diagnosis of systematic errors in atmospheric river...

Diagnosis of Systematic Errors in Atmospheric River Forecasts Using Observations of Integrated Water Vapor

Gary A. WickNOAA Earth System Research Laboratory, Physical Sciences Division

What are atmospheric rivers?Atmospheric rivers are constantly moving, narrow filamentary bands of intense water vapor transport through the lower atmosphere. In a modeling study, Zhu and Newell (1998) found that more than 90% of the meridional poleward water vapor transport in the midlatitudes takes place in these narrow corridors constituting less than 10% of the earth’s circumference and established the term atmospheric river. As such, atmospheric rivers play a critical role in the global hydrologic cycle.

Previous work at the NOAA Earth System Research Laboratory (Ralph et al., 2004) developed objective criteria to identify atmospheric river events in integrated water vapor (IWV) retrievals from the Special Sensor Microwave Imager (SSM/I). As shown to the right, the rivers are identified as narrow bands of less than 1000 km in width with IWV values of 2 cm or greater extending over distances of at least 2000 km.

Why we careRecent studies (e.g., Ralph et al., 2006) demonstrated that these atmospheric rivers were present and were an important contributor to recent extreme precipitation and major flooding events along the west coast. The events also contribute up to 50% of the total water supply in the Sierra Madre mountains.

The problemGiven the impact of these events, it is critical to understand how well they are forecast. This study is an initial attempt to quantify the ability of several leading numerical weather prediction models to reproduce the frequency, size, and intensity of atmospheric river events.

Processing Steps

Isolate top of the tropical water vapor reservoir

Apply median filter to slightly smooth image contours

Threshold IWV values at multiple levels

Compute IWV gradients in image and flag values in excess of a threshold

Identify contiguous thresholded regions and determine clus-ters with sufficient numbers of points and either the pres-ence of strong gradients or high correlation

Compute skeleton for flagged clusters

Loop through each skeleton point, find cases where the mini-mum width of the region at that point is less than 1000 km

Dilate retrieved river center points to close small gaps

Cluster center points into continuous line segments and identify segments of suitable (>2000 km) length

Recompute width and orientation along center points to define final extracted river

Determine if extracted river intersects with land or is poten-tially impacted by gaps in the satellite data

Key Outputs

At any one instant in time:

Identification of number of ARs present in scene

Location of center points along core of atmospheric river

Width of river at points along axis of river and average width

Core IWV values along river axis

Orientation angle of atmospheric river at all points along axis

Flag indication whether river has made landfall

Results from multiple scenes can be combined to further give:

Number or frequency of distinct river events over specified period of time

Frequency of river events making landfall

River lifetime

River propagation speed and characteristics

Source and dissipation regions of distinct rivers

OBJECTIVE IDENTIFICATION OF ATMOSPHERIC RIVERS

APPROACH

CONCLUSIONSAn automated method for identifying and characterizing atmospheric river events from IWV imagery has been developed and applied to the validation of NWP models.

Atmospheric rivers are generally well-predicted in TIGGE models, though initial results suggest some potential tendency for overprediction.

Results indicate that some refinement is required in the objective identification procedure to better handle small gaps and noise in the IWV fields. Improvements are currently being implemented.

Illustration of Satellite/Model Comparisons

Satellite ObservationsIntegrated water vapor retrievals from passive microwave brightness temperatures from the SSM/I

Wentz optimal statistical algorithm

Combination of multiple DMSP satellites

12-hourly composites centered on forecast time

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VERIFICATION OF FORECAST FIELDS

ACKNOWLEDGMENT: The AR research is being conducted in collaboration with P. Neiman and M. Ralph. The model verification studies are being performed with T. Hamill and J. Whitaker.

Ralph, F. M., P. J. Neiman, G. A. Wick, S. I. Gutman, M. D. Dettinger, D. R. Cayan, and A. B. White, 2006: Flooding on California’s Russian River: Role of atmospheric rivers. Geophys Res. Lett., 33, L13801, doi:10.1029/2006GL026689.

Zhu, Y., and R. E. Newell, 1998: A proposed algorithm for moisture fluxes from atmospheric rivers. Mon. Wea. Rev., 126, 725-735.

This section describes the principal steps and outputs of the procedure which is applicable to both satellite-derived and model output IWV fields. The technique is currently implemented through a combination of IDL and Matlab routines.

The ability of several NWP models to reproduce atmospheric river events is evaluated through comparison of their IWV fields with satellite observations. An objective technique for identifying and characterizing atmospheric river events was developed and applied in an identical manner to the satellite-derived and modeled IWV fields. All models were drawn from the THORPEX Interactive Grand Global Ensemble (TIGGE).

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JP1.3

Gary.A.Wick@noaa.gov

INTRODUCTION

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16 November, 2007

ECMWF Modeled Water Vapor

160W 150W 140W 130W 120W 110W

15N

25N

35N

45N

55N

1 2 3 4 5 6 7g/cm2

Objective criteria used to identify the presence of atmospheric rivers in SSM/I IWV data

Example of linkage between atmospheric rivers and extreme precipitation (from Ralph et al. 2006)

18 November, 2005

SSM/I-Derived Integrated Water Vapor

160W 150W 140W 130W 120W 110W

15N

25N

35N

45N

55N

1 2 3 4 5 6 7g/cm2

Original SSM/I IWV imagery

First processing stage showing thresholded IWV regions and points with strong gradients. Thresholds are shown at 2.0, 2.5, and 3.0 cm.

Second stage showing extracted regions with skeletons (red) and points where the river width was found to be < 1000 km

Final stage with extracted river axis (blue) and different estimates of the river edge. The red points correspond to 0.9 times the core IWV value, while the magenta, green and cyan correspond to thresholds of 2.6, 2.3, and 2.0 cm, respectively

Sample Extracted River Properties

Core IWV Along River Axis

0 100 200 300 400Index along river axis

1.5

2.0

2.5

3.0

3.5

4.0

4.5

IWV

(g/c

m2 )

Width Along River Axis

0 100 200 300 400Index along river axis

0

200

400

600

800

1000

Wid

th (k

m)

2.6 cm2.3 cm2.0 cm

0.9 peak

Extracted core IWV value (top) and river width (bottom) for the example case shown to the right. In this case, the river index increases from the SW to the NE.

Observed Atmospheric River

ECMWF 120-hour Forecast

SSM/I-Derived Integrated Water Vapor

February 16, 2004 Descending Passes

170W 160W 150W 140W 130W 120W 110W15N

25N

35N

45N

55N

1 2 3 4 5 6 7g/cm2

IWV >2 cm> 2000 km long

IWV >2 cm< 2000 km wide

AtmosphericRiver

References

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TIGGE Model ProductsControl forecasts of total column water

ECMWF

UK Met Office

JMA

CMC

12Z Initialization

Forecast lead times analyzed: 0, 3, 5, 7, and 10 days Analysis period: October 2007 - March 2008

Analysis region: Northeastern Pacific (15-55N, 110-160 W)

16 November, 2007

SSM/I-Derived Integrated Water Vapor

160W 150W 140W 130W 120W 110W

15N

25N

35N

45N

55N

1 2 3 4 5 6 7g/cm2

Ralph, F. M., P. J. Neiman, and G. A. Wick, 2004: Satellite and CALJET aircraft observations of atmospheric rivers over the eastern North Pacific Ocean during the winter of 1997/98. Mon. Wea. Rev., 132, 1721-1745.

Key QuestionsAre atmospheric rivers predicted with the proper frequency in reanalyses and forecasts?

Are the widths of the atmospheric rivers reproduced accurately?

Are there any biases in the modeled strength of the atmospheric rivers?

Are these results a function of model resolution and forecast lead time?

Ability of Models to Represent Individual Events

The models exhibit good skill in reproducing observed events but some tendency to overpredict events, particularly at longer lead times.

Width Comparison Core Strength Comparison

Probability of Detection False Alarm Rate

No large biases were observed in predicted width or core strength, but notable variability in the predictions were observed.