Extreme Precipitation on the American River Watershed: Strategies for Improving Forecasts

F. Martin (Marty) Ralph, Ph.D. Chief, Regional Weather and Climate Applications Division Environmental Technology Laboratory National Oceanographic & Atmospheric Administration 325 Broadway, Mail Code R/ET7 Boulder, CO 80305 Tel: (303) 497-7099 Fax: (303) 497-6101 E-mail: marty.ralph@noaa.gov Web: PACJET home page: http://www.etl.noaa.gov/programs/pacjet2002/ BIOGRAPHICAL SKETCH Dr. Ralph is a research meteorologist who has focused on studies of phenomena that cause variations in daily weather. A key area of expertise is exploring how to best observe the atmosphere, with an emphasis on what data are needed to improve weather forecasts, especially precipitation and wind forecasts on relatively local (mesoscale) spatial and temporal scales. He has worked closely with the operational weather forecasting community to develop new forecasting techniques based on better physical understanding of the weather and on better use of observations to guide predictions. As the leader of the CALJET and PACJET experiments off the U.S. West Coast in 1997/98, 2000/01 and 2001/02, he has brought together scientists, forecasters, and representatives of critical sectors that depend on weather observations and forecasts in their fields (e.g., emergency management, flood control, marine industry, energy, etc.). From these interactions have come several new ideas on what predictions are needed by users of forecasts, what forecasters require in order to provide these, and how research can help create these capabilities. These include methods for improving regional forecasts during especially high-risk periods associated with the ENSO cycle. He has contributed to forecaster training courses for meteorologists and hydrologists, and has been involved in educational programs such as NOVA. He is currently the Chief of a Division of 30 scientists and engineers responsible for exploring and developing new applications for modern technologies in the atmospheric sciences, weather forecasting and climate arenas.

Extreme precipitation on the American River Watershed:

Strategies for Improving Forecasts

Dr. F. Martin RalphNOAA/Environmental Technology Laboratory,

Boulder, CO

OUTLINE• Evaluate and use snow-level and LLJ

monitoring from wind profilers.

• Deploy a polarimetric X-band scanning radar near Auburn to monitor QPE.

• Use targeted dropsondes over eastern Pacific to improve 24-48 h QPF.

• Evaluate and update the Rhea algorithm using recent study results.

0-12

h

Now

cast

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12-4

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Fore

cast

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PACJETGOAL: Improve 0-24 h prediction of

land-falling Pacific winter storms

METHODS• Physical Process Studies• Observing System Tests• Forecasting Applications

Results from PACJET have been used as a key part of the foundation for recommendations on Forecast Improvement Strategies for the American River

ETL PACJET/CRPAQS Surface Met.ETL PACJET 915-MHz Profiler/RASS/Surface Met.ETL PACJET 915,2875-MHz Profilers/RASS/Surface Met.,Fluxes,GPSETL PACJET 2875-MHz Profiler/Surface Met.,DisdrometerETL CRPAQS Doppler Sodar/Surface Met.ETL CRPAQS 449-MHz Profiler/RASS/Surface Met.ETL CRPAQS 915-MHz Profiler/RASS/Surface Met.ETL CRPAQS 915-MHz Profiler/RASS/Doppler Sodar/Surface Met.non-ETL 915-MHz Profiler/RASS/Surface Met.FSL GPS Integrated Precipitable WaterRawinsondeWSR-88D Radar

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Elev. (m)

AGOATPBBYBKFCCLCCOCZDERKFATFRSGLAGVYKRPLBA

- Angiola- Altamont Pass- Bode ga Bay- Bake rsfield- Chow chilla- Chico- Cazadero- Eureka- Fresno- Fort Ross- Goleta- Gras s Valley- Kings River- Los Banos

LEMLHSMJVNMLNPSPCPRMDSACSIMTJPTMRTRAVISWFD

- Lemore- Lost Hills- Mojave- New Melones Lake- Nav. Postg. School- Pacheco Pass- Richmond- Sacramento- Simi Valley- Tejon Pass- Trimmer- Fairfield- Visalia- Waterford

Combined PACJET/CRPAQS Array

Offshore Sampling

Land based Sampling

PACJET-2001Observations

Nowcasting: Strategies for Monitoring and

Predicting Snow Level

White et al., 2002: An automated brightband height detection algorithm for use with Doppler radar spectral moments. Journal of Atmospheric and Oceanic Technology, Vol. 19 pages 687-697.

Importance of the Snow-level:Watershed Modeling Using the NWSRFS

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Elev. (m)

Smith River at Jed Smith State ParkKlamath River near Turwar Creek

Trinity River at Hoopa

Truckee River at Farad

O R E G O N

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E V

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C A L I F O R N I A

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000Melting level (ft)

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735

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43

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98100

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8796 100

Percentage of river basinbelow this altitude

CNRFC River Forecasting SystemThe sensitivity of watershed

runoff to changes in melting levelfor a given 24-h QPF

24-h QPF:0-6 h = 0.5 in6-12 h = 1.5 in12-18 h = 1.5 in18-24 h = 0.5 in

See May 2002 issue of JTech (White et al., 2002)

2000 ft changes in snow level

tripled runoff in These four Watersheds.

A New Tool for Snow-level MonitoringNOAA/ETL Profiler Snow Level Display

BANDI

II BBY

19 Feb 01 - 10 UTC

GVY

DonnerPass

BBYGVY

DonnerPass

SL: 4100 ft1030 UTC BAND

I

SL: 4600 ft1430 UTC

19 Feb 01 - 14 UTC

II

BBYGVY

DonnerPass

I

II

SL: 4000 ft2030 UTC

19 Feb 01 - 20 UTC

BBYGVY

DonnerPass

II

20 Feb 01 - 00 UTC

SL: 4300 ft0030 UTC

(a) (b)

(c) (d)

Case study of 2 precipitation bands coming ashore

III

Band I

Band II

Onset of Band II

Onset of Band I

rain

snow

Band I

Band II

Station Elevation

III

Onset of Band II

Onset of Band I

snow level

Verification of 2 precipitation bands coming ashore

Open issues on snow-level monitoring

How much lead time can be provided by coastal or buoy profiler data?

With what accuracy can extrapolation from coastal sites predict snow-level in the Sierras?

What are the best potential permanent profiler sites for this application, based on tracks of critical storms?

How to best assimilate this information into the forecast process, e.g., RFC adjusts snow-level in QPF files?

Nowcasting: Strategies for Monitoring and Predicting The Low-Level Jet

Neiman et al., 2002: The Influence of Land-falling Low-level Jets on Rain Rate in California’s Coastal Mountains during CALJET. Monthly Weather Review, Vol. 130 pages 1468-1492.

Seasonal compositeCorrelation profile

Offshore compositeLow-level jet

Low-level jet (LLJ) controls rain rate in coastal mountains

Hei

ght,

MS

L (k

m)

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850

700

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ssur

e (m

b)

Northern couplet

Mean mountain topLow-level jet

0.0 0.2 0.4 0.6 0.8

Low-level jet casesWinter-season analysis

1.0

Linear regression slope (mm h-1) (m s-1)-1:Hourly averaged upslope flow at BBY vs. hourly rain rate at CZD

A

A'

Coastal mountains

Coast

B

B'

Blocked flow

cool

Blocking front

(a)

zv

2h

h

0 1Correlation:

Coastal upslope vs mtn. rain ratev

vh

2h

Coastal mountainsOcean

Feeder cloud

RainLow-level jet

warm, moist

Unblocked

(b)

A A'

v

2h

h

0 1Correlation:

Coastal upslope vs mtn. rain rate

v

vh

2h

Coastal mountainsOcean

Blockedflow

B B'

Blocked

Blocked flow

+

Unblocked Blocked

Wind profiler

Low-level jetwarm, moist

Rain rate in coastal mtns directly linked to upslope flow at coast.Upslope flow at ~1 km best indicator of orographic rains.In blocked flow, near-sfc winds do not provide useful rain-rate info.Connection between upslope flow and rain rate more robust in LLJ conditions. Orographic rain-rate efficiency 50% larger when LLJ is present.

FUTURE: Use serial PACJET soundings & GPS IWV to assess role of moisture & stability in modulating orographic rainfall.

MWR, June 2002, pp. 1468-1492.

Open issues on LLJ and orographic rain

How well do the coastal results apply to Sierras? Is the controlling level higher for the Sierras than for the coast ranges?

The Rhea algorithm keys on 700 mb flow, which differs from the coastal profiler results. Can the Rhea algorithm be evaluated and updated if needed?

With what accuracy can extrapolation from coastal sites predict the LLJ in the Sierras?

What are the best potential permanent profiler sites for this application, based on tracks of critical storms?

How to best assimilate this information into the forecast process: update the Rhea technique?

Nowcasting: Improved Monitoring Of Precipitation Over the American River Watershed

White et al., 2003: Coastal orographic rainfall processes observed by radar during the California land-falling Jets experiment. Journal of Hydrometeorology, Vol. 4 pages 264-282.

W (m/s)

24 23 22 21 20 19 18 17 16 15 14 13 12 11 10Time, UTC (h)

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012

34

Hourly Rainfall (mm):0.00.00.30.38.11.39.12.818.39.111.26.94.63.6

(a) Doppler vertical velocity

(b) radar reflectivity

KMUXWSR-88D 0.5 tilto

23 March 1998CZD

Shallow, Non-bright-band rain is common and hard to detect with NEXRAD

BBB

SLP

FRS = FORT ROSS

CZD = CAZADERO

BBY – BODEGA BAY

BBB = BODEGA BAY BUOY

SLP – SALT POINT

PACJET-2003: NOAA/ETLA Study of Land-falling Pacific Storms on the West Coast of the United States

X-band radar at Fort Ross

X-band

0 5 10 15 20 25 30Reflectivity (dBZ) at 0.5 deg elevation

WSR-88D radar at KMUX0102 UTC 14 Jan 03

KMUX

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60 km

NOAA/ETL X-band radar at FRS0059 UTC 14 Jan 03

Reflectivity (dBZ) at 1.0 deg elevation

0 10 20 30 40 50

PACJET-2003: NOAA/ETL Gap-Filling X-band Radar

• Nearest NEXRAD radar sees no significant echoes approaching flood-prone watershed

• NOAA/ETL’s Coastal X-band radar fills NEXRAD gap

Targeted Dropsondes with NCEP

• Initial conditions used for numerical weather prediction are prone to relatively large errors over the Eastern Pacific Ocean.

• Of particular importance are vertical profiles of wind, temperature and water vapor, which are more regularly available over land.

• The absence of suitable vertical profile information can be addressed by using aircraft to deploy dropsondes in “sensitive areas.”

Determining “Sensitive” Areas for Dropsonde Deployment

The sensitive areas are determined by setting a priori:

• Lead-time (e.g., 24 h) • Forecast variable (e.g., QPF)• Location of desired forecast improvement (West Coast)

Targeted Dropsondes with NCEP• Method identifies areas where

initial condition uncertainty may cause numerical model forecast errors.

• One or more aircraft deploy sondes in “sensitive areas.”

• Lead-time, forecast variable, and location of improved forecast must be set first.

• PACJET added P-3 to make 3 aircraft in one IOP: best improvement of the season.

Open issues on targeted dropsondes

The technique is now operational in NOAA for larger-scale forecasts for about 6 weeks each winter in the Central Pacific.

The CALJET and PACJET studies suggest it can also be used for smaller verification areas (American River watershed?).

Analyses from PACJET-2001 using NOAA’s G-IV aircraft must be completed.

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Eureka

Pt. Arena

Richmond

Pt. Piedras Blancas

Goleta

Redding

ChicoOroville Dam

FolsomDam

Elev. (m)

Integrated Profiler Observing Site 915-MHz Wind Profiler RASS GPS Integrated Water Vapor 10-m Meteorological TowerBuoy-mounted Wind Profiler

Valley

FriantDam

New MelonesLake

Fresno

Shasta Dam

Grass

A Profiler Array to Improve Flood Forecasts For the Sacramento and San Joaquin Rivers

Three linear arrays

• Sierra Foothills0 to 3 h lead time

• Coastal4 to 12 h lead time

• Offshore12 to 18 h lead time

NOAA/ETL First test of a buoy-mounted wind profiler

From 13-17 March 2000 NOAA’s Environmental Technology Laboratory testeda wind profiler on a SCRIPPS 10-m discus buoy.

915 MHz phased array antenna powered by a diesel generator

Moment-level correction for buoy motion (Jordan et al. U. S. Patent)

Wavelet transform suppression of ground and sea clutter(Jordan et al. U. S. Patent; and J. Tech. Article )

ProfilerRadar electronics on buoy

Test bed concept

Marty RalphNOAA/ETL-PACJET

Hydrometeorology TestbedPrimary Goals

• Systematically evaluate promising new methods that can influence both NWP and nowcastingusing the man-machine mix forecasting paradigm.

• Assess their value in terms of improved regional performance on NOAA’s QPF GPRA measure.

•• Use these results as an objective basis for

decisions on transitions to operations both in the test region and nationally.

Hydrometeorology TestbedRegional Implementations/National Impact

West Coast - Cool Season

Carolinas Hurricanes

Plains – Warm Season

Mean annual precipitation (inches)