This study focuses on the considerable spatial variability of precipitation along the western coast of a continent at mid-high latitude and investigates the precipitation climatology and mechanism along the south coast of Alaska, using datasets of spaceborne radars onboard two satellites, namely, the Dual-frequency Precipitation Radar (DPR) KuPR onboard the Global Precipitation Measurement (GPM) core satellite and the Cloud Profiling Radar (CPR) onboard CloudSat. At higher latitudes, differentiating the phase of precipitation particles falling on the ground is crucial in evaluating precipitation. Classification of satellite precipitation products according to the distance from the coastline shows that precipitation characteristics differ greatly on opposite sides of the coastline. Above coastal waters, relatively heavy precipitation with CPR reflectivity larger than 7 dBZ from orographically enhanced nimbostratus clouds, which can be detected by KuPR, is frequently captured. Meanwhile, along coastal mountains, light-to-moderate snowfall events with CPR reflectivity lower than 11 dBZ, which are well detected by the CPR but rarely detected by KuPR, frequently occur, and they are mainly brought by nimbostratus clouds advected from the coast and orographically enhanced shallow cumuliform clouds. There is no clear diurnal variation of precipitation except in summer, and the amplitude of the variation during summer is still low compared with total precipitation especially over the ocean, suggesting that the transport of synoptic-scale water vapor brings much precipitation throughout the year. Case studies and seasonal analysis indicate that frontal systems and moisture flows associated with extratropical cyclones that arrive from the Gulf of Alaska are blocked by terrain and stagnate along the coast to yield long-lasting precipitation along the coastline. The results of this study illustrate the importance of using complementary information provided by these radars to evaluate the precipitation climatology in a region in which both rainfall and snowfall occur.
Local fronts formed near the coast of the Kanto Plain, mainly in the cold season, called “coastal fronts”, tend to be forecast on the inland side of their actual positions by the operational mesoscale Numerical Weather Prediction (NWP; with a horizontal grid spacing of 5 km) model at the Japan Meteorological Agency (JMA). In this study, we confirm a systematic NWP error through statistical validations of coastal fronts that occurred with southerly onshore winds during 2015–2018. Using a nonhydrostatic numerical model, we explore the relevant physical mechanisms through sensitivity experiments involving different horizontal resolutions, envelope orography, and physics parameterization schemes for three cases with typical errors. The operational NWP model was shown to have a systematic error, with local fronts being shifted consistently to the inland side of their actual positions when the forecast period exceeds 5 hours, regardless of precipitation. The sensitivity experiments suggested that the systematic error associated with coastal fronts may be caused primarily by an underestimation of the mountain barrier surrounding the Kanto Plain in the model. The northwestward distance error of coastal fronts, averaged over the three illustrative cases, can be reduced by 27 % and 37 % by increasing the horizontal resolution from 5 km to 2 km and 1 km, respectively, and can be eliminated almost entirely by using the envelope orography. Moreover, the evaporative cooling of precipitation shifts coastal fronts seaward.
Most coastal fronts are thought to take the form of cold air trapped on the southeastern slope of the mountains surrounding the Kanto Plain, where the elevation angle of the frontal surface is roughly controlled dynamically. The local front shifts seaward when the ridgelines of the mountains become higher, or by the reduction of the elevation angle when the trapped air becomes colder.
Precipitation consists of many types of hydrometeors, such as raindrops, ice crystals, graupel, and hail. Due to their impacts, graupel and hail (GH) have received particular attention in the literature. Global Precipitation Measurement (GPM) dual-frequency radar (DPR) has proved to be a very reliable system for global precipitation retrievals. This paper aims to develop a GH identification algorithm for GPM DPR. This algorithm is constructed using a precipitation type index (PTI) defined for DPR. The PTI is effective in separating hydrometeor types and is calculated using measurements of reflectivity, dual-frequency ratio, and storm top height data. The output of the algorithm is a Boolean product representing the existence of graupel or hail along with the vertical profile for each Ku- and Ka-band matched footprint. Cross validation is performed with the Weather Service Radar (WSR-88D) network over continental United States as well as during the Remote sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) experiment. Evaluation of the GH identification algorithm is performed on a global basis, which illustrates promising comparisons with the global lightning and hail precipitation maps generated using radar and radiometer.
Accurate estimation of snowfall rate during snowstorms is crucial. This estimate directly impacts the hydrological and atmospheric models. The snow density plays a very important role in estimating the snowfall rate. In this paper, the snow density is investigated during a huge snowstorm event during the International Collaborative Experiment held during the Pyeongchang 2018 Olympics and Paralympic winter games (ICE-POP 2018). The density is calculated using the terminal velocities and diameters of the snow particles measured by a disdrometer. In this study, we used not only radar reflectivity factor (Z) for snowfall rate (S) estimation, but also dual-frequency ratio (DFR). We derived S-Z and S-Z-DFR relations for snowfall estimation during this snowstorm event after considering the snow density. The comparisons are performed between the National Aeronautics and Space Administration dual-frequency dual-polarization Doppler radar and precipitation gauges using these two power–law relations. The results show that the two relations for snowfall rate estimation agree well with gauges, but the S-Z-DFR method performs the best, which has a lower normalized standard error. The error in the snowfall rate estimates decreases as the time scale becomes large. This shows that the S-Z-DFR algorithm is a promising way for snowfall quantitative precipitation estimation and can be used as a ground validation tool for global precipitation measurement snowfall production evaluations.
In this study, future changes in the rainy season in East Asia are projected based on massive ensemble simulations of about 100 members with a 60-km mesh global atmospheric model (the 60-km model hereinafter) called the “Database for Policy Decision-Making for Future Climate Change (d4PDF)”. For the present-climate, historical observed sea surface temperatures (SSTs) are prescribed to the 60-km model. In the future, 4°C warmer climate relative to the preindustrial climate, six different SST distributions projected by Atmosphere–Ocean General Circulation Models of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are given to the 60-km model. In the future, summer precipitation will generally increase in most regions of East Asia, but will decrease over western Japan. Precipitation decreases in June around 30–35°N over China, Korea and Japan. The Probability Density Function directly was derived from the massive ensemble simulations at each grid point in June and revealed that the most intense precipitation increase will occur in some regions where moderate precipitation decreases will take place in terms of the simple ensemble average. In western Japan, the onset of rainy season will delay and the retreat will occur earlier, resulting in a shorter rainy season. The decrease of precipitation in June over western Japan may be attributed to the counter-effect of the convergence of moisture to the south of Japan, originating in the southward shift of the western North Pacific subtropical high. The projected decrease in June precipitation over western Japan is confirmed to be robust, regardless of model differences in horizontal resolution, convection schemes, and with/without air-sea interactions.
In this study, the polarimetric variables of clear-air echoes (CAEs), that appeared on May 21, 2016 in the Tokyo metropolitan area, Japan, were investigated using the Ka-band (8.6-mm-wavelength) polarimetric cloud radar capable of detecting non-precipitating clouds. The objective was to establish the potential for distinguishing CAEs and hydrometeor echoes in the initial stage of cloud formation using a Ka-band polarimetric cloud radar. On the day being studied, CAEs showed evident diurnal variation. There were no CAEs before sunrise. The equivalent radar reflectivity (Ze) increased with time after sunrise, and horizontally widespread echoes (max. value > −15 dBZ) occurred within the radar observation range in the early afternoon. After sunset and into the early part of the night, Ze decreased rapidly. Range-height indicator observations showed that CAEs were restricted to heights of < 1.5 km. The differential reflectivity (ZDR) values of the CAEs were largely positive (1.8 dB) with a large standard deviation at 18:00 local time, i.e., considerably larger than those of cloud/weak precipitation echoes (0.4 dB) observed simultaneously. In comparison with cloud/precipitation echoes, the copolar correlation coefficient (ρhv) of the CAEs was smaller (< 0.9), whereas the variability of the total differential phase (ΨDP) in the range direction was larger. The upper limit of Ze and the distributions of ZDR and ρhv were inconsistent with the characteristics of the Bragg scattering observed by the S-band (10-cm-wavelength) radar in previous studies. However, the larger ZDR, smaller ρhv, and larger variability of ΨDP in the range direction, associated with the horizontally widespread echoes, were consistent with the characteristics of insect echoes. The depolarization ratio defined using ZDR and ρhv could be effective in distinguishing this type of CAE and hydrometeor echoes observed by Ka-band polarimetric cloud radar. The polarimetric variables obtained by Ka-band polarimetric cloud radar are useful in distinguishing between CAEs and hydrometeor echoes.
The impact of diurnal precipitation over Sumatra Island, the Indonesian Maritime Continent (MC), on synoptic disturbances over the eastern Indian Ocean is examined using high-resolution rainfall data from the Global Satellite Mapping of Precipitation project and the Japanese 55-year Reanalysis data during the rainy season from September to April for the period 2000–2014. When the diurnal cycle is strong, the high precipitation area observed over Sumatra in the afternoon migrates offshore during nighttime and reaches 500 km off the coast on average. The strong diurnal events are followed by the development of synoptic disturbances over the eastern Indian Ocean for several days, and apparent twin synoptic disturbances straddling the equator develop only when the convective center of the Madden–Julian Oscillation (MJO) lies over the Indian Ocean (MJO-IO). Without the MJO, the synoptic disturbances develop mainly south of the equator. The differences in the locations and behaviors of active synoptic disturbances are related to the strength of mean horizontal winds in the lower troposphere. During the MJO-IO, the intensification of mean northeasterly winds in the northern hemisphere blowing into the organized MJO convection in addition to mean southeasterly winds in the southern hemisphere facilitate the formation of the twin disturbances. These results suggest that seed disturbances arising from the diurnal offshore migration of precipitation from Sumatra develop differently depending on the mean states over the eastern Indian Ocean. Furthermore, it is shown that the MJO events with the strong diurnal cycle tend to have longer duration and continuing eastward propagation of active convection across the MC, whereas the convective activities of the other MJO events weaken considerably over the MC and develop again over the western Pacific. These results suggest that the strong diurnal cycle over Sumatra facilitates the smooth eastward propagation of the intraseasonal convection across the MC.
This study systematically evaluates the accuracy, trends, and error sources for western North Pacific tropical cyclone intensity forecasts between 2005 and 2018. The study uses homogeneous samples from tropical cyclone (TC) intensity official forecasts issued by the China Meteorological Administration (CMA), Joint Typhoon Warning Center (JTWC), and Regional Specialized Meteorological Center Tokyo-Typhoon Center (RSMC-Tokyo). The TC intensity forecast accuracy performances are as follows: 24–48 h, JTWC > RSMC-Tokyo > CMA; 72 h, JTWC > CMA > RSMC-Tokyo; and 96–120 h, JTWC > CMA. Improvements in TC intensity forecasting are marginal but steady for all three centers. The 24–72 h improvement rate is approximately 1–2 % yr−1. The improvement rates are statistically significant at the 95 % level for almost half of the verification times from 0–120 h. The three centers tend to overestimate weak TCs over the northern South China Sea, but strong TCs are sometimes underestimated over the area east of the Philippines. The three centers generally have higher skill scores associated with forecasting of rapid weakening (RW) events than rapid intensification (RI) events. Overall, the three centers are not skillful in forecasting RI events more than three days in advance. Fortunately, RW events could be forecasted five days in advance with an accuracy order of CMA > RSMC-Tokyo > JTWC.
In July of 2017 and 2018, heavy rainfall events occurred, leading to significant damage in Japan. This study investigated the rainfall characteristics and environmental conditions for these heavy rainfall events using rain intensity data from operational weather radars and mesoscale analysis data. An automatic algorithm was developed to categorize precipitating cloud systems into five types, one with weaker rainfall (less than 10 mm h−1) and four with stronger rainfall (greater than or equal to 10 mm h−1), i.e., quasi-stationary convective clusters (QSCCs), propagating convective clusters (PCCs), short-lived convective clusters (SLCC), and other convective but unorganized rainfall. The rainfall amount due to the weaker rainfall was found to dominate the total rainfall in most of the analysis region; however, the contribution from the stronger rainfall types became larger than that from the weaker rainfall type in regions that experienced heavy rainfall. Among the stronger rain types, SLCCs dominate over the rainfall contributions from QSCCs or PCCs, whereas rainfalls from convective but unorganized systems are very minor. It was emphasized that the contribution from stronger rains due to organized systems with areas of 200 km2 plays a major role in regions with significant amounts of rainfall during the heavy rainfall events examined here. The examination of the environmental conditions for the development of each system demonstrated that, from the viewpoint of moisture content, the stability conditions were more unstable in 2018 than in 2017. There is also a clear linkage in the time series between rainfall types and the environmental properties of precipitable water and vertical shear. It was found that both the column moisture content and the middle-to-upper-level relative humidity characterize the environmental conditions for the occurrence of the present heavy rainfall events. Features of the rainfall types and their environmental conditions were compared with the QSCC climatology.
Estimation of path attenuation is a critical part of retrieving precipitation parameters using measurements from the Dual-frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement Mission (GPM) satellite. In this paper, we describe the latest implementation of the surface reference technique (SRT) that uses surface scattering properties to infer path attenuation through the precipitation. Both single- and dual-frequency versions of this method are available and although the dual-frequency version appears to be more accurate at moderate rain rates, the single-frequency approach at Ku-band is needed when the Ka-band data are not available. Despite improvements afforded by the dual-frequency version of the method, other methods such as the Hitschfeld-Bordan and standard dual-frequency approaches offer advantages particularly at lighter rain rates and at near-nadir incidence angles over land. Weighted averages of the results from these methods appear to offer the best estimate of path attenuation presently available.