ORCID Profile
0000-0002-8299-9799
Current Organisations
University of Melbourne
,
Institut Pertanian Bogor
,
Wageningen University
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Publisher: Pusat Penelitian dan Pengembangan Badan Meteorologi Klimatologi dan Geofisika
Date: 15-08-2014
Publisher: American Meteorological Society
Date: 08-2019
Abstract: Dynamical seasonal forecasts are afflicted with biases, including seasonal ensemble precipitation forecasts from the new ECMWF seasonal forecast system 5 (SEAS5). In this study, biases have been corrected using empirical quantile mapping (EQM) bias correction (BC). We bias correct SEAS5 24-h rainfall accumulations at seven monthly lead times over the period 1981–2010 in Java, Indonesia. For the observations, we have used a new high-resolution (0.25°) land-only gridded rainfall dataset [Southeast Asia observations (SA-OBS)]. A comparative verification of both raw and bias-corrected reforecasts is performed using several verification metrics. In this verification, the daily rainfall data were aggregated to monthly accumulated rainfall. We focus on July, August, and September because these are agriculturally important months if the rainfall accumulation exceeds 100 mm, farmers may decide to grow a third rice crop. For these months, the first 2-month lead times show improved and mostly positive continuous ranked probability skill scores after BC. According to the Brier skill score (BSS), the BC reforecasts improve upon the raw reforecasts for the lower precipitation thresholds at the 1-month lead time. Reliability diagrams show that the BC reforecasts have good reliability for events exceeding the agriculturally relevant 100-mm threshold. A cost/loss analysis, comparing the potential economic value of the raw and BC reforecasts for this same threshold, shows that the value of the BC reforecasts is larger than that of the raw ones, and that the BC reforecasts have value for a wider range of users at 1- to 7-month lead times.
Publisher: Wiley
Date: 14-06-2022
Publisher: American Geophysical Union (AGU)
Date: 11-11-2022
DOI: 10.1029/2022GL101513
Abstract: A record‐breaking extreme rainfall event, the highest amount recorded since 1866, hit Indonesia's capital, Jakarta, in early January 2020. This torrential rainfall was mainly caused by the convergence of moisture‐rich air due to an unusual blocking of cross‐equatorial northerly surge over Northwest Java by the southerly winds induced by a cyclonic flow over the Indian Ocean. This condition caused a local increase in the amount of water vapor over Jakarta that eventually led to the formation of clouds and heavy precipitation. In addition, the concurrent occurrences of convectively active phases of equatorial waves (Kelvin, TD‐type, and eastward propagating inertia‐gravity waves) and Madden‐Julian Oscillation during the event also partly contributed to the enhanced local moisture over the region by increasing low‐level moisture flux convergence. Together, these large‐scale dynamical drivers provided a convective environment that fostered the development of a massive rain‐producing mesoscale convective system and, consequently, extreme rainfall over the region.
Publisher: Pusat Penelitian dan Pengembangan Badan Meteorologi Klimatologi dan Geofisika
Date: 15-08-2015
Publisher: Indonesian Agency For Agricultural Research and Development (IAARD)
Date: 07-02-2021
Publisher: Wiley
Date: 14-06-2022
Publisher: MDPI AG
Date: 11-07-2022
Abstract: Unusually long duration and heavy rainfall from 5 to 6 February 2021 caused widespread and devastating floods in Semarang, Central Java, Indonesia. The heavy rainfall was produced by two mesoscale convective systems (MCSs). The first MCS developed at 13Z on 5 February 2021 over the southern coast of Sumatra and propagated towards Semarang. The second MCS developed over the north coast of Semarang at 18Z on 5 February 2021 and later led to the first peak of precipitation at 21Z on 5 February 2021. These two MCSs eventually merged into a single MCS, producing the second peak of precipitation at 00Z on 6 February 2021. Analysis of the moisture transport indicates that the strong and persistent north-westerly wind near the surface induced by CENS prior to and during the event created an intensive meridional (southward) tropospheric moisture transport from the South China Sea towards Semarang. In addition, the westerly flow induced by low-frequency variability associated with La Nina and the tropical depression over the North of Australia produced an intensive zonal (eastward) tropospheric moisture transport from the Indian Ocean towards Semarang. The combined effects of the zonal and meridional moisture transport provided favorable conditions for the development of MCSs, and hence extreme rainfall over Semarang. These results provide useful precursors for extreme weather-driven hazard prediction in Semarang and the surrounding regions in the future.
Publisher: MDPI AG
Date: 30-08-2022
Abstract: Nusantara, Indonesia’s new capital city, experienced a rare extreme rainfall event on 27–28 August 2021. This heavy rainfall occurred in August, the driest month of the year based on the monthly climatology data, and caused severe flooding and landslides. To better understand the underlying mechanisms for such extreme precipitation events, we investigated the moisture sources and transport processes using the Lagrangian model HYSPLIT. Our findings revealed that moisture was mostly transported to Nusantara along three major routes: from Borneo Island (BRN, 53.73%), the Banda Sea and its surroundings (BSS, 32.03%), and Sulawesi Island (SUL, 9.05%). Overall, BRN and SUL were the main sources of terrestrial moisture, whereas the BSS was the main oceanic moisture source, having a lower contribution than its terrestrial counterpart. The terrestrial moisture transport from BRN was mainly driven by the large-scale high vortex flow, whereas the moisture transport from the SUL was driven by the circulation induced by boreal summer intraseasonal oscillation (BSISO) and low-frequency variability associated with La Niña. The near-surface oceanic moisture transport from BSS is primarily associated with prevailing winds due to the Australian monsoon system. These insights into moisture sources and pathways can potentially improve the accuracy of predictions of summer precipitation extremes in Indonesia’s new capital city, Nusantara, and benefit natural resource managers in the region.
Publisher: American Meteorological Society
Date: 28-05-2021
Abstract: The seasonal precipitation forecast is one of the essential inputs for economic and agricultural activities and has significant impact on decision making. Large-scale modes of climate variability have strong relationships with seasonal rainfall in Java and are natural candidates for use as potential predictors in a statistical post-processing application. We explore whether using climate indices as additional predictors in the statistical post-processing of ECMWF Seasonal Forecast System 5 (SEAS5) precipitation can improve skill. We use parametric statistical post-processing by applying a logistic distribution-based Ensemble Model Output Statistics (EMOS) technique. We add a variety of potential predictors in the analysis, namely SEAS5 raw and Empirical Quantile Mapping (EQM) bias-corrected precipitation, Nino3.4 index, Dipole Mode Index (DMI), Madden Julian Oscillation (MJO) indices, Sea Surface Temperature (SST) around Java, and several other predictors. We analyze the period of 1981-2010, focusing on July, August, September, and October. We use the Continuous Ranked Probability Skill Score (CRPSS) and Brier Skill Score (BSS) in a comparative verification of raw, EQM and EMOS seasonal precipitation forecasts. We have found that it is essential to use EQM-corrected precipitation as a predictor instead of raw precipitation in the latter. Besides, Nino3.4 and DMI forecasts are not needed as extra predictors to improve monthly precipitation forecasts for the first lead month, except for September. However, for somewhat longer lead months, in September and October when there is more skill than climatology, the model that includes only Nino3.4 and DMI forecasts as potential predictors performs about the same compared to the model that uses only EQM-corrected precipitation as a predictor.
No related grants have been discovered for Dian Nur Ratri.