CSF module produce almost indistinguishable rain type counts in a statistical sense. It is also shown that the DFRm method in the DF CSF module improves the detection of BB.

Abstract

A statistical method to reduce the sidelobe clutter of the Ku-band precipitation radar (KuPR) of the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) Core Observatory is described and evaluated using DPR observations. The KuPR sidelobe clutter was much more severe than that of the Precipitation Radar on board the Tropical Rainfall Measuring Mission (TRMM), and it has caused the misidentification of precipitation. The statistical method to reduce sidelobe clutter was constructed by subtracting the estimated sidelobe power, based upon a multiple regression model with explanatory variables of the normalized radar cross section (NRCS) of surface, from the received power of the echo. The saturation of the NRCS at near-nadir angles, resulting from strong surface scattering, was considered in the calculation of the regression coefficients.

The method was implemented in the KuPR algorithm and applied to KuPR-observed data. It was found that the received power from sidelobe clutter over the ocean was largely reduced by using the developed method, although some of the received power from the sidelobe clutter still remained. From the statistical results of the evaluations, it was shown that the number of KuPR precipitation events in the clutter region, after the method was applied, was comparable to that in the clutter-free region. This confirms the reasonable performance of the method in removing sidelobe clutter. For further improving the effectiveness of the method, it is necessary to improve the consideration of the NRCS saturation, which will be explored in future work.

Abstract

Precipitation observation with the Tropical Rainfall Measuring Mission’s (TRMM’s) precipitation radar (PR) lasted for more than 17 years. To study the changes in the water and energy cycle related to interannual and decadal variabilities of climate, homogeneity of long-term PR data is essential. The aim of the study is to develop a precipitation climate record from the 17-yr PR observation. The focus was on mitigating the discontinuities associated with the switching to redundant electronics in the PR in June 2009. In version 7 of the level-1 PR product, a discontinuity in noise power is found at this timing, indicating a change in the signal-to-noise ratio. To mitigate the effect of this discontinuity on climate studies, the noise power of the B-side PR obtained after June 2009 is artificially increased to match that of the A-side PR. Simulation results show that the storm height and the precipitation frequency detected by the PR relatively decrease by 2.17% and 5.15% in the TRMM coverage area (35°S–35°N), respectively, and that the obvious discontinuity of the time series by the storm height and the precipitation fraction caused by the switching to the redundancy electronics is mitigated. Differences in the statistics of other precipitation parameters caused by the switching are also mitigated. The unconditional precipitation rate derived from the adjusted data obtained over the TRMM coverage area decreases by 0.90% as compared with that determined from the original data. This decrease is mainly caused by reductions in the detection of light precipitation.

Abstract

Precipitation estimates from the Global Satellite Mapping of Precipitation (GSMaP) project are evaluated over the contiguous United States (CONUS) for the period of 2005–06. GSMaP combines precipitation retrievals from the Tropical Rainfall Measuring Mission satellite and other polar-orbiting satellites, and interpolates them with cloud motion vectors derived from infrared images from geostationary satellites, to produce a high-resolution dataset. Four other satellite-based datasets are also evaluated concurrently with GSMaP, to provide a better perspective. The new Climate Prediction Center (CPC) unified gauge analysis is used as the reference data. The evaluation shows that GSMaP does well in capturing the spatial patterns of precipitation, especially for summer, and that it has better estimation of precipitation amount over the eastern than over the western CONUS. Meanwhile, GSMaP shares many of the challenges common to other satellite-based products, including that it underestimates in winter and overestimates in summer. In winter, GSMaP has on average one-half less precipitation over the western region and one-third less over the eastern region, whereas in summer it has about three-quarters and one-quarter more estimated precipitation over the two respective regions, respectively. Most of the summer overestimates (winter underestimates) are from an excessive (insufficient) number of strong events (>20 mm day⁻¹). Overall, GSMaP’s performance is comparable to other satellite-based products, with slightly better probability of detection during summer, and the different satellite-based estimates as a group have better agreement among themselves during summer than during winter.

Abstract

This study compares three TMI rainfall datasets generated by two versions of NASA’s Goddard Profiling algorithm (GPROF2010 and GPROF2017) and JAXA’s Global Satellite Mapping of Precipitation algorithm (GSMaP) over land, coast, and ocean. We use TRMM precipitation radar observations as the reference, and also include CloudSat cloud profiling radar (CPR) observations as the reference over ocean. First, the dynamic thresholds for rainfall detection used by GSMaP and GPROF2017 have better detection capability, indicating by larger Heidke skill score (HSS) values, compared with GPROF2010 over both land and coast. Over ocean, all three datasets have very similar HSS regardless of including CPR observations. Next, intensity analysis shows that no single dataset performs the best according to all three statistical metrics (correlation, root-mean-square error, and relative bias), except that GSMaP performs the best for stratiform precipitation over coast, and GPROF2017 performs the best for convective precipitation over ocean, based on all three metrics. Finally, an error decomposition analysis shows that the total error and its three components have very different characteristics over several regions among these three datasets. For example, the positive total error in GPROF2010 and GSMaP is primarily caused by the positive hit bias over central Africa, while the false bias in GPROF2017 is largely responsible for this positive total error. For future algorithm development, results from this study imply that a convective–stratiform separation technique may be necessary to reduce the large underestimation for convective rain intensity.