Rain Rate Research Articles (Page 1)

Abstract The variability of raindrop size distribution (DSD) across terrain gradients plays a critical role in regulating rainfall microphysics and kinetic energy (KE), yet this variability remains under-explored in semi-arid regions. This study utilized six summers (2019–2024) of 92 disdrometer observations across Shaanxi Province, China, to demonstrate the pronounced south-north DSD gradient across terrains: while the site-averaged mass-weighted mean diameter ( D m ) increased, the normalized intercept parameter (log10 N w ) decreased rapidly from mountains to plateaus. The humid Qinling-Daba Mountains exhibits higher seasonal rainfall (>800 mm) dominated by a high concentration of small raindrops, while the semi-arid Loess Plateau shows frequent occurrences of large raindrops. This DSD shift drives higher KE in the Loess Plateau (25/28 stations >20 J m −2 mm −1 ) than in the Qinling-Daba Mountains (only 2/25 stations >20 J m −2 mm −1 ), thus amplifying soil erosion risks due to vulnerable loessal substrates. Despite the spatial heterogeneity, the site-averaged D m −log 10 N w pairs demonstrate consistent evolution throughout Shaanxi along with the increase in rain rate. We further establish D m as a universal microphysical constraint, deriving robust estimators for KE, accretion/evaporation rates, and mass-weighted terminal velocity of DSD. These relationships overcome the limitations of traditional methods by encoding terrain-mediated DSD heterogeneity through D m parameter. Consequently, they are practical for facilitating high-accuracy estimation of key microphysical quantities and process rates without full DSD resolution. Our findings offer critical semi-arid DSD benchmarks for improving model microphysics parameterization, advancing remote sensing-based KE retrievals, and designing targeted soil conservation strategies for erosion hotspots.

ABSTRACT Vertical mixing in the planetary boundary layer greatly influences thunderstorm activity. The sensitivity of two local (MYJ and MYNN) and one non‐local (YSU) PBL schemes with a combination of Single Layer Urban Canopy Model (SLUCM) of the Weather Research and Forecasting (WRF) model is studied at 2 km horizontal resolution for the evolution of thunderstorms. Twelve thunderstorms over four cities in the eastern Indian region are identified during 2016–2021. Results highlighted that the YSU scheme performs better with a rainfall absolute percentage of error of 27%, while the MYJ and MYNN exhibited comparatively higher errors of 31% and 38%, respectively, within a 50 km area from the city center. The mean timing error of initiation and mature stage against GPM rainfall is 0–1 h in the YSU scheme and 0.5–2 h for both MYJ and MYNN. The lead–lag correlation (0.6 at 00 h) and quantitative rain rate verification also confirm the better performance of YSU. Surface (2 m) and atmospheric dynamical and thermodynamic profiles are replicated well with lower errors in YSU, except for 10 m wind speed. Diagnostic analysis indicates that higher frictional velocities and turbulent kinetic energy in YSU resemble the higher vertical mixing, leading to an unstable atmosphere with stronger updrafts. These PBL characteristics are relatively weaker in MYJ and MYNN as well as the stability indices. Overall, the better performance of the YSU scheme can be attributed to the better transport of surface characteristics, including turbulent fluxes and moisture, to the upper levels in an unstable atmosphere with strong vertical velocities. Further, results highlight that the simulation of urban thunderstorms improved with urban physics when compared with no‐urban simulations. Thus, this study emphasizes the role of PBL along with urban physics in steering the dynamics of urban thunderstorms.

Abstract. The Weather Research and Forecasting (WRF) model includes urban schemes that simulate the influence of urban surfaces on the atmosphere using parameterizations for flux, and radiative exchanges. Three core schemes – the Bulk urban parameterization, Single-Layer Urban Canopy Model (SLUCM), and Multi-Layer Urban Canopy Model (MLUCM) – represent increasing levels of complexity. Although the parameterizations within these urban schemes are described in the literature, their specific implementation remains poorly documented, thus slowing down model development efforts. This manuscript provides a roadmap to the three urban schemes in WRF version 4.5.2, presenting equations using the same symbols as in the model code, along with references to code lines, and including graphics and explanations that connect the code to its physical foundations. Our thorough review of the urban parameterizations implemented in WRF version 4.5.2 highlighted a handful of parameters that may introduce discontinuities in simulations: (i) in the SLUCM, a 1 mm h−1 rain rate threshold is employed to switch between two minimum moisture availability parameterizations, thus impacting latent heat flux calculations; (ii) in the SLUCM a threshold is used to partition shortwave radiation into direct and diffuse components; (iii) in all three urban schemes, the bulk Richardson number is employed to select the similarity function, which influences the vertical distribution of heat and momentum. We also identified a highly simplified treatment of the radiative balance on roof surfaces. The implications of these simplifications can be assessed through targeted observations across relevant conditions, including varying precipitation rates, cloud cover, and transitions between stability regimes. Furthermore, the widespread application of the Monin-Obukhov similarity theory in these urban schemes warrants model evaluation under highly stable and unstable conditions and in heterogeneous urban settings with variable land cover and building heights on scales finer than model resolution. To address these challenges, we offer guidance on observational strategies, emphasizing the need for multi-parametric measurements to capture potential compensating biases and multi-height measurements that align with the levels where quantities are diagnostic and prognosed in the model (i.e., the lowest atmospheric level of the WRF model). Finally, our inspection of the code revealed implementation bugs that have now been corrected in WRF versions 4.6.0 and 4.6.1. Sensitivity tests over the Atlanta urban area show that these corrections affect surface temperatures, underscoring the importance of performing rigorous documentation and verification of the implementation of parameterizations in model code.

Abstract Deep convection and cold‐pool characteristics over Germany during July 2023 are investigated using Deutscher Wetterdienst (DWD) radar observations and a convection‐permitting Weather Research and Forecasting (WRF) model simulation. The analysis combines instantaneous snapshots of convection with a Lagrangian tracking approach to examine the life cycles of isolated convective cells. WRF successfully captures the general morphology and evolution of deep convection and associated cold pools, although it tends to produce smaller, more intense rain‐producing cells. Simulated cold‐pool properties‐including wind gusts and virtual potential temperature () reductions‐align well with observations (e.g., median drop of 2.95 K and wind gusts of , extreme gusts of ), suggesting that the model represents key features of convective outflows reliably. The temporal evolution of convective cell properties shows a downward‐facing parabolic pattern in both model and observations in terms of cell size, rain rate, and reflectivity, although WRF intensifies convection too quickly and consistently overestimates rain rates. An analysis of wind‐energy‐relevant metrics reveals that cold pools induce substantial increases in wind speed, stability, and vertical shear. Estimated power output increases by 35%–60% for long‐lived cells and 33%–50% for short‐lived ones, peaking during the mid‐to‐late cell life cycle. These findings highlight the need to consider cold‐pool dynamics in wind‐energy forecasting and operations.

Abstract Convective overshooting significantly influences atmospheric material and energy cycles and can cause severe social impacts; nevertheless, aerosol effects on its microphysical structure and associated extreme weather remain unclear. Using a more accurate algorithm of detecting convective overshooting and 10 years of high‐resolution data, this paper investigated how aerosols affect convective overshooting, focusing on its long‐term patterns, three‐dimensional microphysical structure of precipitation such as particle size and concentration, as well as the relationship with extreme weather. Results show that convective overshooting occurred more frequently and intensely in the Maritime Continent, exhibiting a significant increasing trend. Aerosols exerted a stronger influence on convective overshooting during water vapor sufficient seasons, and polluted conditions produced larger but sparser raindrops, with raindrop diameters increasing by 1.2–1.76 times while concentration decreased by 20%. Moreover, aerosols enhanced convective overshooting impact on rain rates and lightning by 20% and 50%, respectively, nearly doubling the spatial influence area.

Abstract. Understanding the characteristics of the rain cell, the most basic unit in the natural precipitation system, is helpful in improving the cognition of the precipitation system. In this study, based on the merged precipitation profile data, reflectance and infrared data, and microwave brightness temperature data observed by the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR), visible and infrared scanner (VIRS) and TRMM microwave imager (TMI), rain cells were identified in the PR swath. For the identified valid rain cells, two fitting methods (the minimum bounding rectangle (MBR) and the best fit ellipse (BFE)) were applied to fit the external frame. Then, the geometric and physical parameters of rain cells were also calculated. By analyzing the geometric parameters (length, width, height, and so on) and physical parameters (rain rate, visible reflectance and thermal infrared brightness temperature from cloud top, and microwave brightness temperature from cloud column) of two rain cells (weak rain cell and strong rain cell), the results indicate that the strong rain cell is filled with deep convective precipitation and has low thermal infrared brightness temperature at the cloud top, while the weak rain cell is mainly characterized by stratiform precipitation with low rain rate. Compared to the BFE method, the area of the external frame calculated by the MBR method is generally larger. The filling ratio of the BFE method is slightly higher than that of the MBR method. In general, the results indicate that the rain cell definition parameters using the two fitting methods are reasonable and intuitive. The data used in this paper are freely available at https://doi.org/10.5281/zenodo.15387988 (Wu and Fu, 2025).

Abstract Shallow cumulus cloud fields in subtropical marine trade wind environments, particularly over the tropical Atlantic Ocean, show distinct organizational patterns. Among these, Flower‐type clouds are characterized by expansive stratiform cloud patches surrounded by regions of scattered convection. The objectives of this study were (a) to construct a case study of a time period during the A/ATOMIC field campaign when Flower‐type organization was observed, (b) to evaluate the fidelity of a multi‐model ensemble of large eddy simulations of that case, and (c) to analyze the interaction between cloud and precipitation processes and mesoscale organization in the simulations. The simulations follow a quasi‐Lagrangian trajectory, allowing mesoscale features to develop over time in a domain that follows the boundary‐layer airmass. The results show a broad agreement in simulated thermodynamic properties across different LES codes, with Flower‐type cloud patches appearing within hours of each other. The consensus among models is consistent with observations made during the A/ATOMIC field campaign on the specific day of interest. The cloud structure reveals three distinct peaks in the joint probability densities of cloud base and cloud top height, with the dominant peak at any given time influenced by the stage of cloud organization. The simulated cloud system evolution reveals consistent occurrence of maxima in liquid water path and rain rate before Flower reaches its maximum length scale. Targeted sensitivity tests reveal a weak relationship between Cloud Droplet Number concentration and the extent/degree/type of organization.

Abstract Quantitative precipitation nowcasting (QPN) for the next 3 h is critical in Taiwan due to the threat of extreme rainfall associated with severe weather systems. Conventional QPN extrapolation has achieved fairly good skill for the first-hour predictions but has encountered limitations for longer lead times. This study evaluates the performance of a deep learning QPN model up to a 3-h lead time, addressing the challenges of transitioning artificial intelligence (AI) research into real-world forecasting applications. The baseline model (Deep-QPF) of the convolutional recurrent neural network is trained with a dataset containing radar reflectivity and rain rates at a granularity of 10 min; however, this model tends to produce overpredictions in low rainfall regions. Here, we develop the upgraded model, yielding better skill, which is improved from the baseline model and is additionally driven by heterogeneous weather data. Specifically, an addtional “previous-output-as-next-input (PONI) module” is used to integrate heterogeneous data, including terrain, wind fields, orographic-lifting index, low-level equivalent potential temperature, and seasonality. Statistical verification shows that the “DeepQPF-PONI” is capable of restraining overestimation in drizzle regions and improving heavy rainfall prediction. Another significant contribution of this work is the comprehensive verifications of the DeepQPF-PONI against other operational baselines based on 2022 hindcasts: the proposed DeepQPF-PONI is more accurate than the conventional QPN method and the storm-scale numerical weather prediction. The DeepQPF-PONI substantially outperforms these operational techniques for second-hour and third-hour predictions due to its ability to capture the convective system propagation and the terrain effect on rainfall processes.

Abstract The southeastern United States frequently experience significant precipitation from tropical cyclones (TCs), leading to wind damage, flooding, and related hazards. Understanding the precipitation characteristics of these storms is vital for hydrological assessments. This study aims to characterize the amount and spatial distribution of precipitation contributed to a region by TCs using various precipitation datasets. The analysis focuses on TC Beryl in 2012 during an active drought period. The datasets include near-real-time and postprocessed satellite data (IMERG), merged ground radar observations (Stage IV), interpolated gauge observations [geographic COOP (G-COOP)], and point-based data from Automated Surface Observing System (ASOS). Previous studies have identified significant differences in rainfall characteristics between these products. The analysis reveals that traditional evaluation approaches for quantitative precipitation forecast products (such as the rain rate or the amount or timing of the rain) may not be adequate to compare the TC rainfall data. Instead, an integrated comparison using rainfall intensity and spatial verification metrics provides a more comprehensive evaluation of precipitation estimates during TCs. The spatial verification metrics indicate substantial overlap (∼70%) between satellite-derived and gauge/radar-based precipitation products for the 90th percentile rainfall, highlighting areas of agreement in extreme precipitation estimates. A rainfall threshold of < 65 mm is spatially aligned with a median maximum interest score of > 0.85, suggesting the utility of satellite-derived estimates, particularly due to their global availability. This analysis informs future hydrological studies and highlights the importance of considering rainfall volume in understanding the impacts of landfalling TCs. The findings contribute to a broader perspective on hydrological characteristics derived from different precipitation products, paving the way for further research in this area. Significance Statement Extreme weather events have contributed to trillion dollar losses in the United States since 1980. The flooding caused by tropical cyclones contributes to 40%–50% of this loss. Precipitation being the critical parameter, this study highlights the differences among a few gridded precipitation data products for the case study of TC Beryl 2012. The results show that the volumetric precipitation is more representative of the storm than instantaneous rainfall rates. The study also focuses on the usability of satellite precipitation data for tropical cyclone cases in regions with few in situ precipitation observations.

Abstract The Southeast Asian summer rainy season (SEASuRS) exhibits significant interannual variations, which are a challenge for reliable seasonal prediction. In this study, we provide a complementary approach to existing methodologies for the seasonal outlook of SEASuRS based on observational monitoring of the onset of the wet season. The onset and demise dates of SEASuRS are objectively diagnosed from the first and the last days of the year when the daily rain rates exceed the annual mean climatological rainfall at the granularity of the rainfall analysis. An ensemble of diagnosed onset/demise dates is generated by perturbing the original time series of observed daily rain rates to account for uncertainty at meso- to synoptic scales so that rain events unconnected to the annual cycle do not significantly influence the diagnosis of these dates. Our study shows that the interannual variations in the start dates of SEASuRS are significantly associated with variations in the season’s length and seasonal rainfall. These local relationships are leveraged to generate probabilistic seasonal forecasts, which reveal that longer and wetter seasons associated with early onset or shorter and drier seasons associated with late onset seasons yield very skillful seasonal outlooks at the granularity of the available rainfall analysis. Significance Statement This work leverages the relationship of the onset date variations of the Southeast Asian summer rainy season (SEASuRS) to provide a probabilistic seasonal outlook of the forthcoming season. These seasonal outlooks of the wet season are based on the diagnosis of the onset dates from observed rainfall analysis. We find that an early or later onset of the wet season is closely associated with a longer and wetter or shorter and drier wet season, respectively. We generate an ensemble of seasonal outlooks by perturbing the original time series of daily rain rates to account for uncertainty in observations and in the diagnosis of the onset dates from random precipitation events that may be unconnected to the annual cycle of the rainfall. These ensembles of seasonal outlooks for the SEASuRS show significant skill, especially for anomalous seasons, and are suggested as a complementary approach to existing seasonal prediction techniques, especially when teleconnections with external signals like the ENSO and Indian Ocean dipole are relatively weak. Given the high population density and significant socioeconomic dependence on agriculture such seasonal outlooks could be extremely useful to mitigate the impacts of seasonal climate variability.

Abstract This study uses high-resolution airborne data from the NASA Convective Process Experiments–Cabo Verde field campaign to examine the effects of synoptic conditions on mesoscale convective systems (MCSs) over the western coastal waters of West Africa. African easterly waves (AEWs) are the dominant source of synoptic variability over the region, but their influences on coastal areas remain unclear. This study compares airborne data from 2 days with contrasting synoptic conditions and MCS evolution over the coastal water. On one day, large MCSs with high rain rates persisted over the coastal water when the AEW trough was near the coastline. The flow modulation by the AEW strengthened the near-surface onshore flow and warmed and moistened the boundary layer over the coastal water. These combined effects increased the equivalent potential temperature of inflow air into the coastal MCSs, likely supporting their development. On the other day, an AEW was absent or weak, and coastal MCSs remained small and short lived without the effects of the AEW. The observed modulation of the coastal atmosphere and MCSs by AEWs during the field campaign is analogous to the historical relationship of AEW and coastal MCSs obtained by satellite and reanalysis data. Although not represented by the two field campaign cases, the historical analysis suggests the enhancement of coastal vertical wind shear by AEWs can also intensify coastal MCSs. These results highlight the importance of synoptic modulation in understanding the variability and mechanism of coastal MCSs, which differ from those over land or open ocean.

Abstract. In this work, the T-matrix approach is exploited to produce simulations of spectral polarimetric variables (spectral differential reflectivity, sZDR, spectral differential scattering phase, sδHV, and spectral correlation coefficient, sρHV) for observations of rain acquired from slant-looking W-band cloud radar. The spectral polarimetric variables are simulated with two different methodologies, taking into account instrument noise and the stochastic movement of the raindrops, introduced by raindrop oscillations and by turbulence. The simulated results are then compared with rain Doppler spectra observations from W-band radar for moderate rain rate conditions. Two cases, differing in levels of turbulence, are considered. While the comparison of the simulations with the measurements presents a reasonable agreement for equi-volume diameters less than 2.25 mm, large discrepancies are found in the amplitude (but not the position) of the maxima and minima of sZDR and, more mildly, of sδHV. This pinpoints a general weakness in approximating raindrop as spheroids to simulate radar backscattering properties at the W-band.

Abstract The impacts of the anthropogenic heat (AH) effect on the evolution of a merger‐formation bow echo over the Guangdong‐Hong Kong‐Macao Greater Bay Area are documented. The utilization of radar data assimilation greatly improves the simulated results comparing against observations, strengthening the robustness of analyses in this work. The simulation with AH effect produces the most accurate results compared to observations, exhibiting approximately 62% larger spatial extent of heavy rainfall (>30 mm) and twice the area of strong winds (>10.8 m s−1) compared to the non‐AH simulation. Additionally, the top 1% rain rates and surface winds from the AH‐included simulation are about 25% stronger and 23% greater, respectively, relative to the non‐AH counterpart. On the one hand, higher AH flux tends to enhance the values of convective available potential energy and vertical wind shear within urban areas on average, providing favorable thermodynamic environmental conditions for convective development. On the other hand, greater AH effect triggers stronger convective cell, leading to a more intense merged system. This cell plays a crucial role in the merger process and the formation of bow echo, but it does not persist sufficiently in the non‐AH simulation. A third sensitivity simulation, excluding the urban land cover, produces results comparable to those of the non‐AH simulation. This study quantifies the relative contribution of the AH effect to the evolution of convective systems and the associated weather‐related hazards over the Greater Bay Area, underscoring the significant impacts of AH forcing on the regional flow patterns and the corresponding convection dynamics.

This study examines a Mei-Yu rainfall event using rain gauges (RG) and OTT Parsivel disdrometers to observe precipitation characteristics and raindrop size distributions (RSD), with comparisons made against Weather Research and Forecasting (WRF) model simulations. Results show that Parsivel-derived rain rates (RR) are slightly underestimated relative to RG measurements. Both observations and simulations identify 1–3 mm raindrops as the dominant precipitation contributors, though the model overestimates small and large drop contributions. At low RR, decreased small-drop and increased large-drop concentrations cause corresponding leftward and rightward RSD shifts with decreasing altitude—a pattern well captured by simulations. However, at elevated rainfall rates, the simulated concentration of large raindrops shows no significant increase, resulting in negligible rightward shifting of RSD in the model outputs. Autoconversion from cloud droplets to raindrops (ATcr), collision and breakup between raindrops (AGrr), ice melting (MLir), and evaporation of raindrops (VDrv) contribute more to the number density of raindrops. At 0.1 < RR < 1 mm·h−1, ATcr dominates, while VDrv peaks in this intensity range before decreasing. At higher intensities (RR > 20 mm·h−1), AGrr contributes most, followed by MLir. When the RR is high enough, the breakup of raindrops plays a more important role than collision, leading to a decrease in the number density of raindrops. The overestimation of raindrop breakup from the numerical parameterization may be one of the reasons why the RSD does not shift significantly to the right toward the surface under the heavy RR grade. The RSD near the surface varies with the RR and characterizes surface precipitation well. Toward the surface, ATcr and VDrv, but not AGrr, become similar when precipitation approaches.

This study investigates the linkage between the radar reflectivity slope and raindrop size distribution (DSD) in precipitation with bright bands through coordinated C-band/Ka-band radar and disdrometer observations in southern China. Precipitation is classified into three types based on the reflectivity slope (K-value) below the freezing level, revealing distinct microphysical regimes: Type 1 (K = 0 to −0.9) shows coalescence-dominated growth; Type 2 (|K| > 0.9) shows the balance between coalescence and evaporation/size sorting; and Type 3 (K = 0.9 to 0) demonstrates evaporation/size-sorting effects. Surface DSD analysis demonstrates distinct precipitation characteristics across classification types. Type 3 has the highest frequency of occurrence. A gradual decrease in the mean rain rates is observed from Type 1 to Type 3, with Type 3 exhibiting significantly lower rainfall intensities compared to Type 1. At equivalent rainfall rates, Type 2 exhibits unique microphysical signatures with larger mass-weighted mean diameters (Dm) compared to other types. These differences are due to Type 2 maintaining a high relative humidity above the freezing level (influencing initial Dm at bottom of melting layer) but experiencing limited Dm growth due to a dry warm rain layer and downdrafts. Type 1 shows opposite characteristics—a low initial Dm from the dry upper layers but maximum growth through the moist warm rain layer and updrafts. Type 3 features intermediate humidity throughout the column with updrafts and downdrafts coexisting in the warm rain layer, producing moderate growth.

In this study, the relationship between radar reflectivity and rain rate (Z–R) was investigated. The analysis was conducted using data collected by the OTT Parsivel1 disdrometer during the periods 2012–2014 and 2019–2025 in Warsaw, Poland. As a first step, the parameters a and b of the power-law Z–R relationship were estimated separately for three precipitation types: rain, sleet (rain with snow), and snow. Subsequently, observational data from all 12 months of the annual cycle were used to derive Z–R relationships for 118 individual precipitation events. To date, only a few studies of this kind have been conducted in Poland. In the analysis limited to rain events, the estimated parameters (a = 265, b = 1.48) showed relatively minor deviations from the classical Z–R function for convective rainfall, Z = 300R1.4. However, the parameter a deviated more noticeably from the Z = 200R1.6 relationship proposed by Marshall and Palmer, which is commonly used to convert radar reflectivity into rainfall estimates, including in the Polish POLRAD radar system. The dataset used in this study included rainfall events of varying types, both stratiform and convective, which contributed to the averaging of Z–R parameters. The values for the parameter a in the Z–R relationship estimated for the other two categories of precipitation types, sleet and snow, were significantly higher than those determined for rain events alone. The a values calculated for individual events demonstrated considerable variability, ranging from 80 to 751, while the b values presented a more predictable range, from 1.10 to 1.77. The highest parameter a values were observed during the summer months: June, July, and August. The variability in the Z–R relationship for individual events assessed in this study indicates the need for further research under diverse meteorological conditions, particularly for stratiform and convective precipitation.

Abstract Tropical cyclones (TCs) can produce large rainfall totals which lead to devastating flooding, loss of life, and significant damage to infrastructure. Many studies have examined future changes in TC precipitation; however, few have considered changes owing to differences in the synoptic environment during landfall. Here, we focus on three North Atlantic TCs that impacted the southeastern United States: Hurricanes Floyd (1999), Matthew (2016), and Florence (2018). While these storms were impactful when they occurred, how might the impacts of similar systems change in a future climate? We address these questions using a pseudo–global warming (PGW) approach and ensembles of convection-allowing numerical model simulations. With this method, we compare future changes in precipitation characteristics such as accumulated rainfall and rain-rate frequency and distribution to assess how these changes differ as a function of synoptic environment. Hurricanes Matthew and Floyd, which have more synoptic-scale forcing for ascent while over our study region than Hurricane Florence, exhibit higher average rain rates in the present and future, but Hurricane Florence exhibits the largest increases in rain rates with warming (34% ± 12% vs 23% ± 9% and 21% ± 6% for Hurricanes Matthew and Floyd, respectively). When we consider accumulated precipitation, Hurricanes Matthew and Floyd have larger areal increases in precipitation totals greater than 250 mm than Hurricane Florence (17 600 ± 800 km2 and 22 400 ± 400 km2 vs 9800 ± 500 km2, respectively). These results point to the potential for future TCs in synoptically forced environments to have larger spatial footprints of heavy precipitation but smaller increases in rain rate than storms with less synoptic forcing, especially when considering overland precipitation. Significance Statement Many previous studies demonstrate that tropical cyclone (TC) precipitation will increase in a warmer climate, but few studies consider how TC precipitation responds to climate change as a function of the accompanying weather pattern. Here, we examine future changes in precipitation for TCs in three distinct weather patterns. By analyzing the response of TC rainfall to warming for a diverse set of patterns, we can increase readiness for a variety of future scenarios, with the ultimate goal of maximizing the resilience of future transportation infrastructure.

Abstract We consider the radiative transfer model (RTM) employed by the stepped frequency microwave radiometer (SFMR) and its application in airborne wintertime observations of midlatitude storms and extratropical cyclones. We find that the current RTM, developed and tuned for use in tropical cyclones (TCs), does not adequately model the observed brightness temperatures typically encountered in these cold conditions. While the brightness temperatures observed at several frequencies across the C band are lower, they are more spread apart from each other than the TC RTM predicts. We consider two hypotheses to explain the differences between the measurements and model. One hypothesis assumes the presence of a melting layer between the aircraft and the surface, which imparts enhanced attenuation and emission, which would result in an enhanced spreading of brightness temperatures. The properties of the melting layer scale with rain rate. The other hypothesis is a wind-dependent excess emissivity possibly due to the surface-based layer of mixed-phase droplets lofted from the surface. The latter hypothesis is most consistent with observations when the freezing level, as deduced from the flight-level temperature and an assumed lapse rate, is at or below the surface. We find that the latter hypothesis better represents the observations compared to the first, in large part because there is often little to no rain present in the observations. An excess emissivity model for winter conditions is provided. Significance Statement In addition to its use in hurricanes, the stepped frequency microwave radiometer (SFMR) is also used to measure winds in winter storms over the North Atlantic and Pacific Oceans. Because the algorithm used to estimate winds and rain was derived exclusively in tropical hurricane conditions, estimates of winds and rain in winter storms appear too high. Accurate measurement of extreme winds in winter storms is important for forecasting, satellite microwave instrument calibration/validation, as well as for shipping and commerce. In this paper, we use several years of SFMR measurements in winter conditions to derive a modified model of the environmental conditions. We show that the retrievals with the modified model are more consistent with independent estimates of the surface winds.

The weak linkage between heavy rainfall and strong convection hampers predictability of extreme precipitation and hinders efforts to address challenges related to global warming, especially given unclear microphysical differences between convective clouds generating disparate rain rates. Using multi-year records from spaceborne precipitation radars and cloud-permitting ensemble simulations, here we reveal microphysical distinctions between “heavier” and “lighter” rainfall in convective precipitation events worldwide, as well as their differences in environmental conditions. Results suggest that the near-surface rain rates are dictated mainly by liquid-phase processes, but the more extreme rainfall is mostly produced by vigorous mixed-phase processes combined with a rough balance between breakup and coalescence of liquid drops. With comparable convective intensity, “heavier” rainfall-producing events possess substantially higher raindrop concentration with enhanced coalescence, supported by increased environmental moisture and thicker warm-cloud layers due to enhanced water vapor channels from oceans. The “heavier” rainfall-producing weak convection is formed in the most humid environment, which compensates for the lack of vigorous mixed-phase processes leading to a maritime-like characteristic. These novel insights, along with future projections of larger-scale circulation changes by the state-of-the-art climate models, highlight the increased risk of more frequent and extreme rainfall in southern Asia and the maritime continent islands.

Abstract Extreme precipitation events (EPEs) without severe convective weather signatures such as frequent lightning may be more difficult to forecast and potentially more dangerous. This study investigates the differences in the macro‐ and micro‐structures between EPEs with and without lightning over eastern and southern China, as well as their underlying environmental conditions. EPEs are defined as convective features with maximum hourly rain rate reaching the gauge‐based climatological extreme precipitation threshold (99.9%). Results show that EPEs with lightning (EPE_LIG) account for 51%, and the other 49% EPEs have no lightning (EPE_NoLIG), whose fractional maxima are located along the coast. Most EPE_NoLIGs are embedded in large organized precipitation systems, although their convective cores are smaller than EPE_LIGs. The parent systems of both EPE types are multicellular in nature, but those of EPE_NoLIGs are more likely to produce multiple extreme precipitation centers. EPE_LIGs have the most intense convection, which is stronger than regular thunderstorms (NonEPEs with lightning), while the convective intensity of EPE_NoLIGs is just close to NonEPEs. Microphysical processes of the two types of EPEs differ significantly. The downward increasing radar reflectivity profiles and drop size distribution analyses suggest that warm‐rain processes highly dominate (94.1%) in the formation of extreme precipitation in EPE_NoLIGs. Even for EPEs with very active ice‐based processes (EPE_LIGs), warm‐rain processes still contribute significantly (83.31%). The large‐scale environments of EPE_NoLIGs are featured by a relatively convectively stable but deep moist troposphere and enhanced low‐level southwesterly winds, highlighting the importance of excessive moisture transport and convergence in these events.