An Objective Nowcasting Tool that Optimizes the Impact of Frequent Observations in

Short-Range Forecastsa.k.a. “Stopping Short-Range Gap-osis”

Ralph Petersen1, Robert M Aune2

 

1 Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin – Madison, Madison, Wisconsin, Ralph.Petersen@NOAA.GOV

2 NOAA/NESDIS/ORA, Advanced Satellite Products Team, Madison, Wisconsin, Robert.Aune@NOAA.GOV

Presentation OutlineBasic Mantra: Good data should be believed, used and re-usedBasic Mantra: Good data should be believed, used and re-used

•Focus on 3-6 hour forecast time frame–Expand Forecaster’s tools

–Compliment and Update conventional NWP guidance

•Address the problem of Detecting and Forecasting the Dynamical and Thermodynamical Forces which:

Create the pre-convective environment andHelp trigger convective initiation

• Develop techniques to Expand the utility of Existing and Future Satellite Products

–Go beyond considering data useful only as observations–Apply the techniaues Over Land

•Combine satellite data with other data in daily forecasting–Maximize benefits of both

A basic premise - Any Nowcasting Model Should:

Be used to update/enhance other numerical guidance:

Be Fast

Be run frequently

Not needed to be constrained by longer-range NWP ‘computational stability’ issues

Use all available data:

“Draw closely” to good data

Important for anticipating rapidly developing weather events:

“Perishable” guidance products – rapid delivery

Run Locally? – Few resources beyond comms, users easily trained

We will focus on the “pre-storm environment”We will focus on the “pre-storm environment”

Increase Lead Time / POD and Reduce FARIncrease Lead Time / POD and Reduce FAR

Goals: - Increase the length of time that forecasters can make good use of quality observations (vs. NWP output) for their short range forecasts

- Provide objective tools to help them do this

Making GOES Sounder Derived Product Images (DPI) more useful to forecasters

To increase usefulness, rather than viewing sets of individual profiles, GOES Sounder products are being made available to forecasters as images

Products include: - Total Precipitable Water (TPW) - 3-layers PW - Stability Indices, . . .

DPIs

+ Speed comprehension of information in GOES soundings, and

+ Improve upon Model First Guess, but - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

- Only used as observations, and

- Have no predictive component - Data not used in any NWS NWP models

GOES 900-700 hPa Precipitable Water - 20 July 2005

TPW NWP Guess Error Reduction using GOES-12

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BIAS(mm) SD(mm)

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700-300 hPa

Case of convection on 20 July 2005 which was initially moved over Central Wisconsin, Decayed and then Redeveloping past Chicago

Why did initial convection decay and why did it reform where it did?

1545 UTC

1715 UTC

1915 UTC

Lower-tropospheric GOES Sounder Derived Product Imagery (DPI)

3 layers of Precipitable Water

Sfc-900 hPa900-700 hPa700-300 hPa

0700 UTC

GOES 900-700 hPa Precipitable Water - 20 July 2005

1000 UTC

1300 UTC

1800 UTC

However, after initial storm has developed, cirrus blow-off masks lower-level moisture maximum in

subsequent IR satellite observations

Small cumulus developing in boundary layer later in morning

also mask retrievals in second area.

MoistureMaximum

0700 UTC

1000 UTC

1300 UTC

1800 UTC

Mid-tropospheric GOES Sounder Derived Product Imagery (DPI)

3 layers of Precipitable Water

Sfc-900 hPa900-700 hPa700-300 hPa

Over time, cirrus blow-off also masks the presence of an extended

area of mid-level dryness.

(Differential motion of lower-tropospheric moisture

under upper-tropospheric dryness creates convective instability)

GOES 700-300 hPa Precipitable Water - 20 July 2005

Maximum Dryness

1 - Can we retain the satellite observations that were ‘lost’

after the cirrus shield appeared following the initial storm

development?

2 - Does the underlying premise of data assimilation (that data should make small changes to an already good model)

hold for nowcasting, where small-scale data extremes observed by various different observing systems can make

the difference between a good and bad forecast?

Two basic questions:

For Nowcasting - Have we forgotten the Golden Rule of Forecasting?

Always remember to look out of the window (or at satellite imagery)

What do you see? - Clouds (or water vapor features)

What are they doing? - Moving as ‘entities’ from one place to another

The fundamental question to be addressed by this effort is:

For Nowcasting, are there advantages in exchanging

the non-linear advection terms in ‘classical’ grid-point models

(which ‘recreate’ clouds as they move from grid point to grid point)

with simpler LaGrangian schemes that can be used to

update traditional NWP guidance

by predicting the movement of parcels

that are frequently updated and initialized at observation points.

LaGrangian Trajectories

Clearly show Development of

Interplay between Dry Line from West

and Moist Gulf Flow

from South

Forward Trajectories

Backward Trajectories

Motivation for a LaGrangian Nowcast approach?

Sample LaGrangian Diagnostic Study

of development ofPre-Convective

Environmentusing 3-hourly

Radiosonde DataFrom SESAME

From Kocin et al., 1986.

LaGrangian Techniques formulations based on Greenspan’s Discrete Model Theory (1972,73)

- Data can be used directly without ‘analysis smoothing’

– Retains maxima, minima and extreme gradients

- Can combine Wind / Moisture observations from two sources

- Spatial resolution adjusts to available data density and dynamics

- GOES sounder products can be projected forward at full resolution – even as they move into ‘data void’ cloudy areas

- Use all data at time observed – not binned and averaged

- Parcels from successive nowcasts can be tracked, aged and combined for output

- Use best aspects of all available data setse.g., Cloud Drift winds can be combined with surface obs cloud heights to create a ‘partly cloudy’ parcel with good height and good motion

e.g., Wind Profiler / Aircraft data can be projected forward with accelerations

What are the benefits of a LaGrangian (‘Parcel’) Nowcasting Model?

- It is fast (15 min t) and needs minimal computing resources

- Can be used to ‘update’ other NWP guidance or ‘stand-alone’

DATA DRIVEN

Analytical tests simulating Dynamics of an Idealized Jet StreakFully balanced parcels enter Jet Streak from west

with various balanced flow speeds, Jet Streak magnitudes, Sizes and Motions.

Divergence/Convergence is not divided into four symmetrical quadrants.Divergence on the cyclonic side is offset slight ahead and left of the Geostrophic max and extends well ahead of the core on the anticyclonic side.

Divergence fields are dominated by cross-stream flow components.

Convergence in anticyclonic exit region aloft corresponds with “Dry Slots’ in WV imagery.

Strong Divergence/Convergence transition ahead in area of (CAT,Gravity Waves)

Analytical tests simulating Dynamics of an Idealized Jet Streak

1 - Document the approach2 - Perform analytical accuracy and performance tests3 – Developing a Prototype Objective LaGrangian Nowcasting Model

Initial development focusing on the optimal approaches for providing updates of existing mesoscale model outputs in the free-atmosphere

The objective of these tests is to provide forecasters 3 to 6 hour forecasts of the DPI fields updated every hour

Combines strengths of Short-Range NWP with strength of Satellites - Uses winds from RUC-II analyses + RUC-II geopotential anal/fcsts - Matches these data with GOES Sounder (DPI) Water Vapor Data

GOES Sounder profiles not currently used in RUC over land

- Projects the data matches forward in time (at 15 minute intervals) Additional observations can be included as they arrive

(Satellite updates every 30-60 minutes possible)

What have we done to date?

Returning to 20 July 2005 case

Question: Can GOES Sounder Derived Product Imagery (DPI) be projected forward in time to

provide forecasters more information about second storm

development south of Chicago at ~ 1800 UTC?

0700 UTC

GOES 900-700 hPa Precipitable Water - 20 July 2005

1000 UTC

1300 UTC

1800 UTC

However, after initial storm has developed, cirrus blow-off masks lower-level moisture maximum in

subsequent IR satellite observations

Small cumulus developing in boundary layer also mask retrievals

in second moist area.

MoistureMaximum

0.00 Hr LaGrangian Nowcast900-700 GOES Precipitable Water

0-10 mm – Red, 10-20 mm – Green >20 mm - Blue

20 July 2005 Case StudyNowcasting the Lower-Tropospheric Moisture Source1200 UTC data projected forward to 1800 UTC

Blue = MoistureMaximum

White =Cloudy

0.00 Hr LaGrangian Nowcast900-700 GOES Precipitable Water

0-10 mm – Red, 10-20 mm – Green >20 mm - Blue

0.50 Hr LaGrangian Nowcast900-700 GOES Precipitable Water

0-10 mm – Red, 10-20 mm – Green >20 mm - Blue

1.00 Hr LaGrangian Nowcast900-700 GOES Precipitable Water

0-10 mm – Red, 10-20 mm – Green >20 mm - Blue

1.50 Hr LaGrangian Nowcast900-700 GOES Precipitable Water

0-10 mm – Red, 10-20 mm – Green >20 mm - Blue

2.00 Hr LaGrangian Nowcast900-700 GOES Precipitable Water

0-10 mm – Red, 10-20 mm – Green >20 mm - Blue

2.50 Hr LaGrangian Nowcast900-700 GOES Precipitable Water

0-10 mm – Red, 10-20 mm – Green >20 mm - Blue

3.00 Hr LaGrangian Nowcast900-700 GOES Precipitable Water

0-10 mm – Red, 10-20 mm – Green >20 mm - Blue

3.50 Hr LaGrangian Nowcast900-700 GOES Precipitable Water

0-10 mm – Red, 10-20 mm – Green >20 mm - Blue

4.00 Hr LaGrangian Nowcast900-700 GOES Precipitable Water

0-10 mm – Red, 10-20 mm – Green >20 mm - Blue

4.50 Hr LaGrangian Nowcast900-700 GOES Precipitable Water

0-10 mm – Red, 10-20 mm – Green >20 mm - Blue

5.00 Hr LaGrangian Nowcast900-700 GOES Precipitable Water

0-10 mm – Red, 10-20 mm – Green >20 mm - Blue

5.50 Hr LaGrangian Nowcast900-700 GOES Precipitable Water

0-10 mm – Red, 10-20 mm – Green >20 mm - Blue

6.00 Hr LaGrangian Nowcast900-700 GOES Precipitable Water

0-10 mm – Red, 10-20 mm – Green >20 mm - Blue

6.00 Hr LaGrangian Nowcast900-700 GOES Precipitable Water

0-10 mm – Red, 10-20 mm – Green >20 mm - Blue

Important Lower-Level MoistureInformation about Storm Formation Obscured

by Clouds from Storm in Observations

0.00 Hr LaGrangian Nowcast700-300 GOES Precipitable Water0-5 mm – Purple, 5-10 mm – Black

>10 mm – Light Blue

20 July 2005 Case StudyNowcasting the Middle-Tropospheric Dryness

1200 UTC data projected forward to 1800 UTC

Purple=MaximumDryness

White =Cloudy

0.00 Hr LaGrangian Nowcast700-300 GOES Precipitable Water0-5 mm – Purple, 5-10 mm – Black

>10 mm – Light Blue

0.50 Hr LaGrangian Nowcast700-300 GOES Precipitable Water0-5 mm – Purple, 5-10 mm – Black

>10 mm – Light Blue

1.00 Hr LaGrangian Nowcast700-300 GOES Precipitable Water0-5 mm – Purple, 5-10 mm – Black

>10 mm – Light Blue

1.50 Hr LaGrangian Nowcast700-300 GOES Precipitable Water0-5 mm – Purple, 5-10 mm – Black

>10 mm – Light Blue

2.00 Hr LaGrangian Nowcast700-300 GOES Precipitable Water0-5 mm – Purple, 5-10 mm – Black

>10 mm – Light Blue

2.50 Hr LaGrangian Nowcast700-300 GOES Precipitable Water0-5 mm – Purple, 5-10 mm – Black

>10 mm – Light Blue

3.00 Hr LaGrangian Nowcast700-300 GOES Precipitable Water0-5 mm – Purple, 5-10 mm – Black

>10 mm – Light Blue

3.50 Hr LaGrangian Nowcast700-300 GOES Precipitable Water0-5 mm – Purple, 5-10 mm – Black

>10 mm – Light Blue

4.00 Hr LaGrangian Nowcast700-300 GOES Precipitable Water0-5 mm – Purple, 5-10 mm – Black

>10 mm – Light Blue

4.50 Hr LaGrangian Nowcast700-300 GOES Precipitable Water0-5 mm – Purple, 5-10 mm – Black

>10 mm – Light Blue

5.00 Hr LaGrangian Nowcast700-300 GOES Precipitable Water0-5 mm – Purple, 5-10 mm – Black

>10 mm – Light Blue

5.50 Hr LaGrangian Nowcast700-300 GOES Precipitable Water0-5 mm – Purple, 5-10 mm – Black

>10 mm – Light Blue

6.00 Hr LaGrangian Nowcast700-300 GOES Precipitable Water0-5 mm – Purple, 5-10 mm – Black

>10 mm – Light Blue

6.00 Hr LaGrangian Nowcast700-300 GOES Precipitable Water0-5 mm – Purple, 5-10 mm – Black

>10 mm – Light Blue

Important Upper-Level DrynessInformation about Storm Formation Obscured

by Clouds from Storm in Observations

0.00 Hr LaGrangian Nowcast900-700 GOES Lower-Tropospheric Moisture - >20 mm PW - Blue700-300 GOES Upper-Tropospheric Dryness - 0-5 mm PW– Purple

Sample Nowcasts from 20 July 2005 Case StudyCombining Lower-Level Moisture and Middle-Tropospheric

Dryness to Derive Convective DestabilizationOverlays of 1200 UTC data projected forward to 1800 UTC

0.00 Hr LaGrangian Nowcast900-700 GOES Lower-Tropospheric Moisture - >20 mm PW - Blue700-300 GOES Upper-Tropospheric Dryness - 0-5 mm PW– Purple

0.00 Hr LaGrangian Nowcast900-700 GOES Lower-Tropospheric Moisture - >20 mm PW - Blue700-300 GOES Upper-Tropospheric Dryness - 0-5 mm PW– Purple

1.00 Hr LaGrangian Nowcast900-700 GOES Lower-Tropospheric Moisture - >20 mm PW - Blue700-300 GOES Upper-Tropospheric Dryness - 0-5 mm PW– Purple

1.00 Hr LaGrangian Nowcast900-700 GOES Lower-Tropospheric Moisture - >20 mm PW - Blue700-300 GOES Upper-Tropospheric Dryness - 0-5 mm PW– Purple

2.00 Hr LaGrangian Nowcast900-700 GOES Lower-Tropospheric Moisture - >20 mm PW - Blue700-300 GOES Upper-Tropospheric Dryness - 0-5 mm PW– Purple

2.00 Hr LaGrangian Nowcast900-700 GOES Lower-Tropospheric Moisture - >20 mm PW - Blue700-300 GOES Upper-Tropospheric Dryness - 0-5 mm PW– Purple

3.00 Hr LaGrangian Nowcast900-700 GOES Lower-Tropospheric Moisture - >20 mm PW - Blue700-300 GOES Upper-Tropospheric Dryness - 0-5 mm PW– Purple

3.00 Hr LaGrangian Nowcast900-700 GOES Lower-Tropospheric Moisture - >20 mm PW - Blue700-300 GOES Upper-Tropospheric Dryness - 0-5 mm PW– Purple

4.00 Hr LaGrangian Nowcast900-700 GOES Lower-Tropospheric Moisture - >20 mm PW - Blue700-300 GOES Upper-Tropospheric Dryness - 0-5 mm PW– Purple

4.00 Hr LaGrangian Nowcast900-700 GOES Lower-Tropospheric Moisture - >20 mm PW - Blue700-300 GOES Upper-Tropospheric Dryness - 0-5 mm PW– Purple

5.00 Hr LaGrangian Nowcast900-700 GOES Lower-Tropospheric Moisture - >20 mm PW - Blue700-300 GOES Upper-Tropospheric Dryness - 0-5 mm PW– Purple

5.00 Hr LaGrangian Nowcast900-700 GOES Lower-Tropospheric Moisture - >20 mm PW - Blue700-300 GOES Upper-Tropospheric Dryness - 0-5 mm PW– Purple

6.00 Hr LaGrangian Nowcast900-700 GOES Lower-Tropospheric Moisture - >20 mm PW - Blue700-300 GOES Upper-Tropospheric Dryness - 0-5 mm PW– Purple

6.00 Hr LaGrangian Nowcast900-700 GOES Lower-Tropospheric Moisture - >20 mm PW - Blue700-300 GOES Upper-Tropospheric Dryness - 0-5 mm PW– Purple

6.00 Hr LaGrangian Nowcast900-700 GOES Lower-Tropospheric Moisture - >20 mm PW - Blue700-300 GOES Upper-Tropospheric Dryness - 0-5 mm PW– Purple

1 - Document the approach2 - Perform analytical accuracy and performance tests3 - Develop Initial Prototype4 - Initial testing --- Future work

- Optimize wind level selection to match satellite channel weighting 

- Improve visualization tools to view predicted DPIs in formats identical to the observational products.

Will require improvements to LaGrangian model, including

‘data aging’ and ‘continuous successive image merger’ algorithms

- Integrate Profiler, Aircraft and cloud tracked wind data to provide observed wind as well as moisture data updates.

- Test the impact of higher vertical resolution AIRS soundings in resolving the pre-convective environment.

What are we doing next?

Using the trajectory procedure in both a Forward (forecast) and Backward (diagnosis) mode for OKC tornado case.

-Wind Profiler data taken at 2100UTC (shortly prior to the first severe thunderstorm development) are both traced backward (to trace the origins of the air parcels) and forward (to project their future paths and indicate potential divergence) for 3 hours.

Note:1- Increase in convergence between 2100 and 0000 UTC and 2- Increased cyclonic curvature during the period of storm development, an area which 3 hours earlier had shown divergence.

These results are consistent with diagnostic calculation of divergence and moisture flux divergence made using the Wind Profile and AERI data.

Integrate Profiler wind data to provide observed wind

Incorporating of other data sources Although the Wind Profilers are land based, they provide an excellent source of wind data for calculating Moisture Flux Convergence when used in conjunction with co-located Water Vapor information over land from:

- GEOS - AIRS - AERI - Some MDCRS aircraft - With Wind and Moisture

One advantage of the ‘off-line, stations based’ system used in these tests is that it is very simple and PC based – thereby easily transferred to WFO use – and can be run in ‘true real time’ and refreshed whenever new data arrive

- Data can be inserted (and combined) directly without ‘analysis smoothing’ – retain maxima and minimaProvide Forecast Imagery that are consistent with observations

Summary – An Objective LaGrangian Nowcasting Model

- Quick and minimal resources needed

- Can be used ‘stand-alone’ or to ‘update’ other NWP guidance

DATA DRIVEN

Coordinate Independence

- Initial tests done in pressure for convenience

- Propose developing isentropic system - May be able to reduce dependence on ‘deep’ hybrid surface domain in current models

-Can combined LaGrangian Dynamics with Eulerian surface model

-Forward Data Projection techniques which preserve data and parcel accelerations may also benefit mesoscale data assimilation

Goals:

- Provide objective tools to increase the length of time that forecasters can make good use of dependable observations (vs. only NWP output) for their short range forecasts

-Expand the use of GOES sounder products from subjective observations to objective nowcasting tools