Meteorological analysis of the Banjarnegara Landslide on 12 December 2014
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2005 more than 50 of about 1429 disasters in Indonesia are related to hydro-meteorology, with landslide accounting for 16 Bappenas Bakornas PB, 2006. Therefore, analysis of the
meteorological factor on a landslide event is as important as analysing the geological factor. An accurate prediction of the weather on sites that are prone to landslide can save property
as well as human lives considerably.
Banjarnegara is surrounded by mountains and hills. The area has high rainfall records, especially during the rainy season. These features make Banjarnegara vulnerable to
landslides. There have been 22 landslide events in Banjarnegara between 2005 and 2014 Firdaus, 2014. The latest landslide in Banjarnegara is one of many such events in Indonesia
that had many casualties. A landslide event is difficult to predict. One way to mitigate the impact of a landslide is to identify the meteorological condition, as variations in rainfall and
potential evaporations control indirectly control the initiation and movement of a landslide occurrence Coe Godt, 2012. In Sipayung et al. 2014, landslide is triggered by the rainfall
accumulation 15 days and 3 days before the event. This paper presents the analysis of the meteorological condition before the landslide in Banjarnegara that occurred on 12 December
2014.
2. Data and method
2.1 Data
This paper uses data from observations and model outputs. Rainfall observations are taken from the Climate Prediction Center CPC National Oceanic and Atmospheric
Administration NOAA in the form of CMORPH CPC MORPHing technique data Joyce et al., 2004. Cloud observations are derived from the second generation of Multi-functional
Transport Satellite MTSAT2, while the low-level wind fields are extracted from reanalysis data. CMORPH data are derived from low orbiter satellite microwave observations. The
precipitation estimates of CMORPH are generated from the combination of existing microwave rainfall algorithms. CMORPH data incorporate precipitation estimates from
several different satellites. The CMORPH data used in this paper have temporal resolution of 3 hours and spatial resolution of 0.25 degrees.
MTSAT2 is a geostationary satellite, which means it is located fixed above some position on the Earths surface. MTSAT2 is positioned 35,800 km above equator at 145⁰E.
This satellite is intended to improve the meteorological services, such as weather forecasts, natural-disaster countermeasures, and securing safe transportation, covering East Asia and
the Western Pacific Region. It has five channels, four in the infrared regimes and one channel in the visible spectrum. In this paper, data from the first and second infrared channels IR1
and IR2 are used to derive the cloud types. These infrared data have a temporal resolution of one hour and a spatial resolution of 5 km.
The reanalysis data are obtained from the European Centre for Medium-Range Weather Forecasts ECMWF. The data used in this paper are called the ECMWF Reanalysis
ERA Interim, which is the latest generation of reanalysis from ECMWF. ERA Interim is the intermediate product bridging between ERA-40 1957-2002 and the next-generation
extended reanalysis. The improvement of ERAI dataset over its predecessor is obtained through many model improvements, the use of 4-dimensional variational analysis, a revised
humidity analysis, the use of variational bias correction for satellite data, and other improvements in bias handling Dee et al., 2011. These ERA Interim data have a temporal
resolution of 6 hours and a spatial resolution of 0.125 degrees or about 0.1375 km.
Suaydhi, B. Siswanto
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Results from the outputs of a climate model are also used in this paper. The simulation is used to show the heavy rainfall that hit Karangkobar area in Banjarnegara around the time
of the landslide event. The model used for the simulation of the atmospheric condition is the quasi-uniform Conformal-Cubic Atmospheric Model, known as C-CAM McGregor Dix,
2008. C-CAM is a hydrostatic model with two-time-level semi-implicit time differencing, employing semi-Lagrangian advection with bicubic horizontal interpolation on unstaggered
grid. It can be used to simulate a specific region with high resolution for example: 5 km x 5 km, while a coarser grids are used outside that region.
2.2 Method
The brightness variations obtained from the sensors of MTSAT are converted to the equivalent blackbody temperatures on the top of clouds using a calibration scale provided by
the Japan Meteorological Agency JMA as the owner of the satellite. A two-dimensional threshold diagram 2d-THR from Suseno Yamada 2012 is used to classify the cloud type.
Using this method the cumulonimbus cloud is identified. Cumulonimbus is a cloud type that is associated to a heavy rainfall.
3. Results and discussion
