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Akshara Kaginalkar, Sachin D. Ghude, U. C. Mohanty, Pradeep Mujumdar, Sudheer Bhakare, Hemant Darbari, Arun K Dwivedi, Pallavi Gavali, Srujan Gavhale, Sahidul Islam, Gouri Kadam, Sumita Kedia, Manoj Khare, Neelesh Kharkar, Santosh H. Kulkarni, Sri Sai Meher, A. K. Nath, Mohamed Niyaz, Sagar Pokale, Vineeth Krishnan Valappil, Sreyashi Debnath, Chinmay Jena, Raghu Nadimpalli, Madhusmita Swain, Saimy Davis, Shubha Avinash, C. Kishtawal, Prashant Gargava, S. D. Attri, and Dev Niyogi

Abstract

Global urban population is projected to double by 2050. This rapid urbanization is the driver of economic growth but has environmental challenges. To that end, there is an urgent need to understand, simulate and disseminate information about extreme events, routine city operations and long term planning decisions.

This paper describes an effort underway in India involving an interdisciplinary community of meteorology, hydrology, air quality, computer science from national and international institutes. The urban Collaboratory is a system of systems for simulating weather, hydrology, air quality, health, energy, transport and economy, and its interactions. Study and prediction of urban events involve multi-scale observations and cross-sector models; heterogeneous data management and enormous computing power. The consortia program (NSM_Urban) is part of ‘weather ready cities’, under the aegis of India’s National Supercomputing Mission.

The ecosystem ‘Urban Environment Science to Society (UES2S)’, builds on the integrated cyberinfrastructure with a science gateway for community research and end-user service with modeling and inter-operable data. The Collaboratory has urban computing, stakeholder participation, and a coordinated means to scaffold projects and ideas into operational tools. It discusses the design and the utilization of the High Performance Computing (HPC) as a science cloud platform for bridging urban environment and data science, participatory stakeholder applications and decision making. The system currently integrates models for high impact urban weather, flooding, air quality, and simulating street and building scale wind flow and dispersion. The program with the work underway is ripe for interfacing with regional and international partners and this paper provides an avenue towards that end.

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ALAN GERARD, STEVEN M. MARTINAITIS, JONATHAN J. GOURLEY, KENNETH W. HOWARD, and JIAN ZHANG

Abstract

The Multi-Radar Multi-Sensor (MRMS) system is an operational, state-of-the-science hydrometeorological data analysis and nowcasting framework that combines data from multiple radar networks, satellites, surface observational systems, and numerical weather prediction models to produce a suite of real-time, decision-support products every two minutes over the contiguous United States and southern Canada. The Flooded Locations and Simulated Hydrograph (FLASH) component of the MRMS system was designed for the monitoring and prediction of flash floods across small time and spatial scales required for urban areas given their rapid hydrologic response to precipitation. Developed at the National Severe Storms Laboratory in collaboration with the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) and other research entities, the objective for MRMS and FLASH is to be the world’s most advanced system for severe weather and storm-scale hydrometeorology, leveraging the latest science and observation systems to produce the most accurate and reliable hydrometeorological and severe weather analyses. NWS forecasters, the public and the private sector utilize a variety of products from the MRMS and FLASH systems for hydrometeorological situational awareness and to provide warnings to the public and other users about potential impacts from flash flooding. This article will examine the performance of hydrometeorological products from MRMS and FLASH, and provide perspectives on how NWS forecasters use these products in the prediction of flash flood events with an emphasis on the urban environment.

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Federico Bianchi, Victoria A. Sinclair, Diego Aliaga, Qiaozhi Zha, Wiebke Scholz, Cheng Wu, Liine Heikkinen, Rob Modini, Eva Partoll, Fernando Velarde, Isabel Moreno, Yvette Gramlich, Wei Huang, Markus Leiminger, Joonas Enroth, Otso Peräkylä, Angela Marinoni, Chen Xuemeng, Luis Blacutt, Ricardo Forno, Rene Gutierrez, Patrick Ginot, Gaëlle Uzu, Maria Cristina Facchini, Stefania Gilardoni, Martin Gysel-Beer, Runlong Cai, Tuukka Petäjä, Matteo Rinaldi, Harald Saathoff, Karine Sellegri, Douglas Worsnop, Paulo Artaxo, Armin Hansel, Markku Kulmala, Alfred Wiedensohler, Paolo Laj, Radovan Krejci, Samara Carbone, Marcos Andrade, and Claudia Mohr

Abstract

This paper presents an introduction to the Southern hemisphere high altitude experiment on particle nucleation and growth (SALTENA). This field campaign took place between December 2017 and June 2018 (wet to dry season) at Chacaltaya (CHC), a GAW (Global Atmosphere Watch) station located at 5240 m a.s.l. in the Bolivian Andes. Concurrent measurements were conducted at two additional sites in El Alto (4000 m a.s.l.) and La Paz (3600 m a.s.l.). The overall goal of the campaign was to identify the sources, understand the formation mechanisms and transport, and characterize the properties of aerosol at these stations. State-of-the-art instruments were brought to the station complementing the ongoing permanent GAW measurements, to allow a comprehensive description of the chemical species of anthropogenic and biogenic origin impacting the station and contributing to new particle formation. In this overview we first provide an assessment of the complex meteorology, air mass origin, and boundary layer – free troposphere interactions during the campaign using a 6-month high-resolution WRF (Weather Research and Forecasting) simulation coupled with FLEXPART (FLEXible PARTicle dispersion model). We then show some of the research highlights from the campaign, including i) chemical transformation processes of anthropogenic pollution while the air masses are transported to the CHC station from the metropolitan area of La Paz/El Alto, ii) volcanic emissions as an important source of atmospheric sulfur compounds in the region, iii) the characterization of the compounds involved in new particle formation, and iv) the identification of long-range transported compounds from the Pacific or the Amazon basin. We conclude the article with a presentation of future research foci. The SALTENA dataset highlights the importance of comprehensive observations in strategic high-altitude locations, especially the undersampled Southern Hemisphere.

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Aaron Kennedy, Aaron Scott, Nicole Loeb, Alec Sczepanski, Kaela Lucke, Jared Marquis, and Sean Waugh

Abstract

Harsh winters and hazards such as blizzards are synonymous with the northern Great Plains of the United States. Studying these events is difficult; the juxtaposition of cold temperatures and high winds makes microphysical observations of both blowing and falling snow challenging. Historically, these observations have been provided by costly hydrometeor imagers that have been deployed for field campaigns or at select observation sites. This has slowed the development and validation of microphysics parameterizations and remote-sensing retrievals of various properties. If cheaper, more mobile instrumentation can be developed, this progress can be accelerated. Further, lowering price barriers can make deployment of instrumentation feasible for education and outreach purposes.

The Blowing Snow Observations at the University of North Dakota: Education through Research (BLOWN-UNDER) Campaign took place during the winter of 2019-2020 to investigate strategies for obtaining microphysical measurements in the harsh North Dakota winter. Student led, the project blended education, outreach, and scientific objectives. While a variety of in-situ and remote-sensing instruments were deployed for the campaign, the most novel aspect of the project was the development and deployment of OSCRE, the Open Snowflake Camera for Research and Education. Images from this instrument were combined with winter weather educational modules to describe properties of snow to the public, K-12 students, and members of indigenous communities through a tribal outreach program. Along with an educational deployment of a Doppler on Wheels mobile radar, nearly 1000 individuals were reached during the project.

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Kelvin K. Droegemeier and Neil A. Jacobs

Abstract

For the first time in over 50 years, the United States has, at the direction of Congress, restructured the way in which Federal departments and agencies coordinate to advance meteorological services. The new framework, known as the Interagency Council for Advancing Meteorological Services (ICAMS), encompasses activities spanning local weather to global climate using an Earth system approach. Compared to the previous structure, ICAMS provides a simplified, streamlined framework for coordination across all stakeholders in implementing policies and practices associated with the broad set of services needed by the United States now and into the future. ICAMS also provides improved pathways for research and services integration, as well as mechanisms to more effectively engage the broader community, including academia, industry, nonprofit organizations, and particularly the next generation of educators, researchers, and operational practitioners.

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Adam L. Houston, Lisa M. Pytlikzillig, and Janell C. Walther

Abstract

Inclusion of unmanned aircraft systems (UAS) into the weather surveillance network has the potential to improve short-term (< 1 day) weather forecasts through direct integration of UAS-collected data into the forecast process and assimilation into numerical weather prediction models. However, one of the primary means by which the value of any new sensing platform can be assessed is through consultation with principal stakeholders. National Weather Service (NWS) forecasters are principal stakeholders responsible for the issuance of short-term forecasts. The purpose of the work presented here is to use results from a survey of 630 NWS forecasters to assess critical data gaps that impact short-term forecast accuracy, and explore the potential role of UAS in filling these gaps.

NWS forecasters view winter precipitation, icing, flood, lake-effect/enhanced snow, turbulence, and waves as the phenomena principally impacted by data gaps. Of the ten high-priority weather-related characteristics that need to be observed to fill critical data gaps, seven are either measures of precipitation or related to precipitation-producing phenomena. The three most important UAS capabilities/characteristics required for useful data for weather forecasting are real- or near-real-time data, the ability to integrate UAS data with additional data gathered by other systems, and UASs equipped with cameras to verify forecasts and monitor weather. Of the three operation modes offered for forecasters to consider, targeted surveillance is considered to be the most important compared to fixed site profiling or transects between fixed sites.

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Jian Wang, Rob Wood, Michael P. Jensen, J. Christine Chiu, Yangang Liu, Katia Lamer, Neel Desai, Scott E. Giangrande, Daniel A. Knopf, Pavlos Kollias, Alexander Laskin, Xiaohong Liu, Chunsong Lu, David Mechem, Fan Mei, Mariusz Starzec, Jason Tomlinson, Yang Wang, Seong Soo Yum, Guangjie Zheng, Allison C. Aiken, Eduardo B. Azevedo, Yann Blanchard, Swarup China, Xiquan Dong, Francesca Gallo, Sinan Gao, Virendra P. Ghate, Susanne Glienke, Lexie Goldberger, Joseph C. Hardin, Chongai Kuang, Edward P. Luke, Alyssa A. Matthews, Mark A. Miller, Ryan Moffet, Mikhail Pekour, Beat Schmid, Arthur J. Sedlacek, Raymond A. Shaw, John E. Shilling, Amy Sullivan, Kaitlyn Suski, Daniel P. Veghte, Rodney Weber, Matt Wyant, Jaemin Yeom, Maria Zawadowicz, and Zhibo Zhang

Abstract

With their extensive coverage, marine low clouds greatly impact global climate. Presently, marine low clouds are poorly represented in global climate models, and the response of marine low clouds to changes in atmospheric greenhouse gases and aerosols remains the major source of uncertainty in climate simulations. The Eastern North Atlantic (ENA) is a region of persistent but diverse subtropical marine boundary layer clouds, whose albedo and precipitation are highly susceptible to perturbations in aerosol properties. In addition, the ENA is periodically impacted by continental aerosols, making it an excellent location to study the cloud condensation nuclei (CCN) budget in a remote marine region periodically perturbed by anthropogenic emissions, and to investigate the impacts of long-range transport of aerosols on remote marine clouds. The Aerosol and Cloud Experiments in Eastern North Atlantic (ACE-ENA) campaign was motivated by the need of comprehensive in-situ measurements for improving the understanding of marine boundary layer CCN budget, cloud and drizzle microphysics, and the impact of aerosol on marine low cloud and precipitation. The airborne deployments took place from June 21 to July 20, 2017 and January 15 to February 18, 2018 in the Azores. The flights were designed to maximize the synergy between in-situ airborne measurements and ongoing long-term observations at a ground site. Here we present measurements, observation strategy, meteorological conditions during the campaign, and preliminary findings. Finally, we discuss future analyses and modeling studies that improve the understanding and representation of marine boundary layer aerosols, clouds, precipitation, and the interactions among them.

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Marlene Kretschmer, Samantha V. Adams, Alberto Arribas, Rachel Prudden, Niall Robinson, Elena Saggioro, and Theodore G. Shepherd

Abstract

Teleconnections are sources of predictability for regional weather and climate but the relative contributions of different teleconnections to regional anomalies are usually not understood. While physical knowledge about the involved mechanisms is often available, how to quantify a particular causal pathway from data is usually unclear. Here we argue for adopting a causal inference-based framework in the statistical analysis of teleconnections to overcome this challenge. A causal approach requires explicitly including expert knowledge in the statistical analysis, which allows one to draw quantitative conclusions. We illustrate some of the key concepts of this theory with concrete examples of well-known atmospheric teleconnections. We further discuss the particular challenges and advantages these imply for climate science and argue that a systematic causal approach to statistical inference should become standard practice in the study of teleconnections.

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Dorian J. Burnette
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Aaron R. Naeger, Michael J. Newchurch, Tom Moore, Kelly Chance, Xiong Liu, Susan Alexander, Kelley Murphy, and Bo Wang
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