# Presentation: 398087

## WHAT IS CASA?Kevin Kloesel, College of Geosciences, OU

## Spanish for “house”

- Questions?

## COLLABORATIVE ADAPTIVESENSING of the ATMOSPHEREKevin Kloesel, College of Geosciences, OU

- Presenting on behalf of a cast of thousands...

## NSF - Engineering Research Centers

- ... enabled by fundamental and integrated advances in

**Research resulting in an unprecedented ability to observe the lower portion of the earth’s atmosphere**

- remote sensing
- distributed networked computing
- atmospheric modeling
-     and predicting

**Notes:**
Achieving our vision requires fundamental and integrated advances in the fields of xx, yy,zz

These advances will lay the groundwork for Distributed Collaborative Adaptive Sensing

## NEXRAD System Today

***NEXRAD System Today***

**3 km coverage floor**

** ****Gap**

**	****Earth curvature effects prevent 72% of the **
**troposphere below 1 km from being observed**

**Notes:**
Most people in this room have some sense of how we use big radars today to map winds & precip in the mid to upper parts of the troposphere.

This figure shows the NEXRAD system in the US, 150 radars across the nation that provides comprehensive coverage from 3 km & upward. 

This engineering drawing shows how long-range radars like NEXRAD – are fundamentally unable to map down low – so it is this coverage gap – the region below 3 km – that we seek to fill using DCAS technologies.

## What’s needed to solve this problem?

*What’s needed to solve this problem?*

- Remote sensing
- Microwave engineering
- Networking
- Distributed systems
- Numerical prediction
- Emergency management
- Radar meteorology
- Quantitative inversion
- Climate studies
- Social impact
- Antenna design

- core partners

- expertise

- working
- together

**Notes:**
The core partners span all the expertise needed to solve this problem – from the sensing to the networking to the meteorological prediction to applied social scientists who understand how end-users interact with technical warning systems.

We have a history of working together. We worked to design the large radars that are used in today’s observing system, and we worked together on the NRC report that recommends critical technologies and needs for the future system.  We have a history of training graduate students together such as Leyda whom we’ve allready met. UMAss and OU hold the world record for high-resolution observations of tornadoes, and our trucks and student teams are out there in the planes chasing tornadoes together as we speak. The point I’m making here is that not only do we have the expertise needed to take on this problem, but we have a culture of collaborating together and that’s just what’s needed as a baseline to pull this off. 

## End User Participant Research

- Oklahoma Department of Emergency Management -  Paul Spencer (State EOC)
- Oklahoma Emergency Managers –in-depth interviews and survey interviews
- NWS Forecast Office Norman – Mike Foster (MIC), Dave Andra (SOO), Rick Smith (WCM)
- Baron Services – Greg Wilson
- Vieux and Associates – Jean Vieux

## Objectives

- Identify end users’ perceptions regarding advantages and limitations of current weather observation and warning systems.
- Identify end users’ perceptions regarding how the media and public perceive, understand, and respond to weather forecasts and warning information.
- Examine the communication process, from weather observations to the end users.
- Policy determinations:
- Who will make the decisions regarding the allocation of weather observation  resources?
- In the event of simultaneous weather threats, what priority criteria, should be established to determine the allocation of resources?

## Results: In-depth Interviews

- NWS is seen as the appropriate organization to determine the allocation of system resources.
- The type of weather event and the population density of a region are important considerations in allocation of system resources.
- Precise tracking of tornados is important for Emergency Managers who must make safe shelter decisions when a county wide tornado warning is issued by the NWS.
- 	***“******It would be great to be able to say that there is a large vortex up to a quarter mile wide centered at this intersection, and five minutes from now it’s going to be at this intersection, and to be very specific in that way.”***

## Results: In-depth Interviews

- More frequent updates for radar data would help Emergency Managers make decisions. Currently radar data is updated approximately every  5 minutes. Many cited one minute as an ideal update time.
**	****“****Quicker updates [of radar data] would be the number one thing for me.  Because it’s a long five minutes when you have a tornado coming down your throat here, and you’re hitting the reload button...”**
** **
- Accessing DCAS Data - Sophisticated users want access to radar before it is extensively processed for their own algorithms and visualization products.
- Visualization of the weather phenomena was extremely important for emergency managers. They are looking for enhanced graphics in new products.

## Results: In-depth Interviews

- Floods historically cause more damage and deaths; however, emergency managers interviewed consider tornados the most important weather event because of their unpredictable nature.
- 	**“****if I don’t give the warning for a flood, I’m still going to be here tomorrow...if I don’t blow the sirens before the tornado hits the city limits, I won’t be here tomorrow.”**

## End User Integration Goals

- Incorporate end user needs into the system design from day one.
- Quantify the social and economic impacts to support system trade-offs and eventual technology transfer.
- Effective application of weather technology and information.
- Generating effective public response to hazards and disasters.

## Key Issues

**Two-Way Interface**: End users request and receive data from the system (fundamental shift in observing paradigm.)
**Resource contention:** How the utility of weather information to end users and resource allocation policy determines sensing strategies and use of system resources.
**Technical/Training Requirements:** Data formats, visualization, timing and spatial scales of forecasts for different end users.
**Behavior/Perceptions:** Understanding end user decision making, response, communications, and perception of the strengths and limitations of current weather technology and severe warning systems.
**Social, economic, and behavioral impacts** pre and post CASA.

## Climatological focal point for 
tornadoes, storm genesis, hail, dryline, microbursts, boundary-layer circulations, fronts
Location of major field programs
Co-located with other observing facilities, e.g., ARM/CART radiometers and profilers
Serviced by 3 NEXRAD radars and 1 TDWR radar
OneNet towers and partner buildings available
OK-FIRST emergency managers, Earthstorm K-12 Schools, major TV markets
Covers 4 US Military Bases + OKC Airport + Oklahoma Space Port
Covers edge of Oklahoma winter wheat belt
Oklahoma Mesonet and ARS Micronet

- tornadoes, storm genesis, hail, dryline, microbursts, boundary-layer circulations, fronts
- Location of major field programs
- Co-located with other observing facilities, e.g., ARM/CART radiometers and profilers
- Serviced by 3 NEXRAD radars and 1 TDWR radar
- OneNet towers and partner buildings available
- OK-FIRST emergency managers, Earthstorm K-12 Schools, major TV markets
- Covers 4 US Military Bases + OKC Airport + Oklahoma Space Port
- Covers edge of Oklahoma winter wheat belt
- Oklahoma Mesonet and ARS Micronet

***Why Oklahoma?***

## Who wants it?

**Average Annual Rainfall 32 inches**
**And 60 Rain Days per Year**

**More Than 200 Hail Reports Per**
**Year Over NETRAD Domain**

**About 24 Tornadoes Per Year Over NETRAD Domain**

**Average 6 inches of Snowfall Over NETRAD Domain**

## Test Bed Site – Sept 05-Mar 06

-   Emergency Management Agencies

## Work in Progress:

- Survey Data into Policy Tables

## Oklahoma System Capabilities - Desired

***Capability 1 – Tornado Detection.***** **
- (1a)** **Detect low level circulation associated with a** **tornado within 60 s of formation.
- (1b) Support research related to detecting signatures confirming low level circulation is in contact with the ground and has become a tornado.
***Capability 2 – Tornado Pinpointing.***** **
- (2a) Track centroid of  low level circulation associated with a tornado with spatial errors < 300 m and temporal errors < 60 s.
- (2b) Support research related to tracking the point where a tornado is in contact with the ground.
***Capability 3 – Tornado Anticipation.***** **
- (3a) Support research and provide the real-time data necessary for future demonstration of warning** **algorithms that detect reliable, coherent signatures that precede tornadogenesis.
***Capability 4 - Data Assimilation for Forecasting Models.***** **
- (4a) Provide the real-time (3 sets at 1 minute intervals, every 15 minutes) data** **and
- (4b) Support research for future demonstration of model-based forecasting of severe storm** **threats.
***Capability 5 – Nowcasting.***** **
- (5a) Provide the real-time (1 minute update) data necessary for future nowcasting** **of severe storm threats (hail, severe winds, and storm cell development).
***Capability 6 – End-User Integration and Optimization******.***** **
- (6a) Simultaneously provide severe weather and hazard data to operational end users; data for tornado anticipation, forecast models, and nowcasting to CASA** **researchers
- (6b) resolve resource contention among competing end user needs** **through end user policy.

***Oklahoma System Capabilities - Desired***

##   Can multiple features be detected and interrelated in real time in order to anticipate tornado occurrence?

-   Can multiple features be detected and interrelated in real time in order to anticipate tornado occurrence?

-   How can NEXRAD data be added to CASA Radars and other information to improve our ability to anticipate tornadoes?

## Localized Detection & Pinpointing

** ****How to sample a tornado? How to coordinate radar scanning strategies to pinpoint a tornado?**
**How to modify existing detection algorithms for use in CASA radars?**
** ****How to “knit” together NEXRAD & **
**CASA radar data for a coherent picture **
**to improve detection performance?**

***Localized Detection & Pinpointing***

**Notes:**
This slide describes some of the research questions being addressed in our Predicting thrust, which Kelvin Droegemeier from OU leads. Here the focus is on high-accuracy detection and tracking of tornadoes. The key is that by looking down low, we’ll be directly detecting the tornado vortex, rather than the upper-level mesocyclone. 

## NetRad Concept 

- 3 km

- 25 km

- NetRad Concept

**Goal: Map winds, rain below 3 km**

- > 3 km covered by current technology

## NetRad Concept 

- 3 km

- 6 km

- 25 km

- NetRad Concept

- 1

- 2

- 3

- 4

- 5

- 6

- 7

**Goal: Map winds, rain below 3 km****.**
**7 elevation beam positions scan 0****o****-14****o**** **

## NetRad Concept 

- 3 km

- 25 km

- NetRad Concept

- 1

- 2

- 3

- 4

- 5

- 6

- 7

**Goal: Map winds, rain below 3 km****.**
**7 elevation beam positions scan 0****o****-14****o **
**Neighbor radars map “cone of silence” above a radar. Multiple-Doppler wind measurement throughout.**

## NetRad Sampling Modes

**Sit-and-Spin Mode**

**Targeted (limited sector) Mode**

***NetRad Sampling Modes***

## Coming this fall!!!

- First CASA radar will be deployed near Chickasha, OK in the fall of 2005
- Three other radars will be deployed in SW OK test bed during the next 2 years.
- Data will be available for researchers, NWS, and test bed emergency managers as early as Spring ‘06

## # Sensors Required for Nation-Wide Coverage

**# Sensors Required for Nation-Wide Coverage**

**NETRAD – 30 km**

**300 m floor**

**50 m floor**

**3 km floor**

**Off-the-grid **** – 10 km**