World Meteorological Organization Symposium on Nowcasting - WMO

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lead time moment of 3-4 hours from the latest radar measurement. As the high resolution
NWP applies 4D variational assimilation and as the model is initialized every 6 hours, the
time elapsed from the initial moment in NWP based VSRF is typically 7-12 hours. Especially
in convective cases, the discrepancy between the two forecasts can be substantial.
Meteorologists can perform manual interpolation of the nowcast products to avoid striking
disagreement between the NWP- and radar-based precipitation forecasts.In order to
guarantee a smooth and automatic transition from radar-based precipitation nowcasts to
NWP forecasts we have tested a morphing algorithm. Assuming linear transformation
between the precipitation pattern from the radar nowcast and the pattern from the NWPbased
VSRF, it utilizes the transformation field between them to produce smooth transition
during a time period Δt. During the period the radar pattern is transformed to NWP pattern so
that the location and size change linearly. This transformation trajectory based interpolation
technique yields substantially better results than simple fading between the two forecasts.
The computation is performed applying the commercial CineSat software. There is no
dynamic-physical forcing in the algorithm but at least it can provide in many cases a smooth
transition between radar-based precipitation nowcasts and those from NWP models.
3.20
Assessment of convective forecast uncertainty using high-resolution model ensemble
data
7.3
Towards an analysis ensemble for NWP-model verification
Theresa Gorgas, Reinhold Steinacker
all authors: University of Vienna
One of the great challenges in verification is the definition of the “truth”. Common verification
methods use either observations or analysis fields from a model as reference data. Both
methods have their advantages and drawbacks. Observation data are irregularly distributed
and lack spatial and sometimes also temporal representativeness. Model analyses on the
other hand are not really independent from the verifying model itself. We use the model
independent analysis tool VERA (Vienna Enhanced Resolution Analysis) for NWP –model
intercomparison. One key problem arises from the availability of a dense data set. Within the
WWRP projects D-PHASE and COPS a joint activity has been started to collect GTS and
non-GTS data from the national meteorological services in Central Europe for 2007. Data
from more than 11.000 stations allow to run the analysis with a spatial resolution of 8 km on
an hourly basis. VERA includes a data quality control module which gives a correction
proposal for each station every analysis time. All in all this results in 8760 correction
proposals for each station as a maximum for the whole year. In a next step a statistical
evaluation is performed and finally, the station data are varied randomly within the limits of
the correction proposals. As a result we get analysis maps with some information about the
sensitivity of the analysis in different regions. In the presentation we will give the current
implementation status of the procedure described above.
James Pinto, Mei Xu, David Dowell, Matthias Steiner, and John Williams
NCAR/RAL, Boulder, CO 7.4
Bayesian Procrustes verification of ensemble radar reflectivity nowcasts
The short-term prediction of convective weather is inherently uncertain owing to the
seemingly random nature of the processes that interact to determine the four-dimensional
evolution of thunderstorms. The skill of a particular forecast depends on a number of intrinsic
(i.e., property of the storm – e.g., storm size) and extrinsic (acting on the storm – e.g.,
environment) characteristics of the storm and how well they are represented in the numerical
weather prediction (NWP) model. Toward improved short-term forecasting of aviation
weather hazards, the FAA has funded NOAA-GSD, NCAR-RAL and MIT-LL to develop a
short-term forecasting system that utilizes best available techniques for combining
nowcasting and NWP to produce a seamless, rapidly updating, 0-8 hour forecast of
precipitation intensity, phase and storm top heights (see Dupree et al. abstract for this
conference). An important component of this Collaborative Storm Prediction for Aviation
(CoSPA) forecast system currently under development is the estimation of forecast
uncertainty. In this study we use components of CoSPA including high-resolution
observations from MIT-LL and NWP data from the High-Resolution Rapid Refresh (HRRR)
run by GSD to predict forecast uncertainty. The availability of a new 12-hour long HRRR
forecast every 1 hour can be used to build a time-lagged ensemble that can be compared
with observations to assess the forecast uncertainty. In this paper we explore several
methods (image processing and statistical data mining techniques) for quantifying forecast
uncertainty. A methodology for estimating forecast uncertainty (which can be interpreted as
confidence in the forecast) is presented and demonstrated on selected cases from the
summers of 2008 and 2009.
Neil I. Fox [1] Athanasios C. Micheas [2] Yuqiang Peng [2]
[1] Department of Soil, Environmental and Atmospheric Sciences University of Missouri
Columbia, MO 65211 [2] Department of Statistics University of Missouri Columbia, MO 65211
This paper presents a Bayesian Procrustes approach to the verification of multiple spatial
forecast fields. The Procrustes fit provides measures of dilation, rotation and translation
required to match a forecast of an object to the observed (true) shape of the object. Within
the Bayesian framework multiple two-dimensional shapes can be compared to a ‘truth’ image,
by firstly obtaining the Full Procrustes Fit that provides an average of the forecasts, and
consequently using a Procrustes fit of this average against the truth allows us to evaluate
how well the forecasts are performing (verification of the ensemble). The Bayesian framework
provides a way to capture variability of the forecasts via calculation of credible sets
(confidence intervals). The verification scheme is demonstrated using a limited ensemble of
nowcasts of radar reflectivity. However, it is equally applicable to model precipitation fields or
other forecast products that are usually presented as discrete areas.
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