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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
Samantha Ferrett
,
Thomas H. A. Frame
,
John Methven
,
Christopher E. Holloway
,
Stuart Webster
,
Thorwald H. M. Stein
, and
Carlo Cafaro

Abstract

Forecasting rainfall in the tropics is a major challenge for numerical weather prediction. Convection-permitting (CP) models are intended to enable forecasts of high-impact weather events. Development and operation of these models in the tropics has only just been realized. This study describes and evaluates a suite of recently developed Met Office Unified Model CP ensemble forecasts over three domains in Southeast Asia, covering Malaysia, Indonesia, and the Philippines. The fractions skill score is used to assess the spatial scale dependence of skill in forecasts of precipitation during October 2018–March 2019. CP forecasts are skillful for 3-h precipitation accumulations at spatial scales greater than 200 km in all domains during the first day of forecasts. Skill decreases with lead time but varies depending on time of day over Malaysia and Indonesia, due to the importance of the diurnal cycle in driving rainfall in those regions. Skill is largest during daytime when precipitation is over land and is constrained by orography. Comparison of CP ensembles using 2.2-, 4.5-, and 8.8-km grid spacing and an 8.8-km ensemble with parameterized convection reveals that varying resolution has much less effect on ensemble skill and spread than the representation of convection. The parameterized ensemble is less skillful than CP ensembles over Malaysia and Indonesia and more skillful over the Philippines; however, the parameterized ensemble has large drops in skill and spread related to deficiencies in its diurnal cycle representation. All ensembles are underspread indicating that future model development should focus on this issue.

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