Search Results
Abstract
The tropical west Pacific Ocean and the Philippines are often affected by tropical cyclones (TCs), with threats to human life and of severe economic damage. The performance of the Met Office global operational forecasts at predicting TC-related precipitation is examined between 2006 and 2017, the first time total TC rainfall has been analyzed in a long-term forecast dataset. All precipitation falling within 5° of a TC track point is assumed to be part of the TC rainbands. Forecasts are verified against TC tracks from the JRA-55 reanalysis and precipitation from TRMM 3B42. In composites from the forecasts, the total precipitation (TC and non-TC) is too high and the TC-related precipitation is too low, over both ocean and the Philippines. These biases exist all year-round and generally worsen with lead time, but have improved in recent years with upgrades to the forecasting system. Biases in TC-related precipitation in the Philippines are attributable mainly to TC lifetime being too short over land and ocean and (over land) possibly to individual TCs producing too little rain. There are considerable biases in predicted large-scale conditions related to TC intensification, particularly too little lower-troposphere relative humidity and too strong vertical wind shear. The shear appears to have little impact on the amount of TC precipitation, but dry biases in humidity are consistent with dry biases in TC rainfall. The forecast system accurately reproduces the impact of the MJO on TC precipitation, relative to the forecasts’ own climatology, potentially providing the opportunity for predictability out to several weeks.
Abstract
The tropical west Pacific Ocean and the Philippines are often affected by tropical cyclones (TCs), with threats to human life and of severe economic damage. The performance of the Met Office global operational forecasts at predicting TC-related precipitation is examined between 2006 and 2017, the first time total TC rainfall has been analyzed in a long-term forecast dataset. All precipitation falling within 5° of a TC track point is assumed to be part of the TC rainbands. Forecasts are verified against TC tracks from the JRA-55 reanalysis and precipitation from TRMM 3B42. In composites from the forecasts, the total precipitation (TC and non-TC) is too high and the TC-related precipitation is too low, over both ocean and the Philippines. These biases exist all year-round and generally worsen with lead time, but have improved in recent years with upgrades to the forecasting system. Biases in TC-related precipitation in the Philippines are attributable mainly to TC lifetime being too short over land and ocean and (over land) possibly to individual TCs producing too little rain. There are considerable biases in predicted large-scale conditions related to TC intensification, particularly too little lower-troposphere relative humidity and too strong vertical wind shear. The shear appears to have little impact on the amount of TC precipitation, but dry biases in humidity are consistent with dry biases in TC rainfall. The forecast system accurately reproduces the impact of the MJO on TC precipitation, relative to the forecasts’ own climatology, potentially providing the opportunity for predictability out to several weeks.
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.
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.
