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Mark R. Jury

Abstract

This study reviews Kenya’s fluctuating hydroclimate (3°S–4°N, 35°–40°E) and evaluates products that describe its area-averaged daily rainfall during 2008–18, monthly evaporation during 2000–18, and catchment hydrology via gauge, satellite, and model hindcast/forecast. Using the correlation of rainfall as a metric of skill we found daily satellite versus model hindcasts achieved 75%, while model forecasts at 2–6-day lead achieved 55%–58%. The daily satellite versus model soil moisture had a significant correlation (84%), and model runoff versus gauge streamflow reached 61%. A 2-day delay was noted between rainfall and streamflow response in recent flood events; however, long-range predictability was found to be poor (35%). These outcomes were considered at a local workshop, and ways to sustainably improve the real-time reporting of key hydroclimate parameters for operational data assimilation were suggested as steps toward better monitoring and forecast services in Kenya.

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Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Stephen G. Penny, Jebb Q. Stewart, Amy McGovern, David Hall, John E. Ten Hoeve, Jason Hickey, Hung-Lung Allen Huang, John K. Williams, Kayo Ide, Philippe Tissot, Sue Ellen Haupt, Kenneth S. Casey, Nikunj Oza, Alan J. Geer, Eric S. Maddy, and Ross N. Hoffman

Abstract

Promising new opportunities to apply artificial intelligence (AI) to the Earth and environmental sciences are identified, informed by an overview of current efforts in the community. Community input was collected at the first National Oceanic and Atmospheric Administration (NOAA) workshop on “Leveraging AI in the Exploitation of Satellite Earth Observations and Numerical Weather Prediction” held in April 2019. This workshop brought together over 400 scientists, program managers, and leaders from the public, academic, and private sectors in order to enable experts involved in the development and adaptation of AI tools and applications to meet and exchange experiences with NOAA experts. Paths are described to actualize the potential of AI to better exploit the massive volumes of environmental data from satellite and in situ sources that are critical for numerical weather prediction (NWP) and other Earth and environmental science applications. The main lessons communicated from community input via active workshop discussions and polling are reported. Finally, recommendations are presented for both scientists and decision-makers to address some of the challenges facing the adoption of AI across all Earth science.

Open access
Lei Wang, Tandong Yao, Chenhao Chai, Lan Cuo, Fengge Su, Fan Zhang, Zhijun Yao, Yinsheng Zhang, Xiuping Li, Jia Qi, Zhidan Hu, Jingshi Liu, and Yuanwei Wang

Abstract

Monitoring changes in river runoff at the Third Pole (TP) is important because rivers in this region support millions of inhabitants in Asia and are very sensitive to climate change. Under the influence of climate change and intensified cryospheric melt, river runoff has changed markedly at the TP, with significant effects on the spatial and temporal water resource distribution that threaten water supply and food security for people living downstream. Despite some in situ observations and discharge estimates from state-of-the-art remote sensing technology, the total river runoff (TRR) for the TP has never been reliably quantified, and its response to climate change remains unclear. As part of the Chinese Academy of Sciences’ “Pan-Third Pole Environment Study for a Green Silk Road,” the TP-River project aims to construct a comprehensive runoff observation network at mountain outlets (where rivers leave the mountains and enter the plains) for 13 major rivers in the TP region, thereby enabling TRR to be accurately quantified. The project also integrates discharge estimates from remote sensing and cryosphere–hydrology modeling to investigate long-term changes in TRR and the relationship between the TRR variations and westerly/monsoon. Based on recent efforts, the project provides the first estimate (656 ± 23 billion m3) of annual TRR for the 13 TP rivers in 2018. The annual river runoff at the mountain outlets varies widely between the different TP rivers, ranging from 2 to 176 billion m3, with higher values mainly corresponding to rivers in the Indian monsoon domain, rather than in the westerly domain.

Open access
Leslie A. Duram

Abstract

Previous research indicates the importance of interdisciplinary approaches when teaching about climate change. Specifically, social science perspectives allow students to understand the policy, economic, cultural, and personal influences that impact environmental change. This article describes one such college course that employed active-learning techniques. Course topics included: community resilience, environmental education, historical knowledge timeline, climate justice, social vulnerability, youth action, science communication, hope versus despair, misinformation, and climate refugees. To unify these concepts, engaging activities were developed that specifically address relevant individual, local, state, national, and international climate resilience themes. Students assessed their personal climate footprint, explored social/cultural influences, wrote policy requests to relevant local/state government officials, studied national policy options, and learned about previous global initiatives. The course culminated in a mock global climate summit, which was modeled on a Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC). This final activity required each student to prepare a policy report and represent a nation in negotiating a multilateral climate agreement. It is accepted that climate change education must include physical data on the impacts of anthropogenic emissions. It is also essential that students appreciate the interdisciplinary nature of climate adaptations, become hopeful about addressing change, and gain skills necessary to engage as informed climate citizens.

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Josh Welty and Xubin Zeng

Abstract

Snowmelt is an essential process for the health and sustenance of numerous communities and ecosystems across the globe, though it also presents potential hazards when ablation processes are exceedingly rapid. Using 4 km daily snow water equivalent, temperature, and precipitation data for three decades (1988-2017), here we provide a broad characterization of extreme snowmelt episodes over the conterminous U.S. in terms of magnitude, timing, and coincident synoptic weather patterns. Larger magnitude extreme snowmelt events usually coincide with minimal precipitation and elevated temperatures. However, certain regions, particularly mountainous regions and the northeast U.S., exhibit greater likelihood of extreme snowmelt events during pronounced rain-on-snow events. During snowmelt extremes, snowmelt rate often exceeds precipitation in many regions. Meteorological patterns and associated water vapor transport most directly connected to extreme events over different regions are classified via a machine learning technique. Over the 30-year study period, there is a weakly increasing trend in the frequency of extremes, though this does not necessarily signify an increase in snowmelt magnitudes.

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Wouter Dorigo, Stephan Dietrich, Filipe Aires, Luca Brocca, Sarah Carter, Jean-François Cretaux, David Dunkerley, Hiroyuki Enomoto, René Forsberg, Andreas Güntner, Michaela I. Hegglin, Rainer Hollmann, Dale F. Hurst, Johnny A. Johannessen, Chris Kummerow, Tong Lee, Kari Luojus, Ulrich Looser, Diego G. Miralles, Victor Pellet, Thomas Recknagel, Claudia Ruz Vargas, Udo Schneider, Philippe Schoeneich, Marc Schröder, Nigel Tapper, Valery Vuglinsky, Wolfgang Wagner, Lisan Yu, Luca Zappa, Michael Zemp, and Valentin Aich

CAPSULE

By assessing the capability of available ground-based and remotely sensed observations of water cycle Essential Climate Variables, we discuss gaps in existing observation systems and formulate guidelines for future water cycle observation strategies.

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Michael J. Irvin

Abstract

Kites have been used as weather sensing solutions for over 250 years. The fact that they are simpler to operate and train on than alternative aerial systems, their ability to keep station at a fixed point for a long term, simplified altitude control, and the ease of retrieving their payload attribute to their growing appeal in atmospheric research. NASA, Toyota, and the School of Mechanical and Aerospace Engineering Oklahoma State University are active in developing and deploying high-altitude inflatable kite systems for atmospheric boundary layer (ABL) research—crucial to advancing the accuracy of weather forecasting. Improvements in kite design, as well as instrumentation and supporting infrastructure, are key to further accelerating the use of kites in atmospheric research. The work underway by these researchers is intended to be a deliberate step in the evolutionary development of these beneficial systems.

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I.-I. Lin, Robert F. Rogers, Hsiao-Ching Huang, Yi-Chun Liao, Derrick Herndon, Jin-Yi Yu, Ya-Ting Chang, Jun A. Zhang, Christina M. Patricola, Iam-Fei Pun, and Chun-Chi Lien

Capsules

The explosive intensification of STY Hagibis (2019) and subsequent intensification hindrance was due to the complex interplay across large-scale environmental conditions, vortex-scale properties, and convective-scale features.

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Joshua Wurman, Karen Kosiba, Brian Pereira, Paul Robinson, Andrew Frambach, Alycia Gilliland, Trevor White, Josh Aikins, Robert J. Trapp, Stephen Nesbitt, Maiana N. Hanshaw, and Jon Lutz

Abstract

The Flexible Array of Radars and Mesonets (FARM) Facility is an extensive mobile/quickly-deployable (MQD) multiple-Doppler radar and in-situ instrumentation network.

The FARM includes four radars: two 3-cm dual-polarization, dual-frequency (DPDF), Doppler On Wheels DOW6/DOW7, the Rapid-Scan DOW (RSDOW), and a quickly-deployable (QD) DPDF 5-cm COW C-band On Wheels (COW).

The FARM includes 3 mobile mesonet (MM) vehicles with 3.5-m masts, an array of rugged QD weather stations (PODNET), QD weather stations deployed on infrastructure such as light/power poles (POLENET), four disdrometers, six MQD upper air sounding systems and a Mobile Operations and Repair Center (MORC).

The FARM serves a wide variety of research/educational uses. Components have deployed to >30 projects during 1995-2020 in the USA, Europe, and South America, obtaining pioneering observations of a myriad of small spatial and temporal scale phenomena including tornadoes, hurricanes, lake-effect snow storms, aircraft-affecting turbulence, convection initiation, microbursts, intense precipitation, boundary-layer structures and evolution, airborne hazardous substances, coastal storms, wildfires and wildfire suppression efforts, weather modification effects, and mountain/alpine winds and precipitation. The radars and other FARM systems support innovative educational efforts, deploying >40 times to universities/colleges, providing hands-on access to cutting-edge instrumentation for their students.

The FARM provides integrated multiple radar, mesonet, sounding, and related capabilities enabling diverse and robust coordinated sampling of three-dimensional vector winds, precipitation, and thermodynamics increasingly central to a wide range of mesoscale research.

Planned innovations include S-band On Wheels NETwork (SOWNET) and Bistatic Adaptable Radar Network (BARN), offering more qualitative improvements to the field project observational paradigm, providing broad, flexible, and inexpensive 10-cm radar coverage and vector windfield measurements.

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Stephen W. Nesbitt, Paola V. Salio, Eldo Ávila, Phillip Bitzer, Lawrence Carey, V. Chandrasekar, Wiebke Deierling, Francina Dominguez, Maria Eugenia Dillon, C. Marcelo Garcia, David Gochis, Steven Goodman, Deanna A. Hence, Karen A. Kosiba, Matthew R. Kumjian, Timothy Lang, Lorena Medina Luna, James Marquis, Robert Marshall, Lynn A. McMurdie, Ernani Lima Nascimento, Kristen L. Rasmussen, Rita Roberts, Angela K. Rowe, Juan José Ruiz, Eliah F.M.T. São Sabbas, A. Celeste Saulo, Russ S. Schumacher, Yanina Garcia Skabar, Luiz Augusto Toledo Machado, Robert J. Trapp, Adam Varble, James Wilson, Joshua Wurman, Edward J. Zipser, Ivan Arias, Hernán Bechis, and Maxwell A. Grover

CAPSULE

RELAMPAGO was a multinational field campaign that collected detailed measurements of deep convective storms, high-impact weather, and their effects in Argentina and Brazil.

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