# Presentation: 505707

## The Future of Warning Improvement

- Greg Stumpf
*stumpf@nssl.noaa.gov*
- Don Burgess
*Donald.Burgess@noaa.gov*
- National Severe Storms Laboratory
- Norman, Oklahoma

## NSSL’s contribution to warning improvement

- Detection/Extrapolation Algorithms
- Multi-radar, multi-sensor, fuzzy logic, artificial intelligence
- Model integration – Near Storm Environment
- New decision support tools – WDSS-II
- Integration with other systems
- AutoNowcaster, ARPS
- Increased understanding – Basic Research
- Radar Improvements
- Research Experiments

## The Challenge

- How do we distinguish between severe and non-severe, and tornadic and non-tornadic thunderstorms with the information we have as warning forecasters?

## Users mustintegrate information

- Radar data (multiple radars included)
- Algorithm information
- Trends
- Spotter reports
- Near-Storm Environment (NSE)
- Surface, lightning, and satellite data
- Statistical knowledge of past events
- Basic understanding of storm physics
- Etc.

## NSSL Research and Development Process

- Basic Research

-  Kinematics
-  Morphology
-  Understanding Wx Phenomena

-  Off-line
-  Real-time w/users
-  User Groups

- Evaluation

-  Enhancing and Utilizing Observing Systems
- Relating Signatures to Weather Phenomena

- Applied Research

-  Algorithms
-  Methods
-  Artificial Intelligence
-  Image Processing
-  Prototype Systems

- Detection and Analysis** **
- Techniques, Prototyping

-  Ultimate Applications/Systems for Users

## Detection/Extrapolation Algorithms

## Detection Algorithms

- MDA
- VDDA
- DDPDA
- HDA+NN+NSE
- VCPs and MPDA
- SCIT
- NN and probabilistic guidance
- QPEsums, QIWI, AMBER
- Rapid Update
- Dual-Pol algorithms

## Detection Algorithms

- Guidance tools
- “Safety Net”
- Not intended to replace the human
- Output must be effectively displayed and managed via decision support systems.

## New Mesocyclone Detection Algorithm

- NEXRAD TAC has recommended implementation - ORPG Build 3 (circa Jan 2003).
- New paradigm - detect then diagnose
- Advanced classification schemes (e.g., low-topped meso), and 3D diagnostics (e.g, MSI, Neural Network probabilities).
- Advanced display concepts (filters, tables).
- Better detection and diagnostic skill.

## Supercells of many dimensions

**F1 Colorado**
**Mini-supercell**

**F4 Oklahoma**
**Supercell**

**F1 Phoenix**
**Mini-MINI-supercells**

**All at same Zoom factor!**

***Tornado***
***location***

## Supercells of many dimensions

**Leading-edge vortices**

**Falcon Co**

**Hurr. Opal**

- “mini”

- “large”

- “mini”

- “high-topped”

- “low-topped”

- “low-topped”

- Vertical
- Dimension

- Horizontal
- Dimension

**Pond Bank PA**

## Future of Vortex Detection

- VDDA - _V_ortex _D_etection and _D_iagnosis _A_lgorithm
- Combines MDA and TDA concepts
- “gate-to-gate shear” is not necessarily a TVS, and vice versa..
- Advanced detection techniques (Linear Least Squares derivatives) to remove non-rotation false alarms.
- Multiple radar, multiple sensor (NSE, BWER, hook echo).
- Rapid Update
- Improved vertical and time association.

## NSSL’s MDA/TDA radar Case Studies

- The SWAT team maintains an excellent resource depicting the many varieties of tornadic storms:
*www.nssl.noaa.gov/teams/swat/Cases/cases_pix.html*
- NSSL is also “beta-testing” a Web-based system to truth WATADS case studies:
- For NWSFOs to develop local parameter studies.
- Provides standard guidelines for truthing and scoring algorithm data.
- Incorporates more data from the field into a national repository.

## Damaging Downburst Prediction and Detection Algorithm

- Uses radar-observed precursors to predict downbursts
- Uses linear discriminant analysis to develop a prediction equation on training data.
- Also detects divergent signatures at low-elevations.

- Conceptual Model

- Surface

- Initial Stage

- Descending Core

- Surface Downburst

- Reflectivity
-      Core

- Rotation

- Convergence

**Divergence**

## Hail Detection Algorithm Improvements

**Neural networks are used to make predictions for :**
- Probability of severe hail
- Maximum expected hail size
- Conditional probabilities for three size categories
- Coin-size hail (0.75 - 1.5 inch)
- Golf ball-size hail (1.5 - 2.5 inches)
- Baseball-size hail ( 2.5 inches)

**Uses more radar parameters**:
- Cell-based VIL
- Severe hail index
- Midaltitude rotational velocity
- Storm-top divergence
- Maximum reflectivity
- Base reflectivity
**Uses more environmental parameters**:
- Melting level
- Height of the -20 C level
- Height of the wet-bulb zero
- Vertically-integrated wet-bulb temperature
- Wind speed at the equilibrium level (EL)
- Storm-relative flow at the -20 C level

## New WSR-88D Volume Coverage Patterns (VCPs)

- Three new VCPs approved.  More being testing this year.
- Provides improved temporal/vertical coverage of critical weather phenomena.
- For comparison, the equivalent current VCPs are:
- May integrate Multiple-PRF Dealiasing Algorithm (MPDA).

*VCP ****               4.1 min*

*VCP ****                  5 min*

*VCP 31               10 min*

*VCP 32              10 min*

*VCP 21                6 min*

*VCP ****                10 min*

## SCIT Improvements

- Improved Vertical Association
- Cost functions (distance, attributes, etc.)
- Use entire 3D information
- Area Overlap instead of centroid proximity
- Improved Time Association
- Improvements in Vertical Association
- Cost functions
- Rapid Update

## Probabilistic Tracking

## Neural Networks

- Have been developed for MDA, TDA, and HDA.
- Potential to be used in other algorithms.
- Output is probabilistic guidance.
- Show great improvement in skill, especially when integrated with Near-Storm Environment (NSE) information.

## Reflectivity Feature Detection

- Bounded Weak Echo Region
- Uses fuzzy logic
- Assigns confidence values (0-1) to probable BWERs.
- Hook Echo Identification
- Fuzzy logic and shape extraction
- Supercell Identification
- Integrates MDA, TDA, BWER, Hook Echo, SCIT, and NSE
- All can be integrated to diagnose tornadic probability.

## Near-Storm Environment Algorithm

- Calculates many gridded products derived from RUC model soundings.
- Integrating NSE output with MDA (and HDA) leads to improvements in algorithm diagnostic skill.

- Percent

## 2001 Tornado Warning Guidance

- Larger data set (120 events, many storm types)
- Incorporate new basic knowledge.
- Include NSE attributes in statistics.
- Stratify statistics by range and storm type.

## New Precipitation Estimation Techniques

- QPE SUMS: Quantitative Precipitation Estimation and Segregation Using Multiple Sensors
- AMBER: Areal Mean Basin Estimation Rainfall
- QIWI:  QPE SUMS Interactive Web Interface

## QPE SUMS

- 	Numerous studies have documented the low reliability of operational Precipitation Processing System (PPS) products especially for cool season, stratiform precipitation in complex terrain.
- 	QPE SUMS uses a multi-radar and multi-sensor approach to rainfall and snowfall estimation.
- 	While proof-of-concept testing has taken place in Arizona, the technique may be applicable to many regions.

- Radar-only
- Rainfall

- Multi-Sensor
- Rainfall

## QPE SUMS

- AP removal
- Convective/Stratiform precip segregation
- Adaptive Mosiacking maximizes amount of low-level coverage from adjacent radars.
- Brightband Algorithm used for precip phase segregation
- Rainfall measurements made below the bright band are used to adaptively calibrate satellite cloud-top temperatures in real-time.

## Convective Stratiform Segregation

- Differences in reflectivity-rainfall (Z-R) relationships arise due to the variable nature of cloud drop size distributions.  While a full spectrum of Z-R equations exists, QPE SUMS essentially divides it in half by identifying clouds that are either convective or stratiform.

## Adaptive Mosaicking

- Precipitation rate mosaicking maximizes the amount of *low-level* coverage from adjacent WSR-88D radars.

- _Phoenix Radar Missing_

- _All Radars Reporting_

- _Flagstaff Radar Missing_

## Precipitation Phase Segregation

- Although a radar bright band is a source of contamination, its height reveals where hydrometeors change phase.

- Bright band heights are used in QPE SUMS to estimate the rain/snow line and to determine where representative rainfall and snowfall rates are measured.

## Multi-Sensor Precipitation Estimation

- Rainfall measurements made *below* the bright band are used to adaptively calibrate satellite cloud-top temperatures in real-time.

## The Areal Mean Basin Estimated Rainfall (AMBER) Program

- Input: Precipitation estimates from the Weather Surveillance Radar - 1988 Doppler (WSR-88D)
- Output: Average basin rainfall (ABR) rates and accumulations, updated every volume scan, integrated with flash flood guidance information.

## National Basin Delineation

- Project objective: To delineate individual basins (minimum basin area threshold of 2 sq. mi) for every NWSFO in the country (potential delineated basins could be over 2 million) for use in the Flash Flood Monitoring and Prediction (FFMP) program.
- FFMP functionality is similar to the AMBER.
- The FFMP will be implemented in AWIPS in June 2001.

- Arizona (HUC 15060202) Upper Gila - San Carlos Reservoir Shaded Relief with Synthetic Streams

## QPE SUMS Interactive Web Interface (QIWI)

*Web-based* version of _A_real _M_ean _B_asin _E_stimated _R_ainfall (AMBER) with the following modifications:
- Ingests gridded rainfall accumulations from QPE SUMS
- Lists terrestrial/hydrologic information about each basin
- Basins are flagged if an accumulation exceeds *user-defined* flash flood guidance

## QPE SUMS Interactive Web Interface (QIWI)

## CRAFT

- Level-II Archive done via Internet
- Data compressed to 56 kbs via LDM; eliminated need for expensive T1 lines.
- Eliminate need for 8mm Level-II archive.
- Success in Arizona!
- Real-time Level-II data available online.

## Warning Decision Support System (WDSS) Concept

**To provide timely and accurate severe weather warnings, meteorologists need to integrate data from many sources**

**The real-time counterpart to WATADS.**

*  *Doppler radar
-   Lightning detection network
-   Near-storm environmental information
-   Surface observations (e.g., mesonet)

**To provide timely and accurate severe weather warnings, meteorologists need to have robust feature detection and diagnostic tools/guidance.**

**Because of the short amount of time available to make life-saving decisions, warning decision support systems help manage large amounts of information.**

## WDSS Components

**Detection, Diagnosis, and Prediction Algorithms (RUDDS)**

-   Storm Cell Identification and Tracking
-   Hail Detection
-   Mesocyclone Detection
-   Tornado Detection
-   Damaging Downburst Prediction and Detection
-   Satellite-based Storm Top Evolution
-   Precipitation Accumulation
-   Lightning Association and Threat Area
-   BWER Detection
-   Near-Storm Enviroment algorithm

**Real-time ingest of wideband data and distribution system (RIDDS)**

**Interactive Display designed specifically for rapid access to the most **
**important information (RADS)**

## WDSS-Integrated Information

- Graphic Product Display

## WDSS-II Development Objectives

- Flexible - easy to add a new algorithm to the system or product to the display
- Well documented - others can add products and algorithms
- Customizable - User Interface can be organized to meet needs of user
- Meet needs of operations and research
- Not data source dependent - can use multiple data sources and not centered on a single data source

## Other 

**Other **
**systems**

## WDSS-II Display Features

- Displays WDSS and ORPG Products
- 3D Earth-Relative displays (including cross sections and CAPPIs)
- Base Doppler radar data and derived products (I.e., VIL, precipitation, algorithm tracks).
- Animations of data and products
- Expandable and Flexible design

## WDSS-II Display

- Basic “2-D” Image Display

**Configurable**
**Map Controls**

**Color Scales**

**Reference Point Fix**

**Volume Control**

**Product Control**

**Create Snapshot**
**Image**

**Product Looping**

**Auto Update**

## WDSS-II Display

**Time/Volume**
**Scan Selection**

**Product Selection**

**Data Set Selection**

**“****Sort by” Parameter**

**Launch Trends**

**3D Reference Point Fix**

**Center on “Phenomenon”**

## WDSS-II Display

**Click on any**
**table cell in a data**
**record.    Active **
**frame has 3D fixed **
**reference point**
**on the phenomenon**
**(cell, mesocyclone,**
**tornado).**

## WDSS-II Display

## WDSS-II Display

- 3D Earth Relative Reference

## WDSS-II Display

**No limitation on **
**the number of radar **
**products that can be **
**simultaneously displayed**

## WDSS-II Current Status

- Early release made to Australian Government in August, 1999
- Real-time and offline display functionality (high end graphics workstation)
- NSSL enhanced algorithms execution and display of products
- API to add new rudimentary algorithms

## WDSS/RIDDS sites

## WDSS-II Proof-of-Concept Test Objectives

- To provide a platform for evaluating the accuracy and the operational utility of NSSL’s enhanced severe and hazardous weather prediction algorithms during real-time operational warning situations.
- To gain feedback from operational warning meteorologists on the utility and effectiveness of the Warning Decision Support System concept and its enhanced algorithm and product displays before consideration of their inclusion in operational systems.
- To foster collaboration between NSSL scientists and operational meteorologists.

## WDSS-II Specific Objectives for 2001 Test in Norman NWSFO

- Rapid Update
- New HDA+NN+NSE, pre-JPOL comparisons
- Display filters
- QPESums, QIWI
- Supercell ID
- Algorithm failure analysis (e.g., tracking)
- New WDSSII display concepts

## Other WDSSII Testing in 2001

- Arizona:
- Flash Flood and multi-radar/multi-sensor applications (e.g., QPESums).
- Jackson MS:
- Multi-radar (four) operational system.  Also, QPESums testing.
- Amarillo, Rapid City, Tallahassee, Pittsburgh:
- ROC-supported systems (limited testing)
- Georgia:
- Multiple radar, GTRI
- Sydney Australia

## WDSS Path to AWIPS 

- Through SCAN/THOR

- NSSL

- WDSS

- AWIPS

- FSL
- NWS/
- TDL

- NCAR/
- RAP

- Auto-nowcaster

- Thunderstorm Product

- TDL

- SCAN/THOR

- NSSL
- NCAR
- TDL
- OSF/
- OTB

## Common Operations and Development Environment (CODE)

- A replacement for WATADS
- An algorithm development environment
- An algorithm testing/validation environment
- An algorithm integration environment
- A case study tool

## How does this eventually lead to operational use?

- WDSS-II
- -Extension of WDSS
- -Multiple radars
- -AWIPS data sets
- -Real-time
- -Prototype testing for
-  NEXRAD and AWIPS

- SCAN/THOR (NWS)
- - AWIPS Data Sets
- - Real-time
- - Prototype testing for
-   AWIPS

- AWIPS

- NEXRAD/
- ORPG

- CODE
- -Extension of WATADS
- -Multiple radars
- -AWIPS data sets
- -real-time and archive
- -available to developers
- -AWIPS and ORPG APIs

## Nowcast Guidance:Integration of NSSL applications with other systems

## Nowcasting Techniques

- Expert System

- Extrapolation

- Adapted from:
- Browning et al. 1980,
- Doswell 1986,
- Austin et al. 1987,
- Wilson et al.1998

**Accuracy**

**Forecast Period (hr)**

- 6

- 12

- 18

- 24

- 3

- Explicit Storm Model (ARPS)

- 1km/1min

- 50km/1hr

**Convective Rainfall**

**Still Need **
**Real Statistics**

## Example Auto-Nowcaster Forecast

- Sterling, VA

- Courtesy, Jim Wilson, NCAR

## 5 April 1999 - Impact of Base Data

**12 Z Reflectivity**

*Courtesy S. Weygandt and J. Levit*

- ARPS Model

## 5 April 1999 - Impact of Base Data

*Courtesy S. Weygandt and J. Levit*

**15 Z Reflectivity**

- ARPS Model

## Total Lightning Mapping

- Courtesy WFO MLB

- 23 Feb 98
- Central FL
- Total Ltg
- proxy for
- Updraft
- Intensity

## Polarization Diversity in Radar

- Dual-Polarized radar contains additional moments.
- Can combine DP data with temperature profiles to discriminate precipitation types.
- Provides estimate of drop size distribution, and better precip estimates than conventional radar.

## Algorithm to Classify Hydrometeor Type

- LR = Light Rain
- MR = Moderate Rain
- HR = Heavy Rain
- LD = Large Rain Drops
- R/H = Rain/hail mixture
- GSH = Graupel small hail
- HA = Hail
- DS = Dry Snow
- WS = Wet Snow
- IH = Vertically-oriented ice crystals
- IV = Horizontally-oriented ice crystals

## The National Weather Radar Testbed

***The National Weather Radar Testbed***

**U.S. Navy**
**NOAA/NWS**
**NOAA/OAR**
**Univ. of Oklahoma **
**Oklahoma State Regents**
**Lockheed Martin**
**FAA**
**Research support to be provided by the National Severe Storms Laboratory**

***Phased Array Radar Support***

## Proposed Engineering Studies

- System Design	Dec 2000 - Mar 2001
- Construction 	Mar 2001 - Oct 2002
- Installation & Checkout      	Apr 2003

***Phased Array Radar Timeline***

***Proposed Engineering Studies***

*Radar Improvements*
*Display Improvements*
*Scan Strategies*
*Dual-Polarization*
*Dual Tracking - Aircraft/weather*

*Improved detection of severe weather*
*Improved conceptual models*
*Radar comparison studies*
*Triple Doppler*
*NEXRAD/TDWR/Phased Array*
*Improved Modeling studies*
*Wind Retrieval from Single Doppler*
*Rapid Update of Forecast Models*

***Proposed Meteorological Studies***

## Phased-Array Radar

## Slide 66

## International H20 Project  (IHOP)

- Spring 2002
- Many Participants, including NCAR and NSSL
- Study of Water Vapor in all phases
- Information on storm initiation

## IHOP Continued

- RPV -
- Remotely
- Piloted
- Vehicle

## Bow Echo and MCV Experiment (BAMEX)

- 1. Improve predictability of bow-echo disturbances and severe weather.
- 2. Improve predictability of secondary convection generated by mesoscale vortices.
- 3. Document and understand horizontal circulations within long-lived convective systems.
- 4. Improve 6-24 hour QPF.

## BAMEX Continued

- Late Spring-Summer 2003
- Based in St. Louis
- Mobile Sensors:
- Aircraft (P-3, Electra)
- Portable Dopplers
- Soundings
- Mesonets

## Joint Polarization Experiment (JPOLE)

- Centered on Norman, OK
- Operations Component (in OUN WFO)
-  2001 – CIM DOP
-  2002 & 2003 – KOUN DOP
- Science Component (in field)
- 2003
- Polarization equivalent to JDOP
- Transportable Pol Radars (S-POL, CHILL)
- In-situ Observations (T-28, Mobile Meso)