Binbin ZHou
SAIC@NOAA/NWS/NCEP/EMC
1. Motivation, Background and Goal
The
NCEP's
Short Range Ensemble Forecast(SREF) provides probabilistic forecast
information
on the
mesoscale, and has undergone development by EMC/NCEP
since 1996 (Du, et al, 2003). The SREF system
has been run operationally at NCEP for two years. The
SREF Aviation project, a subset of the NCEP SREF
system, seeks to further improve the utility of SREF
products by applying ensemble techniques to aviation
weather forecasts.
Basically, an ensemble
forecast
is composed of a set of models or "ensemble members" run at the
same
time. The members can be made from the same model using
different initial/ boundaryconditions or physics
schemes, from a multimodel system, or from some
combination
of the above. The output from the various
ensemble members is used to perform
statisticalcomputations
to create probabilistic forecast information
such as an ensemble mean, spread (standard
deviation),
probability of occurence, etc.
The aviation weather
forecast
requires additional forecast products than are typically used for
standard
weather forecasting. Aviation forecasting focuses on
short time scales. Substantial efforts have been made
to improve the aviation weather forecast, such as
utilizing
more sophisticated meteorological models and
employing more complex algorithms to compute
aviation
weather variables such as icing and turbulence.
Traditionally, deterministic weather forecast
approaches
were used. It has been recognized that the
computergenerated weather system does not always
result
in a perfect forecast because of the chaotic
characteristics of the atmosphere. Thus,
probabilistic
or ensemblebased forecast from the SREF system
can provideadditionalinformation in terms of forecast
confidence and uncertainty, and can be useful to
aviation weather forecasters.
Please keep in mind that
current
SREFaviation products are still under development and very
experimental.
As the SREF system is updated and SREF ensemble outputs
are changed, SREF aviation products will be
improved or make use of new algorithms as well.
Also, SREF aviation products will be upgraded based on user
feedback and requirements. We are trying to provide a
useful, efficient, and userfriendly tool to aviation
weather forecasters.
Also please keep in mind that the
primary
goal of SREF system is not only improve the forecast"preciseness",
but more important, to quantify the uncertainties
in the initial conditions and the models
or confidence interms
of a set of statistics based on the
ensemble members. It
gives out information on what the
most likely outcome
might be.
The SREF documentation is located here. Additional ensemble training information can be found here.
2. Ensemble models and members
SREF runs at NCEP twice a day; first at 09Z,
then
at 21Z. The forecasts run out to 63 hours,and data is output
at 3hour intervals.
The current SREF ensemble is made up of 15 members,
based
on 32km Eta model and 40km RSM models.
Model 
Concevtion Schemes 
IC breeding 
ETA 
BettsMillerJanic (BMJ) 
Control + 1 pair 
ETA 
KainFritsh(KF) 
Control + 1 pair 
ETA 
BMJSAT (Saturated moisture profile) 
1 pair 
ETA 
KFDET(Full cloud detrainment) 
1 pair 
RSM 
Simple Arakawa Shubert (SAS) 
Control + 1 pair 
RSM 
Relaxed Arakawa Shubert (RAS) 
1
pair 
While the current SREF system has 15 members, not
every
aviation product is made from all 15 SREF
members. The Eta and RSM have different
postprocessed
data output, and don'talways contain the
variables needed for a particular product.
The table below lists the variousaviation products and which
of the models/physics schemes were used to derive them:























































mean&spread 
Visibility  Eta  10  mean&spread 
3. Multiple initial conditions and boundary conditions
The current SREF system uses a breeding method to
create
different ensemble members. That is, using a
random perturbation field, we add or subtract from the
original initial condition to create a pair of new perturbed
initial conditions.
The boundary conditions come from NCEP's global ensemble output.
4. Model timelines, Data display, Output format and Product update times
The SREFaviation production is divided into 3 parts:
(1) Computation: Copy SREF files and run models, 30 minutes total
09Z cycle: 9:3010:00Z
21Z cycle: 21:3022:00Z
(2) Image creation: Produce image (GIF) files from model output, about 30minutes
(3)
Distribution:
Send GIF files to the NCEP server, 30 minutes. This also includes
updating the html files' time stamp on the server
The total time used to produce
the
new forecast is one and half hours, finishing at about 11:00Z for
the 09Z run
and 23:00Z for the 21Z run.
The images are produced by
GRADS
directly from output files in GRIB format. Each weather parameter
has
22 images so that the results can be animated. Thus,
your browser needs to download all 22 of the GIF files before
a new forecast product can be displayed. Those
who have lowspeed Internet connections or less powerful
computers will notice some delay in viewing SREF
products.
The SREFaviation computation data are
stored
in GRIBextension format. The extension is NCEP's special
addition to regular GRIB format for storing unique
ensemble
data. Because of the limits to NCEP's online system
storage, SREFaviation computation results are kept
only
3 days. If users want these data, we can send them files
in GRIBextension format only. A list of the weather
parameters available in the SREFaviation GRIBextension
file can be found
here. Background information on the NCEP
GRIBextension format is also available on our
website.
SREF BUFR files (for specific locations) are not
available
at present.
There are
many
variables used in the ensemble forecast to create the probabilistic
forecasts
of mean, spread,
"spaghetti", and probability of occurrence. In
SREFaviation, only mean, spread and probability are used. The
"spaghetti plots" of individual ensemble members are
not shown.
Definitionensemble mean: The average value of all members.
Definitionspread:
The standard deviation with respect to ensemble mean. The spread of a
variable
indicates the
forecast diversity of the ensemble mean among all the
members.
Definitionprobability:
For an event, out of all members, in how many members does the event
occur.
Probability
of aevent (variable) is expressedas a percentage.
For example, a probability of
icing
at FL180 equals to 60 % means that 9 of the 15 members, or 60% of the
runs
(9/15 x 100 = 60 %), have icing at that level.
In the images, the mean is
denoted
by contours, spread by colored shading, and probability can be denoted
either
by contours or shading.
6. Definition and method of computation for each variable
(1) Icing (InFlight) probability
In the real world, icing is a
phenomenon
in which ice particles or water droplets freeze on an aircraft,
particularly
the wings. The icing forecast predicts where the kind
of environment favorable for icing events is likely to develop.
The icing environment depends
mostly
on temperature, humidity and cloud water content at certain levels.
There
are many algorithms to predict such an environment,
ranging
from the simple to the very complex. Since the current
RSM lacks cloud water content,we use a TRH
(temperaturerelative
humidity) algorithm to predict icing instead
of a method using cloud water. Thus, current
icing
products can't distinguish between frozen ice and supercooled
large droplets (SLD). The new version of RSM,
which
has cloud water content in its postprocessed output, will
be soon be implemented at NCEP. Then we can use more
sophisticated icing algorithms.
The range of T and RH for icing event is:
10 C < T < 0 C and RH > 70%
Icing is computed at 8 flight levels, from low to high:
FL000:
surface
FL030:
3000 ft (900mb)
FL060:
6000 ft (800mb)
FL090:
9000 ft (725mb)
FL120: 12000
ft (650mb)
FL150: 15000
ft (575mb)
FL180: 18000
ft (500mb)
FL240: 24000
ft (400mb)
At certain
levels
and model gridpoints, if temperature and relative humidity both meet
the
icing criteria above,
then the icing condition is "yes". If M ensemble
members out of all N members predict icing conditions at the same
level and gridpoint, then the icing probability is
M/N x100 %
(2) Clear Air Turbulence (CAT) Probability
CAT is defined as a
turbulence
event occuring at high, cloudless altitudes. Like icing, there
are
morethan a dozen
methods to predict the CAT intensities. The SREF
aviation
turbulence product uses the Ellrod(Gary P. Ellrod, 1992)
algorithm.
The Ellrod algorithm
classifies
CAT intensity into 3 categories: LIGHT to MODERATE, MODERATE, and
MODERATE to SEVERE, and uses a turbulence intensity
index
to determine which categorythe event belongs to.
The Ellrod turbulence
intensity
index is defined as a function of stretching deformation (DST),
shearing
deforma
tion (DSH), vertical wind shear (VWS) and convergence
(CVG). These four factors implicitly reflect the effects of
wind gradients in both the horizontal and vertical
directions,
and the temperature gradient as well. In detail,
Index = VWS * [ DEF + CVG ]
VWS = sqrt(dU*dU + dV*dV) / dz, where U , V are wind u,v components (in m/s), respectively
DEF = sqrt(DST*DST + DSH*DSH)
DST = dU/dx  dV/dy
DSH = dV/dx + dU/dy
CVG =  (dU/dx + dV/dy)
As pointed out by
Ellrod,
defining the 3category index values for different CATintensities
depends
on resolution.
For different horizontal resolutions, Ellrodsuggested
using different index values. For the SREFaviation CAT, all
computations use the following index values:








Please note
that
the turbulence intensity index is computed between 2 levels, i.e.
within
a layer instead of at a
single level. In SREFaviation products, the turbulence
intensity is evaluated within 8 continuous layers, from low
to high:
FL210FL180
or 450500mb
FL240FL210
or 400450mb
FL270FL240
or 350400mb
FL300FL270
or 300350mb
FL330FL300
or 275300mb
FL360FL330
or 225275mb
FL390FL360
or 200225mb
FL420FL390
or 175200mb
(3) Sky (Cloud) Cover Probability
Sky or cloud
cover
is divided into 4 categories, clear, scattered, broken, and overcast.
Each
category has a
range of cloud amount in fractions or percentages, as
listed below:















Eta and RSM models
treat
cloud cover as a percentage instead of a category or
fraction.
SREFaviation
products use the seamless range of percentages shown
in theabove table. So the probability of a particular cloud
cover category is the percentage of ensemble members
predicting a cloud cover percentage that falls in the
category. For example, when the probability of
a clear sky at one gridpoint is 50%, that means half the ensemble
members predict clear (0% cloud cover) at that
gridpoint.
(4)Flight Restriction (LIFR, IFR, MVFR, VFR) probability
LIFR: Low Instrument Flight
Rules
IFR: Instrument
Flight
Rules
MVFR: Marginal Visual Flight Rules
VFR: Visual Flight Rules
Restriction categories are defined by cloud base
height
(ceiling), surface visibility and cloud amount category, as
shown below:





























Here is the pseudo code that describes the computation:
For all members N, find how
many
cloud amounts are broken and overcast, say M.
If M=0
VFR probability = N/N=100 %, i.e. no broken and overcast clouds
Else
(NM) members belong to VFR, some of M belong to either LIFR, or
IFR, or MVFR, or VFR,
Do the following:
Within M, find how many members L1 with cloud base <500 ft or Visb
<1
mile,
LIFR probability = L1/N
Within M, find how many members L2 with 500<=cloudbase<1000 ft or
1<=Visb<3 mile,
IFR probability = L2/N
Within M, find how many members L3 with 1000<=cloudbase<3000 ft
or
3<=Visb<5 mile,
MVFR probability = L3/N
Then the Left in M, i.e. ML1L2L3 members are belong to VFR,
VFR probability = [(ML1L2L3) + (NM)] / N = (NL1L2L3) / N
Done
End if
Note that in the computation, "AND/OR" means
either
"AND" or "OR". When using "AND" the evaluation is
more conservative. We use "OR" in our product.
Ceiling and visibility are also computed and listed for reference. (See sections 9 & 10).
Jet stream probability is computed at 3
levels:
4,500 ft, 18,000 ft and 34,000 ft with 3 wind speed thresholds at
each level.
At 4,500 ft, the
thresholds
are 20, 40, 60 knots
At 18,000 and 34,000 ft
the thresholds are 60, 80, 100 knots
e.g., at 18,000 ft
and
a threshold of 80 knots, the image will show the probability of wind
speed > 80 knots at
18,000
ft.
(I)
Tropopause
height: mean and spread in feet
(II) Temperature
at tropopause height level: mean and spread in C
The first height above the ground where air temperature is below 0 C: mean and height spread.
(I) Mean and
Spread:
averaged cloud total amount (%) and spread (%).
example: cloud contour = 80 and shade color is yellow, which indicates
that the average cloud amount is
80%, and standard deviation (diversity among members) is 20 %
(II) Max cloud amount: maximum cloud amount from all ensemble members.
(III) Min cloud amount: minimum cloud amount from all ensemble members.
example: Min cloud amount at a region = 60%, which indicates the
ensemble mean cloud coverage for
this region is AT LEAST 60 %.
Defined
as
the horizontal visibility at the surface. Visibility is not a model
variable
but is computed from post
processing output, based on surface humidity, rain,
etc.
Two notes:
(a) Eta and RSM do
not
consider the haze directly, but haze forms in high humidity. Thus
SREFvisibilities
implicitly include low visibility caused by haze.
(b) Dust storms
cannot
be detected in the Eta or RSM.
Conditional
ceiling,
defined as the height of the lowest layer of cloud when the sky cover
is
broken or
overcast; the mean and spread are computed in
feet.
If no cloud or sky cover is less 50%, then there is no
ceiling, then its value is defined as model default
14200
m or 46580 feet. The model cloud is defined at each
grid point and level and is not continuously
distributed
in the vertical. The cloud base height is computed
from the lowest level of cloud at each gridpoint
in each ensemble member. But after averaging all the
ensemble members, the cloud base may no longer
correspond exactly to any of the model levels.
The height of the highest layer of cloud; but currently not available from the ensemblemodels.
Convection in
the
SREF is measured by the model convective cloud amount. Convective
cloud is created each
ensemble member from its convective scheme (BMJ, KF,
or SAS). This definition is not the standard used in
aviation, where convection is defined as the mass
convergence
ordivergence. In the SREF product, the location of
convection is expressed in terms of convective
cloudamount
mean and spread.
(13)Convection Speed and direction
Defined as convective storm's
velocity
field. Mean (contour), spread (filled) and vector flow
(direction)
are
plotted in one picture.
(14)Low level wind shear (LLWS)
LLWS here is defined by
the vector change in the layer between the surface and 2000 feet level.
Since winds are
defined at fixed pressure levels with 25mb intervals, the winds at
different location may be at different pressure levels.
Furthermore, 2000feet level will not just at a pressure level.
Therefore, to compute the LLWS, we must first find the
2000 feet level at each grid, then using 10m wind and the 2000 feet
wind to get the wind change.
First serch 2000 feet + 10m height (ie 2030
feet) from the surface upward level by level, after find it, say
between level5 and level6,
then compute the U and V components of the wind at this level by linear
interpolation of level5 wind and level6 wind.
Second step is computing the wind vector change over 10m and
2030 feet. The LLWS is defined as
LLWS = SQRT [ (U_{2030}
 U_{10})^{2} + (V_{2000}
 V_{10})^{2} ]
Where 2030 means 2030 feet, 10 means 10
meter.
The LLWS mean and spread thus can be obtained from
15 ensemble members.
The severe LLWS is defined by (1) LLWS
> 20 knotes over 2000 feet, or
(2) LLWS > 0.16 over any 200 feet layer withn the 2000 feet
The probability of severe LLWS is also computed, but please note that, the pressure level intervals are
fixed 25 mb, or 600 feet, much larger than 200 feet,
so, in current case, only case(1) is the threshold for severe LLWS here.
Probabilities of three kinds of precipitation are defined: rain, snow and freezing rain are calculated.
(16)Surface wind speed and direction at 10 m
The mean of surface wind
speed is computed from average of total wind speeds Wi
where
Wi = SQRT(Ui^{2}+Vi^{2})
The products include
:
Wind speed mean, spread (wind direction is also depicted in the same
image)
The probability for wind speed exceeding 10 knotes
The probability for wind speed exceeding 20 knotes
The probability for wind speed exceeding 30 knotes
The calculations of the mean
and spread of wind direction(North is reference) are a little bit
complex. First, compute
the mean and spread of U and V components respectively, then the mean
and spread of wind direction can be expressed as:
mean of wind direction = arctg ( Umean /
Vmean)
spread of wind direction = arctg (Wspread
/Wmean)
where
Wspread = SQRT ( Uspread^{2} + Vspread^{2})
Wmean = SQRT ( Umean^{2}
+ Vmean^{2})
(17) Fog occurence probability
Fog is detected at a grid point in an ensemble model if following cloud base and
cloud top threshold is satisfied:
Cloud base < 10 m And Cloud top < 400 m
In general, the top of ground fog or radiation fog is less than 200m which is
mainly controlled by surface cooling and weak turbulence, while sea fog or advection
fog is winddriven and can be thicker than radiation fog. So 400 m is used for the
cloud top threshold.
Currently, low level wind shear product has been verified, and showing
that, the skill decreases with the
forecast time, but it i sstillful uptime 63 hours. Please see here fore
detail.
The verification of other SREF aviation products has not yet been
performed because
observed data, either service
or pilot reports, are not available to NCEP. We are
planning
to work with AWC and Aviation Service Branch to do
this work. We will also cooperate with FSL using
its RTVS (Real Time
Verification System) to verify some SREF
aviation products.
Before verification
work
can be done, probabilistic forecastvariables (mean, spread, and
probability)
must be
converted from the NCEP GRIBextension format into a
regular data format such asGRIB and BUFR. Until that
happens, there is no simple way to store the ensemble
data in a standard, easy to use format. AWC, RTVS,
Aviation Service Branch and EMC are working togetherto
solve this problem.
The
SREFaviation
system has been initiated and has been making good progress. But
it is still in the deve
lopment phase. It will be updated along with the
SREF system. Here are some upcoming improvements to the
SREF system:
(1) Add Alaska domain.
(2) Update ensemble members to include more,
different
physics schemes. Currently, breeding pertubations for
initial conditions is the main method used to
differentiate
between the ensemble members. An ensemble with
many different physics schemes and pertubations
will create further diversity among the different members, and a
larger spread in results. Our study already showed
better
skill than with a breedingonly ensemble.
(3) Increase Eta and RSM resolution from the current operational 48 km to 32 km.
(1) Ensemble members
There are several possible
techniques
to create different ensemble members, with the goal of creating the most
diversity among the ensemble members. We are still
experimenting
with different techniques to see which will
result in the highest skill.
(2) Variables and Algorithms
(i) Icing is currently calculated using the
simplest
TRh algorithm. We will switch to a more complex one if the
basic data is available
and computing resources permit. Our goal is not to investigate
which
algorithm is best,
but to try instead to
use the ensemble technique for aviation.
(ii) Ellrod turbulence also is relatively simple algorithm, which may be a candidate for replacement.
(iii) SREF aviation products are created from
regular
forecast variables, which aren't always the same as those
used in aviation. For example, in aviation convection is defined
differently than in the usual meteorological
sense.
(iv) Convection speed and direction are
also
an issue. In SREF, convection speed and direction are computed
from storm UV fields. They might not match the actual convection speed
and direction.
(v) Vertical wind shear is
on
pressure levels of 1000, 950, 900 mb. For mountain regions like the
Rocky
Mountain
states, we can't compute wind shear on pressure surfaces that intersect
or are below the surface.
(3)Verification
How
to
perform verification on SREF aviation products is most important
unsolved
problem. The current
difficulty is output files in NCEP's GRIB format
extension,
not the standard GRIB format. RTVS and AWC can't
read the NCEP format extension & can't help with
verification. We are working on this issue. Storing probabilistic
data in the BUFR format is also a problem.
(4) Ensemble domain restricted to CONUS; Alaska domain must be added.
(5) Probabilistic data format
SREF
aviation
products are gridbased and not available for specific cities or
airports,
since the single station
postprocessing uses BUFR files (not currently output
by SREF). If some users want specific stations, please
contact us. We can do it for you separately.
(6) 90minute update time may be too slow to meet the requirement of aviation users.
(7) Machine production problems
Because SREFaviation products are still in the testing phase they are
not NCEP "official" products. By
NCEP policy, the products must run on the NCEP
development
machine instead of the production machine.
Since the recent upgrade, the development machine is
often unstable goes down from time to time. When that
happens, the SREF cron jobs will be rerun manually,
which will delay product delivery.
We welcome any
comments,
suggestions and feedback from users. If users have new requests,
we will satisfy
them if we can.
3. Federal Meteorological handbook, No. 1 (FMH1),
1995
4. NWS Instruction 10813 of TAF, 2004
Acknoledgement
Following persons are thanked for their technical support, discussions and valuable comments .
Fred Mosher, AWC/NCEP?NOAA
Steven Silberberg, AWC/NCEP/NOAA
Mark Andrews, Aviation Service Branch, NWS//NOAA
Michael Graf, Aviation Service Branch, NWS/NOAA
Jennifer Mahoney, FSL/NOAA
Kevin Baker, ?/NOAA
Kelvin L JohnStone, NWS/NOAA
Also thanks to Mary Hart for her review and editing this document
Contact information:
Binbin Zhou
EMC/NCEP/NOAA
Binbin.Zhou@noaa.gov
Call (301)7638000x7255
or
Jeff McQeen
EMC/NCEP/NOAA
Jeff.McQueen@noaa.gov
Call (301)7638000x7226