Project AutoWARN. Automatic Support for the Weather Warning Service at DWD

Transcription

1 Automatic Support for the Weather Warning Service at DWD Bernhard Reichert Deutscher Wetterdienst, Referat FE ZE

2 Content Project AutoWARN Introduction and Overview AutoWARN Automatic Warning Process Radar Products Statistical Products and LMK-Interpretation

3 AutoWARN Project Goals AutoWARN is part of the new DWD strategy (Headwords: Centralization and Automation). Project goals: 1. Improvement of products serving as a basis for the prediction of hazardous weather events 2. Integration of products in an automated warning process with manual monitoring and decision capabilities by the forecaster

4 Project Environment DWD Board of Directors NinJo Workstation Integration Initiator Interfaces DWD Forecast Dept. DWD NWP Group LMK Project AutoWARN Radar Met. Service Canada Radar DWD Radar Group HP

5 Project Structure Project AutoWARN TP1: Automatic Warning Process TP2: Radar Products TP3: Statistical Products TP4: LMK Interpretation M1: AutoMON Extensions in NinJo (ASG: AutoWARN Status Generator) M1: Quality Assurance of 3D Volume Radar Data M2: Mesocylcone Detection and other Algorithms M1: WarnMOS on ECMWF-Basis M2: CellMOS and Integration in WarnMOS LMK Warning Events M2: EPM Extensions in NinJo (ASE: AutoWARN Status Editor) M3: Integration in NinJo

6 Time Planning Resources Activity 1. Subproject Automatic Warning Process Year III IV I II III IV I II III Scientific Overall Concept Development 2009 IV I II Project leader (Dr. Reichert) 1 Computer Scientist ASG 1 Computer Scientist ASE 1 Computer Scientist AutoMON Data 1 Computer Scientist AutoMON Extensions 2. Subproject Radar Products Development Subproject leader (Heizenreder) 1 Scientist Quality Assurance DWD Volume Data 1 Scientist Quality Assurance DWD Volume Data until > 1 Computer Scientist Radar Data Algorithms 3. Subproject Statistical Products 4. LMK Interpretation Development (external) Development Subproject leader (Dr. Peyinghaus) 1 Scientist LMK

7 Content Project AutoWARN Introduction and Overview AutoWARN Automatic Warning Process Radar Products Statistical Products and LMK-Interpretation

8 Automatic Warning Process: Goals Exploitation and combination of various data sources Arbitrary data combinations (WarnMOS, Nowcasting, Observations, Model runs, etc.) Definition and monitoring of threshold values Generation of a forecast-time dependant automatic warning status Ensures that required warnings cannot be overlooked Supports the warning service Permanent updates of the warning status Permanent manual control and manual modification by the forecaster Export of generated warning status to external system (outside of project AutoWARN) External generation of text and graphical products considering client demands

9 Automatic Warning Process: Overview WarnMOS Observations KONRAD / SCIT Radar Lightning, BlitzMOS AutoMON Automatic Monitoring Warning Events ASG AutoWARN Status Generator Automatic Warning Status Proposal ASE AutoWARN Status Editor Manually Modified Warning Status, Export External Product Generation and distribution LMK (+ other models) MOSMIX PEPS Within AutoWARN Project Outside of AutoWARN

10 Products Generated by the Automatic Warning Process in AutoWARN AutoWARN generates categorical warning status data (extendable to probability information in a future project) for 27 DWD-warning events (with varying quality) on a grid-basis (LMK-grid with 2.8 km resolution; mapping to administrative districts is possible) with hourly temporal resolution up to +72 hours LMK-Grid (421 x 461 Gridpoints, 2.8 km Resolution) Height in m

11 Automatic Warning Process: Component AutoMON Integration in NinJo Layer Management Open AutoMON Main Window Warning Indicators Warning Indicators show status since last confirmation AutoMON can be used as a regular Layer in NinJo A separate AutoMON Main Window can also be opened independently from the NinJo Main Window

12 Automatic Warning Process: Component AutoMON in NinJo

13 Automatic Warning Process: AutoMON Extensions AutoMON Extensions Include far more input data (areal probabilities from WarnMOS and LMK, GRID-Data, more Radar-Data, other probabilistic products, nowcasting products) Allow forecast-time dependant configuration of probability data for the generation of warning categories Allow full spatio-temporal combination of data for warning events Improve stability and reliability of AutoMON for AutoWARN

14 Automatic Warning Process: AutoMON Extensions AutoMON Extensions Perform input data availability checks, i.e. alert the forecaster in case that essential input data for the AutoWARN process are not available within an expected time frame An AutoMON warning indicator in NinJo is used for alerting AW

15 Project AutoWARN Automatic Warning Process: Component EPM in NinJo

16 Automatic Warning Process: AutoWARN Status Editor (ASE) The GUI for manual modifications of the automatically generated warning status proposal by the forecaster consists of 3 Elements: 1. A map for creation and edition of warning areas

17 Automatic Warning Process: AutoWARN Status Editor (ASE) The GUI for manual modifications of the automatically generated warning status proposal by the forecaster consists of 3 Elements: 2. A warning form for specifying heights, times, etc. and sending

18 Automatic Warning Process: AutoWARN Status Editor (ASE) The GUI for manual modifications of the automatically generated warning status proposal by the forecaster consists of 3 Elements: 3. A time series visualization showing temporal relations of warning status units

19 Content Project AutoWARN Introduction and Overview AutoWARN Automatic Warning Process Radar Products Statistical Products and LMK-Interpretation

20 Radar Products: Goals Exploitation of DWD Doppler data, Quality Assurance of 3D-Volume Scans Implementation of Mesocylcone Detection and other Radar Algorithms Improved algorithms for mesocyclone-, gust- and downburst-detection Tornado-Identification algorithms Exploitation of Doppler data Benefit from Canadian Radar know-how Visualization in NinJo Integration of New Products in NinJo and Automatic Warning Process

21 Content Project AutoWARN Introduction and Overview AutoWARN Automatic Warning Process Radar Products Statistical Products and LMK-Interpretation

22 Statistische Produkte und LMK-Interpretation: Ziele Extension of WarnMOS (Areal Probability Data for Warning Events) Implementation of WarnMOS-MIX using GME und ECMWF-Model Implementation of a new Storm Cell Tracking System CellMOS Using GME-Model, Radar Composite, Radar-Analysis (KONRAD), Lightning Data Cell Analysis using Radar Data (KONRAD) and optimized statistical tracking approach (MOS-System) LMK-Interpretation Generation of Probability Data for 23 Warning Events from High Resolution Limited-Area Model LMK (2.8 km resolution) Integration of New Products in NinJo and Automatic Warning Process

23 Statistical Products: CellMOS Typical CellMOS-Output O: Forecasted Cell Track O: Cell at Issue-Time Point(Analysis) O: Observed Cell Track Green Area: Probability > 30% that point in this area is hit by cell

24 Summary Project AutoWARN Goals of AutoWARN are: 1. Improvement of products serving as a basis for the prediction of hazardous weather events 2. Integration of products in an automated warning process with manual monitoring and decision capabilities by the forecaster