© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Corresponding author: Jerald A. Brotzge, jerald.brotzge@wku.edu

Current observation systems that provide data for the analysis and prediction of climate and day-to-day weather are described, along with plans for future systems. The basic principles of satellite, radar, lidar, and sodar measurements are summarized. Temperature and moisture measurements on planetary and synoptic scales, ranging from satellites, the radiosonde network, aircraft, and other sounding systems are described. Wind measurements from satellites, rawinsondes, air composition from satellites, the energy budget, and surface measurements are also discussed. The measuring systems for mesoscale and convective-scale weather are then noted, including satellite-borne radiation instrumentation, and lightning imaging sensors. Operational, fixed-site, and mobile and airborne research radars, surface instrumentation, and ground-based and in-situ profiling systems, aircraft-borne and shipborne instrumentation are also summarized. Special observation issues such as coordination among providers, data assimilation considerations, and data curation are then considered. Special issues for the future are noted in the last section.

This paper summarizes the current challenges in climate and weather research and provides suggestions for future research directions in global observing systems, in modelling and prediction, and in academic environment and education systems.

A 2009 National Research Council study recommended that new mesoscale observing networks be integrated with existing networks to form a nationwide “network of networks”. The report also recommended that research testbeds be established, such as the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) DFW Testbed, to ascertain the potential benefit of proposed observing systems. In this work, we use various conventional and non-conventional observing systems from the DFW Testbed in a series of observing system experiments (OSEs). Of special interest are radar data from Terminal Doppler Weather Radars and CASA X-band radars, as well as novel surface observations. The Advanced Regional Prediction System (ARPS) model is used to perform OSEs that are designed to assess the impact of these observing systems. A three-dimensional variational analysis system and companion complex cloud analysis are used to produce analysis increments, which are assimilated in ARPS using Incremental Analysis Updating. Experiments are performed on a supercell thunderstorm case from 11 April 2016 that produced large, damaging hail. The analysis includes quantitative comparisons of model-derived hail with radar-observed hail, along with verification of surface fields. The CASA radial velocity data benefited the forecasted storm structure, as it positively affected subsequent storm morphology and model-derived hail forecasts. Of note in surface observation impacts, the dewpoint measurements from the non-conventional Earth Networks and CWOP networks slightly degrade the forecasted dewpoint field compared to independent standard observations, but did not prevent the successful prediction of hail.

In this study, the Advanced Regional Prediction System (ARPS), and its associated three-dimensional variational analysis (3DVAR) package are used to simulate a tornadic supercell at 400-m grid spacing. This storm produced an EF3 tornado in Johnson County, TX during the evening of 15 March 2013. Data from Doppler radar, satellite, aircraft, radiosondes, profilers, and surface observations are assimilated in this work. We show that the assimilation of non-conventional surface observations from three networks: the Citizen Weather Observer Program (CWOP), Global Science and Technology (GST) and Automated Weather Stations (AWS) operated by EarthNetworks, in and around the storm inflow, were fundamentally important to the development of an intense low-level mesocyclone. Simulations that did not incorporate this non-conventional data either developed a weak mesocyclone that was displaced to the east of the actual tornado track, or were unable to develop a defined mesocyclone in the first place. In particular, the assimilation of thermodynamic variables (temperature, pressure and moisture) from a subset of AWS observations near the storm’s inflow environment leads to the most accurate simulation of the low-level mesocyclone.

The NOAA Science Advisory Board appointed a task force to prepare a white paper on the use of observing system simulation experiments (OSSEs). Considering the importance and timeliness of this topic and based on this white paper, here we briefly review the use of OSSEs in the United States, discuss their values and limitations, and develop five recommendations for moving forward: national coordination of relevant research efforts, acceleration of OSSE development for Earth system models, consideration of the potential impact on OSSEs of deficiencies in the current data assimilation and prediction system, innovative and new applications of OSSEs, and extension of OSSEs to societal impacts. OSSEs can be complemented by calculations of forecast sensitivity to observations, which simultaneously evaluate the impact of different observation types in a forecast model system.

Greg M. McFarquhar, Elizabeth Smith, Elizabeth A. Pillar-Little, Keith Brewster, Phillip B. Chilson, Temple R. Lee, Sean Waugh, Nusrat Yussouf, Xuguang Wang, Ming Xue, Gijs de Boer, Jeremy A. Gibbs, Chris Fiebrich, Bruce Baker, Jerry Brotzge, Frederick Carr, Hui Christophersen, Martin Fengler, Philip Hall, Terry Hock, Adam Houston, Robert Huck, Jamey Jacob, Robert Palmer, Patricia K. Quinn, Melissa Wagner, Yan (Rockee) Zhang, and Darren Hawk

The deployment of small unmanned aircraft systems (UAS) to collect routine in situ vertical profiles of the thermodynamic and kinematic state of the atmosphere in conjunction with other weather observations could significantly improve weather forecasting skill and resolution. High-resolution vertical measurements of pressure, temperature, humidity, wind speed and wind direction are critical to the understanding of atmospheric boundary layer processes integral to air–surface (land, ocean and sea ice) exchanges of energy, momentum, and moisture; how these are affected by climate variability; and how they impact weather forecasts and air quality simulations. We explore the potential value of collecting coordinated atmospheric profiles at fixed surface observing sites at designated times using instrumented UAS. We refer to such a network of autonomous weather UAS designed for atmospheric profiling and capable of operating in most weather conditions as a 3D Mesonet. We outline some of the fundamental and high-impact science questions and sampling needs driving the development of the 3D Mesonet and offer an overview of the general concept of operations. Preliminary measurements from profiling UAS are presented and we discuss how measurements from an operational network could be realized to better characterize the atmospheric boundary layer, improve weather forecasts, and help to identify threats of severe weather.

The Nationwide Network of Networks (NNoN) concept was introduced by the National Research Council to address the growing need for a national mesoscale observing system and the continued advancement toward accurate high-resolution numerical weather prediction. The research test bed known as the Dallas-Fort Worth (DFW) Urban Demonstration Network was created to experiment with many kinds of mesoscale observations that could be used in a data assimilation system. Many nonconventional observations, including Earth Networks and Citizen Weather Observer Program surface stations, are combined with conventional operational data to form the test bed network. A principal component of the NNoN effort is the quantification of observation impact from several different sources of information. In this study, the GSI-based EnKF system was used together with the WRF-ARW Model to examine impacts of observations assimilated for forecasting convection initiation (CI) in the 3 April 2014 hail storm case. Data denial experiments tested the impact of high-frequency (5 min) assimilation of nonconventional data on the timing and location of CI and subsequent storm evolution. Results showed nonconventional observations were necessary to capture details in the dryline structure causing localized enhanced convergence and leading to CI. Diagnosis of denial-minus-control fields showed the cumulative influence each observing network had on the resulting CI forecast. It was found that most of this impact came from the assimilation of thermodynamic observations in sensitive areas along the dryline gradient. Accurate metadata were found to be crucial toward the future application of nonconventional observations in high-resolution assimilation and forecast systems.

A three-dimensional variational (3DVAR) assimilation technique developed for a convective-scale NWP model—advanced regional prediction system (ARPS)—is used to analyze the 8 May 2003, Moore/Midwest City, Oklahoma tornadic supercell thunderstorm. Previous studies on this case used only one or two radars that are very close to this storm. However, three other radars observed the upper-level part of the storm. Because these three radars are located far away from the targeted storm, they were overlooked by previous studies. High-frequency intermittent 3DVAR analyses are performed using the data from five radars that together provide a more complete picture of this storm. The analyses capture a well-defined mesocyclone in the midlevels and the wind circulation associated with a hook-shaped echo. The analyses produced through this technique are used as initial conditions for a 40-minute storm-scale forecast. The impact of multiple radars on a short-term NWP forecast is most evident when compared to forecasts using data from only one and two radars. The use of all radars provides the best forecast in which a strong low-level mesocyclone develops and tracks in close proximity to the actual tornado damage path.

The Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) Oklahoma test bed has proven the usefulness of a high-resolution, rapidly updating network of radars for a variety of applications. The aim of this project is to quantify the value of the CASA network for the detection of severe wind events. A comparison is made between the performance of Next Generation Doppler Weather Radar (NEXRAD) and CASA radial wind measurements in relation to Oklahoma Mesonet reports of high wind gusts. Two factors inhibit the accurate measurement of winds from weather radar: (1) The viewing angle of the radial velocity beam, and (2) the beam height above ground level. Results show that the CASA radar network performed better overall for detecting and analyzing high wind events within the test bed. CASA dual-Doppler data improved the measurement of winds by 7.27 m/s over all NEXRAD measurements. 1. THE NEXRAD AND CASA RADAR NETWORKS The Next Generation Doppler Weather Radar (NEXRAD) network has greatly improved forecasters abilities to detect and analyze hazardous weather events in real time (Serafin and Wilson, 2000). Yet a few distinct weaknesses in the NEXRAD network remain. First, terrain blockage and the curvature of the earth limit the ability of the network to detect atmospheric winds in the lowest portion of the atmosphere (<1km). More than 70% of the atmosphere below 1-km is not observed by the NEXRAD network (McLaughlin et al. 2009). Second, weather radars are limited to measuring radial velocity. Winds within a thunderstorm are rarely parallel with the beam; true wind velocity can only be found using two or more radars. The final weakness of the NEXRAD network is the temporal resolution of a full volume scan