[Met_help] [rt.rap.ucar.edu #62351] History for Using WPP 3.1.1 with MET 4.1
John Halley Gotway via RT
met_help at ucar.edu
Tue Aug 27 10:59:35 MDT 2013
----------------------------------------------------------------
Initial Request
----------------------------------------------------------------
Hi John,
We have finally upgraded to MET 4.1! I've come across the following
problem while migrating everything over from MET 2.0: When I use MET 4.1
PointStat on Grib1 forecast files generated by WPP 3.1.1, all my _mpr.txt
files record the forecasted value (for every field) as 0.0000. (The
resultant statistics are of course abysmal.) However, if I use MET 2.0
PointStat on the exact same Grib1 forecast files, I get normal looking
forecast values recorded in the _mpr.txt files and normal looking
statistics. I get this behavior with the 4.1 version even when I use the
default PointStat config file that came with the download. Do you know of
any incompatibilities between MET 4.1 and an older version of WPP?
Thanks for any help or insight with this issue.
Kiana
----------------------------------------------------------------
Complete Ticket History
----------------------------------------------------------------
Subject: Re: [rt.rap.ucar.edu #62351] Using WPP 3.1.1 with MET 4.1
From: John Halley Gotway
Time: Tue Jul 23 09:37:52 2013
Kiana,
I am not aware of any incompatibilities with WPP 3.1.1. You could try
turning the verbosity level up to 4 for both to see if there's
additional info that might tell you what's going on. So run
METv2.0 using the -v 4 option and then do the same for METv4.1.
Inspect the output printed the screen to see if anything jumps out at
you.
If not, you could send me a sample GRIB1 file, along with the point
observations you're using, and the Point-Stat config file. I'll run
it here using METv2.0 and METv4.1 to see if I can reproduce the
behavior and figure out what's going on. You can post the files to
our anonymous ftp site, following these instructions:
There have been a lot of updates between versions 2.0 and 4.1
(http://www.dtcenter.org/met/users/support/release_notes/index.php),
so it's possible that one of those is the cause for the differences
you're seeing.
Thanks,
John
On 07/22/2013 06:23 PM, Kiana L Ross via RT wrote:
>
> Mon Jul 22 18:23:38 2013: Request 62351 was acted upon.
> Transaction: Ticket created by Kiana.L.Ross at aero.org
> Queue: met_help
> Subject: Using WPP 3.1.1 with MET 4.1
> Owner: Nobody
> Requestors: Kiana.L.Ross at aero.org
> Status: new
> Ticket <URL:
https://rt.rap.ucar.edu/rt/Ticket/Display.html?id=62351 >
>
>
> Hi John,
>
> We have finally upgraded to MET 4.1! I've come across the following
> problem while migrating everything over from MET 2.0: When I use MET
4.1
> PointStat on Grib1 forecast files generated by WPP 3.1.1, all my
_mpr.txt
> files record the forecasted value (for every field) as 0.0000. (The
> resultant statistics are of course abysmal.) However, if I use MET
2.0
> PointStat on the exact same Grib1 forecast files, I get normal
looking
> forecast values recorded in the _mpr.txt files and normal looking
> statistics. I get this behavior with the 4.1 version even when I use
the
> default PointStat config file that came with the download. Do you
know of
> any incompatibilities between MET 4.1 and an older version of WPP?
>
> Thanks for any help or insight with this issue.
>
> Kiana
>
------------------------------------------------
Subject: Using WPP 3.1.1 with MET 4.1
From: Kiana L Ross
Time: Wed Jul 24 13:45:32 2013
John,
I've uploaded a sample forecast file, observation file, and 4.1
PointStat
config file to the ftp site ("Ross_data" directory). I get the same
number
of matched pairs with PointStat 2.0 vs. PointStat 4.1. The only
difference
is that the reported forecast values in the _mpr.txt files are all
0.0000
with 4.1.
Thanks again for all the help.
Kiana
From: "John Halley Gotway via RT" <met_help at ucar.edu>
To: Kiana.L.Ross at aero.org,
Date: 07/23/2013 08:37 AM
Subject: Re: [rt.rap.ucar.edu #62351] Using WPP 3.1.1 with MET
4.1
Kiana,
I am not aware of any incompatibilities with WPP 3.1.1. You could try
turning the verbosity level up to 4 for both to see if there's
additional
info that might tell you what's going on. So run
METv2.0 using the -v 4 option and then do the same for METv4.1.
Inspect
the output printed the screen to see if anything jumps out at you.
If not, you could send me a sample GRIB1 file, along with the point
observations you're using, and the Point-Stat config file. I'll run
it
here using METv2.0 and METv4.1 to see if I can reproduce the
behavior and figure out what's going on. You can post the files to
our
anonymous ftp site, following these instructions:
There have been a lot of updates between versions 2.0 and 4.1 (
http://www.dtcenter.org/met/users/support/release_notes/index.php), so
it's possible that one of those is the cause for the differences
you're seeing.
Thanks,
John
On 07/22/2013 06:23 PM, Kiana L Ross via RT wrote:
>
> Mon Jul 22 18:23:38 2013: Request 62351 was acted upon.
> Transaction: Ticket created by Kiana.L.Ross at aero.org
> Queue: met_help
> Subject: Using WPP 3.1.1 with MET 4.1
> Owner: Nobody
> Requestors: Kiana.L.Ross at aero.org
> Status: new
> Ticket <URL:
https://rt.rap.ucar.edu/rt/Ticket/Display.html?id=62351 >
>
>
> Hi John,
>
> We have finally upgraded to MET 4.1! I've come across the following
> problem while migrating everything over from MET 2.0: When I use MET
4.1
> PointStat on Grib1 forecast files generated by WPP 3.1.1, all my
_mpr.txt
> files record the forecasted value (for every field) as 0.0000. (The
> resultant statistics are of course abysmal.) However, if I use MET
2.0
> PointStat on the exact same Grib1 forecast files, I get normal
looking
> forecast values recorded in the _mpr.txt files and normal looking
> statistics. I get this behavior with the 4.1 version even when I use
the
> default PointStat config file that came with the download. Do you
know
of
> any incompatibilities between MET 4.1 and an older version of WPP?
>
> Thanks for any help or insight with this issue.
>
> Kiana
>
------------------------------------------------
Subject: Using WPP 3.1.1 with MET 4.1
From: John Halley Gotway
Time: Wed Jul 24 16:09:19 2013
Kiana,
Great, I pulled your data from the ftp site. In your config file, I
see that you're verifying 900mb temperature. My first step was to
plot that field using the plot_data_plane utility in METv4.1:
METv4.1/bin/plot_data_plane WRFPRS_d01.00
WRFPRS_d01.00_TMP_P900.ps 'name="TMP"; level="P900";'
I converted the resulting postscript image to a png, and it's
attached.
I used the METv4.1 PointStat configuration file you sent, and then I
set up an equivalent one for METv2.0. Both are attached.
When I run METv2.0 and METv4.1, both result in finding the same 55
matched pairs. Both of the matched pair (MPR) files are attached as
well. They do differ out in the 5th decimal place due to a
precision issue we fixed somewhere between METv2.0 and METv4.1. But
they're certainly not all 0's. Have you tried executing a "make
clean" followed by a "make" for METv4.1? If not, please give that
a try.
So I'm not able to reproduce the behavior you're reporting.
If doing a clean build of METv4.1 doesn't help, can you please send me
your "user_defs.mk" file and the output of make "make_met.log". I
could inspect those to see if anything jumps out at me.
Thanks,
John
On 07/24/2013 01:45 PM, Kiana L Ross via RT wrote:
>
> <URL: https://rt.rap.ucar.edu/rt/Ticket/Display.html?id=62351 >
>
> John,
>
> I've uploaded a sample forecast file, observation file, and 4.1
PointStat
> config file to the ftp site ("Ross_data" directory). I get the same
number
> of matched pairs with PointStat 2.0 vs. PointStat 4.1. The only
difference
> is that the reported forecast values in the _mpr.txt files are all
0.0000
> with 4.1.
>
> Thanks again for all the help.
>
> Kiana
>
>
>
>
> From: "John Halley Gotway via RT" <met_help at ucar.edu>
> To: Kiana.L.Ross at aero.org,
> Date: 07/23/2013 08:37 AM
> Subject: Re: [rt.rap.ucar.edu #62351] Using WPP 3.1.1 with
MET 4.1
>
>
>
> Kiana,
>
> I am not aware of any incompatibilities with WPP 3.1.1. You could
try
> turning the verbosity level up to 4 for both to see if there's
additional
> info that might tell you what's going on. So run
> METv2.0 using the -v 4 option and then do the same for METv4.1.
Inspect
> the output printed the screen to see if anything jumps out at you.
>
> If not, you could send me a sample GRIB1 file, along with the point
> observations you're using, and the Point-Stat config file. I'll run
it
> here using METv2.0 and METv4.1 to see if I can reproduce the
> behavior and figure out what's going on. You can post the files to
our
> anonymous ftp site, following these instructions:
>
> There have been a lot of updates between versions 2.0 and 4.1 (
> http://www.dtcenter.org/met/users/support/release_notes/index.php),
so
> it's possible that one of those is the cause for the differences
> you're seeing.
>
> Thanks,
> John
>
> On 07/22/2013 06:23 PM, Kiana L Ross via RT wrote:
>>
>> Mon Jul 22 18:23:38 2013: Request 62351 was acted upon.
>> Transaction: Ticket created by Kiana.L.Ross at aero.org
>> Queue: met_help
>> Subject: Using WPP 3.1.1 with MET 4.1
>> Owner: Nobody
>> Requestors: Kiana.L.Ross at aero.org
>> Status: new
>> Ticket <URL:
https://rt.rap.ucar.edu/rt/Ticket/Display.html?id=62351 >
>>
>>
>> Hi John,
>>
>> We have finally upgraded to MET 4.1! I've come across the following
>> problem while migrating everything over from MET 2.0: When I use
MET 4.1
>> PointStat on Grib1 forecast files generated by WPP 3.1.1, all my
> _mpr.txt
>> files record the forecasted value (for every field) as 0.0000. (The
>> resultant statistics are of course abysmal.) However, if I use MET
2.0
>> PointStat on the exact same Grib1 forecast files, I get normal
looking
>> forecast values recorded in the _mpr.txt files and normal looking
>> statistics. I get this behavior with the 4.1 version even when I
use the
>> default PointStat config file that came with the download. Do you
know
> of
>> any incompatibilities between MET 4.1 and an older version of WPP?
>>
>> Thanks for any help or insight with this issue.
>>
>> Kiana
>>
>
>
------------------------------------------------
Subject: Using WPP 3.1.1 with MET 4.1
From: John Halley Gotway
Time: Wed Jul 24 16:09:19 2013
////////////////////////////////////////////////////////////////////////////////
//
// Default point_stat configuration file
//
////////////////////////////////////////////////////////////////////////////////
//
// Specify a name to designate the model being verified. This name
will be
// written to the second column of the ASCII output generated.
//
model = "WRF";
//
// Beginning and ending time offset values in seconds for observations
// to be used. These time offsets are defined in reference to the
// forecast valid time, v. Observations with a valid time falling in
the
// window [v+beg_ds, v+end_ds] will be used.
// These selections are overridden by the command line arguments
// -valid_beg and -valid_end.
//
beg_ds = -5400;
end_ds = 5400;
//
// Specify a comma-separated list of fields to be verified. The
forecast and
// observation fields may be specified separately. If the obs_field
parameter
// is left blank, it will default to the contents of fcst_field.
//
// Each field is specified as a grib code or corresponding grib code
// abbreviation followed by an accumulation or vertical level
indicator.
//
// Each verification field is specified as one of the following:
// GC/ANNN for accumulation interval NNN
// GC/ZNNN for vertical level NNN
// GC/PNNN for pressure level NNN in hPa
// GC/PNNN-NNN for a range of pressure levels in hPa
// GC/LNNN for a generic level type
// GC/RNNN for a specific GRIB record number
// Where GC is the number of or abbreviation for the grib code
// to be verified.
// http://www.nco.ncep.noaa.gov/pmb/docs/on388/table2.html
//
// NOTE: To verify winds as vectors rather than scalars,
// specify UGRD (or 33) followd by VGRD (or 34) with the
// same level values.
//
// NOTE: To process a probability field, add "/PROB", such as
"POP/Z0/PROB".
//
// e.g. fcst_field[] = [ "SPFH/P500", "TMP/P500" ];
//
fcst_field[] = [ "TMP/P900" ];
obs_field[] = [ "TMP/P850-950" ];
//
// Specify a comma-separated list of groups of thresholds to be
applied to the
// fields listed above. Thresholds for the forecast and observation
fields
// may be specified separately. If the obs_thresh parameter is left
blank,
// it will default to the contents of fcst_thresh.
//
// At least one threshold must be provided for each field listed
above. The
// lengths of the "fcst_field" and "fcst_thresh" arrays must match, as
must
// lengths of the "obs_field" and "obs_thresh" arrays. To apply
multiple
// thresholds to a field, separate the threshold values with a space.
//
// Each threshold must be preceded by a two letter indicator for the
type of
// thresholding to be performed:
// 'lt' for less than 'le' for less than or equal to
// 'eq' for equal to 'ne' for not equal to
// 'gt' for greater than 'ge' for greater than or equal to
//
// NOTE: Thresholds for probabilities must be preceeded by "ge".
//
// e.g. fcst_thresh[] = [ "gt80", "gt273" ];
//
fcst_thresh[] = [ "gt273" ];
obs_thresh[] = [];
//
// Specify a comma-separated list of thresholds to be used when
computing
// VL1L2 and VAL1L2 partial sums for winds. The thresholds are
applied to the
// wind speed values derived from each U/V pair. Only those U/V pairs
which meet
// the wind speed threshold criteria are retained. If the
obs_wind_thresh
// parameter is left blank, it will default to the contents of
fcst_wind_thresh.
//
// To apply multiple wind speed thresholds, separate the threshold
values with a
// space. Use "NA" to indicate that no wind speed threshold should be
applied.
//
// Each threshold must be preceded by a two letter indicator for the
type of
// thresholding to be performed:
// 'lt' for less than 'le' for less than or equal to
// 'eq' for equal to 'ne' for not equal to
// 'gt' for greater than 'ge' for greater than or equal to
// 'NA' for no threshold
//
// e.g. fcst_wind_thresh[] = [ "NA", "ge1.0" ];
//
//fcst_wind_thresh[] = [ "NA" ];
fcst_wind_thresh[] = [ "ge1.0" ];
obs_wind_thresh[] = [];
//
// Specify a comma-separated list of PrepBufr message types with which
// to perform the verification. Statistics will be computed
separately
// for each message type specified. At least one PrepBufr message
type
// must be provided.
// List of valid message types:
// ADPUPA AIRCAR AIRCFT ADPSFC ERS1DA GOESND GPSIPW
// MSONET PROFLR QKSWND RASSDA SATEMP SATWND SFCBOG
// SFCSHP SPSSMI SYNDAT VADWND
// ANYAIR (= AIRCAR, AIRCFT)
// ANYSFC (= ADPSFC, SFCSHP, ADPUPA, PROFLR)
// ONLYSF (= ADPSFC, SFCSHP)
//
http://www.emc.ncep.noaa.gov/mmb/data_processing/prepbufr.doc/table_1.htm
//
// e.g. message_type[] = [ "ADPUPA", "AIRCAR" ];
//
message_type[] = [ "ADPUPA" ];
//
// Specify a comma-separated list of grids to be used in masking the
data over
// which to perform scoring. An empty list indicates that no masking
grid
// should be performed. The standard NCEP grids are named "GNNN"
where NNN
// indicates the three digit grid number. Enter "FULL" to score over
the
// entire domain.
// http://www.nco.ncep.noaa.gov/pmb/docs/on388/tableb.html
//
// e.g. mask_grid[] = [ "FULL" ];
//
mask_grid[] = [ "FULL" ];
//
// Specify a comma-separated list of masking regions to be applied.
// An empty list indicates that no additional masks should be used.
// The masking regions may be defined in one of 4 ways:
//
// (1) An ASCII file containing a lat/lon polygon.
// Latitude in degrees north and longitude in degrees east.
// By default, the first and last polygon points are connected.
// e.g. "MET_BASE/data/poly/EAST.poly" which consists of n points:
// "poly_name lat1 lon1 lat2 lon2... latn lonn"
//
// (2) The NetCDF output of the gen_poly_mask tool.
//
// (3) A NetCDF data file, followed by the name of the NetCDF variable
// to be used, and optionally, a threshold to be applied to the
field.
// e.g. "sample.nc var_name gt0.00"
//
// (4) A GRIB data file, followed by a description of the field
// to be used, and optionally, a threshold to be applied to the
field.
// e.g. "sample.grb APCP/A3 gt0.00"
//
// Any NetCDF or GRIB file used must have the same grid dimensions as
the
// data being verified.
//
// MET_BASE may be used in the path for the files above.
//
// e.g. mask_poly[] = [ "MET_BASE/data/poly/EAST.poly",
// "poly_mask.ncf",
// "sample.nc APCP",
// "sample.grb HGT/Z0 gt100.0" ];
//
mask_poly[] = [];
//
// Specify the name of an ASCII file containing a space-separated list
of
// station ID's at which to perform verification. Each station ID
specified
// is treated as an individual masking region.
//
// An empty list file name indicates that no station ID masks should
be used.
//
// MET_BASE may be used in the path for the station ID mask file name.
//
// e.g. mask_sid = "MET_BASE/data/stations/CONUS.stations";
//
mask_sid = "";
//
// Specify a comma-separated list of values for alpha to be used when
computing
// confidence intervals. Values of alpha must be between 0 and 1.
//
// e.g. ci_alpha[] = [ 0.05, 0.10 ];
//
ci_alpha[] = [ 0.05 ];
//
// Specify the method to be used for computing bootstrap confidence
intervals.
// The value for this is interpreted as follows:
// (0) Use the BCa interval method (computationally intensive)
// (1) Use the percentile interval method
//
boot_interval = 1;
//
// Specify a proportion between 0 and 1 to define the replicate sample
size
// to be used when computing percentile intervals. The replicate
sample
// size is set to boot_rep_prop * n, where n is the number of raw data
points.
//
// e.g boot_rep_prop = 0.80;
//
boot_rep_prop = 1.0;
//
// Specify the number of times each set of matched pair data should be
// resampled when computing bootstrap confidence intervals. A value
of
// zero disables the computation of bootstrap condifence intervals.
//
// e.g. n_boot_rep = 1000;
//
n_boot_rep = 1000;
//
// Specify the name of the random number generator to be used. See
the MET
// Users Guide for a list of possible random number generators.
//
boot_rng = "mt19937";
//
// Specify the seed value to be used when computing bootstrap
confidence
// intervals. If left unspecified, the seed will change for each run
and
// the computed bootstrap confidence intervals will not be
reproducable.
//
boot_seed = "";
//
// Specify a comma-separated list of interpolation method(s) to be
used
// for comparing the forecast grid to the observation points. String
values
// are interpreted as follows:
// MIN = Minimum in the neighborhood
// MAX = Maximum in the neighborhood
// MEDIAN = Median in the neighborhood
// UW_MEAN = Unweighted mean in the neighborhood
// DW_MEAN = Distance-weighted mean in the neighborhood
// LS_FIT = Least-squares fit in the neighborhood
//
// In all cases, vertical interpolation is performed in the natural
log
// of pressure of the levels above and below the observation.
//
// e.g. interp_method[] = [ "UW_MEAN", "MEDIAN" ];
//
interp_method[] = [ "UW_MEAN" ];
//
// Specify a comma-separated list of box widths to be used by the
// interpolation techniques listed above. A value of 1 indicates that
// the nearest neighbor approach should be used. For a value of n
// greater than 1, the n*n grid points closest to the observation
define
// the neighborhood.
//
// e.g. interp_width = [ 1, 3, 5 ];
//
interp_width[] = [ 1 ];
//
// When interpolating, compute a ratio of the number of valid data
points
// to the total number of points in the neighborhood. If that ratio
is
// less than this threshold, do not include the observation. This
// threshold must be between 0 and 1. Setting this threshold to 1
will
// require that each observation be surrounded by n*n valid forecast
// points.
//
// e.g. interp_thresh = 1.0;
//
interp_thresh = 1.0;
//
// Specify flags to indicate the type of data to be output:
// (1) STAT and FHO Text Files, Forecast, Hit, Observation Rates:
// Total (TOTAL),
// Forecast Rate (F_RATE),
// Hit Rate (H_RATE),
// Observation Rate (O_RATE)
//
// (2) STAT and CTC Text Files, Contingency Table Counts:
// Total (TOTAL),
// Forecast Yes and Observation Yes Count (FY_OY),
// Forecast Yes and Observation No Count (FY_ON),
// Forecast No and Observation Yes Count (FN_OY),
// Forecast No and Observation No Count (FN_ON)
//
// (3) STAT and CTS Text Files, Contingency Table Scores:
// Total (TOTAL),
// Base Rate (BASER), BASER_CL, BASER_CU,
// Forecast Mean (FMEAN), FMEAN_CL, FMEAN_CU,
// Accuracy (ACC), ACC_CL, ACC_CU,
// Frequency Bias (FBIAS),
// Probability of Detecting Yes (PODY), PODY_CL, PODY_CU,
// Probability of Detecting No (PODN), PODN_CL, PODN_CU,
// Probability of False Detection (POFD), POFD_CL, POFD_CU,
// False Alarm Ratio (FAR), FAR_CL, FAR_CU,
// Critical Success Index (CSI), CSI_CL, CSI_CU,
// Gilbert Skill Score (GSS),
// Hanssen and Kuipers Discriminant (HK), HK_CL, HK_CU,
// Heidke Skill Score (HSS),
// Odds Ratio (ODDS), ODDS_CL, ODDS_CU
//
// (4) STAT and CNT Text Files, Statistics of Continuous Variables:
// Total (TOTAL),
// Forecast Mean (FBAR), FBAR_CL, FBAR_CU,
// Forecast Standard Deviation (FSTDEV), FSTDEV_CL,
FSTDEV_CU
// Observation Mean (OBAR), OBAR_CL, OBAR_CU,
// Observation Standard Deviation (OSTDEV), OSTDEV_CL,
OSTDEV_CU,
// Pearson's Correlation Coefficient (PR_CORR), PR_CORR_CL,
PR_CORR_CU,
// Spearman's Rank Correlation Coefficient (SP_CORR),
// Kendall Tau Rank Correlation Coefficient (KT_CORR),
// Number of ranks compared (RANKS),
// Number of tied ranks in the forecast field (FRANK_TIES),
// Number of tied ranks in the observation field
(ORANK_TIES),
// Mean Error (ME), ME_CL, ME_CU,
// Standard Deviation of the Error (ESTDEV), ESTDEV_CL,
ESTDEV_CU,
// Bias (BIAS = FBAR - OBAR),
// Mean Absolute Error (MAE),
// Mean Squared Error (MSE),
// Bias-Corrected Mean Squared Error (BCMSE),
// Root Mean Squared Error (RMSE),
// Percentiles of the Error (E10, E25, E50, E75, E90)
//
// NOTE: CL and CU values define lower and upper
// confidence interval limits.
//
// (5) STAT and SL1L2 Text Files, Scalar Partial Sums:
// Total (TOTAL),
// Forecast Mean (FBAR),
// = mean(f)
// Observation Mean (OBAR),
// = mean(o)
// Forecast*Observation Product Mean (FOBAR),
// = mean(f*o)
// Forecast Squared Mean (FFBAR),
// = mean(f^2)
// Observation Squared Mean (OOBAR)
// = mean(o^2)
//
// (6) STAT and SAL1L2 Text Files, Scalar Anomaly Partial Sums:
// Total (TOTAL),
// Forecast Anomaly Mean (FABAR),
// = mean(f-c)
// Observation Anomaly Mean (OABAR),
// = mean(o-c)
// Product of Forecast and Observation Anomalies Mean
(FOABAR),
// = mean((f-c)*(o-c))
// Forecast Anomaly Squared Mean (FFABAR),
// = mean((f-c)^2)
// Observation Anomaly Squared Mean (OOABAR)
// = mean((o-c)^2)
//
// (7) STAT and VL1L2 Text Files, Vector Partial Sums:
// Total (TOTAL),
// U-Forecast Mean (UFBAR),
// = mean(uf)
// V-Forecast Mean (VFBAR),
// = mean(vf)
// U-Observation Mean (UOBAR),
// = mean(uo)
// V-Observation Mean (VOBAR),
// = mean(vo)
// U-Product Plus V-Product (UVFOBAR),
// = mean(uf*uo+vf*vo)
// U-Forecast Squared Plus V-Forecast Squared (UVFFBAR),
// = mean(uf^2+vf^2)
// U-Observation Squared Plus V-Observation Squared
(UVOOBAR)
// = mean(uo^2+vo^2)
//
// (8) STAT and VAL1L2 Text Files, Vector Anomaly Partial Sums:
// U-Forecast Anomaly Mean (UFABAR),
// = mean(uf-uc)
// V-Forecast Anomaly Mean (VFABAR),
// = mean(vf-vc)
// U-Observation Anomaly Mean (UOABAR),
// = mean(uo-uc)
// V-Observation Anomaly Mean (VOABAR),
// = mean(vo-vc)
// U-Anomaly Product Plus V-Anomaly Product (UVFOABAR),
// = mean((uf-uc)*(uo-uc)+(vf-vc)*(vo-vc))
// U-Forecast Anomaly Squared Plus V-Forecast Anomaly
Squared (UVFFABAR),
// = mean((uf-uc)^2+(vf-vc)^2)
// U-Observation Anomaly Squared Plus V-Observation Anomaly
Squared (UVOOABAR)
// = mean((uo-uc)^2+(vo-vc)^2)
//
// (9) STAT and PCT Text Files, Nx2 Probability Contingency Table
Counts:
// Total (TOTAL),
// Number of Forecast Probability Thresholds (N_THRESH),
// Probability Threshold Value (THRESH_i),
// Row Observation Yes Count (OY_i),
// Row Observation No Count (ON_i),
// NOTE: Previous 3 columns repeated for each row in the
table
// Last Probability Threshold Value (THRESH_n)
//
// (10) STAT and PSTD Text Files, Nx2 Probability Contingency Table
Scores:
// Total (TOTAL),
// Number of Forecast Probability Thresholds (N_THRESH),
// Reliability (RELIABILITY),
// Resolution (RESOLUTION),
// Uncertainty (UNCERTAINTY),
// Area Under the ROC Curve (ROC_AUC),
// Brier Score (BRIER), BRIER_NCL, BRIER_NCU,
// Probability Threshold Value (THRESH_i)
// NOTE: Previous column repeated for each probability
threshold
//
// (11) STAT and PJC Text Files, Joint/Continuous Statistics of
// Probabilistic Variables:
// Total (TOTAL),
// Number of Forecast Probability Thresholds (N_THRESH),
// Probability Threshold Value (THRESH_i),
// Observation Yes Count Divided by Total (OY_TP_i),
// Observation No Count Divided by Total (ON_TP_i),
// Calibration (CALIBRATION_i),
// Refinement (REFINEMENT_i),
// Likelikhood (LIKELIHOOD_i),
// Base Rate (BASER_i),
// NOTE: Previous 7 columns repeated for each row in the
table
// Last Probability Threshold Value (THRESH_n)
//
// (12) STAT and PRC Text Files, ROC Curve Points for
// Probabilistic Variables:
// Total (TOTAL),
// Number of Forecast Probability Thresholds (N_THRESH),
// Probability Threshold Value (THRESH_i),
// Probability of Detecting Yes (PODY_i),
// Probability of False Detection (POFD_i),
// NOTE: Previous 3 columns repeated for each row in the
table
// Last Probability Threshold Value (THRESH_n)
//
// (13) STAT and MPR Text Files, Matched Pair Data:
// Total (TOTAL),
// Index (INDEX),
// Latitude (LAT),
// Longitude (LON),
// Level (LEVEL),
// Forecast Value (FCST),
// Observation Value (OBS),
// Climatological Value (CLIMO),
// Interpolation Methold (INTERP_MTHD),
// Interpolation Points (INTERP_PNTS)
//
// In the expressions above, f are forecast values, o are observed
values,
// and c are climatological values.
//
// Values for these flags are interpreted as follows:
// (0) Do not generate output of this type
// (1) Write output to a STAT file
// (2) Write output to a STAT file and a text file
//
output_flag[] = [ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 ];
//
// Flag to indicate whether Kendall's Tau and Spearman's Rank
Correlation
// Coefficients should be computed. Computing them over large
datasets is
// computationally intensive and slows down the runtime execution
significantly.
// (0) Do not compute these correlation coefficients
// (1) Compute these correlation coefficients
//
rank_corr_flag = 1;
//
// Specify the GRIB Table 2 parameter table version number to be used
// for interpreting GRIB codes.
// http://www.nco.ncep.noaa.gov/pmb/docs/on388/table2.html
//
grib_ptv = 2;
//
// Directory where temporary files should be written.
//
tmp_dir = "/tmp";
//
// Prefix to be used for the output file names.
//
output_prefix = "METv2.0";
//
// Indicate a version number for the contents of this configuration
file.
// The value should generally not be modified.
//
version = "V2.0";
------------------------------------------------
Subject: Using WPP 3.1.1 with MET 4.1
From: John Halley Gotway
Time: Wed Jul 24 16:09:19 2013
////////////////////////////////////////////////////////////////////////////////
//
// Point-Stat configuration file.
//
// For additional information, see the MET_BASE/data/config/README
file.
//
////////////////////////////////////////////////////////////////////////////////
//
// Output model name to be written
//
model = "WRF";
////////////////////////////////////////////////////////////////////////////////
//
// Forecast and observation fields to be verified
//
fcst = {
wind_thresh = [ NA ];
message_type = [ "ADPUPA" ];
field = [
{
name = "TMP";
level = [ "P900" ];
cat_thresh = [ >273.0 ];
}
];
};
obs = {
wind_thresh = [ NA ];
message_type = [ "ADPUPA" ];
field = [
{
name = "TMP";
level = [ "P850-950" ];
cat_thresh = [ >273.0 ];
}
];
};
////////////////////////////////////////////////////////////////////////////////
//
// Point observation time window
//
obs_window = {
beg = -5400;
end = 5400;
}
////////////////////////////////////////////////////////////////////////////////
//
// Verification masking regions
//
mask = {
grid = [ "FULL" ];
poly = [];
sid = [];
};
////////////////////////////////////////////////////////////////////////////////
//
// Confidence interval settings
//
ci_alpha = [ 0.05 ];
boot = {
interval = PCTILE;
rep_prop = 1.0;
n_rep = 1000;
rng = "mt19937";
seed = "";
};
////////////////////////////////////////////////////////////////////////////////
//
// Interpolation methods
//
interp = {
vld_thresh = 1.0;
type = [
{
method = UW_MEAN;
width = 1;
}
];
};
////////////////////////////////////////////////////////////////////////////////
//
// Statistical output types
//
output_flag = {
fho = BOTH;
ctc = BOTH;
cts = BOTH;
mctc = BOTH;
mcts = BOTH;
cnt = BOTH;
sl1l2 = BOTH;
sal1l2 = BOTH;
vl1l2 = BOTH;
val1l2 = BOTH;
pct = BOTH;
pstd = BOTH;
pjc = BOTH;
prc = BOTH;
mpr = BOTH;
};
////////////////////////////////////////////////////////////////////////////////
obs_quality = [];
duplicate_flag = NONE;
rank_corr_flag = TRUE;
tmp_dir = "/tmp";
output_prefix = "METv4.1";
version = "V4.1";
////////////////////////////////////////////////////////////////////////////////
------------------------------------------------
Subject: Using WPP 3.1.1 with MET 4.1
From: John Halley Gotway
Time: Wed Jul 24 16:09:19 2013
VERSION MODEL FCST_LEAD FCST_VALID_BEG FCST_VALID_END OBS_LEAD
OBS_VALID_BEG OBS_VALID_END FCST_VAR FCST_LEV OBS_VAR OBS_LEV
OBTYPE VX_MASK INTERP_MTHD INTERP_PNTS FCST_THRESH OBS_THRESH
COV_THRESH ALPHA LINE_TYPE TOTAL INDEX OBS_LAT OBS_LON OBS_LVL
OBS_ELV FCST OBS CLIMO
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 1 32.10000 -110.93000 850.00000 1503.00000
299.29007 297.75000 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 2 32.10000 -110.93000 926.00000
-888888.00000 299.29007 301.35001 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 3 32.10000 -110.93000 925.00000 755.00000
299.29007 301.35001 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 4 32.10000 -110.93000 901.00000
-888888.00000 299.29007 300.75000 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 5 32.10000 -110.93000 926.00000
-888888.00000 299.29007 301.35001 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 6 32.83000 -117.12000 925.00000 764.00000
298.77009 297.35001 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 7 32.83000 -117.12000 850.00000 1507.00000
298.77009 298.14999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 8 32.83000 -117.12000 927.00000
-888888.00000 298.77009 297.14999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 9 32.83000 -117.12000 915.00000
-888888.00000 298.77009 297.95001 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 10 32.83000 -117.12000 929.00000
-888888.00000 298.77009 296.75000 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 11 32.83000 -117.12000 874.00000
-888888.00000 298.77009 300.14999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 12 32.83000 -117.12000 890.00000
-888888.00000 298.77009 300.14999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 13 32.83000 -117.12000 940.00000
-888888.00000 298.77009 294.95001 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 14 32.83000 -117.12000 934.00000
-888888.00000 298.77009 295.54999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 15 32.83000 -117.12000 903.00000
-888888.00000 298.77009 298.35001 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 16 32.83000 -117.12000 945.00000
-888888.00000 298.77009 293.14999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 17 32.87000 -114.33000 925.00000 752.00000
303.08023 303.14999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 18 32.87000 -114.33000 850.00000 1502.00000
303.08023 298.54999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 19 32.87000 -114.33000 872.00000
-888888.00000 303.08023 300.54999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 20 32.87000 -114.33000 922.00000
-888888.00000 303.08023 303.14999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 21 33.45000 -111.95000 850.00000 1498.00000
302.25000 298.95001 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 22 33.45000 -111.95000 925.00000 746.00000
302.25000 304.35001 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 23 34.75000 -120.57000 939.00000
-888888.00000 295.47999 284.14999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 24 34.75000 -120.57000 944.00000
-888888.00000 295.47999 281.75000 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 25 34.75000 -120.57000 932.00000
-888888.00000 295.47999 291.54999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 26 34.75000 -120.57000 945.00000
-888888.00000 295.47999 281.35001 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 27 34.75000 -120.57000 872.00000
-888888.00000 295.47999 295.14999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 28 34.75000 -120.57000 927.00000
-888888.00000 295.47999 294.75000 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 29 34.75000 -120.57000 928.00000
-888888.00000 295.47999 294.75000 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 30 34.75000 -120.57000 880.00000
-888888.00000 295.47999 293.54999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 31 34.75000 -120.57000 850.00000
-888888.00000 295.47999 294.95001 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 32 34.75000 -120.57000 860.00000
-888888.00000 295.47999 295.75000 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 33 34.75000 -120.57000 948.00000
-888888.00000 295.47999 281.54999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 34 34.75000 -120.57000 919.00000
-888888.00000 295.47999 294.14999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 35 34.75000 -120.57000 949.00000
-888888.00000 295.47999 281.54999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 36 34.75000 -120.57000 850.00000 1499.00000
295.47999 294.95001 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 37 34.75000 -120.57000 925.00000 768.00000
295.47999 294.75000 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 38 36.05000 -115.18000 850.00000 1491.00000
301.00995 301.54999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 39 36.05000 -115.18000 925.00000 737.00000
301.00995 304.75000 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 40 36.05000 -115.18000 929.00000
-888888.00000 301.00995 304.54999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 41 36.05000 -115.18000 865.00000
-888888.00000 301.00995 302.75000 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 42 36.05000 -115.18000 896.00000
-888888.00000 301.00995 304.54999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 43 36.05000 -115.18000 926.00000
-888888.00000 301.00995 304.75000 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 44 36.05000 -115.18000 929.00000
-888888.00000 301.00995 304.54999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 45 37.75000 -122.22000 925.00000 764.00000
293.96019 289.54999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 46 37.75000 -122.22000 850.00000 1490.00000
293.96019 293.14999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 47 37.75000 -122.22000 922.00000
-888888.00000 293.96019 290.35001 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 48 37.75000 -122.22000 931.00000
-888888.00000 293.96019 287.95001 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 49 37.75000 -122.22000 870.00000
-888888.00000 293.96019 294.14999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 50 37.75000 -122.22000 890.00000
-888888.00000 293.96019 291.54999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 51 37.75000 -122.22000 936.00000
-888888.00000 293.96019 286.54999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 52 37.75000 -122.22000 884.00000
-888888.00000 293.96019 293.75000 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 53 37.75000 -122.22000 940.00000
-888888.00000 293.96019 283.95001 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 54 37.75000 -122.22000 946.00000
-888888.00000 293.96019 281.54999 NA
V2.0 WRF 000000 20130715_120000 20130715_120000 000000
20130715_103000 20130715_133000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 55 37.75000 -122.22000 943.00000
-888888.00000 293.96019 282.14999 NA
------------------------------------------------
Subject: Using WPP 3.1.1 with MET 4.1
From: John Halley Gotway
Time: Wed Jul 24 16:09:19 2013
VERSION MODEL FCST_LEAD FCST_VALID_BEG FCST_VALID_END OBS_LEAD
OBS_VALID_BEG OBS_VALID_END FCST_VAR FCST_LEV OBS_VAR OBS_LEV
OBTYPE VX_MASK INTERP_MTHD INTERP_PNTS FCST_THRESH OBS_THRESH
COV_THRESH ALPHA LINE_TYPE TOTAL INDEX OBS_SID OBS_LAT OBS_LON
OBS_LVL OBS_ELV FCST OBS CLIMO
V4.1 WRF 000000 20130715_120000 20130715_120000 000000
20130715_120000 20130715_120000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 1 000072274 32.10000 -110.93000 850.00000
1503.00000 299.29008 297.75000 NA
V4.1 WRF 000000 20130715_120000 20130715_120000 000000
20130715_120000 20130715_120000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 2 000072274 32.10000 -110.93000 926.00000
-888888.00000 299.29008 301.35001 NA
V4.1 WRF 000000 20130715_120000 20130715_120000 000000
20130715_120000 20130715_120000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 3 000072274 32.10000 -110.93000 925.00000
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ADPUPA FULL UW_MEAN 1 NA NA NA
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ADPUPA FULL UW_MEAN 1 NA NA NA
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ADPUPA FULL UW_MEAN 1 NA NA NA
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ADPUPA FULL UW_MEAN 1 NA NA NA
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 10 000072293 32.83000 -117.12000 929.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 11 000072293 32.83000 -117.12000 874.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 12 000072293 32.83000 -117.12000 890.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 13 000072293 32.83000 -117.12000 940.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 14 000072293 32.83000 -117.12000 934.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 15 000072293 32.83000 -117.12000 903.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 16 000072293 32.83000 -117.12000 945.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 17 000074004 32.87000 -114.33000 925.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 18 000074004 32.87000 -114.33000 850.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 19 000074004 32.87000 -114.33000 872.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 20 000074004 32.87000 -114.33000 922.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 21 000074626 33.45000 -111.95000 850.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 22 000074626 33.45000 -111.95000 925.00000
746.00000 302.25008 304.35001 NA
V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 23 000072393 34.75000 -120.57000 939.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 24 000072393 34.75000 -120.57000 944.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 25 000072393 34.75000 -120.57000 932.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 26 000072393 34.75000 -120.57000 945.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 27 000072393 34.75000 -120.57000 872.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 28 000072393 34.75000 -120.57000 927.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 29 000072393 34.75000 -120.57000 928.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 30 000072393 34.75000 -120.57000 880.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 31 000072393 34.75000 -120.57000 850.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 32 000072393 34.75000 -120.57000 860.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 33 000072393 34.75000 -120.57000 948.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 34 000072393 34.75000 -120.57000 919.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 35 000072393 34.75000 -120.57000 949.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 36 000072393 34.75000 -120.57000 850.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 37 000072393 34.75000 -120.57000 925.00000
768.00000 295.48008 294.75000 NA
V4.1 WRF 000000 20130715_120000 20130715_120000 000000
20130715_120000 20130715_120000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 38 000072388 36.05000 -115.18000 850.00000
1491.00000 301.01008 301.54999 NA
V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 39 000072388 36.05000 -115.18000 925.00000
737.00000 301.01008 304.75000 NA
V4.1 WRF 000000 20130715_120000 20130715_120000 000000
20130715_120000 20130715_120000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 40 000072388 36.05000 -115.18000 929.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 41 000072388 36.05000 -115.18000 865.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 42 000072388 36.05000 -115.18000 896.00000
-888888.00000 301.01008 304.54999 NA
V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 43 000072388 36.05000 -115.18000 926.00000
-888888.00000 301.01008 304.75000 NA
V4.1 WRF 000000 20130715_120000 20130715_120000 000000
20130715_120000 20130715_120000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 44 000072388 36.05000 -115.18000 929.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 45 000072493 37.75000 -122.22000 925.00000
764.00000 293.96008 289.54999 NA
V4.1 WRF 000000 20130715_120000 20130715_120000 000000
20130715_120000 20130715_120000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 46 000072493 37.75000 -122.22000 850.00000
1490.00000 293.96008 293.14999 NA
V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 47 000072493 37.75000 -122.22000 922.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
20130715_120000 20130715_120000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 48 000072493 37.75000 -122.22000 931.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 49 000072493 37.75000 -122.22000 870.00000
-888888.00000 293.96008 294.14999 NA
V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 50 000072493 37.75000 -122.22000 890.00000
-888888.00000 293.96008 291.54999 NA
V4.1 WRF 000000 20130715_120000 20130715_120000 000000
20130715_120000 20130715_120000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 51 000072493 37.75000 -122.22000 936.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 52 000072493 37.75000 -122.22000 884.00000
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V4.1 WRF 000000 20130715_120000 20130715_120000 000000
20130715_120000 20130715_120000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 53 000072493 37.75000 -122.22000 940.00000
-888888.00000 293.96008 283.95001 NA
V4.1 WRF 000000 20130715_120000 20130715_120000 000000
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ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 54 000072493 37.75000 -122.22000 946.00000
-888888.00000 293.96008 281.54999 NA
V4.1 WRF 000000 20130715_120000 20130715_120000 000000
20130715_120000 20130715_120000 TMP P900 TMP P950-850
ADPUPA FULL UW_MEAN 1 NA NA NA
NA MPR 55 55 000072493 37.75000 -122.22000 943.00000
-888888.00000 293.96008 282.14999 NA
------------------------------------------------
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