Medium Range Forecast

- Global System Out To 14 Days

Yuejian Zhu

Ensemble Team Leader

EMC/NCEP/NWS/NOAA

Presents for NWP Forecast Training Class

March 30, 2015, Fuzhou, Fujian, China

Medium Range Forecast- Global system out to 16 days

• Forecast objects– Mainly day 3-7

• Weather pattern, front system, storm, cyclone tracks and etc…• Variables: Temperature, precipitation and etc…

– Week 2• Anomaly forecast, weather pattern transition, blockage and etc…• 500hPa height, temperature (cold/warm), precipitation (dry/wet)

• Forecast Skills– Useful forecast for weather – 60% anomaly correlation– Useful skill for extended range – 50% anomaly correlation– Ensemble mean forecast skill – higher than deterministic forecast– Probabilistic forecast skill

• Forecast confidences– Evaluation/verification– Uncertainty levels– Confidence – good/bad example

• Forecast products– Deterministic – ensemble mean– Uncertainty forecast – ensemble spread, probabilistic forecast above/below thresholds

• Forecast examples– Deterministic or probabilistic?– Winter storm 2008– Specific request project – Hanson Dam– Hurricane Sandy 2012

Case of weekend storm (2/8-9/2013)

http://www.emc.ncep.noaa.gov/gmb/yluo/CCPA.html

~20mm/day

5

http://www.meteo.gc.ca/ensemble/naefs/index_e.html

CMC’s week-2 NAEFS anomaly forecast

Medium Range Forecast- Global system out to 16 days

• Forecast objects– Mainly day 3-7

• Weather pattern, front system, storm, cyclone tracks and etc…• Variables: Temperature, precipitation and etc…

– Week 2• Anomaly forecast, weather pattern transition, blockage and etc…• 500hPa height, temperature (cold/warm), precipitation (dry/wet)

• Forecast Skills– Useful forecast for weather – 60% anomaly correlation– Useful skill for extended range – 50% anomaly correlation– Ensemble mean forecast skill – higher than deterministic forecast– Probabilistic forecast skill

• Forecast confidences– Evaluation/verification– Uncertainty levels– Confidence – good/bad example

• Forecast products– Deterministic – ensemble mean– Uncertainty forecast – ensemble spread, probabilistic forecast above/below thresholds

• Forecast examples– Deterministic or probabilistic?– Winter storm 2008– Specific request project – Hanson Dam– Hurricane Sandy 2012

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 160

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

GFS GEFS NAEFS

NH Anomaly Correlation for 500hPa HeightPeriod: January 1st – December 31st 2014

GFS – 7.85d

GEFS – 8.92dNAEFS – 9.26d

Forecast day(s)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 160.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

N-2014 N-2013 N-2012 N-2011 N-2010 N-2009 N-2008 N-2007

2-day skill improvement for last 7 years

13% AC improvement of day-8 fore-casts for past 7 years ( all skillful fore-

casts)

NH 500hPa height anomaly correlation (NCEP ensembles)

Forecast Day(s)

2007 2008 2009 2010 2011 2012 2013 20140

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

NH 500hPa height AC for day-8 of calendar year mean

GFS GEFS NAEFS

RMS Error and Spread

5 days forecast

10 days forecast

Courtesy of Dr. Yan Luo

Continuous Ranked Probabilistic Skill Scores

5 days forecast

10 days forecast

Courtesy of Dr. Yan Luo

NAEFS Ensembles (left), Manual (right) & Auto (bottom) 8-14 day Precipitation (4/1/2010 –3/31/2011)

Courtesy of Dr. Dan Collins

Heidke Skill Score

6-10 day Precipitation CPC Heidke Skill ScoreApril 1, 2010 –March 31, 2011

All AreasWest/HighPlains South Midwest/NortheastManual 13.8 10.3 17.5 14.8Auto 15.4 17.9 14.5 13.9CDC 20.3 25.2 19.3 15.3NAEFS 24.0 30.7 23.1 16.7

8-14 day Precipitation CPC Heidke Skill ScoreApril 1, 2010 –March 31, 2011

All AreasWest/HighPlains South Midwest/NortheastManual 7.3 5.7 8.6 8.2Auto 10.4 12.6 9.1 9.2CDC 12.2 15.3 13.5 7.1NAEFS 15.8 20.7 16.7 9.3

Courtesy of Dr. Dan Collins

Medium Range Forecast- Global system out to 16 days

• Forecast objects– Mainly day 3-7

• Weather pattern, front system, storm, cyclone tracks and etc…• Variables: Temperature, precipitation and etc…

– Week 2• Anomaly forecast, weather pattern transition, blockage and etc…• 500hPa height, temperature (cold/warm), precipitation (dry/wet)

• Forecast Skills– Useful forecast for weather – 60% anomaly correlation– Useful skill for extended range – 50% anomaly correlation– Ensemble mean forecast skill – higher than deterministic forecast– Probabilistic forecast skill

• Forecast confidences– Evaluation/verification– Uncertainty levels– Confidence – good/bad example

• Forecast products– Deterministic – ensemble mean– Uncertainty forecast – ensemble spread, probabilistic forecast above/below thresholds

• Forecast examples– Deterministic or probabilistic?– Winter storm 2008– Specific request project – Hanson Dam– Hurricane Sandy 2012

RMS Error for Northern Hemisphere (20-80N) 500hPa Height

13.7210.91

62.50

49.9750.06 (day-4)

1-d improvement (25% reduced error) for 10 years

62.46 (day-6)

TWO MAIN ATTRIBUTES OF FORECASTSRELIABILITY – Lack of systematic error

(No conditional bias)

Consider cases with same forecast

Construct pdf of corresponding observtns

If fcst identical to pdf of observations =>

PERFECT RELIABILITY

Reliability CAN BE statistically corrected

(assuming stationary processes)

Climate forecasts are perfectly reliable –

RELIABILITY IN ITSELF HAS NO FCST VALUE

RESOLUTION – Different forecasts

precede different observed events

Consider different classes of fcst events

If all observed classes are preceded by

distinctly different forecasts =>

PERFECT RESOLUTION

Resolution CANNOT BE statistically

corrected

INTRINSIC VALUE OF FCST SYSTEM

Good forecast ??? – you may like it

Bad forecast??? – give you a trouble?

Medium Range Forecast- Global system out to 16 days

• Forecast objects– Mainly day 3-7

• Weather pattern, front system, storm, cyclone tracks and etc…• Variables: Temperature, precipitation and etc…

– Week 2• Anomaly forecast, weather pattern transition, blockage and etc…• 500hPa height, temperature (cold/warm), precipitation (dry/wet)

• Forecast Skills– Useful forecast for weather – 60% anomaly correlation– Useful skill for extended range – 50% anomaly correlation– Ensemble mean forecast skill – higher than deterministic forecast– Probabilistic forecast skill

• Forecast confidences– Evaluation/verification– Uncertainty levels– Confidence – good/bad example

• Forecast products– Deterministic – ensemble mean– Uncertainty forecast – ensemble spread, probabilistic forecast above/below thresholds

• Forecast examples– Deterministic or probabilistic?– Winter storm 2008– Specific request project – Hanson Dam– Hurricane Sandy 2012

Need one slide for median-range forecast

Day 3-7 forecast

Surface pressure

Deterministic

NAEFS 500hPa height mean/spread and mean vorticity

Medium Range Forecast- Global system out to 16 days

• Forecast objects– Mainly day 3-7

• Weather pattern, front system, storm, cyclone tracks and etc…• Variables: Temperature, precipitation and etc…

– Week 2• Anomaly forecast, weather pattern transition, blockage and etc…• 500hPa height, temperature (cold/warm), precipitation (dry/wet)

• Forecast Skills– Useful forecast for weather – 60% anomaly correlation– Useful skill for extended range – 50% anomaly correlation– Ensemble mean forecast skill – higher than deterministic forecast– Probabilistic forecast skill

• Forecast confidences– Evaluation/verification– Uncertainty levels– Confidence – good/bad example

• Forecast products– Deterministic – ensemble mean– Uncertainty forecast – ensemble spread, probabilistic forecast above/below thresholds

• Forecast examples– Deterministic or probabilistic?– Winter storm 2008– Specific request project – Hanson Dam– Hurricane Sandy 2012

Case 1: Deterministic/Probabilistic ForecastQPF .vs. PQPF

• Northern California State Christmas-New Year flooding.

• Winter storm last more than 10 days.

• Total precipitation amount exceeding 660mm over the huge area.

• The homes of 100,000 residents who has been evacuated.

• Some stranded residents has to be rescued by helicopter.

• Caused a lot of damages include road, bridge and resident houses. Photo from Washington Post (1996)

24 hours observationGFS ENS

High predictable heavy precipitation event

February 12-13 1997

(Southern Louisiana flooding)

Location

and

intensity

GFS ENS

GFS made a very good forecast,

But

Ensemble made a excellent forecast.

Case 2: Southern China Winter Storm 2008• Period of 01/15-01/30/2008• Location: South of China – very large area• Continuous precipitation all over the area

– Large area snowfall– Large area freezing rain, ice pellets and mixing rain

• Typical extreme event– Less than 1% chance in climate

• Timing– Chinese Lunar New Year preparing (transportation?)

• Prediction/protection– Very limited???– Protection – cost/loss problem – economic values

• Loss– Lives– Powers– Closed highways and other transportations

• Question left for us: do we need to pay attention to study this case?

What is about NCEP global ensemble forecasts?

500hPa height pattern

RMOP

Unstable patternSince mid of January

Stable weather patternStarting from Jan. 23rd

Key: The trough didn’t move that much for a week

NCEP GEFS real time Probabilistic Precipitation Type Forecast (10-day)

NCEP GEFS real time Probabilistic Precipitation Type Forecast (5-day)

NCEP GEFS real time Probabilistic Precipitation Type Forecast (1-day)

Case 3: Flood Risk ManagementBAMS article 2012: NOAA’S RAPID RESPONSE TO THE HOWARD A. HANSON DAM FLOOD RISK MANAGEMENT CRISIS

NOAA operations and research personnel joined forces to better predict a possible flood and help calm public fears regarding reduced flood protection from a western Washington dam.

Results of sensitivity tests translating hypothetical heavy rainfall on the Green River over 24 h into HHD inflow. The runs were conducted by the NWRFC and used varying antecedent conditions (soil moisture and snow pack). The levels of risk were assigned based on inflow thresholds defined by the USACE. (Refer to BAMS)

ECMWF Ensemble Mean NCEP GFS Ensemble Mean HFIP GFS Ensemble Mean

Case 4: Hurricane Sandy Forecast

Canadian Ensemble Mean Navy Ensemble Mean UKMET Ensemble Mean

25% 50% 75%50%

Climate

Forecast

85%

95%99%

Schematics diagram for anomaly forecast (PDF)

GEFS Based Anomaly Forecast

25%

50%

75%

50%

ClimateForecast

85%95%

98%

Schematics diagram for anomaly forecast (CDF)

Ensemble member #5 (ranked)

Ensemble member #11 (ranked)

Ensemble member #16 (ranked)

68.2% 95.4% 99.6%

8-day fcst 6-day fcst

5-day fcst 4-day fcst