Implementation of Standardized Precipitation Index (SPI) in DEWS
BMKG, Jakarta, 09-11 June 2015
As part of the Joint Cooperation Programme (JCP), a long-term knowledge sharing and capacity building program between Dutch and Indonesian institutes working in the field of climate and water resources management, a Drought Early Warning System (DEWS) has been developed which is in operation since 2012 at BMKG. The development of the DEWS is part of JCP2 Sub-component D1 (Support for farmer services). The objective of this Sub-component is to extend the DEWS with seasonal climate forecasting capabilities and use the forecasts in the generation of base data to enable the upgrade of the currently available static cropping calendar (KATAM, Kalender Tanam, developed by the Indonesian Agency for Agricultural Research and Development, Balitbangtan, partner in JCP2) to a dynamic cropping calendar to support farmers in Indonesia.
As part of the knowledge transfer and capacity building component of JCP a technical training has been held on how to implement SPI into the DEWS. The objective of the training was for the trainees to be able to implement SPI in DEWS using various data sets (both grid as well as ground station data).
The technical DEWS training was hosted at the Climate Early Warning System (CEWS) room of the Climate, Agroclimate and Maritime Climate Division of BMKG in Jakarta from 9-11 June 2015. A total of 14 participants from BMKG and PusAir were trained by staff from Deltares (Ronald Vernimmen, Aditya Riski Taufani).
A training manual was prepared which described the exercises for the training. The training manual was set up in such a way that in principle all the exercises could be completed without the aid of a trainer. A basic background in the use of Delft-FEWS, its general structure and the use of xml configuration files was however a necessary pre-requisite to follow this training. Despite the fact that most of the participants were new to Delft-FEWS the lack in basic background was not an issue in this particular training.
Summary of the training
The training started with a short flashback of the previous training including some points from the evaluation of the previous training.
Some of the suggestions made during the previous evaluation were put into practice. During the training specific attention was paid to the analysis of errors encountered during the training process, so that in future the trainees are able to analyze and correct errors by themselves. To improve interaction between the participants and trainers after each exercise one of the participants were asked to present their experience with the exercise and discuss amongst the participants difficulties encountered (see below).
The first exercise concentrated on importing the TRMM dataset into DEWS. The exercise was intended to refresh the knowledge gained during the previous DEWS training.
After participants were able to import the data, the following exercises included the necessary preparation steps needed before the SPI calculation could be carried out and results imported into DEWS. Since in this training the TRMM dataset was used for SPI analysis, the second exercise was about filling the gap in TRMM and aggregate the 3 hourly data into daily and monthly rainfall.
The third exercise was about the bias correction of the TRMM dataset in DEWS. The bias correction is based on the published work of BMKG and Deltares (Vernimmen et al., 2012).
The fourth and last exercise is the implementation of SPI itself. The implementation of SPI is divided into some steps, which includes the conversion of TRMM grid datatype into scalar time series for each grid cell, export the input datasets into TSD format, run the SPI calculation script via DEWS, import the SPI result and then transform scalar SPI from scalar to grid again. For further analysis, the trainee also calculated average SPI on river basin scale. One of the participants calculated the average SPI on Zona Musim basis.
As the objective of this training was that the participants are able to calculate SPI on different datasets, advanced exercises were provided. The advanced exercise was designed so that the participant can develop their own configuration with only bulleted steps provided.
As mentioned above, an important new activity of this training compared to the previous training was the presentation and discussion session from the participants. After an exercise was successfully conducted, a participant will present his/her work, knowledge and problems. During this session, other participants also join the discussion to share their knowledge and encountered problems as well. When errors were encountered participants were encouraged to send their errors to the newly opened DEWS mailing list so these errors could be discussed within the group.