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Thunderstorm Nowcastingat NOAA-CREST
Presented by Brian Vant-Hull, Robert Rabin
CREST team: Arnold Gruber, Shayesteh Mahani, Reza Khanbilvardi
CREST Students: Nasim Norouzi, Bernard Mhando
NOAA Collaborators: Mamoudou Ba, Robert Kuligowski, Stephan Smith
Meteo-France Contributors: Frederic Autones, Stephane Senesi
Outline
• Principles of satellite identification of thunderstorms
• Tracking and forecasting• Comparison of two identification
algorithms• Ideas for improving detection• Ideas for improving forecasting • Object-based Rainfall Estimation
Identifying Storms by Satellite
• Rapid growth: vertical and horizontal
• Rounded tops
• High local variability in cloud top altitudes.
Storm Trajectories: Past and Future
Object based: past motion predicts future motion.
Field based: future motion predicted by surrounding motion.
Comparison of Two Storm Identification Algorithms
Rapidly Developing Thunderstorm (RDT) model:
• Developed at Meteo-France, used operationally throughout Europe.• Identifies and tracks individual thunderstorms.• Does not project future development of storms, but provides timelines and statistics that may be used for that purpose.
Hydro-Estimator (HE) model:
• Developed at NOAA/NESDIS, run operationally on site.• Estimates precipitation based on local spatial cloud statistics.• HE is the core of a nowcasting module that projects the development of precipitation fields.
The RDT model
Cell detection
Tracking by cell
Identify storms by growth,roundness of tops.
Hydro-Estimator/Nowcaster
x
Precipitation at each pixel calculated by local brightness temperature and local statistics at two areas.
Precipitation features extrapolated by field flow, preserving growth trends.
Comparison: July 27, 2005
RDT Contours + HE rainfall RDT Contours + Radar rainfall
Comparison: Aug 21, 2004
RDT Contours + HE rainfall RDT Contours + Radar rainfall
Improving Storm Detection 1
Water vaporchannel
Visiblechannel
Overshooting tops stand out more in water vapor imagery.
Improving Storm Detection 2
Upper air divergence can often be detected in water vapor images.
Improving Storm Forecasting 2 Correlated K-means
Correlated K-MeansDeveloped by V. Lakshaman
Forecast from image 1 hour agoForecast from image 1 hour agoForecast from image 1 hour agoForecast from image 1 hour agoForecast from image 1 hour ago
Both for comparisonOriginal at 2325Red is convectiveOrange is frontalPurple is split
One hour extrapolation2225 => 2325green is convectiveyellow is frontalpurple is splitRDT run only onExtrapolated images
Unmatched green circle must come from time mismatch.
K-meansextrapolation
with RDT contours
Longer Term Storm Forecasting
1
A
dA
dt
Storm development in the tropics follows a fairly predictable pattern which is easily extrapolated. Is this also true for temperate zones?
growth > > > > > > maturity > > > > > > > > decay
FORTRACC Modeldeveloped by Daniel Vila, used operationally in Brazil
15 minute forecast 30 minute forecast 45 minute forecast 60 minute forecast 75 minute forecast 90 minute forecast105 minute forecast120 minute forecast
Forecasting Convective Initiation
4 3 2 1 0
Elevation (km
)
-20 -10 0 10 20 30Temperature (C)
If sufficient moisture is added to bottom of an otherwise stable layer, it can
become Absolutely Unstable.
Moist
Dry
Predicting such situations is possible by numerical models, but recent work by Ralph Petersen at CIMMS has demonstrated simpler, observation based approaches.
Using Cell Tracking to Improve Precipitation Estimates
• Previous satellite based precipitation estimates have used fixed grids, so that growth rates are a combination of actual growth plus cloud motion.
Tracking clouds provides true growth rates and geometrical structure of cells.
Cooling rate
area
Create Improved Precipitation Rate Tables
Map the Distribution ofPrecipitation Inside Cells
CREST CCNY Satellite Direct Feed
• Reduces processing and distribution time• Allows customized data products
Summary• We are at the beginning of a multi-year project to
produce thunderstorm nowcasting for the New York area.
• We are in the testing phase to determine the best parts of existing models to combine for our own model.
• The tracking algorithm may be used to improve physical variables used for precipitation estimation.
• A direct satellite feed increases the utility of the eventual product, which will be made available via the web.