World Meteorological Organization Symposium on Nowcasting - WMO

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A real-time radar wind analysis system has been developed for monitoring low-level wind
conditions at high (up to 2 km) spatial and (5-10 min) temporal resolution. The system has
capabilities to integrate multi-sensor observations to produce real-time vector wind field. In
additional to WSR-88D radar level II data and Oklahoma Mesonet data, real-time NPN
(NOAA Profiler Network) data are also ingested into the system to improve the accuracy of
retrieved wind field and derived products. This system can display the retrieved horizontal
vector wind field at each selected vertical level or on each conical surface of radar scans
synchronized and overlapped with real-time reflectivity and/or Doppler velocity fields from the
radar. The real-time wind products have been displayed on NSSL/WDSS-II ORPG and made
available to NWS Norman Forecast Office. The early version of the system was delivered to
the Pacific Northwest National Laboratory to provide the very needed real-time radar wind
retrieval capability to derive high-resolution emergency response dispersion models for
homeland security applications. The system’s data quality control package was also tested
and adopted at NRL Monterey for radar data assimilation and nowcast applications. The key
technical elements developed in the system for the radar data quality control and wind
retrieval will be presented with illustrative examples at the workshop.
3.6
Data Assimilation of Hydrometeor Types Estimated from the Polarimetric Radar
Observation
[1] Kosei YAMAGUCHI [2] Eiichi NAKAKITA [3] Yasuhiko SUMIDA
[1] Graduate School of Engineering, Kyoto University [2] Disaster Prevention Research
Institute, Kyoto University [3] Graduate School of Engineering, Kyoto University
It is important for 0-6 hour nowcasting to provide for a high-quality initial condition in a mesoscale
atmospheric model by a data assimilation of several observation data. The polarimetric
radar data is expected to be assimilated into the forecast model, because the radar has a
possibility of measurements of the types, the shapes, and the size distributions of
hydrometeors. Its information is useful for the estimation of the cloud microphysical state by
the data assimilation. In this study, an impact on rainfall prediction of the data assimilation of
hydrometeor types (i.e. raindrop, graupel, snowflake, etc.) is evaluated. The observed
information of hydrometeor types is estimated using the fuzzy logic algorism (Nakakita et al.,
2008). As an implementation, the cloud-resolving nonhydrostatic atmospheric model, CReSS
(Tsuboki and Sakakibara, 2002), which has detail microphysical processes, is employed as a
forecast model. The local ensemble transform Kalman filter, LETKF (Hunt et al., 2007), is
used as a data assimilation method, which uses an ensemble of short-term forecasts to
estimate the flow-dependent background error covariance required in data assimilation. A
heavy rainfall event occurred in Okinawa in 2008 is chosen as an application. As a result, the
rainfall prediction accuracy in the assimilation case of both hydrometeor types and the
Doppler velocity and the radar echo is improved by a comparison of the no assimilation case.
The effects on rainfall prediction of the assimilation of hydrometeor types appear in longer
prediction lead time compared with the effects of the assimilation of radar echo only.
8.10
Detection and monitoring of Convective clouds by satellite
Yann Guillou, F. Autones, Stéphane Sénési
Météo-France, Forecast Department, Nowcasting Development
Meteo-France develops, in the SAF NWC framework, a tool for the detection and tracking of
thunderstorm from satellite imagery. This tool, known as RDT , is particularly useful in areas
without radar network for monitoring thunderstorm. Meteo-France implements it on MSG
(Europe and West Africa) and on GOES (Caribbean Area) to address multiple needs:
research on the African monsoon, meteorological support on ocean areas, meteorological
support for the French armed forces. The identification of convective clouds (or
discrimination) is a mix between a statistical approach and empirical rules. It has been
adapted to the cloud development stage, with a different decision model tuning for the
following three stages : Development (before lightning occurrence for low clouds)
Intermediate (triggering of flashes and quick vertical development) Mature (anvil spread, and
overshoot). Most aspects of the discrimination are separately tuned for each phase : the
choice of input parameters and whether they are time-averaged or instantaneous, and their
respective weights. For instance, the cloud top temperature instantaneous change rate is a
discriminating parameter for development stage but not for mature stage, and inversely for
the GCD index (defined as the difference between brightness temperatures in water vapor
and infrared channels).The following scores are achieved for summer period over Europe
using only satellite data. For each cloud track, periods of significant, somewhat continuous,
electrical activity are considered, and called electrical intervals. More than 70 % of electrical
intervals are detected by RDT. Among it, more than 50% are detected no later than the first
lightning occurrence, more than 80% 30 minutes after. 25% of electrical interval are
discriminated at least 15 minutes before the first lightning occurrence.The developments
undertaken for the year to come have for main objective to improve the diagnosis earliness,
in order to extract satellite information for a development stage which is not well described by
radar networks.
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