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Matthew P. Young, Charles J. R. Williams, J. Christine Chiu, Ross I. Maidment, and Shu-Hua Chen

Abstract

Tropical Applications of Meteorology Using Satellite and Ground-Based Observations (TAMSAT) rainfall estimates are used extensively across Africa for operational rainfall monitoring and food security applications; thus, regional evaluations of TAMSAT are essential to ensure its reliability. This study assesses the performance of TAMSAT rainfall estimates, along with the African Rainfall Climatology (ARC), version 2; the Tropical Rainfall Measuring Mission (TRMM) 3B42 product; and the Climate Prediction Center morphing technique (CMORPH), against a dense rain gauge network over a mountainous region of Ethiopia. Overall, TAMSAT exhibits good skill in detecting rainy events but underestimates rainfall amount, while ARC underestimates both rainfall amount and rainy event frequency. Meanwhile, TRMM consistently performs best in detecting rainy events and capturing the mean rainfall and seasonal variability, while CMORPH tends to overdetect rainy events. Moreover, the mean difference in daily rainfall between the products and rain gauges shows increasing underestimation with increasing elevation. However, the distribution in satellite–gauge differences demonstrates that although 75% of retrievals underestimate rainfall, up to 25% overestimate rainfall over all elevations. Case studies using high-resolution simulations suggest underestimation in the satellite algorithms is likely due to shallow convection with warm cloud-top temperatures in addition to beam-filling effects in microwave-based retrievals from localized convective cells. The overestimation by IR-based algorithms is attributed to nonraining cirrus with cold cloud-top temperatures. These results stress the importance of understanding regional precipitation systems causing uncertainties in satellite rainfall estimates with a view toward using this knowledge to improve rainfall algorithms.

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Felipe M. de Andrade, Matthew P. Young, David MacLeod, Linda C. Hirons, Steven J. Woolnough, and Emily Black

Abstract

This paper evaluates subseasonal precipitation forecasts for Africa using hindcasts from three models (ECMWF, UKMO, and NCEP) participating in the Subseasonal to Seasonal (S2S) prediction project. A variety of verification metrics are employed to assess weekly precipitation forecast quality at lead times of one to four weeks ahead (weeks 1–4) during different seasons. Overall, forecast evaluation indicates more skillful predictions for ECMWF over other models and for East Africa over other regions. Deterministic forecasts show substantial skill reduction in weeks 3–4 linked to lower association and larger underestimation of predicted variance compared to weeks 1–2. Tercile-based probabilistic forecasts reveal similar characteristics for extreme categories and low quality in the near-normal category. Although discrimination is low in weeks 3–4, probabilistic forecasts still have reasonable skill, especially in wet regions during particular rainy seasons. Forecasts are found to be overconfident for all weeks, indicating the need to apply calibration for more reliable predictions. Forecast quality within the ECMWF model is also linked to the strength of climate drivers’ teleconnections, namely, El Niño–Southern Oscillation, Indian Ocean dipole, and the Madden–Julian oscillation. The impact of removing all driver-related precipitation regression patterns from observations and hindcasts shows reduction of forecast quality compared to including all drivers’ signals, with more robust effects in regions where the driver strongly relates to precipitation variability. Calibrating forecasts by adding observed regression patterns to hindcasts provides improved forecast associations particularly linked to the Madden–Julian oscillation. Results from this study can be used to guide decision-makers and forecasters in disseminating valuable forecasting information for different societal activities in Africa.

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Matthew A. Lazzara, John M. Benson, Robert J. Fox, Denise J. Laitsch, Joseph P. Rueden, David A. Santek, Delores M. Wade, Thomas M. Whittaker, and J. T. Young

On 12 October 1998, it was the 25th anniversary of the Man computer Interactive Data Access System (McIDAS). On that date in 1973, McIDAS was first used operationally by scientists as a tool for data analysis. Over the last 25 years, McIDAS has undergone numerous architectural changes in an effort to keep pace with changing technology. In its early years, significant technological breakthroughs were required to achieve the functionality needed by atmospheric scientists. Today McIDAS is challenged by new Internet-based approaches to data access and data display. The history and impact of McIDAS, along with some of the lessons learned, are presented here.

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Nicholas P. Klingaman, Matthew Young, Amulya Chevuturi, Bruno Guimaraes, Liang Guo, Steven J. Woolnough, Caio A. S. Coelho, Paulo Y. Kubota, and Christopher E. Holloway

Abstract

Skillful and reliable predictions of week-to-week rainfall variations in South America, two to three weeks ahead, are essential to protect lives, livelihoods, and ecosystems. We evaluate forecast performance for weekly rainfall in extended austral summer (November–March) in four contemporary subseasonal systems, including a new Brazilian model, at 1–5-week leads for 1999–2010. We measure performance by the correlation coefficient (in time) between predicted and observed rainfall; we measure skill by the Brier skill score for rainfall terciles against a climatological reference forecast. We assess unconditional performance (i.e., regardless of initial condition) and conditional performance based on the initial phase of the Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO). All models display substantial mean rainfall biases, including dry biases in Amazonia and wet biases near the Andes, which are established by week 1 and vary little thereafter. Unconditional performance extends to week 2 in all regions except for Amazonia and the Andes, but to week 3 only over northern, northeastern, and southeastern South America. Skill for upper- and lower-tercile rainfall extends only to week 1. Conditional performance is not systematically or significantly higher than unconditional performance; ENSO and MJO events provide limited “windows of opportunity” for improved S2S predictions that are region and model dependent. Conditional performance may be degraded by errors in predicted ENSO and MJO teleconnections to regional rainfall, even at short lead times.

Open access