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Overview of the NESDIS/CIRARegional and Mesoscale Meteorology Branch

Mark DeMariaNOAA/NESDIS/StAR

RAMM Branch

April 25, 2007Ft. Collins, CO

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RAMMB Branch

• Emphasis on satellite applications to mesoscale weather events, including tropical cyclones, severe storms and mesoscale aspects of winter storms

• Products for hazard detection, including fog, dust, volcanic ash, fires and smoke

• Current and future satellites• Satellite training

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RAMMB Major Accomplishments• NWS RAMSDIS program 1995-2001

– ROL Continues

• Hurricane Mitch Project in Central America 1999-2001• Routine GOES RSO operations in NWS ~2000• Operational Hurricane Products 2001-present

– AMSU TC intensity, GOES/OHC in SHIPS, JTWC models (ST5D, STIPS), New TC wind probability program, TC genesis parameter, RII, Rainfall CLIPER, wind-radii CLIPER, annular hurricane index

• GOES Science Tests – On-going• Training programs – On-going

– VISIT, SHyMet, WMO/International

• GOES-R3 and AWG projects 2003-present

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RAMM Branch – 28 F/T or P/T Staff Members• 6 NOAA/NESDIS FTEs

– DeMaria, Hillger, Knaff, Molenar, Lindsey, Zehr• 5 CIRA Research Scientists

– Connell, Grasso, Brummer (1/2), Sengupta (1/2), Zupanski (1/2)

• 5 CIRA RA Meteorologists– Bikos, Braun, Dostalek, Schumacher, Combs (1/2)

• 3 CIRA RA Computer Staff– Gosden, Watson, Micke (1/2)

• 1 CIRA Admin Assistant– Fryer

• 1 Post Doc– Jirak (shared with Bill Cotton)

• 1 Graduate Student– K. Maclay

• 6 Hourly Employees– D. Coleman, R. Danner, R. DeMaria, G. DeMaria, K.

Jekel, R. Mazur

(underline=PhD)

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FY07 Funding Sources

• NESDIS Base 6 FTEs, travel, pubs, facilities • GIMPAP (NESDIS) 365 K• GOES-R3 (NESDIS) 350 K• GOES-AWG (NESDIS) 75 K• Ground Systems (NESDIS) 70 K• VISIT (NOAA/NWS) 200 K• SHyMet (NESDIS) 75 K• GOES-PSDI (NESDIS) 75 K• JHT (NOAA/NWS) 70 K• Research to Operations (NESDIS) 75 K• Polar Winds (NASA) 43 K

• Total 1398 K

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RAMM BranchResearch Project Plans

• Tropical Cyclone Satellite Research and Applications Development

• Mesoscale and Severe Weather Research and Applications Development– Severe Storms– Fire, volcanic ash, fog and dust detection– Mid-latitude applications

• Satellite Training and Outreach

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RPP-1 Tropical Cyclones

• TC wind structure algorithms from satellite retrievals– Operational (NHC) AMSU algorithm– Development for future satellites

• Intensity forecasting with statistical models– Supports several NHC/JTWC operational algorithms

• Tropical cyclone genesis algorithm– Operational NESDIS product

• Tropical cyclone wind probabilities– Operational NHC/JTWC products

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Tropical Cyclone Wind Structure Algorithms

• Use satellite T/q retrievals as input to hydrostatic equation to calculate height field

• Use balance approximation to estimate winds– Gradient balance in 2-D

– Non-linear balance in 3-D

• Adjust retrievals statistically to account for satellite resolution limitations

• Real-time product using AMSU run at NCEP for NHC and JTWC

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AMSU-A Temperature/Gradient Wind Retrievals(Demuth et al 2006, JAM)

Uncor

rect

ed

Corre

ct

ed

T(r,z) Ps(x,y) V(r,z)

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Hurricane FLOYD – 1515 UTC 14 Sep 99

Hurricane IRIS – 0015 UTC 9 Oct 01

MSLP 932mb

MAX Sustained Winds 125 kt

NE SE SW NW

64 kt 110 75 60 90

50 kt 180 140 105 150

34 kt 250 190 150 190

MSLP 954 mb

MAX Sustained Winds 120 kt

NE SE SW NW

64 kt 15 15 10 15

50 kt 25 25 15 25

34 kt 125 50 40 60

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Experimental RAMMB Product Multi-Platform Satellite Wind analysis

• Combine all available satellite inputs in variation analysis– GOES inner core winds– AMSU nonlinear balance winds– QuikSCAT/Windsat– GOES/Meteosat feature track winds– SSM/I wind speeds

• Global real-time product being demonstrated during 2007– http://rammb.cira.colostate.edu/products/tc_realtime/

• Evaluation by JTWC/NHC• Possible new PSDI project in 2008

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R34 175 180 125 185R50 120 115 80 125R64 80 65 60 60

From satelliteanalysis

R34 150 120 100 150R50 100 90 70 90R64 80 60 45 55

From NHC 18Z advisory

Final Analysis

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• Annular hurricanes are a special class of storms characterized by a large, symmetric eye and very little rain outside of the eyewall.

•An objective Annular Hurricane Index (AHI) based upon GOES and other data was implemented at the National Hurricane Center (NHC) at the end of the 2006 season (for use in 2007)

• Annular hurricanes tend to maintain their intensity longer than average storms

Milestone Significance: By objectively identifying annular hurricanes, the new product will help to improve NHC hurricane intensity forecasts.

7/22/06 0Z: Annular Hurricane Daniel in the Eastern N. Pacific. At this time, the Annular Hurricane Index is 93 (out of 100, with 0 being the lowest probability of annular structure and 100 being the highest).

Operational Transition of a GOES Annular Hurricane Index to NCEP/NHC

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Isabel Eye Sounding from AIRS/AMSU(proxy for NPOESS CrIS/ATMS)

100

200

300

400

500

600

700

800

900

1000

0 2 4 6 8 10 12 14 16 18

Temperature Anomaly (C)

Pre

ss

ure

(h

Pa

)

Eye Sounding

EnvironmentSounding

Eye - Environment Temperature

Integrate Hydrostatic Equation Downward from 100 hPa to SurfaceEnvironment Sounding: Ps = 1012 hPaEye Sounding: Ps = 936 hPaAircraft Recon: Ps = 933 hPa

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Proxy GOES-R/NPOESS TC Datasets(MSG, MODIS, AVHRR, AIRS + future IASI)

2002: Lili (26)

2003:Emily (51) Katrina (68)Rita (69)Stan (17)Wilma (75)Alpha (10)Beta (9)

2005: Ernesto (29)

2005-EP: Hilary (16)

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0

50

100

150

200

250

300

350

400

450

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

Year

MA

E (

nm

i)

120 hr

72 hr

48 hr

24 hr

0

5

10

15

20

25

30

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

YearM

AE

(k

t)

120 hr

72 hr

48 hr

24 hr

Mean Absolute Error of NHC Official Atlantic Track and Intensity Errors1985-2006

3.5% Per Year 0.8% Per Year

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Track vs. Intensity Forecast Skill (Relative to Climatology and Persistence)

NHC Forecast Skill (Atlantic 2001-2005)

0

10

20

30

40

50

60

12 24 36 48 60 72 84 96 108 120

Forecast Interval (hr)

Fo

rec

as

t S

kill

(%

)

Track

Intensity

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Intensity Forecasting With Statistical Models

• Statistical Hurricane Intensity Prediction Scheme (SHIPS)– Developed by RAMMB and NHC for Atlantic and east Pacific– Includes ocean and atmosphere predictors in linear regression– Generally the best operational intensity guidance since 2001– GOES and satellite altimeter predictors added in 2004– Companion “Rapid Intensity Index” using discriminant analysis

• Statistical Typhoon Intensity Prediction Scheme (STIPS)– Developed by RAMMB for JTWC– Implemented operationally in 2003

• Continued improvements under Joint Hurricane Testbed, NRL and NASA to NOAA Research to Operations Programs

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Jason/GFO Altimeter TracksUsed in NHC Ocean Heat Content analysis system

(Pre-Katrina case)

Ocean Surface Height Anomaly(Pre-Katrina Case)

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Impact of OHC on SHIPS/STIPS Intensity Forecasts

0

0.5

1

1.5

2

2.5

3

12 24 36 48 60 72 84 96 108 120

Forecast Interval (hr)

Per

cen

t Im

pro

vem

ent

Atlantic

West Pacific

Atlantic Sample: 3072 SHIPS Model Forecasts 1995-2006West Pacific Sample: 311 STIPS Model Forecasts 2006

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OHC Impact Much Larger for Intense Atlantic Hurricanes

-10.0

-5.0

0.0

5.0

10.0

15.0

20.0

25.0

12 24 36 48 60 72 84 96 108 120

Forecast Interval (hr)

Pe

rce

nt

Imp

rov

em

en

t

All Cases

Isabel 2003

Ivan 2004

Emily 2005

Katrina 2005

Rita 2005

Wilma 2005

Improvements in Operational SHIPS Forecasts from OHC for all Recent Cat 5 Hurricanes

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Tropical Cyclone Genesis Product

• Combines GOES WV imagery and GFS analyses to estimate the probability of tropical cyclone formation in the next 24 hours– Discriminant analysis procedure

• Atlantic/East Pacific operational product implemented at OSDPD in 2006 (PSDI project)– http://www.ssd.noaa.gov/PS/TROP/genesis.html

• New version under development for Central/West Pacific

• GOES-R improved product development using MSG data

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TC Genesis Product

Current Product Domain(GOES-E)

New Product Domain(GOES-W, MT-Sat)

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Tropical Cyclone Wind Probabilities

• Utilize Monte Carlo method to estimate probabilities of 34, 50 and 64 kt winds– Uses NHC/JTWC forecast error

distributions from last 5 years

• Operational implementation in 2006– Graphical and text products– NDFD and AWIPS versions

• New JHT project to use model spread to refine track error distributions

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Tropical Product development and RAMSDIS / CIRA and AOML/HRD

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RPP-2 Severe Weather and HazardsRAMMB Historical Approach

• Image interpretation/feature identification

• Display imagery and experimental products on RAMSDIS and Internet

• Provide examples for training

• Little emphasis on quantitative or operational products

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RPP-2 Severe Weather and HazardsCurrent Projects

• Cloud-top structure analysis– Severe weather nowcasting– Current and future satellite applications

• Mesoscale Convective System Index• Synthetic ABI data from RAMS/Radiative Transfer models• Fog and Dust products from current and future GOES

– Principal component analysis approach

• Mid-latitude applications• Satellite cloud climatologies

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Cloud-Top Structure Analysis• A GOES effective radius retrieval has been developed

• Work is currently underway to understand the link between thunderstorm updraft strength and retrieved ice crystal size

• Possible basis for severe weather now-casting product

• Improvements with ABI using proxy data

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• An index has been developed to predict the organization of Mesoscale Convective Systems (MCS)

• The MCS Index uses NAM model output along with data from the GOES Sounder to make 0-84 hour forecasts

• Now running in Real-Time at http://rammb.cira.colostate.edu/projects/mcsindex/mcsindex.asp

• Potential candidate for new NESDIS operational product, pending user feedback

Milestone Significance: Information about MCS organization is extremely useful for severe storms forecasts (SPC, NWS) as well as quantitative

precipitation forecasts (SAB, HPC)

MCS Index and observed IR satellite cloud tops from 3/1/07 (severe weather outbreak in the Southeast)

Mesoscale Convective System Index

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GOES-R Synthetic ABI Data

• High resolution runs from RAMS mesoscale model– Sophisticated cloud microphysics– Resolution as fine as 400 m

• Radiative transfer code for IR and near IR– Gaseous absorption– Condensate optical properties– 1-D scattering

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Synthetic ABI CasesProvide ABI radiances for six case studies, along with the model fields for “ground truth”.

– 1 severe weather case – 1 lake effect snow case – 2 hurricane cases (Lili and Wilma)– 1 Central America case for fire group– 1 case TBD

GOES-12 at 10.7µm GOES-R at 10.35 µm NPOESS VIIRS at 11.02 µm

4 km 2 km 400 m

2 October 2002

Hurricane Lili

12 February 2003

Lake effect snow

8 May 2003

Severe thunderstorms

Figure 5: Synthetic GOES-12, GOES-R, and NPOESS VIIRS.

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Example of Synthetic ABI Data

• Upcoming work: perform principle component analysis on synthetic datasets

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Fire hot spot simulation at 3.9 µm : high-frequency flickering / diurnal damping

5 min interval, 6 hour forecast period

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GOES Advanced Baseline Imager (ABI) Color Product Development:Utilizing MSG (Meteosat Second

Generation)

Don HillgerNOAA/NESDIS/StAR

hillger@cira.colostate.edudon.hillger@noaa.gov

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ABI Simulations:MSG vs. MODIS

• Disadvantages:– Not as many spectral bands (12 vs. 36 MODIS)– Fewer ABI-equivalent bands (11 vs. 14 of 16 ABI)– Slightly degraded spatial resolution (3 km vs. 1 km

MODIS infrared; ABI will be 2 km infrared)

• Advantages:– High temporal resolution (15 min full-disk, 30 second

rapid-scan vs. several hours)– Feature motion can add to products to help

discriminate among image features

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Comparison of GOES-R ABI with MSG (Meteosat Second Generation) bands

GOES-R ABI MSG

Band Number Wavelength (μm) Band Number Wavelength (μm)

1 (blue) 0.47 No Equivalent No Equivalent

2 (red) 0.64 1 (red) 0.635

3 0.865 2 0.81

4 1.378 No Equivalent No Equivalent

5 1.61 3 1.64

6 2.25 No Equivalent No Equivalent

7 3.90 4 3.92

8 6.19 5 6.25

9 6.95 No Equivalent No Equivalent

10 7.34 6 7.35

11 8.5 7 8.7

12 9.61 8 9.66

13 10.35 No Equivalent No Equivalent

14 11.2 9 10.8

15 12.3 10 12.0

16 13.3 11 13.4

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Fog/stratus in Europe: SW Albedo (with colored IR cloud tops*)

* Albedo for temperatures above -30 deg C; color-coded IR for temperatures below -30 deg C

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Fog/stratus in Europe: “Natural” 3-color Product

Red = 0.6 μm; Green = 0.8 μm; Blue = 1.6 μm

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Fog/stratus in Europe: Daytime Fog/Status 3-color Product

Red = 0.6 μm; Green = 1.6 μm; Blue = SW albedo (3.9 μm)

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Dust in West Africa: 3-color Rosenfeld* dust product

* Daniel Rosenfeld, Hebrew University, Jerusalem

Red = 12.0 - 10.8 μm; Green = 10.8 - 8.7 μm; Blue = 10.8 μm

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Dust in West Africa: 3-color PCI* product

* Red = PCI-2/4; Green = PCI-4/4; Blue = PCI-3/4

From 4 bands: 3.9 μm; 8.7 μm, 10.8 μm; and 12.0 μm

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Summary

• Utilize MSG for ABI color product development:– 11 of 16 ABI bands– Spatial resolution (3 km vs. 2 km IR ABI)– Temporal resolution (15 min vs. 5 min ABI)

• New ABI products leveraged on existing products – fog/stratus and blowing dust as product

examples

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1) Atmospheric Rivers (polar orbiting)

2) TROWAL (geostationary)

3) Polar Winds (polar orbiting)

Midlatitude Cyclone Research

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Bao et al. 2006

Atmospheric Rivers

Use temperature and moisture profiles from ATOVS data

50-hPa height from model

Assume hydrostatic atmosphere

Assume balance condition to retrieve nondivergent wind field

Use ω equation and continuity equation to get irrotational wind field

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Trowal (Trough of warm air aloft)

What can be learned about the trowal by studying satellite imagery alongside observations and model analyses?

e.g. Sometimes upper level clouds present over trowal, sometimes not, convective instability

Project generalized to identify satellite signatures of rapid deeping of cyclones east of the Rockies

Martin 1998

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Polar Winds

Retrieval of winds over Arctic

May apply technique to the study of the breakdown of the polar vortex

500 hPa heights and geostrophic winds

GOES Cloud Climatology Studies

By Cindy Combs and Bernie Connell

August 17, 2006

Background of Satellite Cloud Climatologies at CIRASatellite cloud climatologies have been done at CIRA since the advent of digital images. They have both covered the globe (like CHANCES) and concentrated on small regions.

The general concept behind satellite cloud climatologies is simple. • take aligned satellite images over a given period of time• separate them out according to channel and time of day• for each pixel of each image, use a given algorithm to determine clouds.• Calculate statistics for each pixel based on the results and display in image form.

RAMMT/CIRA

Our group has done several projects that have expanded the original method to produce products tailored to the specific needs of individual NWS offices and other users.

However, to have a true climatology, a bare minimum of 10 years worth of data is required, and 30+ years preferred.

- Oldest of wind regime projects.

-Sea breeze effects on convective development

- Covers summer months

-Forecasters designate the wind regime each morning.

-Uses both Visible and IR imagery

- Currently used by the TAE NWS office in their forecasts.

Tallahassee Summer Sea Breeze Regime ClimatologyBernie Connell and Ken Gould

June-August 1996-2006. At the end of this summer we will have eleven seasons of data. Now with AWIPS running at CIRA, we will investigate incorporating this work as a forecast product. RAMMT/CIRA

U.S. Satellite Cloud Climatology Database

Wind Regime:

• Data from CONUS sector is sectorized to smaller area around each station, then divided into wind regimes.

•Wind regimes are defined by the mean boundary level wind (between 1000-700 mb) over a given AWIPS station divided into 9 regimes: 8 compass points plus calm

• Products from channels 1 and 4 includes cloud percent for each regime for each time period

Now in its 10th year, we have a quality controlled database of satellite images to which new data is continually added. This is the basis for the rest of the special US projects.

CONUS Sector:• Images from GOES East and GOES West imager, 4 km resolution.• Includes every hour during day for visible channel; every other hour for channels 2-6• Products includes min, max and average for all imager channels, plus cloud percent from channel 1 (visible) and channel 4 (10.7 μm)

Jan 1999-20021900 UTCSan Francisco Bay

Calm wind, 31 cases SW wind, 24 cases

Current Special Project: Marine Stratus in Eureka, CA

-Work initiated when contacted by Eureka NWS office’s SOO, Mel Nordquist, asking if we could develop climatologies like Tallahassee over his area focused on marine stratus.

- Starting with general, two hourly cloud composites over summer months.

- Working on formats so that composites can be included in office’s GIS system.

- By end of year, will have website features time loops of composites for Eureka personnel to access.

- Future plans include determining regimes that pertaining to marine stratus burn-off.

-Possible first guess in AWIPS for operational grid of cloud cover

Previous Special ProjectsCentral America:

Investigate the transitions between dry and rainy seasons

Wakefield, VA:

Constructed cloud composites from first three years of data to investigated areas of preferred warm season convection. Now with more months of data, would like to go back and investigate bay convection in early fall period.

Cheyenne, WY:

Constructed cloud composites upstream of the Cheyenne area to search for cloud precursors to high wind events in Cheyenne’s CWA. Found area of increased convection lee of Montana Rockies within 12 hours of Cheyenne event.

Monterey, CA:Construct cloud composites to investigate the formation and dissipation of fog and marine stratus. Provided evidence for suspected relationships between pressure difference between stations and timing of marine stratus dissipation.

RAMMT/CIRA

For more information on these projects, see our webpages at:http://rammb.cira.colostate.edu/research/climatology.asp

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RPP-3 Satellite Training

• Virtual Institute for Satellite Integration Training (VISIT) Program– Instructor-led training for NWS and others– 1 to 1.5 hour sessions with operational focus

• Satellite Hydrology and Meteorology (SHyMet) Program– Structured training for NWS Interns– 8 initial modules

• International Activities

Virtual Institute for Satellite Integration Training (VISIT)

•Mission: Accelerate the transfer of research results into NWS operations via teletraining•Participants from NWS, DOD, other countries•63 courses offered•1048 teletraining sessions administered•15,836 certificates of completion issued

http://rammb.cira.colostate.edu/visit

VISIT teletraining developed by the RAMM team (8/02 – 8/06)

• The GOES 3.9 μm channel• Monitoring Gulf Moisture Return with GOES• Utilizing GOES Imagery within AWIPS to

Forecast Winter Storms • Interactive Cloud Height Algorithm and GOES

Sounder Point Retrievals in AWIPS • Use of GOES/RSO imagery with other Remote

Sensor Data for Diagnosing Severe Weather across the CONUS (RSO 3)

• Wildland Fire Detection using Satellite Imagery

• The Satellite Rainfall Hydro-Estimator (Kadin, Kuligowski, Borneman, Hanna, Scofield)

• Experimental Satellite Derived Tropical Rainfall Potential (TRaP) (Kusselson)

• Fog Detection and Analysis with Satellite Data (Ellrod)

VISIT teletraining developed by NESDIS (from DC

location)

VISIT audio playback sessions recently added to the LMS

• Cyclogenesis• Interactive Cloud Height Algorithm / GOES

sounder point retrievals in AWIPS• Mesoanalysis using GOES RSO imagery• QuikSCAT winds• Utilizing GOES Imagery to forecast winter

storms• Monitoring Gulf Moisture Return with GOES

Imagery• Mesoscale Convective Vortices

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National (SHyMet) and International Training Highlights

Bernie Connell

RAMMB

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Satellite Hydrology Satellite Hydrology and Meteorology and Meteorology

http://rammb.cira.colostate.edu/training/shymet/

Main Objective: To prepare NOAA and NWS users for the latest polar orbiting and geostationary satellite data and products in the warning and forecast process.

A new coursecourse dedicated to operational satellite meteorology

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SHyMet “statistics” April 1, 2006 through March 31, 2007

• 107 NOAA registered participants representing ~45 NWS offices

• 14 Non-NOAA

• 62 individuals completed

• 29 Teletraining sessions offered April through June (CIRA: Jeff Braun, Dan Bikos; CIMSS: Scott Bachmeier)

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SHyMet Plans

• Review feedback

• Conduct survey for input for next SHyMet offering

• Offer SHyMet “Advanced”

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International Training

• Close interaction with the Regional Meteorological Training Centers (RMTCs) in Costa Rica and Barbados since 1996

• Initial focus obtaining satellite imagery and provide basic training.

• WMO sponsored 2-week training events– Mitch Reconstruction Project in Central

America• Live International Weather Briefings• High Profile Training Event Oct 2006

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INTERNATIONAL WEATHER BRIEFINGSLive weather briefings using RAMSDIS Online through VISITview server at CIRA: http://hadar.cira.colostate.edu/vview/vmrmtcrso.htmlvoice through Yahoo Messenger

- Bi-lingual: English and Spanish- Occur once a monthParticipants: CIRA, COMET, CIMSS, the International Desk at NCEP, SAB at NESDIS, Antigua, Argentina , Barbados, Bahamas, Bolivia, Brazil, Cayman, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guyana,Honduras, Jamaica, Mexico, Panama, Peru, Paraguay, Trinidad, and Venezuela.

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RAMMB Issues

• Keep GOES and GOES-R funding stable– GIMPAP changes in 2008?

• Help RAMMB with polar satellite support– IGS, NDE, etc – NPOESS has great promise for tropical cyclone

analysis (CrIS/ATMS and VIIRS)– VISIT-type training needed for NPP

• CIRA re-competition pending• Improve science interaction within StAR

– Re-institute branch chief meetings? – Provide longer lead time on admin requests when

possible