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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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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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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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Shun-ich I. Watanabe
and
Junshi Ito

Abstract

This study evaluates a parameterization scheme for subgrid-scale (SGS) fluxes based on the scale-similarity assumption and employing a large-eddy simulation of an idealized backbuilding convective system. In this parameterization, the SGS fluxes are decomposed into the “Leonard term” which depends only on the resolved scale components, the “Reynolds term” which depends only on the SGS components, and the “cross term” which corresponds to the interaction between the resolved scale and SGS components. Assuming a linear relationship between the Leonard term and the Reynolds and cross terms, SGS fluxes are expressed as the product of an empirical coefficient and the Leonard term. The Leonard term reasonably represents the SGS flux derived by a smooth filter operation, including the counter-gradient vertical SGS transport of potential temperature, which cannot be represented by a traditional eddy-diffusivity model. The dependence of the empirical coefficient on filter width is also evaluated. This dependence is related mainly to the Reynolds term, the magnitude of which varies widely with filter width. The estimation based on the spectral decomposition of the Reynolds term explains the obtained dependence of the empirical coefficient for the vertical flux on filter width. In contrast, the variation of the empirical coefficient with filter width is not required to obtain the horizontal flux. For the parameterization of SGS fluxes in kilometer-scale models that use finite difference or volume methods, the Leonard term is expressed by the horizontal gradient of variables on a discrete grid. The Leonard term on a discrete grid also accurately represents the amplitude and spatial pattern of the SGS flux.

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Dylan W. Reif
,
Howard B. Bluestein
, and
David B. Parsons

Abstract

This study creates a composite sounding for nocturnal convection initiation (CI) events under weakly forced conditions and utilizes an idealized numerical simulation to assess the impact of atmospheric bores on these environments. Thirteen soundings were used to create this composite sounding. Common conditions associated with these weakly forced environments include a nocturnal low-level jet and a Brunt–Väisälä frequency of 0.011 s−1 above 900 hPa. The median lift needed for parcels to realize any convective instability is 490 m, the median convective available potential energy of these convectively unstable parcels is 992 J kg−1, and the median initial pressure of these parcels is 800 hPa. An idealized numerical simulation was utilized to examine the potential influence of bores on CI in an environment based on composite sounding. The characteristics of the simulated bore were representative of observed bores. The vertical velocities associated with this simulated bore were between 1 and 2 m s−1, and the net upward displacement of parcels was between 400 and 650 m. The vertical displacement of air parcels has two notable phases: lift by the bore itself and smaller-scale lift that occurs 100–150 km ahead of the bore passage. The prebore lift is between 50 and 200 m and appears to be related to low-frequency waves ahead of the bores. The lift with these waves was maximized in the low to midtroposphere between 1 and 4 km AGL, and this lift may play a role in assisting CI in these otherwise weakly forced environments.

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Milind Sharma
,
Robin L. Tanamachi
, and
Eric C. Bruning

Abstract

The dual-polarization radar characteristics of severe storms are commonly used as indicators to estimate the size and intensity of deep convective updrafts. In this study, we track rapid fluctuations in updraft intensity and size by objectively identifying polarimetric fingerprints such as Z DR and K DP columns, which serve as proxies for mixed-phase updraft strength. We quantify the volume of Z DR and K DP columns to evaluate their utility in diagnosing temporal variability in lightning flash characteristics. Specifically, we analyze three severe storms that developed in environments with low-to-moderate instability and strong 0–6-km wind shear in northern Alabama during the 2016–17 VORTEX-Southeast field campaign. In these three cases (a tornadic supercell embedded in stratiform precipitation, a nontornadic supercell, and a supercell embedded within a quasi-linear convective system), we find that the volume of the K DP columns exhibits a stronger correlation with the total flash rate. The higher covariability of the K DP column volume with the total flash rate suggests that the overall electrification and precipitation microphysics were dominated by cold cloud processes. The lower covariability with the Z DR column volume indicates the presence of nonsteady updrafts or a less prominent role of warm rain processes in graupel growth and subsequent electrification. Furthermore, we observe that the majority of cloud-to-ground (CG) lightning strikes a carried negative charge to the ground. In contrast to findings from a tornadic supercell over the Great Plains, lightning flash initiations in the Alabama storms primarily occurred outside the footprint of the Z DR and K DP column objects.

Significance Statement

This study quantifies the correlation between mixed-phase updraft intensity and total lightning flash rate in three severe storms in northern Alabama. In the absence of direct updraft velocity measurements, we use polarimetric signatures, such as Z DR and K DP columns, as proxies for updraft strength. Our analysis of polarimetric radar and lightning mapping array data reveals that the lightning flash rate is more highly correlated with the K DP column volume than with the Z DR column volume in all three storms examined. This contrasts with previous findings in storms over the central Great Plains, where the Z DR column volume showed higher covariability with flash rate. Interestingly, lightning initiation in the Alabama storms mainly occurred outside the Z DR and K DP column areas, contrary to previous findings.

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Daniel J. Lloveras
and
Dale R. Durran

Abstract

We present an improved approach to generating moist baroclinically unstable background states for f-plane-channel simulations via potential vorticity (PV) inversion. Previous studies specified PV distributions with constant values in the troposphere and the stratosphere, but this produces unrealistic static-stability profiles that decrease sharply with height in the troposphere. Adding moisture to such environments can yield unrealistically large values of convective available potential energy (CAPE) even for reasonable relative humidity (RH) distributions. In our modified approach, we specify a PV distribution that increases with height in the troposphere and the stratosphere, yielding background states with more realistic values of static stability and CAPE. This modification produces environments that are better suited for representing moist processes, namely, deep convection, in idealized extratropical-cyclone simulations. Also, we present a method for introducing moisture that preserves a specified RH distribution while maintaining hydrostatic balance. Our approach allows for a large degree of control over the initial conditions, as background states with different jet strengths and shapes, average temperatures, moisture contents, or horizontal shears can easily be obtained without changing the underlying PV formula and inadvertently producing unreasonable values of static stability or CAPE. We demonstrate the characteristics of idealized extratropical cyclones developing in our background states by adding localized perturbations that represent an upper-level trough passing over a low-level frontal zone. In particular, we illustrate the impacts of horizontal shear, moisture, and grid spacing on baroclinic-wave development.

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Matthew T. Bray
and
Steven M. Cavallo

Abstract

Arctic cyclones (ACs) are a primary driver of surface weather in the Arctic, contributing to heat and moisture transport and forcing short-term sea ice variability. Still, our understanding of the processes that drive ACs, particularly their large scales and long lifetimes, is limited. ACs are commonly associated with one or more cyclonic tropopause polar vortices (TPVs), potential vorticity (PV) anomalies in the upper troposphere and lower stratosphere that may spur baroclinic development in the surface system, though the exact processes that link the two have yet to be fully explored. In this study, we investigate physical links between TPVs, especially their mesoscale structure and moisture profiles, and ACs with idealized observing system simulation experiments (OSSEs). Starting with a nature run, we simulate different types of dropsonde observations over a TPV during the nascent phase of a nearby AC. The Model for Prediction Across Scales (MPAS) and the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter are then used to run experiments to test the impact of these detailed TPV observations. In addition to a control, five main experiments are conducted, assimilating new observations of temperature and humidity. All experiments reduce forecast errors at the surface and throughout the troposphere. Additional humidity observations alter vertical PV distributions, which in turn impact the development of the AC. Experiments with additional temperature observations exhibit improvements in TPV structure and surrounding PV features and produce stronger surface cyclones with skillful TPV forecasts for up to 36 h longer than the control.

Significance Statement

Arctic cyclones (ACs) are a weather feature that can produce high winds, precipitation, and changes to sea ice cover in the Arctic. As a result, forecasting these storms accurately is important for human and economic interests in the region; however, there are currently gaps in our understanding of how ACs strengthen and persist. In this study, we explore potential links between ACs and weather features higher up in the atmosphere called tropopause polar vortices (TPVs) using computer modeling experiments. This study shows that there are important connections between the characteristics of TPVs and the development of ACs. These findings will be useful for making more accurate forecasts of future events and advancing our knowledge of how sea ice changes relate to the atmosphere.

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