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Zhijun Huang, Huan Wu, Robert F. Adler, Guy Schumann, Jonathan J. Gourley, Albert Kettner, and Nergui Nanding

Abstract

A reliable flood event inventory that reflects the occurrence and evolution of past floods is important for studies of flood hazards and risks, hydroclimatic extremes, and future flood projections. However, currently available flood inventories are based on single-sourced data and often neglect underreported or less impactful flood events. Furthermore, traditional archives store flood events only at sparse geographic points, which significantly limits their further applicability. Also, few publicly available archives contain all-inclusive records of potential natural flooded area over time. To tackle these challenges, we construct two types of multisourced flood event inventories (MFI) for all river basins across the contiguous United States covering the period 1998–2013 on daily and subcatchment scales, which is publicly available at http://flood.umd.edu/download/CONUS/. These archives integrate flood information from in situ observations, remote sensing observations, hydrological model simulations, and five high-quality precipitation products. The first inventory (MFI-Actual) includes all actual floods that occurred in the presence of flood protection infrastructures, while the second, “natural (undefended)” inventory (MFI-Natural) reconstructs the possible “historical” floods without flood protection, which could be more directly influenced by climate variation. In the proposed two inventories, 2,755 and 4,661 flood events were estimated, respectively. MFI-Natural reconstructed 1,597 floods in ungauged basins, and recovered 608 extreme streamflow events in gauged subcatchments where floods would have happened if there were no flood protection. There is an average of four upstream dams located in these flood-recovered subcatchments, which indicates that modern flood defenses efficiently prevent significant flooding from extreme precipitation in many catchments over the country.

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Rachel Hogan Carr, Kathryn Semmens, Burrell Montz, and Keri Maxfield

Abstract

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, CA; Gunnison, CO; Durango, CO; Owego, NY) across the U.S. 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.

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David Halpern

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In 1976, a pilot experiment, called first Equatorial Mooring (EQUA-1), tested an innovative technique for anchoring a taut-line surface mooring at 0°, 150°W where the water depth is 4.5 km. The 36-day deployment contained a wind recorder and fixed-level current meters at 50 and 100 m in the Equatorial Undercurrent (EUC). The following year, in a second pilot experiment, named EQUA-2, a similar mooring was deployed at 0°, 125°W for 99 days. EQUA-2, with current meters at 10, 50, 100, 150 and 200 m, recorded a surge in EUC transport during April 1977 when 3-day averaged eastward current speeds at 50-m depth reached 2 m s‒1. The associated eastward transport per unit meridional width over the 50- to 200-m layer was 190 m2 s‒1. Based on observations recorded in April 1980, the EQUA-2 pulse would correspond to a total EUC transport surge of about 38 Sverdrups and would represent an equatorially trapped first-mode baroclinic Kelvin wave. This paper describes EQUA Project observations and why and how I created the high risk-of-failure opportunity to record pioneering time series measurements at the equator. The enduring legacy of the EQUA Project is the sustained maintenance of in-situ surface wind and upper-ocean current and temperature measurements at numerous sites in the equatorial oceans, starting in the Pacific to improve forecasts of the El Niño and La Niña phenomenon. For example, the 40-year records of surface wind and upper-ocean current and temperature measurements at 0°, 110°W and 0°, 140°W are some of oceanography’s longest time series recorded far from land.

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Russ S. Schumacher, Aaron J. Hill, Mark Klein, James A. Nelson, Michael J. Erickson, Sarah M. Trojniak, and Gregory R. Herman

Abstract

Excessive rainfall is difficult to forecast, and there is a need for tools to aid Weather Prediction Center (WPC) forecasters when generating Excessive Rainfall Outlooks (EROs), which are issued for the contiguous United States at lead times of 1–3 days. To address this need, a probabilistic forecast system for excessive rainfall, known as the Colorado State University-Machine Learning Probabilities (CSU-MLP) system, was developed based on ensemble reforecasts, precipitation observations, and machine learning algorithms, specifically random forests. The CSU-MLP forecasts were designed to emulate the EROs, with the goal being a tool that forecasters can use as a “first guess” in the ERO forecast process. Resulting from close collaboration between CSU and WPC and evaluation at the Flash Flood and Intense Rainfall experiment, iterative improvements were made to the forecast system and it was transitioned into operational use at WPC. Quantitative evaluation shows that the CSU-MLP forecasts are skillful and reliable, and they are now being used as a part of the WPC forecast process. This project represents an example of a successful research-to-operations transition, and highlights the potential for machine learning and other post-processing techniques to improve operational predictions.

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Chris Kidd, George Huffman, Viviana Maggioni, Philippe Chambon, and Riko Oki

Abstract

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 multi-channel 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 summarised 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 (CO). 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.

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Emily Shroyer, Amit Tandon, Debasis Sengupta, Harindra J.S. Fernando, Andrew J. Lucas, J. Thomas Farrar, Rajib Chattopadhyay, Simon de Szoeke, Maria Flatau, Adam Rydbeck, Hemantha Wijesekera, Michael McPhaden, Hyodae Seo, Aneesh Subramanian, R Venkatesan, Jossia Joseph, S. Ramsundaram, Arnold L. Gordon, Shannon M. Bohman, Jaynise Pérez, Iury T. Simoes-Sousa, Steven R. Jayne, Robert E. Todd, G.S. Bhat, Matthias Lankhorst, Tamara Schlosser, Katherine Adams, S.U.P Jinadasa, Manikandan Mathur, M. Mohapatra, E. Pattabhi Rama Rao, A. K. Sahai, Rashmi Sharma, Craig Lee, Luc Rainville, Deepak Cherian, Kerstin Cullen, Luca R. Centurioni, Verena Hormann, Jennifer MacKinnon, Uwe Send, Arachaporn Anutaliya, Amy Waterhouse, Garrett S. Black, Jeremy A. Dehart, Kaitlyn M. Woods, Edward Creegan, Gad Levy, Lakshmi H Kantha, and Bulusu Subrahmanyam

Abstract

In the Bay of Bengal, the warm, dry boreal spring concludes with the onset of the summer monsoon and accompanying southwesterly winds, heavy rains, and variable air-sea fluxes. Here, we summarize the 2018 monsoon onset using observations collected through the multinational Monsoon Intraseasonal Oscillations in the Bay of Bengal (MISO-BoB) program between the US, India, and Sri Lanka. MISO-BoB aims to improve understanding of monsoon intraseasonal variability, and the 2018 field effort captured the coupled air-sea response during a transition from active-to-break conditions in the central BoB. The active phase of the ~20-day research cruise was characterized by warm sea surface temperature (SST > 30°C), cold atmospheric outflows with intermittent heavy rainfall, and increasing winds (from 2 to 15 m s−1). Accumulated rainfall exceeded 200 mm with 90% of precipitation occurring during the first week. The following break period was both dry and clear, with persistent 10−12 m s−1 wind and evaporation of 0.2 mm h−1. The evolving environmental state included a deepening ocean mixed layer (from ~20 to 50 m), cooling SST (by ~ 1°C), and warming/drying of the lower to mid-troposphere. Local atmospheric development was consistent with phasing of the large-scale intraseasonal oscillation. The upper ocean stores significant heat in the BoB, enough to maintain SST above 29°C despite cooling by surface fluxes and ocean mixing. Comparison with reanalysis indicates biases in air-sea fluxes, which may be related to overly cool prescribed SST. Resolution of such biases offers a path toward improved forecasting of transition periods in the monsoon.

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Maude Dinan, Emile Elias, Nicholas P. Webb, Greg Zwicke, Timothy S. Dy, Skye Aney, Michael Brady, Joel R. Brown, Robert R. Dobos, Dave DuBois, Brandon L. Edwards, Sierra Heimel, Nicholas Luke, Caitlin M. Rottler, and Caitriana Steele
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Silke Trömel, Christian Chwala, Carina Furusho, Cintia Carbajal Henken, Julius Polz, Roland Potthast, Ricardo Reinoso-Rondinel, and Clemens Simmer
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A. Gettelman, G. R. Carmichael, G. Feingold, A. M. Da Silva, and S. C. Van Den Heever
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Robert Palmer, David Whelan, David Bodine, Pierre Kirstetter, Matthew Kumjian, Justin Metcalf, Mark Yeary, Tian-You Yu, Ramesh Rao, John Cho, David Draper, Stephen Durden, Stephen English, Pavlos Kollias, Karen Kosiba, Masakazu Wada, Joshua Wurman, William Blackwell, Howard Bluestein, Scott Collis, Jordan Gerth, Aaron Tuttle, Xuguang Wang, and Dusan Zrnic
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