new snowstorm indices and new gis snowstorm database€¦ · northeast east north central central...

Development of New Snowstorm Indicesand

A New GIS Snowstorm Databaseat the

National Climatic Data Center

Mike Squires, Jay Lawrimore, Richard HeimNational Climatic Data Center

David Robinson, Mathieu Gerbush, Thomas EstilowRutgers University

Clay TaborUniversity of North Carolina at Asheville

Anna WilsonSTG

Overview• Goals:

– Bridge the gap between Wxr/Climo and Societal Impacts

– Feedback and identify stakeholders

• Northeast Snowfall Impact Scale

• Data processing issues

• New Regional Snowstorm Indices

• New GIS Snowstorm Database

Northeast Snowfall Impact Scale (NESIS)

• Area of snow, amount of snow, and population affected by >4” of snow

• Societal Impacts

• Puts Northeast snowstorms into historical perspective

Data Processing Using a GIS

• Extract snowfall data from the Global Historical Climate Network – Daily dataset

• Identify stations associated w/ a particular storm

• Quality Control

Unedited Data:January 15-19, 1978

Day 1:January 15, 1978

Presenter

Presentation Notes

The analyst looks at “Daily Weather Maps” to get a general sense of the daily progression of the storm.

Delineated Storm:January 15-19, 1978

Original Delineated

Unrelated Snowfall:January 15, 1978 (Day 1)

Presenter

Presentation Notes

The highlighted stations are the locations that received snow on Day 1. The snowfall in the west is part of the storm being analyzed; the snowfall in the east is part of another storm and must be removed.

Final Dataset:January 15-19, 1978

Delineated Final

Presenter

Presentation Notes

The map on the left shows the final spatial distribution of stations after unrelated snowfall has been removed.

Final Dataset:January 15, 1978

Presenter

Presentation Notes

This slide and the next four slides show the daily evolution of the January 15-19, 1978 snowstorm.

Manual Quality Control of GHCN-D

• Quality Control (QC) is a quasi-subjective process– Want a reliable, robust, repeatable method

• Making QC decisions in the face of uncertainty– Type I error – false positive – throwing out good data (unacceptable)– Type II error – false negative – keeping bad data – The goal is to minimize Type I errors at all costs, so:

• Assume all data is correct unless proven otherwise• In general snowfall is spatially continuous, however

– topography– Lake effect snow– Differences in observation technique– Small scale spatial variability

• Do not change values• Tools: ArcMap, Moran’s I, snowfall grid (bull’s eyes)• Typically ~1% of stations are removed

January 15-17, 1945

Presenter

Presentation Notes

This slide and the next slide show an example of a case where the analyst consulted with the actual observation form to determine the validity of a questionable snowfall total.

Presenter

Presentation Notes

It looks as though the observer meant to report five inches here on day 2; a 7 inch total would be much more reasonable than the 2 inches that we are looking at. This observation was removed.

Regional Snowfall Impact Scale (ReSIS)

• Collaborating with Rutgers University

• Differences from NESIS;– NCDC Regions

– Snowfall and population are confined to specific region

• Finalizing algorithm to compute indices

Intra-Regional Differences

ReSIS = 19.648

ReSIS CalculationMarch 12-13, 1993

Snowfall Population Area (sq mi)2 6,311,630 46,6665 8,110,060 41,237

10 4,135,780 22,17015 3,210,340 29,983

Top of distribution

Bottom of distribution

Middle of distribution

Storm Date Index > 2" > 5" > 10" > 15"1993-Mar-12-14 19.65 15% 17% 27% 41%1996-Jan-06-09 19.51 10% 12% 26% 53%1927-Mar-01-03 16.29 12% 15% 29% 44%1940-Jan-23-24 16.12 20% 27% 29% 25%1922-Jan-26-29 14.98 11% 13% 26% 49%

1942-Mar-02-03 7.34 21% 25% 30% 24%1966-Jan-29-31 7.33 30% 30% 27% 13%1960-Feb-13-15 6.91 29% 32% 32% 7%1958-Feb-14-17 6.63 32% 32% 22% 14%1930-Jan-29-31 6.47 40% 40% 19% 1%

1900-Mar-15-16 3.14 29% 27% 40% 4%1962-Jan-09-11 2.93 84% 16% 0% 0%1926-Jan-07-10 2.65 68% 28% 4% 0%1968-Jan-12-15 2.50 41% 26% 29% 5%1910-Feb-11-13 2.04 63% 33% 4% 0%

Presenter

Presentation Notes

This slide shows the ranked indices and the relative contribution in percent of each of the four terms from the ReSIS algorithm. Using this method, indices for storms in the top of the distributions are being driven by the third and fourth terms. Indices for storms in the bottom of the distribution are being driven by the first and second terms. In the middle of the rankings, the final index value is comprised of contributions from all terms. This pattern of how the individual terms contribute to the index values is a desirable attribute to this method. All of the regions behaved this way.

Top 20 Southeast Snowstorms

Storm Date Area (sq mi) Population Index1993-Mar-12-14 140,056 21,767,810 19.6481996-Jan-06-09 82,232 14,973,350 19.5051927-Mar-01-03 93,361 14,246,920 16.2931940-Jan-23-24 148,685 24,698,910 16.1251922-Jan-26-29 71,959 13,324,460 14.9821979-Feb-18-19 145,034 24,713,830 13.4651980-Mar-01-03 124,958 21,601,770 13.0181930-Dec-17-18 92,438 17,777,300 11.4601983-Feb-10-12 46,397 7,502,419 11.2851936-Feb-06-07 108,419 18,714,421 10.5001987-Jan-21-23 92,044 18,104,674 10.3741973-Feb-09-11 116,780 12,435,609 10.2461962-Mar-05-07 52,134 8,923,510 9.2151902-Feb-14-17 99,665 16,393,906 8.9331988-Jan-06-08 138,258 24,221,031 8.5561960-Mar-01-04 102,288 18,043,010 7.9551966-Jan-25-27 99,953 17,297,296 7.8541914-Feb-25-26 160,198 22,346,073 7.7061942-Mar-02-03 64,108 12,649,065 7.345

Presenter

Presentation Notes

This table and map combination shows how a high ReSIS value and a medium ReSIS value relate to a conventional snowfall map. The stars on these maps represent metropolitan areas with populations over 500,000 people.

Northeast

East North C

entral

Central

Southeast

South

Inde

x Va

lue

0

5

10

15

20

25

30

35

Distribution of Regional Indices

Presenter

Presentation Notes

These box plots represent the distributions of regional indices. In these box plots, all of the outliers are displayed. The boxes encapsulate the central 50% of the distribution, the whiskers define the central 90% of the distribution. The median is represented by the solid horizontal line and the mean is depicted by the dashed horizontal line. Although there are some differences between individual regions, the distributions of all the regional snowfall indices are quite similar. The Wilcoxon-Mann-Whitney rank sum test was applied to this data to formally test if these distributions are similar. The null hypothesis is that the two data samples are from the same distribution. The test was constructed so that each individual regional distribution was compared to the pooled distribution of all regions. The test was repeated five times; once for each of the five individual regions. In all cases the results were found to be insignificant. These results give more evidence to the observation that these distributions are similar to each other.

New National Snowfall Index

• Created any time a regional index is calculated

• Societal impacts and historical perspective

• Allows discrimination between national and regional impacts

• Algorithm under development

New GIS Snowstorm Database

• Collection of top 100-200 U.S. snowstorms from 1900 to present

• “GIS format”

• Will be used to calculate the new national snowfall index

• Could be used by other researchers, policy makers, and the general public

• Societal impact

GIS Snowstorm Database

Miles of Interstate Highway Affected by Snowfall Above a Threshold

Storm Date > 4" > 10" > 15" > 20" > 30"

1977-01-08-11 16,318 2,221 668 58 0

1993-03-12-15 17,249 12,485 7,311 3,295 277

1996-01-06-09 16,063 11,418 8,095 4,490 143

2005-12-07-10 13,039 1,674 43 0 0

Miles of Railroad Affected by Snowfall Above a Threshold

Storm Date > 4" > 10" > 15" > 20">

30"

1977-01-08-11 42,505 5,190 1,478 91 0

1993-03-12-15 38,972 29,357 18,799 9,445 620

1996-01-06-09 32,805 22,219 15,568 9,837 446

2005-12-07-10 33,299 2,795 73 0 0

Pre-Summarized Tables

Presenter

Presentation Notes

This is an example of how the available tools will transmit information to the user via attribute tables. There will be similar information for airports, hospitals, retail centers, and schools.

Potential Interactive Queries

• How many square miles were affected by snowfall >10” during the Jan 1996 storm?

• Which schools in Illinois and Wisconsin have experienced snowstorms >20” in November?

• How many miles of interstate received >30” in New York during the Mar 1993 storm?

• Which hospitals in Pennsylvania have been affected by > 15” of snowfall in any month?

St Louis University & National Weather ServiceAnalog Snowfall Guidance

Storm Date Year Month >4" > 10" > 15" > 20" > 30"19820111_19820115 1982 1 1418 123 0 0 019820404_19820408 1982 4 923 264 53 4 019830210_19830213 1983 2 218 205 428 110 219850129_19850203 1985 1 1463 66 0 0 019870121_19870124 1987 1 790 503 39 9 019880105_19880109 1988 1 1395 282 8 5 019880209_19880214 1988 2 1061 183 27 1 019921209_19921213 1992 12 606 232 72 27 119930214_19930218 1993 2 1238 98 7 0 019930220_19930224 1993 2 1082 152 30 2 019930312_19930315 1993 3 394 608 365 210 1419940116_19940119 1994 1 986 242 63 8 019940222_19940225 1994 2 1023 91 8 0 019950202_19950207 1995 2 703 372 22 6 019951218_19951222 1995 12 810 274 31 1 019960106_19960109 1996 1 396 235 319 464 1219990101_19990104 1999 1 697 298 160 14 020001228_20010101 2000 12 942 56 7 0 020021222_20021226 2002 12 1040 137 38 14 220030214_20030218 2003 2 520 179 445 242 420031204_20031208 2003 12 461 302 144 36 320040124_20040129 2004 1 1566 210 11 7 020050121_20050124 2005 1 900 552 42 90 220051207_20051210 2005 12 1051 138 5 0 0

Snowfall Analog Guidance20-24 February 1993

NCDC GIS Snowstorm DatabaseNumber of Hospitals Affected by Snowfall

Above Specified Threshold

Analogs for 2008/12/19

Rank Date Score

1 1994 12 07 7.741

2 1990 12 15 6.859

3 2000 02 18 6.813

4 1993 02 21 6.758

Presenter

Presentation Notes

St. Louis University and the National Weather Service are developing an Analog Snowfall Guidance product. The purpose is to identify historical snowstorms that are similar to the 48h or 72h GFS forecast. The North American Regional Reanalysis (NARR) is searched using various statistical techniques to identify the top 15 analogs. The goal is not to produce a deterministic forecast but rather give guidance about the magnitude and scale of impending snowstorms. This product can be used with the GIS Snowstorm Database (GSDB) to give an indication of the societal impacts the storm will produce. In this case, analogs are identified for a forecast valid at Dec 19, 2008. The top four analogs are listed. The fourth rated storm is an event that occurred in February of 1993, which has an entry in GSDB. The table for hospitals is shown, indicating that approximately 150 hospitals experienced over 10” of snow during that storm. If the forecaster has confidence in the forecast, it is reasonable to assume that there will be similar impacts for this storm. Of course the actual location of heavy snow in relationship to the locations of the hospitals will affect the actual number of facilities affected. There are also tables available for interstates, railroads, airports, retail centers, and schools.

Issues

• Actively seeking out stakeholders and users• “GIS format”

– OGC: WMS, WFS, WCS, KML– Oracle Spatial/SDE– Method of access?

• Oracle database design• Spatial queries vs. geoprocessing tools

• Implementing tools in Arc Server• How do we archive media reports?• Process to make this a permanent NOAA dataset

Interested Parties

• Saint Louis University– Analog Forecast Guidance

• Munich Reinsurance– Snowfall risk information

• NC Integrated Risk Management Project– NC Dept of Crime Control and Public Safety

– AECOM

• National Weather Service

• Looking for more …

new snowstorm indices and new gis snowstorm database€¦ · northeast east north central central...

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