Aiding Severe Weather Forecasting Valliappa.Lakshmanan@noaa.gov National Severe Storms Laboratory Norman OK, USA http://www.wdssii.org/

What is WDSS-II? The Warning Decision Support System – Integration Information (WDSS-II) An integrated set of loosely coupled tools for: Severe weather diagnosis A collection of meteorological algorithms for severe weather analysis, diagnosis and prediction Hail, tornadoes, wind, lightning, storm tracking Image processing Statistical validation Ground-truth verification Users chain the tools together to accomplish their tasks. 21 February 2019 lakshman@ou.edu

WDSS-II algorithms WDSS-II algorithms are essentially data filters Takes some data as input Produces new data as output One can specify the scientific/validation processing in the middle Without having to worry about data ingest, data formats, notification, etc. But provide a library of common computations on the typical data used. 21 February 2019 lakshman@ou.edu

WDSS-II in the forecast office How can WDSS-II help at the forecast office? New algorithms Interactive 4D display capabilities Multi-sensor case studies Post-event validation Continuous learning WDSS-II may not be the “official” solution But the official solutions should draw on the lessons we have learnt. 21 February 2019 lakshman@ou.edu

Single-radar/Multi-sensor algorithms Some single-radar (multi-sensor) algorithms in WDSS-II 21 February 2019 lakshman@ou.edu

Multi-radar/multi-sensor algorithms A typical multi-radar deployment of WDSS-II 21 February 2019 lakshman@ou.edu

WDSS-II in the forecast office How can WDSS-II help at the forecast office? New algorithms Interactive 4D display capabilities Multi-sensor case studies Post-event validation Continuous learning 21 February 2019 lakshman@ou.edu

Display The WDSS-II display WDSS-II tools exist for Provides 4D analysis capabilities Interactive slicing and dicing Can display all kinds of products Configurable and extensible Not tied to particular sites, product codes or times. On Linux and Windows WDSS-II tools exist for Export to GIS, image and spread-sheet formats 21 February 2019 lakshman@ou.edu

WDSS-II in the forecast office How can WDSS-II help at the forecast office? New algorithms Interactive 4D display capabilities Multi-sensor case studies Clustering and storm tracking Storm-attribute trends Post-event validation Continuous learning 21 February 2019 lakshman@ou.edu

Motion Estimation Uses K-Means clustering and Kalman filters 30 min 30 min Actual dBZ Forecast dBZ 21 February 2019 lakshman@ou.edu

Need for new approach Traditional centroid tracking Accurate at small scales, but not at large scales Inaccurate when storms merge or split Possible to extract trends from the information Flow-based tracking Cross-correlation, Lagrangian methods, etc. Are accurate at large scales, but not at small scales Not useful in decision support because trends of storm properties can not be extracted 21 February 2019 lakshman@ou.edu

K-Means clustering K-Means clustering is a hybrid approach Cluster the input data to find clusters Like centroid-based tracking methods But at different scales. Track the clusters using flow-based methods (minimization of cost-functions) Like flow-based methods Does not involve cluster matching (e.g: Titan) 21 February 2019 lakshman@ou.edu

Example clusters Two different scales shown Both scales are tracked 21 February 2019 lakshman@ou.edu

Extrapolation Smooth the motion estimates spatially using OBAN techniques (Gaussian kernel) temporally using a Kalman filter (assuming constant velocity) Repeat at different scales and choose scale appropriate to extrapolation time period. 21 February 2019 lakshman@ou.edu

WDSS-II in the forecast office How can WDSS-II help at the forecast office? New algorithms Interactive 4D display capabilities Multi-sensor case studies Clustering and storm tracking Storm-attribute trends Post-event validation Continuous learning 21 February 2019 lakshman@ou.edu

Trends The clusters can be used to extract trends of any gridded field. Configurable to extract minimum, maximum, count, sum, time-delta, etc. of gridded fields within cluster Even fuzzy combination of multiple fields Extremely useful for warning decision making! Statistical properties of storms Which clusters are convective? Trends in rain-rates … Which storms intensified after a warning was issued? Trends in cloud-top temperatures … 21 February 2019 lakshman@ou.edu

WDSS-II in the forecast office How can WDSS-II help at the forecast office? New algorithms Interactive 4D display capabilities Multi-sensor case studies Post-event validation Continuous learning 21 February 2019 lakshman@ou.edu

Polygon statistics Using cluster trends is useful for deriving storm properties. What about extracting statistics around a fixed location? Validating probabilistic guidance Maybe at areas of particular interest? NASA launch sites Sporting events WDSS-II has a tool … 21 February 2019 lakshman@ou.edu

Statistics of watch and warning polygons WDSS-II can provide polygon statistics from any gridded field(s) And these polygons can change with time Watch and warning polygons Improved validation of watches and warnings. Does it help to say that 90% of the time that a tornado watch is issued, low-level shear greater than X is observed on radar within the watch area? 21 February 2019 lakshman@ou.edu

Post-event The polygons can be used to examine the decision-making process Post-event For case-studies Easy to run through a whole bunch of data from various sensors Examine the behavior of various gridded fields. Compare to reports, radar observations, etc. Export to GIS/image/spreadsheet formats Move from anecdotal to statistical 21 February 2019 lakshman@ou.edu

WDSS-II in the forecast office How can WDSS-II address some of the issues at the forecast office? New algorithms Interactive 4D display capabilities Multi-sensor case studies Post-event validation Continuous learning 21 February 2019 lakshman@ou.edu

Continuous Learning In real-time … The polygons can be watched in real-time The statistics updated in real-time On observations that arrive in near real-time Why not do the “post-event” analysis during the event? Continuous feedback on existing watches Forecasters can mark certain areas and indicate characteristics they are interested in. And the automated monitoring can tell them if/when those characteristics are met. More information to emergency managers Based on polygons being “watched” for certain characteristics. 21 February 2019 lakshman@ou.edu

Current uses of WDSS-II in the NWS WDSS-II is a leading edge system Provides capabilities not yet in the “official” National Weather Service systems. But getting these capabilities in hasn’t been easy The Storm Prediction Center defines daily threat areas launch a WDSS-II domain automatically configures the data ingest and starts the algorithms. HPCC project: WDSS-II products into GRIB2, GEMPAK and onto N-AWIPS Pioneer grant: increase size of WDSS-II domain to near-CONUS scale NWS forecast offices WDSS-II products are converted into AWIPS format and piped to AWIPS displays in several NWS forecast offices. But the AWIPS display is too restrictive. Therefore … The 4D WDSS-II display can be implemented as a separate app on AWIPS but controlled from within D2D. Consider WDSS-II concepts for next redesign of AWIPS? Algorithm development capabilities 4D visualization Multi-sensor algorithms Adaptive algorithms (forecaster-algorithm feedback loop) 21 February 2019 lakshman@ou.edu

Summary How can WDSS-II help in the forecast office? New algorithms Better guidance Interactive 4D display capabilities Improved analysis Multi-sensor case studies More science in the forecast office Post-event validation Better metrics Continuous learning Growing warning decision making expertise 21 February 2019 lakshman@ou.edu