**Number range CHAPTERSHORT RANGE ENSEMBLE FORECASTING (SREF) AT THE
NATIONAL**

**CENTERS FOR ENVIRONMENTAL PREDICTION^1^**

*(M. Steven Tracton^2^ and Jun Du)*

Environmental Modeling Center, National Centers for Environmental
Prediction

NWS/NOAA Washington, D.C. 20233

**Abstract:** Number range CHAPTEROver the past few years ensemble
prediction has come to the fore as a major element in defining the
future of numerical weather prediction (NWP) and operational weather
forecasting. This stems basically from convergence of increasing
recognition of the importance of explicitly addressing the intrinsic
uncertainties in forecasts with existing/prospective supercomputer
resources and development of ensemble strategies. The net result of this
convergence is an expanding capability to provide quantitative estimates
of those uncertainties. It is widely agreed that ensemble-based
probabilities and/or measures of confidence provide the potential for
considerably enhancing the ability to make user dependent informed
decisions. Indeed, it is likely that many forecast guidance products of
the U.S. National Weather Service (NWS) will evolve to being more
probabilistic in nature, especially those relating to quantitative
precipitation forecasting (QPF). The National Centers for Environmental
Prediction (NCEP) now run operationally a 24-member ensemble each day
with its Global Medium Range Forecast (MRF) model for medium-range (3-14
days) predictions . Results are very encouraging generally and,
specifically, with regard to the operational applications of ensemble
prediction.

The fundamental principles and concepts of ensemble forecasting
exploited for medium-range prediction apply equally well to short -range
(0-3 days) regional model-based ensembles. This paper provides an
overview of the nature and applications of the system, strategies, and
products of the of the short-range ensemble forecasting (SREF) system
developed and now running routinely at NCEP.

The current system is composed of 10 members composed of five members
from both the Eta and Regional Spectral Model (RSM) with 48 km
horizontal resolution. Initial state perturbations are provided by the
Breeding of Growing Modes (BGM) approach, as for the global ensemble
system, but in the context of the regional model systems. Perturbations
to physics, as well as initial conditions, and inclusion of additional
models are anticipated. For illustration on the SREF ensemble products
and their use focus will be on cases of major cyclogenesis, including
the "surprise" snowstorm over the eastern U.S. on January 24-25, 2000. A
principal issue is the relative value of ensembles versus single
deterministic runs at higher resolution. Also considered is the
sensitivity of the forecast models to the imposed perturbations with
regard to the dynamics of storm and frontal evolution.

**1. INTRODUCTION AND BACKGROUND**

Over the past few years ensemble prediction has come to the fore as a
major element in defining the future of numerical weather prediction
(NWP) and operational weather forecasting. This stems basically from
convergence of increasing recognition of the importance of explicitly
addressing the intrinsic uncertainties in forecasts with
existing/prospective supercomputer resources and development of ensemble
strategies. The net result of this convergence is an expanding
capability to provide quantitative estimates of those uncertainties. It
is widely agreed that ensemble-based probabilities and/or measures of
confidence provide the potential for considerably enhancing the ability
to make user dependent informed decisions. Indeed, it is likely that
many forecast guidance products of the U.S. National Weather Service
(NWS) will evolve to being more probabilistic in nature, especially
those relating to quantitative precipitation forecasting (QPF).

The National Centers for Environmental Prediction (NCEP) now run
operationally a 24-member ensemble each day with its Global Medium Range
Forecast (MRF) model for medium-range (3-14 days) predictions. Results
are very encouraging generally and, specifically, with regard to the
operational applications of ensemble prediction. The fundamental
principles and concepts of ensemble forecasting exploited for
medium-range prediction apply equally well to short -range (0-3 days)
regional model-based ensembles. Multiple forecasts valid at the same
time generated as a function of analysis errors and/or model formulation
are run to provide information on the inevitable uncertainties in
forecasts from the spread amongst ensemble members. Those uncertainties,
for example, in the timing, intensity, and location of weather systems
and associated sensible weather are case dependent and vary as a
function of such factors as season, geographical location, forecast lead
time, and circulation regime. In SREF the focus is on smaller space and
time scale phenomena, such as mesoscale convective systems and strong
fronts, rather than the larger-scale, longer-lived systems addressed in
the context of medium-range global model ensembles.

As highlighted in a workshop held at NCEP in 1995, although the basic
ideas and issues for SREF and medium-range ensemble prediction are much
the same, the problems for generating for useful SREFs are potentially
much more difficult (Brooks et al., 1995). Problems generally are
compounded in SREF in regard to observational data, systematic errors in
mesoscale models, insufficient model resolution, inadequate knowledge of
land/surface interactions, and parameterization of convective processes.
Beyond these fundamentally scientific concerns, practical issues
relevant to an operational setting must be dealt with, such as tradeoffs
between model resolution and ensemble size, and development and display
of "user friendly" products derived from ensemble output.

To begin exploring the challenges and prospects of SREF, NCEP launched a
pilot project in 1996 along the lines suggested by the aforementioned
workshop. A 15-member ensemble was generated about once per week for an
extended period using 80 km versions of the Eta model (Black, 1994) and
the Regional Spectral Model (RSM) (Juang and Kanamitsu, 1994).
Perturbations were provided by interpolation of " bred" initial
conditions from the operational global ensemble system (Toth, et al.,
1997) and a variety of in-house analyses. The encouraging

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^1^ Additional information available at
http://Inx48.wwb.noaa.gov/SREF/SREF.html

^2^ Corresponding author address: M. Steven Tracton, NCEP/EMC, 5200 Auth
Road, Camp Springs,

MD 20746; Email: steve.tracton@noaa.gov; Tel: 3017638000 x 7222

results from this study have been documented by Stensrud et al. (1999)
and Hamill and Colucci (1997, 1998). Later, the pilot study was
augmented by including 10 additional members, 5 runs each from the Eta
and RSM initialized with regional "enhancement" of the global bred
perturbations (Tracton and Du, 1998). Additionally, selected cases were
rerun at higher resolution (40 and 48 km for RSM and Eta, respectively).
Among the key findings, based on statistical verification and case study
analysis, were that enhanced diversity of solutions (spread) was
obtained with a multi-model ensemble, higher resolution, and the
regional enhancement. More generally, the study illustrated the
significance of uncertainties in short-range regional-model predictions,
demonstrated the potential of SREF to provide operationally useful
information, and provided a basis for a prototype operational system at
NCEP.

That prototype consisted of the 10 Eta plus RSM members referred to
above. That is, RSM and Eta control (unperturbed) forecasts plus runs of
each model with two pairs of regionally enhanced bred modes from the
global ensemble system. Lateral boundary conditions were provided by the
control and respective perturbations from the global ensembles. This
system was tested with 32 km resolution in the May, 1998 near realtime,
multi-institutional experiment referred to as the Storm and Mesoscale
Ensemble Experiment (SAMEX). The general aspects of the experiment and
preliminary results have been documented by Hou et al (1999). A major
conclusion, consistent with the pilot study referred to above, was that
an "ensemble of multiple systems is close to optimal, probably because
it represents most realistically the current uncertainties in the models
and in the initial conditions". The second major result cited was that
"perturbations in the physics, and lateral boundary conditions
consistent with perturbations in initial conditions are both important
for regional ensemble forecasting". From our own evaluation of the NCEP
component of the complete SAMEX ensemble, a key finding was the
importance of domain size in regard to the influence of lateral boundary
conditions. In order to accommodate the experiment prescribed horizontal
resolution (about 30 km), the domain was constrained to a relatively
small area over the continental U.S. Only after evaluation and
complementary experiments did it become apparent that the influence of
boundaries was distinctly detrimental to the growth of spread in the
interior of the domain (Du and Tracton, 1999).

On the basis of the experiences above, NCEP/EMC has been actively
engaged in constructing an operational SREF capability intended to run
on its new IBM SP Class VIII supercomputer. Its main purpose is to
provide guidance of a probabilistic nature on regional and relatively
short-time scale weather events responsible for severe weather and heavy
precipitation. This paper provides an overview of the strategies,
products and applications of the SREF system developed and which is now
running routinely at NCEP.

**2. SREF SYSTEM**

The current system is composed of 10 members composed of five members
from both the Eta and Regional Spectral Model (RSM) with 48 km
horizontal resolution. It is run twice per day (00 and 12 UTC) to 60
hours over the entire operational Eta North American domain. Initial
state perturbations are provided by through a newly developed regional
breeding system independent of the global ensemble bred modes. This
system works essentially the same as the global breeding (Toth, et al.,
1997), but is done totally in the context of the regional models. Member
dependent lateral boundary conditions are from the operational global
ensemble. In the future, as scientific requirements dictate and both
human and computer resources permit, the SREF system will be enhanced to
run at increased resolution with additional members, inclusion of
additional models, and perturbing physical parameterizations as well as
initial conditions.

**3. SREF PRODUCTS AND APPLICATIONS**

One of the most challenging aspects of ensemble prediction is condensing
the vast amounts of model output and information into an operationally
relevant and useful form. One could, of course, display each of the
standard maps and products for each individual forecast, but this very
quickly becomes extremely cumbersome and difficult to digest and
comprehend. Hence, we have invested considerable effort to convey and
display the essential information from ensembles as compactly as
possible and do so in "user friendly" form. In designing products it is
essential to keep in prospective applications in mind. Applications of
SREF include enhancing skill through ensemble averaging and, more
importantly, providing reliable quantitative estimates of forecast
uncertainty and information on alternative scenarios.

Figure 1 illustrates the ensemble mean and spread of a 60 hour forecast
of 500 mb height. Spread is defined as the standard deviation of
ensemble members from the ensemble mean. In theory and practice (as
demonstrated by verification statistics), the ensemble mean on average
is more skillful than any individual ensemble member. The ensemble mean
will usually be \"smoother\" in appearance than any of the individual
forecasts because the averaging filters the \"unpredictable
components\", where unpredictable here means inconsistencies amongst
ensemble members. Conceptually, considering anything with more detail
than contained in the ensemble mean over specifies the inherent
predictability; however, note that, if most of the ensemble members are
similar in the amplitude and phase of even smaller-scale features, they
will be retained in the ensemble mean. The ensemble mean is just the
first order advantage of ensemble prediction. Its more significant use
is in providing information on uncertainties and/or confidence. The most
basic product addressing this is the ensemble spread. It reflects the
overall degree of variability amongst the ensemble members - the larger
values (towards red) indicating areas of substantial disagreement and
hence less confidence in any individual prediction (or ensemble mean)
and visa versa (more purple) . The maps of spread thus provide an
evolving measure of the relative confidence geographically and with
respect to individual weather systems.

Figure 2 illustrates spaghetti diagrams with that for the 12 hour
accumulated precipitation of .5\". These diagrams are simply composite
charts of a selected contour from each ensemble member plotted on the
same chart. The obvious purpose is to convey the information content of
EACH ensemble member in a sufficiently compact form to enable ready
visualization and interpretation. When viewed over successive times into
the forecasts, they provide information on the relative

![](media/image1.wmf)

![](media/image2.wmf)predictability as a function of forecast lead time
and space (high where and when solutions are close and visa versa). Note
too this is also a form of \"graphical clustering\" in that one can
visually weigh the non-uniform distribution of solutions (if any) and
thereby judge the relative likelihood of specific outcomes in terms of
the number of forecasts pointing in that direction. Spaghetti diagrams
of other parameters can provide specific information with regard to
particular forecast issues. For example, the spaghetti diagrams for
1000-500mb thickness and for 850mb temperature are intended primarily
for assessing the uncertainty in predicting the boundary between frozen
and non-frozen precipitation; contour plots for MSLP relate to the
position and (w.r.t. choice of contour value) the intensity of high and
low pressure systems; isotach composite charts convey information about
jet systems, and etc.

Figure 3 is an example of a probability chart derived from SREF output,
in this case of the Lifted Index (LI) being less than 0^o^ K. The LI is
a measure of the vertical instability and hence an index for the
potential for thunderstorms and/or severe weather. Probability estimates
here are defined simply as the percentage of predictions out of the
total (10) that satisfy the specified criterion. In principle
probability information on any direct or derived model parameter can be
output. Additional examples are precipitation and low-level winds
exceeding various threshold values. Note that the probability charts
convey the net chance for a specific event, while the corresponding
spaghetti diagrams describe the particulars of the alternative scenarios
described by each member of the ensemble.

Many additional products are or soon will become available. They include
clustering, which here refers to grouping together ensemble members that
are similar in some respect in regard to various fields and/or
parameters. The cluster means effectively reduce the number of degrees
of freedom relative to considering the complete set of individual
forecasts, but not so much as the full ensemble mean (except if all the
forecasts are alike). The clustering can be done at various levels over
the model domain as a whole or for specific regions to focus on
individual weather systems. Another set of products under development
are meteograms, time traces at selected point locations. These can
portray the time evolution of various quantities, such as surface
temperature, in terms of the envelope of possibilities or degree of
uncertainty about the ensemble mean (Fig. 4).

![](media/image3.wmf)

![](media/image4.wmf)

![](media/image5.wmf)

![](media/image6.wmf)

**4. CASE STUDY: The January 24-26 2000 Snowstorm**

**4.1 Background**

The storm of 25-26 January 2000 deposited a blanket of heavy snow from
North Carolina northwards to New England and eastern New York State. As
much as 20 inches were recorded in sections of North Carolina and up to
12-15 inches in the metropolitan Washington, D.C. area. The storms
notoriety arises from the fact that it was unpredicted, certainly in
regard at least to its severity in these regions, until about six hours
before heavy snow began accumulating. This largely, though not totally,
reflects the fact that operational forecast models routinely available
gave little, if any, clue to the imminence of this major weather event
as late as the runs from 1200 UTC on 24 January. For the most part the
models predicted the storm track and heavy precipitation too far to the
east. A further overview of the storm and discussion of the short -and
medium-range-model guidance are available at:
http://www.emc.ncep.noaa.gov/mmb/research/blizz2000/. A series of
experiments with various configurations of the Eta model are being run
retrospectively to find a "smoking gun", if any, for the failed
forecasts (see: http: //sgi62. wwb.noaa.gov:8080/BLIZZFCST/).
Additionally, the SREF system has been run retrospectively to assess
whether it would have provided useful information in the context of the
operational forecast problem and decision making process. The SREF
system was as described previously, except that 10 members were run with
both the Eta and RSM. Focus here is on this 20 member multi-model
ensemble run from 1200 UTC 24 January. The 10-member RSM/ETA subset
provided effectively the same signal, but to a lesser degree. As shown
in the following, that signal was a clear "heads up" on the late morning
of the 24th for the possibility of a major snow event, especially when
combined with independent information available from satellite and radar
observations. In actuality, the official forecast for Washington, DC
issued at 21 UTC that day called for only a 40% chance of light snow,
and only some 6 hours later was the forecast changed drastically to
reflect actuality.

**4.2 SREF Results for Snowstorm Case**

![](media/image7.wmf)

about as deep as the observed by 36 hours (\~984mb), and several of the
members correctly tracked closer to the coast than, or example, the
operational Eta (not shown).

Further evaluation of the ensemble forecasts was undertaken to assess
the physical and dynamical mechanisms associated with differences
between the better members and control predictions. The perturbations in
the initial conditions and short term forecast differences thereafter
clearly indicate consistency with expectations from synoptic/dynamic
reasoning. Thus, for example, the better forecasts were associated with
more developmental low-level thermodynamic structure along the southeast
coast (more unstable) and upper level wind fields (upper-level
divergence and low-level convergence). Hence, the bred perturbations are
reliably finding the modes representative of dynamically important
analysis uncertainties. A collorary is that the nature of the initial
differences between the control and better forecasts suggests what and
where additional observations might have made the control analysis
better. And, the results indicate that analysis errors alone, exclusive
of model deficiencies, were in large part responsible for the failure of
the operational runs.

![](media/image8.wmf)

**4.3 Discussion of Snowstorm Case**

The bottom line here was that the ensemble sent a distinct signal from
the runs of 12Z 24 Jan that there was the possibility of a major
snowstorm along the East Coast. An issue is whether that signal (say
30-40%) would have made a difference in the NWS operational forecasts
which, for example, did not indicate a chance of a significant snowfall
in the Washington region until after 00 UTC on 25 January. This question
relates intimately to the more general issue of conveying uncertainty in
forecast products. Certainly in potentially high risk, significant
weather scenarios we feel it is incumbent to communicate the
possibilities of threats (and do so reliably) so that users of NWS
products, including emergency managers, have the requisite information
necessary to take or not take action depending upon the relevant costs
and losses that might be incurred. Finally, we note that in this
snowstorm case, the probabilities provided by the ensembles could have
been tilted upwards given the independent information available from
radar and satellite data by late morning/early afternoon of 24 January.

![](media/image9.png)

**5.0 CONCLUDING REMARKS**

A prototype system for regional model based short-range ensemble
forecasting (SREF) has been developed and is now running routinely twice
per day at NCEP for test and evaluation. The objective is to demonstrate
the operational utility and feasibility of SREF as a function of the
relevant and practical questions of atmospheric predictability. While
the system as now constructed is considered potentially quite valuable
for operational use, considerable room for improvement in strategy and
capability exists. Among the issues under investigation are ensemble
size versus domain, initial condition versus model formulation
sensitivities, and the relative information content from ensembles
versus single (deterministic) runs. A crucial aspect of the later is the
value of the ensembles, even when they must run at lower resolution and
possibly lesser physical sophistication than a deterministic run because
of computer resource limitations.

To more fully exploit SREF it is incumbent to educate and train
forecasters in its use. At present forecast product suites are mostly
deterministic - forecasters produce and issue their single best estimate
of the coming weather - even when they know in their heart it's a
low-confidence prediction. SREF provides a tool (but only a tool) for
forecasters to refine that degree of confidence and to assess the
relative likelihood of alternative possibilities. The next step must be
to modify the products issued to explicitly reflect that information.
One can start with a semi-quantitative message or simple graphic
expressing the confidence (high /low), for example, of the "best bet"
single prediction. From there one can add explicit statements or
graphics of alternative scenarios and their relative likelihood and,
finally, proceed to full consideration of quantitative probability
estimates. In this construct, the human forecaster most certainly
retains a crucial role. For the most part today, forecasters evaluate,
adjust, and otherwise tune the relatively few model runs available on
the basis of their experience, knowledge of model biases, and
internalized data processing (not amenable to computer algorithms). In
the era of ensemble prediction similar sorts of skills are necessary for
interpreting output derived from SREF (e.g., probability distributions,
cluster populations, and alternative scenarios of storm motion and
intensification).

**6.0 REFERENCES**

Brooks, H. E., M.S. Tracton, D.J. Stensrud, G. DiMego, and Z. Toth,
1995: Short range ensemble forecasting: Report from a workshop, 25-27
July 1994, Bull.Amer.Meteor.Soc.,76,1617-1624.

Black, T., 1994: The new NMC mesoscale Eta model: Description and
forecast examples. Wea. Forecasting, 9, 265-278

Du, J., S.L. Mullen and F. Sanders, 1997: Short-range ensemble
forecasting of quantitative precipitation. Mon.Wea.Rev., 125, 2427-2459.

Du, J.,and M.S. Tracton, 1999: Impact of lateral boundary conditions on
regional-model ensemble prediction. Research Activities in Atmospheric
and Oceanic Modeling (edited by H.Ritchie), Report 28, CAS/JSC Working
Group Numerical Experimentation (WGNE),WMO/TD-No. 942, 6.7-6.8.

Hamill, T.M and S.J Colucci,1997: Verification of Eta-RSM short-range
ensemble forecasts. Mon. Wea. Rev., 125, 1322-1327.

Hamill, T.M., and S.J. Colucci, 1998: Evaluation of Eta-RSM ensemble
probabilistic precipitation forecasts. Mon.Wea.Rev., 126, 711-724.

Hou,D., E.Kalnay, and K. Drogemeir, 1999:Objective verification of the
SAMEX '98 ensemble forecasts. Submitted to Mon.Wea.Rev. (see also:
http://meto.umd.edu/\~ekalnay/SAMEX/SAMEX.html)

Juang, H.M., and M. Kanamitsu, 1994: The NMC nested regional spectral
model. Mon.Wea.Rev., 122, 3-26.

Stensrud, D.J., H.E. Brooks, J.Du, M.S.Tracton, and E. Rogers, 1999:
Using ensembles for short-range forecasting. Mon.Wea.Rev., 127, 433-446.

Toth, Z., and E. Kalnay, 1997: Ensemble forecasting at NCEP and the
breeding method. Mon. Wea. Rev, 125, 3297-3319.

Tracton, S., J. Du, Z. Toth, and H. Juang,1998:Short-range ensemble
forecasting (SREF) at NCEP/EMC. Preprints, 12th Conf. on Numerical
Weather Prediction. Phoenix, AZ, Amer. Meteor.Soc., 269-272.