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Numerous recent tropical cyclones have caused extreme rainfall and flooding events in the CONUS. Climate change is contributing to heavier extreme rainfall around the world. Modeling studies have suggested that tropical cyclones may be particularly efficient engines for transferring the additional water vapor in the atmosphere into extreme rainfall. This paper develops a new indicator for climate change using the enhanced rainfall metric to evaluate how the frequency and/or intensity of extreme rainfall around tropical cyclones has changed. The enhanced rainfall metric relates the amount of rain from a storm over a given location to the 5-yr return period rainfall in that location to determine the severity of the event. The annual area exposed to tropical-cyclone-related 5-yr rainfall events is increasing, which makes it a compelling climate change indicator. Quantile regression illustrates that the distribution of tropical cyclone rainfall is also changing. For tropical storms, all quantiles are increasing. However, major hurricanes show large increases in their most extreme rainfall. This study does not attempt to make any detection claims (vs natural variability) or attribution of the observed trends to anthropogenic forcing. However, the sensitivity of the results to natural variability in tropical cyclone frequency was somewhat constrained by comparing 2 decades from the previous active era (1951–70) with two from the current era (2001–20). This comparison also shows that both the mean rainfall and the maximum rainfall associated with tropical cyclones are increasing over most areas of the eastern CONUS with the most significant increases from northern Alabama to the southern Appalachians. Significance Statement: The purpose of this study is to analyze the changes in frequency and magnitude of extreme precipitation events associated with tropical cyclones with the goal of developing a new indicator for climate change. This is important because heavy rainfall and associated flooding is one of the primary causes of tropical cyclone destruction and fatalities, especially in inland locations away from where storms initially make landfall. Our results show that both the frequency and magnitude of extreme rainfall events from tropical cyclones have increased over the CONUS. The strongest storms (major hurricanes) also show more of an increase in extreme rainfall than storms of weaker intensities. [ABSTRACT FROM AUTHOR]

This paper describes the rainfall and microphysical structure of precipitation associated with Tropical Cyclone Ockhi using polarimetric Doppler weather radar (PDWR) products. The study reports the statistical analysis of precipitation types of tropical cyclone cloud systems over the north Indian Ocean, by combining the observations of the PDWR and disdrometer for the first time. There are few studies that mention initial DWR observations of TC over this low-latitude region below 23.5°N. We tried to further carry out a statistical analysis of precipitating clouds in a cyclone from its depression stage to severe cyclonic stage. This study tries to classify and quantify the contribution of convective and stratiform rain to the total TC rainfall. Precipitating clouds have been classified into convective and stratiform based on reflectivity measurements. The vertical profiles (VPRs) of the radar reflectivity Z and the differential reflectivity ZDR obtained for the stratiform and the convective events are compared. A study of the VPR of the convective events reveals that the Z and ZDR parameters tend to increase as the raindrops descend toward the ground owing to enhanced collision–coalescence processes. The VPR of stratiform rain shows signatures of the bright band (BB). The drop size distribution (DSD) parameters and rainfall rate pertaining to the two different precipitation regimes are estimated from the radar data and have been compared. The Joss–Waldvogel Disdrometer measurements have been used to derive DSD parameters and polarimetric rain-rate relation. [ABSTRACT FROM AUTHOR]

Because of its high spatial and temporal variability, rainfall remains one of the most challenging meteorological variables to measure accurately. Obtaining high-quality rainfall products is essential for flood monitoring, disaster warning, and weather forecasting systems, but this is not always possible on the basis of current rainfall observation networks. Innovative alternatives draw inspiration from "citizen science" and "crowdsourcing," allowing for opportunistic sensing of rainfall from existing measurements at a low cost, which has become a popular topic and is beginning to play an important role in developing rainfall observation systems. This paper reviews the current state of new rainfall observation approaches and explores their opportunities to complement more traditional ways of rainfall data collection in a hydrological context. Furthermore, the challenges of each new approach are discussed. Although these new options show great potential in enhancing the current rainfall network, they still face problems in terms of their accuracy, real-time accessibility, and limited applicability when individually employed. In contrast, the fusion of new measurements with traditional observation networks is feasible and will be effective for regional rainfall monitoring. This study also serves as an important reference in developing monitoring techniques for other environmental factors. Significance Statement: New rainfall observation techniques provide a meaningful supplement to current rainfall networks in terms of spatiotemporal resolution and accuracy. In this paper, we present a comprehensive overview of the innovations in rainfall observation and their popularity in different regions around the world. Then, the application value and future opportunities that new techniques bring to hydrological research are analyzed. It is anticipated that this paper will be of value to researchers with an interest in improving the quality of rainfall data, thus paving the way to accelerate these studies, as well as the application and implementation of their findings, to the next stage. Furthermore, we expect to prompt a rethink on utilizing and exploiting these new rainfall products to enhance our understanding and optimization of current rainfall sensing systems. [ABSTRACT FROM AUTHOR]

Existing precipitation-type algorithms have difficulty discerning the occurrence of freezing rain and ice pellets. These inherent biases are not only problematic in operational forecasting but also complicate the development of model-based precipitation-type climatologies. To address these issues, this paper introduces a novel light gradient-boosting machine (LightGBM)-based machine learning precipitation-type algorithm that utilizes reanalysis and surface observations. By comparing it with the Bourgouin precipitation-type algorithm as a baseline, we demonstrate that our algorithm improves the critical success index (CSI) for all examined precipitation types. Moreover, when compared with the precipitation-type diagnosis in reanalysis, our algorithm exhibits increased F1 scores for snow, freezing rain, and ice pellets. Subsequently, we utilize the algorithm to compute a freezing-rain climatology over the eastern United States. The resulting climatology pattern aligns well with observations; however, a significant mean bias is observed. We interpret this bias to be influenced by both the algorithm itself and assumptions regarding precipitation processes, which include biases associated with freezing drizzle, precipitation occurrence, and regional synoptic weather patterns. To mitigate the overall bias, we propose increasing the precipitation cutoff from 0.04 to 0.25 mm h−1, as it better reflects the precision of precipitation observations. This adjustment yields a substantial reduction in the overall bias. Finally, given the strong performance of LightGBM in predicting mixed precipitation episodes, we anticipate that the algorithm can be effectively utilized in operational settings and for diagnosing precipitation types in climate model outputs. Significance Statement: Freezing rain can have significant impacts on transportation and infrastructure, making accurate prediction of precipitation types crucial. In this study, we use a machine learning method known as LightGBM to predict precipitation types. We show that the new algorithm performs better than the existing methods for all precipitation types examined. Additionally, we compute a freezing-rain climatology over the eastern United States. Although the resulting climatology pattern corresponds well to observations, the algorithm overpredicts freezing-rain occurrence. We argue that this bias can be substantially reduced by increasing the precipitation cutoff from 0.04 to 0.25 mm h−1. Overall, this work highlights the potential of the LightGBM algorithm for both weather forecasting and diagnosing precipitation types in climate models. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of the environmental conditions during the first half of the 2020 mei-yu season (Y20) and the southwest vortex (SWV), as well as their interaction, on heavy precipitation in southern Taiwan during late May 2020, based on a quantitative approach through ensemble simulations. The control experiment successfully replicates observed heavy precipitation in southern and central Taiwan and reveals a positive spatial correlation between precipitation occurrence probabilities and mean accumulated precipitation, emphasizing continuous rainfall accumulation over intermittent extreme events. Comparative analyses with sensitivity experiments elucidate that the Y20, featuring an extended western North Pacific subtropical high, intensify pressure gradients and southwesterly flow near Taiwan, favoring precipitation in windward regions but hindering it in the east. The SWV creates a moist and vortical environment near Taiwan, amplifying moisture supply and westerly winds, promoting precipitation in southern Taiwan, and enhancing frontal activity. The interaction between the SWV and the Y20, though limited in its impact on providing favorable wind and moisture conditions for precipitation southwest of Taiwan, significantly contributes to precipitation in southern Taiwan. The reason is that although the SWV primarily enhances moisture and the Y20 predominantly boost southwesterly flow, creating favorable conditions for rainfall, substantial precipitation occurs only when both factors converge in a nonlinear interaction. The interaction increases frontal activity over the Taiwan Strait and influences the movement and strength of the SWV, enhancing southwesterly flow and moisture flux in southwestern Taiwan. [ABSTRACT FROM AUTHOR]

The Yarlung Zsangbo Grand Canyon (YGC) is an important pathway for water vapor transport from southern Asia to the Tibetan Plateau (TP). This area exhibits one of the highest frequencies of convective activity in China, and precipitation often induces natural disasters in local communities, which can dramatically affect their livelihoods. In addition, the produced precipitation gives rise to vast glaciers and large rivers around the YGC. In 2018, the Second Tibetan Plateau Scientific Expedition and Research Program tasked a research team to conduct an "investigation of the precipitation process in the water vapor channel of the Yarlung Zsangbo Grand Canyon" (INVC) in the southeastern TP. This team subsequently established a comprehensive observation system of land–air interaction, water vapor, clouds, and rainfall activity in the YGC. This paper introduces the developed observation system and summarizes the preliminary results obtained during the first two years of the project. Using this INVC observation network, herein, we focus on the development of rainfall events on the southeastern TP. This project also helps to monitor geohazards in the key area of the Sichuan–Tibet railway, which traverses the northern YGC. The observation datasets will benefit future research on mountain meteorology. [ABSTRACT FROM AUTHOR]

Mixed-phase clouds contain both supercooled cloud liquid and ice crystals. In principle, precipitation may be initiated either by the liquid phase or by the ice phase. Ice crystals may grow by vapor diffusion to become snow ("ice crystal process"), forming "cold" precipitation. Equally, cloud droplets, when large enough, coalesce to form "warm" precipitation by the "warm rain process." Warm rain could be supercooled and freeze as "warm" graupel. In the present paper, a new simplified theoretical analysis is provided to examine the microphysical system consisting of three species of hydrometeor, namely, cloud liquid, "cold ice" (crystals, snow), and "warm rain" (frozen or supercooled). This is obtained by nondimensionalizing and simplifying the evolution equations for the mass of each species. Analytical formulas are given for equilibria. Feedback analysis shows that the sign of the feedback is linked to the abundance of precipitation, with a neutral surface in the 3D phase space. The system's precipitation amount explodes while in the initial unstable regime, crossing the neutral surface and approaching the equilibrium point that is a stable attractor. Positive and negative feedbacks are elucidated. In a standard case, the cold ice mass is about 1000 times larger than the warm rain mass. To illustrate the physical behavior of the theory, sensitivity tests are performed with respect to environmental conditions (e.g., aerosol, updraft speed) and microphysical parameters (e.g., riming and sedimentation rates for cold ice). Cold ice prevails, especially in fast ascent, due to its low bulk density, favoring slow sedimentation and a wide cross-sectional area for riming. Significance Statement: The theory elucidates how the ice phase can prevail in the precipitation from any mixed-phase clouds with supercooled cloud liquid and crystals. The ice phase radically suppresses cloud liquid by riming when active and "wins" the competition against coalescence. This prevalence of ice is shown to arise from the low bulk density of snow. The cloud is viewed as a system of negative and positive feedbacks that prevail in realms of stability and instability in a 3D phase space. [ABSTRACT FROM AUTHOR]

Intraseasonal and diurnal variations are two basic periodic oscillations in global/regional climate and weather. To investigate their joint impacts over East Asia, this paper categorizes the boreal summer intraseasonal oscillations (ISOs) in 1998–2019 into two groups with different diurnal cycles. It is shown that the active ISOs with large diurnal cycles feature a northwestward-moving anomalous anticyclone with strong southerlies at the western flank. These ISOs have in-phase patterns of geopotential height anomaly between low and midlatitudes over East Asia, associated with the simultaneous expansions of the western Pacific subtropical high (WPSH) and South Asian high (SAH). They couple with the anomalous ABL heating by daytime solar radiation over East Asia, which acts to enhance monsoon southerlies at midnight. The nocturnally strengthened southerlies facilitate dynamic lifting, moisture transport, and convective instability for producing midnight to morning rainfall at their northern terminus, thereby yielding a remarkable northward propagation of the monsoon rain belt. In contrast, the other ISOs with small diurnal cycles are related to a westward-moving anomalous anticyclone, while the WPSH and SAH have relatively small expansions and the westerly trough is active at middle latitudes. They lead to the dipole patterns of geopotential height anomaly and weak ABL heating over East Asia. The daily-mean southerlies and moisture conditions as well as their nocturnal enhancements are relatively weak, and thus, the northward shift of the monsoon rain belt is less pronounced. These results highlight that the large-scale conditions of ISOs can be distinguished by their different couplings with regional-scale diurnal forcings, which help the understanding and prediction of multiscale rainfall activities. [ABSTRACT FROM AUTHOR]

Fluvial flooding is a major cause of death and damages from tropical cyclones (TCs), so it is important to understand the predictability of river flooding in TC cases, and the potential of global ensemble flood forecast systems to inform warning and preparedness activities. This paper demonstrates a methodology using ensemble forecasts to follow predictability and uncertainty through the forecast chain in the Global Flood Awareness System (GloFAS) to explore the connections between the skill of the TC track, intensity, precipitation, and river discharge forecasts. Using the case of Hurricane Iota, which brought severe flooding to Central America in November 2020, we assess the performance of each ensemble member at each stage of the forecast, along with the overall spread and change between forecast runs, and analyze the connections between each forecast component. Strong relationships are found between track, precipitation, and river discharge skill. Changes in TC intensity skill only result in significant improvements in discharge skill in river catchments close to the landfall location that are impacted by the heavy rains around the eyewall. The rainfall from the wider storm circulation is crucial to flood impacts in most of the affected river basins, with a stronger relationship with the post-landfall track error rather than the precise landfall location. We recommend the wider application of this technique in TC cases to investigate how this cascade of predictability varies with different forecast and geographical contexts in order to help inform flood early warning in TCs. Significance Statement: This study demonstrates a methodology to analyze the cascade of predictability and uncertainty through the various stages of the tropical cyclone (TC) flood forecasting chain, illustrating how it can provide useful information to modelers interested in optimizing flood forecast skill, and to those who prepare and communicate flood forecasts with stakeholders and end-users in TC cases. The results highlight the importance of improving verification of ensemble TC precipitation forecasts, and of focusing on more than just the category of the storm and landfall location when forecasting and communicating flood impacts in TC cases. [ABSTRACT FROM AUTHOR]

Generating plausible future climate time series is needed for bottom-up climate impact modeling, as well as downscaling climate model output for hydrological applications. A novel method for generating multisite daily stochastic climate series is developed based on 1) linear regression between climate teleconnection time series (e.g., IPO/SOI) and annual rainfall, 2) clustered method of fragments for subannual disaggregation, and 3) a regression-based approach to daily potential evapotranspiration (PET) for hydrological modeling. We demonstrate that bias (i.e., oversampling) occurs with the standard method of fragments disaggregation in the multisite context and show that selection of an analog year from clustered rainfall amounts provides better sampling properties than the standard method of fragments. Using hydrological data for southeastern Australia, we model runoff from observed and simulated rainfall and PET using the GR4J (Génie Rural à 4 paramètres Journalier) model. Simulated annual and daily rainfall and runoff characteristics from the new method are similar to existing methods, with improvements demonstrated in wet–wet transition probabilities and spatial (between-site) correlations. Significance Statement: In this paper we develop a novel method for generating multisite daily stochastic climate series regressing climate teleconnections (e.g., ENSO, IPO) to annual site rainfall, and disaggregation using a clustered version of the method of fragments. The modular nature of the method also allows for the possibility of the generation of replicates from GCM output of climate teleconnections, as well as perturbed climate futures for scenario-neutral modeling. Results demonstrate that rainfall and runoff metrics of interest for water resource modeling are reproduced well using the model. [ABSTRACT FROM AUTHOR]

It is typically interpreted that more moisture in the atmosphere leads to more intense rains. This notion may be supported, for example, by taking a scatterplot between rain and column precipitable water. The present paper suggests, however, that the main consequence of intense rains with more moisture in the atmosphere is that there is a higher chance of occurrence rather than an increase in the expected magnitude. This tendency equally applies to any rains above 1 mm (6 h)−1 to a lesser extent. The result is derived from an analysis of 33 local rain gauge station data and a shared sounding over Friuli Venezia Giulia, northeast Italy. Significance Statement: Moisture is the source of clouds. Clouds, in turn, are source of rain. So we may expect that more moisture in the atmosphere causes more intense rains. We may further speculate that with more moisture in the atmosphere as a consequence of the global warning, we must face more catastrophic rain events and floods. However, this paper, by analyzing data over Friuli Venezia Giulia, northeast Italy, suggests otherwise: more moisture indeed increases frequencies of intense rains, but not their magnitudes as much. [ABSTRACT FROM AUTHOR]

Severe rainfall has become increasingly frequent and intense in the Taipei metropolitan area. A complex thunderstorm in the Taipei Basin on 14 June 2015 produced an extreme rain rate (>130 mm h-1 ), leading to an urban flash flood. This paper presents storms’ microphysical and dynamic features during the organizing and heavy rain stages, mainly based on observed polarimetric variables in a Doppler radar network and ground-based raindrop size distribution. Shallower isolated cells in the early afternoon characterized by big raindrops produced a rain rate >10 mm h-1, but the rain showers persisted for a short time. The storm’s evolution highlighted the behavior of merged convective cells before the heaviest rainfall (exceeding 60 mm within 20 min). The columnar features of differential reflectivity (ZDR) and specific differential phase (KDP) became more evident in merged cells, which correlated with the broad distribution of upward motion and mixed-phase hydrometeors. The KDP below the environmental 0°C level increased toward the ground associated with the melted graupel and resulted in subsequent intense rain rates, showing the contribution of the ice-phase process. Due to the collision–breakup process, the highest concentrations of almost all drop sizes and smaller mass-weighted mean diameter occurred during the maximum rainfall stage. [ABSTRACT FROM AUTHOR]

The Excessive Rainfall Outlook (ERO), issued operationally from the Weather Prediction Center (WPC), serves the Weather and Water Enterprise and decision-makers with probabilistic guidance and messaging context out to 3 days for excessive rainfall leading to impacts, including flash flooding. Eighty-three percent of all flood-related damages and 39% of all flood-related fatalities reported in Storm Data from 2010 to 2020 occurred in or near an ERO High Risk. Given that a High Risk is issued on only 4% of days, the presence of such risk can serve as an important step in raising situational awareness of a greater likelihood of a damaging and deadly flash flood day. This paper details the operational construction of the ERO at WPC and discusses the role of the ERO in the National Weather Service collaborative forecast process for heavy rainfall. Case studies where the ERO achieved a High Risk are presented for the deadly Montecito, California, flash flood and debris flow (2018) and Hurricane Harvey (2017). More broadly, challenges are highlighted which could be overcome by research to further improve ERO utility. [ABSTRACT FROM AUTHOR]

The spatial distribution of summer rainfall anomalies over eastern China often shows a tripole pattern with rainfall anomalies over the Yangtze River basin varies in opposite phase with North China and South China. It is not clear whether this tripole pattern is an intrinsic atmospheric mode or it is remotely forced. Using two sets of model outputs from 20 models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5), this paper investigates the driving mechanisms of this leading rainfall mode and its major influencing factors. One set (piControl) is fully coupled atmosphere–ocean simulations under constant preindustrial forcing and the other (sstClim) is atmosphere-alone models forced by prescribed climatological sea surface temperatures (SSTs). By comparing results from these two different sets of simulations, it is found that the tripole pattern is the leading mode of summer precipitation variability over eastern China with or without oceanic forcing. It can be regarded as an intrinsic atmospheric mode although air–sea interaction can modify its temporal variability. The cyclonic–anticyclonic atmospheric circulation anomaly over the northern North Pacific is identified as a key factor in both experiments. As atmospheric internal variability, it is related to a circumglobal zonal wave train propagating along the westerly jet stream. When air–sea interactions involved, modulation from SST anomalies is exerted through the meridional Pacific–Japan/East Asia–Pacific wave train propagating along the East Asian coast. Our results suggest that the North Pacific could be another key region providing potential predictability to the East Asian monsoon in addition to the Indo-Pacific. [ABSTRACT FROM AUTHOR]

This paper proposes a new quantitative precipitation estimation (QPE) technique to provide accurate rainfall estimates in complex terrain, where conventional QPE has limitations. The operational radar QPE in Taiwan is mainly based on the simplified relationship between radar reflectivity Z and rain rate R [R(Z) relation] and only utilizes the single-point lowest available echo to estimate rain rates, leading to low accuracy in complex terrain. Here, we conduct QPE using deep learning that extracts features from 3D radar reflectivities to address the above issues. Convolutional neural networks (CNN) are used to analyze contoured frequency by altitude diagrams (CFADs) to generate the QPE. CNN models are trained on existing rain gauges in northern and eastern Taiwan with the 3-yr data during 2015–17 and validated and tested using 2018 data. The weights of heavy rains (≥10 mm h−1) are increased in the model loss calculation to handle the unbalanced rainfall data and improve accuracy. Results show that the CNN outperforms the R(Z) relation based on the 2018 rain gauge data. Furthermore, this research proposes methods to conduct 2D gridded QPE at every pixel by blending estimates from various trained CNN models. Verification based on independent rain gauges shows that the CNN QPE solves the underestimation of the R(Z) relation in mountainous areas. Case studies are presented to visualize the results, showing that the CNN QPE generates better small-scale rainfall features and more accurate precipitation information. This deep learning QPE technique may be helpful for the disaster prevention of small-scale flash floods in complex terrain. [ABSTRACT FROM AUTHOR]

There are many sources of uncertainty in satellite precipitation retrievals of warm rain. In this paper, the second of a two-part study, we focus on uncertainties related to spatial heterogeneity and surface clutter. A cloud-resolving model simulation of warm, shallow clouds is used to simulate satellite observations from three theoretical satellite architectures—one similar to the Global Precipitation Measurement Core Observatory, one similar to CloudSat, and one similar to the planned Atmosphere Observing System (AOS). Rain rates are then retrieved using a common optimal estimation framework. For this case, retrieval biases due to nonuniform beamfilling are very large, with retrieved rain rates negatively (low) biased by as much as 40%–50% (depending on satellite architecture) at 5 km horizontal resolution. Surface clutter also acts to negatively bias retrieved rain rates. Combining all sources of uncertainty, the theoretical AOS satellite is found to outperform CloudSat in terms of retrieved surface rain rate, with a bias of −19% as compared with −28%, a reduced spread of retrieval errors, and an additional 17.5% of cases falling within desired uncertainty limits. The results speak to the need for additional high-resolution modeling simulations of warm rain so as to better characterize the uncertainties in satellite precipitation retrievals. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel strategy to robustly diagnose the onset and demise of the rainy season using daily observed rainfall over seven specific regions across Australia, as demarcated by the Natural Resource Management (NRM) agency of Australia. The methodology lies in developing an ensemble spread of the diagnosed onset and demise from randomly perturbing the observed daily time series of rainfall at synoptic scales to obtain a measure of the uncertainty of the diagnosis. Our results indicate that the spread of the ensemble in the diagnosis of the onset and demise dates of the rainy season is higher in the subtropical region than the tropical region. Secular change of earlier onset, later demise, longer length, and wetter season are also identified in many of these regions. The influence of the PDO at decadal scale, ENSO and Indian Ocean dipole at interannual scale, and MJO at intraseasonal scale also reveals significant influence on the evolution of the rainy season over these regions in Australia. Most important, the covariability of the onset date with the length of the season and seasonal rainfall anomaly of the season is highlighted as a valuable relationship that can be exploited for real-time monitoring and providing an outlook of the forthcoming rainy season, which could serve some of the NRM regions. Significance Statement: We document the rainfall variability during the rainy season over tropical, subtropical, and semiarid regions of Australia and relate them to modes of climate variability spanning from intraseasonal to secular time scales. The study highlights the varied influence of the modes of climate variability on various aspects of the evolution of the rainy season, such as its onset and demise dates and the seasonal rainfall anomaly over these Australian regions. The study uses 114 years of data and shows that the variations in the length of the rainy season and its seasonal rainfall anomaly are strongly dictated by variations of rainy-season onset date. This provides a quick seasonal outlook of the forthcoming rainy season by just monitoring the onset-date evolution in these regions. [ABSTRACT FROM AUTHOR]

A bin (or spectral) model is developed to investigate the sensitivity of warm rain initiation to cloud condensation nuclei (CCN). It explicitly represents CCN with a formula whose parameters come from the Twomey relationship (or CCN measurements). By seamlessly integrating CCN activation and drop collection with thousands of bins, the model can replicate the effect of CCN on rain initiation, providing a benchmark to test the process parameterizations in rain initiation. The model is used to simulate two extreme cases with CCN parameters of maritime and continental clouds, respectively, where other actual cases usually lie between these two extreme cases. Its simulations show that rain can initiate within half an hour or less as observed in cumulus clouds. The fast rain initiation modeled is attributed mainly to a new process: the condensational conversion of cloud drops to raindrops via collision–coalescence initiators (or drops with radius between 28 and 100 μm). Since the new process is more important in rain initiation than the autoconversion of cloud drops to raindrops when large CCN exist, it is suggested that the process be parameterized into the weather and climate models to better represent CCN and subsequently remove the common bias of "too dense clouds." Significance Statement: The current weather and climate models represent aerosols via implicit parameterizations and have a bias of "too dense clouds." Their implicit parameterizations of aerosols usually overlook (or misrepresent) some cloud processes. In this paper and its preceding part (Zeng and Li 2020) we proposed a new framework to explicitly parameterize one subset of aerosols: cloud condensation nuclei (CCN). To embody an explicit parameterization of CCN, we still need quantitative information to connect CCN activation and rain initiation, which motivates this study. In the study we developed an accurate microphysical model to simulate the growth of small CCN to large raindrops, providing information on the sensitivity of rain initiation to CCN. We performed many sensitivity simulations and found the condensational conversion of cloud drops to raindrops via collision–coalescence initiators is a vital process in warm rain initiation. Since the process has been overlooked by all the weather and climate models, the study suggests that the process be introduced in the weather and climate models to properly represent the fast warm rain initiation observed and subsequently remove the bias of "too dense clouds." [ABSTRACT FROM AUTHOR]

The Dynamical–Statistical–Analog Ensemble Forecast for Landfalling Typhoon Daily Precipitation (DSAEF_LTP_D) model is introduced in this paper. To improve the DSAEF_LTP_D model's forecasting ability, tropical cyclone (TC) translation speed was introduced. Taking Supertyphoon Lekima (2019), which produced widespread heavy rainfall from 9 to 11 August 2019 as the target TC, two simulation experiments associated with the prediction of daily precipitation were conducted: the first involving the DSAEF_LTP_D model containing only the TC track (the actual trajectory of the TC center), named DSAEF_LTP_D-1; and the second containing both TC track and translation speed, named DSAEF_LTP_D-2. The results show the following: 1) With TC translation speed added into the model, the forecasting performance for heavy rainfall (24-h accumulated precipitation exceeding 50 and 100 mm) on 9 and 10 August improves, being able to successfully capture the center of heavy rainfall, but the forecasting performance is the same as DSAEF_LTP_D-1 on 11 August. 2) Compared with four numerical weather prediction (NWP) models (i.e., ECMWF, GFS, GRAPES, and SMS-WARMS), the TS100 + TS50 (the sum of TS values for predicting 24-h accumulated precipitation of ≥100 and ≥50 mm) of DSAEF_LTP_D-2 is comparable to the best performer of the NWP models (ECMWF) on 9 and 10 August, while the performance of DSAEF_LTP_D model for predicting heavy rainfall on 11 August is poor. 3) The newly added similarity regions make up for the deficiency that the similarity regions are narrower when the TC track is northward, which leads to DSAEF_LTP_D-2 having a better forecasting performance for heavy rainfall on 11 August, with the TS100 + TS50 increasing from 0.3021 to 0.4286, an increase of 41.87%. [ABSTRACT FROM AUTHOR]

Daily rainfall accumulation estimates have been derived from 1-min volume data collected via self-syphon rain gauges deployed in the Tropical Atmosphere–Ocean (TAO) array of oceanographic buoys. The underlying high-resolution volume data were obtained directly from the Global Tropical Moored Buoy Array (GTMBA) Project Office of NOAA/Pacific Marine Environmental Laboratory. The derived accumulations have been incorporated into the Pacific Rainfall (PACRAIN) database as estimated daily values to augment existing sea level oceanic rainfall records gathered using traditional rain gauges. They have also been included in the PACRAIN historical, monthly gridded rainfall product. The methodology presented, which employs differencing of least squares–regressed sensor levels about 0000 UTC and rain gauge syphon events, is shown to offer improved error characteristics over the methodology used to compute previously published GTMBA rain rates. In particular, the PACRAIN method yields larger coefficients of determination and smaller standard errors than the duplicated GTMBA method when applied to synthetic rainfall data with noise magnitude and decorrelation times encompassing those observed in the real 1-min data. These results are shown to be consistent with mathematical expectations. Sources of instrument and catchment errors, as well as evaporation, are discussed in the context of their potential effects on accumulation estimates for periods of a day or longer. Significance Statement: In this paper, we describe the derivation of daily rainfall amounts from raw rain gauge data obtained from buoy-mounted rain gauges. These new accumulation estimates expand the store of rainfall estimates from locations approximating the open-ocean conditions of the tropical Pacific Ocean. The derivation technique we describe exhibits better performance than the method used to generate previously published estimates using the same dataset. [ABSTRACT FROM AUTHOR]

The impacts of rainy days (>24 mm) on the physics of the surface atmosphere and upper ocean are characterized in the central Pacific Ocean (140°–170°W) on the equator, where deep-cycle turbulence substantially influences the sea surface temperature and air–sea heat flux on diurnal and longer time scales. Here, rainfall is relatively weak on average (1–3 mm day−1), and enough rain to substantially alter the diurnal cycle of upper-ocean buoyancy only occurs on the order of once in 100 days, albeit more frequently to the west and during El Niño and boreal winter. Rainy days are associated with multiple systematic changes in the surface atmosphere, but the freshwater and the reduction in daily downwelling shortwave radiation (by ∼50 W m−2) are codominant and drive systematic changes in the ocean during and the day after the rainy day. These two drivers explain ensemble average reductions in the upper-ocean salinity (−0.12 psu at 1 m) and temperature (−0.16°C at 1 m) and increases in buoyancy (+0.0005 m s−2 at 1 m), which are typically confined to a shallow fresh/warm mixing layer on the order of 10 m thick in the daytime. At deeper depths, the intrinsic ocean temperature, salinity, and velocity variability make it challenging to extract an ensemble average response to rainy days in observations, but some examples from observations and large-eddy simulations suggest that rainfall can significantly reduce the vertical extent and heat flux in the deep-cycle turbulence, although the bulk energetics and buoyancy flux of the turbulence are not necessarily modified by rain. Significance Statement: Rain significantly impacts social and ecological systems in many ways that are readily apparent in populated areas, but the impacts of rain over the ocean are not as well known. In this paper, sustained in situ observations over decades and highly resolved numerical simulations of ocean turbulence during a few rain events are used to characterize the impacts of rainy days on the surface–atmosphere and upper-ocean physics in the center of action of El Niño in the central equatorial Pacific. These results contribute to broader efforts to observe, understand, and accurately model the surface atmosphere, the upper ocean, and air–sea interaction in the central Pacific and thereby improve long-range weather and climate observations and predictions. [ABSTRACT FROM AUTHOR]

The southwest vortex (SWV) is a critical weather system in China, but our knowledge of this system remains incomplete. Here, we investigate the cloud properties in the SWV. First, we search for the SWVs with time steps and center locations that are consistent between the SWV yearbook and ERA-Interim reanalysis data. Second, we supplement these SWVs' life spans and movement paths. Third, we relocate the Fengyun (FY) satellite FY-4A cloud retrievals in the 10° × 10° region centered on each SWV and analyze the cloud occurrence frequency (COF), cloud-top height (CTH), and cloud optical thickness (COT). A distribution mode of cloud types is summarized from the COFs, with water clouds, supercooled clouds, mixed clouds, ice clouds, cirrus clouds, and overlap clouds occurring sequentially from west to east. The CTH probability density (PD) distribution features a significant north–south difference. In addition, the COT PD distributions exhibit a common trend: with increasing COT, the PD increases rapidly and then slowly before peaking, whereupon the PD decreases abruptly. From spring to summer, the region with the highest convective COF shifts from the northeast to the northwest, and an east–west gradient of the convective COF appears in autumn and winter. Furthermore, we investigate the cloud properties during SWV-related heavy rainfall. Heavy rain occurs mainly in the west of the SWV, and convective clouds are mainly in the northwest, partly in the southwest and near the SWV center. The average CTH in heavy rainfall is generally higher than 6 km, and the average COT is greater than 20. Significance Statement: The southwest vortex (SWV) is an important weather system in China. However, we do not yet comprehensively know this weather system. The cloud properties can indicate the structures of weather systems and are key parameters in numerical weather prediction (NWP) models. Thus, investigating cloud properties is necessary and meaningful to understand the SWV and accurately predict SWV-related precipitation in NWP models. In this paper, a typical distribution mode of six cloud types in the SWV is summarized from the cloud occurrence frequency, and the distribution features of convective clouds, cloud-top height, and cloud optical thickness in the SWV are analyzed. Furthermore, the cloud properties in SWV-related heavy rain are also studied. [ABSTRACT FROM AUTHOR]

A simple—yet powerful—indicator for monitoring agricultural drought is the water requirement satisfaction index (WRSI). In data-sparse, food-insecure areas, the WRSI is used to guide billions of dollars of aid every year. The WRSI uses precipitation (PPT) and reference evapotranspiration (RefET) data to estimate water availability relative to water demand experienced over the course of a growing season. If the season is in progress, to-date conditions can be combined with climatological averages to provide insight into potential end-of-season (EOS) crop performance. However, if the average is misrepresented, these forecasts can hinder early warning and delay precious humanitarian aid. While many agencies use arithmetic average climatologies as proxies for "average conditions," little published research evaluates their effectiveness in crop-water balance models. Here, we use WRSI hindcasts of three African regions' growing seasons, from 1981 to 2019, to assess the adequacy of the arithmetic mean climatological forecast—the Extended WRSI. We find that the Extended WRSI is positively biased, overestimating the actual EOS WRSI by 2%–23% in East, West, and southern Africa. The presented alternative combines to-date conditions with data from previous seasons to produce a series of historically realistic conclusions to the current season. The mean of these scenarios is the WRSI Outlook. In comparison with the Extended WRSI, which creates a single forecast scenario using average inputs that are not covarying, the WRSI Outlook employs an ensemble of scenarios, which more adequately capture the historical distribution of distribution of rainfall events along with the covariability between climate variables. More specifically, the impact of dry spells in individual years is included in the WRSI Outlook in a way that is smoothed over in the Extended WRSI. We find that the WRSI Outlook has a near-zero bias score and generally has a lower RMSE. In total, this paper highlights the inadequacies of the arithmetic mean climatological forecast and presents a less biased and more accurate scenario-based approach. To this end, the WRSI Outlook can improve our ability to identify agricultural drought and the concomitant need for humanitarian aid. [ABSTRACT FROM AUTHOR]

Research on the interaction between the Madden–Julian oscillation (MJO) and rainfall around Jakarta is limited, although the influence of the MJO on increased rainfall is acknowledged as one of the primary causes of flooding in the region. This paper investigates the local rainfall response around Jakarta to the MJO. We used C-band Doppler radar in October–April during 2009–12 to study rain-rate characteristics at much higher resolution than previous analyses. Results show that the MJO strongly modulates rain rates over the region; however, its effect varies depending on topography. During active phases, MJO induces a high rain rate over the ocean and coast, meanwhile during suppressed phases, it generates a high rain rate mainly over the mountains. In phase 2 of the MJO we find the strongest increase in mean and extreme rain rate, which is earlier in the MJO cycle than most studies reported, based on lower-resolution data. This higher rain rate is likely due to increases in convective and stratiform activities. The MJO promotes more stratiform rain once it resides over Indonesia. In phase 5, over the northwestern coast and western part of the radar domain, the MJO might bring forward the peak of the hourly rain rate that occurs in the early morning. This is likely due to a strong westerly flow arising from MJO superimposed westerly monsoonal flow, blocked by the mountains, inducing a strong wind propagating offshore resulting in convection near the coast in the morning. Our study demonstrates the benefits of using high-resolution radar for capturing local responses to the larger-scale forcing of the MJO in Indonesia. Significance Statement: Rainfall in Jakarta and its surroundings is highly variable and often heavy resulting in devastating floods. In this region, in the wet season, rainfall is influenced by large-scale climate variability including the Madden–Julian oscillation (MJO) characterized by eastward propagation of clouds near the equatorial regions on intraseasonal time scales. The MJO has been known to increase the probability of rainfall occurrence and its magnitude, but we show that the impact differs in varying topography. The frequency and intensity of rainfall increase over land areas including mountains even when MJO has not arrived in Indonesia. Meanwhile, once MJO moves through Indonesia, the frequency and magnitude of the rainfall increases over the northern coast and ocean as well as in the west of the radar domain. [ABSTRACT FROM AUTHOR]

Globally, extreme rainfall has intense impacts on ecosystems and human livelihoods. However, no effort has yet been made to forecast the extreme rainfall indices through machine learning techniques. In this paper, a new extreme rainfall indices forecasting model is proposed using a random forest (RF) model to provide effective forecasts of monthly extreme rainfall indices. In addition, RF feature importance is proposed in this study to identify the most and least important features for the proposed model. This study forecasts only statistically significant extreme rainfall indices over Bangladesh including consecutive dry days (CDD), the number of heavy rain days (R10mm; rainfall ≥ 10 mm), and the number of heavy rain days (R20mm; rainfall ≥ 20 mm) within 1–3 months of lead time. The proposed model uses monthly antecedent CDD, R10mm, and R20mm including atmospheric parameters and ocean–atmospheric teleconnections, namely, convective available potential energy (CAPE), relative humidity (RH), air temperature (TEM), El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), and North Atlantic Oscillation (NAO), as the inputs to the model. Results show that the proposed model yields the best performance to forecast CDD, R10mm, and R20mm with only the antecedent of these indices as input. Ocean–atmospheric teleconnections (IOD, ENSO, and NAO) are useful for CDD forecasting, and local atmospheric parameters (CAPE, RH, and TEM) are useful for R10mm and R20mm forecasting. The results suggest that adding atmospheric parameters and ocean–atmospheric teleconnections is useful to forecast extreme rainfall indices. [ABSTRACT FROM AUTHOR]

Better understanding of how moisture, clouds, and precipitation covary under climate warming lacks a comprehensive observational view. This paper analyzes the tropical atmospheric water cycle's evolution with sea surface temperature (SST), using for the first time, the synergistic dataset of instantaneous observations of the relative humidity profile from the Megha-Tropiques satellite, clouds from the CALIPSO satellite, and near-surface precipitation from the CloudSat satellite, and quantifies their rates of change with SST warming. The dataset is partitioned into three vertical velocity regimes, with cloudy grid boxes categorized by phase (ice or liquid), opacity (opaque or thin), and the presence of near-surface precipitation. Opaque cloud cover is always larger in the presence of near-surface precipitation (high ice clouds especially). Low liquid water clouds in the descending regime dominate for SSTs < 299.25 K, where the free troposphere is dry (~20%), and opaque liquid water cloud cover decreases with SST warming (−8% K−1) and thin liquid water cloud cover stays constant (~20%). High ice clouds dominate the ascending regime in which, for 299.25 < SST < 301.75 K, humidity increases with SST in the lower free troposphere and peaks around 302 K. Over the warm SST range (>301.75 K), in the ascending regime, opaque high ice cloud cover decreases with SST (−13% K−1), while thin ice cloud cover increases (+6% K−1). Over the warm SST range, total cloudiness decreases with warming in all regimes. This paper characterizes fundamental relationships between aspects of the tropical atmospheric water cycle and SST. [ABSTRACT FROM AUTHOR]

Monitoring precipitation forecast skill in global numerical weather prediction (NWP) models is an important yet challenging task. Rain gauges are inhomogeneously distributed, providing no information over large swathes of land and the oceans. Satellite-based products, on the other hand, provide near-global coverage at a resolution of ∼10–25 km, but limitations on data quality (e.g., biases) must be accommodated. In this paper the stable equitable error in probability space (SEEPS) is computed using a precipitation climatology derived from the Tropical Rainfall Measuring Mission (TRMM) TMPA 3B42 V7 product and a gauge-based climatology and then applied to two global configurations of the Met Office Unified Model (UM). The representativeness and resolution effects on an aggregated SEEPS are explored by comparing the gauge scores, based on extracting the nearest model grid point, with those computed by upscaling the model values to the TRMM grid and extracting the TRMM grid point nearest the gauge location. The sampling effect is explored by comparing the aggregate SEEPS for this subset of ∼6000 locations (dictated by the number of gauges available globally) with all land points within the TRMM region of 50°N and 50°S. The forecast performance over the oceanic areas is compared with performance over land. While the SEEPS computed using the two different climatologies should never be expected to be identical, using the TRMM climatology provides a means of evaluating near-global precipitation using an internally consistent dataset in a climatologically consistent way. [ABSTRACT FROM AUTHOR]

This paper studies the influences of the Madden–Julian oscillation (MJO) on East Asian (EA) winter rainfall using the singular value decomposition (SVD) approach. This method uses two-dimensional instead of latitudinally averaged variables in the commonly used real-time multivariate MJO (RMM) index. A comparison of the two approaches is made using the same OLR and zonal wind data over 37 boreal winter seasons of December–March. The SVD composite reveals a more conspicuous and coherent variation throughout the MJO cycle, while the RMM composite is more ambiguous. In particular, the SVD analysis identifies the convection anomalies over the Maritime Continent and the subtropical western Pacific (MCWP) as a major cause of enhanced rainfall in EA at RMM phases 8 and 1. This is at least one-eighth of a cycle earlier than the phases of convection development over the Indian Ocean (IO) that were emphasized by previous studies. A linearized global baroclinic model is used to demonstrate the mechanism of MJO forcing on EA rainfall during various phases, with a focus on the MCWP cooling. The result shows that the anomalous MCWP cooling and the resultant low-level anticyclonic flow interact with the East Asian jet, leading to an overall weakened EA winter monsoon circulation. The associated anomalous overturning circulation, with ascending motion and low-level horizontal moisture convergence in EA, contributes to the enhanced rainfall. This model result supports the interpretation of the SVD analysis, in that the MCWP cooling induced anomalous meridional circulation is a more direct cause of enhanced EA rainfall than the IO heating (or the IO–MCWP heating dipole) induced Rossby wave teleconnection. [ABSTRACT FROM AUTHOR]

This paper estimates rainfall totals at 17 Greenland meteorological stations, subjecting data from in situ precipitation gauge measurements to seven different precipitation phase schemes to separate rainfall and snowfall amounts. To correct the resulting snow/rain fractions for undercatch, we subsequently use a dynamic correction model (DCM) for automatic weather stations (AWS, Pluvio gauges) and a regression analysis correction method for staffed stations (Hellmann gauges). With observations ranging from 5% to 57% for cumulative totals, rainfall accounts for a considerable fraction of total annual precipitation over Greenland’s coastal regions, with the highest rain fraction in the south (Narsarsuaq). Monthly precipitation and rainfall totals are used to evaluate the regional climate model RACMO2.3. The model realistically captures monthly rainfall and total precipitation (R = 0.3–0.9), with generally higher correlations for rainfall for which the undercatch correction factors (1.02–1.40) are smaller than those for snowfall (1.27–2.80), and hence the observations are more robust. With a horizontal resolution of 5.5 km and simulation period from 1958 to the present, RACMO2.3 therefore is a useful tool to study spatial and temporal variability of rainfall in Greenland, although further statistical downscaling may be required to resolve the steep rainfall gradients. [ABSTRACT FROM AUTHOR]

Urban floods due to intense precipitation are a major problem in many tropical regions as in Africa. Rainfall measurement using microwave links from cellular communication networks has been proposed as a cost-effective solution to monitor rainfall in these areas where the gauge network is scarce. The method consists in retrieving rainfall from the attenuation estimated along the commercial microwave links (CMLs) thanks to the power levels provided by an operator. In urban areas where the network is dense, rainfall can be estimated and mapped for hydrological prediction. Rainfall estimation from CMLs is subject to uncertainties. This paper analyzes the advantages and limitations of this rainfall data for a distributed hydrological model applied to an urban area. The case study is in West Africa in Ouagadougou, Burkina Faso, where a hydrological model has been set up. The analysis is based on numerical simulations, using high-resolution rain maps from a weather radar to emulate synthetic microwave links. Two sources of uncertainty in the rain estimation and on the simulated discharge are analyzed by simulations: (i) the precision of the raw information provided by the operator and (ii) the density and geometry of the network. A coarse precision (1 dB) in the signal provided by the operator can lead to substantial underestimation of rainfall and discharge, especially for links operating at low frequency (below 10 GHz) or of short length (less than 1 km). The density of the current mobile networks in urban areas is appropriate to analyze hydrological impact of tropical convective rainfall. [ABSTRACT FROM AUTHOR]

Interactions over South Asia between tropical depressions (TDs) and extratropical storms known as western disturbances (WDs) are known to cause extreme precipitation events, including those responsible for the 2013 floods over northern India. In this study, existing databases of WD and TD tracks are used to identify potential WD–TD interactions from 1979 to 2015; these are filtered according to proximity and intensity, leaving 59 cases that form the basis of this paper. Synoptic charts, vorticity budgets, and moisture trajectory analyses are employed to identify and elucidate common interaction types among these cases. Two broad families of interaction emerge. First, a dynamical coupling of the WD and TD, whereby either the upper- and lower-level vortices superpose (a vortex merger), or the TD is intensified as it passes into the entrance region of a jet streak associated with the WD (a jet-streak excitation). Second, a moisture exchange between the WD and TD, whereby either anomalous moisture is advected from the TD to the WD, resulting in anomalous precipitation near the WD (a TD-to-WD moisture exchange), or anomalous moisture is advected from the WD to the TD (a WD-to-TD moisture exchange). Interactions are most common in the post-monsoon period as the subtropical jet, which brings WDs to the subcontinent, returns south; there is a smaller peak in May and June, driven by monsoon onset vortices. Precipitation is heaviest in dynamically coupled interactions, particularly jet-streak excitations. Criteria for automated identification of interaction types are proposed, and schematics for each type are presented to highlight key mechanisms. [ABSTRACT FROM AUTHOR]

Record-breaking precipitation due to a back-building convective system occurred in Kyushu Island, Japan, on 5 July 2017. In this paper, a quasi-stationary back-building convective system is reproduced using a regional weather prediction model initialized with a single representative sounding in which the land–sea distribution around the observed convective system is crudely simplified. The simulated convective system and heavy precipitation are reasonably similar to observations. Horizontal resolution finer than 1 km is found to be necessary for reproducing the convective system. The area of heavy precipitation tends to shift downstream with finer horizontal resolution. The surface temperature contrasts at the northern and southern coastlines cause sea breezes and a stationary convergence line between them continuously triggers cumulus clouds. The horizontal convergence near the surface is further enhanced by preceding cumulus clouds that cause the latent heating aloft and generate a mesoscale surface pressure depression. Vertical shear of the environmental wind is also found to be important for organizing the convective system but veering of its wind direction and a cold pool are not essential. [ABSTRACT FROM AUTHOR]

Partial beam blockage (PBB) correction is an indispensable step in weather radar data quality control and subsequent quantitative applications, such as precipitation estimation, especially in urban and/or complex terrain environments. This paper developed a novel PBB correction procedure based on the improved ZPHI method for attenuation correction and regional specific differential propagation phase (KDP)–reflectivity (ZH) relationship derived from in situ raindrop size distribution (DSD) measurements. The practical performance of this PBB correction technique was evaluated through comparing the spatial continuity of reflectivity measurements, the consistency between radar-measured and DSD-derived KDP and ZH relationships, as well as rainfall estimates based on R(ZH) and R(KDP). The results showed that through incorporating attenuation and PBB corrections (i) the spatial continuity of ZH measurements can effectively be enhanced; (ii) the distribution of radar-measured KDP versus ZH is more consistent with the DSD-derived KDP versus ZH; (iii) the measured ZH from a C-band radar in the PBB-affected area becomes more consistent with collocated S-band measurements, particularly in the rainstorm center area where ZH is larger than 30 dBZ; and (iv) rainfall estimates based on R(ZH) in the PBB-affected area are incrementally improved with better spatial continuity and the performance tends to be more comparable with R(KDP). [ABSTRACT FROM AUTHOR]

The companion of this paper, Part I, discovered the characteristics of the rainfall progression in tropicaldepression (TD)-type waves over the western North Pacific. In Part II, the large-scale controls on the convective rainfall progression have been investigated using the ERA-Interim data and the TRMM 3B42 precipitation-rate data during June-October from 1998 to 2013 through budgets of moist static energy (MSE) and moisture. A buildup of columnintegratedMSE occurs in advance of deep convection, and an export ofMSE occurs following deep convection, which is consistent with the MSE recharge-discharge paradigm. The MSE recharge-discharge is controlled by horizontal processes, whereby horizontal moisture advection causes net MSE import prior to deep convection. Such moistening by horizontal advection creates a moist midtroposphere, which helps destabilize the atmospheric column, leading to the development of deep convective rainfall. Following the heaviest rainfall, negative horizontal moisture advection dries the troposphere, inhibiting convection. Such moistening and drying processes explain why deep convection can develop without preceding shallow convection. The advection of moisture anomalies by the mean horizontal flow controls the tropospheric moistening and drying processes. As the TD-type waves propagate northwestward in coincidence with the northwestward environmental flow, the moisture, or convective rainfall, is phase locked to the waves. The critical role of the MSE import by horizontal advection in modulating the rainfall progression is supported by the anomalous gross moist stability (AGMS), where the lowest AGMS corresponds to the quickest increase in the precipitation rate prior to the rainfall maximum. [ABSTRACT FROM AUTHOR]

In southern Africa, models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) predict robust future drying associated with a delayed rainy-season onset in the austral spring and a range of wetting and drying patterns in the austral summer. This paper relates these rainfall changes to dynamical shifts in two classes of weather systems: the Congo Air Boundary (CAB) and tropical lows. Objective algorithms are used to track these features in CMIP5 model output. It is then established that the climatological locations and frequencies of these systems are reasonably well represented in the CMIP5 models. RCP8.5 end-of-twenty-first-century projections are compared with historical end-of-twentieth-century simulations. Future projections in tropical-low locations and frequencies diverge, but indicate an overall average decrease of 15% and in some cases a northward shift. The projected spatial change in the tropical-low frequency distribution is weakly positively correlated to the projected spatial change in the austral summer rainfall distribution. Meanwhile, future projections indicate a 13% increase in CAB frequency from October to December. This is associated with the gradual climatological CAB breakdown occurring half a month later on average in end-of-twenty-first-century RCP8.5 projections. A delay in the gradual seasonal decline of the CAB prevents rainfall to the south of the CAB’s mean position, most of which is shown to occur on CAB breakdown days, hence creating the austral spring drying signal and delayed wet-season onset. Intermodel variability in the magnitude of CAB frequency increase is able to explain intermodel variability in the projected drying. [ABSTRACT FROM AUTHOR]

Cyclones can be identified from gridded pressure data at different levels of the troposphere, with vertical structure known to influence the temporal development and impacts of midlatitude cyclones. However, studies of midlatitude cyclones typically focus on cyclones identified at a single atmospheric level. This paper examines how the frequency of vertically organized or deep cyclones varies around the world, with a focus on southeastern Australia. About 50% of global cyclones identified from mean sea level pressure show a coherent vertical structure extending to at least 500 hPa, based on ERA-Interim reanalysis data, and shallow cyclones are most common in the global midlatitudes. Using a combination of reanalysis data and satellite-based rainfall and lightning, we show that in southeast Australia deep cyclones have higher intensities, longer durations, and more severe winds and rainfall than either shallow surface cyclones or upper-level cyclones with no surface low, motivating a three-dimensional approach for future cyclone analyses. [ABSTRACT FROM AUTHOR]

In this paper, a methodology is proposed to quantitatively evaluate precipitation products for multiple purposes. Evaluation mainly focuses on rainfall characteristics relevant to hydrological or agricultural applications: spatial distribution pattern, effect of aggregation over time, the capture of small-scale variability and seasonality, detection of dry spells and wet spells, and timing and volume of heavy rainfall events. Verification statistics were modified and metrics were reported for extreme weather performance, such as flood and drought monitoring. The analysis was performed for different rainfall categories, over regions dominated by different weather systems or with different topographical structures. The latest versions of seven commonly available, high-resolution rainfall estimates have been evaluated by the method against daily data from 16 rain gauge stations over Tanzania, during 1998-2006. They were TRMM 3B42, CHIRPS, TAMSAT, CMORPH_RAW, CMORPH_BLD, WFDEI_CRU, and CPCU. All products, except for CMORPH_BLD and CPCU, were poorly correlated to gauge data at daily time scale with correlation coefficients,0.5. Five-day aggregation was the minimum time scale that can be used for the products to reach an accuracy better than monthly-mean of gauge data. Their performance varied across different climatic or topographical regions and different rainfall seasons. Timing of precipitation was inaccurately estimated by all products, particularly for heavy rains, with less than 40% hits. The results of the evaluation procedure allow discrimination between available products and better selection of the product to be used for a specific application, such as crop insurance or flood early warning, under particular climatic conditions. [ABSTRACT FROM AUTHOR]

A new framework is proposed for the bulk parameterization of rain microphysics: the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS). It is designed to facilitate direct constraint by observations using Bayesian inference. BOSS combines existing process-level microphysical knowledge with flexible process rate formulations and parameters constrained by observations within a Bayesian framework. Using a raindrop size distribution (DSD) normalization method that relates DSD moments to one another via generalized power series, generalized multivariate power expressions are derived for the microphysical process rates as functions of a set of prognostic DSD moments. The scheme is flexible and can utilize any number and combination of prognostic moments and any number of terms in the process rate formulations. This means that both uncertainty in parameter values and structural uncertainty associated with the process rate formulations can be investigated systematically, which is not possible using traditional schemes. In this paper, BOSS is compared to two- and three-moment versions of a traditional bulk rain microphysics scheme (denoted as MORR). It is shown that some process formulations in MORR are analytically equivalent to the generalized power expressions in BOSS using one or two terms, while others are not. BOSS is able to replicate the behavior of MORR in idealized one-dimensional rainshaft tests, but with a much more flexible and systematic design. Part II of this study describes the application of BOSS to derive rain microphysical process rates and posterior parameter distributions in Bayesian experiments using Markov chain Monte Carlo sampling constrained by synthetic observations. [ABSTRACT FROM AUTHOR]

Machine learning algorithms are trained on a 10-yr archive of composite weather radar images in the Swiss Alps to nowcast precipitation growth and decay in the next few hours in moving coordinates (Lagrangian frame). The hypothesis of this study is that growth and decay is more predictable in mountainous regions, which represent a potential source of practical predictability by machine learning methods. In this paper, artificial neural networks (ANN) are employed to learn the complex nonlinear dependence relating the growth and decay to the input predictors, which are geographical location, mesoscale motion vectors, freezing level height, and time of the day. The average long-term growth and decay patterns are effectively reproduced by the ANN, which allows exploring their climatology for any combination of predictors. Due to the low intrinsic predictability of growth and decay, its prediction in real time is more challenging, but is substantially improved when adding persistence information to the predictors, more precisely the growth and decay and precipitation intensity in the immediate past. The improvement is considerable in mountainous regions, where, depending on flow direction, the root-mean-square error of ANN predictions can be 20%–30% lower compared with persistence. Because large uncertainty is associated with precipitation forecasting, deterministic machine learning predictions should be coupled with a model for the predictive uncertainty. Therefore, we consider a probabilistic perspective by estimating prediction intervals based on a combination of quantile decision trees and ANNs. The probabilistic framework is an attempt to address the problem of conditional bias, which often characterizes deterministic machine learning predictions obtained by error minimization. [ABSTRACT FROM AUTHOR]

In karst terrains, fractures and conduits often occur in clusters, forming groundwater quick flow belts, which are the major passages of groundwater and solute transport. We propose a cost-effective method that utilizes precipitation and spring discharge data to identify groundwater quick flow belts by the multitaper method (MTM). In this paper, hydrological processes were regarded as the transformation of precipitation signals to spring discharge signals in a karst spring catchment. During the processes, karst aquifers played the role of signal filters. Only those signals with high energy could penetrate through aquifers and reflect in the spring discharge, while other weak signals were filtered out or altered by aquifers. Hence, MTM was applied to detect and reconstruct the signals that penetrate through aquifers. Subsequently, by analyzing the reconstructed signals of precipitation with those of spring discharge, we acquired the hydraulic response time and identified the quick flow belts. Finally, the methods were applied to the Niangziguan Spring (NS) catchment, China. Results showed that the hydraulic response time of the spring discharge to precipitation was 3 months at Pingding County; 4 months at Yuxian County, Yangquan City, Xiyang County, and Heshun County; and 27 months at Shouyang County and Zouquan County. These results suggested that Pingding County is located at a groundwater quick flow belt, which is a major passage of groundwater and contaminants, in the NS catchment. This is important since Pingding County is not only the key development area of karst groundwater but also the key conservation area for sustainable development of karst groundwater resources in NS catchment. [ABSTRACT FROM AUTHOR]

The intensity of interannual variation of spring precipitation over southern China during 1979–2014 and possible reasons for it are investigated in this paper. There is a significant interdecadal change in the intensity of interannual variation of spring precipitation over southern China around 1995/96. The intensity of interannual variation of spring rainfall over South China is stronger during 1979–95 than that during 1996–2014. The possible reason may be the larger amplitude of the sea surface temperature anomaly (SSTA) in the western Pacific Ocean (WP) before 1995/96. The cooler (warmer) SSTA in WP may trigger an abnormal local anticyclone (cyclone) at lower levels. The anomalous southwesterly (northeasterly) flow at the northwestern flank of the WP anticyclone (cyclone) covers South China, transporting more (less) moisture to South China. Meanwhile, the anomalous winds converge (diverge) in South China at lower levels and diverge (converge) at upper levels, which causes the anomalous ascent (descent) to enhance (reduce) the precipitation over there. However, during 1996–2014, the intensity of interannual variation of spring rainfall over the middle and lower reaches of the Yangtze River valley becomes much stronger than that during 1979–95, which is related to the intensified interannual variation of the atmospheric circulation in the middle and high latitudes over Eurasia. The weak (strong) Siberian high and East Asian trough may reduce (enhance) the northerly wind from the middle and high latitudes. As a result, the middle and lower reaches of the Yangtze River valley are subjected to the anomalous southerly wind, favoring more (less) precipitation over there. [ABSTRACT FROM AUTHOR]

This study investigates how multifrequency cloud radar observations can be used to evaluate the representation of rain microphysics in the WRF Model using two bulk microphysics schemes. A squall line observed over Oklahoma on 12 June 2011 is used as a case study. A recently developed retrieval technique combining observations of two vertically pointing cloud radars provides quantitative description of the drop size distribution (DSD) properties of the transition and stratiform regions of the squall-line system. For the first time, the results of this multifrequency cloud radar retrieval are compared to more conventional retrievals from a nearby polarimetric radar, and a supplementary result of this work is that this new methodology provides a much more detailed description of the DSD vertical and temporal variations. While the extent and evolution of the squall line is well reproduced by the model, the 1-h low-reflectivity transition region is not. In the stratiform region, simulations with both schemes are able to reproduce the observed downdraft and the associated significative subsaturation below the melting level, but with a slight overestimation of the relative humidity. Under this subsaturated air, the simulated rain mixing ratio continuously decreases toward the ground, in agreement with the observations. Conversely, the profiles of the mean volume diameter and the concentration parameter of the DSDs are not well reproduced. These discrepancies pinpoint at an issue in the representation of rain microphysics. The companion paper, investigates the sources of the biases in the microphysics processes in the rain layer by performing numerical sensitivity studies. [ABSTRACT FROM AUTHOR]

Compared to traditional single-polarization radar, dual-polarization radar has a number of advantages for quantitative precipitation estimation because more information about the drop size distribution and hydrometeor type can be gleaned. In this paper, an improved dual-polarization rainfall methodology is proposed, which is driven by a region-based hydrometeor classification mechanism. The objective of this study is to incorporate the spatial coherence and self-aggregation of dual-polarization observables in hydrometeor classification and to produce robust rainfall estimates for operational applications. The S-band dual-polarization data collected from the NASA Polarimetric (NPOL) radar during the GPM Iowa Flood Studies (IFloodS) ground validation field campaign are used to demonstrate and evaluate the proposed rainfall algorithm. Results show that the improved rainfall method provides better performance than a few single- and dual-polarization algorithms in previous studies. This paper also investigates the impact of radar beam broadening on various rainfall algorithms. It is found that the radar-based rainfall products are less correlated with ground disdrometer measurements as the distance from the radar increases. [ABSTRACT FROM AUTHOR]

This study examines the univariate error covariances of hourly rainfall accumulations using two different NWP models and a mosaic of radar reflectivity over a continental-scale domain. The study focuses on two main areas. The focus of the first part of the paper is on the ensemble-based and the innovation-based error variance and correlation estimations. An ensemble of forecasts and a set of observations provide the basis for estimating the errors in two different ways. The results indicate that both ensemble- and innovation-based methods lead to comparable variance estimations, while the local error correlation estimates have larger differences due to the sensitivity of calculations to the gradient of the variance field. The second part of the paper uses innovations for identifying the errors. The focus of this part is on a prognostic method for estimating the error statistics from the background based on the Bayesian inference technique. The case study shows that the predictive model produces a similar result regarding the magnitude and the dispersion of variance in comparison with the innovation and ensemble-based variances. This study represents a step toward estimating local error variances and local error correlations to construct a nonhomogeneous and precipitation-dependent error covariance matrix of rainfall. These results will be used in a future paper in the design of a 2D-VAR Assimilation Method for Blending Extrapolated Radars (AMBER) with NWP precipitation forecast to form a precipitation nowcasting model. [ABSTRACT FROM AUTHOR]

An intense mesoscale convective system (MCS) in the Guinea Coast region caused one of the highest ever recorded daily rainfall amounts at the Nigerian station Abakaliki on 12 June 2016 (223.5 mm). This paper provides a detailed analysis of the meso- and synoptic-scale factors leading to this event, including some so far undocumented dynamical aspects for southern West Africa. The MCS formed over the Darfur Mountains due to diurnal heating, then moved southwestward along a mid- to lower-tropospheric trough, and developed into a classical West African squall line in a highly sheared environment with pronounced midlevel dryness. Strong moisture flux convergence over Nigeria prior to the MCS passage led to extreme values in precipitable water and was caused by the formation of a local, short-lived heat low. According to the pressure tendency equation, the latter resulted from tropospheric warming due to MCS-forced subsidence as well as surface insolation in the resulting almost cloud-free atmosphere. In this extremely moist environment, the MCS strongly intensified and initiated the formation of a lower-tropospheric vortex, which resulted in a deceleration of the MCS and high rainfall accumulation at Abakaliki. Following the vorticity equation, the vortex formation was realized through strong low-level vortex stretching and upper-level vertical vorticity advection related to the MCS, which became "dynamically large" compared to the Rossby radius of deformation. Eventually, moisture supply and lifting associated with the vortex are suggested to promote the longevity of the MCS during the subsequent westward movement along the Guinea Coast. [ABSTRACT FROM AUTHOR]

Freezing rain can cause severe impacts, particularly when it persists for many hours. In this paper, we present the climatology of long-duration (6 or more hours) freezing rain events in the United States and Canada from 1979 to 2016. We identify three focus regions from this climatology and examine the archetypal thermodynamic evolution of events in each region using surface and radiosonde observations. Long-duration events occur most frequently in the northeastern United States and southeastern Canada, where freezing rain typically begins as lower-tropospheric warm-air advection develops the warm layer aloft. This warm-air advection and the latent heat of fusion released when rain freezes at the surface erode the cold layer, and freezing rain transitions to rain once the surface temperature reaches 0°C. In the southeastern United States, a larger percentage of events are of long duration than elsewhere in North America. Weak surface cold-air advection and evaporative cooling in the particularly dry onset cold layers there prevent surface temperatures from rising substantially during events. Finally, the south-central United States has a regional maximum in the occurrence of the top 1% of events by duration (18 or more hours), despite the relative rarity of freezing rain there. These events are associated with particularly warm/deep onset warm layers, with persistent low-level cold-air advection maintaining the cold layer. The thermodynamic evolutions we have identified highlight characteristics that are key to supporting persistent freezing rain in each region and may warrant particular attention from forecasters tasked with predicting these events. [ABSTRACT FROM AUTHOR]

This paper reports a consistent seesaw relationship between interdecadal precipitation variability over North China and the Southwest United States, which can be found in observations and simulations with several models. Idealized model simulations suggest the seesaw could be mainly driven by the interdecadal Pacific oscillation (IPO), through a large-scale circulation anomaly occupying the entire northern North Pacific, while the Atlantic multidecadal oscillation (AMO) contributes oppositely and less. Modulation of precipitation by the IPO tends to be intensified when the AMO is in the opposite phase, but weakened when the AMO is in the same phase. The warm IPO phase is associated with an anomalous cyclone over the northern North Pacific; consequently, anomalous southwesterly winds bring more moisture and rainfall to the Southwest United States, while northwesterly wind anomalies prevail over North China with negative rainfall anomalies. The east–west seesaw of rainfall anomalies reverses sign when the circulation anomaly becomes anticyclonic during the cold IPO phase. The IPO-related tropical SST anomalies affect the meridional temperature gradient over the North Pacific and adjacent regions and the mean meridional circulation. In the northern North Pacific, the atmospheric response to IPO forcing imposes an equivalent barotropic structure throughout the troposphere. An important implication from this study is the potential predictability of drought-related water stresses over these arid and semiarid regions, with the progress of our understanding and prediction of the IPO and AMO. [ABSTRACT FROM AUTHOR]

This paper scrutinizes the zonal oscillation of the western North Pacific subtropical high (WNPSH) via diagnosing its two extreme phases, which are defined by the top 10% strongest (positive phase) and the weakest (negative phase) WNPSH index (WNPSHI) days during summers in 1979–2016. Key findings include the following: a tripole pattern consisting of intensified (weakened) precipitation over the Maritime Continent and the East Asian summer monsoon regions, and suppressed (strengthened) precipitation over the western North Pacific summer monsoon region during positive (negative) WNPSH phases; a westward movement of WNPSH-induced precipitation anomalies that subsequently affects eastern China, Japan, and the Korean Peninsula at different time lags; an OLR–vorticity pattern explained by atmospheric responses to thermal sources is suggested to drive the oscillation; and the competitive interaction of local air–sea feedbacks, especially during the positive phase. In addition, moderate-to-strong positive correlations between the WNPSHI and the Niño-3.4 index are found on 1–2-, 2–3-, and 3–6-yr time scales; both exhibit decadal shifts to a higher-frequency mode, suggesting the intensification of both the zonal WNPSH oscillation and the ENSO under the changing climate and their close interdecadal association. A nonlinear quasi-biennial WNPSH–ENSO relationship is identified: the positive (negative) WNPSH phase sometimes occurs during 1) a decaying El Niño (La Niña) in the preceding summer/autumn, and/or 2) a developing La Niña (El Niño) in the current summer/autumn. A full ENSO transition from moderate-to-strong El Niño to La Niña is often seen during the positive phase, offering potential in predicting ENSO events and extreme WNPSH phases and thereby the summer monsoon rainfall in East Asia. [ABSTRACT FROM AUTHOR]

Rain gauges are considered the most accurate method to estimate rainfall and are used as the "ground truth" for a wide variety of applications. The spatial density of rain gauges varies substantially and hence influences the accuracy of gridded gauge-based rainfall products. The temporal changes in rain gauge density over a region introduce considerable biases in the historical trends in mean rainfall and its extremes. An estimate of uncertainty in gauge-based rainfall estimates associated with the nonuniform layout and placement pattern of the rain gauge network is vital for national decisions and policy planning in India, which considers a rather tight threshold of rainfall anomaly. This study examines uncertainty in the estimation of monthly mean monsoon rainfall due to variations in gauge density across India. Since not all rain gauges provide measurements perpetually, we consider the ensemble uncertainty in spatial average estimation owing to randomly leaving out rain gauges from the estimate. A recently developed theoretical model shows that the uncertainty in the spatially averaged rainfall is directly proportional to the spatial standard deviation and inversely proportional to the square root of the total number of available gauges. On this basis, a new parameter called the "averaging error factor" has been proposed that identifies the regions with large ensemble uncertainties. Comparison of the theoretical model with Monte Carlo simulations at a monthly time scale using rain gauge observations shows good agreement with each other at all-India and subregional scales. The uncertainty in monthly mean rainfall estimates due to omission of rain gauges is largest for northeast India (~4% uncertainty for omission of 10% gauges) and smallest for central India. Estimates of spatial average rainfall should always be accompanied by a measure of uncertainty, and this paper provides such a measure for gauge-based monthly rainfall estimates. This study can be further extended to determine the minimum number of rain gauges necessary for any given region to estimate rainfall at a certain level of uncertainty. [ABSTRACT FROM AUTHOR]

Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months. [ABSTRACT FROM AUTHOR]