1. Production of Snowfall and Snow Depth Climatologies
for NWS Cooperative Observer Sites.
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2. Snowfall Extremes and Return Period Statistics
for the Contiguous US and Alaska.
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1. Production of Snowfall and Snow Depth Climatologies
for NWS Cooperative Observer Sites.
|
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2. Snowfall Extremes and Return Period Statistics
for the Contiguous US and Alaska.
|
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1. Production of Snowfall and Snow Depth Climatologies.
for NWS Cooperative Observer Sites.
1.1. Objective.
The purpose of this project was to
generate snowfall and snow depth statistics for several
thousand non-airport stations in the National Weather
Service (NWS) Cooperative (COOP) Network. Stations in the
Lower 48 States and Alaska were considered. The Automated
Surface Observing System (ASOS) instrumentation being
installed at airport locations detects weather phenomena,
including the occurrence of snow, using a standard
observational methodology, however the ASOS automated
instruments are not able to measure the amount of snowfall
or snow on the ground (snow depth). This project
established snow climatologies for COOP stations which could
be used to support NWS real-time snow operations in the ASOS
observation era. These snow climatologies also enable the
National Oceanic and Atmospheric Administration (NOAA) to
better respond to user requests for snow information for use
in economic and engineering decision-making, and provide
the Federal Emergency Management Agency with an objective basis
for declaring federal snow disasters. |
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1.2. Data.
This project analyzed daily snowfall and snow depth
data from NCDC's TD-3200 Cooperative Summary of the Day data
base. The digital period of record
was examined. Daily maximum and minimum temperature and
precipitation were used to quality control (QC) the snow
data. |
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1.3. Quality Control.
Three levels of quality control were employed in order
to obtain the best snow data possible. The first level
involved using the ValHiDD edited TD-3200 values. The
second level employed a number of internal consistency
checks. The third level was an extremes check. |
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1.3.1. First Level QC: ValHiDD.
During the 1990's, an automated quality control system
called ValHiDD (Validation of Historical Daily Data) was
applied to the entire TD-3200 data base to remove gross
errors in daily maximum and minimum temperature,
precipitation, snowfall, and snow depth. ValHiDD is a
rules-based method for detecting and correcting
discrepancies (due to digitizing errors and observer errors)
in the TD-3200 data base. The checks employed by ValHiDD
include a limits check, internal consistency checks,
flatliner temperature check, precipitation/snowfall/snow
depth (PSFSD) relationship check, temperature spike check,
multiple rule-group failures check, and failed fix check (
Reek, et al., 1992).
Although the number of discrepancies uncovered and
resolved by ValHiDD was small compared to the total number
of data values examined, their removal/correction was
important and ValHiDD significantly contributed to the
improvement of the overall TD-3200 data base. However, two
factors relevant to this project should be noted:
(1) In some PSFSD cases, ValHiDD could not identify which
element should be corrected, so the values were flagged as
suspect and not altered.
(2) The PSFSD relationship check assumed that all three
elements were observed at the same hour. For most volunteer
COOP observers this assumption holds. However, for airport
stations, this is not the case: snow depth is observed at 7
a.m. local time, while daily snowfall and precipitation
amount are reported as of midnight. This airport station
observation time discrepancy complicated the PSFSD
relationship check. |
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1.3.2. Second Level QC: Internal Consistency Checks.
This level of QC included temporal checks (comparing
today's snow depth values to yesterday's values) and
additional inter-element checks beyond those performed by
ValHiDD. Snowfall and snow depth values which failed the
internal consistency checks were corrected (where possible)
or set to missing. Temperature or precipitation values were
not examined for accuracy at this level.
The second level QC included the following checks. The
following abbreviations are used here: TMIN = minimum
temperature (deg. F), TMAX = maximum temperature (deg. F),
P = precipitation (inches), SF = snowfall (inches), and SD =
snow depth (inches).
(1) Factor of 10 error for SF: if P >= 0.01 and SF >= 1.0
and the ratio, SF/P, was greater than 80.0, then the SF
value was corrected by dividing by 10. The corrected
SF value was similarly checked and set to missing if
the new SF/P ratio was greater than 50.
(2) Hail check: nonzero SF values were set to zero if
TMIN >= 40.
An alternative check was used for those cases where the
minimum temperature was missing (stations measuring
both temperature and precipitation where the day's TMIN
was missing, and stations which measured only
precipitation). This alternative involved examining
the day's climatological median extreme minimum
temperature (CMEMT) for the state. The CMEMT was
computed for each of the 365 days of the year (the
value for February 28 was used for February 29 leap
days) for each state from the daily extreme minimum
temperature values for all stations in the state, from
the period 1961-1990. Nonzero SF values were set to
zero if CMEMT > 25.
(3) Nonzero SF values were set to missing if:
(I) SF > 0.4 but P = 0; or
(II) today's P is missing.
(4) Factor of 10 error for SD (where previous day's SD =
zero or trace): SD was compared to SF and corrected if
it was identified as being off by a factor of 10. If
the SD was greater than ten times SF, the SD was set to
missing. (There were a few cases where the observer
inconsistently recorded SD off by a factor of ten for a
string of years. This check was used to identify the
beginning and ending years of such periods, so the
station's data could be later examined manually. If
the error was not consistent, the snow depth from this
string of years was subsequently deleted from the
analysis.)
(5) Second check for factor of 10 error for SD: if the
difference between today's SD and yesterday's SD was
greater than today's SF (plus an adjustment factor due
to difference in units resolution), today's SD was
divided by 10. The corrected SD value was similarly
checked and set to missing if the difference in SD was
still greater than today's SF.
(6) Zero SD values were set to missing if:
yesterday's SD > 7 and today's SF > 2.0.
(7) Nonzero SD values were set to missing if:
(I) today's SD & yesterday's SD with today's SF = 0;
or
(II) today's SF is missing; or
(III)yesterday's SD missing and today's SD & (today's
SF + SD of last day with non-missing SD).
(8) Today's SD was set to missing if today's P < 0.05 and:
(I) yesterday's SD >= (4 + today's SD), and today's
TMAX < 40; or
(II) yesterday's SD >= (7 + today's SD), and today's
TMAX < 45; or
(III)yesterday's SD >= (10 + today's SD), and today's
TMAX > 44; or
(IV) yesterday's SD >= (7 + today's SD), and today's
TMAX missing.
The snowfall and snow depth values which were corrected
or set to missing (by the above 8 checks) were tallied and
the counts were saved to a metadata file to be used later in
a station quality assessment step.
The following additional checks were made. Values
failing these checks were not changed, but the number of
flagged values was similarly saved to a metadata file.
(9) Questionable SF values (the SF/P ratios were unusual):
(I) 1 < SF < 3, and SF > 50*P; or
(II) 3 <= SF <= 6, and SF > 40*P, and TMAX > 24; or
(III)SF > 6, and SF > 20*P, and TMAX > 24; or
(IV) SF > 6, and SF > 30*P, and TMAX < 25.
(10) Questionable SD values (unusual decrease in SD):
today's SD <= yesterday's SD, today's SF > 2.0, and
today's TMAX < 30.
As in the ValHiDD discussion (see section 1.3.1), it is
critical to these automated tests that the temperature,
precipitation, snowfall, and snow depth observations be
taken at the same hour. This is the case for most volunteer
COOP observers. However, NWS and Federal Aviation
Administration (FAA) airport stations observe snow depth at
7 a.m. local time, while the remaining elements are reported
as of midnight. This airport station observation time
discrepancy impacts checks (4)-(7) and (10) above, and can
result in valid snow depth values being flagged as erroneous
and being changed. |
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1.3.3. Third Level QC: Extremes Checks.
The daily snowfall values were compared, on a state-by-
state basis, to known statewide 24-hour snowfall extremes.
The known extremes published by weather historian David
Ludlum (
Ludlum, 1982) were, for most states, multiplied by
an acceptability factor (1.4) in order to account for new
daily extremes that may have been set since his book was
published, and to account for the difference in time frame
(a moving 24-hour time frame versus daily values taken at a
fixed ob time). Special subjective estimates were used for
Colorado, Florida, and New York.
These adjusted statewide extremes were used in the
snowfall extremes check. If a station's daily snowfall
value exceeded the corresponding statewide extreme, the
value was set to missing and the occurrence was tallied.
The counts were saved to a metadata file to be used later in
a station quality assessment step.
Snow depth varies widely in states with mountain
topography. For example, the extremes for coastal stations
in southern California would be considerably lower than the
extremes for stations in the Sierra Nevada range. This made
it difficult to establish an appropriate statewide snow
depth extreme, so a standard snow depth extreme of 2000
inches was used for all stations in all 49 states. If a
station's snow depth value exceeded 2000 inches, the value
was set to missing and the occurrence was similarly tallied
and saved. |
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1.4. Analysis Procedures.
The NWS Office of Meteorology (OM) solicited input on
suggested methodology and desired output statistics from NWS
regional and field offices and from non-NOAA snow experts.
This input was reviewed by NCDC and NWS OM and incorporated,
as appropriate, into the project.
The properties of snow make it difficult to accurately
and consistently measure snowfall and snow depth. Snow
often melts as it lands or as it lies on the ground, snow
settles as it lies on the ground, and snow is easily blown
and redistributed. These properties can be affected by
location, time of day the observations are taken, and how
often they are measured (
Doesken and Judson, 1997). For
these reasons, it is important for observers to adhere to a
standard methodology (
National Weather Service, 1972) for
observing and reporting snow. Unfortunately, stations
change location, observers, and sometimes observation time.
Such changes introduce inhomogeneities into the snow record.
No acceptable adjustment algorithms exist to statistically
adjust daily snow data for inhomogeneities. The alternative
for creating a reasonably high quality set of snow
statistics, therefore, is to use stations which have a low
risk of having inhomogeneous data.
For this project, the entire TD-3200 data base was
examined. QC (
Section 1.4.1) and inventory (
Section 1.4.2)
indicators were computed for data from the period 1948-1996.
Several station metadata files were examined and metadata
indicators were computed (
Section 1.4.3). The QC,
inventory, and station metadata indicators were used to
assess the quality of each station (
Section 1.4.4). The
stations included in the final station list were selected
based upon this objective assessment of their quality, as
well as (where human resources allowed) a subjective
assessment (based on experience with operational processing
of the stations' data).
Two sets of QC and inventory indicators were computed
for each station, one for snowfall and one for snow depth.
As a result, some stations will have output products and
statistics for snowfall but not snow depth, some for snow
depth but not for snowfall, and some for both snowfall and
snow depth. |
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1.4.1. Data QC Indicators.
Daily COOP data have been digitized operationally
beginning in 1948. Over the years, pre-1948 data have been
keyed on a special project case-by-case basis, and are more
likely to have gaps of missing data. About 18 percent of
the stations had data beginning before 1948, while less than
one percent (0.36%) had data which ended before 1948.
Consequently, the QC indicators were computed for the 1948-
1996 period (however, the entire data base was QC'd and
analyzed for the computation of the statistics). The QC
indicators include the following:
(1) the number of non-missing daily values read;
(2) the number of daily values that were flagged as suspect
by the QC checks, including those flagged values that
were corrected and those flagged values for which no
corrective action was taken;
(3) the number of daily values that failed the quality
control checks and were set to missing; and
(4) the percent of the non-missing daily values read that
were flagged as suspect or set to missing. |
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1.4.2. Data Inventory Indicators.
For the reasons noted in
Section 1.4.1, the inventory
indicators were computed over the period 1948-1996. They
include the following:
(1) number of years in the TD-3200 data base between the
first and last years with data;
(2) number of years in the TD-3200 data base having some
data (at least one day);
(3) number of months having complete data (no days
missing), and percent of possible months having
complete data;
(4) number of usable daily values processed;
(5) number of daily values missing, and percent of daily
values missing; and
(6) information concerning the number of breaks (or gaps)
in the data record, where a break is defined as at
least one month completely missing. The information
included the number of breaks of different lengths, the
total number of breaks (breaks with any number of
months missing), and the length of the biggest break.
For example, if a station had one break of three months
length and two breaks of five months length, then it
would have two breaks of different lengths, three
breaks in total, and the biggest break would be five
months long. |
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1.4.3. Station Metadata Indicators.
Station metadata from three files were utilized in the
creation of the metadata indicators: the Cooperative
Station History File (COOP); the U.S. Historical Climatology
Network Station History File (HCN); and the 1961-1990
Climate Normals historical time of observation (ob time)
file (CLIM81). The COOP file contains metadata for all
(approximately 24,000) stations, past and present, in the
Cooperative Network, but some metadata elements are not
complete. The HCN file contains complete metadata for the
1221 "best" stations in the Cooperative Network (
Easterling, et al., 1996). The CLIM81 file contains complete ob time
metadata for the 40 years, 1951-1990, for the 6662 stations
for which 1961-1990 monthly climate normals were computed.
Metadata from these three files were examined in order to
capture the most comprehensive metadata information for as
many stations as possible.
The following station metadata indicators were
computed:
(1) from the COOP file: number of years the station is in
the file, the number of times the station changed
location, and the number of times the station changed
location divided by how long it is in the file.
Location is measured in the COOP file by latitude,
longitude, elevation, and a "relocation" (station moved
x distance in y direction) parameter. Latitude,
longitude, and elevation information was available for
the period of record, however relocation information
was not available for 1948-1980.
Some tolerance was built into this indicator. A
location change occurred if any of the following
criteria were met:
A. any change in latitude or longitude (both measured
to the nearest minute);
B. an elevation change greater than 20 feet; or
C. the relocation parameter indicated a move greater
than one tenth of a mile. Of the 5631
decipherable relocations in the metadata base,
approximately 26% of them were less than or equal
to a tenth of a mile.
(2) from the COOP file: the number of times the station's
ob time changed, and the number of times the station's
ob time changed divided by how long it is in the file.
Ob time information is available from only 1981-
present. There are three pronounced peaks in a plot of
ob time change: one at 1 hour, one at 9-10 hours, and
one at 16-17 hours. One can safely assume that an ob
time change of 1 or 2 hours will not introduce a
significant inhomogeneity into the snow record. Ob
time changes of 3-5 hours are rare. For these reasons,
some tolerance was built into this indicator, as well,
with an ob time change being counted only if it
exceeded 2 hours.
(3) from the HCN file: number of years the station is in
the file, the number of times the station changed
location, and the number of times the station changed
location divided by how long it is in the file.
The discussion for the COOP location indicator applies
to the HCN location indicator, except the HCN location
information was available for the entire period of
record.
(4) from the HCN file: the number of times the station's
ob time changed, and the number of times the station's
ob time changed divided by how long it is in the file.
The discussion for the COOP ob time change indicator
applies to the HCN ob time change indicator, except the
HCN ob time information was available for the entire
period of record.
(5) from the HCN file: the number of times the station's
observer name changed, and the number of times the
station's observer name changed divided by how long it
is in the file.
(6) from the CLIM81 file: number of years the station is
in the file, the number of times the station's ob time
changed, and the number of times the station's ob time
changed divided by how long it is in the file.
The discussion for the COOP ob time change indicator
applies to the CLIM81 ob time change indicator, except
the CLIM81 ob time information was available for the
CLIM81 file's period of record. |
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1.4.4. Station Quality Assessment.
The following QC, inventory, and station metadata
indicators were used to assess the quality of each station.
Examination of frequency distribution charts of these
indicators did not provide meaningful guidance in
determination of cutoff criteria. Therefore, the specific
criteria chosen were selected in order to maximize both the
quality of the station dataset and the number of stations
included in the dataset. The data and metadata indicators
for all stations are included in the metadata files, in the
event the user wishes to apply different criteria.
In order to be included in the project's final station
list (for snowfall and/or snow depth), the station had to
meet the following requirements:
(1) have at least 15 years of non-missing data for each of
the 12 months (January-December) for selected climatic
elements (number of days with snowfall, monthly total
snowfall amount, greatest daily snowfall amount, number
of days with snow depth, and daily snow depth amount);
(2) have at least 15 years of non-missing data for each of
the 365 days of the year for selected climatic elements
(daily snowfall amount and daily snow depth amount);
(3) have at least 70% of the months from the data period
with complete data (no days missing);
(4) have 33 or fewer breaks per 100 years;
(5) have no more than 25% of the daily values missing out
of the total number of (daily values missing plus daily
values with data);
(6) have 3 or fewer ob time changes, based on the COOP
metadata file;
(7) have 10 or fewer location changes per 100 years, based
on the COOP metadata file (it should be noted that
latitude, longitude, and/or elevation may have changed
due to the switch from manually-based surveys to
satellite-based surveys when, in fact, the station did
not move); and
(8) have 10 or fewer ob time changes per 100 years, based
on the CLIM81 metadata file.
Stations which met these criteria were then examined
for station type. NWS offices and NWS and FAA airport
stations were deleted from the snow depth list, due to QC
considerations as discussed in
sections 1.3.1 and
1.3.2. |
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1.5. Computational Methodology.
A suite of statistics (mean, median, first and third
quartiles, extremes [both amounts and dates of occurrence],
and frequencies/probabilities) was generated for several
climatic parameters. The specific statistics that were
computed vary with parameter, but the number of years with
non-missing data (NYRS) was computed for each parameter.
The beginning and ending years and NYRS information are
crucial for any inter-station or inter-seasonal comparisons
the user may wish to make.
A daily climatology and a monthly/seasonal climatology
were created for each station. The statistics for the daily
climatology were generated for each day from the years of
data available for the day. The statistics for the
monthly/seasonal climatology were generated from year-month
or year-season sequential values. |
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1.5.1. Climatic Parameters.
The climatic parameters include the following:
(1) number of days (1-day periods, 2-day periods, and 3-day
periods) with daily snowfall amount equal to zero or a
trace;
(2) number of days (1-day periods, 2-day periods, and 3-day
periods) with daily snowfall amount greater than or
equal to several thresholds (0.1, 1.0, 2.0, 5.0, 10.0,
12.0, 18.0, 24.0, and 36.0 inches);
(3) monthly and seasonal total snowfall amount;
(4) number of consecutive days with daily snowfall amount
greater than or equal to several thresholds (0.1, 1.0,
2.0, and 5.0 inches);
(5) dates of first and last occurrence of daily snowfall
amount greater than or equal to several thresholds
(1.0, 4.0, and 6.0 inches);
(6) daily snowfall amount, both with all days examined
(whether they had snowfall or not) and only days having
snowfall examined;
(7) greatest multiple-day (2-, 3-, 4-, 5-, 6-, and 7-day)
total snowfall amount (where snow fell on each day) in
a month;
(8) greatest 2-day and 3-day total snowfall amount (whether
it snowed each day or not) in a month;
(9) number of days with snow depth equal to zero or a
trace;
(10) number of days with snow depth greater than or equal to
several thresholds (1.0, 2.0, 5.0, and 10.0 inches);
(11) number of consecutive days with snow depth greater than
or equal to various thresholds (1.0, 2.0, 5.0, 10.0,
12.0, 18.0, 24.0, and 36.0 inches);
(12) daily snow depth amount, both with all days examined
(whether they had snow cover or not) and with only days
with snow cover examined; and
(13) number of years (from which frequencies were derived)
with snowfall or snow depth greater than or equal to
several thresholds (0.1, 1.0, 2.0, 5.0, 10.0, and 12.0
inches) for each day of the year. |
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1.5.2. Monthly/Seasonal Climatology Computational Considerations.
The multiple-day extremes parameters (see group 7 in
Section 1.5.1) are based on snow falling on each of the days
in the time unit. If snow fell on some days but not all
days, then that value was not included in the analysis for
that multiple-day time unit, but it may qualify for
inclusion in a shorter time unit. This could result in some
longer time units having smaller extreme snowfall amounts
than the corresponding shorter time units.
The date (year, month, and/or day) of an extreme is the
date of the most recent occurrence (except for statistics
G1-G0). The date listed for multiple-day parameters is the
last day of the multiple-day period.
Monthly statistics (for January through December) were
computed based on the days in the month under consideration.
Seasonal statistics were computed for winter, spring,
summer, autumn, annual, and snow season, with the seasons
corresponding to the following months, respectively:
December-February, March-May, June-August, September-
November, January-December, and August-July. The seasonal
statistics are not based on the monthly statistics; they
were computed from the daily values corresponding to each
season in each year of the record. (For example, the mean
winter statistics are not the average or total of the
December, January, and February mean statistics; they are
based on the sequential winter periods in the data record.)
For the first and last occurrence of snowfall (group 5 in
Section 1.5.1), the (incomplete) years at the beginning and
end of the data period were included in the analysis for the
seasonal statistics. For these reasons, the seasonal
statistics may not agree with the corresponding monthly
statistics.
For the first and last occurrence of snowfall (group 5
in
Section 1.5.1), if no snow occurred, then there was no
data from which to compute the dates and the first and last
years of data will be coded as "missing" (-99). For these
elements, the NYRS statistics refers to the number of years
with nonzero data, which is somewhat broader than "the
number of years with non-missing data."
Likewise for the daily snowfall (snow depth) amount,
where only days having snowfall (snow depth) were examined
(groups 6 and 12 in
Section 1.5.1): if no snow occurred,
then there was no data from which to compute a value.
Consequently, the NYRS statistics refers to the number of
years with nonzero data, which is somewhat broader than "the
number of years with non-missing data."
The greatest number of consecutive days with snow depth
parameter (element 71) was computed for just the August-July
snow season. It was not computed for the other seasons or
the individual months. |
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1.5.3. The Effect of Missing Data.
The impact of missing data varies, depending on the
element and statistic computed. Total snowfall amount had
no tolerance for missing data. If even one day was missing
in a month or season, the total snowfall could not be
computed for that year's month or season. Consequently, the
number of years with non-missing data will vary with month
and season. The six seasons (especially annual and August-
July) have a greater chance of experiencing missing data
and, generally, will have fewer years with non-missing data
when compared to the individual months.
The median daily value for a month had no tolerance for
missing data. All days in a month had to have data in order
for a median daily value to be computed for that year-month.
The number of days with snowfall or snow depth
parameters had no tolerance for missing data. Data for leap
days (February 29) were included in the analysis. Due to
this fact and due to rounding error, the sum of the values
for the equal zero, equal trace, and greater than or equal
to 0.1 inch (for snowfall, 1.0 inch for snow depth)
thresholds may not exactly equal the maximum possible number
of days in the month or season. This will be especially
noticeable for the number of days with 2-day and 3-day
snowfall parameters.
The consecutive days with snowfall or snow depth, or
"runs" parameters, had no tolerance for missing data for
each specific threshold. A run of consecutive days for a
given threshold was delineated by days (immediately before
and after the run) which had values less than the run's
threshold value. Consequently, a run had to have no missing
days during the run and on the days immediately before the
run started and after the run ended in order to be included
in the analysis. If a nonzero day outside of this range was
missing, however, then the runs for the lower thresholds
would be affected and would be treated as missing in the
sequential data for that year. This could result in
statistics for runs with higher thresholds being larger than
the corresponding statistics for lower thresholds.
The daily extreme, multiple-day extreme, and date of
occurrence parameters had a greater tolerance for missing
data. Data were analyzed even if a month had up to 5 days
missing. This could result in apparent discrepancies
between these and other parameters.
The greatest 2-day and 3-day total snowfall amount,
whether it snowed each day or not (group 8 in
Section 1.5.1), could tolerate up to 5 missing days per month.
However, missing days within a 2- or 3-day period were
excluded from the analysis. (For example, if a 2-day period
had snow, but the day before and the day after this 2-day
period were missing, then the snowfall total would be
included in the 2-day analysis but not in the 3-day
analysis.) Missing days might, in some cases, result in
extreme 2-day snowfall totals being greater than the
corresponding extreme 3-day snowfall totals.
A complicated tolerance for missing data was built into
the probability of receiving measurable snowfall parameter
(statistic PR). The data in a month (or season) were
examined to determine if it snowed in a given year-month (or
year-season). If even one day in a month (or season) had
measurable snowfall, then that year was counted in the
computations, regardless of how many days were missing.
However, to determine if no snow occurred in a month (or
season), the month (season) had to have no days missing.
The probability for each month (season) was computed by
summing the number of years with one or more days of
measurable snowfall, then dividing by the number of years
that qualified (i.e., where it could be determined that it
did or did not snow). |
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2. Snowfall Extremes and Return Period Statistics
for the Contiguous US and Alaska.
2.1. Objective.
The purpose of this project was to prepare snowfall
extremes and return period statistics for official weather
stations across the contiguous United States and Alaska,
which the Federal Emergency Management Agency (FEMA) may use
as an aid in making disaster declarations for record or
near-record snowstorms. This work was performed as an
adjunct to the Production of Snowfall and Snow Depth
Climatologies for NWS Cooperative Observer Sites project.
|
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2.2. Data.
This project analyzed daily snowfall data from NCDC's
TD-3200 Cooperative Summary of the Day data base. The
digital period of record was
examined. Daily maximum and minimum temperature and
precipitation were used to quality control (QC) the snow
data. |
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2.3. Quality Control.
Three levels of quality control were employed in order
to obtain the best snow data possible. The first level
involved using the ValHiDD edited TD-3200 values. The
second level employed a number of internal consistency
checks. The third level was an extremes check. |
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2.3.1. First Level QC: ValHiDD.
Same as
Section 1.3.1 of the Production of Snowfall and
Snow Depth Climatologies for NWS Cooperative Observer Sites
project. |
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2.3.2. Second Level QC: Internal Consistency Checks.
This level of QC included additional inter-element
checks beyond those performed by ValHiDD. Snowfall values
which failed the internal consistency checks were corrected
(where possible) or set to missing. Temperature or
precipitation values were not examined for accuracy at this
level.
The second level QC included the following checks. The
following abbreviations are used here: TMIN = minimum
temperature (deg. F), TMAX = maximum temperature (deg. F),
P = precipitation (inches), SF = snowfall (inches), and SD =
snow depth (inches).
(1) Factor of 10 error for SF: if P >= 0.01 and SF >= 1.0
and the ratio, SF/P, was greater than 80.0, then the SF
value was corrected by dividing by 10. The corrected
SF value was similarly checked and set to missing if
the new SF/P ratio was greater than 50.
(2) Hail check: nonzero SF values were set to zero if
TMIN >= 50, or TMIN >= 40 with TMAX >= TMIN + 20.
(3) Nonzero SF values were set to missing if:
SF > 0.4 but P = 0.
The snowfall values which were corrected or set to
missing (by the above checks) were tallied and the counts
were saved to a metadata file to be used later in a station
quality assessment step.
The following additional checks were made. Values
failing these checks were not changed, but the number of
flagged values was similarly saved to a metadata file.
(4) Questionable SF values (the SF/P ratios were unusual):
(I) 1 > SF < 3, and SF > 50*P; or
(II) 3 <= SF <= 6, and SF > 40*P, and TMAX > 24; or
(III)SF > 6, and SF > 20*P, and TMAX > 24; or
(IV) SF > 6, and SF > 30*P, and TMAX < 25. |
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2.3.3. Third Level QC: Extremes Checks.
The daily snowfall values were compared, on a state-by-
state basis, to known statewide 24-hour snowfall extremes.
The known extremes published by weather historian David
Ludlum (
Ludlum, 1982) were, for most states, multiplied by
an acceptability factor (1.4) in order to account for new
daily extremes that may have been set since his book was
published, and to account for the difference in time frame
(a moving 24-hour time frame versus daily values taken at a
fixed ob time). Special subjective estimates were used for
Colorado, Florida, and New York.
These adjusted statewide extremes were used in the
snowfall extremes check. If a station's daily snowfall
value exceeded the corresponding statewide extreme, the
value was set to missing and the occurrence was tallied.
The counts were saved to a metadata file to be used later in
a station quality assessment step. |
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2.4. Analysis Procedures.
FEMA needed snow statistics for as many stations as
possible, so all of the "current" stations were selected. A
station was considered to be "current" if it had snowfall
data in the TD-3200 data base in 1996, or if it was listed
as a currently-open station in the Cooperative Station
History File. Some stations were listed as currently open
yet did not have current snowfall data in the data base.
These were included in the product sent to FEMA. The NCDC
archive product, however, includes these stations as well as
non-current stations that were included in the station list
for the Production of Snowfall and Snow Depth Climatologies
for NWS Cooperative Observer Sites project.
As noted in the Analysis Procedures section for the
Production of Snowfall and Snow Depth Climatologies for NWS
Cooperative Observer Sites project (
Section 1.4), the
properties of snow make it difficult to accurately and
consistently measure snowfall. The QC, inventory, and
metadata quality assessment indicators computed by that
project for all stations should be consulted before using
the return period statistics of the FEMA project. |
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2.5. Computational Methodology.
Data from the digital period of record were analyzed. The output for this
project includes observed extreme snowfall values and the
extreme snowfall values that correspond to four return
periods. These values were computed as follows:
(1) Four values (corresponding to four time units) were
determined for each year of the data period: the
greatest 1-day, greatest 2-day, and greatest 3-day
snowfall amounts, and the August-July total snowfall
amount.
(2) Each time unit was analyzed separately. For example,
1-day snowfall might have had 35 values (35 extreme
values, one for each of 35 years), 2-day snowfall might
have had 30 values, 3-day snowfall might have had 26
values, and August-July total snowfall might have had
21 values.
(3) The highest of these values was selected as the
observed maximum snowfall value.
(4) These extreme values were analyzed using the
Generalized Extreme-Value statistical distribution
estimated using the method of L-moments and L-moment
ratios described by
Hosking and Wallis (1997). This
analysis method can be used to compute the extreme
snowfall values corresponding to any desired return
period (i.e., probability level). Extreme snowfall
values corresponding to the 10-year, 25-year, 50-year,
and 100-year return periods were computed for each of
the four time units. |
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2.5.1. Statistical Considerations.
It should be noted that a statistical distribution can
be determined only from nonzero values. If too many values
are zero (which will happen, for example, in warm climate
regions such as the Gulf Coast states, southern New Mexico,
southern Arizona, and coastal and southern California, where
it rarely snows), then a statistical distribution cannot be
determined and return period statistics cannot be computed.
Hosking and Wallis note that at least 20 nonzero values are
needed in order to determine the statistical distribution,
but a study by Guttman (1994) indicates that at least 30
nonzero values are needed for stable return period
statistics.
In this study, return period statistics were computed
if at least 20 nonzero values were available, in order to
generate return period statistics for as many stations as
possible. The number of years with nonzero data are
included in the output to enable the user to decide if they
want to use a particular station's values. If the number of
years with nonzero data is 20 or more but less than 30, then
the user should exercise caution when using the return
period statistics for that time unit.
Some time units may have had 20 or more years with
nonzero data, while other time units for the same station
may have had fewer years. In these cases, only some of the
return period statistics were computed; the others have a
"not available" code. |
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2.5.2. Data Completeness Considerations.
Guidance from the World Meteorological Organization
(WMO) was used, where available, for determining the values
for each year. The August-July total seasonal snowfall was
computed by adding the total monthly snowfall amounts from
the corresponding months for each August-July "year." If
any month was missing, the August-July value for that "year"
was missing. The total monthly snowfall amounts were
computed by summing the daily snowfall amounts in the month.
If any daily snowfall value was missing, the monthly total
was missing. In this way, the August-July total seasonal
snowfall has no tolerance for missing data.
For the 1-day, 2-day, and 3-day snowfall extremes, the
data for a month was discarded if more than five days were
missing. If five or fewer days were missing, then the
highest value for that month was used for the month. The
extreme value for the August-July snow "year" was the
highest value of the twelve available corresponding months.
It may be possible, for some locations that experience only
a few days of snowfall each year, to have a year with no
snowfall when, in fact, snow did fall but the data was
discarded because it failed a QC test.
As noted above, the August-July total seasonal snowfall
has no tolerance for missing data. The 1-day, 2-day, and
3-day snowfall extremes are more tolerant of missing data.
For this reason, the August-July total seasonal snowfall
will always have the same or fewer number of years with non-
missing data than the 1-day, 2-day, and 3-day extremes. It
may be possible for the 1-day, 2-day, or 3-day extreme
value(s) to be greater than the August-July extreme seasonal
total if the corresponding year(s) for the August-July total
value was missing.
The 1-day extreme snowfall value is the greatest single
daily snowfall amount. The 2-day extreme snowfall value is
the greatest two-day snowfall amount, where data were
available for both days. If heavy snow fell on one day but
the day before and the day after were both missing, then
that snowfall amount could not be included in a two-day
total or a three-day total. In this way, it may be possible
for the 1-day extreme snowfall value to be larger than the
2-day or 3-day extreme snowfall value. Likewise for 3-day
snowfall. The 3-day extreme snowfall value is the greatest
three-day snowfall amount, where data were available for
each of the three days. If heavy snow fell on one or two
days, but the day before and the day after this one-day or
two-day period were missing, then that snowfall amount could
not be included in a 3-day total. In this way, it may be
possible for the 1-day or 2-day extreme snowfall value to be
larger than the 3-day extreme snowfall value. |
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2.5.3. Cautions and Warnings.
The users of these snow extremes and return period
statistics should use considerable caution if they compare
the values from one station to those of another station.
The stations may not be directly comparable to one another,
due to several reasons:
(1) The properties of snow make it difficult to accurately
and consistently measure snowfall. As noted by
Doesken and Judson (1997), snow often melts as it lands or as
it lies on the ground, snow settles as it lies on the
ground, and snow is easily blown and redistributed.
These properties can be affected by location, time of
day the observations are taken, and how often they are
measured.
(2) The synoptic weather patterns that generate snow can
result in snowfall amounts that vary greatly over small
distances (snow bands).
(3) Local topography can have a major effect. Snowfall
amounts can vary greatly depending on elevation and on
slope aspect, steepness, and orientation (especially
with regard to the prevailing wind patterns and the
wind patterns associated with any given storm).
(4) The data period is an important factor. Ideally, the
same data period (with no missing data) would be
desired for all stations if any inter-station
comparisons were to be made. In reality, the stations
have varying data periods with differing amounts of
missing data.
(5) The results of a statistical analysis partly depend on
how much data are analyzed (sample size). A bigger
sample size (60 or 70 years of nonzero data) would
provide more stable results for this type of analysis (
Guttman, 1994). Unfortunately, this amount of data
was not available. The preferred minimum sample size
is 20 to 30 years of nonzero data, but the user should
exercise caution when using return period statistics if
the number of years with nonzero data is 20 or more but
less than 30.
(6) Even if two stations have the same number of years with
nonzero data, the history of snowfall at a location can
affect the shape of the statistical distribution, which
determines the snowfall amounts corresponding to the
selected return periods. |
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2.6. References.
Doesken, N.J. and A. Judson, 1997: A Guide to the Science,
Climatology, and Measurement of Snow in the United
States, Second Edition, Colorado State University
Department of Atmospheric Science: Fort Collins.
Easterling, D.R., T.R. Karl, E.H. Mason, P.Y. Hughes, and
D.P. Bowman (R.C. Daniels and T.A. Boden, editors),
1993: United States Historical Climatology Network
(U.S. HCN): Monthly Temperature and Precipitation Data.
Carbon Dioxide Information Analysis Center,
Environmental Sciences Division, Publication No. 4500,
ORNL/CDIAC-87, NDP-019/R3, Oak Ridge, TN.
Guttman, N.B., 1994: "On the sensitivity of sample L-Moments
to sample size." Journal of Climate, vol. 7, pp. 1026-1029.
Ludlum, D.M., 1982: The American Weather Book, Houghton
Mifflin Co.: Boston.
Hosking, J.R.M. and J.R. Wallis, 1997: Regional Frequency
Analysis: An Approach Based on L-Moments, Cambridge
University Press
National Weather Service, 1972: National Weather Service
Observing Handbook No. 2: Substation Observations,
First Edition, Revised December 1972 (Supersedes
Circular B), U.S. Dept. of Commerce, National Oceanic
and Atmospheric Administration, Silver Spring, MD.
Reek, T., S.R. Doty, and T.W. Owen, 1992: "A deterministic
approach to the validation of historical daily
temperature and precipitation data from the Cooperative
Network." Bulletin of the American Meteorological
Society, vol. 73, pp. 753-762. |
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2.7. Acknowledgments.
This project was supported by ESDIM grant NWS-97-04 and FEMA grant EMF-2000-IA-0039.
The project leaders would like to specially thank Dr. David
Robinson of Rutgers University, Mr. Nolan Doesken of the
Colorado Climate Center, and Mr. Grant Goodge of Asheville,
NC for their valuable input and suggestions, and Ms. Nina Stroumentova for valuable programming support. |
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