The climate research community uses atmospheric reanalysis data sets to understand a wide range of processes and variability in the atmosphere, yet different reanalyses may give very different results for the same diagnostics. The Stratosphere–troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP) is a coordinated activity to compare reanalysis data sets using a variety of key diagnostics. The objectives of this project are to identify differences among reanalyses and understand their underlying causes, to provide guidance on appropriate usage of various reanalysis products in scientific studies, particularly those of relevance to SPARC, and to contribute to future improvements in the reanalysis products by establishing collaborative links between reanalysis centres and data users. The project focuses predominantly on differences among reanalyses, although studies that include operational analyses and studies comparing reanalyses with observations are also included when appropriate. The emphasis is on diagnostics of the upper troposphere, stratosphere, and lower mesosphere. This paper summarizes the motivation and goals of the S-RIP activity and extensively reviews key technical aspects of the reanalysis data sets that are the focus of this activity. The special issue “The SPARC Reanalysis Intercomparison Project (S-RIP)” in this journal serves to collect research with relevance to the S-RIP in preparation for the publication of the planned two (interim and full) S-RIP reports
This paper synthesizes multiple methods for machine learning (ML) model interpretation and visualization (MIV) focusing on meteorological applications. ML has recently exploded in popularity in many fields, including meteorology. Although ML has been successful in meteorology, it has not been as widely accepted, primarily due to the perception that ML models are “black boxes,” meaning the ML methods are thought to take inputs and provide outputs but not to yield physically interpretable information to the user. This paper introduces and demonstrates multiple MIV techniques for both traditional ML and deep learning, to enable meteorologists to understand what ML models have learned. We discuss permutation-based predictor importance, forward and backward selection, saliency maps, class-activation maps, backward optimization, and novelty detection. We apply these methods at multiple spatiotemporal scales to tornado, hail, winter precipitation type, and convective-storm mode. By analyzing such a wide variety of applications, we intend for this work to demystify the black box of ML, offer insight in applying MIV techniques, and serve as a MIV toolbox for meteorologists and other physical scientists.
The spatiotemporal variability and three-dimensional structures of mesoscale convective systems (MCSs) east of the U.S. Rocky Mountains and their large-scale environments are characterized across all seasons using 13 years of high-resolution radar and satellite observations. Long-lived and intense MCSs account for over 50% of warm season precipitation in the Great Plains and over 40% of cold season precipitation in the southeast. The Great Plains has the strongest MCS seasonal cycle peaking in May–June, whereas in the U.S. southeast MCSs occur year-round. Distinctly different large-scale environments across the seasons have significant impacts on the structure of MCSs. Spring and fall MCSs commonly initiate under strong baroclinic forcing and favorable thermodynamic environments. MCS genesis frequently occurs in the Great Plains near sunset, although convection is not always surface based. Spring MCSs feature both large and deep convection, with a large stratiform rain area and high volume of rainfall. In contrast, summer MCSs often initiate under weak baroclinic forcing, featuring a high pressure ridge with weak low-level convergence acting on the warm, humid air associated with the low-level jet. MCS genesis concentrates east of the Rocky Mountain Front Range and near the southeast coast in the afternoon. The strongest MCS diurnal cycle amplitude extends from the foothills of the Rocky Mountains to the Great Plains. Summer MCSs have the largest and deepest convective features, the smallest stratiform rain area, and the lowest rainfall volume. Last, winter MCSs are characterized by the strongest baroclinic forcing and the largest MCS precipitation features over the southeast. Implications of the findings for climate modeling are discussed.
Mesoscale convective systems (MCSs) are frequently observed over the U.S. Great Plains during boreal spring and summer. Here, four types of synoptically favorable environments for spring MCSs and two types each of synoptically favorable and unfavorable environments for summer MCSs are identified using self-organizing maps (SOMs) with inputs from observational data. During spring, frontal systems providing a lifting mechanism and an enhanced Great Plains low-level jet (GPLLJ) providing anomalous moisture are important features identified by SOM analysis for creating favorable dynamical and thermodynamic environments for MCS development. During summer, the composite MCS environment shows small positive convective available potential energy (CAPE) and convective inhibition (CIN) anomalies, which are in stark contrast with the large positive CAPE and negative CIN anomalies in spring. This contrast suggests that summer convection may occur even with weak large-scale dynamical and thermodynamic perturbations so MCSs may be inherently less predictable in summer. The two synoptically favorable environments identified in summer have frontal characteristics and an enhanced GPLLJ, but both shift north compared to spring. The two synoptically unfavorable environments feature enhanced upper-level ridges, but differ in the strength of the GPLLJ. In both seasons, MCS precipitation amount, area, and rate are much larger in the frontal-related MCSs than in nonfrontal MCSs. A large-scale index constructed using pattern correlation between large-scale environments and the synoptically favorable SOM types is found to be skillful for estimating MCS number, precipitation rate, and area in spring, but its explanatory power decreases significantly in summer. The low predictability of summer MCSs deserves further investigation in the future.
Rossby wave breaking is an important mechanism for the two-way exchange of air between the tropical upper troposphere and lower stratosphere and the extratropical lower stratosphere. The authors present a 30-yr climatology (1981–2010) of anticyclonically and cyclonically sheared wave-breaking events along the boundary of the tropics in the 350–500-K potential temperature range from ECMWF Interim Re-Analysis (ERA-Interim). Lagrangian transport analyses show net equatorward transport from wave breaking near 380 K and poleward transport at altitudes below and above the 370–390-K layer. The finding of poleward transport at lower levels is in disagreement with previous studies and is shown to largely depend on the choice of tropical boundary. In addition, three distinct modes of transport for anticyclonic wave-breaking events are found near the tropical tropopause (380 K): poleward, equatorward, and symmetric. Transport associated with cyclonic wave-breaking events, however, is predominantly poleward. The three transport modes for anticyclonic wave breaking are associated with specific characteristics of the geometry of the mean flow. In particular, composite averages show that poleward transport is associated with a “split” subtropical jet where the jet on the upstream side of the breaking wave extends eastward and lies poleward and at lower altitudes of the subtropical jet on the downstream side, producing a substantial longitudinal overlap between the two jets. Equatorward transport is not associated with a split subtropical jet and is found immediately downstream of stationary anticyclones in the tropics, often associated with monsoon circulations. It is further shown that, in general, the transport direction of breaking waves is determined primarily by the relative positions of the jets.
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