Abstract

The Baiu front is a subtropical convergence zone that is formed over east Asia in early summer (hereinafter referred to as the Baiu period). In this study, an overocean precipitation retrieval algorithm is developed to retrieve precipitation for the Baiu period from brightness temperatures (TBs) supplied by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The basic idea of the algorithm is to find the optimal precipitation that gives radiative transfer model (RTM)-calculated, field-of-view–averaged TBs that fit best with the TMI TBs at 10.7, 19.7, and 85.5 GHz with vertical polarization. For the RTM calculation, spatial precipitation inhomogeneity and freezing-level height are estimated from TMI TBs. The optimal precipitation with 10-km resolution is obtained by solving the gradient equation of a cost function that is a weighted sum of squares of TB differences between the TMI observation and the RTM calculation. Precipitation retrieved by this algorithm was validated using TRMM precipitation radar (PR) data from the western part of Japan during June–July of 1998. The results indicate the following.

1. Mesoscale (∼100 km) structures of precipitation disturbances were retrieved successfully with the algorithm. However, there were discrepancies in position and strength of individual rain cells between the precipitation retrievals and PR data.

2. Precipitation retrieved by the algorithm agreed well with PR data within the precipitation range of 1–25 mm h⁻¹, irrespective of precipitation type.

Abstract

The comparison between cloud information and rainfall is studied using infrared and radar data from the Tropical Rainfall Measuring Mission. Cloud information from the visible and infrared scanner was compared with rain information from precipitation radar (PR) for rain cases assembled during June 1998 over a frontal zone in east Asia. The authors selected the following four parameters: 1) radiance ratio of 0.6 and 1.6 μm [channel 1/channel 2 (Ch1/Ch2)], 2) brightness temperature difference between 11 and 12 μm (BTD45), 3) brightness temperature difference between 3.8 and 11 μm (BTD34), and 4) brightness temperature (TBB) in channel 4 (Ch4) as the cloud information. The flags of “rain certain,” stratiform rain, brightband existence, and convective rain observed by PR, and integrated rain rate from the rain bottom to rain top were used as the rainfall information.

From the comparison between rain–no-rain information by PR and the four cloud parameters, it is found that values of the radiance ratio of Ch1/Ch2 larger than 25, BTD45 smaller than 1.5 K, and BTD34 smaller than 8 K are effective in delineating rain area. The probability of detection (POD), false alarm ratio (FAR), and skill score (SS) are computed and compared for the following rain and no-rain algorithms: 1) single cloud threshold of 235 K in Ch4 TBB as in the Geostationary Operational Environmental Satellite Precipitation Index, 2) single threshold of 260 K in Ch4 TBB, 3) Ch1/Ch2 larger than 25 and Ch4 TBB colder than 260 K (C12), 4) BTD45 smaller than 1.5 K and Ch4 TBB colder than 260 K (C45), and 5) BTD34 smaller than 8 K and Ch4 TBB colder than 260 K. The C12 method shows the highest SS, and the C45 method shows the highest POD. The BTD34 scores better in FAR than the BTD45 and is better than BTD45 in delineating the thicker part of cirrus clouds. The use of the second channel shows better scores than does use of the single infrared threshold algorithm.

The cloud characteristics for convective rain and stratiform rain are also studied using Ch1/Ch2, BTD45, and BTD34. The percentage of occurrence of stratiform rain shows a local maximum for clouds of small BTD45/BTD34 with Ch4 TBB of 220–250 K. The higher percentage of convective rain corresponds well to the optically thicker (smaller BTD45) clouds colder than 210 K. However, there is no significant difference in Ch1/Ch2 between convective and stratiform rain, because significant convective cases are not included in the data that were processed.

Abstract

The relationship of tropical cyclone (TC) future intensity change to current intensity and current axisymmetricity deduced from hourly Global Satellite Mapping of Precipitation (GSMaP) data was investigated. Axisymmetricity is a metric that correlates positively with the magnitude of the axisymmetric component of the rainfall rate and negatively with the magnitude of the asymmetric component. The samples used were all of the TCs that existed in the western North Pacific basin during the years 2000–15. The results showed that, during the development stage, the intensification rate at the current time, and 6 and 12 h after the current time was strongly related to both the current intensity and axisymmetricity. On average, the higher the axisymmetricity, the larger the intensity change in the next 24 h for TCs with a current central pressure (maximum sustained wind) between 945 and 995 hPa (85 and 40 kt). The mean value of the axisymmetricity for TCs experiencing rapid intensification (RI) was much higher than that for non-RI TCs for current intensities of 960–990 hPa. The new observational evidence for the intensification process presented here is consistent with the findings of previous theoretical studies emphasizing the role of the axisymmetric component of diabatic heating.

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

This paper discusses the benefits of using a hybrid ensemble Kalman filter and four-dimensional variational (4D-Var) data assimilation (DA) system rather than a 4D-Var system employing the National Meteorological Center (NMC, now known as NCEP) method (4D-Var-Bnmc) to predict severe weather events. An adjoint-based 4D-Var system was employed with a background error covariance matrix constructed from the NMC method and perturbations in a local ensemble transform Kalman filter system. The DA systems are based on the Japan Meteorological Agency’s nonhydrostatic model. To reduce the sampling noise, three types of implementation (the spatial localization, spectral localization, and neighboring ensemble approaches) were tested. The assimilation of a pseudosingle observation of sea level pressure located at a tropical cyclone (TC) center yielded analysis increments physically consistent with what is expected of a mature TC in the hybrid systems at the beginning of the assimilation window, whereas analogous experiments performed using the 4D-Var-Bnmc system did not. At the end, the structures of the 4D-Var-based increments became similar to one another, while the analysis increment by the 4D-Var-Bnmc system was broad in the horizontal direction. Realistic DA experiments showed that all of the hybrid systems provided initial conditions that yielded more accurate TC track and intensity forecasts than those achievable by the 4D-Var-Bnmc system. The hybrid systems also yielded some statistically significant improvements in forecasting local heavy rainfall events in terms of fraction skill scores when a 160 km × 160 km window size was used. The overall skills of the hybrid systems were relatively independent of the choice of implementation.

The “ Wakasa Bay Experiment” was conducted in order to refine error models for oceanic precipitation from the Advanced Microwave Sounding Radiometer-Earth Observing System (AMSR-E) measurements and to develop algorithms for snowfall. The NASA P-3 aircraft was equipped with microwave radiometers, covering a frequency range of 10.7–340 GHz, and radars at 13.4, 35.6, and 94 GHz, and was deployed to Yokota Air Base in Japan for flights from 14 January to 3 February 2003. For four flight days (27–30 January) a Gulfstream II aircraft provided by Core Research for Environmental Science and Technology (CREST), carrying an extensive cloud physics payload and a two-frequency (23.8 and 31.4 GHz) microwave radiometer, joined the P-3 for coordinated flights. The Gulfstream II aircraft was part of the “Winter Mesoscale Convective Systems Observations over the Sea of Japan in 2003” (“WMO-03”) field campaign sponsored by Japan Science and Technology Corporation (JST). Extensive data were taken, which addressed all of the experimental objectives. The data obtained with the NASA P-3 are available at the National Snow and Ice Data Center (NSIDC), and they are available free of charge to all interested researchers.

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.

Abstract

The Statistical Hurricane Intensity Prediction Scheme (SHIPS) is a multiple regression model for forecasting tropical cyclone (TC) intensity [both central pressure (Pmin) and maximum wind speed (Vmax)]. To further improve the accuracy of the Japan Meteorological Agency version of SHIPS, five new predictors associated with TC rainfall and structural features were incorporated into the scheme. Four of the five predictors were primarily derived from the hourly Global Satellite Mapping of Precipitation (GSMaP) reanalysis product, which is a microwave satellite-derived rainfall dataset. The predictors include the axisymmetry of rainfall distribution around a TC multiplied by ocean heat content (OHC), rainfall areal coverage, the radius of maximum azimuthal mean rainfall, and total volumetric rain multiplied by OHC. The fifth predictor is the Rossby number. Among these predictors, the axisymmetry multiplied by OHC had the greatest impact on intensity change, particularly, at forecast times up to 42 h. The forecast results up to 5 days showed that the mean absolute error (MAE) of the Pmin forecast in SHIPS with the new predictors was improved by over 6% in the first half of the forecast period. The MAE of the Vmax forecast was also improved by nearly 4%. Regarding the Pmin forecast, the improvement was greatest (up to 13%) for steady-state TCs, including those initialized as tropical depressions, with slight improvement (2%–5%) for intensifying TCs. Finally, a real-time forecast experiment utilizing the hourly near-real-time GSMaP product demonstrated the improvement of the SHIPS forecasts, confirming feasibility for operational use.