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David Halpern

Abstract

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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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 Spirig, Christian Feigenwinter, Markus Kalberer, Eberhard Parlow, and Roland Vogt

Abstract

Dolueg is a two-component framework to dynamically display time series. It serves as outreach to other researchers and the local public, educational resource and quality control tool. The first component is a set of Python functions. These create different types of visualisation with meta information about the data in the zoomable, modern SVG format. The second component is a simple but highly customizable website, that groups these figures according to the displayed data. We provide the code in two separate repositories on GitHub for interested parties including more detailed instructions for the installation.

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Yunxia Zhao, Hamid Norouzi, Marzi Azarderakhsh, and Amir AghaKouchak

Abstract

Most previous studies of extreme temperatures have primarily focused on atmospheric temperatures. Using 18 years of the latest version of the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data, we globally investigate the spatial patterns of hot and cold extremes as well as diurnal temperature range (DTR). We show that the world’s highest LST of 80.8 °C, observed in the Lut Desert in Iran and the Sonoran Desert in Mexico, is over ten degrees above the previous global record of 70.7 °C observed in 2005. The coldest place on Earth is Antarctica with the record low temperature of -110.9 °C. The world’s maximum DTR of 81.8 °C is observed in a desert environment in China. We see strong latitudinal patterns in hot and cold extremes as well as DTR. Biomes worldwide are faced with different levels of temperature extremes and DTR: we observe the highest zonal average maximum LST of 61.1 ± 5.3 °C in the deserts and xeric shrublands; the lowest zonal average minimum LST of -66.6 ± 14.8 °C in the Tundra; and the highest zonal average maximum DTR of 43.5 ± 9.9 °C in the montane grasslands and shrublands. This global exploration of extreme LST and DTR across different biomes sheds light on the type of extremes different ecosystems are faced with.

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Louise Crochemore, Carolina Cantone, Ilias G. Pechlivanidis, and Christiana S. Photiadou

Abstract

In a context that fosters the evolution of hydro-climate services, it is crucial to support and train users in making the best possible forecast-based decisions. Here, we analyze how decision-making is influenced by the seasonal forecast performance based on the Call For Water serious game in which participants manage a water supply reservoir. The aim is twofold: (1) train participants in the concepts of forecast sharpness and reliability, and (2) collect participants’ decisions to investigate the levels of forecast sharpness and reliability needed to make informed decisions. In the first game round, participants are provided with forecasts of varying reliability and sharpness, while in the second round, they have the possibility to pay for systematically reliable and sharp forecasts (improved forecasts). Exploitable answers were collected from 367 participants, predominantly researchers, forecasters and consultants in the water resources and energy sectors. Results show that improved forecasts led to better decisions, enabling participants to step out of purely conservative strategies and successfully take risks. Reliability levels of 60% are necessary for decision-making while both reliability levels above 70% and sharpness are required for informed risk-prone strategies. Improved forecasts are judged more valuable in extreme years, for instance when hedging against water shortage risks. Additionally, participants working in the energy, air quality and agriculture sectors, as well as traders, decision-makers and forecasters invested the most in forecasts. Finally, we discuss the potential of serious games to foster capacity development in hydro-climate services, and provide recommendations for forecast-based service development.

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Jonathan D. W. Kahl, Brandon R. Selbig, and Austin R. Harris

Abstract

Wind gusts are common to everyday life and affect a wide range of interests including wind energy, structural design, forestry, and fire danger. Strong gusts are a common environmental hazard that can damage buildings, bridges, aircraft, and trains, and interrupt electric power distribution, air traffic, waterways transport, and port operations. Despite representing the component of wind most likely to be associated with serious and costly hazards, reliable forecasts of peak wind gusts have remained elusive. A project at the University of Wisconsin-Milwaukee is addressing the need for improved peak gust forecasts with the development of the meteorologically stratified gust factor (MSGF) model. The MSGF model combines gust factors (the ratio of peak wind gust to average wind speed) with wind speed and direction forecasts to predict hourly peak wind gusts. The MSGF method thus represents a simple, viable option for the operational prediction of peak wind gusts. Here we describe the results of a project designed to provide the site-specific gust factors necessary for operational use of the MSGF model at a large number of locations across the United States. Gust web diagrams depicting the wind speed- and wind direction-stratified gust factors, as well as peak gust climatologies, are presented for all sites analyzed.

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