International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
Owing to the extreme cloudiness over Malaysia during heavy
down pour, this study envisages the development of a
Nowcasting system for the monitoring of Langat river
catchment (Fig 2). The operational coupling quantitative
precipitation forecasting (QPF) using cloud indexing and
modeling based techniques with Mike 11 hydrodynamic
oriented GIS in the bid to implement a fully automated
simulation and early warning system (Fig 3). According to
Karlsson (1999), Nowcasting defines forecasting in the
7
A
approximate range +0-9 hours from observation time.
However, due to data processing restrictions and present
deficiencies in the shortest numerical weather prediction
(NWP) model forecasts, special forecasting techniques are
necessary for bridging the gap in the knowledge of weather
evolution between the latest observation information and the
information available from NWP models. He concluded that the
challenge in Nowcasting is to utilize the latest observations
together with models to improve very short forecasts.
4 QPF Modeling (P CI.9.1) H&HD Modeling (Mike 11)
3 NOAA-AVHRR & Rainfall in -Hydrological Parameters
GMS-5 data ASCII Format Rainfall
| - QPF Models, Time Series data
ins of - Classifications -Hydraulic Parameters
ms c | - Computation Rain- Pre
: - Networ chainage
ement - Cross section£ R. Depth
itative | e - Runoff point,
paper
OAA- |
lentify |
umber
s been | N 1
s like 11 GIS (Arc View)
on the Catchment geometry Dev
xi2.5 Surface DEM Dev
3D built-up areas
Road Network overlay
Simulation
Water level profiling
system Runoff & flow simulation
at Visualization & interpretation of
ied in P
to. the flooding process
C ZZ
recasts
ending
sts and : i : ;
cs LS Fig 3. QPF& Hydrodynamic oriented GIS Flood Early Warning
| flood
arning.
of rain 2 Cloud base Modeling Techniques using NOAA-AVHRR rate and rain area in convective stratiform technique (CST).
is and Local clouds in IR 7B are screened to eliminate thin, non-
remote Satellite images are currently an in dispensable tool for precipitating clouds. The governing formulas are as follows:
itation forecasters to obtain a synoptic view of the distribution of cloud
of few and cloud structures over region. Over the years various
methods and techniques have been explored for rainfall
estimation from cloud cover . Arkin, (1979) described the cloud
indexing method to be based on the high correlations and
probability fraction of cloud cluster colder than 235 K in
infrared (IR). This model was initially developed for NOAA
data and later adopted for Geostationary images. Rain days are
identified from the occurrence of IR brightness temperature
(TB) below a threshold at given location. Details of this method
are given by (Arkin,(1979), Arkin and Janowiak,(1991), Ba and
Nicholson (1998), (Todd et al., 1995,1999).
The cloud model-based technique is another method that
introduces cloud physics into the retrieval process for a
quantitative improvement deriving from the overall better
physical description of the rain formation processes. Details of
these studies are found in (Gruber, (1973), Anagnostou et al.,
(1999), Reudenbach et al., (2001), Bendix, (1997, 2002)). This
one dimensional cloud model, relates cloud temperature to rain-
721
Slope S is calculated for each temperature minimum T min-
Parameters defined as
S7 T 1-6 - Tmin. Tmin is Ave Temp of six closest pixels.
The rain area (Ar) is assumed to be five times the model up
Ar= Smr2 . Rmean = VRR/Ar,, Rmean= 74.89-0.266 Te, Ar =
exp(15.27-0.0465T;)
Our study area, the Langat river basin catchment area is about
1,988 km? The average annual rainfall depth is approximately
2,400 mm ranging from 1,800 to 3,000 mm. The highest
rainfall occurs in the month of April and November with a
mean of 280 mm. The lowest rainfall occurs in the month of
June with a mean of 115 mm. The wet seasons occur in the
transitional periods of the monsoons, from March to April and
from October to November. In this study NOAA-AVHRR data
was used because of moderately good spatial resolution of
1.1km and because of it repeated coverage of about 6 hr daily,
