Most-cited articles · 2022

The 10 most-cited articles in Monthly Weather Review, 2022

Ranked by OpenAlex citation count on 29 October 2026, out of 181 eligible articles Monthly Weather Review published in 2022. Ranks are frozen; this page will not change. Number one, with 71 citations: GEFSv12 Reforecast Dataset for Supporting Subseasonal and Hydrometeorological Applications.

Cohort
181 articles
Listed
Top 10 (5.5%)
Open access
8 of 10
Snapshot
2026-10-29

The most-cited articles published in Monthly Weather Review in 2022 cluster around several themes, including subseasonal to seasonal forecasting and predictability, tropical cyclone structure and intensity, and convection, heavy rainfall, and lightning in complex terrain. The top paper, by Hong Guan, Yuejian Zhu, Eric Sinsky and colleagues, describes the GEFSv12 reforecast dataset and has 71 citations. Other highly cited entries address explainable machine learning for subseasonal temperature forecasts, airborne Doppler radar analyses of tropical cyclone vortices, and deep convection observed during the CACTI field campaign.

The list offers a view of which 2022 Monthly Weather Review articles have attracted the most citations to date, as counted by OpenAlex. It covers a range of subjects, from forecast datasets and ensemble filtering methods to satellite radiance assimilation and wind-farm wake parameterization. Readers can use the ranking to identify frequently cited work in the journal from that year, while noting that citation counts reflect accumulated attention rather than a judgment of scientific quality.

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  1. 1
    top 0.6%

    GEFSv12 Reforecast Dataset for Supporting Subseasonal and Hydrometeorological Applications

    Hong Guan, Yuejian Zhu, Eric Sinsky, Bing Fu, Wei zhong Li and 13 more

    71 citationsOpen access (PUBLIC-DOMAIN)doi:10.1175/mwr-d-21-0245.1OpenAlex

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    Abstract For the newly implemented Global Ensemble Forecast System, version 12 (GEFSv12), a 31-yr (1989–2019) ensemble reforecast dataset has been generated at the National Centers for Environmental Prediction (NCEP). The reforecast system is based on NCEP’s Global Forecast System, version 15.1, and GEFSv12, which uses the Finite Volume 3 dynamical core. The resolution of the forecast system is ∼25 km with 64 vertical hybrid levels. The Climate Forecast System (CFS) reanalysis and GEFSv12 reanalysis serve as initial conditions for the Phase 1 (1989–99) and Phase 2 (2000–19) reforecasts, respectively. The perturbations were produced using breeding vectors and ensemble transforms with a rescaling technique for Phase 1 and ensemble Kalman filter 6-h forecasts for Phase 2. The reforecasts were initialized at 0000 (0300) UTC once per day out to 16 days with 5 ensemble members for Phase 1 (Phase 2), except on Wednesdays when the integrations were extended to 35 days with 11 members. The reforecast dataset was produced on NOAA’s Weather and Climate Operational Supercomputing System at…

  2. 2
    top 1.2%

    Using Explainable Machine Learning Forecasts to Discover Subseasonal Drivers of High Summer Temperatures in Western and Central Europe

    Chiem van Straaten, Kirien Whan, Dim Coumou, Bart van den Hurk, Maurice J. Schmeits

    50 citationsOpen access (CC-BY)doi:10.1175/mwr-d-21-0201.1OpenAlex

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    Abstract Reliable subseasonal forecasts of high summer temperatures would be very valuable for society. Although state-of-the-art numerical weather prediction (NWP) models have become much better in representing the relevant sources of predictability like land and sea surface states, the subseasonal potential is not fully realized. Complexities arise because drivers depend on the state of other drivers and on interactions over multiple time scales. This study applies statistical modeling to ERA5 data, and explores how nine potential drivers, interacting on eight time scales, contribute to the subseasonal predictability of high summer temperatures in western and central Europe. Features and target temperatures are extracted with two variations of hierarchical clustering, and are fitted with a machine learning (ML) model based on random forests. Explainable AI methods show that the ML model agrees with physical understanding. Verification of the forecasts reveals that a large part of predictability comes from climate change, but that reliable and valuable subseasonal forecasts are…

  3. 3
    top 1.7%

    An Analysis of Tropical Cyclone Vortex and Convective Characteristics in Relation to Storm Intensity Using a Novel Airborne Doppler Radar Database

    Michael S. Fischer, Paul D. Reasor, Robert F. Rogers, John F. Gamache

    48 citationsdoi:10.1175/mwr-d-21-0223.1OpenAlex

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    Abstract This analysis introduces a novel airborne Doppler radar database, referred to as the Tropical Cyclone Radar Archive of Doppler Analyses with Re-centering (TC-RADAR). TC-RADAR comprises over 900 analyses from 273 flights into TCs in the North Atlantic, eastern North Pacific, and central North Pacific basins between 1997 and 2020. This database contains abundant sampling across a wide range of TC intensities, which facilitated a comprehensive observational analysis on how the three-dimensional, kinematic TC inner-core structure is related to TC intensity. To examine the storm-relative TC structure, we implemented a novel TC center-finding algorithm. Here, we show that TCs below hurricane intensity tend to have monopolar radial profiles of vorticity and a wide range of vortex tilt magnitudes. As TC intensity increases, vorticity becomes maximized within an annulus inward of the peak wind, the vortex decays more slowly with height, and the vortex tends to be more aligned in the vertical. The TC secondary circulation is also strongly linked to TC intensity, as more intense…

  4. 4
    top 2.3%

    Deep Convection Initiation, Growth, and Environments in the Complex Terrain of Central Argentina during CACTI

    Zhe Feng, Adam C. Varble, Joseph Clinton Hardin, James N. Marquis, Alexis Hunzinger and 2 more

    48 citationsOpen access (OTHER-OA)doi:10.1175/mwr-d-21-0237.1OpenAlex

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    Abstract This study characterizes the wide range of deep convective cloud life cycles and their relationships with ambient environments observed during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign near the Sierras de Córdoba (SDC) range in central Argentina. We develop a novel convective cell tracking database for the entire field campaign using C-band polarimetric radar observations. The cell tracking database includes timing, location, area, depth, merge/split information, microphysical properties, collocated satellite-retrieved cloud properties, and sounding-derived environmental conditions. Results show that the SDC exerts a strong control on convection initiation (CI) and growth. CI preferentially occurs east of the SDC ridge during the afternoon, and cells often undergo upscale growth through the evening as they travel eastward toward the plains. Larger and more intense cells tend to occur in more unstable and humid low-level environments, and surface-based cells are stronger than elevated cells. Midtropospheric relative humidity and vertical…

  5. 5
    top 2.8%

    Assimilation of AMSU-A in All-Sky Conditions

    David Duncan, Niels Bormann, Alan Geer, Peter Weston

    47 citationsOpen accessdoi:10.1175/mwr-d-21-0273.1OpenAlex

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    Abstract Radiances from microwave temperature sounders have been assimilated operationally at ECMWF for two decades, but observations significantly affected by clouds and precipitation have been screened out. Extending successful assimilation beyond clear-sky scenes is a challenge that has taken several years of development to achieve. In this paper we describe the all-sky treatment of AMSU-A, which enables greater numbers of temperature sounding radiances to be used in meteorologically active parts of the troposphere. Successful all-sky assimilation required combining lessons learned from the clear-sky assimilation of AMSU-A with the approach initially developed for humidity-sensitive microwave radiances. This concerned particularly observation thinning, error modeling, and variational quality control. As a result of the move to all-sky assimilation, the forecast impact of AMSU-A now replicates and exceeds that of the previous clear-sky usage. This is shown via trials in comparison to the current ECMWF assimilation system, judged with respect to forecast scores and background fits…

  6. 6
    top 3.4%

    A Pan-Alpine Climatology of Lightning and Convective Initiation

    Agostino Manzato, Stefano Serafin, Mario Marcello Miglietta, Daniel J. Kirshbaum, Wolfgang Schulz

    36 citationsOpen access (CC-BY)doi:10.1175/mwr-d-21-0149.1OpenAlex

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    Abstract A new lightning-flash and convective initiation climatology is developed over the Alpine area, one of the hotspots for lightning activity in Europe. The climatology uses cloud-to-ground (CG) data from the European Cooperation for Lightning Detection (EUCLID) network, occurring from 2005 to 2019. The CG lightning data are gridded at a resolution of approximately 2 km and 10 min. A new and simple method of identifying convective initiation (CI) events applies a spatiotemporal mask to the CG data to determine CI timing and location. Although the method depends on a few empirical thresholds, sensitivity tests show the results to be robust. The maximum activity for both CG flashes and CI events is observed from mid-May to mid-September, with a peak at the end of July; the peak in the diurnal cycle occurs in the afternoon. CI is mainly concentrated over and around the Alps, particularly in northern and northeastern Italy. Since many thunderstorms follow the prevailing midlatitude westerly flow, a peak of CG flashes extends from the mountains into the plains and coastal areas of…

  7. 7
    top 3.9%

    A Quantile-Conserving Ensemble Filter Framework. Part I: Updating an Observed Variable

    J. G. Anderson

    36 citationsOpen accessdoi:10.1175/mwr-d-21-0229.1OpenAlex

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    Abstract A general framework for deterministic univariate ensemble filtering is presented. The framework fits a continuous prior probability density function (PDF) to the prior ensemble. A functional representation for the observation likelihood is combined with the prior PDF to get a continuous analysis (posterior) PDF. Cumulative distribution functions for the prior and analysis are also required. The key innovation is that an analysis ensemble is computed so that the quantile of each ensemble member is the same as its prior quantile. Many choices for the prior PDF family and the likelihood function are described. A choice of normal prior with normal likelihood is equivalent to the ensemble adjustment Kalman filter. Some other choices for the prior include gamma, inverse gamma, beta, beta prime, lognormal, and exponential distributions. Both prior distributions and likelihoods can be defined over a set of intervals giving additional flexibility that can be used to implement methods like a Huber likelihood for observations with occasional outliers. Priors and likelihoods can also…

  8. 8
    top 4.5%

    Impacts of Coastal Terrain on Warm-Sector Heavy-Rain-Producing MCSs in Southern China

    Murong Zhang, Kristen Lani Rasmussen, Zhiyong Meng, Yipeng Huang

    34 citationsdoi:10.1175/mwr-d-21-0190.1OpenAlex

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    Abstract Warm-sector heavy rainfall in southern China refers to the heavy rainfall that occurs within a weakly forced synoptic environment under the influence of monsoonal airflows. It is usually located near the southern coast and is characterized by poor predictability and a close relationship with coastal terrain. This study investigates the impacts of coastal terrain on the initiation, organization, and heavy rainfall potential of MCSs in warm-sector heavy rainfall over southern China using quasi-idealized WRF simulations and terrain-modification experiments. Typical warm-sector heavy rainfall events were selected to produce composite environments that forced the simulations. MCSs in these events all initiated in the early morning and developed into quasi-linear convective systems along the coast with a prominent back-building process. When the small coastal terrain is removed, the maximum 12-h rainfall accumulation decreases by ∼46%. The convection initiation is advanced ∼2 h with the help of orographic lifting associated with flow interaction with the coastal hills in the…

  9. 9
    top 5%

    Evaluation of the Fitch Wind-Farm Wake Parameterization with Large-Eddy Simulations of Wakes Using the Weather Research and Forecasting Model

    Alfredo Peña, Jeffrey D. Mirocha, P. van der Laan

    32 citationsOpen access (OTHER-OA)doi:10.1175/mwr-d-22-0118.1OpenAlex

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    Abstract Wind-farm parameterizations in weather models can be used to predict both the power output and farm effects on the flow; however, their correctness has not been thoroughly assessed. We evaluate the wind-farm parameterization of the Weather Research and Forecasting Model with large-eddy simulations (LES) of the wake performed with the same model. We study the impact on the velocity and turbulence kinetic energy (TKE) of inflow velocity, roughness, resolution, number of turbines (one or two), and inversion height and strength. We compare the mesoscale with the LES by spatially averaging the LES within areas correspondent to the mesoscale horizontal spacing: one covering the turbine area and two downwind. We find an excellent agreement of the velocity within the turbine area between the two types of simulations. However, within the same area, we find the largest TKE discrepancies because in mesoscale simulations, the turbine-added TKE has to be highest at the turbine position to be advected downwind. Within the downwind areas, differences between velocities increase as the…

  10. 10
    top 5.6%

    The Influence of Convectively Coupled Kelvin Waves on African Easterly Waves in a Wave-Following Framework

    Quinton A. Lawton, Sharanya J. Majumdar, Krista Dotterer, Chris D. Thorncroft, Carl J. Schreck

    32 citationsOpen accessdoi:10.1175/mwr-d-21-0321.1OpenAlex

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    Abstract While considerable attention has been given to how convectively coupled Kelvin waves (CCKWs) influence the genesis of tropical cyclones (TCs) in the Atlantic Ocean, less attention has been given to their direct influence on African easterly waves (AEWs). This study builds a climatology of AEW and CCKW passages from 1981 to 2019 using an AEW-following framework. Vertical and horizontal composites of these passages are developed and divided into categories based on AEW position and CCKW strength. Many of the relationships that have previously been found for TC genesis also hold true for non-developing AEWs. This includes an increase in convective coverage surrounding the AEW center in phase with the convectively enhanced (“active”) CCKW crest, as well as a buildup of relative vorticity from the lower to upper troposphere following this active crest. Additionally, a new finding is that CCKWs induce specific humidity anomalies around AEWs that are qualitatively similar to those of relative vorticity. These modifications to specific humidity are more pronounced when AEWs are at…

How to read this list

The cohort is every article, review and book chapter OpenAlex attributes to Monthly Weather Review with publication year 2022, excluding retracted works and paratext such as editorials and errata. The top 10 by citation count are listed; the percentile is the rank divided by the cohort size. 10 of the 10 entries have an abstract in OpenAlex. Counts were read on 29 October 2026 and will not be updated on this page, so a quoted rank stays verifiable. Full rules on the methodology page.

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