NCEP

In NCEP's Seamless Suite of Products, with its essentialClimate -Weather -Water linkages, ALL model applicationsare essentially driven by the global model system ,which in

turn is DRIVEN BY GLOBAL OBSERVATIONS - both POES and GOES

WHERE AMERICA’S CLIMATE AND WEATHER SERVICES BEGIN

NPEC's view of GOES - An Essential Componentof the Future Integrated Observing System

Ralph A. PetersenDeputy Director, NCEP/EMC

________________________________________________________________________________________

NCEP’s Seamless Suite of Products

Covers events from Climate to Weather to Oceans

Spans ranges of time from Seasons to Weeks to Days to Hours in the future

Lindzen’s recent statements underwrite the broad impact of GOES

Improved Spectral and Spatial coverage

of Future GOES Imagers and Sounders

will be critical to meet NCEP’s future goals

- Important for:- NWP

- Objective Nowcasting

- Realtime Forecaster Products

- Must:- Eliminate the conflict between supporting ASOS and forecast products

- Fill in the time, space and information gaps left by other observations

Where will NCEP’s models be by 2010?Model Resolution improvements will have neared completion by then

Domestic models at about 2 km

Threats models finer than 1 km

Major improvements in model physics In the Atmosphere

- Detailed Moisture/Cloud Characteristics Over Land

- Details of Surface Properties Over Oceans

- Diurnal SST structure Probabilistic Forecasts

- Will need better measures of analysis (and therefore data) confidence - Ensembles of physics packages will use detailed cloud, moisture, aerosol and surface specifications, as well as tendencies

New Service Areas Air Quality Models

- Major needs for observations - domestically and internationally -

Role of Future GOES compared to other data systems

Surface Based Systems:Radiosonde - A primary source of wind, temperature and moisture data

Strengths - High absolute accuracy and vertical resolution Weaknesses - Land only, twice daily, ~400 km spacing

Aircraft - Increasingly important source of Wind and Temperatures

Strengths - Good accuracy, vertical resolution, multiple obs per day

Weaknesses - Tropospheric only, Primarily over land, Airport spacing, moisture less certain

Wind: Profiler/VAD - Becoming integral to NWS operations, esp. in Midwest

Strengths - Good accuracy, good vertical and temporal resolution

Weaknesses - Wind only, limited areas

Radar - Essential observations of Precipitation

Strengths – Precip. location, intensity, vertical/horizontal structure

Weaknesses - Clouds and “reflectors” only, no temperature or RH

Role of Future GOES compared to other data systems

Satellite Sounding Systems:Polar (NPOESS)- A primary global data source

Strengths - “All Weather”, Global coverage, Increasing vertical resolution, Moderately course horizontal resolution

Weaknesses - 2-4 (6) times daily, Gaps in coverage

Microwave - Coarse vertical/horizontal resolution, rain contamination

IR - Finer vertical resolution, Cloud contamination

- Major component to NWP - Underutilized by forecasters

GOES - Major source of hemispheric/domestic forecaster information

Strengths - High temporal and spatial resolution, Increasing vertical resolution, Good cloud/surface detail, Captures diurnal changes, Only observations available of detailed horizontal moisture structure and time changes

Weaknesses - IR-only “profile” information, No polar coverage, Limited by “Cloud Contamination”, Underutilized in NWP

GOES: Spatial Details and Time Tendencies

- Major Source of Moisture Observations

- Horizontal and vertical moisture gradients

- Future systems will observe 6-8 individual layers

- Moisture fluxes possible with sufficient horizontal resolution

- Need identified by NAOS

- Cloud properties

- Cloud location, top height, ice vs. water, . . .

- Surface Properties

- Land - Vegetation, Surface emissivity,

- Oceans - SST, Ocean color,

Surface motion, . . .

- Trace gases, etc.

- Fire, Smoke, Dust, O3, CO2, . . .

Information Content for Moist Atmospheres

0

2

4

6

8

10

12

14

16

12 18 18N 50N 2000 RAOB

Channels

Num

ber

of

independent Pie

ces o

f

Info

rmatio

n

TemperatureMoisture

Improvements can be seen in aGOES Sounder Emulation TEST

Radiance data from:- A ‘TRUTH’ atmosphere - (based on available radiosondes

and high spectral-resolution ground-based observations - AERI)

Were used to simulate satellite observations from:- Current Operational Radiometers (GOES)

- Advanced Geostationary Interferometers (GIFTS)

Time series of thunderstorm prediction parameter Lifted Index were also derived using a surface-based parcel.

(Note – Conventional radiosonde data would only be availableat end of the time series – after the storms had occurred.)

Test prior to severe storm event near Lamont, OK GOES-8 Visible

20 UTC 21 UTC

23 UTC 00 UTC

Time series of low-level vertical temperature structure during9 hours prior to Oklahoma/Kansas tornadoes on 3 May 1999

Truth>

GIFTS>

Note GIFTS improves depiction of boundary layer

heating and surface inversion

Current GOES>

GIFTS traces evolution of 800 hPa inversion with 60-80% error reduction

GIFTS captures important vertical temperature variations at ~1 km resolution

GIFTS/GOES Retrieved-Temperature Errors

Truth>

Note – Independent GIFTS Information

every 100 mb

GIFTS Errors>Standard Deviation = 0. 6o

Note - GIFTS reduces errors by

80% and captures 800mb inversion

GOES Errors>Standard Deviation = 3. 5o

Time series of low-level vertical moisture structure during9 hours prior to Oklahoma/Kansas tornadoes on 3 May 1999

Truth>

GIFTS>

Note GIFTS retains strong

vertical gradients for monitoring

convective instability

Current GOES>

GIFTS traces moisture peaks and gradients with greatly reduced errors

GIFTS captures important vertical moisture variations at ~1.5 km resolution

GIFTS/GOES Retrieved-Moisture (g/kg) Errors

Truth>

Note – Independent GIFTS Information every 100-150 mb

GIFTS Errors>Standard Dev. = 0.9 g/kg

Note - GIFTS reduces errors and captures

low-level moisture peaks and vertical

gradients

GOES Errors>Standard Dev. = 2.4 g/kg

UW-Madison/CIMSS

Time (UTC)

Lif

ted

Ind e

x (

K)

GIFTS depicts LI values and rapid atmospheric destabilization prior to onset of convection better than current GOES. (Note – Super-adiabatic layer near surface

deepened after 2000 UTC - difficult to retrieve without including surface data.)

Time series of Lifted Index during9 hours prior to Oklahoma/Kansas Tornadoes - 3 May

1999

NCEP’s Seamless Suite of Products – Role of Future GOES

- Let’s look at 3 categories of use of future GOES observations for Weather Forecasting

- NWP -

- Objective Nowcasting -

- Subjective forecaster use of

real-time Image Products -

Need to integrate ALL data sources

GOES in Numerical Weather Prediction 0-2 Days

----------------------------------------------------------------------------------------------- - Build on activities of Joint Center for Satellite Data Assimilation

- GOES impact on weather forecasts likely to be greatest in first 2 days

For precipitation forecasting- Improve specification of precipitation location and intensity

- Could be key to location and timing of “Storm Initiation”

- Differential moisture flux

Assimilation over land “easier” for smaller, IR FOVs- Direct use of radiances for vertical moisture and temperature gradients

- Need to define “reasonable” subset of representative channels

- Build on AIRS

- Build on GIFTS

- Direct use of Cloud Imagery

- Cloud composition, placement, removal, motion and trends

-Apply a measure of “confidence” in the observation – especially important for

ensemble and targeting strategies.

GOES in “Objective Nowcasting 0-6 hours - - - - The “Watch Period”

----------------------------------------------------------------------------------------------- - By 2010, forecasters will not be able to look at all observations individually

- Emerging new users need frequent updates- Energy sector (Max/min temps for days and hours)

- Need to provide forecaster tools that depict all data as closely as possible- Allow users to see high-resolution, 4-D depictions of “Gridded Obs”

- Less “dynamically constrained” than longer-range NWP

- Needs fast turn-around

- “Instant integration”

- Combine best of all systems

- Combine Raob, Aircraft, Profile winds and

GOES RH to produce Moisture Flux

- Need to capture both current and past observations

- Add/subtract/move clouds

- Need new ideas

- Note Different roles of Products and Radiances vs. NWP

- Need to identify areas of “Confidence” in analysis and nowcasts

Direct use of GOES Products by forecasters0-2 hours - - - - The “Warning Period”

-----------------------------------------------------------------------------------------------

- Will continue to rely on “Products” instead of radiance* Derived Product Image (DPIs) information will become increasingly

important- Easy to interpret

- Easily integrated with “Nowcasting” products

- Need techniques for “Auto Alerting”

* Applications likely to expand Lifted Index - Pre-convective storm location/intensity

- Icing

- Fog - Development/Dissipation

- Wild fires

- What the user needs - especially for

products from new sounder

* Real-time model verification - Model products will be displayed as

“Synthetic GOES Images”

Is this Vision Realistic?Is this Vision Realistic?

Test Using Other Existing Data SetsTest Using Other Existing Data Sets

Ralph A. Petersen, NCEP, Camp Springs, MDRalph A. Petersen, NCEP, Camp Springs, MD

Wayne F. Feltz, UW/CIMSS, Madison, WIWayne F. Feltz, UW/CIMSS, Madison, WI

Joseph Schaefer, NCEP/SPC, Norman, OKJoseph Schaefer, NCEP/SPC, Norman, OK

Russell Schneider, NCEP/SPC, Norman, OKRussell Schneider, NCEP/SPC, Norman, OK

Study the development of the pre-convective environment using co-located AERI Temperature/Moisture profiles and Wind Profiler data

20 UTC 21 UTC

23 UTC 00 UTC

High temporal frequency Wind, Temperature and Moisture profiles were available in clear skies for the 5 hours preceding convection at the Lamont, Vici and Purcell ARM-CART/Wind Profiler sites

These data are used to diagnose the dynamical processes responsible for supplying the moisture that supported the tornadic storms in this area

Begin by diagnosing evolution of

temperature and moisture fields using AERI data

Then diagnose evolution of wind fields

using Profiler data

Finally, data from both systems are combined to determine moisture flux convergence and the relative roles of divergence and

advection in supplying moisture and maintaining convective instability.

Time series of Equivalent Potential Temperature from AERI combine thermal and moisture observations (in clear skies) show:

1 – Continual increase in strength and depth of low-level maximum of total thermal energy after 18Z, which

2 – Combines with drier air aloft, causing 3 – Marked Increase in Convective Instability, especially in the

period immediately before cloud formation

Tropospheric Velocity Divergence fields show a major transition near 18Z, changing from

1 – A pattern of boundary-layer divergence and mid-tropospheric convergence, to

2 – One dominated by low-level convergence and upper-tropospheric divergence.

To diagnose the dynamical processes responsible for supplying the moisture to the area where severe convection developed,

AERI Moisture and Profiler Wind data can be combined to derive

Moisture Flux Divergence

Moisture Flux Divergence shows marked transition after 18Z, with:1 – Moisture extraction occurring between 0.5 and 1.5 km in first 3 hours

(coincident with the observed drying), followed by

2 – Rapid development and maintenance of strong low-level moisture convergence in the lowest 1.5 km,

3 - Capped by a shallow layer of moisture divergence immediately aloft, locally enhancing convective instability

Determining how (1) this increase in local supply of moisture and convective instability is controlled by (2) divergence and (3) advection

qV = q(V) + V q

(1) (2) (3)

allows us to understand: a) How much of the thunderstorm prediction problem can be addressed primarily with local wind observations and divergence forecasts, versus

b) How much is the result of advective processes which require high-resolution specification of moisture gradients

Moisture-weighted Velocity Divergence shows:

1 – Major low-level moisture extraction prior to 18Z, followed by

2 – Rapid transition to moisture convergence around 19Z, which

3 – Decreases in strength toward the end of the period

Moisture Advection shows:1 – A weakening low-level moisture source early in the period,

2 – Persistent drying aloft, including dominant sink at the top of the “lid” during the later time periods (helps maintain convective instability)

3 – Rapid increase of low-level moisture convergence sourcearound 19Z, which

4 – Strengthens and dominates at the end of the period and supports growth of subsequent convection

SummarySummary

Combined AERI/Wind Profiler dataCombined AERI/Wind Profiler data provide continuous monitors of low- provide continuous monitors of low-level moisture convergence processes responsible for increasing convective level moisture convergence processes responsible for increasing convective

instability and providing sufficient moisture instability and providing sufficient moisture to support convectionto support convection

Fine temporal scale structures showed:Fine temporal scale structures showed:

1 – Rapid transition from low-level moisture flux divergence to convergence,1 – Rapid transition from low-level moisture flux divergence to convergence,

2 – Initial increase in low-level moisture flux convergence related to 2 – Initial increase in low-level moisture flux convergence related to increase in increase in velocity convergencevelocity convergence

[ related to high-resolution wind data and forecasts ][ related to high-resolution wind data and forecasts ] and and

3 – Maintenance of both low-level moisture flux convergence supporting 3 – Maintenance of both low-level moisture flux convergence supporting convection and overlaying moisture flux divergence (drying) enhancing convection and overlaying moisture flux divergence (drying) enhancing

convective instability is driven by convective instability is driven by moisture advectionmoisture advection [ related to high-resolution moisture gradient observations ][ related to high-resolution moisture gradient observations ]

Follow-on tests using “Emulated GIFTS” temperature &moisture profiles and Follow-on tests using “Emulated GIFTS” temperature &moisture profiles and “Emulated GIFTS” temperature, moisture &WV Wind profiles will further “Emulated GIFTS” temperature, moisture &WV Wind profiles will further

define impact of GIFTSdefine impact of GIFTS

How do we get there?

- Build Upon Planned Research/Development Programs – Like GIFTS

-Include Total Infrastructure

- Ground Receivers

- Data Ingest Systems

-Applications Development- Data Assimilation

- Product Generation

- User Information Displays

- Data System Emulations-Know what you will see

Remember the End User