Use of SWIRLS nowcasting system for quantitative precipitation forecast using Indian DWR data

Abstract

Local severe storms are extreme weather events that last only for a few hours and evolve rapidly. Very often the mesoscale features associated these local severe storms are not well-captured synoptically. Forecasters have to predict the changing weather situation in the next 0-6 hrs based on latest observations. The operational process to predict the weather in the next 0-6 hrs is known as “nowcast”. Observational data that are typically suited for nowcasting includes Doppler Weather Radar (DWR), wind profiler, microwave sounder and satellite radiance. To assist forecasters, in predicting the weather information and making warning decisions, various nowcasting systems have been developed by various countries in recent years. Notable examples are Auto-Nowcaster (U.S.), BJ-ANC (China-U.S.), CARDS (Canada), GRAPES-SWIFT (China), MAPLE (Canada), NIMROD (U.K.), NIWOT (U.S.), STEPS (Australia), SWIRLS (Hong Kong, China), TIFS (Australia), TITAN (U.S.) (Dixon and Wiener, 1993) and WDSS (U.S.). Some of these systems were used in the two forecast demonstration projects organized by WMO for the Sydney 2000 and Beijing 2008 Olympic. A common feature of these systems is that they all use rapidly updated radar data, typically once every 6 minutes.The nowcasting system SWIRLS (“Short-range Warning of Intense Rainstorms in Localized Systems”) has been developed by the Hong Kong Observatory (HKO) and was put into operation in Hong Kong in 1999. Since then system has undergone several upgrades, the latest known as “SWIRLS-2” to support the Beijing 2008 Olympic Games. SWIRLS-2 is being adapted by India Meteorological Department (IMD) for use and test for the Commonwealth Games 2010 at New Delhi with assistance from HKO. SWIRLS-2 ingests a range of observation data including SIGMET/IRIS DWR radar product, raingauge data, radiosonde data, lightning data to analyze and predict reflectivity, radar-echo motion, QPE, QPF, as well as track of thunderstorm and its associated severe weather, including cloud-to-ground lightning, severe squalls and hail, and probability of precipitation. SWIRLS-2 uses a number of algorithms to derive the storm motion vectors. These include TREC (“Tracking of Radar Echoes by Correlation”), GTrack (Group tracking of radar echoes, an object-oriented technique for tracking the movement of a storm as a whole entity) and lately MOVA (“Multi-scale Optical flow by Variational Analysis”). This latest algorithm uses optical flow, a technique commonly used in motion detection in image processing, and variational analysis to derive the motion vector field. By cascading through a range of scales, MOVA can better depict the actual storm motion vector field as compared with TREC and GTrack which does well in tracking small scales features and storm entity respectively. In this paper the application of TREC and MOVA to derive the storm motion vector, reflectivity and QPF using Indian DWR data has been demonstrated for the thunderstorm events over Kolkata and New Delhi. The system has been successfully operationalized for Delhi and neighborhood area for commonwealth games 2010. Real time products are available on IMD website

Evaluating pySTEPS optical flow algorithms for convection nowcasting over the Maritime Continent using satellite data

“…However, because of the principle that underlies optical flow -that each pixel maintains its intensity between time stepsit is unable to capture the IGD of convection. Smaller-scale convection exhibits higher rates of change in its evolution (Venugopal et al, 1999) and so has the greatest uncertainty associated with it. Initially, this justifies why the majority of low skill is seen at smaller scales (Fig.…”

Assessments of Use of Blended Radar–Numerical Weather Prediction Product in Short-Range Warning of Intense Rainstorms in Localized Systems (SWIRLS) for Quantitative Precipitation Forecast of Tropical Cyclone Landfall on Vietnam’s Coast

“…The HKO has been developing SWIRLS since 1999 and released the community version named Com-SWIRLS [31,48]. This system has been widely used to provide QPFs, as well as TC-induced rainfall forecasts [49][50][51][52][53].…”

Quadratic Growth Hypothesis, Classification of Cumulus Based On Rate of Growth, Concept of Reaction Time and Cumulus Motion Speed

“…Hence any exercise to control hailstone growth within the cloud must be attempted prior to its embryonic stage. Lower threshold of radar reflectivity of hailstone containing cloud has been well accepted as 45dBZ (Witt 1990;Srivastava et al, 2011) albeit it may be argued to be observed at 52 dBZ, too. Also in areas where there are heavy convective rains, 45 dBZ can also be achieved with no hail present.…”