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Jianshun Wang, Qiang Zhang, Liang Zhang, Ying Wang, Ping Yue, Yanbin Hu, and Peilong Ye

Abstract

As impacted by climate change and further global warming, drought turns out to be the most frequent meteorological extreme event worldwide, which severely affects agriculture, ecosystem, water management and even human survival. In this study, the global pattern and development trends & directions on drought monitoring were presented based on Web of Science database by conducting a bibliometric analysis from 1983 to 2020. The following conclusions were drawn. (1) The USA and China were found as the most productive and influential nations, accounting for 24.63% and 14.30% in publication outputs and taking up 5023 and 2040 in local citations, respectively. (2) Chinese Academy of Science was reported as the core institution with 5.73% publication outputs and 829 local citations. (3) Remote Sensing of Environment and Remote Sensing were found as the most influential journals and the most productive journals with 1045 local citations and 210 publication outputs, respectively. (4) Agricultural drought profoundly affecting food security was found as the most concerned drought type in the world. The drought monitoring research mainly focus on the research and development of drought index, the response of terrestrial ecosystems to drought, and the trends and dynamics of drought in context of climate change. This study explored key findings, contradictions, and limitations of drought monitoring studies were summarized and explored. In addition, the development trend and research direction of drought monitoring researches in the future were also explored.

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Pavlos Kollias, Robert Palmer, David Bodine, Toru Adachi, Howie Bluestein, John Y. N. Cho, Casey Griffin, Jana Houser, Pierre. E. Kirstetter, Matthew R. Kumjian, James M. Kurdzo, Wen Chau Lee, Edward P. Luke, Steve Nesbitt, Mariko Oue, Alan Shapiro, Angela Rowe, Jorge Salazar, Robin Tanamachi, Kristofer S. Tuftedal, Xuguang Wang, Dusan Zrnic, and Bernat Puigdomenech Treserras

Abstract

Phased array radars (PARs) are a promising observing technology, at the cusp of being available to the broader meteorological community. PARs offer near instantaneous sampling of the atmosphere with flexible beam forming, multi-functionality, low operational and maintenance costs, and without mechanical inertia limitations. These PAR features are transformative compared to those offered by our current reflector-based meteorological radars. The integration of PARs into meteorological research has the potential to revolutionize the way we observe the atmosphere. The rate of adoption of PARs in research will depend on many factors including i) the need to continue educating the scientific community on the full technical capabilities and trade-offs of PARs through an engaging dialogue with the science and engineering communities and ii) the need to communicate the breadth of scientific bottlenecks that PARs can overcome in atmospheric measurements and the new research avenues that are now possible using PARs in concert with other measurement systems. The former is the subject of a companion article that focuses on PAR technology while the latter is the objective here.

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L. Magnusson, D. Ackerley, Y. Bouteloup, J.-H. Chen, J. Doyle, P. Earnshaw, Y. C. Kwon, M. Köhler, S. T. K Lang, Y.-J. Lim, M. Matsueda, T. Matsunobu, R. McTaggart-Cowan, A. Reinecke, M. Yamaguchi, and L. Zhou

Abstract

In the DIMOSIC (DIfferent MOdels, Same Initial Conditions) project, forecasts from different global medium-range forecast models have been created based on the same initial conditions. The dataset consists of 10-day deterministic forecasts from seven models and includes 122 forecast dates spanning one calendar year. All forecasts are initialized from the same ECMWF operational analyses to minimize the differences due to initialization. The models are run at or near their respective operational resolutions to explore similarities and differences between operational global forecast models. The main aims of this study are: (1) evaluate the forecast skill and how it depends on model formulation, (2) assess systematic differences and errors at short lead times, (3) compare multi-model ensemble spread to model uncertainty schemes, and (4) identify models that generate similar solutions. Our results show that all models in this study are capable of producing high-quality forecasts given a high-quality analysis. But at the same time we find a large variety in model biases, both in terms of temperature errors and precipitation. We are able to identify models whose forecasts are more similar to each other than they are to those of other systems, due to the use of similar model physics packages. However, in terms of multi-model ensemble spread, our results also demonstrate that forecast sensitivities to different model formulations skill are substantial. We therefore believe that the diversity in model design that stems from parallel development efforts at global modeling centers around the world remains valuable for future progress in the numerical weather prediction community.

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Katia Lamer, Edward P. Luke, Brian Jr. Walsh, Steven Andrade, Zackary Mages, Zeen Zhu, Erin Leghart, Bernat P. Treserras, Ann Emrick, Pavlos Kollias, Andrew Vogelmann, and Martin Schoonen

Abstract

The Brookhaven National Laboratory Center for Multiscale Applied Sensing (CMAS) aims to address environmental equity needs in the context of a changing climate. As a first step towards this goal, the center developed a one-of-a-kind observatory tailored to the study of highly heterogeneous urban environments.

This article describes the features of the mobile observatory that enable its rapid deployment either on or off the power grid, as well as its instrument payload. Beyond its unique design, the observatory optimizes data collection within the obstacle-laden urban environment using a new smart sampling paradigm. This setup facilitated the collection of previously poorly documented environmental properties including wind profiles throughout the atmospheric column.

The mobile observatory captured unique observations during its first few intensive observation periods (IOPs).. Vertical air motion and infrared temperature measurements collected along the faces of the supertall One Vanderbilt skyscraper in Manhattan, NY reveal how solar and anthropogenic heating affect wind flow and thus the venting of heat, pollution, and contaminants in urban street canyons. Also, air temperature measurements collected during travel along a 150-km transect between Upton and Manhattan, NY offer a high-resolution view of the urban heat island and reveal that temperature disparities also exist within the city across different neighborhoods.

Ultimately, the datasets collected by CMAS are poised to help guide equitable urban planning by highlighting existing disparities and characterizing the impact of urban features on the urban microclimate with the goal of improving human comfort.

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Faisal Hossain, Margaret Srinivasan, Nicolas Picot, Santiago Pena-Luque, and Bradley Doorn
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Ralf Merz, Arianna Miniussi, Stefano Basso, Karl-Jonas Petersen, and Larisa Tarasova

Abstract

Conceptual hydrological models are irreplaceable tools for large scale (i.e., from regional to global) hydrological predictions. Large scale modeling studies typically strive to employ one single model structure regardless of the diversity of catchments under study. However, little is known on the optimal model complexity for large scale applications. In a modelling experiment across 700 catchments in the contiguous US, we analyze the performance of a conceptual (bucket style) distributed hydrological model with varying complexity (5 model versions with 11 to 45 parameters) but with exactly the same inputs, spatial and temporal resolution, and implementing the same regional parameterization approach. The performance of all model versions compares well with those of contemporary large scale models tested in the US, suggesting that the applied model structures reasonably account for the dominant hydrological processes. Remarkably, our results favor a simpler model structure where the main hydrological processes of runoff generation and routing through soil, groundwater and the river network are conceptualized in distinct but parsimonious ways. As long as only observed runoff is used for model validation, including additional soil layers in the model structure to better represent vertical soil heterogeneity seems not to improve model performance. More complex models tend to have lower model performance and may result in rather large uncertainties in simulating states and fluxes (soil moisture and groundwater recharge) in model ensemble applications. Overall, our results indicate that simpler model structures tend to be a more reliable choice, given the limited validation data available at large scale.

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Harald Sodemann, Franziska Aemisegger, and Camille Risi
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Robert A Weller, Roger Lukas, James Potemra, Albert. J. Plueddemann, Chris Fairall, and Sebastien Bigorre

Abstract

There is great interest in improving our understanding of the respective roles of the ocean and atmosphere in variability and change in weather and climate. Due to the sparsity of sustained observing sites in the open ocean, information about the air-sea exchanges of heat, freshwater, and momentum is often drawn from models. In this paper observations from three long-term surface moorings deployed in the tradewind regions of the Pacific and Atlantic Oceans are used to compare observed means and low-passed air-sea fluxes from the moorings with coincident records from three atmospheric reanalyses (ERA5, NCEP2, and MERRA2) and from CMIP6 coupled models. To set the stage for the comparison, the methodologies of maintaining the long-term surface moorings, known as Ocean Reference Stations (ORS), and assessing the accuracies of their air-sea fluxes are described first. Biases in the reanalyses’ means and low-passed wind stresses and net air-sea heat fluxes are significantly larger than the observational uncertainties and in some case show variability in time. These reanalyses and most CMIP6 models fail to provide as much heat into the ocean as observed. In the discussion and conclusion section, long-term observing sites in the open ocean are seen as essential, independent benchmarks not only to document the coupling between the atmosphere and ocean but also to promote collaborative efforts to assess and improve the ability of models to simulate air-sea fluxes.

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Hanna Lee, Nadine Johnston, Lars Nieradzik, Andrew Orr, Ruth H. Mottram, Willem Jan van de Berg, and Priscilla A. Mooney
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Neil A. Stuart, Gail Hartfield, David M. Schultz, Katie Wilson, Gregory West, Robert Hoffman, Gary Lackmann, Harold Brooks, Paul Roebber, Teresa Bals-Elsholz, Holly Obermeier, Falko Judt, Patrick Market, Daniel Nietfeld, Bruce Telfeyan, Dan Depodwin, Jeffrey Fries, Elliot Abrams, and Jerry Shields

ABSTRACT

A series of webinars and panel discussions were conducted on the topic of the evolving role of humans in weather prediction and communication, in recognition of the 100th anniversary of the founding of the AMS. One main theme that arose was the inevitability that new tools using artificial intelligence will improve data analysis, forecasting, and communication. We discussed what tools are being created, how they are being created, and how the tools will potentially affect various duties for operational meteorologists in multiple sectors of the profession. Even as artificial intelligence increases automation, humans will remain a vital part of the forecast process as that process changes over time. Additionally, both university training and professional development must be revised to accommodate the evolving forecasting process, including addressing the need for computing and data skills (including artificial intelligence and visualization), probabilistic and ensemble forecasting, decision support, and communication skills. These changing skill sets necessitate that both the U.S. government’s Meteorologist General Schedule-1340 requirements and the AMS standards for a bachelor’s degree need to be revised. Seven recommendations are presented for student and forecaster preparation and career planning, highlighting the need for students and operational meteorologists to be flexible life-long learners, acquire new skills, and be engaged in the changes to forecast technology in order to best serve the user community throughout their careers. The article closes with our vision for the ways that humans can maintain an essential role in weather prediction and communication, highlighting the interdependent relationship between computers and humans.

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