- 5 December 200=
6 - fvs.hlp VERSION 2006.12
Keit=
h F.
Brill
keith.brill@noaa.gov
WELCOME TO fvs -- the EMC/HPC forecast
verification system
The fvs script performs one or more of four functions.
functions are triggered by the presence of a single
character
within a single character string forming only one
command-line
input. I=
f no
input is given, this help information is printed.
The system
controlled by fvs consists of a combination of<=
br>
unix
C-shell scripts and fortran programs.
Here is a
summary of the actions caused by individual characters
found in the command-line input to fvs:
Character
Action
=
h =
Prints out this help information
=
v =
Prints out version number and change summary
=
u =
Starts user input for search conditions
=
s =
Starts the search software for display output
=
c =
Starts the search software for VSDB output
=
p =
Starts the graphical display software
Entering <=
span
class=3DSpellE>husp will perform all four functions in that order.
the performance of the functions.
A second i=
nput
may be given on the fvs command line. If the
first input is not h, the second input may be any char=
acter
or
string of characters.=
In this case, the presence of the second
input triggers fvs to rese=
t the
path that points to the database;
fvs
will request input from the user to reset the VSDB_DATA
environment variable.
Entering <=
span
class=3DSpellE>fvs h followed by one of the parameters listed below =
gives
specific help information on that item. Most of them are "fvs p"
mode parameters.
border -
graph mode parm
panel - gr=
aph
mode parm
c_codes - fvs<=
/span>
compute codes parsevgf - PARSEVGF help file
clear &nbs=
p;
- graph mode parm
pbs - General =
PBS prob file
colors -
graph mode parm ptype &nb=
sp;
- graph mode parm
ctl_edit - YL's edit trace.ctl reflin - graph mode parm
device -
graph mode parm
revers - graph mode parm
factor -
graph mode parm
rstrcs - graph mode parm
fvs - this
gd2obs - GD2OBS help file ryaxis <=
/span>-
graph mode parm
gdfho - GDFHO help file
sigtst1 - significance=
test1
gdgbs - GDGBS help file
title - gr=
aph
mode parm
gdl1l2 - GDL1L2 help file
trace - gr=
aph
mode parm
gdpbsfho - GDPBSFHO help f=
ile t_text <=
/span>-
graph mode parm
getter - KB's edit trace.ctl version - info on fvs=
versions
grphprm - graph parm
list
witlin - graph mode parm
hstclr - graph mode parm
wwx - WWX PBS =
prob file
lablev - graph mode parm
xaxis - graph mode parm
line =
;
- graph mode parm
xlabel - graph mode parm
l_text <=
/span>-
graph mode parm
xudlbl - graph mode parm
marker -
graph mode parm
yaxis - graph mode parm
mkplot - mkplo=
t_vsdb
help &n=
bsp;
ylabel - graph mode parm
mkvsdb - mkvsd=
b_vsdb
help &n=
bsp;
yrlbel - graph mode parm
For more
information on VSDB data, see the VSDB database
documentation below.
The fvs script is found in $VSDB_SCRIPTS and must be in
$PATH.
The $PATH
environmental variable must also include the following
fortran programs:
mkflnm_vsdb
mkvsdb_vsdb
These prog=
rams
must be specifically built for your workstation
and operating system type.
Graphical
displays are done by GEMPLT, the GEMPAK graphics
primitive software library.
Function u=
(user
interface for search conditions)
This funct=
ion
initiates a script that queries the user for
information regarding search conditions for data to be
displayed
in a plot or a set of plots. Prompting information is given
to assist in making choices. The search conditions for a total<=
br>
of up to twenty-four traces may be specified. These traces may be
all in one plot or constitute several different plots.=
This funct=
ion
creates a file called trace.ctl.
The user is
asked to set search conditions for dependent and
independent variables.
It is helpful to have a thorough visual
concept of the type of plot you want before setting the
search
conditions. It
may be helpful to do a rough hand tracing of
the type of graph you want to see before proceeding.
following definitions may help you in setting search
conditions.
Definitions:
categorically binned
data plot =
- a plot whose independent variable is
=
&nb=
sp;
determined by sets of search conditions
=
&nb=
sp;
x axis.
data combination - the process of adding
together data
=
&nb=
sp;
values that occur at the same point on
=
&nb=
sp;
the abscissa (x axis) as determined by
=
=
the independent variable search conditions
dependent variable - numbers whose values=
are
graphed along
=
&nb=
sp;
the ordinate (y axis) of a graph
dependent variable
search condition - any search condition that
determines how
=
&nb=
sp;
data will be found and combined with
=
&nb=
sp;
other data but does not by itself
=
&nb=
sp;
determine where the data would be
=
&nb=
sp;
plotted on the x axis (see independent
=
&nb=
sp;
variable search condition)
independent variable - a general term for=
that
which
=
&nb=
sp;
determines positions on the abscissa
=
&nb=
sp;
(x axis) of a graph
independent variable
search condition - a search condition t=
hat
determines
=
&nb=
sp;
where data would be plotted in the set
=
&nb=
sp;
of locations along the abscissa (x axis).=
=
&nb=
sp;
The "values" along the abscissa may be
=
&nb=
sp;
character strings (e.g., regions, names
=
&nb=
sp;
threshold values, level values).
plot - a display consisting of a set of o=
ne to
eight traces
=
that are usually displayed together on th=
e same
graph
scatter plot - a plot formed by two traces
whose dependent
=
&nb=
sp;
variable values are functions of the same=
=
&nb=
sp;
independent variable (usually time). In this
=
&nb=
sp;
case, the x and y axis values are both
=
&nb=
sp;
determined by the values of the dependent=
=
&nb=
sp;
variable.
time series - a plot formed by traces of =
data
whose
=
&nb=
sp;
independent variable is time
trace - a sequence of numbers representing
values of some
=
dependent variable as a function of an independent
=
variable.
Many values may have been combined to
=
make the number at a point on a trace.
The user is
asked to set some numbers controlling consistency
checking, which is done when the search for data is
completed.
One form of
consistency constraint is to make sure that the number
of data records contributing to the combined values is=
the
same
from point to point along a trace and/or through corre=
sponding
points on multiple traces. Another form of constraint is to
make
sure that the same set of verifying times and values of
any
dependent variables for which a data combination list
exists
contributed to the combined values at points on the da=
ta
traces.
The form o=
f the
constraint is determined by a single number chosen
by the user.
Traces identified to be on the same plot are made
consistent in accordance with the selected number. Another number
determines whether the consistency constraint applies
vertically
through the traces (with point to point variation) or
horizontally
along the traces (with trace to trace variation), or b=
oth
(for total
consistency, no variation). It is possible to set a
consistency
constraint that results in a count of zero values for a
trace.
In any cas=
e, a
brief message is printed telling how the data were
combined.
It is poss=
ible
to have the software automatically determine the
consistency constraint.
When this is the case, the user is asked
to enter a data loss tolerance percentage. This is the percentage
of the data that you would be willing to sacrifice to
maintain the
consistency constraint.
If more than this percentage would be lost
in satisfying the consistency constraint, then the
consistency
constraint is removed, and all data found in the searc=
h are
used
in the data combination.
Functions =
s and
c (search the data base)
In this mo=
de, fvs makes no queries to the user. It performs
a search of the data base to which the environmental
variable
VSDB_DATA points.&nbs=
p;
The file trace.ctl must exist to provide=
the search instructions.
A long sea=
rch
involving many input VSDB files will go faster
if the process of generating the file list can be
bypassed.
If the sea=
rch to
be executed involves the same time range and
models as the previous search, then copy the file
named
vsdb_files_found into vsdb_files_found.save.
The latter file
will be used as the source for the file names, thus
saving
the time required to build the list of file names.
The functi=
on s
generates a file called trace.dat. The function
c generates a file called vsdb.da=
t.
You may edit and clone different ve=
rsions
of trace.ctl without
actually executing function u. You must make sure that the
appropriate file is named trace.c=
tl
in the local directory
before initiating function s or c if you are bypassing=
function
u.
Editing of=
trace.ctl should be attempted with great caution.
When the s=
earch
is completed, a summary of the number of VSDB
data records found, accepted, and rejected is printed
out. The
following gives a description of each column of that
output:
RECORDS
FOUN=
D =
of records actually found for this trace.
=
&nb=
sp;
If each point on the trace has the same
=
&nb=
sp;
number of records contributing to it,
=
&nb=
sp;
then this column shows the product of
that
=
&nb=
sp;
number and the number of points along
=
&nb=
sp;
the trace.
RECORDS
PADDED =
count FHO (forecast, hits, obs) records<=
br>
=
&nb=
sp;
added to this trace before consistency
=
&nb=
sp;
checking is done so that consistency
=
&nb=
sp;
checking does not fail. For example,
=
&nb=
sp;
if there are no forecasts or observations=
=
&nb=
sp;
of precipitation exceeding two inches,
=
&nb=
sp;
then that threshold and higher thresholds=
=
&nb=
sp;
could be missing from the data archive.
=
&nb=
sp;
as though they were found. The total
=
&nb=
sp;
number added is shown. This column is
=
=
nonzero only when threshold value is the
=
&nb=
sp;
independent variable for the data plot.
TOTA=
L =
This column is just the sum of the
RECORDS =
preceding two.
NUMBER =
This column reports the
number of VSDB
ACCEPTED
=
&nb=
sp;
checking is complete. Primary consistency
=
&nb=
sp;
checking is done by matching verification=
=
times and/or counting the number of
=
&nb=
sp;
records contributing to each point on
=
&nb=
sp;
an individual trace and comparing that
=
&nb=
sp;
number to either or both the correspondin=
g
=
&nb=
sp;
points on the other traces comprising the=
=
&nb=
sp;
plot or the other points along the trace.=
=
&nb=
sp;
The degree and type of consistency checking
=
&nb=
sp;
is under user control.
=
&nb=
sp;
CONSISTENC=
Y =
This is the number rejected by consistency
REJECTIONS =
checking. It is=
the
difference between
=
&nb=
sp;
the numbers reported in the last two
columns:
=
=
TOTAL
RECORDS minus NUMBER ACCEPTED.
ADDITIONAL=
=
This column reports additional rejections
REJECTIONS =
that occur when points have to be eliminated
=
&nb=
sp;
from one or more traces because those
points
=
&nb=
sp;
do not exist on at least one other trace.=
=
&nb=
sp;
This count is non-zero when strict
=
&nb=
sp;
consistency constraints require an exact<=
br>
=
&nb=
sp;
point for point match among two or more
TOTAL # OF=
=
This column is the sum of the preceding two
REJECTIONS =
columns.
FINAL #
ACCEPTED after=
all
rejections have been subtracted.
# MISSING<=
span
style=3D'mso-spacerun:yes'> =
This column reports the number of empty bins
POINTS =
for a categorically binned data plot; other-
=
&nb=
sp;
wise, it contains N/A (not applicable),
which
=
&nb=
sp;
also is the case for wild card bin
conditions.
=
&nb=
sp;
Empty bins account for disparities in the
=
&nb=
sp;
finally accepted number among traces for
which
=
&nb=
sp;
consistency checking is in force, since
data
=
&nb=
sp;
totally missing at a point on one trace
is
=
&nb=
sp;
not allowed to eliminate consistently
matching
=
&nb=
sp;
data on other traces of the same plot set=
.
Function p=
(plot
statistics)
This funct=
ion
queries the user for information on how to
display a graph of data in trace.=
dat,
generated by applying
function s. Help
information is provided in this function.
Please read
instructions carefully before making choices.
Function p=
may
be applied repeatedly to data found in trace.dat.
The file <=
span
class=3DSpellE>trace.dat may be saved under another name so that it<=
br>
is not necessary to re-execute function s to view the
data.
Always mak=
e sure
that the file trace.dat contains the
appropriate data before running function p (fvs p).
The
search conditions used to find the data in trace.dat are
saved at the beginning of the tra=
ce.dat
file.
Other
information:
Required
environmental variables:
VSDB=3D
VSDB_TBL=
=3D path to tables u=
sed by
fvs scripts
VSDB_HLP=
=3D path to help
information used by fvs scripts
VSDB_SCRIP=
TS=3D
path to fvs scripts
VSDB_DAY1=
=3D the earliest possible =
day
for all data
VSDB_DATA=
=3D path to VSDB data
directories
VSDB_TEMP=
=3D path to temporary disk=
space
(usually /tmp)
Example of
setting environmental variables:
setenv VSDB
/export/hp52/wd22kb/vsdb
setenv VSDB_TBL /export/hp=
52/wd22kb/vsdb/tables
setenv VSDB_HLP
/export/hp52/wd22kb/vsdb/help
setenv VSDB_SCRIPTS
/export/hp52/wd22kb/vsdb/scripts
setenv VSDB_DAY1
199601010000
setenv VSDB_DATA
/export/mmbsrv/usr1/wd20er/data/vsdb
setenv VSDB_TEMP /tmp
How do I p=
oint fvs to my own statistical data base?
1. Make your database on some network=
accessable disk.
a. Each
model or forecast system to be verified must have
=
its own directory under the $VSDB_DATA
path.
b. All of the verification dat=
a for
a single day must be in
=
a single file whose name is by convention=
the
following:
=
x_YYYYMMDD.vsdb
=
where x is the model name, and YYYYMMDD i=
s the
year, month,
=
and day. Note that x is also the subdirec=
tory
name and the
=
name preceding / in the model header field
(field # 2) in
=
all of the data records in the files. The slash / is not
=
used if no qualifiers follow the model
name.
Data
may also be stored in monthly files named in the
=
following form:
=
x_YYYYMM.vsdb
2. Once several days of data are in t=
he
data base, execute the
=
script $VSDB_SCRIPTS/update.files
for each of the nine data
header fields.
This script generates table files used by
=
function u that are tailored to that part=
icular
data. These
=
files are . files
under $VSDB_DATA. Do the
following:
=
a. Execute
$VSDB_SCRIPTS/update.files for all fields---
=
$VSDB_SCRIPTS/update.files ALL
=
b. If
you change the contents of one of the header fields
=
in your data, rerun $VSDB_SCRIPTS/update.files for that
=
particular field---
=
$VSDB_SCRIPTS/update.files n
=
where n =3D 1, 2, 3, 4, 5, 6, 7, 8, or 9<=
br>
=
For more information on header fields, see the database
=
description included below.
3. Make sure $VSDB_DATA points to the
correct statistical
=
data base before running fvs. You may have =
fvs
set the
=
data path on the fly by entering any char=
acter
as a second
=
input on the command line. You will be queried by fvs for
=
the path.
4. Make symbolic links to point to ot=
her
data directories that
=
contain results to be compared with your
data. This step is
=
optional.
=
EMC/HPC fvs INSTALLATION INFORMATION
=
&nb=
sp;
Keith F. Brill
=
&nb=
sp;
keith.brill@noaa.gov
=
&nb=
sp;
20 April 1998
=
&nb=
sp;
UPDATED: 20 January
2000
=
&nb=
sp;
UPDATED: 07 August
2000
=
&nb=
sp;
UPDATED: 06 March 2003=
=
&nb=
sp;
UPDATED: 03 May 2004
To run fvs, the NCEP statistical database access and display=
system,
the following must be in your path:
fvs - the dri=
ver
C-shell script
mkflnm_vsdb - fortran program that makes required VSDB
=
&nb=
sp;
file names (legacy program as of March 20=
03)
mkplot_vsdb - fortran program that plots graphs
mktlst_vsdb - fortran program that lists out trace
=
&nb=
sp;
information
mktrcs_vsdb - fortran program that reads trace data and
=
&nb=
sp;
combines statistical values for plotting<=
br>
mkvsdb_vsdb - fortran program that reads trace data and
=
&nb=
sp;
combines statistical values for VSDB
output
mkymrg_vsdb - fortran program that scans a search control
=
&nb=
sp;
file to create a list of models and a
list
=
&nb=
sp;
of date-time ranges
$GEMEXE/gplt - GEMPAK graphics subprocess
$GEMEXE/xw - GEMPAK X-windo=
ws
driver subprocess
$GEMEXE/ps - GEMAPK PostScr=
ipt
driver subprocess
$GEMEXE/gf - GEMPAK GIF
where GEMEXE is an environmental variable
defined to be
the path to the GEMPAK executables for yo=
ur
workstation and
operating system.
The following
environmental variables must be set
setenv VSDB /complete path=
to fvs scripts, help, tables, etc.
setenv VSDB_TBL $VSDB/tabl=
es
setenv VSDB_HLP $VSDB/help=
setenv VSDB_SCRIPTS
$VSDB/scripts
setenv VSDB_DAY1 199601010=
000 OR
your earliest date
setenv VSDB_DATA /complete=
path to
default VSDB data
setenv VSDB_TEMP /tmp
setenv DDAPP /complete path
executable file directories
The fortran programs must have=
been
linked to run on your work-
station
and operating system. The fol=
lowing
executables are
available:
$DDAPP/exe_hp HP OS
$DDAPP/exe_sg6
SGI OS 6
$DDAPP/exe_lnx Linux
$DDAPP/exe_aix IBM workstations
Executing source
$VSDB_SCRIPTS/for_fvs on a supported NCEP
workstation
should setup everything required to run fvs.
that
you may have to redefine VSDB_DATA to look at the data you
want. There is a script designed to help=
you
set the correct
definition
for VSDB_DATA. The script must
ALWAYS be invoked by
source:
source $VSDB_SCRIPTS/set_vsdb_data
The script promp=
ts you
for input. fvs will invoke this script for
you
when you enter a second input on the command line.
In a non-NCEP
environment, modify either (or both) for_fvs_generic
(C shell) or
environment
appropriate to your workstation configuration. These
scripts
are located under $VSDB_SCRIPTS.
To build the nec=
essary
executable files to run fvs, cd
to $VSDB/lib
Execute the comp=
ile
script and then the bld script. For example,
to
do a build on a LINUX workstation, enter the following:
compile
lnx ALL
bld lnx ALL
=
fvs VERIFICAT=
ION
STATISTICS DATABASE
=
&nb=
sp;
Keith F. Brill
=
=
Mark
D. Iredell
=
&nb=
sp;
20 January 2000
=
MODIFIED: Keith F.
=
MODIFIED: Keith F.
=
MODIFIED: Keith F.
=
MODIFIED: Keith F.
=
MODIFIED: Keith F.
=
MODIFIED: Keith F.
The fvs Verification Statistics Database (VSDB)=
is an
ASCII text file.
Records in the file are separated by linefeeds (X'0A'). The maximum
record size is 512 bytes. Each record contains one or possib=
ly
more
statistic values.
The record is defined by blank-separated text fields.
The maximum field size is 24 bytes.
The fields must not be either null
or contain an embedded blank. Fields do not have to line up in
columns.
All characters are assumed to be upper case.
There will be one VSDB file for each day for each model. The naming
convention for the file will be name_YYYYMMDD.vsdb,
where name is the
model name, and YYYYMMDD is the year, month, and
day. This will include
all verifications done on the particular day YYY=
YMMDD
for that model.
The models will placed in separate directories. The directory name
must agree with name_ in the .vsdb
file name. Also permissible
is
name_YYYYMM.vsdb.
VSDB files may be compressed, in which case .Z is appended to the file
name, or gzipped, in=
which
case .gz is appended. A directory may
contain a mix of .gz=
, .Z,
and regular text files. When a
search is
done, the files will be uncompressed or gunzipped into a directory
named vsdb under
$VSDB_TEMP. $VSDB_TEMP must be
large enough and have
write permission.
If the directory vsdb does not exist, it=
will
be
created with permission open to all. If vsdb
already exists, it must
have write permission. All files in the existing vsdb directory will
be deleted before the search commences. Any files placed in this
directory will remain after the search is done.<=
br>
The first set of fields consist of the header
fields. The header fields
identify the verification statistic(s). There are usually 11 header
fields but more can be added compatibly. The contents of the header
fields should conform to the standards below:
Header f=
ield 1 : (char) verification dat=
abase
version
Header f=
ield 2 : (char) forecast model
verified
Header f=
ield 3 : (char) forecast hour
verified
Header f=
ield 4 : (char) verifying date
Header f=
ield 6 : (char) verifying grid or
region
Header f=
ield 7 : (char) statistic type
Header field 10-- Not yet
defined
Following the header fields is a separator field consisting of a single
equals sign (=3D).
Separator field
: (char) =3D
The next set of fields consist of the data
fields. The first data field<=
br>
is typically the number of values used. The following data fields are
one or more statistics values. The statistic type header field
infers
the order of the statistic values. A missing value for the data
fields
is -1.1e31.&nbs=
p;
All data values must have no more than 9 digits following
the decimal point (e.g., 24.123456789 is valid, =
while
24.1234567891 is
not valid).
Data field 1 =
:
(real) number of values used (gridpoints or
Data field 2 =
:
(real) actual statistic value(s)
Optional=
data
fields.
Examples:
V01 AVNB 24 1996090100 FNL NHX ACORR(1-20) Z P50=
0 =3D
3600 94.32
V01 ERL 36 1996090100 MB_PCP G211 FHO>2.5 APCP/24 SFC =3D 6045 .40 .50
.30
V01 ETAX 24 1996090100 MESO G211 TENDCORR SLP MSL =3D 10000 77.77
V01 ECM 24 1996090100 FNL NHX RMSE Z P1000 =3D 3600 -1.1E31
V01 AVN 12 1996090100 AIRCFT/GOOD NHX RMSE T P250-200 =3D 3600 1.4321E+00
If the same entry in the header field is found in the next VSDB record,
then it may be replaced by ". This allows for more compact VSDB
files.
The " may be repeated for that header field=
in
following VSDB records
until the entry changes.
HEADER FIELD STANDARDS
Header field 1: verification database version
V01
Vnn =
Future
versions
Header field 2: forecast model verified
AVN =
Aviation forecast model
AVNX =
AVN Parallel X
AVNY =
AVN Parallel Y
AVNZ =
AVN Parallel Z
AVNU =
AVN Parallel U
AVNV =
AVN Parallel V
AVN? =
AVN future parallel ?
BAWX =
HPC Basic Weather Desk
COM =
Combined Ensemble (SREF)
ECM =
ECMWF =
ENSnn =
Ensemble member nn
ENSxx =
Ensemble
product xx
ETA =
Early Eta model
ETAL =
Eta Parallel L
ETAV =
Eta Parallel V
ETAX =
Eta Parallel X
ETAY =
Eta Parallel Y
ETA? =
Eta future parallel =
?
FNL =
Final GDAS
GFS =
Global Forecast System
KFETA =
Kain-Fritsch Eta
LFM =
Limited Fine Mesh Model
MEDR =
MESO =
Mesoscale model
MRF =
Medium-range forecast model
MRFX
MRF
Parallel X
MRFY =
MRF Parallel Y
MRFZ =
MRF Parallel Z
MRFU =
MRF Parallel U
MRFV =
MRF Parallel V
MRF? =
MRF future parallel ?
NGM =
Nested-grid forecast model
NOGAPS =
Navy Global Model
PER/X =
Persistence from model X analysis
QPF/nnn =
HPC quantitative precipitation forecast
RSM =
Regional Spectral Model
RSMH =
Hawaiian Regional Spectral Model
RUC =
Rapid Update Cycle
SPEC &n=
bsp;
Spectral
Model
SREF =
SRMEAN =
TSname =
Hurricane model
UKM =
UKMET =
USRname =
User-defined
experiment
nnn =
Forecaster number identifier
##s/nnn =
HPC Snowfall probability forecast
The model name may be followed by an optional slash preceding
a grid number, indicating the output grid from w=
hich
the
model data was interpolated for the verification=
. For example,
eta/212 implies that grid 212 was the source of =
the eta model
data used in the verification.
The slash may also be followed by a qualifying character
string.
For example, AVN/ANL would be the AVN analysis as
opposed to the AVN initialization, both of which=
would
be
assigned forecast hours of 00. Ensemble member names may
follow the slash, e.g., ETA/N1, ETA/CTL, ETA/P1.=
Members
may also be MEAN, MED, BST, OPL, or others. Users must consult
ensemble model developers for specific definitio=
ns of
these
qualifiers.&nbs=
p;
Qaulifiers following a slash may be wildcarded in
specifying fvs search
conditions by terminating the header
field with / followed by nothing (end of line).<=
span
style=3D'mso-spacerun:yes'> This feature
should not be used if consistency constraints ar=
e set
for the
search.
For best results when consistency constraints are set,
explicitly list the elements to be found and com=
bined
in creating
the trace.ctl file.<=
br>
The model name may be followed by an optional @ sign preceding
a two-digit model cycle time, e.g., ETA@12, for =
the
12Z run of
the ETA model.
The HPC snowfall forecast is denoted by ##s/nnn,
where ## is
the product identifying number (e.g., 93, 94, or=
98)
and nnn
is the reference number of the forecaster who ma=
de the
forecast.
The HPC QPF forecast is denoted by QPF/nnn, whe=
re nnn is the
the forecaster refer=
ence
number, if available.
Header field 3: forecast hour
hhhh.d/w
wh=
ere
hhhh.d is the hour in the forecast that lies at
the
mi=
dpoint
of the time interval w. w is usually the interval
ov=
er
which observations have been interpolated.=
The interval
be=
tween
forecasts used in the interpolation is w/2. If w is
ze=
ro,
then /w is omitted, and no time interpolation is implied
(e=
.g.,
grid-to-grid verification).
Header field 4: verifying date
yyyymmddhh.d/w/i
wh=
ere
yyyymmddhh.d is the beginning, midpoint, or end=
ing
of
a<=
/span>
time interval of width w hours with a data increment of i,
wh=
ich
gives the time interval in hours between the data times
co=
ntributing
to the stored statistical result.
If /w/i are
absent,
th=
ey
are both assumed to be 0. If =
w is
preceded by a plus (+)
si=
gn,
then yyyymmddhh.d is the beginning of the time
interval.
If w is preceded by a =
minus
(-) sign, then yyyymmddhh.d is the
en=
ding
of the time interval. Otherwi=
se,
unsigned w indicates that
<=
span
class=3DGramE>yyyymmddhh.d is the midpoint of an interval w h=
ours
in duration.
Note that .d is the
fractional part of an hour and may be omitted
if=
it is 0.
To standardize certain
commonly requested time searches, the
fo=
llowing
conventions are imposed for yyyymmddhh.d/w:
h=
h.d
=3D 12.0 and w=3D24 implies entire day yyyymmdd=
d=
dhh.d
=3D 15xx.0 and w =3D 730 implies entire month yyyymm
m=
mddhh.d
=3D 0215xx.0 and w =3D 2190 implies first quarter of y=
yyy
m=
mddhh.d
=3D 0515xx.0 and w =3D 2190 implies second quarter of =
yyyy
m=
mddhh.d
=3D 0815xx.0 and w =3D 2190 implies third quarter of y=
yyy
m=
mddhh.d
=3D 1115xx.0 and w =3D 2190 implies last quarter of yy=
yy
m=
mddhh.d
=3D 0401xx.0 and w =3D 4380 implies first half of yyyy=
m=
mddhh.d
=3D 1001xx.0 and w =3D 4380 implies last half of yyyy<=
/span>
m=
mddhh.d
=3D 0701xx.0 and w =3D 8760 implies entire year yyyy
m=
mddhh.d
=3D 0115xx.0 and w =3D 2190 implies climatological
winter
=
&nb=
sp; =
season for yyyy
m=
mddhh.d
=3D 0415xx.0 and w =3D 2190 implies climatological
spring
=
&nb=
sp; =
season for yyyy
m=
mddhh.d
=3D 0715xx.0 and w =3D 2190 implies climatological
summer
=
&nb=
sp; =
season for yyyy
m=
mddhh.d
=3D 1015xx.0 and w =3D 2190 implies climatological
fall
=
&nb=
sp; =
season for yyyy
Here xx is the valid h=
our of
the averaged forecasts.
The search software wi=
ll
look for these specific criterion on
re=
quest
for daily, monthly, quarterly, semi-annually, annually,
or=
seasonally tagged data.
The search software wi=
ll NOT
make any attempt to decide whether
a<=
/span>
specific yyyymmddhh.d lies within intervals def=
ined
in the data
ba=
se
using w. It will, however, be=
able
to match the string
y=
yyymmddhh.d/w/i. It will only discriminate on the b=
asis
of w
wh=
en
daily, monthly, quarterly, semi-annually, annually, or
se=
asonally
tagged data is requested.
as=
a search criterion only. If i=
t is
not present in the data
fi=
eld,
it will be assumed to have a zero value.
Header field 5: verifying data source or analysis
Any name that can be used in field 2 plus:
MB_PCP =
Mike Baldwin's Precipitation Analysis
ADPUPA =
Conventional upper-air
ADPSFC =
Conventional surface
AIRCAR =
ACARS
AIRCFT =
Conventional aircraft
ANYAIR =
Any upper-air data source
ANYSFC =
Any surface data source
CDTP =
GOES cloud-top pressure
COOP =
Cooperative observer network
CSQ =
Combination of COOP, surface, and QPE data
ERS1DA =
ERS Scatterometer data
GCDTT =
GOES cloud-top temperature
GLBANL =
Global Analysis
HPC/SFC =
NCEP/HPC surface analysis
Knnn =
Observation
PREPRO type nnn
ONLYSF =
Surface data verified against 2/10-m forecast data
PROFLR =
Profiler
QPE =
Quantitative Precipitation Estimates
SATEMP =
Satellite radiances
SATWND =
Satellite winds
SFCSHP =
Conventional marine
SPSSMI =
SSM/I
SREF_VF =
Verifying analyses used for SREF
TOCC =
Total cloud cover
TOCC_THR
Total Cloud Cover with thresholds
VADWND =
VAD WSR88D wind profiles
A verifying data source name may be followed by /Knnn<=
/span>,
where nnn
is observation type number.
The SREF_VF refers to the analyses used to verify the
Ensemble Forecast (SREF) fields.
These are derived from various
data assimilation systems, including EDAS, GDAS,=
and
stage-2
precipitation analyses. SREF verification is a
grid-to-grid
comparison.
A data quality flag may be entered after each verifying data type.
The following flags are standard:
/GOOD --
useable data
/BAD -- rejected
data
/GOOD may be omitted when only GOOD data is used.
The data searching software must match these verbatim.
Header field 6: verifying grid or region
Bnnnnn =
Buoy, where nnnnn is the buoy number
CONUS =
Continental
EASTR
Eastern ha=
lf of
CONUS
WESTR =
Western half of CONUS
GBL =
Global
NHX =
Northern hemisphere extropics (20N-80N)<=
br>
SHX =
Southern hemisphere extropics (80S-20S)<=
br>
TRO =
Tropics (20S-20N)
Gnnn =
NCEP grid GRIB type nnn
Gnnn/SUBSET NCEP grid
subset
Rnnnnn =
Rawinsonde station =
nnnnn
Rxxnnn =
Rawinsonde set xxnn=
n
USRname =
User-defined grid
USRname/SUBSET Subset<=
/span>
of user-defined grid
x:y:c:d =
Zonal band from longitude x to longitude y
=
centered on latitude c, d degrees of
=
latitude wide
Grid 104 SUBSET 3-character names:
1 ATC (1) Arctic verificat=
ion
region
2 WCA (2)
3 ECA (3)
4 NAK (4)
5 SAK (5)
6 HWI (6)
7 NPO (7)
8 SPO (8)
9 NWC (9)
10 SWC
(A) Souther=
n West
Coast verification region effective 6/03
11 NMT
(B)
12 SMT
(C) Southern
Mountain verification region
12r GRB (C) Great
Basin Verification region effective 6/03
13 NFR
(D)
13r SMT (D)
Southern Mountain verification region effective 6/03
14 SFR
(E)
14r SWD (E)
15 NPL
(F) Northern
Plains verification region effective 6/03
16 SPL
(G) Southern
Plains verification region effective 6/03
17 NMW (H)
17r MDW (H)
18 SMW
(I)
18r LMV (I)
19 APL
(J)
20 NEC
(K)
21 SEC
(L)
22 NAO
(M)
23 SAO
(N)
24 PRI
(O) Puerto =
Rico
&
25 MEX
(P)
26 GLF
(Q) Gulf of=
27 CAR
(R)
28 CAM
(S)
29 NSA
(T)
30 GMC
(U)
MDA ( ) Middle Atlantic =
subset
of NEC (used by HPC 5/04)
The sequence numbers and alpha-numeric characters inside parentheses
refer to the labelling of
the points within the regions on grid 104
displays.
The lowercase "r" following some sequence numbers above
indicate that the 6/03 modification of the regio=
ns
resulted in this
item replacing the region previously defined for=
that
number.
The Grid 104 subset regions were modified effective 1 June 2003.
The modifications are described as follows:
1. The GMC region was added by
extracting territory from the
SPL
and SMW regions.
2. The SMW region was extende=
d further
to the north, taking
territory
away from NMW, and renamed LMV.
3. The APL region was extended
slightly further towards the
southwest,
taking a little area away from the old SMW region.
4. The NPL and SPL regions we=
re
shifted westward.
5. Both the NFR and SFR regio=
ns
were eliminated, yielding some
territory
to the plains regions and some to the mountains.
6. The NWC region was extende=
d to
south of the
Bay area and was
narrowed slightly east to west.
7. The SWC region yielded some
territory to the new Southwest
desert
region.
8. A new
former
southern and northern mountain regions.
HPC Phase error SUBSET names:
SEUS
CEUS Cent=
ral
NEUS
SCUS South
CCUS Cent=
ral
NCUS Nort=
h
SWUS
CWUS Central
NWUS
HPC Snow/Ice accumulation verification regions:
NE -
MA -
Middle
SE -
AP -
MW -
Midwestern
GP -
NR -
SR -
DSW - Desert
SGB -
NGB -
NPC - Northern
SPC - Southern
HPC PMSL verifcation regions:
MRDG
Continental US NPS grid
MRDG/PNW
MRDG/PSW Pacific
southwest
MRDG/DSW Desert southwe=
st
including southern CA
MRDG/IMN Northern
inter-mountain region of western US
MRDG/IMS Southern inter=
-mountain
region of western US
MRDG/GPN
MRDG/GPS
MRDG/MVN Northern Missi=
ssippi
Valley including
MRDG/MVS Southern
MRDG/ANE
MRDG/ASE
These regions will be defined in a table file.
Header field 7 : statistic type
ACTIVE statistic types consist of numbers from which other
statistics can be computed by the display
software.
PASSIVE statistic types consist of pre-computed numbers that
can only be found and displayed.
ACTIVE TYPES:
ESL1L2/n
n-member ensemble mean L1 L2 norms for scalars
EVL1L2/n
n-member ensemble mean L1 L2 norms for vectors
FHO<>* =
F,H, and O (three values), where
=
F =3D Forecasted fraction above/below threshold
=
H =3D Correct fraction above/below threshold (hits)
=
O =3D Observed fraction above/below threshold
GBS|<>/range
General Brier Score for single threshold
GBS|c1|c2|... Gen=
eral
Brier Score with multiple categories,
=
where c1, c2, etc., are category
definitions
PBS_94E =
Partitioned Brier score for HPC excessive rainfall
=
guidance
PBS_ENS:n/# Partitioned
Brier score for n-member ensemble
PBS_WWD/# Parti=
tioned
Brier score for HPC winter weather desk
=
forecast verification (7 values)
PBS_WWX/#
Partitioned Brier score for HPC 93s, 94s, and 98s
=
forecast verification (7 values)
PHSE:# =
Truncated Zonal trig phase and amplitude errors
=
for scalars (10 values)
RPS|<> =
Ranked Probability Score for defined categories
RPS/# =
RPS for # number of undefined categories
SAL1L2(*)
Anomaly L1 and L2 values for scalars (5 values)
SL1L2(*)
L1 and L2 values for Scalars (5 values)
SSAL1L2(*)
Standardized Anomaly L1 and L2 values for
=
scalars (5 values)
RHET =
Ranked histogram array of probabilities that
=
observed or analyzed data falls within
intervals
=
of values determined by the ensemble
members,
=
including the tails
VAL1L2(*)
Anomaly L1 and L2 values for vectors (7 values)
VL1L2(*)
L1 and L2 values for Vectors (7 values)
VSAL1L2(*)
Standardized Anomaly L1 and L2 values for
=
vectors (7 values)
PASSIVE TYPES:
ACORR(*)
Anomaly correlation
ACORWG(*)
Anomaly correlation for waves 1-20, 1-3, 4-9, 10-20
AVGFR(*)
Forecast mean
AVGOB(*)
Observed mean
BIAS(*) =
Forecast mean minus obs mean
CORR(*) =
Correlation
FARR =
False alarm rate for ROC curve
GDET =
GFS legacy deterministic statistics
LRPS =
Legacy Ranked Probability scores
MAXE(*) =
Maximum difference
PODR =
Probability of detection for ROC curve
RLE/# =
Relative Location Error
RMDIF(*)
RMS & MEAN differences (see below)
RMSESP =
Ensemble mean RMS error and ensemble spread
RMSE(*) Root Mean Square Error
S1(*) =
Skill score for gradients
SDERR(*)
Standard deviation of error =3D (forecast - obs=
)
SDFR(*) =
Standard deviation of the forecasts
SDOB(*) =
Standard deviation of the obs
TENDCORR(*) Tendency
correlation
RHNT =
Ranked histogram array of probabilities that
=
observed or analyzed data falls closest to
each
=
ensemble member
VLCEK =
Vlcek's statistics group (see below)
???(*) =
User defined
B<>* =
Bias above/below threshold
CSI<>* =
Critical Success Index above /below threshold
ETS<>* =
Equitable threat score above/below threshold
FAR<>* =
False alarm rate above/below threshold
PA<>* =
Postagreement above/below threshold
PF<>* =
Prefigurance above/below threshold
POD<>* =
Probability of detection above/below threshold
TS<>* =
Threat score above/below threshold
The qualifier parenthetically enclosed following the statistic
type may be any character string. The qualifier is optional.
The searching software must match both the parameter name and
the qualifier to find the statistic values. SREF relates to
verification of the Short Range Ensemble Forecast
(SREF)
products.
The scalar anomaly L1L2 data are composed of five numbers in
addition to the data count:
MEAN [f-c], MEAN [o-c], MEAN [(f-c)*(o-c)], MEAN [(f-c)=
**2],
MEAN [(o-c)**2].
For standardized anomalies, the deviations from the mean are
divided by the standard deviation.
The scalar L1L2 data are composed of five numbers in addition to
the data count:
MEAN [f], MEAN [o], MEAN [f*o], MEAN (f**2), MEAN (o**2).
In these expressions, f are forecast values, o are observed values,
and c are climatological
values.
The vector anomaly L1L2 data are composed of seven numbers in
addition to the data count:
MEAN [uf-c], MEAN [vf-c],
MEAN [uo-c], MEAN [<=
span
class=3DSpellE>vo-c],
MEAN [(uf-c)*(uo-c<=
span
class=3DGramE>)+(vf-c)*(vo-c)],
MEAN [(uf-c)**2+(vf=
-c)**2],
MEAN [(uo-c)**2+(
The vector L1L2 data are composed of seven numbers in addition to
the data count:
MEAN [uf], MEAN [vf], MEAN [uo], MEA=
N [vo], MEAN [uf*uo+vf*vo],
MEAN [uf**2+vf**2], =
MEAN [uo**2+vo**2]
In the case of ensemble L1L2 (e.g., ESL1L2, EVL1L2) norms, the
usual scalar and vector L1L2 values are followed=
by
the variance
of the members about the ensemble mean. This variance is n-1
weighted, where n is the number of ensemble
members. The
individual variances for each contributing forec=
ast
are combined
over all ensemble forecasts represented by the V=
SDB
record. These
values, weighted by the number of forecasts for =
each
VSDB record,
are added together when VSDB records are combine=
d by fvs. Th=
e
combined variance is obtained by dividing by the=
total
number of
forecasts.
In other words, this variance is treated just like
any other L1L2 norm. An optional additional value may be
included
in these records.
It is the fraction of ensemble members lying
three or more standard deviations from the ensem=
ble
mean.
The scalar ESL1L2 data are composed of six or seven numbers in
addition to the data count. The vector EVL1L2 data are
composed
of eight or nine numbers in addition to the data
count.
Note that the statistic type determines whether vector or scalar
treatment is appropriate in the computation of t=
he
following
statistical quantities for SAL1L2, VAL1L2, SL1L2,
VL1L2 and
their ensemble equivalents:
variance and standard deviation of foreca=
st
values
variance and standard deviation of observ=
ed
values
root mean square error
bias
covariance
correlation
In the case of thresholds, the information is not enclosed in
parentheses, but it is given as a real number pr=
eceded
by either
< or >, according to whether the value is =
an
upper bound or a lower
bound, respectively. The statistic types followed by
<>* in the
listing above must ALWAYS be accompanied by a
threshold qualifier.
The searching software will be binning this kind of data on the basis
of the thresholds.
Note that F, H, and O can be used to compute FAR, TS, ETS, POD,
PA, B, PF, and CSI. Diagnostic software will be includ=
ed to
compute
the latter from the former, which MUST be stored=
under
the FHO
statistic type.=
The values of F, H, and O are always entered as
decimal values between 0 and 1.0. The number of events is simply
the product of the value and the count.
The RHNT is the array of probabilities of ensemble members being
nearest the verification. RHET is the array of probabilities
of
the verification falling into bins defined by the
ensemble members
ordered from lowest to highest value. If there are Nm ensemble
members, there will be Nm probabilities associat=
ed
with RHNT, but
Nm+1 probabilities associated with RHET. In both cases, the
last probability is omitted from the record sinc=
e it can
be
computed as a residual. The probabilities must always sum =
to
1.
The data record for RHNT contains the following:
DATA Field =
Contents
1 Numb=
er of
contributing verification events
2
probability analysis closest to ensemble member # 1
3
probability analysis closest to ensemble member # 2
...
Nm
probability analysis closest to ensemble member # Nm-1
residual =3D&nb=
sp;
probability analysis closest to ensemble member $ Nm
The data record for RHET contains the following:
DATA Field =
Contents
1 Numb=
er of
contributing verification events
2
probability analysis less than lowest ensemble member
3
probability analysis between lowest and next lowest
...
Nm+1 probability anal=
ysis
between next highest and highest
residual =3D&nb=
sp;
probability analysis greater than highest ensemble member
The residual is one minus the sum of the probabilities stored from
element two onward. The residual itself is not stored.=
RHNT is a
passive statistic type; while RHET is an active =
type
from which the
probabilities of the analysis being, below, abov=
e,
within, or outside
the range of the ensemble members may be
computed.
The PHSE:# statistic type denotes a group of err=
or
values related
to the difference between a forecast and an anal=
ysis
in a truncated
zonal band.&nbs=
p;
The # represents the wave number within the zonal band
to which the record applies. The wave number is not optional,
it
must be present for accurate documentation. PHSE:# =
may be
followed
by /WL, where WL is the wavelength to the nearest
whole kilometer.
/WL is optional. Computationa=
lly, meridional averaging reduces the
band to a single one dimensional array of numbers
which are
subjected to trigonometric approximation allowing
phase and
amplitude error calculation for those wavelengths
making comparable
contributions to the total variation across the
zone. The data
record contains eleven data fields:
DATA Field =
Contents
1 =
"1"
(the data count is always one)
2 =
Phase error (PE, KM)
3 =
Amplitude error (AE, units of data)
4 =
Forecast variance contribution * PE
5 =
Forecast variance contribution (units**2)
6 =
Analysis variance contribution * PE
7 =
Analysis variance contribution (units**2)
8 =
Analysis phase angle (APA, degrees)
9 =
Analysis amplitude (AA, units of data)
10 =
Total forecast variance (units**2)
11 =
Total analysis variance (units**2)
GBS|<>/range is a general Brier score with optional threshold
specifications and an optional probability range
specification. The
latter option is to be used for single-threshold=
(two
category)
situtations. If the GBS statistic requires
specification of multiple
categories, then the | character is used to sepa=
rate
the category
descriptions.&n=
bsp;
When the | character is used, threshold checking is
turned off, so multiple use of < and > is
permitted to define the
categories.&nbs=
p;
The categories are given in order following the GBS.
For example, GBS|>:100 means that category on=
e is
all events greater
than or equal to 100, and category two is all ev=
ents
less than 100.
(Note that : is used in place of =3D because =3D=
has
special meaning as
a separator between header and data information =
in a
VSDB record.)
The /range specification is provided to allow computation of the
observed probability for a given range of foreca=
st
probabilities.
This is intended for the case when there are only two categories.
If ranges are specified, they can be combined for a total Brier score
by terminating the GBS|<> statistic type w=
ith a
forward slash (/) in
setting search conditions, because, when nothing
follows the forward
slash, the search will accept all data to be
combined. The optional
threshold follows the > or < sign. In setting search conditions
for GBS, the threshold should always be included=
in
the statistic
type, and no separate threshold search condition
should be set. No
explicit threshold checking is done when the | is
present. The data
record allows for multiple categories of
outcomes:
DATA Field =
Contents
1 Number of verifi=
cation
events (N)
2 Category 1 mean
product of observed & forecast probabilites=
3 Category 1 mean =
of
squares of forecast probabilities
4 Fraction of N ob=
served
in category 1
5 Category 2 mean
product of observed & forecast probabilites=
6 Category 2 mean =
of
squares of forecast probabilities
7 Fraction of N ob=
served
in category 2
8 Category 3 mean
product of observed & forecast probabilites=
9 Category 3 mean =
of
squares of forecast probabilities
10 Fraction of N ob=
served
in category 3
11 Category 4 mean
product of observed & forecast probabilites=
12 Category 4 mean =
of
squares of forecast probabilities
The fraction of N observed in the last category is computed as a
residual and should not be included in the data
record. For example,
if there are only two categories, there would on=
ly be
6 entries in
the data field.=
The number of categories is the number of entries
divided by 3.&n=
bsp;
The Brier score is computed as follows:
GBS =3D .5 * SUM=
( MEAN (F*F) - 2 * MEAN (F*O) + Fraction )
where the SUM is over all categories. The perfect GBS is 0, the worst
is 1.
RPS|<> is the ranked probability score, where category thresholds
are
given in the same way as for the GBS statistic t=
ype
described above.
RPS/# is also allowed, where the # simply gives the number of categories
and, therefore, the number of probability values
constituting each
individual forecast. The |<> and /# suffixes are
optional and may be
omitted.
The data record contains the following:
DATA Field =
Contents
1
Number of contributing verification events (N)
2
Ranked probability score
3
Positive RPS fraction
4 Climatological ranked probability score (optional)
PBS_ENS:n/# is the
partitioned Brier score statistic type for
ensembles.
The number of members is given by n. The threshold
specification replaces #. This statistic type is used for
making
reliability diagrams and computing the Brier Sco=
re and
its
decomposition terms. The record entries are as described
for
PBS_WWX below except that more risk or probability categories are
allowed.
If there are M categories, then there are 2*M data fields.
The first is always the number of events.&=
nbsp;
The fraction of events
with no risk forecast is always computed as a
residual. The last
M/2 data values are fractions of events correctly forecast, one
for each probability category. Probabilities for the categories
the probabilities are computed internally with e=
qual
values
assigned to categories 2 through M-1. Values of 0 and 1,
respectively, are assigned to categories 1 and
M.
PBS_WWX is a single threshold probability verification using the
Partitioned Brier (PB) score for H=
PC's
winter weather forecasts.
The
threshold is given following a slash postfixed to the statistic type
identifier.&nbs=
p;
The data record contains these eight values:
DATA Field =
Contents
1
3
4
5
6
7
8
The probabilities of exceeding the threshold for each risk
category are 0, .25, .5, and 1.0 for no, low,
moderate, and high
risk, respectively. These default values may be overri=
dden
by
specifying them in a file named pbs_wwx.prob. The file must have
the following structure:
HEADER RECORD - can be anything
NO_RISK_PROBABILITY
0.0
LOW_RISK =
.25
MODERATE_RISK =
.50
HIGH_RISK =
1.00
With the data values and these probabilities, the PB score can be
computed using the following equation:
PBS =3D SUM [
n(r) * ( f(r)**2 - 2*f(r)*p(r) + p(r) ) ]
where n(r) is the number of events in category r
divided by the
total number of events over all categories, p(r)=
is
the number of
events in category r observed to exceed the thre=
shold
divided by
the total number of events in category r, f(r) i=
s the
forecast
probability of exceeding the threshold associate=
d with
the risk
category r, and the summation is over risk categ=
ories,
r. The
perfect PBS is zero, the worst possible PBS is 1=
. The observed
frequency or probability of observations exceedi=
ng the
threshold
(p(r)) can be displayed for each risk category.<=
br>
Skill scores like those computed from FHO values can be computed
from the PBS_WWX numbers.
ACORWG is composed of five numbers, the first being the data count.
RMDIF is composed of five numbers:
the data count, RMS (f-a),
MEAN (f-a), RMS (f-c), and MEAN (f-c), where f is forc=
ast,
a is
analysis, and c is climatology.
The Relative Location Error (RLE) records a displacement of the
forecast relative to the observation. Descriptor field element
6 gives the direction from the point of observation to the
forecast location using one of the following
designations:
N - north NNE - north northeast NE - northeast E=
NE -
east northeast
E - east =
ESE
- east southeast SE - southea=
st SSE
- south southeast
S - south SSW - south southwest SW - southwest W=
SW -
west southwest
W - west =
WNW
- west northwest NW - northwe=
st NNW
- north northwest
0 - 0 error
RLE is usually followed by /n where n quantifies the forecast and
observation.&nb=
sp;
The data field contains the following:
DATA Field =
Contents
1
2
3
4 =
The longitude of the observation
The VLCEK statistic type denotes a group of statistical values
preceded by a data count value of 1. The values, in the assumed
order, are:
=
S1 BIAS SDERR RMSE AVGOB SDOB
These parameter names are defined in the list above. All are
"passive" parameters. The VLCEK data base begins at 1200=
UTC
on
1 October 1977 and ends at 1200 UTC on 31 December 1999, with
the possibility that it may be continued into th=
e year
2000. The
data are roughly monthly values generated by the=
SUMAC
program.
The passive statisic type, GDET, is defined for
legacy deterministic
statistics.&nbs=
p;
The data record consists of the following eight values:
pattern anomaly correlation for waves 1--3 (PAC/=
1-3),
PAC/4-9,
PAC/10-20, PAC/1-20, RMSE for the actual forecast, bias (mean error)
for the actual forecast, RMSE for the forecast
anomaly, and bias for
the forecast anomaly. These values follow a data count v=
alue
of 1;
thus, the data record has nine values.
Two passive statistic types are defined to support display of ROC
diagrams for ensembles. The PODR and FARR statistic types =
are
the
probability of detection and false alarm rate,
respectively. Each
data record consists of a list of probabilities,=
one
for each ensemble
member, following a data count value of 1. The data depth of a
record currently permits 23 members. The ROC diagram is displayed
as a scatter plot using code 9002 to turn the da=
ta
fields into two
traces, one for PODR and one for FARR.
The RMSESP statistic type is defined for comparing ensemble spread
to the RMS error of the ensemble mean. Each data record consists of
three values:&n=
bsp;
1) the data count =3D 1, 2) the root-mean-squared error
of the ensemble mean, and 3) the ensemble spread=
. This is a passive
statistic type.
The LRPS statistic type accomodates legacy Rank=
ed
probability scores.
The data record consists of three values:&=
nbsp;
1) the data count =3D 1,
2) the ranked probability score, and 3) the climatological ranked
probability score. This is a passive statistic type.<=
br>
Header field 8 : parameter identifier
APCP/12 =
12-h Accumulated total precipitation
APCP/24 =
24-h Accumulated total precipitation
APCP/nn =
nn-h Accumulated total precipitation
CFR =
Cloud fraction
CPCP/12 =
12-h
Convective precipitation
CPCP/24 =
24-h Convective precipitation
ER =
Excessive Rainfall (HPC)
H =
Height above ground level
HI =
Heat Index
IA/xx =
Ice accumulation over xx hours (inches)
Knnn =
NCEP parameter GRIB type nnn
Kxxxxx =
5-character NCEP (Russ Jones) identifier
PMSL =
Sea level pressure
PxxI =
xx-h Accumulated total precipitation (inches)
Q =
Specific humidity
QPF =
Quantitative Precipitation Forecast
RH =
Relative humidity
S =
Snowfall
SF =
Snowfall
SF/xx =
Snowfall over xx hours (inches)
SLP =
Sea level pressure
SPCP/12 =
12-h Grid scale precipitation
SPCP/24 =
24-h Grid scale precipitation
T =
Temperature (sensible)
TV =
Virtual temperature
U =
U wind component
V =
V wind component
VWND =
Vector wind
WDIR =
Wind Direction
WSPD =
Wind
Speed
Z =
Height
ZR =
Freezing Rain
Note that accumu=
lation
or averaging periods follow the
parameter
name with / as the separator.
Header field 9 : level identifier
Bx-y =
Constant pressure depth boundary layer
Dx-y =
Depth
Hx-y =
Height above ground level
Px-y =
Pressure
Sx-y =
Sigma
Tx-y =
Potential temperature
Zx-y =
Height
ATMOS =
Entire atmosphere
FRZDN =
Lower freezing level
FRZUP =
Upper freezing level
MSL =
MWND =
Maximum wind
SFC =
Surface
TROP =
Tropopause
where
x-y gives the bounding values of the levels for a layer.
If -y is not giv=
en,
then a single level value is specified.
For B, D, H,
specified.
Data field : count followed by data value(s)
For data combina=
tion,
the count will always multiply the data
value
before summing. The counts wi=
ll be
summed also.