TWO-YEAR ASSESSMENT OF NOWCASTING PERFORMANCE IN THE CASA SYSTEM Evan Ruzanski 1, V. Chandrasekar 2, and Delbert Willie 2 1 Vaisala, Inc., Louisville, Colorado, USA 2 Colorado State University, Fort Collins, Colorado, USA July 27, 2011

Introduction DARTS AlgorithmSystem ArchitectureDataResults Conclusions Intro The Collaborative and Adaptive Sensing of the Atmosphere radar network uses nowcasting in a distributed closed-loop system –For emergency decision support (1–20 min lead time) –For radar scanning adaptation (1–5 min lead time) Can nowcasting be done operationally in a geographically distributed processing environment? –Fast radar data update cycles (1 min) –An efficient algorithm is needed for motion estimation

Introduction DARTS AlgorithmSystem ArchitectureDataResults Conclusions Overarching CASA - Distributed Collaborative Adaptive Sensing (DCAS) Concept Intro Meteorological Command and Control steers radars to scan when and where user needs are greatest NWS forecasters, emergency managers, researchers

Introduction DARTS AlgorithmSystem ArchitectureDataResults Conclusions Intro NWS forecasters evaluate CASA data E. Bass and B. Philips, CASA researchers CASA data and products being used at the NWS Norman, Oklahoma, forecast office

DARTS Algorithm System ArchitectureDataResults Conclusions Intro Discretize the general continuity equation using FFT Formulate linear system Solve linear system; recover motion estimates using IFFT E. Ruzanski, V. Chandrasekar, and Y. Wang, “The CASA Nowcasting System,” J. Atmos. Oceanic Technol., vol. 28, no. 5, pp. 640–655, 2011.

System Architecture DARTS Algorithm System Architecture DataResults Conclusions Intro Real-time data transfer via LDM Data processing/nowcasting software development and evaluation The CASA radar network (KSAO, KCYR, KLWE, KRSP) System Operations Control Center (SOCC) and Meteorological Command and Control (MC&C) Nowcasting system operation

System Architecture DARTS Algorithm System Architecture DataResults Conclusions Intro Ingest Merge and Grid Nowcasting (DARTS) Radial reflectivity cuts from each CASA radar node MC&C LDM SOCC Internet Display

Data DARTS AlgorithmSystem Architecture Data Results Conclusions Intro Verification was done using reflectivity data from 24 weather events collected during the CASA IP1 experiments Feb. 2009–May 2010 –Avg. duration of each event was ~3 hrs. (total ~95 hrs.) –Data set includes a wide range of precipitation types (super-cellular, quasi-linear, multi-cellular events) Ground clutter filtering and attenuation correction were applied at each radar node Data were gridded to 1-km AGL CAPPIs covering a +/- 70 km area with avg. resolution of 0.5 km/1 min. using a 20 dBZ threshold

Results DARTS AlgorithmSystem ArchitectureData Results Conclusions Intro Example CASA observation and corresponding 10-min. prediction (web display)

Results DARTS AlgorithmSystem ArchitectureData Results Conclusions Intro Example CASA observation and corresponding 10-min. prediction sequences

Results DARTS AlgorithmSystem ArchitectureData Results Conclusions Intro (a) CSI, (b) POD, (c) FAR, (d) MAE scores for 2009–2010 events

Results DARTS AlgorithmSystem ArchitectureData Results Conclusions Intro Forecaster feedback on adaptive scanning was positive –1 min update rates is important Steering using the latest observation vs nowcasting has drawbacks –Sector scans can be too narrow –Important areas of the storm are missed Forecaster feedback suggested steering using nowcasting eliminated sector scanning issues

Results DARTS AlgorithmSystem ArchitectureData Results Conclusions Intro MC&C observation showing steering using previous observations (left) vs steering using previous observations and 5-min. DARTS nowcasts (right). The leading edge of the storm cut-off on the left. Leading edge missed without nowcasting Leading edge observed with nowcasting support Storm motion

Conclusions DARTS AlgorithmSystem ArchitectureDataResults Conclusions Intro Nowcasting has been successfully demonstrated in the CASA system –Nowcasting 0–20 min is beneficial for emergency decision-making support –Nowcasting 1–5 min is used to set up the radar network scanning strategy Computational efficiency is a key concern given the high resolution of the data and distributed nature of the system –The DARTS algorithm estimates storm motion using LLSE in the Fourier domain

Conclusions DARTS AlgorithmSystem ArchitectureDataResults Conclusions Intro Approximately 95 h (5700 frames) of data from Feb. 2009–May 2010 were used for evaluation Quantitative and qualitative scores were favorable –CSI, POD, FAR and MAE scores showed nowcasting consistently outperformed a persistence forecast –Forecaster surveys suggested steering using nowcasting eliminated sector scanning issues