NOAA’s Snow Climatology Dataset & User Perspectives

1. NOAA’s Snow Climatology Dataset & User Perspectives Richard R. Heim Jr. NOAA/NESDIS/National Climatic Data Center Asheville, North Carolina Timothy Kearns NOAA/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

2. 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/

3. *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 data RP: return periods for 1-day, 2-day, 3-day, monthly SF ** meeting several specific thresholds

4. FEMA Snow Disaster Declaration Statistics *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 data RP: return periods for 1-day, 2-day, 3-day, monthly SF ** meeting several specific thresholds

5. 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

6. 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)

7. 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)

8. 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

9. 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

10. 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

11. NWS Perspective onNCDC Snow Climatology Primary Reasons for Differences in Extreme Values NCDC versus NWS NCDC Employs rigid Quality Control Intolerant of Missing Data NWS Limited Quality Control

12. 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

13. What Type of Data is Requested NWS’s Customers Requests Departure from Normal Extremes and where winter/month/day ranks

14. NWS Perspective onNCDC Snow Climatology Extreme Example of Differences Between NCDC Snow Climatology and NWS Local Database

15. 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

16. NWS Perspective onNCDC Snow Climatology Breakout Session Discussion Points Where’s the Middle Ground? What do our Customers Want?

17. Thank You! Richard.Heim@noaa.gov Jay.Lawrimore@noaa.gov Timothy.Kearns@noaa.gov Snow Climatology: http://www.ncdc.noaa.gov/ussc/ NCDC Climate Monitoring Branch Reports & Products: http://www.ncdc.noaa.gov/oa/climate/research/monitoring.html