NOAA National Severe Storms Laboratory

The conservation and distribution of water substance in atmospheric circulations is considered within a frame of continuity principles, model air flows, and models of microphysical processes. The simplest considerations of precipitation involve its vertical distribution in an updraft column, where condensate appears immediately as precipitation with uniform terminal fallspeed. The study also treats steady two-dimensional air circulations in which time-dependent distributions of water vapor, cloud and precipitation respond to model microphysical processes. The approach throughout is essentially kinematic, although results provide numerous insights into the dynamical properties of a cloudy or stormy atmosphere. Water distributions are explained in relation to the air’s horizontal divergence, vertical velocity and compressibility, and physical pictures are presented frequently. The findings are compared with various observations on precipitating weather systems. Detailed summaries of this paper by Sections are presented in Sections 1 and 15.

An approach to forecasting the potential for flash flood-producing storms is developed, using the notion of basic ingredients. Heavy precipitation is the result of sustained high rainfall rates. In turn, high rainfall rates involve the rapid ascent of air containing substantial water vapor and also depend on the precipitation efficiency. The duration of an event is associated with its speed of movement and the size of the system causing the event along the direction of system movement. This leads naturally to a consideration of the meteorological processes by which these basic ingredients are brought together. A description of those processes and of the types of heavy precipitation-producing storms suggests some of the variety of ways in which heavy precipitation occurs. Since the right mixture of these ingredients can be found in a wide variety of synoptic and mesoscale situations, it is necessary to know which of the ingredients is critical in any given case. By knowing which of the ingredients is most important in any given case, forecasters can concentrate on recognition of the developing heavy precipitation potential as meteorological processes operate. This also helps with the recognition of heavy rain events as they occur, a challenging problem if the potential for such events has not been anticipated. Three brief case examples are presented to illustrate the procedure as it might be applied in operations. The cases are geographically diverse and even illustrate how a nonconvective heavy precipitation event fits within this methodology. The concept of ingredients-based forecasting is discussed as it might apply to a broader spectrum of forecast events than just flash flood forecasting.

Two time-splitting methods for integrating the elastic equations are presented. The methods are based on a third-order Runge-Kutta time scheme and the Crowley advection schemes. The schemes are combined with a forward-backward scheme for integrating high-frequency acoustic and gravity modes to create stable splitexplicit schemes for integrating the compressible Navier-Stokes equations. The time-split methods facilitate the use of both centered and upwind-biased discretizations for the advection terms, allow for larger time steps, and produce more accurate solutions than existing approaches. The time-split Crowley scheme illustrates a methodology for combining any pure forward-in-time advection schemes with an explicit time-splitting method. Based on both linear and nonlinear tests, the third-order Runge-Kutta-based time-splitting scheme appears to offer the best combination of efficiency and simplicity for integrating compressible nonhydrostatic atmospheric models.

Abstract Precipitation is a key source of freshwater; therefore, observing global patterns of precipitation and its intensity is important for science, society, and understanding our planet in a changing climate. In 2014, the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) launched the Global Precipitation Measurement (GPM) Core Observatory (CO) spacecraft. The GPM CO carries the most advanced precipitation sensors currently in space including a dual-frequency precipitation radar provided by JAXA for measuring the three-dimensional structures of precipitation and a well-calibrated, multifrequency passive microwave radiometer that provides wide-swath precipitation data. The GPM CO was designed to measure rain rates from 0.2 to 110.0 mm h−1 and to detect moderate to intense snow events. The GPM CO serves as a reference for unifying the data from a constellation of partner satellites to provide next-generation, merged precipitation estimates globally and with high spatial and temporal resolutions. Through improved measurements of rain and snow, precipitation data from GPM provides new information such as details on precipitation structure and intensity; observations of hurricanes and typhoons as they transition from the tropics to the midlatitudes; data to advance near-real-time hazard assessment for floods, landslides, and droughts; inputs to improve weather and climate models; and insights into agricultural productivity, famine, and public health. Since launch, GPM teams have calibrated satellite instruments, refined precipitation retrieval algorithms, expanded science investigations, and processed and disseminated precipitation data for a range of applications. The current status of GPM, its ongoing science, and its future plans are presented.

All of the 0000 UTC soundings from the United States made during the year 1992 that have nonzero convective available potential energy (CAPE) are examined. Soundings are classified as being associated with nonsupercell thunderstorms, supercells without significant tornadoes, and supercells with significant tornadoes. This classification is made by attempting to pair, based on the low-level sounding winds, an upstream sounding with each occurrence of a significant tornado, large hail, and/or 10 or more cloud-to-ground lightning flashes. Severe weather wind parameters (mean shear, 0-6-km shear, storm-relative helicity, and storm-relative anvil-level flow) and CAPE parameters (total CAPE and CAPE in the lowest 3000 m with buoyancy) are shown to discriminate weakly between the environments of the three classified types of storms. Combined parameters (energy-helicity index and vorticity generation parameter) discriminate strongly between the environments. The height of the lifting condensation level also appears to be generally lower for supercells with significant tornadoes than those without. The causes for the very large false alarm rates in the tornadic/nontornadic supercell forecast, even with the best discriminators, are discussed.

A sample of 413 soundings in close proximity to tornadic and nontornadic supercells is examined. The soundings were obtained from hourly analyses generated by the 40-km Rapid Update Cycle-2 (RUC-2) analysis and forecast system. A comparison of 149 observed soundings and collocated RUC-2 soundings in regional supercell environments reveals that the RUC-2 model analyses were reasonably accurate through much of the troposphere. The largest error tendencies were in temperatures and mixing ratios near the surface, primarily in 1-h forecast soundings immediately prior to the standard rawinsonde launches around 1200 and 0000 UTC. Overall, the RUC-2 analysis soundings appear to be a reasonable proxy for observed soundings in supercell environments.

The Tropospheric Ozone Assessment Report (TOAR) is an activity of the International Global Atmospheric Chemistry Project. This paper is a component of the report, focusing on the present-day distribution and trends of tropospheric ozone relevant to climate and global atmospheric chemistry model evaluation. Utilizing the TOAR surface ozone database, several figures present the global distribution and trends of daytime average ozone at 2702 non-urban monitoring sites, highlighting the regions and seasons of the world with the greatest ozone levels. Similarly, ozonesonde and commercial aircraft observations reveal ozone’s distribution throughout the depth of the free troposphere. Long-term surface observations are limited in their global spatial coverage, but data from remote locations indicate that ozone in the 21st century is greater than during the 1970s and 1980s. While some remote sites and many sites in the heavily polluted regions of East Asia show ozone increases since 2000, many others show decreases and there is no clear global pattern for surface ozone changes since 2000. Two new satellite products provide detailed views of ozone in the lower troposphere across East Asia and Europe, revealing the full spatial extent of the spring and summer ozone enhancements across eastern China that cannot be assessed from limited surface observations. Sufficient data are now available (ozonesondes, satellite, aircraft) across the tropics from South America eastwards to the western Pacific Ocean, to indicate a likely tropospheric column ozone increase since the 1990s. The 2014–2016 mean tropospheric ozone burden (TOB) between 60°N–60°S from five satellite products is 300 Tg ± 4%. While this agreement is excellent, the products differ in their quantification of TOB trends and further work is required to reconcile the differences. Satellites can now estimate ozone’s global long-wave radiative effect, but evaluation is difficult due to limited in situ observations where the radiative effect is greatest.

The Oklahoma mesonet is a joint project of Oklahoma State University and the University of Oklahoma. It is an automated network of 108 stations covering the state of Oklahoma. Each station measures air temperature, humidity, barometric pressure, wind speed and direction, rainfall, solar radiation, and soil temperatures. Each station transmits a data message every 15 min via a radio link to the nearest terminal of the Oklahoma Law Enforcement Telecommunications System that relays it to a central site in Norman, Oklahoma. The data message comprises three 5-min averages of most data (and one 15-min average of soil temperatures). The central site ingests the data, runs some quality assurance tests, archives the data, and disseminates it in real time to a broad community of users, primarily through a computerized bulletin board system. This manuscript provides a technical description of the Oklahoma mesonet including a complete description of the instrumentation. Sensor inaccuracy, resolution, height with respect to ground level, and method of exposure are discussed.

Knowledge of severe local storms has been increasing rapidly in recent years as a result of both observational studies and numerical modeling experiments. This paper reviews that knowledge as it relates to development of new applications for forecasting of severe local storms. Many of these new applications are based on physical understanding of processes taking place on the storm scale and thus allow forecasters to become less dependent on empirical relationships. Refinements in pattern recognition and severe weather climatology continue to be of value to the operational severe local storms forecasters, however. Current methodology for forecasting severe local storms at the National Severe Storms Forecast Center is described. Operational uses of new forecast applications, new “real-time” data sources (such as wind profilers and Doppler radars), and improved numerical model products are discussed.

Abstract Electrification and lightning are simulated for a small continental multicell storm. The results are consistent with observations and thus provide additional understanding of the charging processes and evolution of this storm. The first six observed lightning flashes were all negative cloud-to-ground (CG) flashes, after which intracloud (IC) flashes also occurred between middle and upper levels of the storm. The model simulation reproduces the basic evolution of lightning from low and middle levels to upper levels. The observed lightning indicated an initial charge structure of at least an inverted dipole (negative charge above positive). The simulations show that noninductive charge separation higher in the storm can enhance the main negative charge sufficiently to produce negative CG flashes before upper-level IC flashes commence. The result is a “bottom-heavy” tripole charge structure with midlevel negative charge and a lower positive charge region that is more significant than the upper positive region, in contrast to the traditional tripole structure that has a less significant lower positive charge region. Additionally, the occurrence of cloud-to-ground lightning is not necessarily a result of excess net charge carried by the storm, but it is primarily caused by the local potential imbalance between the lowest charge regions. The two-moment microphysics scheme used for this study predicted mass mixing ratio and number concentration of cloud droplets, rain, ice crystals, snow, and graupel. Bulk particle density of graupel was also predicted, which allows a single category to represent a greater range of particle characteristics. (An additional hail category is available but was not needed for the present study.) The prediction of hydrometeor number concentration is particularly critical for charge separation at higher temperatures (−5° < T < −20°C) in the mixed phase region, where ice crystals are produced by rime fracturing (Hallett–Mossop process) and by splintering of freezing drops. Cloud droplet concentration prediction also affected the rates of inductive charge separation between graupel and droplets.

Abstract 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.

Accurate storm identification and tracking are basic and essential parts of radar and severe weather warning operations in today’s operational meteorological community. Improvements over the original WSR-88D storm series algorithm have been made with the Storm Cell Identification and Tracking algorithm (SCIT). This paper discusses the SCIT algorithm, a centroid tracking algorithm with improved methods of identifying storms (both isolated and clustered or line storms). In an analysis of 6561 storm cells, the SCIT algorithm correctly identified 68% of all cells with maximum reflectivities over 40 dBZ and 96% of all cells with maximum reflectivities of 50 dBZ or greater. The WSR-88D storm series algorithm performed at 24% and 41%, respectively, for the same dataset. With better identification performance, the potential exists for better and more accurate tracking information. The SCIT algorithm tracked greater than 90% of all storm cells correctly. The algorithm techniques and results of a detailed performance evaluation are presented. This algorithm was included in the WSR-88D Build 9.0 of the Radar Products Generator software during late 1996 and early 1997. It is hoped that this paper will give new users of the algorithm sufficient background information to use the algorithm with confidence.

A new synthesis of information forming the foundation for rule-based systems to deduce dominant bulk hydrometeor types and amounts using polarimetric radar data is presented. The information is valid for a 10-cm wavelength and consists of relations that are based on an extensive list of previous and recent observational and modeling studies of polarimetric signatures of hydrometeors. The relations are expressed as boundaries and thresholds in a space of polarimetric radar variables. Thus, the foundation is laid out for identification of hydrometeor types (species), estimation of characteristics of hydrometeor species (size, concentrations, etc.), and quantification of bulk hydrometeor contents (amounts). A fuzzy classification algorithm that builds upon this foundation will be discussed in a forthcoming paper.

The state of knowledge regarding trends and an understanding of their causes is presented for a specific subset of extreme weather and climate types. For severe convective storms (tornadoes, hailstorms, and severe thunderstorms), differences in time and space of practices of collecting reports of events make using the reporting database to detect trends extremely difficult. Overall, changes in the frequency of environments favorable for severe thunderstorms have not been statistically significant. For extreme precipitation, there is strong evidence for a nationally averaged upward trend in the frequency and intensity of events. The causes of the observed trends have not been determined with certainty, although there is evidence that increasing atmospheric water vapor may be one factor. For hurricanes and typhoons, robust detection of trends in Atlantic and western North Pacific tropical cyclone (TC) activity is significantly constrained by data heterogeneity and deficient quantification of internal variability. Attribution of past TC changes is further challenged by a lack of consensus on the physical link- ages between climate forcing and TC activity. As a result, attribution of trends to anthropogenic forcing remains controversial. For severe snowstorms and ice storms, the number of severe regional snowstorms that occurred since 1960 was more than twice that of the preceding 60 years. There are no significant multidecadal trends in the areal percentage of the contiguous United States impacted by extreme seasonal snowfall amounts since 1900. There is no distinguishable trend in the frequency of ice storms for the United States as a whole since 1950.

Radar can produce detailed precipitation information for large areas from a single location in real time. Although radar has been used experimentally for nearly 30 years to measure rainfall, operational implementation has been slow. Today we find that data are underutilized and both confusion and misunderstanding exist about the inherent ability of radar to measure rainfall, about factors that contribute to errors, and about the importance of careful calibration and signal processing. Areal and point rainfall estimates are often in error by a factor of two or more. Error sources reside in measurement of radar reflectivity factor, evaporation and advection of precipitation before reaching the ground, and variations in the drop-size distribution and vertical air motions. Nevertheless, radar can be of lifesaving usefulness by alerting forecasters to the potential for flash flooding. The most successful technique for improving the radar rainfall estimates has been to “calibrate” the radar with rain gages. Simple techniques that combine sparse gage reports (one gage per 1000–2000 km2) with radar produce smaller measurement errors (10–30%) than either system alone. When high accuracy rainfall measurements are needed (average error less than about 10–20%) the advantage of radar is diminished, since the number of gages required for calibration is itself sufficient to provide the desired accuracy.

Abstract This paper contains a description of the most recent version of the hydrometeor classification algorithm for polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D). This version contains several modifications and refinements of the previous echo classification algorithm based on the principles of fuzzy logic. These modifications include the estimation of confidence factors that characterize the possible impacts of all error sources on radar measurements, the assignment of the matrix of weights that characterizes the classification power of each variable with respect to every class of radar echo, and the implementation of a class designation system based on the distance from the radar and the parameters of the melting layer that are determined as functions of azimuth with polarimetric radar measurements. These additions provide considerable flexibility and improve the discrimination between liquid and frozen hydrometeors. The new classification scheme utilizes all available polarimetric variables and discerns 10 different classes of radar echoes. Furthermore, a methodology for the new fuzzy logic classification scheme is discussed and the results are illustrated using polarimetric radar data collected with the Norman, Oklahoma (KOUN), WSR-88D prototype radar during a mesoscale convective system event on 13 May 2005.

Abstract Real-time forecasts of five landfalling Atlantic hurricanes during 2005 using the Advanced Research Weather Research and Forecasting (WRF) (ARW) Model at grid spacings of 12 and 4 km revealed performance generally competitive with, and occasionally superior to, other operational forecasts for storm position and intensity. Recurring errors include 1) excessive intensification prior to landfall, 2) insufficient momentum exchange with the surface, and 3) inability to capture rapid intensification when observed. To address these errors several augmentations of the basic community model have been designed and tested as part of what is termed the Advanced Hurricane WRF (AHW) model. Based on sensitivity simulations of Katrina, the inner-core structure, particularly the size of the eye, was found to be sensitive to model resolution and surface momentum exchange. The forecast of rapid intensification and the structure of convective bands in Katrina were not significantly improved until the grid spacing approached 1 km. Coupling the atmospheric model to a columnar, mixed layer ocean model eliminated much of the erroneous intensification of Katrina prior to landfall noted in the real-time forecast.

Weather and climate extremes have been varying and changing on many different time scales. In recent decades, heat waves have generally become more frequent across the United States, while cold waves have been decreasing. While this is in keeping with expectations in a warming climate, it turns out that decadal variations in the number of U.S. heat and cold waves do not correlate well with the observed U.S. warming during the last century. Annual peak flow data reveal that river flooding trends on the century scale do not show uniform changes across the country. While flood magnitudes in the Southwest have been decreasing, flood magnitudes in the Northeast and north-central United States have been increasing. Confounding the analysis of trends in river flooding is multiyear and even multidecadal variability likely caused by both large-scale atmospheric circulation changes and basin-scale “memory” in the form of soil moisture. Droughts also have long-term trends as well as multiyear and decadal variability. Instrumental data indicate that the Dust Bowl of the 1930s and the drought in the 1950s were the most significant twentieth-century droughts in the United States, while tree ring data indicate that the megadroughts over the twelfth century exceeded anything in the twentieth century in both spatial extent and duration. The state of knowledge of the factors that cause heat waves, cold waves, floods, and drought to change is fairly good with heat waves being the best understood.

Abstract During the 2005 NOAA Hazardous Weather Testbed Spring Experiment two different high-resolution configurations of the Weather Research and Forecasting-Advanced Research WRF (WRF-ARW) model were used to produce 30-h forecasts 5 days a week for a total of 7 weeks. These configurations used the same physical parameterizations and the same input dataset for the initial and boundary conditions, differing primarily in their spatial resolution. The first set of runs used 4-km horizontal grid spacing with 35 vertical levels while the second used 2-km grid spacing and 51 vertical levels. Output from these daily forecasts is analyzed to assess the numerical forecast sensitivity to spatial resolution in the upper end of the convection-allowing range of grid spacing. The focus is on the central United States and the time period 18–30 h after model initialization. The analysis is based on a combination of visual comparison, systematic subjective verification conducted during the Spring Experiment, and objective metrics based largely on the mean diurnal cycle of the simulated reflectivity and precipitation fields. Additional insight is gained by examining the size distributions of the individual reflectivity and precipitation entities, and by comparing forecasts of mesocyclone occurrence in the two sets of forecasts. In general, the 2-km forecasts provide more detailed presentations of convective activity, but there appears to be little, if any, forecast skill on the scales where the added details emerge. On the scales where both model configurations show higher levels of skill—the scale of mesoscale convective features—the numerical forecasts appear to provide comparable utility as guidance for severe weather forecasters. These results suggest that, for the geographical, phenomenological, and temporal parameters of this study, any added value provided by decreasing the grid increment from 4 to 2 km (with commensurate adjustments to the vertical resolution) may not be worth the considerable increases in computational expense.

Recent studies showed that anomalous dry conditions and limited moisture supply roughly between 1998 and 2008, especially in the Southern Hemisphere, led to reduced vegetation productivity and ceased growth in land evapotranspiration (ET). However, natural variability of Earth's climate system can degrade capabilities for identifying climate trends. Here we produced a long-term (1982-2013) remote sensing based land ET record and investigated multidecadal changes in global ET and underlying causes. The ET record shows a significant upward global trend of 0.88 mm yr(-2) (P < 0.001) over the 32-year period, mainly driven by vegetation greening (0.018% per year; P < 0.001) and rising atmosphere moisture demand (0.75 mm yr(-2); P = 0.016). Our results indicate that reduced ET growth between 1998 and 2008 was an episodic phenomenon, with subsequent recovery of the ET growth rate after 2008. Terrestrial precipitation also shows a positive trend of 0.66 mm yr(-2) (P = 0.08) over the same period consistent with expected water cycle intensification, but this trend is lower than coincident increases in evaporative demand and ET, implying a possibility of cumulative water supply constraint to ET. Continuation of these trends will likely exacerbate regional drought-induced disturbances, especially during regional dry climate phases associated with strong El Niño events.