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Elizabeth E. Ebert, John E. Janowiak, and Chris Kidd

An increasing number of satellite-based rainfall products are now available in near–real time over the Internet to help meet the needs of weather forecasters and climate scientists, as well as a wide range of decision makers, including hydrologists, agriculturalists, emergency managers, and industrialists. Many of these satellite products are so newly developed that a comprehensive evaluation has not yet been undertaken. This article provides potential users of short-interval satellite rainfall estimates with information on the accuracy of such estimates. Since late 2002 the authors have been performing daily validation and intercomparisons of several operational satellite rainfall retrieval algorithms over Australia, the United States, and northwestern Europe. Short-range quantitative precipitation forecasts from four numerical weather prediction (NWP) models are also included for comparison.

Synthesis of four years of daily rainfall validation results shows that the satellite-derived estimates of precipitation occurrence, amount, and intensity are most accurate during the warm season and at lower latitudes, where the rainfall is primarily convective in nature. In contrast, the NWP models perform better than the satellite estimates during the cool season when non-convective precipitation is dominant. An optimal rain-monitoring strategy for remote regions might therefore judiciously combine information from both satellite and NWP models.

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Elizabeth E. Ebert, Ulrich Damrath, Werner Wergen, and Michael E. Baldwin

Twenty-four-hour and 48-h quantitative precipitation forecasts (QPFs) from 11 operational numerical weather prediction models have been verified for a 4-yr period against rain gauge observations over the United States, Germany, and Australia to assess their skill in predicting the occurrence and amount of daily precipitation.

Model QPFs had greater skill in winter than in summer, and greater skill in midlatitudes than in Tropics, where they performed only marginally better than “persistence.” The best agreement among models, as well as the best ability to discriminate raining areas, occurred for a low rain threshold of 1–2 mm d−1. In contrast, the skill for forecasts of rain greater than 20 mm d−1 was generally quite low, reflecting the difficulty in predicting precisely when and where heavy rain will fall. The location errors for rain systems, determined using pattern matching with the observations, were typically about 100 km for 24-h forecasts, with smaller errors occurring for the heaviest rain systems.

It does not appear that model QPFs improved significantly during the four years examined. As new model versions were introduced their performance changed, not always for the better. The process of improving model numerics and physics is a complicated juggling act, and unless the accurate prediction of rainfall is made a top priority then improvements in model QPF will continue to come only slowly.

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Eric Gilleland, David A. Ahijevych, Barbara G. Brown, and Elizabeth E. Ebert

Numerous new methods have been proposed for using spatial information to better quantify and diagnose forecast performance when forecasts and observations are both available on the same grid. The majority of the new spatial verification methods can be classified into four broad categories (neighborhood, scale separation, features based, and field deformation), which themselves can be further generalized into two categories of filter and displacement. Because the methods make use of spatial information in widely different ways, users may be uncertain about what types of information each provides, and which methods may be most beneficial for particular applications. As an international project, the Spatial Forecast Verification Methods Inter-Comparison Project (ICP; www.ral.ucar.edu/projects/icp) was formed to address these questions. This project was coordinated by NCAR and facilitated by the WMO/World Weather Research Programme (WWRP) Joint Working Group on Forecast Verification Research. An overview of the methods involved in the project is provided here with some initial guidelines about when each of the verification approaches may be most appropriate. Future spatial verification methods may include hybrid methods that combine aspects of filter and displacement approaches.

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Elizabeth E. Ebert, Michael J. Manton, Philip A. Arkin, Richard J. Allam, Gary E. Holpin, and Arnold Gruber

Three algorithm intercomparison experiments have recently been conducted as part of the Global Precipitation Climatology Project with the goal of (a) assessing the skill of current satellite rainfall algorithms, (b) understanding the differences between them, and (c) moving toward improved algorithms. The results of these experiments are summarized and intercompared in this paper.

It was found that the skill of satellite rainfall algorithms depends on the regime being analyzed, with algorithms producing very good results in the tropical western Pacific and over Japan and its surrounding waters during summer, but relatively poor rainfall estimates over western Europe during late winter. Monthly rainfall was estimated most accurately by algorithms using geostationary infrared data, but algorithms using polar data [Advanced Very High Resolution Radiometer and Special Sensor Microwave/Imager (SSM/I)] were also able to produce good monthly rainfall estimates when data from two satellites were available. In most cases, SSM/I algorithms showed significantly greater skill than IR-based algorithms in estimating instantaneous rain rates.

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Manfred Dorninger, Eric Gilleland, Barbara Casati, Marion P. Mittermaier, Elizabeth E. Ebert, Barbara G. Brown, and Laurence J. Wilson

Abstract

Recent advancements in numerical weather prediction (NWP) and the enhancement of model resolution have created the need for more robust and informative verification methods. In response to these needs, a plethora of spatial verification approaches have been developed in the past two decades. A spatial verification method intercomparison was established in 2007 with the aim of gaining a better understanding of the abilities of the new spatial verification methods to diagnose different types of forecast errors. The project focused on prescribed errors for quantitative precipitation forecasts over the central United States. The intercomparison led to a classification of spatial verification methods and a cataloging of their diagnostic capabilities, providing useful guidance to end users, model developers, and verification scientists. A decade later, NWP systems have continued to increase in resolution, including advances in high-resolution ensembles. This article describes the setup of a second phase of the verification intercomparison, called the Mesoscale Verification Intercomparison over Complex Terrain (MesoVICT). MesoVICT focuses on the application, capability, and enhancement of spatial verification methods to deterministic and ensemble forecasts of precipitation, wind, and temperature over complex terrain. Importantly, this phase also explores the issue of analysis uncertainty through the use of an ensemble of meteorological analyses.

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Paul A. Kucera, Elizabeth E. Ebert, F. Joseph Turk, Vincenzo Levizzani, Dalia Kirschbaum, Francisco J. Tapiador, Alexander Loew, and M. Borsche

Advances to space-based observing systems and data processing techniques have made precipitation datasets quickly and easily available via various data portals and widely used in Earth sciences. The increasingly lengthy time span of space-based precipitation data records has enabled cross-discipline investigations and applications that would otherwise not be possible, revealing discoveries related to hydrological and land processes, climate, atmospheric composition, and ocean freshwater budget and proving a vital element in addressing societal issues. The purpose of this article is to demonstrate how the availability and continuity of precipitation data records from recent and upcoming space missions is transforming the ways that scientific and societal issues are addressed, in ways that would not be otherwise possible.

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Tony Bannister, Elizabeth E. Ebert, Ted Williams, Philip Douglas, Alan Wain, Maree Carroll, Jeremy Silver, Ed Newbigin, Edwin R. Lampugnani, Nicole Hughes, Clare Looker, Vanora Mulvenna, Danny Csutoros, Penelope J. Jones, Janet M. Davies, Cenk Suphioglu, Paul J. Beggs, Kathryn M. Emmerson, Alfredo Huete, and Ha Nguyen

Abstract

In November 2016, an unprecedented epidemic thunderstorm asthma event in Victoria, Australia, resulted in many thousands of people developing breathing difficulties in a very short period of time, including 10 deaths, and created extreme demand across the Victorian health services. To better prepare for future events, a pilot forecasting system for epidemic thunderstorm asthma (ETSA) risk has been developed for Victoria. The system uses a categorical risk-based approach, combining operational forecasting of gusty winds in severe thunderstorms with statistical forecasts of high ambient grass pollen concentrations, which together generate the risk of epidemic thunderstorm asthma. This pilot system provides the first routine daily epidemic thunderstorm asthma risk forecasting service in the world that covers a wide area, and integrates into the health, ambulance, and emergency management sector. Epidemic thunderstorm asthma events have historically occurred infrequently, and no event of similar magnitude has impacted the Victorian health system since. However, during the first three years of the pilot, 2017–19, two high asthma presentation events and four moderate asthma presentation events were identified from public hospital emergency department records. The ETSA risk forecasts showed skill in discriminating between days with and without health impacts. However, even with hindsight of the actual weather and airborne grass pollen conditions, some high asthma presentation events occurred in districts that were assessed as low risk for ETSA, reflecting the challenge of predicting this unusual phenomenon.

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