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FY10 GOES-R3 Project Proposal Title Page•

Title: Transitioning GOES-Based Nowcasting Capability into the GOES-R Era

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Project Type: Product development Proposal

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Status: Renewal

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Duration: 3 years FY08 - FY10

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Leads: Stephan Smith, Mamoudou Ba, NOAA/NWS Meteorological

Development Laboratory (MDL), Robert J. Kuligowski, NOAA/NESDIS

Center for Satellite Applications and Research (STAR), Robert Rabin, NOAA/OAR National Severe Storms Laboratory (NSSL), Valliappa Lakshmanan, CIMMS, Reza Khanbilvardi, Arnold Gruber, Shayesteh Mahani and Brian Vant-Hull, NOAA/CUNY Cooperative Remote Sensing Science and Technology Centre (CREST)

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Other Participants: Interactions with Eumetsat Nowcasting SAF and French developers of RDT from Météo-France

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2. Project SummaryGoal is: transition of satellite based nowcasting capability into the GOES-R era

This work involves:•

Evaluating nowcasting models for the US using GOES Data. New York Metropolitan area is selected as the test-bed. Models being studied at NOAA-CREST include:

» Rapidly Developing Thunderstorm (RDT): operational model used by Eumetsat SAF» Segmotion: operational clustering and tracking algorithm in use at NSSL» SatCast: Convective initiation detection in use at CIMMS and UAH

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Enhance nowcasting models for use with GOES-R•

Coordinate with AWG in evaluating candidate Day-1 nowcasting algorithms for GOES-R

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Support AWG via research and development of enhanced Day-2 rainfall estimation and nowcasting algorithms (SCaMPR)

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Research enhancements include:» Extrapolation of storm development into the future» Inclusion of lightning data,» Use of NWP wind shear and convective products, and ABI upper level divergence to

improve nowcasting of development and decay of storms,» Improvements to rain rate algorithms for nowcasting storms.

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3. Motivation/Justification•

Supports NOAA Mission Goal: Serve society's needs for weather and water information.

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The objective of the work proposed here is to support the work of the AWG for a Day-1 nowcasting model and develop improved GOES R nowcasting algorithms for Day 2 and beyond by:» Assisting in the algorithm selection process by producing output from

the RDT and other nowcasting algorithms as needed ( e.g. HN) for use in the Hydrology Algorithm Team intercomparison.

» Conducting research to support further improvements to the algorithm beyond the initial version that will be presented to the GOES-R Prime Contractor in early 2009.

• Serve as nowcasting testbed for NOAA/MDL’s work in implementing satellite based nowcasting into System for Convection Analysis and Nowcasting

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4. Methodology•

Use lightning and precipitation radar data as validation for nowcasting models with GOES-R inputs.

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Evaluate the potential impact of additional inputs into nowcasting algorithms, including:» Data from the GOES Lightning Mapper (GLM)» Wind shear products and convective products from the ABI» Derived upper-level divergence

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Investigate potential improvements to the Version 1 rainfall rate algorithm by using nowcasting tools to create Lagrangian variables (growth and cooling rates) for use as precipitation predictors.

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Use lifecycle studies to produce extrapolation of storm cells.

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Test bed sites will be centered on the NYC metropolitan area, Norman (OK), and Reno (NV)

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5. Summary of Previous Results•

Contact established with RDT developers at Meteo-France and operational staff at EUMETSAT

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The Hydro-Estimator and Hydro-Nowcaster models from NESDIS and RDT model from Météo-France/EuMetSat were installed at CREST.

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RDT has been modified to use GOES-IR instead of MSG data as input

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RDT installed on site with implementation of 24×7 web based for NYC metro-area using real time GOES-IR from CREST satellite receiver antenna.

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RDT Performance Evaluations to Detect:- convective initiation and growth rate, using SatCast comparison- early convective initiation, using cloud to ground lightning data &

precipitation, - small and/or under 15 minute convective cells, using GOES rapid scan

data (7.5 minutes)

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5. Summary of Previous Results (Cont.)•

Initial studies performed to evaluate the possibility of using RDT as the basis for a rainfall detection algorithm.

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Software has been developed to display Lightning Mapper Array (LMA) data in real time.

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A lifecycle study of the convective tower elements selected by RDT demonstrated that on an individual basis they did not exhibit the regular behaviour of mesoscale convective clusters. (Stable results using averaged lifecycle data is described later).

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Cloud cell life cycle analysis that is continuing using both RDT and WDSSII segmotion trajectories.

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Produced documentation and training materials that can be used to train students to run the RDT thunderstorm identification algorithm.

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Assembled satellite, radar and SPC model data for study of full life cycle of convective storms. ( Rabin, NSSL).

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6a. Expected FY10 Outcomes•

A peer-reviewed journal publication about RDT-SatCast comparison.

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A peer-reviewed journal publication about RDT Cloud Lifecycle studies and application to extrapolation and nowcasting.

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Incorporate new precipitation variables (e.g., cell growth rates) into SCaMPR for improving precipitation products.

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Apply the results of cloud lifecycle study and identified correlated variables to forecast precipitation.

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Incorporate results into enhanced nowcasting model and evaluate for test sites and nationwide.

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RDT Lifecycle Studies (directed undergraduate research)

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Lifecycle studies of the RDT cells are largely complete. Cells of similar lifetime have been grouped together, averaged, and Gaussian functions of time fit to the results. The curve fitting will be used for extrapolation of cell development. Results show that the area extrapolation is fairly stable, but the parabolic position extrapolation is not.

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Began design of software to evaluate the quality of the RDT extrapolation.

6b. FY10 Accomplishments & Results

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6b. FY10 Accomplishments & Results (Cont) Satcast-RDT Comparison

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Rebuilt some of the code for the RDT-SatCast comparison (lost by stolen computer), specifically to ingest rainfall data, and to output summary trajectories including the rainfall.

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Precipitation was added as validation. RDT contours with SatCast convective detection does better at predicting rainfall than lightning. With pure RDT convective detection, lightning is predicted with better skill. This is expected as SatCast was calibrated against radar echos. Imagery that shows SatCast convective indications tend to appear in highly textured cloud masses, while RDT detects large plumes.

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Performed test of shorter RDT towers, but found an insignificant change in the number of SatCast pixels inside the RDT contours.

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6b. FY10 Accomplishments & Results (Cont.) Segmotion Lifecycle Studies

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Installed Segmotion at CREST

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Created code (unix, C, WDSII) to calculate cooling rates on a pixel basis using the SegMotion algorithm. The code will be ingested into the SCAMPR (Self CAlibrating Multivariate Precipitation Retrieval) rainfall algorithm to see if the extra information improves the skill of precipitation estimation.

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Developed code to ingest rainfall (national mosaic NMQ) and lightning (NDLN) data into the segmotion output for lifecycle studies. This will be needed until the NSSL output is usable. Currently the NSSL output uses 10 degree bins that are too coarse to capture convective initiation.

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Completed code for segmotion lifecycle study using operational output. As noted above, this will not be useful until the algorithm temperature bins are retuned.

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6b. FY10 NSSL Accomplishments &

Ongoing•

Enhancements were made to time-series of variables associated with convective cloud tracks using the k-means technique developed by V. Lakshaman.

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New parameters include mean cloud top temperature, precipitation rate from radar, and precipitable water. These parameters are being used to develop a statistical algorithm for storm intensification and dissipation at CREST.

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Provided time-series plots of environmental parameters along storm tracks (including upper-level divergence, deep layer shear, and CAPE) for sample storms, by B. Rabin.

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Developed on-line web page to facilitate qualitative comparisons of hourly rainfall from radar and forecasts from the \RUC HRRR (high resolution rapid refresh model, 3km) to be used for comparison with the developed Nowcasting model, by B. Rabin.

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6c. FY10 Ongoing & Future DirectionsSatcast-RDT Comparison & Lifecycle Studies

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Prepare RDT-SatCast and RDT lifecycle studies for publication.

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Break down RDT SatCast comparison by individual convective indicators, as opposed to the sum. This will pinpoint why RDT fails to capture convective initiation.

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Design temperature bin array for use by the NSSL algorithm that can capture thunderstorm development with no significant increase of computation time. This will be used to continue the lifecycle study until acquisition of a new computer allows finer resolution.

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Identification of Lagrangian variables from lifecycle study that correlate to precipitation.

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Apply results of the lifecycle study to forecast precipitation based on the life history of a cell.

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6c. FY10 Ongoing & Future Directions•

Segmotion lifecycle study results will be evaluated for nowcasting precipitation and lightning.

• Discussions are underway to combine 3-D wind fields estimated from WSR- 88D Doppler radars using the VDRAS assimulation technique (NCAR) with GOES-R wind and stability products (CIMSS and CIRA) to improve diagnosis of convective storm initiation and evolution

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Incorporate new precipitation variables (e.g., cell growth rates) into SCaMPR for improving precipitation products.

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Incorporate results into enhanced nowcasting model and evaluate for test sites and nationwide.

Cloud Tracking Extrapolation •

Develop a stable spatial extrapolation scheme so the algorithm can be presented on the web site.

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Positional extrapolation based on NWM winds will be explored

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Outline•

Eumetsat RDT: tracking and extrapolation of storms

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RDT, SatCast, and Convective Initiation

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Segmotion (Kmeans), lifecycles and environmental parameters

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Cross collaborations

Goals and Approach•

Use the best ideas from existing algorithms to produce a nowcasting suite:- Convective Initiation: Satcast- Tracking: RDT and SegMotion- Mature thunderstorm detection: RDT- Extrapolation to the future: FORTRACC

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Apply lifecycle analysis to nowcasting precipitation/lightning.

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RDT Lifecycles

Gaussians fitted to the average areas and tower heights as a function of time: cells of similar lifetimes grouped together. We see monotonic pattern up to 2 hr lifetimes.

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Applyingextrapolation: FORTRACC

method.

Parabolic position doesn’t work so well at times. Other approaches are under consideration.

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Area

Time

Precipitation is also part of a storm’s lifecycle…..

Area and growth rate uniquely define storm intensity and where it is in its lifecycle, presumably including precipitation. Recent work by Delgado et al show good correlation of total rain to area growth rate and cooling rate.

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RDT and Precipitation

~ 50% of heavy precipitation falls outside RDT tower structures.

White < 243 K

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RDT versus Satcast Convective Detection: May 5-10, 2009

RDT tested with Lightning/Precipitation (35 dbZ)Hits: 83/30 Misses: 301/1642 False Alarms: 65/118

POD: 22% / 2% FAR: 44% / 80%

RDT detection leads event in 5/8 cases, avg lead time: 74/111 minSimultaneous: 34/6 cases, avg 53/89 minutes into storm lifecycleRDT detection lags event in 44/16 cases, avg lag time: 57/63 min

SatCast/RDT tested with Lightning/Precipitation (35 dbZ)Hits: 62/286 Misses: 322/1386 False Alarms: 476/252

POD: 16% / 17% FAR: 88% / 47%

SatCast detection leads event in 12/74 cases, avg lead time: 37/47 minSimultaneous: 15/92 cases, avg 19/16 minutes into storm lifecycleSatCast detection lags event in 35/120 cases, avg lag time: 58/39 min

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Satcast and Convective Initiation

Top of water vapor layer Ti

Initiation layerTi +5

Ti +20

8 convective initiation indicators, including presence in initiation layers and growth rates

Freezing level

Ti -5

Ti -20

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Satcast can be used to find which pixels have convectively active characteristics, and then see which types of behavior fall inside the RDT contours.

Below, pixels with relevant brightness temperatures and cooling rates are classified by their RDT contour areas.

Satcast

and RDT

Green pixels:Satcast

Red:RDT storms

Orange:RDT regular

Satcast detected pixelswithin 20° C below water vapor level

Outside the contours: 0 areaInside the contours, by area

Satcast detected pixelswith significant cooling rate

Outside the contours: 0 areaInside the contours, by area

Area => Area =>

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Current Evaluation of RDT

• Good job tracking storms.• Moderate job detecting developed storms as identified by

lightning (new version will do better!).• Poor job detecting initiating/newly developed storms.• Poor job predicting heavy rain: half falls outside the

contours.

The failures are due mainly to the initial detection of clouds via tower structures, less to subsequent classification of detected clouds as storms.

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Segmotion and Environmental Bins

The Segmotion algorithm (aka ‘Kmeans’) is running operationally at NSSL. It tracks cloud patches, calculates statistics, and associates cloud patches with data from weather model analysis, radar, and lightning. Many of these variables were added for this project.

We group trajectories of similar lifetime together, then use the model data to stratify trajectory averages by the following environmental parameters:

• Total Precipitable Water• Wind Shear• CAPE

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Segmotion Lifecycle Averages

Overall Averages

area

lightning

precip

High Cape

Mid Cape

Low Cape

AREA

High Cape

Mid Cape

Low Cape

Precipitation

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Needed Adjustments to Operational Segmotion

Standard BT view with RDT contoursCurrent Segmotion View with 10 K temperature bins

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NSSL Storm Tracker Enhancements

Upper level divergence and lightningPrecipitation and Precipitable waterPrecipitation and Wind speed (shear)return

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Crossover with AWG: Precipitation

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Wrote algorithm based on Segmotion to calculate cooling

rates for input to AWG precipitation algorithm (SCAMPR).

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Have contracted to evaluate how scale of the segmotion

algorithm affects extrapolation of the AWG precipitation

product.

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Summary of FY2010 Results

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A Gaussian extrapolation scheme based on lifecycle statistics is being evaluated for the RDT algorithm

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Due to the reliance on cloud tower structures, RDT does not do well with convective initiation or precipitation estimation

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A lifecycle study based on environmental parameters is underway with segmotion to be applied to nowcasting of precipitation/lightning, but awaits a more optimal selection of temperature bins.

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Work is being done with AWG to improve the precipitation estimation and extrapolation algorithms.

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7. Proposed FY11 Outcomes

• Nationwide web product of enhanced nowcasting algorithm, including extrapolation of storm position, size, precipitation and lightning.

• Incorporation of satellite wind and stability products into the VDRAS technique, which currently combines radar data with a high resolution numerical model to obtain 3D winds and thermodynamic structure of individual storms.

• Evaluate object based variables (such as cell volume growth rate) for use in the GOES-R operational precipitation algorithm.

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8. Funding Profile ($K)

9. Budget Profile ($)

$130,000$700

$10,000$119,300

FY10

NESDISNSSLCREST

Expected Budget

TOTAL BUDGET

0KOther Sources

Funding Sources Purchase Items FY10

(See next slide) 130KGOES-R3

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10. Supplemental Information•

Coordination with other Groups» Interaction with the developers of RDT in Meteo-France

continues.

» Dr. Bob Rabin from NSSL has visited CREST several times and has helped students with MCIDAS and precipitation data.

» Coordination with other groups and GOES R work comes through different team members, e.g.,

– Auto-nowcaster- Dr. Ba, (MDL)

– GOES R rainfall algorithms, SCaMPR, Dr. Kuligowski (NESDIS)– GIMPAP developments, Dr. Rabin (NSSL)– FORTRACC comparisons with Daniel Vila (CICS)– SatCast comparison with Dr. John Mecikalski (UAH & NSSTC)– K-means tracking, Dr. Valliappa Lakshmanan (CIMMS)