World Meteorological Organization Symposium on Nowcasting - WMO

52
3.14
A WRF-based rapid updating cycling forecast system of BMB and its performance
during the summer and Olympic Games 2008
Min Chen[1], Shui-yong Fan[1], Jiqin Zhong[1], Xiang-yu Huang[2], Yong-Run Guo[2], Wei
Wang[2], Yingchun Wang[1], Bill Kuo[2]
[1]Institute of Urban Meteorology, CMA, Beijing, China [2] National Center of Atmospheric
Science, Boulder, CO
A real-time rapid updated cycling forecast system (BJ-RUC) based on WRF model and
WRFVAR data assimilation system was built in BMB with the primary goal to improve the
short-range forecasts and support the weather service for the XXIX Olympic Games held in
Beijing, 2008. A preliminary evaluation was performed for the operational forecasts of BJ-
RUC system during the summer season from June to September of 2008 including the
Olympic and Paralympic Games period. The forecast quality for precipitation and
conventional variables of the system were evaluated against AWS and conventional
observations. In addition, two strong convective cases that occurred during the Olympic
Games were examined. As the forecast length was 24 hours, there’re 8 comparable forecasts
from cycling runs with different initial time in each 3-hr period such as 00-03utc, 03-
06utc,…,21-00utc. To give an idea about which run has the best forecast quality for each 3-hr
time interval, the forecast performances were evaluated with 3-hr time interval. For all
verification thresholds, better precipitation forecast skills were illustrated for the very initial 0-
3hr and 0-6hr forecasts. This can be ascribed to the fact that the spin-up problem has been
partly better resolved via the rapid updating cycling assimilation style than the cold-start runs.
AWS rainfall observations revealed an obvious diurnal cycle of precipitation in Beijing area
that most convection occurs in late afternoon and nighttime. All cycling runs were found to be
capable of forecasting this diurnal cycle of precipitation whatever their initial time or the valid
forecast length. Especially, the best precipitation forecast were in 15-18UTC, and the worst
forecasts were in 03-06utc, corresponding to local 21-00BJT and the time at noon,
respectively. Herein, the cycling runs initiating from 06, 09, 12 and 15utc and their 0-6 hour
short-range precipitation forecasts would be more significant for forecasters to predict the
convection occurring at night. For surface forecasts, there was very significant systematic
error for 2-m temperature and moisture, while most error of 10-m wind was due to nonsystematic
error, such as random error, etc. For each identical valid time with 3-hour interval,
the analysis of the cycling runs initiating at the current time did have the best quality when
comparing with the forecasts from other 7 cycles, especially at 12UTC, the cold-start initial
time. There’re significant distances between analysis and 3-hr forecast of previous cycles.
For cold-start runs initiating from 12UTC, the distance was the largest, that is, the
accumulated model forecast errors would have a chance to be reduced to a new low level; for
hot-start runs, these distances could be regarded as analysis increments, which modified the
error of the background (3-hr forecast of previous cycle) and pushed it toward observations.
From this point of view, the data assimilation by WRFVAR was effectively performed. Also for
the surface forecast valid at each 3-hour interval, nearly all of the best forecast quality came
from the very latest cycle; while other cycles whose forecast range among 6-24 hours had
similar forecast skills. So it is found that the positive impacts brought by the assimilation of
latest observations would at least last for 6 hours, which was also consistent to the better
SESSION 8: Satellite
8.1
Satellite Nowcasting Applications
Marianne Koenig
EUMETSAT
Geostationary satellite data with their fast image repeat cycles play a key role for a number of
nowcasting applications, e.g. fog detection, monitoring large dust outbreaks, general airmass
identification, and tracking of volcanic ash clouds. Satellite images are especially useful to
support the nowcasting and short term forecasting of possibly severe convective storms by
providing information on the pre-convective air mass conditions, and monitoring the cloud
development, especially identifying the convective initiation phase.
The European Meteosat Second Generation (MSG) satellite data with its multi-spectral
imagery and 15 minute repeat cycle can well support these aspects of convection forecasting.
In order to provide air mass properties with respect to tropospheric stability, a so-called
“Global Instability Indices” (GII) product is operationally derived from the MSG imagery data.
The GII data is only available for clear skies, i.e. it shows the pre-convective conditions.
Once first clouds appear, MSG data can be used to detect the convective initiation (CI) phase
of individual cumulus-type clouds. Within a current research project, the already established
CI technique for the US GOES satellites is modified and refined using the additional spectral
information provided by MSG, and this concept is currently being validated against storm
reports, lightning data, and radar observations.
In addition, during the entire life cycle of clouds, their microphysical properties can be
retrieved from the multi-spectral satellite data. Although satellite views only provide
information on the very cloud top, a time sequence of such parameters, together with their
local spatial variability, helps to detect the vigour of such storms.
This presentation will show application examples, for both Europe and Africa, of this suite of
products that can be derived from MSG. In the end, an outlook will be given on the future new
capabilities of the Meteosat Third Generation suite of satellites, which will provide higher
spectral, temporal, and spatial resolution.
89