Thunderstorm Nowcasting at 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 rainfallRDT Contours + Radar rainfall

Comparison: Aug 21, 2004 RDT Contours + HE rainfallRDT Contours + Radar rainfall

Improving Storm Detection 1 Water vapor channel Visible channel 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-Means Developed by V. Lakshaman Forecast from image 1 hour ago

Both for comparison Original at 2325 Red is convective Orange is frontal Purple is split One hour extrapolation 2225 => 2325 green is convective yellow is frontal purple is split RDT run only on Extrapolated images Unmatched green circle must come from time mismatch. K-means extrapolation with RDT contours

Longer Term Storm Forecasting 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 Model developed 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 forecast 120 minute forecast

Forecasting Convective Initiation Elevation (km) Temperature (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 areaarea Create Improved Precipitation Rate Tables Map the Distribution of Precipitation 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.