Violeta E. Toma

Were the 2010 Pakistan floods predictable

During July 2010, a series of monsoonal deluges over northern Pakistan resulted in catastrophic flooding, loss of life and property and an agricultural crisis that may last for years. Was the rainfall abnormal compared to previous years? Furthermore, could a high probability of flooding have been predicted? To address these questions, regional precipitation is analyzed using three dataset sets covering the 1981–2010 time period. It is concluded that the 2010 average May to August (MJJA) rainfall for year 2010 is somewhat greater in magnitude than previous years. However, the rainfall rate of the July deluges, especially in North Pakistan was exceptionally rare as deduced from limited data. The location of the deluges over the mountainous northern part of the country lead to the devastating floods. The European Centre for Medium Range Weather Forecasts (ECMWF) 15-day Ensemble Prediction System (EPS) is used to assess whether the rainfall over the flood affected region was predictable. A multi-year analysis shows that Pakistan rainfall is highly predictable out to 6–8 days including rainfall in the summer of 2010. We conclude that if these extended quantitative precipitation forecasts had been available in Pakistan, the high risk of flooding could have been foreseen. If these rainfall forecasts had been coupled to a hydrological model then the high risk of extensive and prolonged flooding could have anticipated and actions taken to mitigate their impact.

Asian summer monsoon prediction in ECMWF System 4 and NCEP CFSv2 retrospective seasonal forecasts

The seasonal prediction skill of the Asian summer monsoon is assessed using retrospective predictions (1982–2009) from the ECMWF System 4 (SYS4) and NCEP CFS version 2 (CFSv2) seasonal prediction systems. In both SYS4 and CFSv2, a cold bias of sea-surface temperature (SST) is found over the equatorial Pacific, North Atlantic, Indian Oceans and over a broad region in the Southern Hemisphere relative to observations. In contrast, a warm bias is found over the northern part of North Pacific and North Atlantic. Excessive precipitation is found along the ITCZ, equatorial Atlantic, equatorial Indian Ocean and the maritime continent. The southwest monsoon flow and the Somali Jet are stronger in SYS4, while the south-easterly trade winds over the tropical Indian Ocean, the Somali Jet and the subtropical northwestern Pacific high are weaker in CFSv2 relative to the reanalysis. In both systems, the prediction of SST, precipitation and low-level zonal wind has greatest skill in the tropical belt, especially over the central and eastern Pacific where the influence of El Nino-Southern Oscillation (ENSO) is dominant. Both modeling systems capture the global monsoon and the large-scale monsoon wind variability well, while at the same time performing poorly in simulating monsoon precipitation. The Asian monsoon prediction skill increases with the ENSO amplitude, although the models simulate an overly strong impact of ENSO on the monsoon. Overall, the monsoon predictive skill is lower than the ENSO skill in both modeling systems but both systems show greater predictive skill compared to persistence.

Predictability and Prediction Skill of the MJO in Two Operational Forecasting Systems

AbstractThe authors examine the predictability and prediction skill of the Madden–Julian oscillation (MJO) of two ocean–atmosphere coupled forecast systems of ECMWF [Variable Resolution Ensemble Prediction System (VarEPS)] and NCEP [Climate Forecast System, version 2 (CFSv2)]. The VarEPS hindcasts possess five ensemble members for the period 1993–2009 and the CFSv2 hindcasts possess three ensemble members for the period 2000–09. Predictability and prediction skill are estimated by the bivariate correlation coefficient between the observed and predicted Wheeler–Hendon real-time multivariate MJO index (RMM). MJO predictability is beyond 32 days lead time in both hindcasts, while the prediction skill is about 27 days in VarEPS and 21 days in CFSv2 as measured by the bivariate correlation exceeding 0.5. Both predictability and prediction skill of MJO are enhanced by averaging ensembles. Results show clearly that forecasts initialized with (or targeting) strong MJOs possess greater prediction skill compared to...

Development and implementation of South Asia's first heat-health action plan in Ahmedabad (Gujarat, India).

Recurrent heat waves, already a concern in rapidly growing and urbanizing South Asia, will very likely worsen in a warming world. Coordinated adaptation efforts can reduce heat’s adverse health impacts, however. To address this concern in Ahmedabad (Gujarat, India), a coalition has been formed to develop an evidence-based heat preparedness plan and early warning system. This paper describes the group and initial steps in the plan’s development and implementation. Evidence accumulation included extensive literature review, analysis of local temperature and mortality data, surveys with heat-vulnerable populations, focus groups with health care professionals, and expert consultation. The findings and recommendations were encapsulated in policy briefs for key government agencies, health care professionals, outdoor workers, and slum communities, and synthesized in the heat preparedness plan. A 7-day probabilistic weather forecast was also developed and is used to trigger the plan in advance of dangerous heat waves. The pilot plan was implemented in 2013, and public outreach was done through training workshops, hoardings/billboards, pamphlets, and print advertisements. Evaluation activities and continuous improvement efforts are ongoing, along with plans to explore the program’s scalability to other Indian cities, as Ahmedabad is the first South Asian city to address heat-health threats comprehensively.

MJO Propagation across the Maritime Continent in the ECMWF Ensemble Prediction System

AbstractThe characteristics of the MJO propagation across the Maritime Continent are investigated using a 20-yr reforecast dataset from the ECMWF ensemble prediction system. Analysis of the MJO events initialized over the Indian Ocean (phase 2) shows that the initial MJO amplitude and prediction skill relationship is not linear, particularly when the predictions start in moderate (between strong and weak) MJO amplitude category. To examine the key factors that determine the prediction skill, reforecasts in the moderate category are grouped into high- and low-skill events, and the differences in their ocean–atmospheric conditions as well as the physical processes during reforecast period are examined. The initial distribution of OLR anomalies in high-skill events shows a clear dipole pattern of convection with an enhanced convective anomalies over the Indian Ocean and strongly suppressed convective anomalies in the western Pacific Ocean. This dipole mode may support the MJO propagation across the Maritime ...

An Atmospheric–Hydrologic Forecasting Scheme for the Indus River Basin

AbstractSouth Asia is vulnerable to extreme weather events, and the Indus River basin (IRB) is subject to flood risk. Flooding occurred in Pakistan during the monsoon seasons from 2010 to 2012, causing major economic damage and significant loss of life. The nature of slow-rise discharge on the Indus and overtopping of riverbanks in the IRB indicates the need for extended warning time (1–10 days). Daily mean streamflow at Tarbela Dam, a major control structure on the Indus River, is reproduced by using numerical weather prediction initialization data from the ECMWF to drive the Variable Infiltration Capacity macroscale hydrologic model. A one-dimensional routing model conducts base flow and surface runoff from each grid cell through a stream network. Comparisons of reconstructions with inflow data at Tarbela Dam over a 3-yr period yield an R2 correlation of 0.93 and RMSE of 692 m3 s−1. From 2010 to 2012, 276 daily ensemble hindcasts are generated. In comparing the ensemble mean of 10-day hindcasts to recon...