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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 object-based verification procedure described in a recent paper by Duda and Turner was expanded herein to compare forecasts of composite reflectivity and 6-h precipitation objects between the two most recent operational versions of the High-Resolution Rapid Refresh (HRRR) model, versions 3 and 4, over an expanded set of warm season cases in 2019 and 2020. In addition to analyzing all objects, a reduced set of forecast–observation object pairs was constructed by taking the best forecast match to a given observation object for the purposes of bias-reduction and unequivocal object comparison. Despite the apparent signal of improved scalar metrics such as the object-based threat score in HRRRv4 compared to HRRRv3, no statistically significant differences were found between the models. Nonetheless, many object attribute comparisons revealed indications of improved forecast performance in HRRRv4 compared to HRRRv3. For example, HRRRv4 had a reduced overforecasting bias for medium- and large-sized reflectivity objects, and all objects during the afternoon. HRRRv4 also better replicated the distribution of object complexity and aspect ratio. Results for 6-h precipitation also suggested superior performance in HRRRv4 over HRRRv3. However, HRRRv4 was worse with centroid displacement errors and more severely overforecast objects with a high maximum precipitation amount. Overall, this exercise revealed multiple forecast deficiencies in the HRRR, which enables developers to direct development efforts on detailed and specific endeavors to improve model forecasts. Significance Statement: This work builds upon the authors' prior work in assessing model forecast quality using an alternative verification method—object-based verification. In this paper we verified two versions of the same model (one an upgrade from the other) that were making forecasts covering the same time window, using the object-based verification method. We found that the updated model was not statistically significantly better, although there were indications it performed better in certain aspects such as capturing the change in the number of storms during the daytime. We were able to identify specific problem areas in the models, which helps us direct model developers in their efforts to further improve the model. [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]

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]

This paper explores whether particles within uniformly spaced generating cells falling at terminal velocity within observed 2D wind fields and idealized deformation flow beneath cloud top can be reorganized consistent with the presence of single and multibanded structures present on WSR-88D radars. In the first experiment, two-dimensional wind fields, calculated along cross sections normal to the long axis of snowbands observed during three northeast U.S. winter storms, were taken from the initialization of the High-Resolution Rapid Refresh model. This experiment demonstrated that the greater the residence time of the particles in each of the three storms, the greater particle reorganization occurred. For experiments with longer residence times, increases in particle concentrations were nearly or directly collocated with reflectivity bands. For experiments with shorter residence times, particle reorganization still conformed to the band features but with less concentration enhancement. This experiment demonstrates that the combination of long particle residence time and net convergent cross-sectional flow through the cloud depth is sufficient to reorganize particles into locations consistent with precipitation bands. Increased concentrations of ice particles can then contribute, along with any dynamic forcing, to the low-level reflectivity bands seen on WSR-88D radars. In a second experiment, the impact of flow deformation on the reorganization of falling ice particles was investigated using an idealized kinematic model with stretching deformation flow of different depths and magnitudes. These experiments showed that deformation flow provides for little particle reorganization given typical deformation layer depths and magnitudes within the comma head of such storms. Significance Statement: Past research with vertically pointing and scanning radars presents two different perspectives regarding snowfall organization in winter storms. Vertically pointing radars often observe cloud-top generating cells with precipitation fallstreaks descending into a broad stratiform echo at lower altitudes. In contrast, scanning radars often observe snowfall organized in quasi-linear bands. This work attempts to provide a connection between these two perspectives by examining how two-dimensional convergent and deformation flow occurring in winter storms can contribute to the reorganization of snowfall between cloud top and the ground. [ABSTRACT FROM AUTHOR]

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6. Significance Statement: Soil moisture links the land surface water, energy, and carbon cycles. NASA Soil Moisture Active Passive (SMAP) satellite observations and observation-based precipitation data are merged into a numerical model of land surface water and energy balance processes to generate the global, 9-km resolution, 3-hourly Level-4 Soil Moisture (L4_SM) data product. The product is available with ∼2.5-day latency to support Earth science research and applications, such as flood prediction and drought monitoring. We show that a recent L4_SM algorithm update using satellite- and gauge-based precipitation inputs from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products improves the temporal variations in the estimated soil moisture, particularly in otherwise poorly instrumented regions in South America, Africa, Australia, and East Asia. [ABSTRACT FROM AUTHOR]

Inland flooding and mudslides from tropical cyclone (TC) rainstorms are among the most destructive natural hazards in China, resulting in considerable direct economic losses and large numbers of fatalities. In this paper, a TC precipitation model (TCPM) is improved by incorporating the effects of complex terrain through a set of new parameters (e.g., slope, roughness, and attenuation distance) for a more accurate assessment of TC rainfall hazards in China. Moreover, by introducing parameterized spiral rainbands, the model could more accurately capture the intensity of extreme precipitation. The model comprehensively considers dynamic and thermodynamic precipitation factors and is adept at capturing the climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China. The model is verified by providing two comparisons. One is analysis including detailed results of three typical TC cases, and the other uses empirical cumulative distribution functions for extreme observations and simulations of historical landfalling TCs in China during the period 1960–2018. The comparisons reveal that the TCPM shows impressive performance for strong TCs with heavy precipitation within 200–300 km of the TC center. Moreover, both the modeled extreme hourly and total TC precipitation probability distributions are consistent with the observations. However, the model needs to be further improved for TCs with dispersive or long-distance precipitation. Significance Statement: In this paper, an optimized and physics-based model for the simulation of tropical cyclone precipitation is described and used to estimate the risk of TC rainfall hazards in China. The work is innovative in that it considers the effect of complex terrain from three perspectives, including slope, roughness, and attenuation distance. The simulations demonstrated that the model is adept at capturing the main climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China, which is simple to run several hundred thousand times, with bright application prospects in catastrophe risk assessment. [ABSTRACT FROM AUTHOR]

Adverse weather such as rain, snow, and fog may significantly reduce visibility or change adhesion properties and, as a consequence, affect drivers’ sense of safety, driving comfort, and their reaction to a changing driving environment (i.e., lower speed and increased headways). The changed behavior of individual drivers affects both traffic flow characteristics, that is, average speed and headways, and parameters related to highway performance such as free-flow speed and capacity. Thus, understanding the impact may be important in the context of predicting and assessing traffic conditions on planned or existing road facilities. The paper discusses the effects of adverse weather conditions and time of day on traffic flow characteristics and the parameters related to highway performance. Using real traffic and weather data from a Polish expressway, the paper aims to identify factors related to weather and time of day that significantly influence traffic flow parameters and traffic conditions and to analyze and quantify this impact. The results of the study may help to develop coefficients of weather-related adjustment factors that will make it possible to estimate, for example, average speed of vehicles in the nighttime or in conditions of rain or limited visibility. The results of the study may contribute to a new Polish method for capacity estimation and traffic conditions assessment for uninterrupted traffic facilities. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is characterized by abundant warm and cold structures that influence the overlying atmospheric boundary layer dynamics through two different mechanisms. First, turbulence and large eddies in the lower troposphere are affected by atmospheric stability, which can be modified by local SST, resulting in enhanced vertical mixing and larger surface winds over warmer waters. Second, the thermodynamic adjustment of air density to the underlying SST structures and the subsequent changes in atmospheric pressure drive secondary circulations. This paper aims to disentangle the effects of these processes and explore the environmental conditions that favor them. Two main environmental variables are considered: the large-scale air–sea temperature difference (proxy for stability) and wind speed. Using 5 years of daily reanalyses data, we investigate the 10-m wind response to SST structures. Based on linear regression between wind divergence and SST derivatives, we show that both mechanisms operate over a large spectrum of conditions. Ten-meter wind divergence is strongly impacted by the local SST via its effect on vertical mixing for midwind regimes in slightly unstable to near-neutral conditions, whereas the secondary circulation is important in two distinct regimes: low wind speed with a slightly unstable air column and high background wind speed with a very unstable air column. The first regime is explained by the prolonged Lagrangian time that the air parcel stays over an SST structure while the second one is related to strong heat fluxes at the air–sea interface, which greatly modify the marine atmospheric boundary layer properties. Location and frequency of the environmentally favorable conditions are discussed, as well as the response in low-cloud cover and rainfall. Significance Statement: The main objective of this study is to explore the wind response to thermal structures at the sea surface under different environmental conditions using the latest atmospheric reanalysis. Recent literature suggests that fine-scale air–sea interactions affect a large spectrum of atmospheric dynamics, from seasonal to weather-type regimes. It is thus important to characterize the atmospheric response to ocean surface variability. Our findings describe the environmental conditions for which the two main physical processes through which the atmosphere responds to sea surface temperature structures are active the most and can guide the development of high-resolution observing missions and campaigns in specific geographical locations and seasons to retrieve data that can be used to improve parameterization in models. [ABSTRACT FROM AUTHOR]

Midlatitude cyclones approaching coastal mountain ranges experience flow modifications on a variety of scales including orographic lift, blocking, mountain waves, and valley flows. During the 2015/16 Olympic Mountain Experiment (OLYMPEX), a pair of scanning ground radars observed precipitating clouds as they were modified by these orographically induced flows. The DOW radar, positioned to scan up the windward Quinault Valley, conducted RHI scans during 285 h of precipitation, 80% of which contained reversed, down-valley flow at lower levels. The existence of down-valley flow in the Quinault Valley was found to be well correlated with upstream flow blocking and the large-scale sea level pressure gradient orientated down the valley. Deep down-valley flow occurred in environments with high moist static stability and southerly winds, conditions that are common in prefrontal sectors of midlatitude cyclones in the coastal Pacific Northwest. Finally, a case study of prolonged down-valley flow in a prefrontal storm sector was simulated to investigate whether latent heat absorption (cooling) contributed to the event. Three experiments were conducted: a Control simulation and two simulations where the temperature tendencies from melting and evaporation were separately turned off. Results indicated that evaporative cooling had a stronger impact on the event's down-valley flow than melting, likely because evaporation occurred within the low-level down-valley flow layer. Through these experiments, we show that evaporation helped prolong down-valley flow for several hours past the time of the event's warm frontal passage. Significance Statement: This paper analyzes the characteristics of down-valley flow over the windward Quinault Valley on the Olympic Peninsula of Washington State using data from OLYMPEX, with an emphasis on regional pressure differences and blocking metrics. Results demonstrate that the location of precipitation over the Olympic Peninsula is shifted upstream during events with deep down-valley flow, consistent with blocked upstream airflow. A case study of down-valley flow highlights the role of evaporative cooling to prolong the flow reversal. [ABSTRACT FROM AUTHOR]

Precipitation (P) gauge undercatch (PUC) is an important source of error when using observed meteorological datasets for hydrologic modeling studies in regions with cold and windy winters. Preliminary simulations using the Variable Infiltration Capacity (VIC) hydrological model forced with different meteorological datasets showed significant underprediction of simulated streamflow throughout the domain. A new hybrid gridded meteorological dataset at 1/16° resolution based on observed station data was assembled over the U.S. Midwest and Great Lakes region from 1915 to 2021 at a daily time step. Correction of primary station data using existing techniques is generally difficult or infeasible in the United States due to missing station metadata and lack of local wind speed (WS) measurements. We developed and tested several different postprocessing adjustment techniques using regridded WS obtained from the NCEP–NCAR reanalysis. The most effective approach corrected rain or mixed P using WS alone, and P as snow using a regressed snow-to-P ratio from a group of wind-shielded reference stations (to account for different and generally unknown snow measurement techniques). The PUC-corrected gridded products were validated against high-quality shielded stations and corrected Global Historical Climatology Network stations with in situ WS, showing good overall agreement. Observed monthly streamflow at 40 river basins was also compared to hydrologic model simulations forced by datasets with and without PUC corrections. The best PUC-corrected dataset produced improvements in streamflow simulations in at least 80% of the streamflow locations for three validation metrics (r2, Nash–Sutcliff efficiency, bias in the mean), demonstrating its value for hydrometeorological studies in the greater Midwest region. Significance Statement: Many applications in hydrology require in situ precipitation (P) measurements, which are known to have a systematic low bias due to the effects of wind, also known as precipitation undercatch (PUC). Addressing PUC is problematic in the United States due to limited access to detailed station metadata (SMD) and local wind speed (WS) measurements. In this paper we develop a set of procedures to create gridded precipitation datasets for the U.S. Midwest region that incorporate corrections for PUC without needing either (i) detailed SMD or (ii) local WS measurements. Among other tests, results in 40 test basins throughout the Midwest show substantial improvements in simulated streamflow in 32 out of 40 basins when PUC corrections are included in meteorological driving datasets. [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]

This study uses Global Historical Climate Network (GHCN) data in each season to identify the days with the most extreme precipitation ("EP days") in the mid-Atlantic and northeast United States between 1979 and 2019. These days are sorted according to the fraction of extreme precipitation attributed to tropical cyclone (TC), atmospheric river (AR), and extreme integrated vapor transport (IVT) influences. In winter and spring, there have been increases in seasonal precipitation from the most extreme days, associated with a combination of frequency and intensity changes. These increasing trends come primarily from atmospheric rivers. In summer and fall, there have also been large increases in precipitation on extreme days, in this case due entirely to increased event frequency. These changes come from a combination of AR, TC, and extreme IVT influences. Synoptic characteristics of AR-related EP days in winter and spring have changed significantly. In winter, there has been an amplification of the Atlantic ridge and a deepening of the upstream trough over the upper Great Plains, as well as enhanced AR detection and IVT on these days. The composite low has shifted north and intensified. In spring, the trough has weakened and 1000–500-hPa thickness has increased broadly to the south. These changes are related to changes in the large-scale flow. In winter and spring, the North Atlantic subtropical high (NASH) has strengthened and shifted west, leading to increased southwesterly IVT over the mid-Atlantic and Northeast United States. In summer, southerly IVT along the east coast has increased, and 1000–500-hPa climatological thickness has increased broadly in all seasons. Significance Statement: This paper studies the days with the most extreme precipitation over the mid-Atlantic and Northeast United States in each season and finds that there have been large changes in the frequency, intensity, and characteristics of some of these days. Extreme days associated with atmospheric rivers (ARs) and tropical cyclones (TCs) have become more frequent. Precipitation on AR-related days has also become more intense in some areas. On winter extreme days associated with ARs, the pattern has become more amplified and moisture fluxes have become stronger; these changes are likely associated with strengthened high pressure over the Atlantic. Throughout all seasons, there is evidence of warming on extreme days. This is important because it adds to our understanding of how total and AR-related extreme precipitation is changing in a warming climate. [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]

The seamless combination of nowcasting and numerical weather prediction (NWP) aims to provide a functional basis for very-short-term forecasts, which are essential (e.g., for weather warnings). In this paper we propose a statistical method for precipitation using neural networks (NN) that combines nowcasting data from DWD’s radar-based RadVOR system with postprocessed forecasts of the high resolving NWP ensemble COSMO-DE-EPS. The postprocessing is performed by Ensemble-MOS of DWD. Whereas the quality of the nowcasting projections of RadVOR is excellent at the beginning, it declines rapidly after about 2 h. The postprocessed forecasts of COSMO-DE-EPS in contrast start with lower accuracy but provide meaningful information on longer forecast ranges. The combination of the two systems is performed for probabilities that the expected precipitation amounts exceed a series of predefined thresholds. The resulting probabilistic forecasts are calibrated and outperform both input systems in terms of accuracy for forecast ranges from 1 to 6 h as shown by verification. The proposed NN-model generalizes a previous statistical model based on extended logistic regression, which was restricted to only one threshold of 0.1 mm. The various layers of the NN-model are related to the conventional design elements (e.g., triangular functions and interaction terms) of the previous model for easier insight. [ABSTRACT FROM AUTHOR]

The northeastern Tibetan Plateau is located in a climatic junction, which is considered an ideal region to explore the interactions between the summer monsoons and the westerly circulation patterns. However, to date, the needed long-term precipitation-based isotopic dataset is too limited to predict the interactions and patterns. This paper presents an evaluation of hydrometeorological processes and climate dynamics in the northeastern Tibetan Plateau based on a 7-yr precipitation isotope dataset covering the summer monsoon periods from 2012 to 2018. Results illustrated remarkable seasonal isotopic variability, characterized by lower δ18O and δ2H values in June with an average of −10‰ and −66.7‰, respectively. Higher δ18O and δ2H values in July averaged −6.7‰ and −39.5‰, respectively. This clear isotopic variability is largely related to seasonal changes of moisture sources and hydrometeorological processes. These precipitation isotopic values were primarily determined by the amount of precipitation, relative humidity, and convective activity, but showed no correlation with air temperature. Backward trajectory model results showed that Xinjiang, northern China, the Arctic, central Asia, and the South China Sea (SCS) were the primary sources of precipitation for the study site with varying seasonal contributions. The maritime moisture source of the SCS primarily resulted in the lowest precipitation δ18O values during the prevailing summer monsoon, which is mainly as a result of the strong convective activity and rainout processes along the air trajectory. The higher average deuterium excess (d-excess) value of precipitation in September indicated continental sources from central Asia (e.g., 75.4%) as land vapor recycling increases d-excess concentration in the atmosphere. These findings provide further insights into the main factors of precipitation isotopic variability related to atmospheric processes along the trajectory and the relevant factors in the monsoon regions. Significance Statement: Recently, scientists and policy makers have become aware that Tibetan hydroclimate variability provides evidence of changes in regional and global circulation patterns that may result in the intensification of climate-driven extremes. However, these studies largely depend on crucial paleoclimate records of past precipitation isotopes in monsoon regions, which contain great uncertainties because of the complex relationship between climatic variability and precipitation isotopes. This study first presented a 7-yr isotopic dataset to understand the hydrological processes and climate dynamics controlling the isotopic variability in the northeastern Tibetan Plateau. The findings reveal important factors on the isotopic variability associated with atmospheric processes and their key climatic variables, which can enhance our interpretation of the paleoclimate records in monsoon regions. [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]

This paper provides an updated analysis of observed changes in extreme precipitation using high-quality station data up to 2018. We examine changes in extreme precipitation represented by annual maxima of 1-day (Rx1day) and 5-day (Rx5day) precipitation accumulations at different spatial scales and attempt to address whether the signal in extreme precipitation has strengthened with several years of additional observations. Extreme precipitation has increased at about two-thirds of stations and the percentage of stations with significantly increasing trends is significantly larger than that can be expected by chance for the globe, continents including Asia, Europe, and North America, and regions including central North America, eastern North America, northern Central America, northern Europe, the Russian Far East, eastern central Asia, and East Asia. The percentage of stations with significantly decreasing trends is not different from that expected by chance. Fitting extreme precipitation to generalized extreme value distributions with global mean surface temperature (GMST) as a covariate reaffirms the statistically significant connections between extreme precipitation and temperature. The global median sensitivity, percentage change in extreme precipitation per 1 K increase in GMST is 6.6% (5.1% to 8.2%; 5%–95% confidence interval) for Rx1day and is slightly smaller at 5.7% (5.0% to 8.0%) for Rx5day. The comparison of results based on observations ending in 2018 with those from data ending in 2000–09 shows a consistent median rate of increase, but a larger percentage of stations with statistically significant increasing trends, indicating an increase in the detectability of extreme precipitation intensification, likely due to the use of longer records. [ABSTRACT FROM AUTHOR]

Short-termintense precipitation (SIP; i.e., convective precipitation exceeding 20mmh21) nowcasting is important for urban flood warning and natural hazards management. This paper presents an algorithm for coupling automatic weather station data and single-polarization S-band radar data with a graph model and a random forest for the nowcasting of SIP. Different from the pixel-by-pixel precipitation nowcasting algorithm, this algorithm takes the convective cells as the basic units to consider their interactions and focuses on multicell convective systems. In particular, the following question could be addressed: Will a multicell convective system cause SIP events in the next hour? First, a method based on spatiotemporal superposition between cells is proposed for multicell systems identification. Then, the graph model is used to represent cell physical attributes and the spatial distribution of the entire system. For each graph model, a fusion operation is used to form a 42-dimensional graph feature vector. Finally, combined with the machine learning approaches, a random forest classifier is trained with the graph feature vector to predict the precipitation. In the experiment, this algorithm achieves a probability of detection (POD) of 79.2% and a critical success index (CSI) of 68.3% with the data between 2015 and 2016 in North China. Compared with other precipitation nowcasting algorithms, the graph model and random forest could predict SIP events more accurately and produce fewer false alarms. [ABSTRACT FROM AUTHOR]

Characterizing and developing drought climatology continues to be a challenging problem. As decision makers seek guidance on water management strategies, there is a need for assessing the performance of drought indices. This requires the adaptation of appropriate drought indices that aid in monitoring droughts and hydrological vulnerability on a regional scale. This study aims to assist the process of developing a statewide water shortage and assessment plan (WSP) for the state of Indiana by conducting a focused hydroclimatological assessment of drought variability. Drought climatology was assessed using in situ observations and regional reanalysis data. A summary of precipitation and evaporation trends, estimated drought variability, worst-case drought scenarios, drought return period, and frequency and duration was undertaken, using multiple drought indices and streamflow analysis. Results indicated a regional and local variability in drought susceptibility. The worst-case (200-yr return period) prediction showed that Indiana has a 0.5%% probability of receiving 45%% of normal precipitation over a 12-month drought in any year. Consistent with other studies, the standardized precipitation index (SPI) was found to be appropriate for detecting short-term drought conditions over Indiana. This recommendation has now been incorporated in the 2009 Indiana water shortage plan. This study also highlights the difficulties in identifying past droughts from available climatic data, and the authors' results suggest a persistent, high degree of uncertainty in making drought predictions using future climatic projections. [ABSTRACT FROM AUTHOR]

Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including 1) spatiotemporal characteristics metrics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from phases 5 and 6 of the Coupled Model Intercomparison Project. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development. [ABSTRACT FROM AUTHOR]

Deep learning models have been shown to perform well in terms of the radar-based precipitation nowcasting problem when compared with advection-based models. Yet, most of the existing literature has used locally trained models, and relatively little is known about how to construct deep learning–based nowcasting models applicable in wider domains. We conduct experiments for precipitation nowcasting using deep learning models spanning the region around Japan with the Japan Meteorological Agency radar precipitation dataset with 1-km spatial resolution and 5-min temporal resolution. We trained the model with radar data sampled from all over Japan, then applied transfer learning with regional data. Through this experiment, it is found that combining data from all over Japan is effective in improving the forecast performance of heavy precipitation with deep learning models. Significance Statement: This paper proposes a methodological improvement for the problem of short-term rainfall forecasting (nowcasting) using deep learning. We trained deep learning models with data sampled from all over Japan, then applied transfer learning with regional data. We compared the performance metrics of several modeling approaches and demonstrated that learning from heavy precipitation examples collected from wider domains improves the performance significantly. This finding suggests that there are a lot of commonalities in the time evolution of rainfall patterns at different geographical locations, which could be exploited to improve the model's performance. A future research topic could be to learn from a larger dataset, including precipitation datasets from other countries. [ABSTRACT FROM AUTHOR]

This paper utilizes statistical and statistical–dynamical methodologies to select, from the full observational record, a minimal subset of dates that would provide representative sampling of local precipitation distributions across the contiguous United States (CONUS). The CONUS region is characterized by a great diversity of precipitation-producing systems, mechanisms, and large-scale meteorological patterns (LSMPs), which can provide favorable environment for local precipitation extremes. This diversity is unlikely to be adequately captured in methodologies that rely on grossly reducing the dimensionality of the data—by representing it in terms of a few patterns evolving in time—and thus requires data thinning techniques based on high-dimensional dynamical or statistical data modeling. We have built a novel high-dimensional empirical model of temperature and precipitation capable of producing statistically accurate surrogate realizations of the observed 1979–99 (training period) evolution of these fields. This model also provides skillful hindcasts of precipitation over the 2000–20 (validation) period. We devised a subsampling strategy based on the relative entropy of the empirical model's precipitation (ensemble) forecasts over CONUS and demonstrated that it generates a set of dates that captures a majority of high-impact precipitation events, while substantially reducing a heavy-precipitation bias inherent in an alternative methodology based on the direct identification of large precipitation events in the Global Ensemble Forecast System (GEFS), version 12 reforecasts. The impacts of data thinning on the accuracy of precipitation statistical postprocessing, as well as on the calibration and validation of the Hydrologic Ensemble Forecast Service (HEFS) reforecasts are yet to be established. Significance Statement: High-impact weather events are usually associated with extreme precipitation, which is notoriously difficult to predict even using highly resolved state-of-the-art numerical weather prediction models based on first physical principles. The same is true for statistical models that use past data to anticipate the future behavior likely to stem from an observed initial state. Here we use both types of models to identify the occurrences of the states, over the historical climate record, which are likely to lead to extreme precipitation events. We show that the overall statistics of precipitation over the contiguous United States is encapsulated in a greatly reduced set of such states, which could substantially alleviate the computational burden associated with testing of hydrological forecast models used for decision support. [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]

Accurate quantitative precipitation estimates (QPEs) at high spatial and temporal resolution are difficult to obtain in regions of complex terrain due to the large spatial heterogeneity of orographically enhanced precipitation, sparsity of gauges, precipitation phase variations, and terrain effects that impact the quality of remotely sensed estimates. The large uncertainty of QPE in these regions also makes the evaluation of high-resolution quantitative precipitation forecasts (QPFs) challenging, as it can be difficult to choose a reference QPE that is reliable at both high and low elevations. In this paper we demonstrate a methodology to combine information from multiple high-resolution hourly QPE products to evaluate QPFs from NOAA's High-Resolution Rapid Refresh (HRRR) model in a region of Northern California. The methodology uses the quantiles of monthly QPE distributions to determine a range of hourly precipitation that correspond to "good," "possible," "underestimated," or "overestimated" QPFs. In this manuscript, we illustrate the use of the methodology to evaluate QPFs for seven atmospheric river events that occurred during the 2016–17 wet season in Northern California. Because the presence of frozen precipitation is often not captured by traditional QPE products, we evaluate QPFs both for all precipitation, and with likely frozen precipitation excluded. The methodology is shown to provide useful information to evaluate model performance while taking into account the uncertainty of available QPE at various temporal and spatial scales. The potential of the technique to evaluate changes between model versions is also shown. [ABSTRACT FROM AUTHOR]

A better understanding of the interannual variability in temperature and precipitation datasets used as forcing fields for hydrologic models will lead to a more complete description of hydrologic model uncertainty, in turn helping scientists study the larger goal of how the Arctic terrestrial systemis responding to global change. Accordingly, this paper investigates temporal and spatial variability in monthly mean (1992--2000) temperature and precipitation datasets over the Western Arctic Linkage Experiment (WALE) study region. The six temperature datasets include 1) the fifth-generation Pennsylvania State University--National Center for Atmospheric Research MesoscaleModel (MM5); 2) the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); 3) the Advanced Polar Pathfinderall-sky temperatures (APP); 4) National Centers for Environmental Prediction--National Center for Atmospheric Research (NCEP--NCAR) reanalyses (NCEP1); 5) the Climatic Research Unit/University of East Anglia CRUTEM2v (CRU); and 6) the Matsuura and Wilmott 0.5° × 0.5° Global Surface Air Temperature and Precipitation (MW). Comparisons of monthly precipitation are examined for MM5, ERA-40, NCEP1, CRU, and MW. Results of the temporal analyses indicate significant differences between at least two datasets (for either temperature or precipitation) in almost every month. The largest number of significant differences for temperature occurs in October, when there are five separate groupings; for precipitation, there are four significantly different groupings from March through June, and again in December. Spatial analyses of June temperatures indicate that the greatest dissimilarity is concentrated in the central portion of the study region, with the NCEP1 and APP datasets showing the greatest differences. In comparison, the spatial analysis of June precipitation datasets suggests that the largest dissimilarity is concentrated in the eastern portion of the study region. These results indicate that the choice of forcing datasets likely will have a significant effecton the output from hydrologic models, and several different datasets should beused for a robust hydrologic assessment. [ABSTRACT FROM AUTHOR]

Subdaily precipitation extremes are high-impact events that can result in flash floods, sewer system overload, or landslides. Several studies have reported an intensification of projected short-duration extreme rainfall in a warmer future climate. Traditionally, regional climate models (RCMs) are run at a coarse resolution using deep-convection parameterization for these extreme events. As computational resources are continuously ramping up, these models are run at convection-permitting resolution, thereby partly resolving the small-scale precipitation events explicitly. To date, a comprehensive evaluation of convection-permitting models is still missing. We propose an evaluation strategy for simulated subdaily rainfall extremes that summarizes the overall RCM performance. More specifically, the following metrics are addressed: the seasonal/diurnal cycle, temperature and humidity dependency, temporal scaling, and spatiotemporal clustering. The aim of this paper is as follows: (i) to provide a statistical modeling framework for some of the metrics, based on extreme value analysis, (ii) to apply the evaluation metrics to a microensemble of convection-permitting RCM simulations over Belgium against high-frequency observations, and (iii) to investigate the added value of convection-permitting scales with respect to coarser 12-km resolution. We find that convection-permitting models improved precipitation extremes on shorter time scales (i.e., hourly or 2 hourly), but not on 6-24-h time scales. Some metrics such as the diurnal cycle or the Clausius-Clapeyron rate are improved by convection-permitting models, whereas the seasonal cycle appears to be robust across spatial scales. On the other hand, the spatial dependence is poorly represented at both convection-permitting scales and coarser scales. Our framework provides perspectives for improving high-resolution atmospheric numerical modeling and datasets for hydrological applications. [ABSTRACT FROM AUTHOR]

The Integrated Multisatellite Retrievals for Global Precipitation Measurement Mission (IMERG) is a global precipitation product that uses precipitation retrievals from the virtual constellation of satellites with passive microwave (PMW) sensors, as available. In the absence of PMW observations, IMERG uses a Kalman filter scheme to morph precipitation from one PMW observation to the next. In this study, an analysis of convective systems observed during the Convective Process Experiment (CPEX) suggests that IMERG precipitation depends more strongly on the availability of PMW observations than previously suspected. Following this evidence, we explore systematic biases in IMERG through bulk statistics. In two CPEX case studies, cloud photographs, pilot's radar, and infrared imagery suggest that IMERG represents the spatial extent of precipitation relatively well when there is a PMW observation but sometimes produces spurious precipitation areas in the absence of PMW observations. Also, considering an observed convective system as a precipitation object in IMERG, the maximum rain rate peaked during PMW overpasses, with lower values between them. Bulk statistics reveal that these biases occur throughout IMERG Version 06.Wefind that locations and times without PMW observations have a higher frequency of light precipitation rates and a lower frequency of heavy precipitation rates due to retrieval artifacts. These results reveal deficiencies in the IMERG Kalman filter scheme, which have led to the development of the Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood (SHARPEN; described in a companion paper) that will be applied in the next version of IMERG. [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]

Recent field observations suggest that cloud-top precipitation generating cells (GCs) are ubiquitous in the warm-frontal and comma-head regions of midlatitude winter cyclones. The presence of fallstreaks emanating from the GCs and their persistence either to the surface or until merging into precipitation bands suggests that GCs are a critical component of the precipitation process in these cyclones. This paper is the second part of a three-part series that investigates the dynamics of GCs through very-high-resolution idealized Weather Research and Forecasting (WRF) Model simulations. This paper assesses the role of cloud-top instability paired with nighttime, daytime, or no radiative forcing on the development and maintenance (or lack) of GCs. Under initially unstable conditions at cloud top, GCs develop regardless of radiative forcing but only persist clearly with radiative forcing. Cloud-top destabilization due to longwave cooling leads to development of GCs even under initially neutral and stable conditions, providing a physical explanation for the observed ubiquity of GCs atop winter cyclones. GCs do not develop in initially stable simulations with no radiation. Decreased range in vertical velocity spectra under daytime radiative forcing is consistent with offset of the destabilizing influence of longwave cooling by shortwave heating. [ABSTRACT FROM AUTHOR]

Infrequent lightning flashes occurring outside of surface precipitation pose challenges to Impact-Based Decision Support Services (IDSS) for outdoor activities. This paper examines the remote sensing observations from an event on 20 August 2019 where multiple cloud-to-ground flashes occurred over 10 km outside surface precipitation (lowest radar tilt reflectivity, 10 dBZ and no evidence of surface precipitation) in a trailing stratiform region of a mesoscale convective system. The goal is to demonstrate the fusion of radar with multiple lightning observations and a lightning risk model to demonstrate how reflectivity and differential reflectivity combined provided the best indicator for the potential of lightning where all of the other lightning safety methods failed. A total of 13 lightning flashes were observed by the Geostationary Lightning Mapper (GLM) within the trailing stratiform region between 2100 and 2300UTC. The average size of the 13 lightning flashes was 3184 km², with an average total optical energy of 7734 fJ. A total of 75 NLDN flash locations were coincident with the 13GLMflashes, resulting in an average of 5.8 NLDN flashes [in-cloud (IC) and cloud-to-ground (CG)] per GLM flash. In total, five of the GLM flashes contained at least one positive cloud-to-ground flash (1CG) flash identified by the NLDN, with peak amplitudes ranging between 66 and 136 kA. All eight CG flashes identified by the NLDN were located more than 10 km outside surface precipitation. The only indication of the potential of these infrequently large flashes was the presence of depolarization streaks in differential reflectivity (ZDR) and enhanced reflectivity near the melting layer. [ABSTRACT FROM AUTHOR]

Over the last two decades, the Central Weather Bureau of Taiwan and the U.S. National Severe Storms Laboratory have been involved in a research and development collaboration to improve the monitoring and prediction of river flooding, flash floods, debris flows, and severe storms for Taiwan. The collaboration resulted in the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system. The QPESUMS system integrates observations from multiple mixed-band weather radars, rain gauges, and numerical weather prediction model fields to produce high-resolution (1 km) and rapid-update (10 min) rainfall and severe storm monitoring and prediction products. The rainfall products are widely used by government agencies and emergency managers in Taiwan for flood and mudslide warnings as well as for water resource management. The 3D reflectivity mosaic and QPE products are also used in high-resolution radar data assimilation and for the verification of numerical weather prediction model forecasts. The system facilitated collaborations with academic communities for research and development of radar applications, including quantitative precipitation estimation and nowcasting. This paper provides an overview of the operational QPE capabilities in the Taiwan QPESUMS system. [ABSTRACT FROM AUTHOR]

This paper is the first attempt to document a simple convection-tracking method based on the IMERG precipitation product to generate an IMERG-based Convection Tracking (IMERG-CT) dataset. Up to now, precipitation datasets have been Eulerian accumulations. Now with IMERG-CT, we can estimate total rainfall based on Lagrangian accumulations, which is a very important step in diagnosing cloud-precipitation process following the evolution of air masses. Convection-tracking algorithms have traditionally been developed on the basis of brightness temperature (Tb) from satellite infrared (IR) retrievals. However, vigorous rainfall can be produced by warm-topped systems in a moist environment; this situation cannot be captured by traditional IR-based tracking but is observed in IMERG-CT. Therefore, an advantage of IMERG-CT is its ability to include the previously missing information of shallow clouds that grow into convective storms, which provides us more-complete life cycle records of convective storms than traditional IR-based tracking does. This study also demonstrates the utility of IMERG-CT through investigating various properties of convective systems in terms of the evolution before and after peak precipitation rate and amount. For example, composite analysis reveals a link between evolution of precipitation and convective development: the signature of stratiform anvils remaining after the storm has produced the maximum rainfall, as average Tb stays almost constant for 5 h after the peak of precipitation. Our study highlights the importance of joint analysis of cloud and precipitation data in time sequence, which helps to elucidate the underlying dynamic processes producing tropical rainfall and its resultant effects on the atmospheric thermodynamics. [ABSTRACT FROM AUTHOR]

This paper evaluates subseasonal precipitation forecasts for Africa using hindcasts from three models (ECMWF, UKMO, and NCEP) participating in the Subseasonal to Seasonal (S2S) prediction project. A variety of verification metrics are employed to assess weekly precipitation forecast quality at lead times of one to four weeks ahead (weeks 1-4) during different seasons. overall, forecast evaluation indicates more skillful predictions for ECMWF over other models and for East Africa over other regions. Deterministic forecasts show substantial skill reduction in weeks 3-4 linked to lower association and larger underestimation of predicted variance compared to weeks 1-2. Tercile-based probabilistic forecasts reveal similar characteristics for extreme categories and low quality in the near-normal category. Although discrimination is low in weeks 3-4, probabilistic forecasts still have reasonable skill, especially in wet regions during particular rainy seasons. Forecasts are found to be overconfident for all weeks, indicating the need to apply calibration for more reliable predictions. Forecast quality within the ECMWF model is also linked to the strength of climate drivers' tele- connections, namely, El Niño-Southern oscillation, Indian ocean dipole, and the Madden-Julian oscillation. The impact of removing all driver-related precipitation regression patterns from observations and hindcasts shows reduction of forecast quality compared to including all drivers' signals, with more robust effects in regions where the driver strongly relates to precipitation variability. Calibrating forecasts by adding observed regression patterns to hindcasts provides improved forecast associations particularly linked to the Madden-Julian oscillation. Results from this study can be used to guide decision-makers and forecasters in disseminating valuable forecasting information for different societal activities in Africa. [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]

This paper presents a global picture of the dynamic processes and synoptic characteristics of extratropical extreme precipitation events (EPEs), defined as annual maximum daily precipitation averaged over 7.58 3 7.58 regional boxes. Based on the quasigeostrophic omega equation, extreme precipitation can be decomposed into components forced by large-scale adiabatic disturbances and amplified by diabatic heating feedback. The spatial distribution of the diabatic feedback parameter is largely controlled by atmospheric precipitable water and captured by a simple model. Most spatial heterogeneities of EPEs in the middle and high latitudes are due to the spatial variations of large-scale adiabatic forcing. The adiabatic component includes the processes of vorticity advection, in which the synoptic vorticity advection by background wind dominates; temperature advection, in which the total meridional temperature advection by synoptic wind dominates; and boundary forcing. The synoptic patterns of EPEs in all extratropical regions can be classified into six clusters using the self-organizing map method: two clusters in low latitudes and four clusters in middle and high latitudes. Synoptic disturbances are characterized by strong pressure anomalies throughout the troposphere over the coastal regions and oceans and feature upper-level shortwave disturbances and a large westward tilt with height over land. Synoptic configurations favor moisture transport from ocean to land over coastal regions. [ABSTRACT FROM AUTHOR]

Gridded precipitation data are very important for hydrological and meteorological studies. However, gridded precipitation can exhibit significant statistical bias that needs to be corrected before application, especially in regions where high wind speeds, frequent snowfall, and sparse observation networks can induce significant uncertainties in the final gridded datasets. In this paper, we present a method for the production of gridded precipitation on the Tibetan Plateau (TP). This method reduces the statistical distribution error by correcting for wind-induced undercatch and optimizing the interpolation method. A gridded precipitation product constructed by this method was compared with previous products on the TP. The results show that undercatch correction is necessary for station data, which can reduce the distributional error by 30% at most. Athin-plate splines interpolation algorithm considering altitude as a covariate is helpful to reduce the statistical distributional error in general. Our method effectively inhibits the smoothing effect in gridded precipitation, and compared to previous products, results in a higher mean value, larger 98th percentile, and greater temporal variance. This study can help to improve the quality of gridded precipitation over the TP. [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]

Global monthly rainfall estimates and other hydrological products have been produced from 1987 to the present using measurements from the Defense Meteorological Satellite Program (DMSP) series of the Special Sensor Microwave Imager (SSM/I). The aim of this paper is twofold: to present the recent efforts to improve the quality control (QC) of historical antenna temperature of the SSM/I sensor (1987-2008) and how this improvement impacts the different hydrological products that are generated at NOAA/National Environmental Satellite, Data, and Information Service (NESDIS). Beginning in 2005, the DMSP Special Sensor Microwave Imager/Sounder (SSMI/S) has been successfully operating on the F-16, F-17, and F-18 satellites. The second objective of this paper is focused on the application of SSMI/S channels to evaluate the performance of several hydrological products using the heritage of existing SSM/I algorithms and to develop an improved strategy to extend the SSM/I time series into the SSMI/S era, starting with data in 2009 for F-17. The continuity of hydrological products from SSM/I to SSMI/S has shown to be a valuable contribution for the precipitation and climate monitoring community but several sensor issues must be accounted for to meet this objective. [ABSTRACT FROM AUTHOR]

Monitoring of precipitation using higher-frequency radar systems such as X band is becoming popular. At X-band frequency, weather radar signals are attenuated along their paths due to precipitation. A network-based reflectivity retrieval technique has been developed for the Collaborative Adaptive Sensing of the Atmosphere (CASA) system, which is a radar network that can observe a weather event simultaneously by multiple radars. This paper describes the design and implementation of an architectural framework for real-time processing of the network-based attenuation correction system. The benchmarks presented here show that the system accomplishes networked attenuation correction within a few seconds, making it well suited for the CASA system. This paper presents the performance of the network-based attenuation correction system in terms of the metrics of attenuation correction as well as computational performance using CASA Integrated Project 1 (IP1) data during 2007--09 field experiments. The results show that the network-based attenuation correction algorithm works robustly in real time while retrieving attenuation-corrected reflectivity. [ABSTRACT FROM AUTHOR]

The Bay Area of California and surrounding region receives much of its annual precipitation during the October–March wet season, when atmospheric river events bring periods of heavy rain that challenge water managers and may exceed the capacity of storm sewer systems. The complex terrain of this region further complicates the situation, with terrain interactions that are not currently captured in most operational forecast models and inadequate precipitation measurements to capture the large variability throughout the area. To improve monitoring and prediction of these events at spatial and temporal resolutions of interest to area water managers, the Bay Area Advanced Quantitative Precipitation Information project was developed. To quantify improvements in forecast precipitation, model validation studies require a reference dataset to compare against. In this paper we examine 10 gridded, high-resolution (≤10 km, hourly) precipitation estimates to assess the uncertainty of high-resolution quantitative precipitation estimates (QPE) in areas of complex terrain. The products were linearly interpolated to 3-km grid spacing, which is the resolution of the operational forecast model to be validated. Substantial differences exist between the various products at accumulation periods ranging from hourly to annual, with standard deviations among the products exceeding 100% of the mean. While the products seem to agree fairly well on the timing of precipitation, intensity estimates differ, sometimes by an order of magnitude. The results highlight both the need for additional observations and the need to account for uncertainty in the reference dataset when validating forecasts in this area. [ABSTRACT FROM AUTHOR]

Using a two-year dataset (2016–17) from 17 one-minute rain gauges located in the moist forest region of Ghana, the performance of Integrated Multisatellite Retrievals for GPM, version 6b (IMERG), is evaluated based on a subdaily time scale, down to the level of the underlying passive microwave (PMW) and infrared (IR) sources. Additionally, the spaceborne cloud product Cloud Property Dataset Using SEVIRI, edition 2 (CLAAS-2), available every 15 min, is used to link IMERG rainfall to cloud-top properties. Several important issues are identified: 1) IMERG's proneness to low-intensity false alarms, accounting for more than a fifth of total rainfall; 2) IMERG's overestimation of the rainfall amount from frequently occurring weak convective events, while that of relatively rare but strong mesoscale convective systems is underestimated, resulting in an error compensation; and 3) a decrease of skill during the little dry season in July and August, known to feature enhanced low-level cloudiness and warm rain. These findings are related to 1) a general oversensitivity for clouds with low ice and liquid water path and a particular oversensitivity for low cloud optical thickness, a problem which is slightly reduced for direct PMW overpasses; 2) a pronounced negative bias for high rain intensities, strongest when IR data are included; and 3) a large fraction of missed events linked with rainfall out of warm clouds, which are inherently misinterpreted by IMERG and its sources. This paper emphasizes the potential of validating spaceborne rainfall products with high-resolution rain gauges on a subdaily time scale, particularly for the understudied West African region. [ABSTRACT FROM AUTHOR]

In this paper, it is shown that westward-propagating monsoon low pressure system–like disturbances in the South Asian monsoon region can be simulated in an idealized moist general circulation model through the addition of a simplified parameterization of land. Land is parameterized as having one-tenth the heat capacity of the surrounding slab ocean, with evaporation limited by a bucket hydrology model. In this model, the prominent topography of the Tibetan Plateau does not appear to be necessary for these storm systems to form or propagate; therefore, focus is placed on the simulation with land but no topography. The properties of the simulated storms are elucidated using regression analysis and compared to results from composites of storms from comprehensive GCMs in prior literature and reanalysis. The storms share a similar vertical profile in anomalous Ertel potential vorticity to those in reanalysis. Propagation, however, does not seem to be strongly dictated by beta drift. Rather, it seems to be more closely consistent with linear moisture vortex instability theory, with the exception of the importance of the vertical advection term in the Ertel potential vorticity budget toward the growth and maintenance of disturbances. The results presented here suggest that a simplified GCM configuration might be able to be used to gain a clearer understanding of the sensitivity of monsoon low pressure systems to changes in the mean state climate. [ABSTRACT FROM AUTHOR]

In the U.S. Great Plains (GP), diagnosing precipitation variability is key in developing an understanding of the present and future availability of water in the region. Building on previous work investigating U.S. GP pluvial years, this study uses ERA twentieth century (ERA-20C) reanalysis data to investigate key circulation anomalies driving GP precipitation anomalies during a subset of GP pluvial years (called in this paper Pattern pluvial years). With previous research showing links between tropical Pacific sea surface temperature (SST) anomalies and GP climate variability, this study diagnoses the key circulation anomalies through an analysis of SSTs and their influence on the atmosphere. Results show that during Pattern southern Great Plains (SGP) pluvial years, central tropical Pacific SST anomalies are coincident with key atmospheric anomalies across the Pacific basin and North America. During northern Great Plains (NGP) Pattern pluvial years, no specific pattern of oceanic anomalies emerges that forces the circulation anomaly feature inherent in specific NGP pluvial years. Utilizing the results for SGP pluvial years, a conceptual model is developed detailing the identified pathway for the occurrence of circulation patterns that are favorable for pluvial years over the SGP. Overall, results from this study show the importance of the identified SGP atmospheric anomaly signal and the potential for predictability of such events. [ABSTRACT FROM AUTHOR]

This paper investigates the ability of the Weather Research and Forecasting (WRF) Model in simulating multiple small-scale precipitation bands (multibands) within the extratropical cyclone comma head using four winter storm cases from 2014 to 2017. Using the model output, some physical processes are explored to investigate band prediction. A 40-member WRF ensemble was constructed down to 2-km grid spacing over the Northeast United States using different physics, stochastic physics perturbations, different initial/boundary conditions from the first five perturbed members of the Global Forecast System (GFS) Ensemble Reforecast (GEFSR), and a stochastic kinetic energy backscatter scheme (SKEBS). It was found that 2-km grid spacing is adequate to resolve most snowbands. A feature-based verification is applied to hourly WRF reflectivity fields from each ensemble member and the WSR-88D radar reflectivity at 2-km height above sea level. The Method for Object-Based Diagnostic Evaluation (MODE) tool is used for identifying multibands, which are defined as two or more bands that are 5–20 km in width and that also exhibit a >2:1 aspect ratio. The WRF underpredicts the number of multibands and has a slight eastward position bias. There is no significant difference in frontogenetical forcing, vertical stability, moisture, and vertical shear between the banded versus nonbanded members. Underpredicted band members tend to have slightly stronger frontogenesis than observed, which may be consolidating the bands, but overall there is no clear linkage in ambient condition errors and band errors, thus leaving the source for the band underprediction motivation for future work. [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]

Numerous circulation indices have been applied in practical climate services focused on regional precipitation. It is beneficial to identify the most influential or decisive indices, but this is difficult with conventional correlation analyses because of the underlying nonlinear mechanisms for precipitation. This paper demonstrates a set of the most influential indices for July–August precipitation in North China, based on the recursive random forest (RRF) method. These decisive circulation indices include the Polar–Eurasia teleconnection, North African subtropical high ridge position, India–Burma trough, Antarctic Oscillation, Northern Hemisphere polar vortex central latitude, North Atlantic Oscillation, and western Pacific subtropical high northern boundary position. Some of these factors have been recognized as directly influential to the regional precipitation, for example, those of the northwestern Pacific subtropical high; however, some are not easily understood. Decision tree (DT) models using these indices were developed to facilitate composite analyses to explain the RRF results. Taking one of the most interesting DT rules as an example, when the North African subtropical high ridge position is sufficiently far south, an anomalous anticyclone occurs in the upstream and an anomalous cyclone in the downstream of North China. This is unfavorable for northward moisture transport in eastern China and hence causes less precipitation in North China than climatology. The present results are not only helpful for improving diagnostic models of regional precipitation, but also enlightening for exploring how global climate change could impact a region by modulating large-scale circulation patterns. [ABSTRACT FROM AUTHOR]

Based on the abrupt-4XCO2 scenario in phase 6 of the Coupled Model Intercomparison Project (CMIP6), this study investigates the response of the rainfall changes to El Niño–like SST warming and the role of ocean dynamical processes in the salinity changes in the tropical Pacific. The results show that the Walker circulation weakening and eastward shift, related to El Niño–like SST warming, dominates the zonal precipitation change. Precipitation decreases (increases) in the Maritime Continent (the equatorial Pacific), partly offsetting the effect of specific humidity. At the same time, the El Niño–like warming triggers convergence of meridional winds, which leads to a precipitation increase in the equatorial Pacific and a decrease in the intertropical convergence zone and the South Pacific convergence zone, following the "warmer-get-wetter" mechanism. Unlike the spatial pattern of precipitation changes, the sea surface salinity changes become fresher in the tropical western Pacific, related to the precipitation and the mean horizontal advection. The precipitation increase leads to negative salinity anomalies in the equatorial central Pacific. The westward climatological zonal currents transport the negative salinity anomalies westward. The meridional currents advect the salinity anomalies to both sides of the equator, partly offsetting the contribution of the freshwater flux on the salinity change. In addition, shallower mixed layer depth and weakening upwelling bring less high-salinity water to the surface and impact salinity redistribution through the vertical process in the equatorial regions. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite series R (GOES-R) will greatly expand the ability to observe the earth from geostationary orbit compared to the current-generation GOES, with more than 3 times as many spectral bands and a 75% reduction in footprint size. These enhanced capabilities are beneficial to rainfall rate estimation since they provide sensitivity to cloud-top properties such as phase and particle size that cannot be achieved using the limited channel selection of current GOES. The GOES-R rainfall rate algorithm, which is an infrared-based algorithm calibrated in real time against passive microwave rain rates, has been previously described in an algorithm theoretical basis document (ATBD); this paper describes modifications since the release of the ATBD, including a correction for evaporation of precipitation in dry regions and improved calibration updates. These improvements have been evaluated using a simplified version applicable to current-generation GOES to take advantage of the high-resolution ground validation data routinely available over the conterminous United States. Correcting for subcloud evaporation using relative humidity from a numerical model reduced false alarm rainfall by half and reduced the overall error by 35% for hourly accumulations validated against the National Centers for Environmental Prediction stage IV radar-gauge field; however, the number of missed events did increase slightly. Reducing the size of the calibration regions and increasing the training data requirements improved the consistency of the retrieved rates in time and space and reduced the overall error by an additional 4%. [ABSTRACT FROM AUTHOR]