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S. Maldonado

Bio: S. Maldonado is an academic researcher from Ohio State University. The author has contributed to research in topics: Climate change & Weather Research and Forecasting Model. The author has an hindex of 1, co-authored 1 publications receiving 63 citations.

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TL;DR: The Arctic System Reanalysis, version 2 (ASRv2) as mentioned in this paper is a multi-agency, university-led retrospective analysis (reanalysis) of the greater Arctic region using blends of the polaroptimized version of the Weather Research and Forecasting (Polar WRF) Model and three-dimensional variational data assimilated observations for a comprehensive integration of the regional climate of the Arctic for 2000-12.
Abstract: The Arctic is a vital component of the global climate, and its rapid environmental evolution is an important element of climate change around the world. To detect and diagnose the changes occurring to the coupled Arctic climate system, a state-of-the-art synthesis for assessment and monitoring is imperative. This paper presents the Arctic System Reanalysis, version 2 (ASRv2), a multiagency, university-led retrospective analysis (reanalysis) of the greater Arctic region using blends of the polar-optimized version of the Weather Research and Forecasting (Polar WRF) Model and WRF three-dimensional variational data assimilated observations for a comprehensive integration of the regional climate of the Arctic for 2000–12. New features in ASRv2 compared to version 1 (ASRv1) include 1) higher-resolution depiction in space (15-km horizontal resolution), 2) updated model physics including subgrid-scale cloud fraction interaction with radiation, and 3) a dual outer-loop routine for more accurate data assimi...

91 citations


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TL;DR: The American Meteorological Society (AMS) as discussed by the authors has a policy that any use of material in this work that is determined to be fair use under Section 107 of the U.S. Copyright Act (17 USC §108) does not require the AMS's permission.
Abstract: © Copyright 19 June 2019 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center ( http://www.copyright.com ). Questions about permission to use materials for which AMS holds the copyright can also be directed to permissions@ametsoc.org. Additional details are provided in the AMS Copyright Policy statement, available on the AMS website ( http://www.ametsoc.org/CopyrightInformation ).

120 citations

Journal ArticleDOI
TL;DR: The first atmospheric regional reanalysis for Australia (BARRA-R) was published in 2019 by the Bureau of Meteorology Atmospheric high-resolution (B BARRA).
Abstract: . The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) is the first atmospheric regional reanalysis over a large region covering Australia, New Zealand, and Southeast Asia. The production of the reanalysis with approximately 12 km horizontal resolution – BARRA-R – is well underway with completion expected in 2019. This paper describes the numerical weather forecast model, the data assimilation methods, the forcing and observational data used to produce BARRA-R, and analyses results from the 2003–2016 reanalysis. BARRA-R provides a realistic depiction of the meteorology at and near the surface over land as diagnosed by temperature, wind speed, surface pressure, and precipitation. Comparing against the global reanalyses ERA-Interim and MERRA-2, BARRA-R scores lower root mean square errors when evaluated against (point-scale) 2 m temperature, 10 m wind speed, and surface pressure observations. It also shows reduced biases in daily 2 m temperature maximum and minimum at 5 km resolution and a higher frequency of very heavy precipitation days at 5 and 25 km resolution when compared to gridded satellite and gauge analyses. Some issues with BARRA-R are also identified: biases in 10 m wind, lower precipitation than observed over the tropical oceans, and higher precipitation over regions with higher elevations in south Asia and New Zealand. Some of these issues could be improved through dynamical downscaling of BARRA-R fields using convective-scale ( km) models.

73 citations

Journal ArticleDOI
TL;DR: In this article, the authors compared four datasets (Cloud, Albedo, Radiation dataset Edition 2 (CLARA), Surface Solar Radiation dataset (SARAH), ECMWF Reanalysis 5 (ERA5) and Arctic System Reanalysis v2 (ASR) on high latitude locations.

68 citations

Journal ArticleDOI
TL;DR: In this article, the authors compared the performance of the ERA5 reanalysis over the Greenland Ice Sheet (GrIS) to the Arctic System Reanalysis (ASR) and a state-of-the-art polar regional climate model (MAR), and found that ERA5 does not outperform MAR significantly when compared with near-surface climate observations over GrIS.
Abstract: . The ERA5 reanalysis, recently made available by the European Centre for Medium-Range Weather Forecasts (ECMWF), is a new reanalysis product at a high resolution replacing ERA-Interim and is considered to provide the best climate reanalysis over Greenland to date. However, so far little is known about the performance of ERA5 over the Greenland Ice Sheet (GrIS). In this study, we compare the near-surface climate from the new ERA5 reanalysis to ERA-Interim, the Arctic System Reanalysis (ASR) as well as to a state-of-the-art polar regional climate model (MAR). The results show (1) that ERA5 does not outperform ERA-Interim significantly when compared with near-surface climate observations over GrIS, but ASR better models the near-surface temperature than both ERA reanalyses. (2) Polar regional climate models (e.g., MAR) are still a useful tool to downscale the GrIS climate compared to ERA5, as in particular the near-surface temperature in summer has a key role for representing snow and ice processes such as the surface melt. However, assimilating satellite data and using a more recent radiative scheme enable both ERA and ASR reanalyses to represent more satisfactorily than MAR the downward solar and infrared fluxes. (3) MAR near-surface climate is not affected when forced at its lateral boundaries by either ERA5 or ERA-Interim. Therefore, forcing polar regional climate models with ERA5 starting from 1950 will enable long and homogeneous surface mass balance reconstructions.

55 citations