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jun-ichi Yano

Abstract

Objectively identifying a phenomenon from observation is often difficult. This essay reflects upon this problem from a philosophical perspective by taking the Madden-Julian oscillation (MJO) as an example. I argue that it can be considered as a problem of Gestalt. This concept is introduced by closely following Ludwig Wittgenstein’s two philosophical works, “Philosophical Investigations (Philosophische Untersuchungen)” and “Remarks on the Philosophy of Psychology (Bemerkungen über die Philosophie der Psychologie)”. Reflections upon the concept of Gestalt suggest why an objective identification of a phenomenon is so difficult. Importantly, the problem should not be reduced to that of a “pattern recognition”. Rather a given phenomenon must be considered as a whole, including a question of a driving mechanism.

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Alexander R. Gottlieb and Justin S. Mankin

Abstract

Warmer and shorter winters from climate change will reduce snowpacks in most seasonally snow-covered regions of the world, with consequences for freshwater availability in spring and summer when people and ecosystems demand water most. Recent record low snowpacks, such as those in the winters of 2013/14 and 2014/15 in the Western United States, have led to a surge in research on ‘snow droughts,’ which are pointed to as harbingers of global warming that pose significant societal hazards. Yet despite the importance of understanding snow droughts to best prepare for their attendant impacts, the concept remains amorphous, with no agreed-upon definition of what they are, how best to measure them, and how such snow droughts connect to warm-season impacts. These knowledge gaps limit our understanding of the risks posed by snow droughts in the present and future, and thus our preparedness for their differential impacts on freshwater resources. To address these issues, we compile a hemispheric ensemble of in situ, satellite, and reanalysis snowpack datasets. We use this ensemble to evaluate the scientific challenges and uncertainties arising from differences in defining and measuring snow droughts, and identify opportunities to leverage this information to better understand the significance of snow droughts. We show that a clearer quantification of what constitutes a snow drought, including its uncertainties, improves our ability to anticipate costly and disruptive warm-season droughts, which is vital for informing risk management and adaptation to changing snow regimes.

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Christopher J. White, Daniela I. V. Domeisen, Nachiketa Acharya, Elijah A. Adefisan, Michael L. Anderson, Stella Aura, Ahmed A. Balogun, Douglas Bertram, Sonia Bluhm, David J. Brayshaw, Jethro Browell, Dominik Büeler, Andrew Charlton-Perez, Xandre Chourio, Isadora Christel, Caio A. S. Coelho, Michael J. DeFlorio, Luca Delle Monache, Francesca Di Giuseppe, Ana María García-Solórzano, Peter B. Gibson, Lisa Goddard, Carmen González Romero, Richard J. Graham, Robert M. Graham, Christian M. Grams, Alan Halford, W. T. Katty Huang, Kjeld Jensen, Mary Kilavi, Kamoru A. Lawal, Robert W. Lee, David MacLeod, Andrea Manrique-Suñén, Eduardo S. P. R. Martins, Carolyn J. Maxwell, William J. Merryfield, Ángel G. Muñoz, Eniola Olaniyan, George Otieno, John A. Oyedepo, Lluís Palma, Ilias G. Pechlivanidis, Diego Pons, F. Martin Ralph, Dirceu S. Reis Jr., Tomas A. Remenyi, James S. Risbey, Donald J. C. Robertson, Andrew W. Robertson, Stefan Smith, Albert Soret, Ting Sun, Martin C. Todd, Carly R. Tozer, Francisco C. Vasconcelos Jr., Ilaria Vigo, Duane E. Waliser, Fredrik Wetterhall, and Robert G. Wilson

Abstract

The subseasonal-to-seasonal (S2S) predictive timescale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this timescale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a ‘knowledge-value’ gap, where a lack of evidence and awareness of the potential socio-economic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development – demonstrating both skill and utility across sectors – this dialogue can be used to help promote and accelerate the awareness, value and co-generation of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting timescale.

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Hylke E. Beck, Albert I.J.M. van Dijk, Pablo R. Larraondo, Tim R. McVicar, Ming Pan, Emanuel Dutra, and Diego G. Miralles

Abstract

We present Multi-Source Weather (MSWX), a seamless global gridded near-surface meteorological product featuring a high 3-hourly 0.1° resolution, near real-time updates (~3-hour latency), and bias-corrected medium-range (up to 10 days) and long-range (up to 7 months) forecast ensembles. The product includes ten meteorological variables: precipitation, air temperature, daily minimum and maximum air temperature, surface pressure, relative and specific humidity, wind speed, and downward shortwave and longwave radiation. The historical part of the record starts January 1, 1979, and is based on ERA5 data bias-corrected and downscaled using high-resolution reference climatologies. The data extension to within ~3 hours of real-time is based on analysis data from GDAS. The 30-member medium-range forecast ensemble is based on GEFS and updated daily. Finally, the 51-member long-range forecast ensemble is based on SEAS5 and updated monthly. The near real-time and forecast data are statistically harmonized using running-mean and cumulative distribution function-matching approaches to obtain a seamless record covering 1979 to 7 months from now. MSWX presents new and unique opportunities for hydrological modeling, climate analysis, impact studies, and monitoring and forecasting of droughts, floods, and heatwaves (within the bounds of the caveats and limitations discussed herein). The product is available at www.gloh2o.org/mswx.

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S. Kalluri, C. Barnet, M. Divakarla, R. Esmaili, N. Nalli, K. Pryor, T. Reale, N. Smith, C. Tan, T. Wang, J. Warner, M. Wilson, L. Zhou, and T. Zhu

Abstract

Infrared and microwave sounder measurements from polar-orbiting satellites are used to retrieve profiles of temperature, water vapor, and trace gases utilizing a suite of algorithms called the National Oceanic and Atmospheric Administration (NOAA) Unique Combined Atmospheric Processing System (NUCAPS). Meteorologists operationally use the retrievals similar to radiosonde measurements to assess atmospheric stability and aid them in issuing forecasts and severe weather warnings. Measurements of trace gases by NUCAPS enable detection, tracking, and monitoring of greenhouse gases and emissions from fires that impact air quality. During the polar winters, when ultraviolet measurements of ozone are not possible, absorption features in the infrared spectrum of the sounders enable the assessment of ozone concentration in the stratosphere. These retrievals are used as inputs to monitor the ozone hole over Antarctica. This article illustrates the utility of NUCAPS atmospheric profile retrievals in assessing meteorological events using several examples of severe thunderstorms, tropical cyclones, fires, and ozone maps.

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Konstantine P. Georgakakos, Theresa M. Modrick, Eylon Shamir, Rochelle Campbell, Zhengyang Cheng, Robert Jubach, Jason A. Sperfslage, Cristopher R. Spencer, and Randall Banks

Abstract

At the beginning of the 21st Century a research-to-operations program was initiated to design and develop operational systems to support local forecasters in their challenging task to provide advance warning for flash floods worldwide. Twenty some years later, the Flash Flood Guidance System with global coverage provides real-time assessment and guidance products to more than 60 countries, serving nearly 3 billion people. The implementation domains cover a wide range of hydroclimatological, geomorphological and land-use regimes worldwide. This flexible and evolving system combines meteorology and hydrology data and concepts as well as supports product utility for flash-flood disaster mitigation on very large scales with high spatial and temporal resolution. Through quality control procedures, it integrates remotely-sensed data of land-surface precipitation and of land-surface properties from geostationary and polar orbiter satellite platforms, reflectivity data from a variety of weather radar systems, as well as asynchronous precipitation data from ground-based automated precipitation gauges, in order to produce assessments and short-term forecasts that support forecasters and disaster managers in real time. For each region, it also integrates mesoscale meteorological model forecasts with land-surface model response to produce longer-term guidance products. It contains components and interfaces that allow real-time forecaster adjustments to products based on local last-minute field information and relevant forecaster experience. Assessments of utility for flash flood warning operations by national forecasting agencies worldwide are positive. The article exemplifies the process of realization and evolution of the FFGS from research in interdisciplinary fields to operations in diverse environments, and discusses lessons learned.

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Hisham Eldardiry, Faisal Hossain, Margaret Srinivasan, and Vardis Tsontos

Abstract

For nearly three decades, satellite nadir altimeters have provided essential information to understand, primarily ocean, and also, inland water dynamics. A variety of parameters can be inferred via altimeter measurements, including sea surface height, sea surface wind speeds, significant wave heights, and topography of land, sea ice, and ice sheets. Taking advantage of these parameters with the long record of altimeter data spanning multiple decades has allowed a diverse range of societal applications. As the constellation of altimeter satellites grows, the proven value of the missions to a diverse user community can now be demonstrated by highlighting a selection of verifiable success stories. In this paper, we review selected altimeter success stories which incorporate altimetry data, alone or in conjunction with numerical models or other Earth observations, to solve a key societal problem. First, we define the problem or the key challenge of each use case, and then we articulate the uptake of the successful altimeter-based solution. Our review revealed steady progress by scientific and stakeholder communities in bridging the gap between data availability and their actual uptake to address a variety of applications. Highlighting these altimeter-based success stories can serve to further promote the widespread adoption of future satellite missions such as the Surface Water and Ocean Topography (SWOT) mission scheduled for launch in 2022. Knowledge of the breadth of current utility of altimeter observations can help the scientific community to demonstrate the value in continuing radar altimeter and similar missions, particularly those with expanded capabilities, such as SWOT.

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Rachel H. White, Kai Kornhuber, Olivia Martius, and Volkmar Wirth

Abstract

A notable number of high impact weather extremes have occurred in recent years, often associated with persistent, strongly meandering atmospheric circulation patterns known as Rossby waves. Because of the high societal and ecosystem impacts, it is of great interest to be able to accurately project how such extreme events will change with climate change, and to predict these events on seasonal to subseasonal (S2S) timescales. There are multiple physical links connecting upper atmosphere circulation patterns to surface weather extremes, and it is asking a lot of our dynamical models to accurately simulate all of these. Subsequently, our confidence in future projections and S2S forecasts of extreme events connected to Rossby waves remains relatively low. We also lack full fundamental theories for the growth and propagation of Rossby waves on the spatial and temporal scales relevant to extreme events, particularly under strongly non-linear conditions. By focussing on one of the first links in the chain from upper atmospheric conditions to surface extremes -- the Rossby waveguide -- it may be possible to circumvent some model biases in later links. To further our understanding of the nature of waveguides, links to persistent surface weather events and their representation in models, we recommend: exploring these links in model hierarchies of increasing complexity, developing fundamental theory, exploiting novel large ensemble data sets, harnessing deep learning, and increased community collaboration. This would help increase understanding and confidence in both S2S predictions of extremes and of projections of the impact of climate change on extreme weather events.

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Ghassan J. Alaka Jr., Xuejin Zhang, and Sundararaman G. Gopalakrishnan

Abstract

To forecast tropical cyclone (TC) intensity and structure changes with fidelity, numerical weather prediction models must be “high definition”, i.e., horizontal grid spacing ≤ 3 km, so that they permit clouds and convection and resolve sharp gradients of momentum and moisture in the eyewall and rainbands. However, resolutions in operational global models remain too coarse to accurately predict these structures that are critical to TC intensity. Storm-following nests are a solution to this problem because they are computationally efficient at fine resolutions, providing a practical approach to improve TC intensity forecasts. Under the Hurricane Forecast Improvement Program, the operational Hurricane Weather Research and Forecasting (HWRF) system was developed to include telescopic, storm-following nests for a single TC per model integration. Subsequently, HWRF evolved into a state-of-the-art tool for TC predictions around the globe, although its single-storm nesting approach does not adequately simulate TC-TC interactions as they are observed. Basin-scale HWRF (HWRF-B) was developed later with a multi-storm nesting approach to improve the simulation of TC-TC interactions by producing high-resolution forecasts for multiple TCs simultaneously. In this study, the multi-storm nesting approach in HWRF-B was compared with a single-storm nesting approach using an otherwise identical model configuration. The multi-storm approach demonstrated TC intensity forecast improvements, including more realistic TC-TC interactions. Storm-following nests developed in HWRF and HWRF-B will be foundational to NOAA’s next-generation hurricane application in the Unified Forecast System.

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Simone Tilmes, Andrea Smith, Peter Lawrence, Tim Barnes, Greeshma Gadikota, Wojciech Grabowski, Douglas G. MacMartin, Brian Medeiros, Monica Morrison, Andreas Prein, Roy Rasmussen, Karen Rosenlof, Dale S. Rothman, Anton Seimon, Gyami Shrestha, and Britton B. Stephens

Capsule summary

Community Climate Intervention Strategies Workshop, October 28-30, 2020

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