Static and dynamic quality index (QI) maps are generated as the base products of Doppler weather radar (DWR). The quality characterization of radar reflectivity and radial velocity in terms of their QI is presented for the operational DWRs in India. A static composite QI has been generated using the maximum method. These static maps enable the detection of a low QI region in advance for the Indian radars. The QI of reflectivity is above 0.5 in all regions except in the regions of blockage, high attenuation due to rain, and beam broadening, whereas the QI of radial velocity is good for values >0.8 except for the ambiguous region and the region affected by nonmeteorological echoes. This shall help in the quick preprocessing of radar observations, since the regions of low QI can be masked. A sample case of gridded radar rainfall is presented by employing the QI scheme.
This study demonstrates the data assimilation perspective of vertical profiles of winds derived from the Doppler weather radar (DWR) data using the volume velocity processing (VVP) technique. The VVP information from the nine Indian DWR stations is used in this study. The winds from the Indian DWR network are assessed for their quality based on the National Center for Medium Range Weather Forecasting unified model (NCUM). This paper describes the quality of DWR VVP winds, preprocessing of VVP wind data, and their use in NCUM 4 D-Var assimilation systems. The VVP winds are compared against an NCUM background (short forecast) to understand the observation bias. Comparison of VVP winds is also made with colocated radiosonde wind observations. The VVP winds show less bias when compared against model background especially in the region of strong wind flow. The correlation between the observations and the model background is greater than 0.7 for most of the radars. The VVP winds provide reasonably accurate estimates of the vertical wind structure in the troposphere over radar locations, which can be effectively used in the numerical weather prediction system.
In the northern region of India, hailstorms are common phenomena during winter. Four cases of hailstorms around Delhi (28° 58975′ N, 77° 22195′ E) region during winter have been analyzed in the present study. It is mainly based on the observations of polarimetric radar variables (ZH and ZDR), as observed by Delhi C-band Doppler weather radar and supplementary thermodynamic variables (CAPE, CIN, wind shear). The thermodynamic properties of the atmosphere during the hailstorm events have been studied using radiosonde observations. The hailstorms over the study region are classified into two types with and without large CAPE. Although ZH is higher for all events, the ZDR differs for the two categories. The events with small CAPE and strong shear produce storms with larger ZDR (rain mixed with small hail), while those with large CAPE and weak shear produce smaller ZDR (strong hail).
Doppler Weather Radar (DWR) can provide tropospheric wind observations with high temporal and spatial resolutions. The Volume Velocity Processing (VVP) technique is one of the processing methods which can provide vertical profiles of mean horizontal winds. The DWR observed VVP winds gives a continuous observation of the wind field at various atmospheric levels. The quality of the VVP winds is studied against the short-range forecast of the NCUM model (model background). The biases of the observation are calculated against model background. This study focuses on the quality of VVP winds and seasonal variation of bias of the observed wind. This results shows that the VVP winds provides reasonably accurate estimates of the vertical wind structure in the troposphere over radar locations which can be effectively used in the numerical weather prediction system.
A tool for the entire Indian weather radar network using the static composite QI (Quality Index) map is generated. Various customized modules are used for this generation of the radar mosaic. The characterization of quality of DWR (Doppler weather Radar) data in terms of their QI is essential for assimilating the data into NWP (Numerical Weather Prediction) models. The static QI maps give a quick overview about the inherent errors in the DWR data. Quality control algorithms are applied for the generation of composite QI. The near real time access to the DWR data at NCMRWF enables the generation of an accumulated gridded radar rainfall product. This gridded rainfall map is useful for generating products like high resolution rainfall product, QPE (quantitative precipitation estimate) and for other applications. Results of some case studies shall be presented.