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

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

Thumb Rule for Nowcast of Dust Storm and Strong Squally Winds over Delhi NCR using DWR Data

ABSTRACTSqually winds are the natural hazards and are often associated with the severe thunderstorms (TS), which mostly affects plains of North West India during pre monsoon season (March to May). Squally winds of the order more than 60 kmph are very devastating. Under influence of these strong squally winds trees, electricity poles, advertisement sign boards fall, sometimes human life is also lost. The main objective of this study is to find out the thumb rule based on Doppler Weather Radar (DWR) Data to Nowcast the squally winds over a region. To detect thumb rule, five cases of thunder storm accompanied with squally winds ranging from (55 kmph to 110 kmph) are taken in to consideration. These TS’s occurred over Delhi NCR (National Capital Region) during May – June 2018. Maximum reflectivity (Max Z) data of Delhi DWR, Cloud Top Temperature (CTT) data from INSAT and squally winds along with other weather parameters observed at Safdarjung and Palam observatories are utilized to find out the Thumb Rule.Based on the analysis, it is concluded that presence of a western disturbance (WD), presence of East-West trough from North-west Rajasthan upto East UP through south Haryana and very high temperature of the order of 40 degree Celsius over the nearby area are very conducive for occurrence of squally winds accompanied with thunderstorms. Thumb rule find out in this study is that, squally winds of the order of 55 kmph or more will effect a station if a thunderstorm (having Max Z echo with vertical extension of cell >7 km, reflectivity >45 dBz and at a distance of more than 100 km from the station) moving towards station is present in one to two hour before images of Doppler Weather Radar.

Impact of radar data assimilation for the simulation of a heavy rainfall case in central Italy using WRF–3DVAR

The aim of this study is to investigate the role of the assimilation of Doppler weather radar (DWR) data in a mesoscale model for the forecast of a heavy rainfall event that occurred in Italy in the urban area of Rome from 19 to 22 May 2008. For this purpose, radar reflectivity and radial velocity acquired from Monte Midia Doppler radar are assimilated into the Weather Research Forecasting (WRF) model, version 3.4.1. The general goal is to improve the quantitative precipitation forecasts (QPF): with this aim, several experiments are performed using the three-dimensional variational (3DVAR) technique. Moreover, sensitivity tests to outer loops are performed to include non-linearity in the observation operators. In order to identify the best initial conditions (ICs), statistical indicators such as forecast accuracy, frequency bias, false alarm rate and equitable threat score for the accumulated precipitation are used. The results show that the assimilation of DWR data has a large impact on both the position of convective cells and on the rainfall forecast of the analyzed event. A positive impact is also found if they are ingested together with conventional observations. Sensitivity to the use of two or three outer loops is also found if DWR data are assimilated together with conventional data.

Impact of radar data assimilation using WRF three-dimensional variational system, for the simulation of a heavy rainfall case in Central Italy

This work is a first assessment of the role of Doppler Weather radar (DWR) data in a mesoscale model for the prediction of a heavy rainfall. The study analyzes the event occurred during 19–22 May 2008 in the urban area of Rome. The impact of the radar reflectivity and radial velocity acquired from Monte Midia Doppler radar, on the assimilation into the Weather Research Forecasting (WRF) model version 3.2, is discussed. The goal is to improve the WRF high resolution initial condition by assimilating DWR data and using ECMWF analyses as First Guess thus improving the forecast of surface rainfall. Several experiments are performed using different set of Initial Conditions (ECMWF analyses and warm start or cycling) and a different assimilation strategy (3 h-data assimilation cycle). In addition, 3DVAR (three-dimensional variational) sensitivity tests to outer loops are performed for each of the previous experiment to include the non-linearity in the observation operators. In order to identify the best ICs, statistical indicators such as forecast accuracy, frequency bias, false alarm rate and equitable threat score for the accumulated precipitation are used. The results show that the assimilation of DWR data has a positive impact on the prediction of the heavy rainfall of this event, both assimilating reflectivity and radial velocity, together with conventional observations. Finally, warm start results in more accurate experiments as well as the outer loops strategy.