The 10 most-cited articles in Monthly Weather Review, 2020

Abstract The representation of tropical precipitation is evaluated across three generations of models participating in phases 3, 5, and 6 of the Coupled Model Intercomparison Project (CMIP). Compared to state-of-the-art observations, improvements in tropical precipitation in the CMIP6 models are identified for some metrics, but we find no general improvement in tropical precipitation on different temporal and spatial scales. Our results indicate overall little changes across the CMIP phases for the summer monsoons, the double-ITCZ bias, and the diurnal cycle of tropical precipitation. We find a reduced amount of drizzle events in CMIP6, but tropical precipitation occurs still too frequently. Continuous improvements across the CMIP phases are identified for the number of consecutive dry days, for the representation of modes of variability, namely, the Madden–Julian oscillation and El Niño–Southern Oscillation, and for the trends in dry months in the twentieth century. The observed positive trend in extreme wet months is, however, not captured by any of the CMIP phases, which simulate…

Abstract As wind farms grow in number and size worldwide, it is important that their potential impacts on the environment are studied and understood. The Fitch parameterization implemented in the Weather Research and Forecasting (WRF) Model since version 3.3 is a widely used tool today to study such impacts. We identified two important issues related to the way the added turbulent kinetic energy (TKE) generated by a wind farm is treated in the WRF Model with the Fitch parameterization. The first issue is a simple “bug” in the WRF code, and the second issue is the excessive value of a coefficient, called C TKE , that relates TKE to the turbine electromechanical losses. These two issues directly affect the way that a wind farm wake evolves, and they impact properties like near-surface temperature and wind speed at the wind farm as well as behind it in the wake. We provide a bug fix and a revised value of C TKE that is one-quarter of the original value. This 0.25 correction factor is empirical; future studies should examine its dependence on parameters such as atmospheric stability,…

Abstract Data assimilation combines forecasts from a numerical model with observations. Most of the current data assimilation algorithms consider the model and observation error terms as additive Gaussian noise, specified by their covariance matrices and , respectively. These error covariances, and specifically their respective amplitudes, determine the weights given to the background (i.e., the model forecasts) and to the observations in the solution of data assimilation algorithms (i.e., the analysis). Consequently, and matrices significantly impact the accuracy of the analysis. This review aims to present and to discuss, with a unified framework, different methods to jointly estimate the and matrices using ensemble-based data assimilation techniques. Most of the methods developed to date use the innovations, defined as differences between the observations and the projection of the forecasts onto the observation space. These methods are based on two main statistical criteria: 1) the method of moments, in which the theoretical and empirical moments of the innovations are assumed to…

Abstract In this study, a nocturnal extreme rainfall event induced by the urban heat island (UHI) effects of the coastal city of Guangzhou in South China on 7 May 2017 is examined using observational analyses and 18-h cloud-permitting simulations with the finest grid size of 1.33 km and the bottom boundary conditions nudged. Results show that the model reproduces convective initiation on Guangzhou’s downstream side (i.e., Huashan), where a shallow thermal mesolow is located, the subsequent back-building of convective cells as a larger-scale warm-moist southerly flow interacts with convectively generated cold outflows, and their eastward drifting and reorganization into a localized extreme-rain-producing storm near Jiulong under the influences of local orography. In particular, the model produces the maximum hourly, 3- and 12-hourly rainfall amounts of 146, 315, and 551 mm, respectively, at nearly the right location compared to their corresponding observed extreme amounts of 184, 382, and 542 mm. In addition, the model reproduces an intense meso-γ-scale vortex associated with the…

Abstract The Mediterranean storm “Vaia” developed within a typical autumn synoptic circulation, generally associated with heavy rain conditions over the western Mediterranean Sea basin. Intense precipitation was responsible for floods over Italy between 27 and 30 October 2018, and the storm was accompanied by explosive cyclogenesis, storm surge, and extremely intense wind gusts that caused casualties and extensive damage, especially to the Alpine forests. This study investigates the contribution of different moisture sources to the extreme precipitation by means of numerical model simulations using the Bologna Limited Area Model (BOLAM). In particular, the attention is focused on the significant amount of water vapor transported into the Mediterranean basin from the Atlantic Ocean tropical area and organized along a narrow corridor across the African continent. First, a newly developed detection algorithm is applied to identify this transport as an atmospheric river (AR). Then, the implementation of an atmospheric water budget diagnostic, supported by sensitivity experiments, allows…

Abstract Using nine years of historical forecasts spanning April 2003–April 2012 from NOAA’s Second Generation Global Ensemble Forecast System Reforecast (GEFS/R) ensemble, random forest (RF) models are trained to make probabilistic predictions of severe weather across the contiguous United States (CONUS) at Days 1–3, with separate models for tornado, hail, and severe wind prediction at Day 1 in an analogous fashion to the Storm Prediction Center’s (SPC’s) convective outlooks. Separate models are also trained for the western, central, and eastern CONUS. Input predictors include fields associated with severe weather prediction, including CAPE, CIN, wind shear, and numerous other variables. Predictor inputs incorporate the simulated spatiotemporal evolution of these atmospheric fields throughout the forecast period in the vicinity of the forecast point. These trained RF models are applied to unseen inputs from April 2012 to December 2016, and their forecasts are evaluated alongside the equivalent SPC outlooks. The RFs objectively make statistical deductions about the relationships…

Abstract This paper describes the development of convolutional neural networks (CNN), a type of deep-learning method, to predict next-hour tornado occurrence. Predictors are a storm-centered radar image and a proximity sounding from the Rapid Refresh model. Radar images come from the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) and Gridded NEXRAD WSR-88D Radar dataset (GridRad), both of which are multiradar composites. We train separate CNNs on MYRORSS and GridRad data, present an experiment to optimize the CNN settings, and evaluate the chosen CNNs on independent testing data. Both models achieve an area under the receiver-operating-characteristic curve (AUC) well above 0.9, which is considered to be excellent performance. The GridRad model achieves a critical success index (CSI) of 0.31, and the MYRORSS model achieves a CSI of 0.17. The difference is due primarily to event frequency (percentage of storms that are tornadic in the next hour), which is 3.52% for GridRad but only 0.24% for MYRORSS. The best CNN predictions (true positives and negatives) occur for strongly…

Abstract To produce more precise descriptions of air–sea exchanges under tropical cyclones (TCs), spaceborne synthetic aperture radar (SAR) instruments provide unique capabilities to probe the ocean surface conditions, at very high spatial resolution, and on synoptic scales. Using highly resolved (3 km) wind fields, an extensive database is constructed from RadarSat-2 and Sentinel-1 SAR acquisitions. Spanning 161 tropical cyclones, the database covers all TC intensity categories that have occurred in 5 different TC basins, and include 29 cases coincident with SFMR measurements. After locating the TC center, a specific methodology is applied to filter out areas contaminated by heavy precipitation to help extract, for each acquisition, the maximum wind speed (Vmax), its associated radius (Rmax), and corresponding outer wind radii (R34/50/64 kt). These parameters are then systematically compared with best track (BTK), and when available, SFMR airborne measurements. For collocated SFMR and SAR observations, comparisons yield root-mean-squares of 3.86 m s −1 and 3 km for ocean surface…

Abstract Through conducting dynamic and thermodynamic diagnoses as well as a series of numerical sensitivity simulations, we investigated the effects of the terrain, coastline, and cold pools on convection initiation (CI) and its subsequent upscale convective growth (UCG) during a case of heavy rainfall along the coast of South China. CI occurred at the vertex of the coastal concave mountain geometry as a combined result of coastal convergence, orographic lifting, and mesoscale ascent driven by the terminus of a marine boundary layer jet (MBLJ). In numerical simulations with the coastline or terrain of South China removed, the coastal CI does not occur or becomes weaker as the MBLJ extends farther north, suggesting that the coastline and terrain play a role in CI. In addition, local small-scale terrain can modulate the detailed location and timing of CI and UCG. When the coastal concave terrain and coastline near the CI are artificially removed or filled by additional mountains, the orographic lifting and the local convergence along the coast correspondingly change, which strongly…

Abstract Understanding and predicting midlatitude cold spells is of scientific and public interest, given often associated severe impacts. However, large-scale atmospheric dynamics related to these events are not fully understood. The winter of 2017/18 was characterized by several cold spells affecting large parts of North America and Eurasia. Here, the role of stratosphere–troposphere coupling for the occurrence of cold spells in this winter is investigated using different wave propagation diagnostics. While the European cold spell in late February 2018 was influenced by a major sudden stratospheric warming (SSW) associated with wave absorption, the cold spells over North America at the end of December 2017 and early February 2018 were related to downward reflected waves over the North Pacific. Previously proposed wave reflection indices, however, either miss these reflection events or are not able to distinguish them from the major SSW related to wave absorption. To overcome this, a novel simple index based on eddy heat flux is proposed here, capturing regional wave reflection…