World Meteorological Organization Symposium on Nowcasting - WMO

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reduce damages, it’s possible to modify the crops microclimate by different techniques. For
instance, short-term forecast of minimum night temperatures is a useful tool for farmers to
organize their actions. Accordingly, the aim of this work is to provide a brief description and
the results of the operational frost nowcasting system developed in the framework of the
Satellite and Environmental Systems Division (DSA) at the Center for Weather Prediction and
Climate Studies (CPTEC) of the National Institute for Space Research (INPE) using landsurface
temperature product derived from real-time METEOSAT Second Generation
(MSG)/Spinning Enhanced Visible and Infrared Imager (SEVIRI) high-rate data every 15
minutes. The frost nowcasting system forecasts night land-surface temperature with a time
resolution of one hour and provides information on expected frost conditions up to 12 hours
ahead. The validation of the system is performed using data from different automatic weather
stations located at Santa Catarina and Paraná estates during the year 2008. Results show
that the system allows early warning information about the 2 frost events and also more
accurate quantification of the frost impact, contributing to reduce speculations in the
economical area and to inspect the agricultural insurance.
4.4
Canadian airport nowcasting project (CAN-Now): Short term forecasts for airports
George A. Isaac [1], M. Bailey [1], F. Boudala [1], W. Chang [1], B. Clark [1], S. G. Cober [1],
R. Crawford [1], N. Donaldson [1], N. Driedger [1], M. Fournier [2], I. Gultepe [1], I. Heckman
[1], L. Huang [1], A. Ling [3], J. Reid [1] and Z. Vukovic[1]
[1] Science and Technology Branch, Environment Canada, Toronto, Ontario, Canada [2]
Canadian <strong>Meteorological</strong> Aviation Centre East, Montreal, Quebec, Canada [3] Canadian
<strong>Meteorological</strong> Aviation Centre West, Edmonton, Alberta, Canada
The Canadian Airport Nowcasting Project (CAN-Now) is developing an advanced prototype
all-season weather forecasting and nowcasting system that can be used at major airports.
This system uses numerical model data, pilot reports, ground sensor observations
(precipitation, ceiling, visibility, winds, etc) as well as remote sensing (satellite, radar,
radiometer) information to provide detailed nowcasts out to approximately 6 hours. The
nowcasts, or short term weather forecasts, should allow decision makers at airports such as
pilots, dispatchers, de-icing crews, ground personnel or air traffic controllers to make
decisions with increased margins of safety and improved efficiency. The system is being
developed and tested at Toronto Pearson International Airport (CYYZ) with an eventual aim
of adapting the resulting concept and forecasts to other Canadian airports such as Montreal,
Calgary and Vancouver. The preliminary displays being tested, which include high glance
value situation displays, will be discussed along with initial evaluations. Plans for future work
will also be described including finalizing the system products, testing and validating them,
and implementing them operationally
6.9
Improved short-term quantitative precipitation forecast combining radar data with a
high-resolution NWP model
Zachary M. DuFran, Richard L. Carpenter, Jr., Brent L. Shaw
Weather Decision Technologies, Inc., Norman, Oklahoma, USA
Techniques of short term extrapolation have been moderately successful for precipitation
nowcasting, but lack the ability to forecast precipitation initiation, growth and decay that are
seen in dynamical numerical weather prediction models. Although NWP models provide the
best automated guidance beyond 6-12 hours in the future, they are generally less accurate
than extrapolation techniques for the first few hours of a forecast due to initialization errors,
resolution, and deficiencies in even the best microphysical parameterizations. The
Adjustment of Rain from Models with Radar (ARMOR) algorithm was developed at McGill
University to combine the latest radar data with a model forecast of precipitation to produce a
phase and intensity-corrected forecast with enhanced skill out to six hours.The ARMOR
algorithm has been licensed and implemented at Weather Decision Technologies, Inc. WDT
uses a high-resolution (10-km horizontal grid spacing with 15-minute temporal resolution)
implementation of the Weather Research and Forecast (WRF) model to produce ARMOR
forecasts over the contiguous United States. ARMOR combines the WRF15-minute
precipitation accumulation with 1 km resolution North American low altitude radar mosaics
which are updated every 5 minutes.ARMOR uses the WRF model forecast as the
background, taking advantage of the model’s ability to replicate mesoscale precipitation
patterns that occur over the 0-12 hour time frame. Model spatial phase errors and
precipitation intensity correction are derived by comparing the past radar mosaics with the
model forecasts over the last hour. Corrections are applied to the WRF model forecast at
each time step to produce the ARMOR forecast.This presentation will explain the ARMOR
algorithm, provide samples of QPF output along with real-time radar verification and discuss
the observed skill of ARMOR compared with other techniques.
6.10
High resolution precipitation analysis and forecast validation over complex terrain
using an inverse VERA approach
Benedikt Bica, Stefan Schneider, Reinhold Steinacker
Department of Meteorology and Geophysics, University of Vienna
Precipitation diagnosis and prognostics over mountainous terrain still poses a challenge due
to the complex influence of topography. Both stratiform and convective precipitation usually
show patterns that can hardly be resolved by an observation network. Similarly, the data
quality control, analysis and modelling of precipitation fields is subject to limitations that arise
from the steep gradients which might occur in precipitation fields over mountain regions. In
the proposed presentation, the VERA (Vienna Enhanced Resolution Analysis) method is
applied to precipitation fields in order to assess its suitability for analysis, nowcasting and
model validation purposes. VERA is based on the variational principle and further allows the
inclusion of supplemental knowledge of typical patterns of meteorological parameters
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