MALAYSIAN METEOROLOGICAL DEPARTMENT
MINISTRY OF SCIENCE, TECHNOLOGY AND INNOVATION
Variational Data Assimilation of Doppler Radar Reflectivity Data into WRFV3.2
Kumarenthiran Subramaniam, Ling Leong Kwok and Wan Azli Wan Hassan
ABSTRACT
Radar reflectivity data assimilation into Numerical Weather Prediction models has been adopted
in most advanced Meteorological and Hydrological Services worldwide to improve weather
forecasts. The enhancement of the radar network to 12 Doppler radars over Malaysia has been
the main motivation of this study. The WRFV3.2 was chosen for this purpose. The radar data
was assimilated by using 3DVAR technique based on the Grid point Statistical Interpolation
(GSI) package from National Center for Atmospheric Research (NCAR), USA. Experiments
were carried out with and without radar data for the period 01 October 2011 – 31 October 2011.
All runs were initialized at 12UTC and run for a forecast range of 12 hours. Results indicate an
improvement for 0-12 hour forecast for the month of October 2011, however further tests on
individual cases of extreme events and other months are required. Radar data quality control
remains a challenge and needs to be improved.
1.
Introduction
Assimilation of high resolution Doppler radar observations has long been recognized as
an efficient way to improve short-range quantitative precipitation forecasting (QPF). The
potential of improving QPF arises from a combination of developments. NWP models from
agencies such as the Japan Meteorological Agency (JMA) and Korea Meteorological
Administration (KMA) are now running routinely at resolutions of the order of 2km. Such high
resolution models require correspondingly high resolution wind and moisture data for
initialization, which radar networks are well placed to deliver. Secondly NWP data assimilation
techniques have advanced considerably in the 1990’s, with the arrival of techniques capable of
extracting information from time sequences of observations only indirectly related to model
prognostic variables. The next few years are likely to see further improvements in computing
power, microphysical parameterization and assimilation methods which will enable better
exploitation of the information available from weather radars. Thirdly, developments in radar
networking and processing are beginning to reach a maturity which makes feasible the routine
operational delivery of quality controlled radar information of accuracy sufficient for worthwhile
NWP assimilation.
Since the enhancement of the Doppler radar network over Malaysia in
2011 by the Malaysian Meteorological Department (MMD) various methods to assimilate
Doppler radar data into the Weather Research and Forecasting (WRF) Model has been explored.
The main purpose is to enhance to improve the current model performance and aid weather
forecasters to improve the forecast skill.
2.
Doppler Radar Network over Malaysia
In 2011, the Malaysian Meteorological Department enhanced the existing radar network
by installing 6 S-Band Doppler radars over Peninsula. Over Sabah and Sarawak, 4 C-band and 1
S-band (Miri) Doppler radar was installed. Figure 1 shows the Doppler radar network over
Malaysia.
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Figure1: Doppler radar network over Malaysia
3.
Methodology
3.1
Overview
In this study, the Doppler radar reflectivity is assimilated via variational assimilation
using the modified Community Gridpoint Statistical Interpolation (GSI) system. The variational
assimilation system updates the NWP model control variables. The GSI system is a variational
data assimilation system capable of 3DVAR data assimilation. GSI is designed to be a flexible,
state-of-art system that is efficient on available parallel computing platforms. The GSI system is
in the public domain and is freely available for community use. This package was developed at
the Development Test bed Centre (DTC), National Centre for Atmospheric Research (NCAR)
USA. However modifications are required to use this package for Doppler radar reflectivity data
assimilation. The NWP model used was the WRFV3.2 developed at NCAR as well. Table 1
indicates the domain configuration for WRFV3.2 model. Table 1 indicates the model physics and
general configuration information.
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Figure 2: WRF domain configuration
Table 1: WRFV3.2 model configuration
Boundary Condition Data
GFS forecasts data with a horizontal resolution of 0.5º
(Gaussian grid of 768 X 384, equivalent to 0.5 X 0.5 degree
latitude/longitude) and a 6-hour time interval between two
consecutive forecast outputs.
Horizontal resolution
36km, 12km and 4km
Cumulus parameterization
36 km (Betts-Miller-Janjic ), 12km (Grell – Devenyi)
4km(KF-ETA)
PBL Scheme
Yonsei University(YSU)
Surface Layer Physics
Monin-Obukhov (Janjic) scheme
Forecast length
3 days (72 hours)
Forecast time
(12UTC)
Forecast range
12-hour
Forecast Interval
3 hour
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3.2
Radar Reflectivity Assimilation
In order to assimilate radar data in NWP models, the reflectivity values observed at a
certain range and height have to be converted to rainfall rates at ground level. This conversion
introduces errors in the resulting rainfall map because of the variability of the Z-R relationship,
bright band effects, vertical reflectivity profile and incomplete beam filling. Joss and Germann
(2000) have given an extensive overview of the problems and solutions when applying
qualitative and quantitative information from weather radar. The variability of the Z-R
relationship originates from differences in the droplet-spectrum which depends on the
precipitation situation and climatological circumstances. Although there are many different Z-R
relationships as described in Collier, (1989), the Z-R relationship, Z = 200R1.6 is widely accepted
and is also selected to be used in this study.
Operational radar-based rainfall estimation generally uses correction for the vertical
profile of reflectivity (including bright band effects) and adjustment to gauge accumulations
(Joss and Lee, 1995; Harrison et al., 2000). Even after these corrections, the mean difference
between radar-based rainfall estimates and gauge accumulations will typically still be a factor of
two. Representativeness errors, however, make up a significant part of this difference (Harrison
et al., 2000). To keep the radar rainfall estimates as accurate as possible, the operational radar
systems are adjusted using rain gauge measurements on a regular basis. The precipitation
processing system (PPS) of the VAISALA-IRIS software, which is used to produce radarderived rainfall products, has the capability to adjust the radar rainfall estimates to hourly rain
gauge accumulations (Fulton et al., 1998). The gauge correction is applied to the radar data. Also
other QC procedures such as Anomalous Propagation (AP) and removal of land and sea clutter
are based on the VAISALA-IRIS software.
Reflectivity or post-processed information from reflectivity is assimilated with different
approaches in different NWP models. The main approaches in use are nudging, variational
analysis and Ensemble Kalman Filtering. In this study, the variational analysis method is used.
The assimilation of reflectivity information requires complicated observation operators including
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moist physics. Figure 3 shows the general steps involved in the radar reflectivity assimilation
process. Figure 4 shows the 3DVAR assimilation process in step 6.
Step 1: Radar data decoding
Step 2: Radar reflectivity QC
Step 3: Single Radar Cartesian Coordinate Transformation
Step 4: Output reference in Cartesian Grid
Step 5: Reflectivity in 3D-Mosaic
Step 6: GSI variational assimilation of reflectivity data
Figure 3: Radar reflectivity assimilation processes
Figure 4: The detailed assimilation processes as in Step 6 (Figure 3)
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In 3DVAR, the information from radar reflectivity is taken into account through a new
cost function, Jo term (Figure 5), which measures the distance of the background and the radar
information (reflectivity, rain, or derived temperature and humidity profiles). In this study, the
observation operators providing the ‘model to reflectivity’ transformation are directly
assimilated. Here the control variable is the total water content (qt), which is first repartitioned to
model rain water (qr), cloud water (qc) and water vapor (qv) through warm rain physics. The
model rain water (qr) is then used to compute simulated ‘model’ reflectivity. Jo is then computed
in the ‘reflectivity space’ and its gradient is computed with respect to reflectivity. Finally, the
adjoint of the reflectivity operator and the repartitioning warm rain physics are used to compute
the gradient with respect to the control variable (qt), i.e gradJo and gradJb can be added and a
new search of the minimization can start.(Chapter 6, GSI User Guide V3.0a)
Figure 5: 3DVAR cost function equations. Excerpts from the GSI User Guide V3.0a
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Thinning of the radar data is performed by using the Sorted Position Radar INTerpolation
(SPRINT) software from NCAR. The mapping of the data into Cartesian grid is done by using
the Custom Editing and Display of Reduced Information in Cartesian-space (CEDRIC) from
NCAR.
The threat scores (TS) and Bias score (Jolliffe and Stephenson, 2003) were used to
evaluate the model performance for 31 days starting from 1200 UTC 01 October 2011 to 1200
UTC 31 October 201.1
4.
Experiments
Comparison experiments for 31 days starting from 1200 UTC 01 October 2011 to 1200
UTC 31 October 2011 were conducted by creating parallel runs with and without Doppler radar
data assimilation. Observations from 12 Doppler radars (Figure 1) were assimilated in the
Doppler radar data-assimilation runs during the period. These radar data-assimilation runs were
designed with 3 hour analysis and forecast cycling and numerical forecasts lasted 12 hours
initialized at 1200 UTC.
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5.
Results and Discussion
Figures 6(a) and 6(b) shows verification of the 3 hour rainfall accumulation forecast
against the MMD Automatic Weather Station (AWS) rainfall observations during the 31 days.
Figure 6(a): The threat score of the comparison runs for 3-h rainfall accumulation forecasts with
(radar) and without (no radar) Doppler radar data assimilation for 20 mm/3h threshold. (The blue
bars represent TS in the no-radar data-assimilation runs, and the red bars TS and scores in the
radar data-assimilation runs).
Figure 6(a) indicates that the radar data assimilation produces higher threat scores (TS)
(Hamill and Colucci, 1996) than the forecasts without radar data assimilation for all lead times.
Figure 6(b) shows that the BIAS (Hamill and Colucci, 1996) scores (which measure the ratio of
the frequency of forecasted events to the frequency of observed events) with Doppler radar data
assimilation for the 3 and 6 hour forecast are almost perfect, however beyond the 9 hour mark
there is a marked over forecast indicating improvements are required on the nudging schemes
used. The BIAS scores for forecasts without radar data assimilation shows an under forecast for
the 3 and 6 hour forecast and an over forecast for the 9 and 12 hour forecast. The over forecast
for the 3 and 9 hour forecast for forecasts without Doppler radar data assimilation is lower than
the forecast with Doppler radar data assimilation.
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Figure 6 (b): The BIAS score (solid lines) of the comparison runs for 3-h rainfall accumulation
forecasts with (radar) and without (no radar) Doppler radar data assimilation for 20 mm/3h
threshold. (The blue line represents BIAS scores in the no-radar data-assimilation runs, and the
red line represents BIAS scores in the radar data-assimilation runs).
Overall, the experiments and the verifications indicated a statistically significant positive
impact of the Doppler radar data assimilation on the short-range QPF (0–12 h). In addition to the
one-month verification shown in Figures 6(a) and 6(b), further case studies, including squall
lines and other severe thunderstorm events are being carried out.
6.
Concluding remarks
Although the assimilation results indicate a significant impact for the short range forecast
of 12 hours, this may wary from case by case and from month to month. Further case studies
have to be carried out. Radar data quality control remains a challenge and needs to be further
improved as poor quality radar data will deteriorate the model’s performance. The runs are
mostly carried out with cold start and in 3 hours cycle. Hence, more frequent cycling tests needs
to be implemented.
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