Search Results (20)

The Tibetan Plateau vortex (TPV), as an α-scale mesoscale weather system, often brings severe weather conditions like torrential rain and severe convective storms. Based on the detections from the Global Precipitation Measurement (GPM) Core Observatory’s Dual-frequency Precipitation Radar (DPR) and the FY-4A satellite’s Advanced Geostationary Radiation Imager (AGRI), combined with ERA5 reanalysis data, the precipitation characteristics of a TPV moving eastward during 8–13 July 2021 at different developmental stages are explored in this study. It was clear that the near-surface precipitation rate of the TPV during the initial stage at the eastern Tibetan Plateau (TP) was below 1 mm·h⁻¹, implying overall weak precipitation dominated by stratiform clouds. After moving out of the TP, the radar reflectivity factor (Ze), precipitation rate, and normalized intercept parameter (dBNw) significantly increased, while the proportion of convective clouds gradually rose. Following the TPV movement, the distribution range and vertical thickness of Ze, mass-weighted mean diameter (Dm), and dBNw tended to increase. The high-frequency region of Ze appeared at 15–20 dBZ, while Dm and dBNw occurred at around 1 mm and 33 mm⁻¹·m⁻³, respectively. Near the melting layer, Ze was characterized by a significant increase due to the aggregation and melting of ice crystals. The precipitation rate of convective clouds was generally greater than that of stratiform clouds, whilst both of them increased during the movement of the TPV. Particularly, at 01:00 on 12 July, there was a significant increase in the precipitation rate and Dm of convective clouds, while dBNw noticeably decreased. These findings could provide valuable insights into the three-dimensional structure and microphysical characteristics of the precipitation during the movement of the TPV, contributing to a better understanding of cloud precipitation mechanisms. Full article

A series of typhoons and tropical storms have produced extreme precipitation events in Vietnam during the first part of the 2020 monsoon season: events of this magnitude pose significant challenges to remote sensing Quantitative Precipitation Estimation (QPE) techniques. The weather-monitoring needs of modern human activities require that these challenges be overcome. In order to address this issue, in this work, seven precipitation products were validated with high spatial and temporal detail against over 1200 rain gauges in Vietnam during six case studies tailored around the most intense events of 2020. The data sources included the Vietnamese weather radar network, IMERG Early run and Final run, the South Korean GEO-KOMPSAT-2A and Chinese FengYun-4A geostationary satellites, DPR on board the GPM-Core Observatory, and European ERA5-Land reanalysis. All products were resampled to a standardized 0.02° grid and compared at hourly scale with ground stations measurements. The results indicated that the radars product was the most capable of reproducing the information collected by the rain gauges during the selected extreme events, with a correlation coefficient of 0.70 and a coefficient of variation of 1.38. However, it exhibited some underestimation, approximately 30%, in both occurrence and intensity. Conversely, geostationary products tended to overestimate moderate rain rates (FY-4A) and areas with low precipitation (GK-2A). More complex products such as ERA5-Land and IMERG failed to capture the highest intensities typical of extreme events, while GPM-DPR showed promising results in detecting the highest rain rates, but its capability to observe isolated events was limited by its intermittent coverage. Full article

The northeastern China cold vortex (NCCV) is the main weather system affecting Northeast China. Based on the precipitation products from the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement core observatory (GPM) satellite, the precipitation structures and microphysical properties for different rain types in 6432 NCCVs from 2014 to 2019 were studied using dynamic composite analysis. Our results show that the precipitation in NCCVs is dominated by stratiform precipitation. Regions with high stratiform and convective precipitation frequency have a comma shape. The growth mechanism of precipitation particles changes at ~4 km in altitude, the lower particles grow through collision (more pronounced in convective precipitation), and the upper hydrometeors grow through the Bergeron process. Additionally, the precipitation structures and microphysical properties exhibit great regional variations in NCCVs. The rainfall for all rain types is the strongest in the southeast region within an NCCV, mainly characterized by higher near-surface droplet concentration, while precipitation events occur more frequently in the southeast region for all rain types. There are active rimming growth processes above the melting layer for convective precipitation in the western region of an NCCV. In the southeast region of an NCCV, the collision growth of droplets in both types of precipitation is the most obvious. Full article

The impacts of hailstorms on human beings and structures and the associated high economic costs have raised significant interest in studying storm mechanisms and climatology, thus producing a substantial amount of literature in the field. To contribute to this effort, we have explored the hail frequency in the Mediterranean basin during the last two decades (1999–2021) on the basis of hail occurrences derived from the observations of the microwave radiometers on board satellites of the Global Precipitation Measurement Constellation (GPM-C) from 2014 (date of GPM Core Observatory launch) onwards and merging multiple other satellite platforms prior to 2014. According to the MWCC-H method, two hail event categories (hail and super hail) are identified, and their spatiotemporal distributions are evaluated to identify the hail development areas in the Mediterranean and the corresponding monthly climatology of hail occurrences. Our results show that the northern sectors of the domain (France, Alpine Region, Po Valley, and Central-Eastern Europe) tend to be hit by hailstorms from June to August, while the central sectors (from Spain to Turkey) are more affected as autumn approaches. The trend analysis shows that the mean number of hail events over the entire domain tends to substantially increase, showing a higher increment during 2010–2021 than during 1999–2010. This behavior was particularly enhanced over Southern Italy and the Balkans. Our findings point to the existence of “sub-hotspots”, i.e., Mediterranean regions most susceptible to hail events and thus possibly more vulnerable to climate change effects. Full article

Global precipitation measurement (GPM) is one of the effective means employed to observe orographic precipitation, and its inversed GPM DPR data can be used to study the microphysical structure of precipitation particles. This study considers statistics on convective precipitation (CP) and stratiform precipitation (SP) events over three types of terrain (plains, mountains, and high mountains) using the DPR onboard the GPM Core Observatory from May to September of 2014–2021 to analyze the vertical structure of heavy CP and SP. In mountain areas and high mountain areas, the updraft rendered by topography or seeder-feeder mechanism is not only conducive to the collision and merger of raindrops into large raindrops, but also increases the concentration of small drops, which is the main reason why the occurrence probability of not only large but also small raindrops increases and the horizontal distribution domain of mass weighted average raindrop diameter (D_m) widens. For heavy SP, the occurrence probability of medium-diameter precipitation particles below the freezing height (FzH) over high mountains is greater than those over plains. The precipitation particles above 10 km altitude of high mountains have characteristics, such as lower droplet number concentration and larger diameter, compared with those over plains. Furthermore, the study also analyzed the correlation between storm top altitude (STA) and D_m, water vapor and STA respectively. This study is helpful to further understand the effect of topography on heavy precipitation through cloud microphysical processes and the vertical structure of precipitation. Full article

Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud–convection–precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote sensing measurements that help us distinguish them. The Global Precipitation Measurement (GPM) Core Observatory’s microwave imager (GMI) and dual-frequency precipitation radar (DPR) together provide ample information on global precipitation characteristics. As an active sensor, the DPR provides an accurate precipitation type assignment, while passive sensors such as the GMI are traditionally only used for empirical understanding of precipitation regimes. Using collocated precipitation type flags from the DPR as the “truth”, this paper employs machine learning (ML) models to train and test the predictability and accuracy of using passive GMI-only observations together with ancillary information from a reanalysis and GMI surface emissivity retrieval products. Out of six ML models, four simple ones (support vector machine, neural network, random forest, and gradient boosting) and the 1-D convolutional neural network (CNN) model are identified to produce 90–94% prediction accuracy globally for five types of precipitation (convective, stratiform, mixture, no precipitation, and other precipitation), which is much more robust than previous similar effort. One novelty of this work is to introduce data augmentation (subsampling and bootstrapping) to handle extremely unbalanced samples in each category. A careful evaluation of the impact matrices demonstrates that the polarization difference (PD), brightness temperature (Tc) and surface emissivity at high-frequency channels dominate the decision process, which is consistent with the physical understanding of polarized microwave radiative transfer over different surface types, as well as in snow and liquid clouds with different microphysical properties. Furthermore, the view-angle dependency artifact that the DPR’s precipitation flag bears with does not propagate into the conical-viewing GMI retrievals. This work provides a new and promising way for future physics-based ML retrieval algorithm development. Full article

Typhoon In-Fa hit continental China in July 2021 and caused an unprecedented rainfall amount, making it a typical case to examine the ability of numerical models in forecasting landfalling typhoons. The record-breaking storm was simulated using a 3-km-resolution weather research and forecast (WRF) model with spectral bin microphysics scheme (BIN) and two-moment seven-class bulk parameterization scheme (BULK). The simulations were then separated into three different typhoon landfall periods (i.e., pre-landfall, landfall, and post-landfall). It was found that typhoon intensity prediction is sensitive to microphysical schemes regardless of landfall periods, while typhoon track prediction tends to be more (less) sensitive to microphysical schemes after (before) typhoon landfall. Moreover, significant differences exist between BIN and BULK schemes in simulating the storm intensity, track, and rainfall distribution. BIN scheme simulates stronger (weaker) typhoon intensity than BULK scheme after (before) landfall, while BULK scheme simulates typhoon moving faster (slower) than BIN scheme before (after) landfall. BIN scheme produces much more extensive and homogeneous typhoon rainbands than BULK scheme, whereas BULK scheme produces stronger (weaker) rainfall in the typhoon inner (outer) rainbands. The possible reasons for such differences are discussed. At present, the ability of WRF and other mesoscale models to accurately simulate the typhoon precipitation hydrometeors is still limited. To evaluate the performances of BIN and BULK schemes of WRF model in simulating the condensed water in Typhoon In-Fa, the observed microwave brightness temperature and radar reflectivity from the core observatory of Global Precipitation Mission (GPM) satellite are directly used for validation with the help of a satellite simulator. It is suggested that BIN scheme has better performance in estimating the spatial structure, overall amplitude, and precise location of the condensed water in typhoons before landfall. During typhoon landfall, the performance of BIN scheme in simulating the structure and location of the condensate is close to that of BULK scheme, but the condensate intensity prediction by BIN scheme is still better; BULK scheme performs even better than BIN scheme in the prediction of condensate structure and location after typhoon landfall. Both schemes seem to have poorer performances in simulating the spatial structure of precipitation hydrometeors during typhoon landfall than before/after typhoon landfall. Moreover, BIN scheme simulates more (less) realistic warm (cold) rain processes than BULK scheme, especially after typhoon landfall. BULK scheme simulates more cloud water and larger convective updraft than BIN scheme, and this is also reported in many model studies comparing BIN and BULK schemes. Full article

The primary goal of the dual-frequency precipitation radar (DPR) aboard the Global Precipitation Measurement (GPM) Core Observatory satellite is to infer precipitation rate and raindrop/particle size distributions (DSD/PSD). The focus of this paper is threefold: (1) to describe the DPR retrieval algorithm that uses an adjustable relationship between rain rate (R) and the mass-weighted diameter (D_m) or an R-D_m relationship in solving for R and D_m simultaneously; (2) to evaluate the DPR algorithm based on the physical simulations that employ measured DSD/PSD to understand the mechanism and error characteristics of the retrieval method; (3) to review ground validation studies for DPR products as well as to analyze the strengths and weaknesses of ground radar and rain gauge/disdrometer validations. Overall, the DPR Version 6 algorithm provides reasonably accurate estimates of R and D_m in rain. Non-uniformity in the rain profile, however, tends to degrade the accuracy of the R and D_m estimates to some extent as the range-independent assumption of the adjustable parameter (ε) of the R-D_m relation is not able to fully account for natural variation of DSD in the vertical profile. The DPR snow rate is underestimated as compared with the independent dual-frequency ratio (DFR) technique. This is possibly the result of the constraint associated with the path integral attenuation (PIA)/differential PIA (δPIA) used in the DPR algorithm to find the best ε and range-independent ε assumption. A range-variable ε model, proposed in the DPR Version 7 algorithm, is expected to improve rain and snow retrieval. Full article

Tropical-like cyclone (TLC or medicane) Ianos formed during mid-September 2020 over the Southern Mediterranean Sea, and, during its mature stage on days 17–18, it affected southern Italy and especially Greece and its Ionian islands, where it brought widespread disruption due to torrential rainfall, severe wind gusts, and landslides, causing casualties. This study performs a sensitivity analysis of the mature phase of TLC Ianos with the WRF model to different microphysics parameterization schemes and initial and boundary condition (IBC) datasets. Satellite measurements from the Global Precipitation Measurement Mission-Core Observatory (GPM-CO) dual-frequency precipitation radar (DPR) and the Advanced Scatterometer (ASCAT) sea-surface wind field were used to verify the WRF model forecast quality. Results show that the model is most sensitive to the nature of the IBC dataset (spatial resolution and other dynamical and physical differences), which better defines the primary mesoscale features of Ianos (low-level vortex, eyewall, and main rainband structure) when using those at higher resolution (~25 km versus ~50 km) independently of the microphysics scheme, but with the downside of producing too much convection and excessively low minimum surface pressures. On the other hand, no significant differences emerged among their respective trajectories. All experiments overestimated the vertical extension of the main rainbands and display a tendency to shift the system to the west/northwest of the actual position. Especially among the experiments with the higher-resolution IBCs, the more complex WRF microphysics schemes (Thompson and Morrison) tended to outperform the others in terms of rain rate forecast and most of the other variables examined. Furthermore, WSM6 showed a good performance while WDM6 was generally the least accurate. Lastly, the calculation of the cyclone phase space diagram confirmed that all simulations triggered a warm-core storm, and all but one also exhibited axisymmetry at some point of the studied lifecycle. Full article

Precipitation is one of the integral components of the global hydrological cycle. Accurate estimation of precipitation is vital for numerous applications ranging from hydrology to climatology. Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, the Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation product was released. The IMERG provides global precipitation estimates at finer spatiotemporal resolution (e.g., 0.1°/half-hourly) and has shown to be better than other contemporary multi-satellite precipitation products over most parts of the globe. In this study, near-real-time and research products of IMERG have been extensively evaluated against a daily rain-gauge-based precipitation dataset over India for the southwest monsoon period. In addition, the current version 6 of the IMERG research product or Final Run (IMERG-F V6) has been compared with its predecessor, version 5, and error characteristics of IMERG-F V6 for pre-GPM and GPM periods have been assessed. The spatial distributions of different error metrics over the country show that both near-real-time IMERG products (e.g., Early and Late Runs) have similar error characteristics in precipitation estimation. However, near-real-time products have larger errors than IMERG-F V6, as expected. Bias in all-India daily mean rainfall in the near-real-time IMERG products is about 3–4 times larger than research product. Both V5 and V6 IMERG-F estimates show similar error characteristics in daily precipitation estimation over the country. Similarly, both near-real-time and research products show similar characteristics in the detection of rainy days. However, IMERG-F V6 exhibits better performance in precipitation estimation and detection of rainy days during the GPM period (2014–2017) than the pre-GPM period (2010–2013). The contribution of different rainfall intensity intervals to total monsoon rainfall is captured well by the IMERG estimates. Furthermore, results reveal that IMERG estimates under-detect and overestimate light rainfall intensity of 2.5–7.5 mm day⁻¹, which needs to be improved in the next release. The results of this study would be beneficial for end-users to integrate this multi-satellite product in any specific application. Full article

Since early March 2014, the NASA/JAXA Global Precipitation Measurement Core- Observatory (GPM-CO) satellite has allowed analysis of precipitation systems around the globe, thanks to the capabilities of the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR). In this work, we demonstrate how GPM-CO measurements obtained from 4 years of observations over the Mediterranean area can be used as an extremely effective tool to study the main climatological characteristics of the most intense Mediterranean storm structures. DPR and GMI-based Precipitation Features (PFs) parameters are used as proxies of the vertical structure and microphysical properties of these events, and their statistical distribution is analyzed to identify extremes. The analysis of annual, seasonal and geographical distribution of the identified deep convective systems highlights substantial differences in their diurnal cycle and in the distribution between land-sea and summer-winter. There is a general shift of the convective systems from the south (mostly over the sea) in the cold season, to the north (mostly over land) in the warm season. The analysis shows also that the inferred convective intensity is not always related to heavy precipitation. Known DPR and GMI-based criteria were adopted to identify overshooting top events and potential hailstorms, identify extreme deep convection signatures, like those observed for tropical and subtropical systems, and the most intense occur mostly over the sea. Although the analysis is limited to four years, the results show that the GPM-CO offers unprecedented measurements to identify and characterize extreme weather events in the Mediterranean region, with unique potentials for future long-term climatology and interannual variability analysis. Full article

This paper presents the results of the first characterization of coincident Ku- and Ka-band ocean surface normalized radar cross section measurements at earth incidence angles 0°–18° using one year of wide swath Global Precipitation Measurement (GPM) mission dual frequency precipitation radar (DPR) data. Empirical geophysical model functions were derived for both bands, isotropic and directorial sensitivity were assessed, and finally, sea surface temperature (SST) dependence of radar backscatter, at both bands, were investigated. The Ka-band exhibited higher vector wind sensitivity for a low-to-moderate wind speeds regime, and the SST effects were also observed to be substantially larger at Ka-band than at Ku-band. Full article

This paper explores the capability of high frequency microwave measurements at vertical and horizontal polarizations in detecting snowfall over land. Surface in-situ meteorological data were collected over Conterminous US during two winter seasons in 2014–2015 and 2015–2016. Statistical analysis of the in-situ data, matched with Global Precipitation Measurement (GPM) Microwave Imager (GMI) measurements on board NASA/JAXA Core Observatory, showed that the polarization difference at 166 GHz had the highest correlation to measured snowfall rate compared to the single channel high frequency measurements and the polarization difference at 89 GHz. A logistic regression model applied to the match-up data, using the polarization difference at 166 and 89 GHz as predictors, yielded an overall snowfall classification rate of 69.0%, with the largest contribution coming from the polarization difference at 166 GHz. Logistic regression using the four single channels as predictors (at 89 and 166 GHz, horizontal and vertical polarizations) further indicated that the horizontal polarization at 166 GHz was the most important contributor. An overall classification rate of 73% was achieved by including the 183.31 ± 3 GHz and 183.31 ± 7 GHz vertical polarization channels in the final logistic regression model. Evaluation of the final algorithm demonstrated skill in snowfall detection of two significant events. Full article

This study shows how satellite-based passive and active microwave (MW) sensors can be used in conjunction with high-resolution Numerical Weather Prediction (NWP) simulations to provide insights of the precipitation structure of the tropical-like cyclone (TLC) Numa, which occurred on 15–19 November 2017. The goal of the paper is to characterize and monitor the precipitation at the different stages of its evolution from development to TLC phase, throughout the storm transition over the Mediterranean Sea. Observations by the NASA/JAXA Global Precipitation Measurement Core Observatory (GPM-CO) and by the GPM constellation of MW radiometers are used, in conjunction with the Regional Atmospheric Modeling System (RAMS) simulations. The GPM-CO measurements are used to analyze the passive MW radiometric response to the microphysical structure of the storm, while the comparison between successive MW radiometer overpasses shows the evolution of Numa precipitation structure from its early development stage on the Ionian Sea into its TLC phase, as it persists over southern coast of Italy (Apulia region) for several hours. Measurements evidence stronger convective activity at the development phase compared to the TLC phase, when strengthening or weakening phases in the eye development, and the occurrence of warm rain processes in the areas surrounding the eye, are identified. The weak scattering and polarization signal at and above 89 GHz, the lack of scattering signal at 37 GHz, and the absence of electrical activity in correspondence of the rainbands during the TLC phase, indicate weak convection and the presence of supercooled cloud droplets at high levels. RAMS high-resolution simulations support what inferred from the observations, evidencing Numa TLC characteristics (closed circulation around a warm core, low vertical wind shear, intense surface winds, heavy precipitation), persisting for more than 24 h. Moreover, the implementation of DPR 3D reflectivity field in the RAMS data assimilation system shows a small (but non negligible) impact on the precipitation forecast over the sea up to a few hours after the DPR overpass. Full article

Observational data from the Global Precipitation Measurement (GPM) Core Observatory during four summers (2014–2017) has been used to investigate deep convection systems (DCSs) over the Tibetan Plateau (TP) and its south slope (SS). The frequency, geographical distribution diurnal variation, and vertical structure of DCSs over the TP and SS are compared among these two regions. The frequency of DCSs over the SS (0.98%) was far higher than over the TP (0.15%), suggesting that stronger DCSs occur to the east and south of the TP. The maximum number of DCS occurred in July and August. A clear diurnal variation in DCS was found over the whole region, DCSs over the TP and SS both have a greatest amplitude in the afternoon. The probability of DCSs from 1200 to 1800 local time (LT) was 76.3% and 44.1% over TP and SS respectively, whereas the probability of DCSs being generated from 2200 (LT) to 0600 on the next day LT was 0.03% and 33.1% over the TP and SS respectively. There was a very low frequency of DCSs over the TP during the night. Five special echo top heights were used to investigate the vertical structure of DCSs. DCSs over the TP were both weaker and smaller than those over the SS. Full article