World Meteorological Organization Symposium on Nowcasting - WMO

More documents

Recommendations

Info

36 P1.15 2.3 A Bayesian Hierarchical Particle Filter model for storm cell motion forecasting Operational multi-sensor nowcasting of severe convective storms in the Swiss Alpine area A. M. Hering, U. Germann, P. Ambrosetti, I. Giunta, L. Clementi and L. Nisi MeteoSwiss, ML, Locarno-Monti, Switzerland For the automatic detection, tracking and analysis of intense convective cells MeteoSwiss uses operationally the real-time nowcasting system TRT (Thunderstorms Radar Tracking), as part of its severe thunderstorms nowcasting and short-term warning facility. The presentation will focus on the current version of the TRT system including the latest operational improvements in the hail products, and show first preliminary results from two new projects that can be included into TRT.TRT is a multi-sensor system that uses volumetric reflectivity data of multi-radar composites (mosaic) to analyse the 3D storm structure and compute severe weather attributes based on heuristic and centroid-methods, as well as cloud-toground lightning flashes. Gridded fields and cell-based attributes of individual storms like e.g. VIL, Echo Tops, altitude of maximum reflectivity, hail probability, are computed every 5min and are available in real-time to the forecasters as 2D animations and time series. TRT was used in real-time during MAP D-PHASE (a FDP of the WWRP of WMO) and was available to end-users on the project visualisation platform. A “cell severity ranking” product, developed and tested for MAP D-PHASE, was introduced operationally to find and highlight the most dangerous and strongest storms based on their severity. It includes also the computation of a 60 minutes position forecast, based on the motion of individual cells. The uncertainty of the expected position is taken into account increasing the area of the forecasted cell proportionally to the spread (standard deviation) of the velocity vectors from the last three 5min time steps. The cell ranking is successfully used by forecasters and allows them to focus on the most severe cells maintaining situational awareness and to speed-up the decision process of thunderstorms warnings.The latest operational improvement in TRT is the computation of the estimated probability of hail of any size, as well as the maximum expected hail size. These products are based on the difference between the altitude of the Echo Tops at 45 and 51 dBZ, and the freezing level, determined from the high-resolution, nonhydrostatic NWP model for the Alpine region COSMO-2. For a future improvement of the TRT-system we plan to include the results of two initiating international projects. The first will develop an improved extrapolation technique that combines Lagrangian persistence of radar precipitation fields with orographic forcing in complex orography. This information can be used to extend the lead-time of the nowcasting warnings. The second aims to merge severe convection predictors, retrieved from satellites, with the properties of evolving thunderstorms. The satellite based anticipation of convection initiation will be available in form of continuously updated probability maps. This will allow it to improve nowcasting, combining satellite information during the whole thunderstorm cycle, from early detection until dissipation, with the TRT system and to better characterise the different phases of a convective storm. Neil I. Fox [1] Yong Song [2] Christopher K. Wikle [2] [1] Department of Soil, Environmental and Atmospheric Sciences University of Missouri Columbia, MO 65211 [2] Department of Statistics University of Missouri Columbia, MO 65211 A new storm motion forecasting method is proposed that uses a particle filter concept within a Bayesian Hierarchical framework to better predict nonlinear cell motion. The motion of individual cells is predicted using a spatio-temporal model within which the cells are characterized by as having mass, parameterized by maximum radar reflectivity, and are allowed to interact with one another. The model is based on Euler-Lagrangian dynamics and uses a Markov Chain Monte Carlo (MCMC)) based particle filter to assess dynamic state and parameter estimation. This estimation is completed by finding the Bayesian posterior distribution of state locations and model parameters. There are opportunities within the parameterization to utilize additional information such as wind velocity. Tests were conducted on selected cases featuring both linear and nonlinear motion of convective cells where interaction between those cells may or may not be evident. Results show that the scheme is capable of predicting storm cells moving both linearly and nonlinearly while allowing one cell to affect the motion of its neighbors. P1.16 Quality Control of Aircraft Moisture Soundings Seth Gutman [1] and David Helms [2] [1] NOAA Earth System Research Laboratory, Boulder, Colorado USA [2] NOAA National Weather Service, Silver Spring, Maryland USA The growing availability of moisture observations from Tropospheric Airborne <strong>Meteorological</strong> Data Report (TAMDAR) and Water Vapor Sensing System (WVSS-II) sensors installed on commercial aircraft and the ability to rapidly assimilate them into numerical weather prediction models such as the Local Analysis and Prediction System (LAPS) and the Rapid Refresh (RR next generation RUC) provides forecasters with a greatly improved severe weather nowcasting capability. All observing systems, including these, are subject to systematic and random errors. Characterization of systematic errors is a necessary precondition for effective data assimilation, but even mature observing systems such as radiosondes have systematic errors that have largely gone undetected until recently. The detection of random errors is even more difficult unless independent observations with high precision and comparable accuracy are available. This presentation describes a technique to assess the accuracy of aircraft moisture soundings in near real-time. The technique was developed to assess the accuracy of Radiosonde Replacement System moisture soundings made by GPS radiosondes for the U.S. National Weather Service. The method integrates the (calculated or measured) mixing ratio during aircraft ascent or decent and compares it with a Global Navigation Satellite System (GNSS) estimate of total precipitable water vapor made at a 105
104 technique of TREC, and storm locations in the next hour are provided.These algorithms have been applied in the B08FDP. It is found that more than 1500 storms are identified and the mean absolute errors in the X-axis and Y-axis for 30-minute storm forecast are about 7 and 6 kilometers respectively. With the increase of forecast length, the mean absolute errors of the storm product become larger, and the X-axis error is greater than that in the Y-axis. P1.14 A composite approach of radar echo extrapolation based on radar-derived vectors in combination with model-predicted winds Liang Qiaoqian, Feng Yerong, Zeng Qin, Hu Sheng, Wang Ying Guangdong <strong>Meteorological</strong> Bureau, China Extending the lead time of precipitation forecast is vital to operational heavy rainfall warning. Usually an approach for rainfall nowcast that utilizes radar motion vectors to extrapolate radar-observed precipitation echoes provides a relatively poor skill if the lead time is beyond 30 min. This is because the derived radar vectors, i.e., the TREC (Tracking Radar Echo by Correlation) winds, represent only the instant trend of precipitation echo motion. For a longer lead time, the effect that background air flow exerts on echo movement is of importance. In this paper, an extrapolation architecture that extended forecast lead time up to 3 hours was developed to fuse radar-derived motion vectors with model-predicted winds. The TREC vector fields were derived from radar reflectivity patterns over the 3-km CAPPI (Constant Altitude Plan Position Indicator) mosaics through cross correlation technique. The background steering winds were provided by the analyses or predictions of the rapid update assimilation model CHAF (Cycle of Hourly Assimilation and Forecast). A similarity index was designed to determine the level on which model winds were applied to the extrapolation process via a comparison between model winds and radar vectors. Verification showed that the introduction of background steering air flow in the extrapolation provided considerable gain in prediction skill, compared to the radar-only extrapolation technique, in a summer rainfall case that was investigated. 2.4 Thunderstorm risk monitoring service Brovelli Pascal, Etienne Arbogast, Michel Bouzom, Jérôme Reynaud, Frédéric Autones, Yann Guillou, Isabelle Bernard-Bouissières, Stéphane Sénési Météo-France – Forecast Department – Nowcasting Development, 42, av. Gaspard Coriolis 31057 Toulouse France The SIGnificant weather Object Oriented Nowcasting System (SIGOONS) is based on a scheme combining forecaster’s expertise and observation data advanced automated processing ; it is an object oriented system for detection and forecasting significant phenomena at a few hours range. Downstream, SIGOONS feed warnings automated generation. Today, SIGOONS manages thunderstorms only. SIGOONS development follows two streams: o Operating a “fully automated” SIGOONS to produce thunderstorm risk warnings, in order to demonstrate the capability of warnings service for Météo-France customers at the short nowcasting range. At this stage of automation, warnings are limited to a range of one hour o Ensure interaction feasibility and efficiency to match forecaster’s expertise on thunderstorms forecasting, for improving warnings timeliness, intensity and location. The 2009 SIGOONS schedule was populated by the marketing of the thunderstorms warnings service named “Thunderstorm risk monitoring service” and by experiments with the seven regional forecasting services in real-time to assess adding expert value to warnings. Beyond, the goals are to operate thunderstorms expertise routinely using SIGOONS, to improve automation in thunderstorms description using new radar data (3D, doppler, polarization data) and mesoscale numerical weather prediction data, to introduce a probabilistic description of warnings location and intensity, and to manage another phenomena, namely the strong wind events. 2.5 A Decision-support System for Winter Weather Maintenance of Roads, Bridges, and Runways Michael B. Chapman [1], Sheldon Drobot [1], William Mahoney [1], Jim Cowie [1], Seth Linden [1] [1] National Center for Atmospheric Research, Research Applications Laboratory, Boulder, Colorado USA Maintaining control of snow/ice buildup on roadway surfaces during winter storms is challenging for road maintenance entities. Some of the more critical challenges include making effective and efficient decisions for treatment types, timing of treatments, and location of greatest impact to the roadway based on precipitation rates/types and other weather conditions. These decisions are critical because of the implications to roadway safety, as well as economic impacts to the agency and the environmental impacts of treatments. In order to mitigate the challenges associated with winter road maintenance, the United States Department of Transportation (USDOT) Federal Highway Administration (FHWA) initiated the 37
Page 1 and 2: 140 World
Page 3 and 4: 138 3 Programme George Isaac: Chair
Page 5 and 6: 136 5 Welcome participants to the I
Page 7 and 8: 134 SESSION 1: Olympic Nowcasting (
Page 9 and 10: 132 P2.27 Comparison of nowcasting
Page 11 and 12: 130 P2.24 Nowcast of the Rainstorm
Page 13 and 14: 128 P2.20 Nowcast of the Rainstorm
Page 15 and 16: 126 P2.17 Automatic procedures and
Page 17 and 18: 124 P2.14 15:00 7.7 The network of
Page 19 and 20: 122 P2.10 Systematic variation of D
Page 21 and 22: 120 P2.7 Studying on How to Maximiz
Page 23 and 24: 118 P2.5 Impact Analysis on Forecas
Page 25 and 26: 116 a map where this nowcasting exp
Page 27 and 28: 114 same quantities, where the uniq
Page 29 and 30: 112 1.7 P1.25 The development of fu
Page 31 and 32: 110 P1.22 Regional Multiple Doppler
Page 33 and 34: 108 1.11 P1.19 Storm severity nowca
Page 35: 106 location in close proximity to
Page 39 and 40: 102 implementation of the methods a
Page 41 and 42: 100 P1.8 A numerical Case Study on
Page 43 and 44: 98 Very short-time quantitative pre
Page 45 and 46: 96 POSTER SESSION 1 P1.1 Assimilati
Page 47 and 48: 94 47 3.7 8.9 THE GOES-R SATELLITE
Page 49 and 50: 92 8.6 Progress Toward Satellite-ba
Page 51 and 52: 90 3.12 8.2 Translating Ensemble We
Page 53 and 54: 88 7.7 The network of EUMETCast sta
Page 55 and 56: 86 7.5 An Operational Perspective f
Page 57 and 58: 84 provide guidance on the types of
Page 59 and 60: 82 (fingerprint) in the analysis pr
Page 61 and 62: 80 4.5 6.8 Towards the Blending of
Page 63 and 64: 78 6.6 Impact of local observations
Page 65 and 66: 76 4.10 Nowcasting flash floods in
Page 67 and 68: 74 5.10 The 0-8 Hour Collaborative
Page 69 and 70: 72 optionally be applied through so

World Meteorological Organization Symposium on Nowcasting - WMO

Create successful ePaper yourself

Delete template?

Save as template?