Automatic raingauge data often serve as an important input to hydrological and weather warning operations. They are not only fundamental in quantitative rainfall analysis, but also act as the ground truth in warning operation and forecast validation. Quality control is required before the data can be used quantitatively due to systematic and random errors. Extremely large random errors and unreasonably small or false zero values can hamper effective monitoring of heavy rain. Yet both are difficult to detect in real-time by objective means. In an attempt to address these problems, a rainfall data quality-control scheme based on radar-raingauge co-kriging analysis was developed. The important threshold values required in the data quality control of 60-min raingauge rainfall were determined from a detailed analysis of the distributions of rainfall residuals defined as the arithmetic difference and the logarithm of the ratio between a raingauge measurement and its co-kriging estimate. The scheme has been developed and is in real-time use in Hong Kong, a coastal city of about 1100 km 2 area with more than 150 raingauges installed. Geographically, it is located in the subtropics and dominated by heavy convective rainfall in the summer. As a basis of the quality-control scheme, the co-kriging rainfall analysis was shown through a verification exercise to be superior to those obtained by the Barnes analysis and ordinary kriging of raingauge data. The performance of the quality-control algorithm was assessed using selected cases and controlled tests, and was found to be satisfactory, with a high error detection rate for the two targeted types of error. Limitations and operational issues identified during a real-time trial of the quality-control scheme are also discussed.Key words raingauge; rainfall; data quality control; co-kriging; radar; quantitative precipitation estimation Développement d'un système opérationnel de contrôle de qualité des données de précipitations basé sur l'analyse par co-krigeage des données d'un radar et de pluviographes Résumé Les données issues de pluviographes automatiques constituent souvent une entrée importante des systèmes d'alerte hydrologiques et météorologiques. Elles ne sont pas seulement fondamentales pour l'analyse quantitative des précipitations, mais représentent aussi la vérité terrain dans l'élaboration des alertes et la validation des prévisions. Un contrôle de qualité est nécessaire avant que les données ne soient utilisées quantitativement en raison de possibles erreurs systématiques et aléatoires. Des erreurs aléatoires extrêmement importantes, des valeurs beaucoup trop faibles ou de faux zéros peuvent compromettre la surveillance efficace des fortes pluies. Ces erreurs sont pourtant difficiles à détecter en temps réel par des moyens objectifs. Afin de tenter de résoudre ces problèmes, nous avons élaboré un système de contrôle de qualité des données de précipitations basé sur l'analyse par co-krigeage des données d'un radar et de pluviographes. Les valeurs des seuils...
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
Temperatures over Hong Kong have shown a marked increasing trend since the 1970s due to global warming and urbanization, but outbreaks of intense winter monsoon can bring very low temperatures in Hong Kong at times. This study aims at establishing criteria of extreme cold surges that suit the climatological characteristics of Hong Kong. Surges in this study were selected through percentile ranking of three weather attributes of each cold event: the lowest temperature, the largest temperature drop and the maximum sustained wind speed. Out of 152 cold events in 1991–2020, only four significant cold events in 1991, 1993, 2010 and 2016 met the most extreme 10th percentile of the three attributes concurrently and could be classified operationally as “extreme cold surge”. Very cold temperatures (at or below 7.0 °C), a temperature drop of at least 8.0 °C in two days and gale force wind speed (at or above 17.5 m/s) were recorded in all four surges. The results of classification are illustrated by selected cases. As ensemble products of some numerical weather prediction models tend to have a stable indication of extremity of cold events, the potential applications of cross-referencing the forecast and actual extremity in operational forecasting are also discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.