Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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Classification of Precipitation Type and its Diurnal Cycle in REMO
Simulation and in Observations
Andi Walther 1 , Ralf Bennartz 1, 2 , Daniela Jacob 3 and Jürgen Fischer 1
1
Institut für Weltraumwissenschaften, Freie Universität Berlin,Carl-Heinrich-Beckerweg 6-10, D-12165 Berlin, Germany
2
Atmospheric & Oceanic Sciences, University of Wisconsin, Madison, WI
3
Max Planck Institut für Meteorologie, Hamburg, Germany
E-mail: andi.walther@wew.fu-berlin.de
1. Introduction
The overall objective of the BALTIMOS project is the
development of a coupled model system for the Baltic Sea
and its catchment basin in order to understand and model
exchange processes between atmosphere, sea, land surface,
and lakes including hydrology. BALTIMOS is a
contribution to the BMBF research program DEKLIM.
Observations and modeling of spatial and temporal
variability of precipitation are an important factor for
understanding the water cycle. Sumner (1988) classifies
precipitation as an expression of origin and considered three
main types: convectional, cyclonic and orographic. One
focal point of this presentation is the application of a method
to divide precipitation in frontal and non-frontal fraction.
Models simulate the diurnal cycle of precipitation often
wrongly. Operational models tend to produce the maximum
of precipitation over land at about local noon, corresponding
to the time of maximum heating (Trenberth et al. 2003).
Observations have shown that this timing is a few hours
before. One further goal of this study is to compare the
diurnal cycle of REMO simulations with observations from
radar data.
2. Regional model REMO
The regional climate model REMO that will be used in this
study is developed by the Max Planck Institut for
Meteorology (MPI) in Hamburg (e.g. Jacob and
Claussen,M.. (1995)). It is a three-dimensional, hydrostatic
atmosphere model, that is based on the DWD physical
parameterization model. The horizontal resolution is 1/6° or
in a grid size equal of about 18 x 18 km². Our investigations
were processed in the “climate” modus.
3. Radar Data Source
The primary data used in our study are the BALTRAD
composite radar reflectivity data set. The properties of this
product include 2 km spatial grid with 15 minutes temporal
resolution and 8-bit dBZ converter. The BALTRAD data are
gauge-adjusted and can be transformed into rain intensity
information with two fixed seasonal-dependent Z-R
relations. This procedure was undertaken by the Swedish
Weather service (SMHI) and introduced by Michelson et al.
(1999).
In order to compare with REMO output it was necessary to
transform the data from a 2 km spatial grid into REMO grid
(1/6 ° - about 18 km spatial resolution). The rain rates of all
BRDC-pixel, rain or non-rain pixel inside a REMO pixel are
averaged.
4. Frontal/Non-frontal Distinction
The main attribute is to classify contiguous precipitation
systems in contrast to classification of each individual pixel
in other investigations (e.g. Steiner et al. (1995)). That
means, that each pixel of a contiguous rain area gets a
common classification, frontal or non-frontal. Threshold for
rain pixel was defined as 0.2 mm/hr. Any rain areas
smaller than 4,000 km² are initially considered, by virtue
of their space scale, to be non-frontal. Larger echoes are
subject of a classification algorithm with help of an
artificial neural network (ANN) algorithm. Input
parameters of the ANN are 9 texture and shape
information. Texture information is a result of statistical
analysis of spatial rain rate differences and gives a hint of
homogeneity and the inner structure of the rain field.
Shape parameters are the size of the major axis, the
eccentricity and the size of the area. 400 scenes from the
available dataset of BALTRAD network were randomly
selected to classify manually with help by weather
reanalysis maps. Both, the calculated parameters of a rain
area, and the corresponding classification represent one
vector of the training dataset of the ANN.
The resulting algorithm are applied to BALTRAD as well
as REMO products. Figure 1 shows the fraction of frontal
rain events for BALTRAD data in 2000.
Figure 1. Fraction of frontal rain events of all rain events
in 2000. Incoherence in Middle Sweden and close to
Stockholm is due to bad quality of radar data at individual
radar sites.
5. Diurnal Cycle
Diurnal variations were analyzed by Fourier
decomposition. For expediency, both, the radar and
REMO products were grouped by the hour. Instead of
using the rain rate and averaging, the final field is the
fraction of time that a rain intensity exceeds a threshold of
0.2 mm/hr at each grid point.
The zeroth and first components of the discrete Fourier
decomposition corresponds to the daily mean and the
diurnal cycle of precipitation. This procedure was
undertaken for all precipitation events as well as separated
in precipitation type.