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Kelly Lombardo
and
Miranda Bitting

Abstract

The annual, seasonal, and diurnal spatiotemporal heavy convective precipitation patterns over a pan-European domain are analyzed in this study using a combination of datasets, including the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) precipitation rate product, E-OBS ground-based precipitation gauge data, European climatological gauge-adjusted radar precipitation dataset (EURADCLIM), Operational Programme for the Exchange of Weather Radar Information (OPERA) ground-based radar-derived precipitation rates, and fifth major global reanalysis produced by ECMWF (ERA5) total and convective precipitation products. Arrival Time Difference Network (ATDnet) lightning data are used in conjunction with IMERG and EURADCLIM precipitation rates, with an imposed threshold of 10 mm h−1 to classify precipitation as convective. Annually, the largest convective precipitation accumulations are over the European seas and coastlines. In summer, convective precipitation is more common over the European continent, though relatively large accumulations exist over the northern coastal waters and the southern seas, with a seasonal localized maximum over the northern Adriatic Sea. Activity shifts southward to the Mediterranean and its coastlines in autumn and winter, with maxima over the Ionian Sea, the eastern Adriatic Sea, and the adjacent coastline. Over the continent, 1%–10% of the total precipitation accumulated is classified as convective, increasing to 10%–40% over the surrounding seas. In contrast, 30%–50% of ERA5 precipitation accumulations over land is produced by the convective parameterization scheme and 40%–60% over the seas; however, only 1% of ERA5 convective precipitation accumulations are from rain rates exceeding 10 mm h−1. Regional analyses indicate that convective precipitation rates over the inland mountains follow diurnal heating, though little to no diurnal pattern exists in convective precipitation rates over the seas and coastal mountains.

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Yang Zhou
,
Binshuo Liu
,
Boyang Lei
,
Qifan Zhao
,
Shanlei Sun
, and
Haishan Chen

Abstract

The ERA5 reanalysis during cold months (November-March) of 1979-2020 was used for determining four cluster centroids through the k-means for classifying regional anomalies of the daily geopotential height at 500 hPa (H500) over northeastern China. EOF was used to reduce dimensionality. Four clusters were linked to the EOF patterns with clear meteorological meanings, which are associated with the evolutions of ridge and trough over northeastern China. Those systems relate to warm and cold advections at 850 hPa. In each H500 cluster, the advection is the major contributor leading to temperature changes at 850 hPa, which significantly relates to the changes and anomalies of daily minimum air temperature at 2m (T2min). Furthermore, the jet activities over Asia relate to more or less occurrence of specific H500 clusters in jet phases. This is because anomalous westerlies are generally in favor of positive anomalies of vorticity tendency at 500 hPa. For the reforecasts during 2004-2019 in the CMA S2S model, the hit rates above 50% for all the H500 clusters are within 9.5 days, which are in between those for the first two and the last two clusters. The correct prediction of H500 anomalies improves the T2min prediction up to 12 days, compared with 8 days for the incorrect one. The good prediction of the jet activities leads to more accurate prediction of H500 anomalies. Therefore, improvement of the model prediction of the jet activities and the H500 anomalies will lead to better prediction of winter weather near the ground over northeastern China.

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Zhibo Zhang
,
David B. Mechem
,
J. Christine Chiu
, and
Justin A. Covert

Abstract

Because of the coarse grid size of Earth system models (ESMs), representing warm-rain processes in ESMs is a challenging task involving multiple sources of uncertainty. Previous studies evaluated warm-rain parameterizations mainly according to their performance in emulating collision–coalescence rates for local droplet populations over a short period of a few seconds. The representativeness of these local process rates comes into question when applied in ESMs for grid sizes on the order of 100 km and time steps on the order of 20–30 min. We evaluate several widely used warm-rain parameterizations in ESM application scenarios. In the comparison of local and instantaneous autoconversion rates, the two parameterization schemes based on numerical fitting to stochastic collection equation (SCE) results perform best. However, because of Jessen’s inequality, their performance deteriorates when grid-mean, instead of locally resolved, cloud properties are used in their simulations. In contrast, the effect of Jessen’s inequality partly cancels the overestimation problem of two semianalytical schemes, leading to an improvement in the ESM-like comparison. In the assessment of uncertainty due to the large time step of ESMs, it is found that the rainwater tendency simulated by the SCE is roughly linear for time steps smaller than 10 min, but the nonlinearity effect becomes significant for larger time steps, leading to errors up to a factor of 4 for a time step of 20 min. After considering all uncertainties, the grid-mean and time-averaged rainwater tendency based on the parameterization schemes is mostly within a factor of 4 of the local benchmark results simulated by SCE.

Open access
Mohammadvaghef Ghazvinian
,
Luca Delle Monache
,
Vesta Afzali Gorooh
,
Daniel Steinhoff
,
Agniv Sengupta
,
Weiming Hu
,
Matthew Simpson
,
Rachel Weihs
,
Caroline Papadopoulos
,
Patrick Mulrooney
,
Brian Kawzenuk
,
Nora Mascioli
, and
Fred Martin Ralph

Abstract

This study introduces a deep learning (DL) scheme to generate reliable and skillful probabilistic quantitative precipitation forecasts (PQPFs) in a postprocessing framework. Enhanced machine learning model architecture and training mechanisms are proposed to improve the reliability and skill of PQPFs while permitting computationally efficient model fitting using a short training dataset. The methodology is applied to postprocessing of 24-h accumulated PQPFs from an ensemble forecast system recently introduced by the Center for Western Weather and Water Extremes (CW3E) and for lead times from 1 to 6 days. The ensemble system was designed based on a high-resolution version of the Weather Research and Forecasting (WRF) Model, named West-WRF, to produce a 200-member ensemble in near–real time (NRT) over the western United States during the boreal cool seasons to support Forecast-Informdayed Reservoir Operations (FIRO) and studies of prediction of heavy-to-extreme events. Postprocessed PQPFs are compared with those from the raw West-WRF ensemble, the operational Global Ensemble Forecast System version 12 (GEFSv12), and the ensemble from the European Centre for Medium-Range Weather Forecasts (ECMWF). As an additional baseline, we provide PQPF verification metrics from a recently developed neural network postprocessing scheme. The results demonstrate that the skill of postprocessed forecasts significantly outperforms PQPFs and deterministic forecasts from raw ensembles and the recently developed algorithm. The resulting PQPFs broadly improve upon the reliability and skill of baselines in predicting heavy-to-extreme precipitation (e.g., >75 mm) across all lead times while maintaining the spatial structure of the high-resolution raw ensemble.

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Chih-Chi Hu
,
Peter Jan van Leeuwen
, and
Jeffrey L. Anderson

Abstract

The particle flow filter (PFF) shows promise for fully nonlinear data assimilation (DA) in high dimensional systems. However, its application in atmospheric models has been relatively unexplored. In this study, we develop a new algorithm, PFF-DART, in order to conduct DA for high-dimensional atmospheric models. PFF-DART combines the PFF and the two-step ensemble filtering algorithm in the Data Assimilation Research Testbed (DART), exploiting the highly parallel structure of DART. To evaluate the performance of PFF-DART, we conduct an Observing System Simulation Experiment (OSSE) in a simplified atmospheric general circulation model, and compare the performance of PFF-DART with an existing linear and Gaussian DA method. Using the PFF-DART algorithm, we demonstrate, for the first time, the capability of the PFF to yield stable results in a year-long cycling DA OSSE. Moreover, PFF-DART retains the important ability of the PFF to improve the assimilation of nonlinear and non-Gaussian observations. Finally, we emphasize that PFF-DART is a versatile algorithm that can be integrated with numerous other non-Gaussian DA techniques. This quality makes it a promising method for further investigation within a more sophisticated numerical weather prediction model in the future.

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Xinxin Xie
,
Xiao Xiao
,
Jieying He
,
Pablo Saavedra Garfias
,
Tiejian Li
,
Xiaoyu Yu
,
Songyan Gu
, and
Yang Guo

Abstract

This study investigates precipitation observed by a set of collocated ground-based instruments in Zhuhai, a coastal city located at the southern tip of the Pearl River Delta of Guangdong Province in South China. Seven months of ground-based observations from a tipping-bucket rain gauge (RG), two laser disdrometers (PARSIVEL and Present Weather Sensor 100 (PWS)], and a vertically pointing Doppler Micro Rain Radar-2 (MRR), spanning from December 2021 to July 2022, are statistically evaluated to provide a reliable reference for China’s spaceborne precipitation measurement mission. Rainfall measurement discrepancies are found between the instruments though the collocated deployment mitigates uncertainties originating from spatial/temporal variabilities of precipitation. The RG underestimates hourly rain amounts at the observation site, opposite to previous studies, leading to a percent bias (Pbias) of 18.2% of hourly rain amounts when compared to the PARSIVEL. With the same measurement principle, the hourly accumulated rain between the two laser disdrometers has a Pbias of 15.3%. Discrepancies between MRR and disdrometers are assumed to be due to different temporal/spatial resolution, instrument sensitivities, and observation geometry, with a Pbias of mass-weighted mean diameter and normalized intercept parameter of gamma size distribution less than 9%. The vertical profiles of drop size distribution (DSD) derived from the MRR are further examined during extreme rainfalls in the East Asian monsoon season (May, June, and July). Attributed to the abundant moisture which favors the growth of raindrops, coalescence is identified as the predominant effective process, and the raindrop mass-weighted mean diameter increases by 33.7% when falling from 2000 to 600 m during the extreme precipitation event in May.

Significance Statement

The performance and reliability of ground-based observations during precipitation scenarios are evaluated over the coastal area of South China, in preparation for China’s spaceborne precipitation measurement mission. A comparison study, which is carried out to assess the accuracy of rainfall and drop size distribution (DSD), demonstrates that the observation results are relatively reliable though discrepancies between the instruments still exist, while the accompanying microphysical process during extreme precipitation can be quantified with profiling capabilities at the observatory. An accurate and reliable rainfall characterization over the coastal region in South China can contribute to the validation of satellite rainfall products and provide further insights into the microphysical parameterization schemes during extreme precipitation.

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Sylvain Dupont
,
Mark R. Irvine
, and
Caroline Bidot

Abstract

Turbulence in canopy plays a crucial role in biosphere–atmosphere exchanges. Traditionally, canopy turbulence has been analyzed under stationary conditions based on atmospheric thermal stability, disregarding the time of the day and the atmospheric boundary layer (ABL) depth, although recent studies have suggested that daytime canopy turbulence might be influenced by ABL-scale motions. The morning transition offers an intriguing period when the ABL grows and when an increasing influence of large-scale motions on canopy turbulence might be anticipated. Using large-eddy simulations resolving both canopy and ABL turbulence, we investigate here how the turbulence and exchanges at the canopy top change along the morning transition according to the wind intensity. Under significant wind, simulations show that canopy turbulence and exchanges are dominated by mixing-layer-type motions whose characteristics remain constant during the morning transition even though ABL-scale motions imprint on the canopy’s instantaneous velocity fields as the ABL grows. Under low wind, the canopy turbulence is dominated by plumes, whose horizontal sizes extend with the ABL, while their vertical sizes reach a limit before the morning transition ends. In the early morning, canopy-top exchanges are influenced by sources from both the canopy top and the ABL entrainment zone, explaining some of the dissimilarity in turbulent transport between scalars, apart from the differences in the location of canopy scalar sources. When reaching the residual layer, the ABL grows quicker, with intense water vapor and carbon dioxide exchanges, dominated by large-scale motions penetrating deep within the canopy, releasing into the atmosphere the nocturnal accumulated carbon dioxide.

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Xavier Chartrand
,
Louis-Philippe Nadeau
, and
Antoine Venaille

Abstract

The quasi-biennial oscillation (QBO) is understood to result from wave–mean-flow interactions, but the reasons for its relative stability remain a subject of ongoing debate. In addition, consensus has yet to be reached regarding the respective roles of different equatorial wave types in shaping the QBO’s characteristics. Here, we employ Holton–Lindzen–Plumb’s quasilinear model to shed light on the robustness of periodic behavior in the presence of multiple wave forcings. A comprehensive examination of the various dynamical regimes in this model reveals that increased vertical wave propagation at higher altitudes favors periodicity. In the case of single standing wave forcing, enhanced vertical propagation is controlled by the wave attenuation length scale. The occurrence of nonperiodic states at high forcing amplitudes is explained by the excitation of high vertical unstable modes. Increasing the attenuation length scale prevents the emergence of such modes. When multiple wave forcing is considered, the mean flow generated by a dominant primary wave facilitates greater vertical propagation of a perturbation wave. Raising the altitude where most of the wave damping occurs favors periodicity by preventing the development of secondary jets responsible for the aperiodic behavior. This mechanism underscores the potential role of internal gravity waves in supporting the periodicity of a QBO primarily driven by planetary waves.

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Tianao Liu
,
Yilun Chen
,
Haosheng Zuo
,
Aoqi Zhang
,
Xiao Pan
,
Shumin Chen
, and
Weibiao Li

Abstract

Cloud and precipitation microphysics in tropical cyclones (TCs) moving toward Northeast China may exhibit significant distinctions compared with the typical precipitation of this region. In this study, observations from ground-based particle size velocity (Parsivel) disdrometers in Liaoning Province, China, and cloud property data from the Himawari geostationary satellite were utilized to analyze the raindrop size distribution (DSD) characteristics and cloud vertical evolution associated with the outer rainbands of Typhoon Maysak (2020). A comparative analysis was conducted with a typical precipitation event in Northeast China induced by a cold vortex (cold-core low). Our findings reveal distinctive DSD characteristics related to the TC, where medium-sized raindrops dominate, with a smaller diameter but higher concentration in the TC case compared to the typical cold-vortex-induced precipitation case in Northeast China. Convective precipitation falls between maritime-like and continental-like patterns, leaning more toward continental convection. This varies significantly with TCs in Southeast China but is similar to that observed in coastal-front-like rainbands, suggesting extratropical influence. A detailed analysis of the vertical profile of cloud droplets shows a unique “top-down” phenomenon during the extratropical transition process of the TC, where the development of lower-level clouds follows that of upper-level clouds, inconsistent with previous studies and the case for comparison. Further investigation indicates the significant role of the intrusion of dry and cold air from upper levels and the presence of high humidity in low levels in driving this phenomenon. Our results will provide novel insights into cloud and precipitation microphysics associated with TCs in midlatitude regions.

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Xuan Zhou
,
Lu Wang
,
Pang-chi Hsu
,
Tim Li
, and
Baoqiang Xiang

Abstract

The prediction skill for individual Madden-Julian Oscillation (MJO) events is highly variable, but the key factors behind this remain unclear. Using the latest hindcast results from the Subseasonal-to-Seasonal (S2S) Phase II models, this study attempts to understand the diverse prediction skill for the MJO events with an enhanced convective anomaly over the eastern Indian Ocean (IO) at the forecast start date, by investigating the preference of the prediction skill to the MJO-associated convective anomalies and low-frequency background states (LFBS). Compared to the low-skill MJO events, the high-skill events are characterized by a stronger intraseasonal convection-circulation couplet over the IO before the forecast start date, which could result in a longer zonal propagation range during the forecast period, thereby leading to a higher score for assessing the prediction skill. The difference in intraseasonal fields can further be attributed to the LFBS of IO sea surface temperature (SST) and quasi-biannual oscillation (QBO), with the high- (low-) skill events corresponding to a warmer (colder) IO and easterly (westerly) QBO phase. The physical link is that a warm IO could increase the low-level convective instability and thus amplify MJO convection over the IO, whereas an easterly QBO phase could weaken the Maritime Continent barrier effect through weakening the static stability near the tropopause, thus favoring eastward propagation of the MJO. It is also found that the combined effects of IO SST and QBO phases are more effective in influencing MJO prediction skill than individual LFBS.

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