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.