ALBERT

Description: Ensemble sensitivity analysis (ESA) is a statistical technique applied within an ensemble to reveal the atmospheric flow features that relate to a chosen aspect of the flow. Given its ease of use (it is simply a linear regression between a chosen function of the forecast variables and the entire atmospheric state earlier or simultaneously in time), ensemble sensitivity has been the focus of several studies over roughly the last ten years. Such studies have primarily tried to understand the relevant dynamics and/or key precursors of high-impact weather events. Other applications of ESA have been more operationally oriented, including observation targeting within data assimilation systems and real-time adjustment techniques that attempt to utilize both sensitivity information and observations to improve forecasts.While ESA has gained popularity, its fundamental properties remain a substantially underutilized basis for realizing the technique’s full scientific potential. For example, the relationship between ensemble sensitivity and the pure dynamics of the system can teach us how to appropriately apply various sensitivity-based applications, and combining sensitivity with other ensemble properties such as spread can distinguish between a fluid dynamics problem and a predictability one. This work aims to present new perspectives on ensemble sensitivity, and clarify its fundamentals, with the hopes of making it a more accessible, attractive, and useful tool in the atmospheric sciences. These new perspectives are applied in part to a short climatology of severe convection forecasts to demonstrate the unique knowledge that can gained through broadened use of ESA.

Description: Surface downward radiation (SDR), including shortwave downward radiation (SWDR) and longwave downward radiation (LWDR), is of great importance to energy and climate studies. Considering the lack of reliable SDR data with a high spatiotemporal resolution in the East Asia-Pacific (EAP) region, we derived SWDR and LWDR at 10-min and 0.05° resolutions for this region from 2016-2020 based on the next-generation geostationary satellite Himawari-8 (H-8). The SDR product is unique in terms of its all-sky features, high accuracy and high resolution levels. The cloud effect is fully considered in the SDR product, and the influence of high aerosol loadings and topography on the SWDR are considered. Compared to benchmark products of the radiation, such as Clouds and the Earth’s Radiant Energy System (CERES) and the European Centre for Medium-Range Weather Forecasts (ECMWF) next-generation reanalysis (ERA5), and the Global Land Surface Satellite (GLASS), not only is the resolution of the new SDR product notably much higher but the product accuracy is also higher than that of those products. In particular, hourly and daily root mean square errors of the new SWDR are 104.9 and 31.5 Wm−2, respectively, which are much smaller than those of CERES (at 121.6 and 38.6 Wm−2, respectively), ERA5 (at 176.6 and 39.5 Wm−2, respectively) and GLASS (daily of 36.5 Wm−2). Meanwhile, RMSEs of hourly and daily values of the new LWDR are 19.6 and 14.4 Wm−2, respectively, which are comparable to that of CERES and ERA5, and even better over high altitude regions.

Description: Forecasts from numerical weather prediction (NWP) models play a critical role in many sectors of the American economy. Improvements to operational NWP model forecasts are generally assumed to provide significant economic savings through better decision making. But is this true? Since 2014, several new versions of the High-Resolution Rapid Refresh (HRRR) model were released into operation within the National Weather Service. Practically, forecasts have an economic impact only if they lead to a different action than what would be taken under an alternative information set. And in many sectors, these decisions only need to be considered during certain weather conditions. We estimate the economic impacts of improvements made to the HRRR, using 12-hour wind, precipitation, and temperature forecasts in several cases where they can have “economically meaningful” behavioral consequences. We examine three different components of the U.S. economy where such information matters: 1) better integration of wind energy resources into the electric grid, 2) increased worker output due to better precipitation forecasts that allow workers to arrive to their jobs on time, and 3) better decisions by agricultural producers in preparing for freezing conditions. These applications demonstrate some of the challenges in ascertaining the economic impacts of improved weather forecasts, including highlighting key assumptions that must be made to make the problem tractable. For these sectors, we demonstrate that there was a marked economic gain for the U.S. between HRRR versions 1 and 2, and a smaller, but still appreciable economic gain between versions 2 and 3.

Description: With the continued social distancing requirements of the novel COVID-19 pandemic, many in-person educational programs were halted in 2020, including specialty education and research experiences for undergraduates. However, some Research Experiences for Undergraduates (REUs) progressed in Summer 2020 in a fully virtual format. The importance of understanding how these practical STEM skills translated in a virtual REU format, in addition to areas of improvement going forward, are critical to the development of effective online STEM learning through REUs. Two survey instruments were designed to capture data from both the REU mentors (including the PIs) and the students in the programs. Questions included information on the REU they participated in, their perceptions of the best and worst aspects, their overall satisfaction with the experience, and their likelihood to seek out virtual REUs in the future. Overall, both students and faculty involved in virtual REUs were glad to have had the experience and were satisfied with it. The benefits of flexibility, the ease of communication and scheduling, and the increased access to online resources were echoed as the strengths of the virtual format. However, many believe that an in-person REU had benefits that could not be replicated in a virtual environment including community building and hands-on experiences. Several were bogged down by technical difficulties. With more effort made to include community building to a greater extent, as well as considerations and planning for technical demands, the future of widely accessible online REU experiences is a bright one.

Description: MOSES (Modular Observation Solutions for Earth Systems) is a novel observation system that is specifically designed to unravel the impact of distinct, dynamic events on the long-term development of environmental systems. Hydro-meteorological extremes such as the recent European droughts or the floods of 2013 caused severe and lasting environmental damage. Modelling studies suggest that abrupt permafrost thaw events accelerate Arctic greenhouse gas emissions. Short-lived ocean eddies seem to comprise a significant share of the marine carbon uptake or release. Although there is increasing evidence that such dynamic events bear the potential for major environmental impacts, our knowledge on the processes they trigger is still very limited. MOSES aims at capturing such events, from their formation to their end, with high spatial and temporal resolution. As such, the observation system extends and complements existing national and international observation networks, which are mostly designed for long-term monitoring.Several German Helmholtz Association centers have developed this research facility as a mobile and modular “system of systems” to record energy, water, greenhouse gas and nutrient cycles on the land surface, in coastal regions, in the ocean, in polar regions, and in the atmosphere – but especially the interactions between the Earth compartments. During the implementation period (2017-2021), the measuring systems were put into operation and test campaigns were performed to establish event-driven campaign routines. With MOSES’ regular operation starting in 2022, the observation system will then be ready for cross-compartment and cross-discipline research on the environmental impacts of dynamic events.

Description: Uncertainty is everywhere and understanding how individuals understand and use forecast information to make decisions given varying levels of certainty is crucial for effectively communicating risks and weather hazards. To advance prior research about how various audiences use and understand probabilistic and deterministic hydrologic forecast information, a social science study involving multiple scenario-based focus groups and surveys at four locations (Eureka, California; Gunnison, Colorado; Durango, Colorado; Owego, New York) across the United States was conducted with professionals and residents. Focusing on the Hydrologic Ensemble Forecast System, the Advanced Hydrologic Prediction Service, and briefings, this research investigated how users tolerate divergence in probabilistic and deterministic forecasts and how deterministic and probabilistic river level forecasts can be presented simultaneously without causing confusion. This study found that probabilistic forecasts introduce a tremendous amount of new, yet valuable, information but can quickly overwhelm users based on how they are conveyed and communicated. Some were unaware of resources available, or how to find, sort, and prioritize among all the data and information. Importantly, when presented with a divergence between deterministic and probabilistic forecasts, most sought out more information while some others reported diminished confidence in the products. Users in all regions expressed a desire to “ground truth” the accuracy of probabilistic forecasts, understand the drivers of the forecasts, and become more familiar with them. In addition, a prototype probabilistic product that includes a deterministic forecast was tested, and suggestions for communicating probabilistic information through the use of briefing packages is proposed.

Description: Wintertime episodes of high aerosol concentrations occur frequently in urban and agricultural basins and valleys worldwide. These episodes often arise following development of persistent cold-air pools (PCAPs) that limit mixing and modify chemistry. While field campaigns targeting either basin meteorology or wintertime pollution chemistry have been conducted, coupling between interconnected chemical and meteorological processes remains an insufficiently studied research area. Gaps in understanding the coupled chemical–meteorological interactions that drive high-pollution events make identification of the most effective air-basin specific emission control strategies challenging. To address this, a September 2019 workshop occurred with the goal of planning a future research campaign to investigate air quality in western U.S. basins. Approximately 120 people participated, representing 50 institutions and five countries. Workshop participants outlined the rationale and design for a comprehensive wintertime study that would couple atmospheric chemistry and boundary layer and complex-terrain meteorology within western U.S. basins. Participants concluded the study should focus on two regions with contrasting aerosol chemistry: three populated valleys within Utah (Salt Lake, Utah, and Cache Valleys) and the San Joaquin Valley in California. This paper describes the scientific rationale for a campaign that will acquire chemical and meteorological datasets using airborne platforms with extensive range, coupled to surface-based measurements focusing on sampling within the near-surface boundary layer, and transport and mixing processes within this layer, with high vertical resolution at a number of representative sites. No prior wintertime basin-focused campaign has provided the breadth of observations necessary to characterize the meteorological–chemical linkages outlined here, nor to validate complex processes within coupled atmosphere–chemistry models.

Description: Prediction of ice formation in clouds presents one of the grand challenges in the atmospheric sciences. Immersion freezing initiated by ice-nucleating particles (INPs) is the dominant pathway of primary ice crystal formation in mixed-phase clouds, where supercooled water droplets and ice crystals coexist, with important implications for the hydrological cycle and climate. However, derivation of INP number concentrations from an ambient aerosol population in cloud-resolving and climate models remains highly uncertain. We conducted an aerosol–ice formation closure pilot study using a field-observational approach to evaluate the predictive capability of immersion freezing INPs. The closure study relies on collocated measurements of the ambient size-resolved and single-particle composition and INP number concentrations. The acquired particle data serve as input in several immersion freezing parameterizations, which are employed in cloud-resolving and climate models, for prediction of INP number concentrations. We discuss in detail one closure case study in which a front passed through the measurement site, resulting in a change of ambient particle and INP populations. We achieved closure in some circumstances within uncertainties, but we emphasize the need for freezing parameterization of potentially missing INP types and evaluation of the choice of parameterization to be employed. Overall, this closure pilot study aims to assess the level of parameter details and measurement strategies needed to achieve aerosol–ice formation closure. The closure approach is designed to accurately guide immersion freezing schemes in models, and ultimately identify the leading causes for climate model bias in INP predictions.

Description: To address the need to map precipitation on a global scale, a collection of satellites carrying passive microwave (PMW) radiometers has grown over the last 20 years to form a constellation of about 10–12 sensors at any one time. Over the same period, a broad range of science and user communities has become increasingly dependent on the precipitation products provided by these sensors. The constellation presently consists of both conical and cross-track-scanning precipitation-capable multichannel instruments, many of which are beyond their operational and design lifetime but continue to operate through the cooperation of the responsible agencies. The Group on Earth Observations and the Coordinating Group for Meteorological Satellites (CGMS), among other groups, have raised the issue of how a robust, future precipitation constellation should be constructed. The key issues of current and future requirements for the mapping of global precipitation from satellite sensors can be summarized as providing 1) sufficiently fine spatial resolutions to capture precipitation-scale systems and reduce the beam-filling effects of the observations; 2) a wide channel diversity for each sensor to cover the range of precipitation types, characteristics, and intensities observed across the globe; 3) an observation interval that provides temporal sampling commensurate with the variability of precipitation; and 4) precipitation radars and radiometers in low-inclination orbit to provide a consistent calibration source, as demonstrated by the first two spaceborne radar–radiometer combinations on the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) mission Core Observatory. These issues are critical in determining the direction of future constellation requirements while preserving the continuity of the existing constellation necessary for long-term climate-scale studies.

Description: Heavy precipitation events and their associated flooding can have major impacts on communities and stakeholders. There is a lack of knowledge, however, about how stakeholders make decisions at the subseasonal-to-seasonal (S2S) time scales (i.e., 2 weeks to 3 months). To understand how decisions are made and S2S predictions are or can be used, the project team for “Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods” (PRES2iP) conducted a 2-day workshop in Norman, Oklahoma, during July 2018. The workshop engaged 21 professionals from environmental management and public safety communities across the contiguous United States in activities to understand their needs for S2S predictions of potential extended heavy precipitation events. Discussions and role-playing activities aimed to identify how workshop participants manage uncertainty and define extreme precipitation, the time scales over which they make key decisions, and the types of products they use currently. This collaboration with stakeholders has been an integral part of PRES2iP research and has aimed to foster actionable science. The PRES2iP team is using the information produced from this workshop to inform the development of predictive models for extended heavy precipitation events and to collaboratively design new forecast products with our stakeholders, empowering them to make more-informed decisions about potential extreme precipitation events.