Two-year assessment of Nowcasting performance in the CASA system

TWO-YEAR ASSESSMENT OF NOWCASTING PERFORMANCE IN THE

CASA SYSTEM

Evan Ruzanski1, V. Chandrasekar2, and Delbert Willie2

1Vaisala, Inc., Louisville, Colorado, USA2Colorado State University, Fort Collins, Colorado,

USA

July 27, 2011

Introduction

DARTS Algorithm System Architecture

Data Results

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


Data Results

Conclusions

Users

MC&C

Data Fusion / Algorithm

Radar Network

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


Data Results

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

DARTS AlgorithmSystem Architecture

Data Results

Conclusions

Intro

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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


Data Results

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


Data Results

Conclusions

Intro

Ingest Merge and Grid

Nowcasting(DARTS)

Radial reflectivity cuts from each CASA radar node

MC&C

LDM

SOCC Intern

et D

isplay

Data


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


Data ResultsConclusions

Intro

Example CASA observation and corresponding 10-min. prediction (web display)

Results



Intro

Example CASA observation and corresponding 10-min. prediction sequences

Results


Data Results

Conclusions

Intro

(a) CSI, (b) POD, (c) FAR, (d) MAE scores for 2009–2010 events

Results


Data 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



Intro

MC&C observation 20090517-0246005 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 Storm motion

Conclusions


Data Results

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


Data Results

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

Sports

Two-year assessment of Nowcasting performance in the CASA system