noaa’s snow climatology dataset & user perspectives

National Climatic Data Center

NOAA’s Snow Climatology Dataset & User Perspectives

Richard R. Heim Jr.NOAA/NESDIS/National Climatic Data Center

Asheville, North Carolina

Timothy KearnsNOAA/National Weather Service WFO, Aberdeen, South Dakota

Snowfall Observations and Products in the 21st Century: Meeting the Needs of FEMA and the Climate Community

Estes Park, CO – 25-27 May 2011


Snow Climatology Background

Developed in late 1990s Snowfall & Snow Depth climatologies for COOP stations

Support NWS operations 1997 ESDIM grant

SnowClim generated

CD – flat ascii files

Support FEMA snow disaster declarations 2000 FEMA grant

online access – tabular format

Other applications media, monitoring, research

http://www.ncdc.noaa.gov/ussc/


Variables & Statistics Mean Quartiles* NY* Extremes* Prob* RP* NF*

Snowfall X X

Daily & multi-day SF X X X X X

Total SF (monthly, seasonal, annual)

X X X X X

Number of days with SF** X X X

Number of consecutive days with SF**

X X X X

First/last occurrence of SF** MD X X

Number of days with snow cover (SD)**

X X X

Daily SD X X X

Number of consecutive days with snow cover (SD)**

X X X X

Snow Cover (SD) X X

*Legend: Quartiles: first quartile, median (MD), third quartile Extremes: greatest, top/bottom 10 (for monthly SF), earliest/latest (for first/last occurrence of SF) Prob: probability of receiving daily SF in a month NF: number of years with SF or SD on that day meeting the SF or SD threshold; when used in conjunction with NY, daily probability or frequency of snow = NF / NY

NY: number of years with non-missing dataRP: return periods for 1-day, 2-day, 3-day, monthly SF

** meeting several specific thresholds


Variables & Statistics Mean Quartiles* NY* Extremes* Prob* RP* NF*

Snowfall X X

Daily & multi-day SF X X X X X

Total SF (monthly, seasonal, annual)

X X X X X

Number of days with SF** X X X

Number of consecutive days with SF**

X X X X

First/last occurrence of SF** MD X X

Number of days with snow cover (SD)**

X X X

Daily SD X X X

Number of consecutive days with snow cover (SD)**

X X X X

Snow Cover (SD) X X

*Legend: Quartiles: first quartile, median (MD), third quartile Extremes: greatest, top/bottom 10 (for monthly SF), earliest/latest (for first/last occurrence of SF) Prob: probability of receiving daily SF in a month NF: number of years with SF or SD on that day meeting the SF or SD threshold; when used in conjunction with NY, daily probability or frequency of snow = NF / NY

NY: number of years with non-missing dataRP: return periods for 1-day, 2-day, 3-day, monthly SF

** meeting several specific thresholds

FEMA Snow Disaster Declaration Statistics


Snow Climatology Processing

Stations analyzed All COOP stations analyzed – 24,040

TD-3200: Summary of the Day, 1948-present, operational QC TD-3206: Data Rescue, pre-1948, limited QC

A subset of stations made available Exclude stations will short records (< 15 years of non-missing data) Current and closed stations for historical climatologies 9099 stations for FEMA snow disaster declaration support Not all counties represented

The mix of data sources required stringent QC Accepted TD-3200/3206 QC Applied additional QC


Snow Climatology Processing Quality Control Applied

Accepted the ValHiDD (Reek et al., 1992) QC applied to TD-3200 data 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

In some PSFSD cases, ValHiDD could not identify which element should be corrected, so the values were flagged as suspect and not altered

Additional QC applied – data values changed if fail temporal checks (today’s SD compared to yesterday’s SD & SF) factor of 10 check for SF (if SF/P > 80, then SF=SF/10) hail check (nonzero SF set to zero if TMIN >= 40) nonzero SF set to missing if

SF > 0.4 but P = 0, or today’s P is missing

factor of 10 check for SD (SD divided by 10 or set to missing) zero SD set to missing if yesterday’s SD > 7 and today’s SF > 2 nonzero SD set to missing depending on SD, SF, &/or TMAX criteria SF & SD extremes check (based on state extremes from Ludlum [1982] & NCDC

data base extremes)


Snow Climatology Processing Quality Control Applied

Additional QC applied – data values not changed questionable SF values flagged but not changed if SF/P ratios were unusual questionable SD values flagged but not changed based on SD, SF, & TMAX criteria

QCI (Quality Control – Inventory – Metadata) Statistics Useful for selecting the best quality stations Data QC indicators – number of non-missing daily values read, flagged, corrected,

not corrected, failed QC (& percent of total for these indicators)

Data Inventory indicators – number of years in data base, complete months, number & percent daily values missing & processed, gap (break) info

Station Metadata indicators – number of location changes, ob time changes, observer changes (& these indicators scaled by number of years in metadata base)


Snow Climatology Processing Daily to Monthly Values – Tolerance for Missing Data

Total SF – zero tolerance for missing data means & other statistics computed from year-month sequential data year-month sequential monthly or seasonal or annual SF value not computed if

even one day was missing 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

Median daily value for a month – zero tolerance Number of days with SF or SD – zero tolerance Consecutive days with SF or SD – zero tolerance Daily extreme, multiple-day extreme, date of occurrence

could tolerate up to 5 missing days in a month

Greatest 2-day & 3-day SF could tolerate up to 5 missing days in a month but if a 2-day or 3-day period had a missing day within that period, then that period

was omitted from the analysis


Snow Climatology Processing Daily to Monthly Values – The Effect of Missing Data

Impact: Very stringent QC Potentially “good” daily SF values may be

set to missing or changed Fewer monthly (& seasonal) total SF values available for the

climatologies

Therefore …

Snow Climatology monthly SF extremes (and other monthly statistics) may differ from monthly SF extremes from other sources

Snow Climatology daily SF extremes may differ from daily SF extremes from other sources

Question: When We Reprocess Later This Summer … Revise QC? – Revise criteria for computing monthly totals?

Breakout Session

NWS Perspective onNCDC Snow Climatology

- Surveyed 10 Snowy WFO’s- 8 WFO’s responded- 7 were not familiar with NCDC’s Snow Product- All 8 of the WFO’s found differences between

- WFO’s Database- NCDC’s Snow Climatology- ACIS Database

- WFO’s Reported Significant Differences with NCDC on Extreme Values


- Primary Reasons for Differences in Extreme Values NCDC versus NWS

- NCDC - Employs rigid Quality Control- Intolerant of Missing Data

- NWS- Limited Quality Control

NWS Perspective Who Uses Snow Data

• NWS’s Primary Snow Customers• Public• Press• Emergency Managers• City Planners (snow removal)• Farmers• Insurance Companies• Other Government Agencies

What Type of Data is Requested

• NWS’s Customers Requests

• Departure from Normal

• Extremes and where winter/month/day ranks


- Extreme Example of Differences Between NCDC Snow Climatology and NWS Local Database

Top Ten Snowiest YearsAugust - July

• Aberdeen’s Database

• 1 109.8 1937• 2 94.6 1897 • 3 79.3 2011 • 4 76.8 1994• 5 75.9 1997• 6 74.6 2001 • 7 74.5 1915 • 8 71.3 1907 • 9 68.0 1936• 10 67.5 1893 • POR 1893 - 2011

• NCDC’s Snow Climatology

• 1 52.8 1935• 2 39.2 1933• 3 29.5 1944• 4 27.5 1990• 5 24.7 1934• 6 24.4 1946• 7 23.4 1945• 8 19.9 1942• 9 19.8 1941• 10 19.1 1957• POR 1893 - 2006


• Discussion Points

• Where’s the Middle Ground?

• What do our Customers Want?

Breakout Session


Thank You!

[email protected]

[email protected]

[email protected]

Snow Climatology:http://www.ncdc.noaa.gov/ussc/

NCDC Climate Monitoring Branch Reports & Products:http://www.ncdc.noaa.gov/oa/climate/research/monitoring.html

noaa’s snow climatology dataset & user perspectives

Documents

wfos snow data

ncdcs snow productall

wfos local database

responding wfos

different wfos

wfos databasesome

significant differences

snowy wfos8 wfos responded7