In this historical paper, we trace the scientific-and engineering-based steps at the National Severe Storms Laboratory (NSSL) and in the larger weather radar community that led to the development of NSSL's first 10-cm-wavelength pulsed Doppler radar. This radar was the prototype for the current Next Generation Weather Radar (NEXRAD), or Weather Surveillance Radar-1998 Doppler (WSR-88D) network.

We track events, both political and scientific, that led to the establishment of NSSL in 1964. The vision of NSSL's first director, Edwin Kessler, is reconstructed through access to historical documents and oral histories. This vision included the development of Doppler radar, where research was to be meshed with the operational needs of the U.S. Weather Bureau and its successor—the National Weather Service.

Realization of the vision came through steps that were often fitful, where complications arose due to personnel concerns, and where there were always financial concerns. The historical research indicates that 1) the engineering prowess and mentorship of Roger Lhermitte was at the heart of Doppler radar development at NSSL; 2) key decisions by Kessler in the wake of Lhermitte's sudden departure in 1967 proved crucial to the ultimate success of the project; 3) research results indicated that Doppler velocity signatures of mesocyclones are a precursor of damaging thunderstorms and tornadoes; and 4) results from field testing of the Doppler-derived products during the 1977-79 Joint Doppler Operational Project—especially the noticeable increase in the verification of tornado warnings and an associated marked decrease in false alarms—led to the government decision to establish the NEXRAD network.

On 15 October 1980, a weather system that had been to the west of Colorado was forecast to move into the state, and to bring with it light to moderate snow in the Rockies, and generally light rain and thundershower activity over the plains to the east. In most regions this forecast was adequate. However, substantially heavier activity (including a small tornado, large hail, heavy rain, and snow) also occurred in some areas. In this paper we show how all relevant real-time data, when properly merged, could have enabled formulation of a useful short-term forecast. In addition we point out how mesonet surface data gathered after the fact could have helped narrow down the forecast area of severe weather and heavy precipitation.

Abstract

To study tropical cyclones and generate forecast applications using satellite observations, researchers often consolidate disparate sources of raw and ancillary data. Data consolidation involves obtaining, collocating, and intercalibrating data from different sensors and derived products; calculating environmental diagnostics from a homogeneous source; and standardizing these various products for a straightforward analysis. To alleviate preprocessing issues and provide a long-term, global digital dataset of tropical cyclone satellite observations, we construct the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED). TC PRIMED contains tropical cyclone–centric 1) intercalibrated, multichannel, multisensor microwave brightness temperatures, 2) retrieved rainfall from NASA’s Goddard Profiling Algorithm (GPROF), 3) nearly coincident geostationary satellite infrared brightness temperatures and derived metrics, 4) tropical cyclone position and intensity information, 5) ECMWF fifth-generation reanalysis fields and derived environmental diagnostics, and 6) precipitation radar observations from the TRMM and GPM Core Observatory satellites. TC PRIMED consists of over 176,000 overpasses of 2,101 storms from 1998 to 2019, providing researchers with an analysis-ready dataset to promote and support research into improving our understanding of the relationship between tropical cyclone convective and precipitation structure, intensity, and environment. Here, we briefly describe data sources and processing steps to create TC PRIMED. To demonstrate TC PRIMED’s potential utility for studying important tropical cyclone processes and for application development, we present a shear-relative composite analysis of several multisensor satellite variables relative to the tropical cyclone lifetime maximum intensity. The composite analysis provides a simple example of how TC PRIMED can benefit future studies to advance our understanding of tropical cyclones and improve forecasts.

The deployment of a space-based Doppler lidar would provide information that is fundamental to advancing the understanding and prediction of weather and climate.

This paper reviews the concepts of wind measurement by Doppler lidar, highlights the results of some observing system simulation experiments with lidar winds, and discusses the important advances in earth system science anticipated with lidar winds.

Observing system simulation experiments, conducted using two different general circulation models, have shown 1) that there is a significant improvement in the forecast accuracy over the Southern Hemisphere and tropical oceans resulting from the assimilation of simulated satellite wind data, and 2) that wind data are significantly more effective than temperature or moisture data in controlling analysis error. Because accurate wind observations are currently almost entirely unavailable for the vast majority of tropical cyclones worldwide, lidar winds have the potential to substantially improve tropical cyclone forecasts. Similarly, to improve water vapor flux divergence calculations, a direct measure of the ageostrophic wind is needed since the present level of uncertainty cannot be reduced with better temperature and moisture soundings alone.

Accurate quantitative precipitation estimates (QPE) and very short term quantitative precipitation forecasts (VSTQPF) are critical to accurate monitoring and prediction of water-related hazards and water resources. While tremendous progress has been made in the last quarter-century in many areas of QPE and VSTQPF, significant gaps continue to exist in both knowledge and capabilities that are necessary to produce accurate high-resolution precipitation estimates at the national scale for a wide spectrum of users. Toward this goal, a national next-generation QPE and VSTQPF (Q2) workshop was held in Norman, Oklahoma, on 28–30 June 2005. Scientists, operational forecasters, water managers, and stakeholders from public and private sectors, including academia, presented and discussed a broad range of precipitation and forecasting topics and issues, and developed a list of science focus areas. To meet the nation's needs for the precipitation information effectively, the authors herein propose a community-wide integrated approach for precipitation information that fully capitalizes on recent advances in science and technology, and leverages the wide range of expertise and experience that exists in the research and operational communities. The concepts and recommendations from the workshop form the Q2 science plan and a suggested path to operations. Implementation of these concepts is expected to improve river forecasts and flood and flash flood watches and warnings, and to enhance various hydrologic and hydrometeorological services for a wide range of users and customers. In support of this initiative, the National Mosaic and Q2 (NMQ) system is being developed at the National Severe Storms Laboratory to serve as a community test bed for QPE and VSTQPF research and to facilitate the transition to operations of research applications. The NMQ system provides a real-time, around-the-clock data infusion and applications development and evaluation environment, and thus offers a community-wide platform for development and testing of advances in the focus areas.

This paper examines the potential of regional environmental prediction by focusing on the local forecasting effort in the Pacific Northwest. A consortium of federal, state, and local agencies have funded the development and operation of a multifaceted numerical prediction system centered at the University of Washington that includes atmospheric, hydrologic, and air quality models, the collection of real-time regional weather data sources, and a number of real-time applications using both observations and model output. The manuscript reviews northwest modeling and data collection systems, describes the funding and management system established to support and guide the effort, provides some examples of regional real-time applications, and examines the national implications of regional environmental prediction.

Abstract

The primary goal of the Second Wind Forecast Improvement Project (WFIP2) is to advance the state-of-the-art of wind energy forecasting in complex terrain. To achieve this goal, a comprehensive 18-month field measurement campaign was conducted in the region of the Columbia River basin. The observations were used to diagnose and quantify systematic forecast errors in the operational High-Resolution Rapid Refresh (HRRR) model during weather events of particular concern to wind energy forecasting. Examples of such events are cold pools, gap flows, thermal troughs/marine pushes, mountain waves, and topographic wakes. WFIP2 model development has focused on the boundary layer and surface-layer schemes, cloud–radiation interaction, the representation of drag associated with subgrid-scale topography, and the representation of wind farms in the HRRR. Additionally, refinements to numerical methods have helped to improve some of the common forecast error modes, especially the high wind speed biases associated with early erosion of mountain–valley cold pools. This study describes the model development and testing undertaken during WFIP2 and demonstrates forecast improvements. Specifically, WFIP2 found that mean absolute errors in rotor-layer wind speed forecasts could be reduced by 5%–20% in winter by improving the turbulent mixing lengths, horizontal diffusion, and gravity wave drag. The model improvements made in WFIP2 are also shown to be applicable to regions outside of complex terrain. Ongoing and future challenges in model development will also be discussed.

The Convective Storm Initiation Project (CSIP) is an international project to understand precisely where, when, and how convective clouds form and develop into showers in the mainly maritime environment of southern England. A major aim of CSIP is to compare the results of the very high resolution Met Office weather forecasting model with detailed observations of the early stages of convective clouds and to use the newly gained understanding to improve the predictions of the model.

A large array of ground-based instruments plus two instrumented aircraft, from the U.K. National Centre for Atmospheric Science (NCAS) and the German Institute for Meteorology and Climate Research (IMK), Karlsruhe, were deployed in southern England, over an area centered on the meteorological radars at Chilbolton, during the summers of 2004 and 2005. In addition to a variety of ground-based remote-sensing instruments, numerous rawinsondes were released at one- to two-hourly intervals from six closely spaced sites. The Met Office weather radar network and Meteosat satellite imagery were used to provide context for the observations made by the instruments deployed during CSIP.

This article presents an overview of the CSIP field campaign and examples from CSIP of the types of convective initiation phenomena that are typical in the United Kingdom. It shows the way in which certain kinds of observational data are able to reveal these phenomena and gives an explanation of how the analyses of data from the field campaign will be used in the development of an improved very high resolution NWP model for operational use.

Abstract

Forecasts by mid-2015 for a strong El Niño during winter 2015/16 presented an exceptional scientific opportunity to accelerate advances in understanding and predictions of an extreme climate event and its impacts while the event was ongoing. Seizing this opportunity, the National Oceanic and Atmospheric Administration (NOAA) initiated an El Niño Rapid Response (ENRR), conducting the first field campaign to obtain intensive atmospheric observations over the tropical Pacific during El Niño.

The overarching ENRR goal was to determine the atmospheric response to El Niño and the implications for predicting extratropical storms and U.S. West Coast rainfall. The field campaign observations extended from the central tropical Pacific to the West Coast, with a primary focus on the initial tropical atmospheric response that links El Niño to its global impacts. NOAA deployed its Gulfstream-IV (G-IV) aircraft to obtain observations around organized tropical convection and poleward convective outflow near the heart of El Niño. Additional tropical Pacific observations were obtained by radiosondes launched from Kiritimati , Kiribati, and the NOAA ship Ronald H. Brown, and in the eastern North Pacific by the National Aeronautics and Space Administration (NASA) Global Hawk unmanned aerial system. These observations were all transmitted in real time for use in operational prediction models. An X-band radar installed in Santa Clara, California, helped characterize precipitation distributions. This suite supported an end-to-end capability extending from tropical Pacific processes to West Coast impacts. The ENRR observations were used during the event in operational predictions. They now provide an unprecedented dataset for further research to improve understanding and predictions of El Niño and its impacts.

Abstract

The Southeast Atmosphere Studies (SAS), which included the Southern Oxidant and Aerosol Study (SOAS); the Southeast Nexus (SENEX) study; and the Nitrogen, Oxidants, Mercury and Aerosols: Distributions, Sources and Sinks (NOMADSS) study, was deployed in the field from 1 June to 15 July 2013 in the central and eastern United States, and it overlapped with and was complemented by the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC⁴RS) campaign. SAS investigated atmospheric chemistry and the associated air quality and climate-relevant particle properties. Coordinated measurements from six ground sites, four aircraft, tall towers, balloon-borne sondes, existing surface networks, and satellites provide in situ and remotely sensed data on trace-gas composition, aerosol physicochemical properties, and local and synoptic meteorology. Selected SAS findings indicate 1) dramatically reduced NOx concentrations have altered ozone production regimes; 2) indicators of “biogenic” secondary organic aerosol (SOA), once considered part of the natural background, were positively correlated with one or more indicators of anthropogenic pollution; and 3) liquid water dramatically impacted particle scattering while biogenic SOA did not. SAS findings suggest that atmosphere–biosphere interactions modulate ambient pollutant concentrations through complex mechanisms and feedbacks not yet adequately captured in atmospheric models. The SAS dataset, now publicly available, is a powerful constraint to develop predictive capability that enhances model representation of the response and subsequent impacts of changes in atmospheric composition to changes in emissions, chemistry, and meteorology.