World Meteorological Organization Symposium on Nowcasting - WMO

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6.7
4.6
Evaluation of a hydrometeorological forecast system for the metropolitan area of São
Paulo
Utilization of frequently-updated mesoscale analysis for thunderstorm nowcasting: an
examination of relevant predictors.
Ernani de Lima Nascimento [1] Stéphane Sénési [2]
A. J. Pereira Filho1, O. Massambani1, R. Hallak1, F. Vemado1, L. R. Pereira1, F. Vemado1,
C. G. M. Ramos1
Universidade Federal de Santa Maria, Brazil [1] Météo-France, France [2]
1 University of São Paulo, DCA/IAGUSP Data from an automated mesoscale objective analysis system updated at 15-min intervals
are assessed as part of a diagnostic tool for describing the relevant mesoscale environment
A Hydrometeorological Forecast System (HFS) has been implemented for Metropolitan Area
of São Paulo (MASP) to mitigate the effects of severe rainfall events. This work shows an
evaluation of ARPS high resolution precipitation forecasting and MXPOL rainfall nowcasting
over the MASP for the rainy seasons of 2008 and 2009. Significant improvements of ARPS
rainfall forecasting have been accomplished by using explicit cloud microphysics. On the
other hand, the MXPOL higher sensitivity and polarimetric measurements have increased the
leading time on the inset of heavy rainfall convective systems by monitoring boundary layer
features associated with local circulation in the MASP.
around and downstream of ongoing thunderstorms which is relevant for nowcasting the
evolution of convective activity within the 120-min range. Following an ingredients-based
forecast methodology, the objective is to search for physically meaningful and statistically
significant relationships between meteorological parameters obtained from the rapidlyupdated
mesoscale analysis and trends in radar reflectivity for the evolving thunderstorms.
These relationships are evaluated in both qualitative and quantitative ways through a multiple
linear regression approach in which the predictand is the change in (the spatially-smoothed
field of) reflectivity over distinct time intervals, ranging from 30-min to 120-min. The main
atmospheric ingredients examined for generating the predictors include (but are not restricted
4.7
Probabilistic thunderstorm guidance from a time-lagged ensemble of High Resolution
Rapid Refresh (HRRR) forecasts
to): conditional instability, low-level moisture availability, low-level convergence, and deep
layer vertical wind shear. A total of eleven convective episodes that occurred over northcentral
France during the warm season of 2006 are analyzed. The final goal is to expand the
resources of the current automated nowcasting system developed at Météo-France by
Curtis Alexander [1 and 3], Doug Koch [2 and 3], Steve Weygandt [3], Tanya Smirnova [1 and
3], Stan Benjamin [3], and Huiling Yuan [1 and 3]
[1] Cooperative Institute for Research in Environmental Sciences (CIRES) University of
Colorado, [2] University of Northern Colorado (UNC), [3] National Oceanic and Atmospheric
Administration (NOAA) Earth System Research Lab (ESRL) Global Systems Division (GSD)
incorporating the capability of predicting storm intensification and decay (in response to nonlinear
processes) to the already existing capability to nowcast storm position based on linear
extrapolation. The results shown here refer to the predictor screening and the goodness of fit
and explained variance of the reflectivity in the regression procedure.A considerable case-tocase
variability was found in terms of the linear relationship between the atmospheric
parameters and the reflectivity trend. Best results were found for slower-moving
Since September 2007, ESRL GSD has been running an hourly updated convectionresolving
model known as the High Resolution Rapid Refresh (HRRR) (see Weygandt et al.
at this symposium). The HRRR uses the hourly updated data assimilation cycle of the Rapid
Update Cycle (RUC) model including a highly effective radar reflectivity assimilation
procedure (see Benjamin et al. at this symposium). An experimental probabilistic
thunderstorm guidance (thunderstorm likelihood) product known as the HRRR convective
probabilistic forecast (HCPF) is being developed.For each hourly HRRR initialization, 0-12
hour forecasts are produced with model output available once per hour during the 12 hour
integration period. Using staggered lead times from consecutive HRRR forecast cycles, sets
of forecasts with the same valid time are constructed as time-lagged ensembles. Model
reflectivity and instability fields are used to identify regions of convection among the timelagged
ensemble members. The HCPF is generated using the fraction of total grid points
across the ensemble and within specified radii of each grid point that exceed model
reflectivity and surface lifted index thresholds (currently lower than +2 °C).Performance of the
HCPF (skill and reliability) at various lead times is being optimized by changing the weighted
contribution of individual HRRR forecasts and adjusting other parameters including reflectivity
thresholds and adjacent grid point radius of influence. Verification of the HCPF is performed
using the 4 km National Convective Weather Diagnostic (NCWD) product on an hourly basis.
Demonstration and evaluation of HRRR time-lagged ensembles in providing 1-12 hr
thunderstorms evolving under weak synoptic forcing, for which the predictors and adjusted
equations explained a fairly large part of the variation in the (spatially-smoothed) reflectivity
change. Among the selected predictors, moisture convergence, height of the lifting
condensation level (LCL) and a combination of CAPE and moisture convergence were the
ones with best performance from a univariate standpoint. Interestingly, better results were
obtained: (i) when sampling the mesoscale environment at the current location of the
convective activity rather then downstream of it; and (ii) for longer nowcast ranges. Our
results suggest that the later finding is due to a better representation of the variance of the
reflectivity change when stratifying this field in a longer time interval. We also found that
having a more frequent cycle of mesoscale analysis (15-min intervals) yields better results in
terms of goodness-of-fit when compared to a regular 1hr-spaced analysis. Implications of
these preliminary findings to the predictive skill of the procedure are also discussed.