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Investigating the typical development of thunderstorms using satellite, radar and lightning observations

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Abstract

Wind gusts, heavy precipitation, and hail are the fundamental meteorological hazards associated with thunderstorms. Even though numerical weather models have some skills to predict convection, the exact location of the convective initialization and the path covered by thunderstorms, in general, cannot be predicted by these models. Hence, there is a strong interest to improve the short-term forecast by extrapolating the current observation to the following hours by mean of statistical algorithms. Several object oriented algorithms have been designed for monitoring and tracking convective cells. They support forecasters providing real time analyses and automatic warnings. For this purpose two nowcasting algorithms have been developed at MeteoSwiss. The Thunderstorm Radar Tracking (TRT) algorithm uses radar information and data from NWP models to judge the current damage potential and to predict the future path of thunderstorms. The Context and Scale Oriented Thunderstorm Satellite Predictors Development (COALITION) algorithm is designed to detect convective initialization by means of a multi-sensor approach, employing SEVIRI, radar and lightning observations as well as NWP data and orographic information. Furthermore, COALITION heuristically estimates the change of the convective intensity for the near future. For predicting the development of convective systems in a statistical way, a precise knowledge about the convective development is crucial. In this study we use observations of the Swiss meteorological radar and Météorage lighting network, SEVIRI measurements as well as forecasts of the MeteoSwiss regional model COSMO2 to investigate the life cycle of convection over of Switzerland. Special attention is given to the characteristic evolutionary events like convective initiation, cloud top glaciation, start, maximum, and end of precipitation and lightning activities. The focus of this study is on the innovative use of satellite observation. The convective development is related to reflectances and brightness temperatures observed from satellites and their temporal changes. Additionally, retrieved physical cloud properties of state-of-the-art cloud remote sensing algorithms like the cloud top height, multilayer flags, cloud phase, optical depth and effective radius are employed. The effect of the definition of the cell boundary on the mean properties of the convective cell is discussed. And a first assessment of the capacity of the applied data as predictors for convective intensity is presented. The insights of this study will be used to improve the early detection of convection and the convective intensity forecast of TRT and COALITION.
Investigating the typical
development of thunderstorms using
satellite, radar and lightning observations
U. Hamann, L. Nisi, A. Hering, L. Clementi , M. Gabella, U. Germann
MeteoSwiss, Locarno Monti, Switzerland
ulrich.hamann@meteoswiss.ch
Motivation Objectives
Results: Case study of an Convective System over the Po valley, 23th of July 2014
During the warm season severe thunderstorms regularly affect the Alpine region.
Heavy precipitation, wind gusts, and hail are a thread for aviation, traffic, agriculture and tourism.
Multi sensor measurements completes the observation of convection.
The typical development of thunderstorms can be used in Nowcasting algorithms.
Early warning of strong convection and precipitation contributes to saving lives and property.
1) Observation of convection with a multiple sensors
2) Investigation of the typical development of convective systems
3) Early identification of potentially severe thunderstorms
4) Prediction of the intensification/decay of convective cells
5) Forecast of speed and direction of the thunderstorm movement
Hering, A. M., Morel, C., Galli, G., Sénési, S., Ambrosetti, P., and Boscacci, M.
(2004), Nowcasting thunderstorms in the alpine region using a radar based
adaptive thresholding scheme. Proceedings, Third ERAD Conference, Visby,
Sweden, 206-211.
Mecikalski, J. R., MacKenzie. W. M. Jr., König, M., and Muller, S. (2010), Cloud-
Top Properties of Growing Cumulus prior to Convective Initiation as Measured by
Meteosat Second Generation. Part II: Use of Visible Reflectance. J. Appl. Meteor.
Climatol., 49, 2544–2558
Morel, C., Sénési, S., Autones, F., and Labatut, L. (2000), The Rapid Developing
Thunderstorms (RDT) product of the nowcasting SAF. Prototyping activities and
quality assessment using GOES images. Proceedings, The 2000 Meteorological
Satellite Data Users' Conference, Eumetsat and CNR, Bologna, Italy, 698-705.
Nisi, L., Ambrosetti, P. and Clementi, L. (2014), Nowcasting severe convection in
the Alpine region: the COALITION approach. Q.J.R. Meteorol. Soc., 140: 1684–
1699. doi: 10.1002/qj.2249
Zinner, T., Mannstein, H., and Tafferner, A. (2008), Cb-TRAM: Tracking and
monitoring severe convection from onset over rapid development to mature phase
using multi-channel Meteosat-8 SEVIRI data. Meteorology and Atmospheric
Physics, Volume 101, Issue 3-4, pp. 191-210
Thanks to the entire staff of MeteoSwiss Locarno-Monti for their help. We
also would like to thank J. Mecikalski (University of Alabama) for the
SATCAST algorithm and M. König (EUMETSAT) for the scientific support.
Federal Office of Meteorology
and Climatology MeteoSwiss
Thunderstorm multi sensor observation Detection of Convection
Literature
Convective Development
SEVIRI channel combination suitable for the
detection of convective initiation (Mecikalski, 2010)
09UTC 12UTC 15UTC 18UTC 21UTC 24UTC 09UTC 12UTC 15UTC 18UTC 21UTC 24UTC
HR overviewMicro-physicsAirmass
Convection Radar
echo max
WV6.2-IR10.8
WV6.2-WV7.3
trispectral
IR8.7-IR10.8
IR12.0-IR10.8
PyTROLL
Radar
echo top 15dBZ
Conclusions & Outlook
This case study shows the
observation from radar and
satellite for the 23th of July
2014. The Swiss radars mea-
sures reflectivities in the order
of 35 to 40 dBZ with the highest
reflectivities from 15:00-
18:00UTC. In the HRoverview
some structure of the cloud top
are observed. The micro-
physics and fog RGBs show
the spread of the ice cloud
cover in redish colors. The
Airmass RGB indicates a cold
air mass approaching from the
West. The convection RGB
indicates active convection in
orange and the most active
parts in yellowish. On the right
hand side the channel differen-
ces proposed by Mecikalsky
are shown. The mean ± one
standard deviation is indicated
in color. All suggested channel
combination show a skill to
detect convection, some suffer
from noisy structure.
The objective of PyTROLL is to provide different free and
open source python modules for the reading, interpretation,
and writing of weather satellite data, among others from
MSG SEVIRI. PyTROLL supports geostationary and polar
platforms, it can read various data formats as hdf, grib,
HRIT, and netCDF. It is able to remap and interpolate
datasets on user defined projections and areas.
Furthermore, PyTROLL can produce standard RGB
composites and offers an interphase for user defined RGB
modes.
The PYTROLL software package is used extensively in this study. The
authors would like to thank DMI and SHMI for developing PYTROLL and
making it available.
Radar and satellite observations have been used to observe
the life cycle of an convective system. The lifting and
glaciation of the cloud top as well as an indication of the ice
particle size are illustrated by standard RGB images.
Additionally, the airmass RGB shows an cold airmass
approaching from the west. The satellite channel differences
proposed by Mecikalski (2010) show good skills to identify
convectively active regions.
In future, further parameters like lightning observation,
temporal changes of satellite radiances and brightness
temperatures, retrieved cloud physical products as well as
structural features of the cloud top are investigated for their
potential to identify convective systems as well as to predict
the development of the convective intensity. An additional
focus is on the forecast of the damage potential. The
combination of different predictors can be archived by
physical, statistical, or heuristic methods. A combination of
several methods is possible, too.
Fog
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