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HomeMy WebLinkAboutContract 44200 (2)CITY SECRETARY , t CONTRACT NO....4.'3 MEMORANDUM OF UNDERSTANDING Between The City of Fort Worth and The University of Massachusetts, Amherst 1. PARTIES. The parties to this Memorandum of Understanding (MOU) are the University of Massachusetts, Amherst (UMass) on behalf of its Center for Collaborative Adaptive Sensing of the Atmosphere ("CASA"), a National Science Foundation (NSF) funded Engineering Research Center with the lead institution being University of Massachusetts at Amherst located at 70 Butterfield Terrace, Amherst, MA 01003 and the City of Fort Worth, Texas, ("City") located at 1000 Throckmorton Street, Fort Worth, Texas, 76102. 2. PURPOSE. The purpose of this MOU is to confirm the collaboration between the City and UMass and the financial commitment of the City in accordance with the Accelerating Innovation Research (AIR) grant (NSF IIP-1237767 awarded to UMass 3. RESPONSIBILITIES. a. University of Massachusetts, Amherst: 1. UMass shall participate in a collaborative effort with the city in accordance with the terms and conditions of the NSF grant and the scope of work contained in the grant proposal. 2. UMass, using best efforts, will work with its collaborators to meet the goals, metrics and milestones described in Section VI. B, Urban Flash Flood Warning Systems, of the AIR grant proposal (Appendix A). b. City of Fort Worth: 1. The City agrees to participate in a collaborative manner with UMass in accordance with the grant proposal throughout the life of the AIR grant. 2. The City shall pay UMass an amount of $150,000.00 for year 1 of the AIR grant. Upon authorization by the NSF of year 2 of the AIR grant, the City shall pay UMass an additional amount of $150,000.00. All funds provided to UMass shall be considered third party matching funds in accordance with City's letter dated September 28, 2012. 4. POINTS OF CONTACT. a. City of Fort Worth The primary POC is Amy Cannon, (817) 392-2289. Email address: amy.cannon@fortworthtexas.gov 02--2 "1-1 : P03 : i "1 IN �i �c s(-{-lui 1j U r;\fo b. University of Massachusetts, Amherst The primary technical POC is Brenda Philips, (413)-478-4460 Email address: bphilips@ees.umass.edu The primary administrative POC is Theresa Girardi (413) 545-0698, Email address: twg@research.umass.edu 5. ENTIRETY OF AGREEMENT. This MOU, consisting of 3 pages, represents the entire and integrated agreement between the parties and supersedes all prior discussions, negotiations, representations and agreements whether written or oral as related specifically to the teuus of the AIR grant. 6. OTHER PROVISIONS. Nothing in this MOU is intended to conflict with current law or regulation or the directives of UMass, the City or the terms of the NSF grant. If any term of this MOU is found to be inconsistent with such authority, then that term shall be declared invalid, but remaining terms and conditions of this MOU shall remain in full force and effect. 7. TERM. This MOU is effective on 1/15/13 .and will remain in place until the NSF grant end date of 6/30/14 unless terminated under Paragraph 9. 8. MODIFICATION. This MOU may be modified upon the mutual written consent of the parties. Any such written communications should be sent to the following persons at the following addresses: City of Fort Worth: Amy Cannon Professional Engineer Transportation and Public Works 1000 Throckmorton Street Fort Worth, TX 76102 UMass: Brenda Philips PI 209 Knowles Engineering Building 151 Holdsworth Way Amherst, MA 01003 Copies to: Theresa Girardi 9. TERMINATION. This MOU may be terminated prior to the NSF grant end date per mutual written agreement of the parties. [SIGNATURES APPEAR ON FOLLOWING PAGE] Executed by: CITY OF FORT WORTH, TEXAS c"2"301440.4 Fernando Costa Assistant City Manager Date: 2/ZO/IS APPROVAL RECOMMENDED: By: Douglas Wrsig Director, Transportation & Publi orks z ci APPROVED AS TO FORM AND LEGALITY A*r By: itery_ ftg Douglas`VJ. Black Assistant City Attorney ATT aa_ (,$clary"J. Kayser City Secretary M&C No.: M&C Date: C-26055 1/15/2013 • • UNIVERSITY OF MASSACHUSETTS, AMHERST Theresa Girardi, Assistant Director Grant and Contract Admin. Date: OFFICIAL RECORD CITY SECRETARY Fr WORTHS TX • • COVER SHEET FOR PROPOSAL TO THE NATIONAL SCIENCE FOUNDATION PROGRAM ANNOUNCEMENT/SOLICITATION NO./CLOSING DATER no in response to a program announc menUsolicitation enter NSF 11-1 NSF 12-511 03/01/12 FOR CONSIDERATION BY NSF ORGANIZATION UNIT(S) (Indicate the most specific unit known, i.e. program, division, etc.) Rsrch,(continued) „ccelerating Innovation I DATE RECEIVED NUMBER OF COPIES DIVISION ASSIGNED FUND C 103/01/2012 2 0707000011P 8019 EMPLOYER IDENTIFICATION NUMBER (EIN) OR SHOW PREVIOUS AWARD NO. IF THIS IS TAXPAYER IDENTIFICATION NUMBER (TIN) 0 A RENEWAL 0 AN ACCOMPLISHMENT -BASED RENEWAL 043167352 NAME OF ORGANIZATION TO WHICH AWARD SHOULD BE MADE University of Massachusetts Amherst AWARDEE ORGANIZATION CODE (IF KNOWN) 0022210000 NAME OF PRIMARY PLACE OF PERF University of Massachusetts Amherst FOR NSF USE ONLY NSF PROPOSAL NUMBER 1237767 ODE DUNS# (Data Universal Numbering System) 153926712 FILE LOCATION 07/17/2012 3:04pm S IS THIS PROPOSAL BEING SUBMITTED TO ANOTHER FEDERAL AGENCY? YES ❑ NO w IF YES, LIST ACRONYM(S) ADDRESS OF AWARDEE ORGANIZATION, INCLUDING 9 DIGIT ZIP CODE Research Administration Building 70 Butterfield Terrace AMHERST MA 01003-9242 ADDRESS OF PRIMARY PLACE OF PERF, INCLUDING 9 DIGIT ZIP CODE University of Massachusetts Amherst 70 Butterfield Terrace Amherst ,MA ,010039242 ,US. IS AWARDEE ORGANIZATION (Check AU That Apply) ■ SMALL BUSINESS 0 MINORITY BUSINESS I 0 IF THIS IS A PRELIMINARY PROPOSAL (See GPG II.0 For Definitions) ❑ FOR -PROFIT ORGANIZATION 0 WOMAN -OWNED BUSINESS THEN CHECK HERE TITLE OF PROPOSED PROJECT CASA Warning System Innovation Institute REQUESTED AMOUNT PROPOSED DURATION (1-60 MONTHS) REQUESTED STARTING DATE $ 766,664 24 months 09/01/12 CHECK APPROPRIATE BOX(ES) IF THIS PROPOSAL INCLUDES ANY OF THE ITEMS LISTED BELOW ❑ BEGINNING INVESTIGATOR (GPG I.G.2) ❑ DISCLOSURE OF LOBBYING ACTIVITIES (GPG II.C.1.e) ❑ PROPRIETARY & PRIVILEGED INFORMATION (GPG I.D, II.C.1.d) HISTORIC PLACES (GPG II.C.21) EAGER* (GPG II.D.2) 0 RAPID** (GPG II.D.1) VERTEBRATE ANIMALS (GPG II.D.6) IACUC App. Date PHS Animal Welfare Assurance Number PI/PD DEPARTMENT Engineering Research Center ■ ■ ■ PI/PD FAX NUMBER 413-577-1995 NAMES (TYPED) PI/PD NAME Brenda J Philips CO-PI/PD CO-PI/PD CO-PI/PD CO-PI/PD SHOW RELATED PRELIMINARY PROPOSAL NO. IF APPLICABLE CO HUMAN SUBJECTS (GPG II.D.7) Human Subjects Assurance Number Exemption Subsection or IRB App. Date Pending 0 INTERNATIONAL COOPERATIVE ACTIVITIES: COUNTRY/COUNTRIES INVOLVED (GPG ■ HIGH RESOLUTION GRAPHICS/OTHER GRAPHICS WHERE EXACT COLOR REPRESENTATION IS REQUIRED FOR PROPER INTERPRETATION (GPG I.G.1) PI/PD POSTAL ADDRESS Research Administration Building 70 Butterfield Terrace AMHERST MA 010039242 United States High Degree Yr of Degree Telephone Number Electronic Mail Address MBA 1986 413-577-2213 bphilips@ecs.umass.edu Page 1 of 2 Electronic Signature COVER SHEET FOR PROPOSAL TO THE NATIONAL SCIENCE FOUNDATION FOR CONSIDERATION BY NSF ORGANIZATION UNIT(S) - continued from page 1 (Indicate the most specific unit known, I.e. program, division, etc.) IIP - PARTNRSHIPS FOR INNOVATION-PFI Continuation Page PROJECT SUMMARY This Accelerating Innovation Research (AIR) project will launch an innovation ecosystem for weather warning systems technologies, and processes developed by the Collaborative Adaptive Sensing of Atmosphere (CASA) Engineering Research Center. CASA's partners are the Stormwater Management Division in the City of Fort Worth and the National Weather Service's Office of Science and Technology, and Ft. Worth Weather Forecast Office. Additional partners include: University of Texas Arlington, University of North Texas, and Fort Worth Emergency Management Operations. The focus of the activities to be performed under this AIR project will take place in the Dallas Fort Worth (DFW) Metroplex, the 4th largest urban area in the U.S. in 4 — 8 radar test bed that disseminates data in real time to users for decision -making. Intellectual Merits This AIR project will enable very high -resolution lower atmospheric observations to transition into operations and use, and create new knowledge in hydrology, hazards response, and system engineering. CASA quantitative precipitation estimates will be coupled with enhanced high resolution hydrologic and hydraulic models to enable improved, finer scale risk assessment and warning for urban floods. In addition, guidelines for the optimal scale of rainfall -runoff modeling given the available scale of the rainfall rate products will be assessed. Social science methods will evaluate human response to flood warnings with recommended warning strategies. This will be one of the first deployments of X-band radars for flood warning in the United States. In addition, the AIR project will support translational research in high resolution analyses of the current state of the atmosphere that pinpoint areas where storm development will occur. These activities will result in the creation of new knowledge in the fields of flash flood warning, storm initiation, and urban -scale decision -making. Broader Impacts Weather has significant societal impacts. 3.4% or $485 billion of the 2008 US gross domestic product is sensitive to weather variability, and the extreme weather of 2011 resulted in at least 922 deaths and more than $70 billion in total economic losses. CASA networks deployed in urban areas could mitigate some of this socioeconomic vulnerability. This AIR project will develop new ownership/operations models for deploying radar networks, or other safety infrastructure. Rather than rely on federal funding and federal ownership, a locally -driven, model in which a regional catalyst brings together multiple private/public, local/national stakeholders to fund hardware and operational costs of a regional warning system will be created. The goal is to create a replicable model for other urban areas in the United States. Key Words: Radar Meteorology, Hydrology, Sense and Response Systems, Meteorological Assimilation and Analyses, Business Plan Development, Hazardous Weather I. Overview The socioeconomic impacts of weather range from minor traffic delays due to a flooded or icy road to loss of life, property, and severe economic disruption due to tornadoes, ice storms, and flash flooding. For example, 3.4% or $485 billion of the 2008 US gross domestic product is sensitive to weather variability (Lazo et al. 2011), and the extreme weather of 2011 resulted in at least 922 deaths (NCDC 2011) and more than $70 billion in total economic losses (Insurance Information Institute 2012). In the United States, the 159 long-range NEXRAD radars owned and operated by the National Weather Service (NWS) are currently the main source of sensor data for severe weather warnings and forecasts of tornadoes and flash flooding. The problem with these radars is that they have gaps in lower atmospheric sensing, in spatial and temporal resolution, and in user -oriented information. To address these gaps in the current weather observation system, the NSF Engineering Research Center (ERC) for Collaborative Adaptive Sensing of Atmosphere (CASA) has developed a new paradigm for weather warning systems based on dense networks of small radars (McLaughlin, et al., 2009). This new paradigm addresses the complete end -to -end weather warning system from radar observation to forecaster and emergency manager decision -making through human response. The success of the test bed demonstrations of this socio-technical system from 2006-2011 in Oklahoma has led to several National Research Council reports recommending the adoption and deployment of CASA-type weather warning systems as a supplement to existing or future large -radar weather observing systems (NRC reports). Traditionally transfer of a technology such as CASA's to the weather enterprise — the collection of public and private organizations that collect, disseminate and use weather information — has been driven by National Weather Service requirements and funded through the federal budget. The 159 NEXRAD radars were built and deployed through a large federal appropriation to the NWS and then contracted out to industry (Friday 1994). However, in some cases, the pace of technology development exceeded the weather enterprises' ability to move new science and technology discoveries into practice. For example, dual -polarization upgrades to the NEXRAD system to improve rain measurement are being implemented over the next two years, but the technology was validated over 20 years ago (Cate 2010). In other cases, as now, due to the economy and the current political environment, it has become very difficult for the NWS to obtain the budget needed for acquiring and operating new sensor infrastructure. CASA-ERC will collaborate with National Weather Service -Office of Science and Technology and City of Fort Worth, Transportation and Public Works Department, Stormwater Management Division to establish the CASA Warning System Innovation Institute (CASA-WSII), This innovation ecosystem seeks to develop public -private partnerships to accelerate the translation of its warning system and related technologies into operation. The focus of the activities to be performed under this AIR project will take place in the Dallas Fort Worth (DFW) Metroplex, the 4th largest urban area in the U.S and will focus on four key activities: 1. Shared ownership/operations model. Rather than rely on federal funding and federal ownership, CASA-WSII will pilot a locally -driven model in which a regional catalyst brings together multiple private/public, local/national stakeholders to fund hardware and operational costs of a regional warning system. The goal is to create a replicable model for other urban areas in the United States. 2. Systems -level translational research to quantify the value of CASA warning systems to a variety of different stakeholders to encourage their participation in the shared ownership/operations funding model 3. Technology -level translational research of major sub -components of the CASA warning system, such as the radars, signal processing algorithms, and radar control architecture to start-ups and existing instrument manufacturers and suppliers. 4. Involvement of students in translational research, business plan creation and market assessment. The platform for translational research and shared ownership model is the 4-node radar network (with planned expansion to 8 radars) that the CASA-ERC is currently in the process of deploying in the DFW Metroplex. Starting in spring 2012, the CASA DFW radar network will disseminate real-time weather information to public and private stakeholders for evaluation and decision -making as severe weather events occur in the Metroplex. In this way, this end -to -end system from radar observation to socioeconomic impact, will serve as a living lab for achieving the goals of CASA WSII. It will serve as a platform where university researchers, start-ups, existing companies, public practitioners and organizations, and third party investors collaborate, and students learn about the technology transfer process. Initial projects for CASA-WSII include development of high resolution flood warning systems for urban areas productization of the radar network control architecture; storm initiation products through fusing CASA observations with other sensor observations for NWS Forecasters; piloting of a shared ownership and operations model for CASA radar networks; creation of economic models that estimate the job creation and tax revenue benefits of CASA information or other data; and entrepreneurship education. II. Partners CASA-ERC (University of Massachusetts, Colorado State University, University of Oklahoma, University of Colorado, Colorado Springs) will enter into synergistic partnerships with: • National Weather Service. Office of Science and Technology. Ft. Worth Weather Forecast Office {federally funded). The Office of Science and Technology's (OST) mandate is to drive science and technology advances into NWS operations. OST will enable access to NWS personnel and facilities such as systems engineers to integrate CASA real-time data into the display platform for the Ft Worth Weather Forecast Office; collaborations with Subject Matter Experts (SME) on operational meteorology through the Ft. Worth forecast office NWS is also interested in new models of deploying and operating sensors. • City of Fort Worth. Transportation and Public Works Department (TPWD), municipally funded CASA-WSII will develop a high -resolution flood warning system integrating CASA rainfall rate data in collaboration with personnel from the TPWD's Stormwater Division, and also access historical data, and hydraulic models. This flood warning system can be a model, both regionally and nationally of CASA s capabilities. Other translational research partners include: • U. ofTexas Arlington. Civil Engineering Department • U. of North Texas, Denton. Murahv Center for Entrepreneurship. Discovery Park business incubator • AECOM Technical Services. a Fortune 500 technical and engineering services firm • Ridgeline Instruments. an X-Band radar start-up comvanv Third party investors are: • City of Fort Worth. Transportation and Public Works Department. Storm Water Division,— $300,000 cash • National Weather Service Office of Science and Technology — $275,000 cash • North Central Texas Council of Governments (NCTCOG) --$60,000 in -kind. NCTCOG is a regional voluntary organization, funded by local governments, that strengthens both the individual and collective power of local governments and helps them recognize regional opportunities. NCTCOG serves the 16 county area of DFW. As part of the AIR proposal, NCTCOG will play the role of the regional catalyst behind the shared ownership model. They will establish a non-profit organization that will house the CASA-WSII, and donate senior executive and staff time to the project • Ridgeline Instruments — $75,000 in -kind contribution of commercial grade X-band radar from this start-up. 2 • Fort Worth Emergency Management Overations — 456,000 in kind contribution of staff time to collaborate with university researchers on decision making impacts of flood warning or severe weather warning products. III. CASA Engineering Research Center For the past 9 years, the CASA-ERC has been dedicated to revolutionizing the nation's ability to observe, understand predict, and respond to hazardous weather events. The center has pursued an innovative, low- cost, densely networked X-Band radar sensing paradigm to overcome the resolution and coverage limitations of the current WSR-88D NEXRAD weather radars (McLaughlin et al. 2009). The short-range and close spacing of CASA radars gives them the ability to scan low to the ground with very high spatial resolution. Overlapping coverage allows each voxel in the network to be simultaneously viewed by two or more radars, allowing for multi -Doppler wind vector retrievals and a solution to the increased attenuation experienced at X-band A radar control architecture that adaptively steers the beams of the radars gives the system high temporal updates of where the weather is occurring. CASA's fundamental, technology and systems -level research were evaluated in a prototype system -level test bed located in southwestern Oklahoma. CASA operated 4 dual-pol multi -Doppler radars where high resolution observations, derived products and forecasts were disseminated in real time to off -duty NWS forecasters and on -duty local emergency managers for evaluation and decision making This proof -of - concept test bed demonstrated networked X-band radar engineering and meteorology (Junyent and Chandrasekar 2009, McLaughlin et al 2007); user -responsive networked system architecture (Zink et al. 2010; Pepyne et al 2008; Philips et al. 2008); precipitation estimation (Wang and Chandrasekar 2010); nowcasting, real-time analysis and numerical weather prediction (Brewster et al. 2005 Hu et al. 2006; Brotzge et al. 2010; Brewster et al. 2010) integrated warning systems and decision making (Philips et al. 2010; Bass et al 2011; League et al. 2010), and societal vulnerability and public response to weather information (Donner 2007; Trainor 2011). These all came together in an integrated system when on May 24, 2011 an emergency manager using CASA data made life saving decisions as an EF4 tornado with estimated peak winds of 190 mph approached his city (BostonGlobe.com 2011, Philips et al. 2012) (Figure 1) •N Figure 1. Comparison of NEXRAD (left) and CASA (right) reflectivity data as an EF4 tornado looms over the city of Chickasha, Oklahoma. The precision of CASA data enables facilities, equipment and other resources information in supplementary information on CASA. CASA-ERC is supporting this effort to sustain the center's activities after NSF-ERC funding ends in September 2013 The AIR proposal opportunity enables CASA-ERC to fund collaborations with new research partners; address business models; attract critical third -party funding; and enable the establishment of a new organizational entity the can sustain CASA's research effort after the grant ends. See the documents section for more IV. Dallas Fort Worth Urban Demonstration Network The Dallas Fort Worth Urban Demonstration Network will be a key infrastructure to speed the transition of the warning systems and technology into practice. Whereas the Oklahoma test bed focused on demonstrating proof -of -concept, Dallas Fort Worth test bed (through the AIR proposal) will focus on 3 demonstrating proof -of -value to key stakeholders who can support the commercialization of a CASA network. This end -to -end test bed will operate a multi -Doppler, dual-polarimetric radar network covering majority of the 6.5 million people in the region. In addition, CASA-ERC will incorporate existing sensors in the region such as airport (TDWR) radars, rain gauges and privately -owned sensors for creating new products through data fusion and for validation purposes. IT infrastructure for data mining, radar control, and data dissemination will be housed at NWS Southern Region Headquarters (SRH). CASA-ERC and the North Central Texas Council of Governments have signed a Memorandum of Understanding to operate the DFW test bed and to conduct joint fundraising to support on -going operations for the next five years. Plans for radar operations are as follows: Figure 2. Layout of the 8 radars test bed in the DFW Metroplex . Each radar has a range of 40 km. Phase 1. Four research -grade UMass CASA radars test bed for operations starting in spring 2012. Phase 2. Four commercial -grade CASA-style X- band radars with networking capabilities will be added. Two radars will be provided by OU/EEC (Enterprise Electronics Corporation) through cost - share committed to CASA-ERC; one from industry partner EWR and• one radar through a new start-up company Ridgeline Instruments a joint collaboration of Colorado State University through an in -kind contribution that is part of this AIR proposal (Figure 2) Phase 3. Expansion of the network to a total of 16 — 20 radars through the shared ownership/ operations to cover the entire 16 county region of the Metroplex. See the facilities, equipment and other resources information in supplementary documents section for more information on CASA and DFW Urban Demonstration Network. V. CASA Warning System Innovation Institute: Strategy CASA-WSII seeks to develop a shared ownership/operations model for regional networks of radars. The regional network could be in an urban area or in an area with poor lower atmospheric radar coverage The focus during the two years of the AIR grant will be on urban areas that would t) benefit from low- level, "neighborhood scale" data and ii) have a diversity of stakeholders who might pay for the system. To provide a sense of the potential of the market, the top 50 Metropolitan Statistical Areas (MSA), represent 54% of the total US population and occupy 3.5 million square miles (US Census 2010). In order to provide coverage for the entire 16 county, 12 223 square mile area surrounding Dallas Fort Worth, an estimated 20 CASA-type small radars would need to be deployed. Applying this coverage ratio to the top 50 MSAs, the urban market represents approximately 472 radars At a per radar cost of $500k — $700k (installed), a rough estimate of the potential urban market ranges from $223 million to $312 million; however, the average city, with a 5303 sa. mile footprint. would only nay $4.5 — $6.2 million for an 8 node CASA system for their region. Our initial per annum estimate for operations and maintenance of an 8-node network is $500,000 (based on experience in the test bed). It seems very feasible for an urban area to share that level of cost among its public and private stakeholders. Considering, for example, the budgets allocated annually by major Texas cities on flood water monitoring 4 and management, the flash flood warning alone could fund the operations of a CASA network when one considers that a CASA network is equivalent to having rainfall rates every 800 feet. From the point of view of a small to medium size radar vendor, such as Ridgeline Instruments, a startup specializing in CASA-type radar technologies, a 400+ unit market is quite attractive. The potential market for regional deployments that address low -altitude gaps in radar coverage is an order of magnitude larger approximately 70% of the US lacks NEXRAD coverage below 1 km. Using the same coverage ratio, the gap filling market is equivalent to - 4,000 radars or a roughly $2 billion market. A modified shared ownership model could be used for this market also. CASA-WSII strategy focuses on: • Continuing to move the system and component technology towards operational usage through translational research projects; offering cost-effective rapid prototyping and validation in a live environment through the test bed. • Developing the business models that support deployment of systems of 8 — 30 radars, including market assessment, hardware acquisition models, data licensing models, and scalable operations models. • Demonstrating value to stakeholders in applications such as transportation, stormwater management, aviation through the DFW test bed. Milestones for each of the projects are listed at the end of the description. VI. CASA Warning System Innovation Institute: Projects A. Business Models/Commercialization 1. Topics in Entrepreneurship Course - Through the University of North Texas Murphy Center for Entrepreneurship a new course, "Topics in Entrepreneurship" will be offered to graduate students and seniors interested in exploring market analysis, business models and commercialization strategies for CASA technologies (and other UNT research). Student Venture Teams comprised of UNT graduate students from Business, Computer Science and Engineering, Electrical Engineering Economics, the Texas Center for Digital Knowledge, the Emergency Management program and Natural Hazards Research will form interdisciplinary teams. These teams will work together, with involvement from the graduate students and staff researchers involved in CASA- WSII translational research activities, to evaluate the technologies and processes for commercial potential and prepare documentation to secure funding to bring the best ideas to the marketplace. Student Venture Teams will work closely with mentors from the DFW area, who have expertise in Emergency Management. Coordination with researchers and faculty from participating universities will also be coordinated through the Murphy Center for Entrepreneurship Graduate students, participating on the Venture Teams, will gain academic credit and valuable experience in understanding business decisions involved in technology commercialization Insight specific to opportunities in weather radar systems and markets they serve will also be an added value to students. In addition to business plans, the course will address market assessment, hardware acquisition models, data licensing models; and scalable operations models for the urban market. Tony Mendes, Entrepreneurship, University of North Texas 2. Job Growth and Tax Revenue — The Weather Enterprise lags behind other industries in being able to quantify economic impacts. This project will develop models demonstrating the job creation and tax revenue generation potential of the DFW network through input/output (I/O) analysis. Based on Bureau of Economic Analysis annual surveys, I/O data shows the structure of an industry, how much materials, labor, capital is required to produce a unit of output. They also show the linkages among different industry sectors in the economy. In this way, I/O tables can show how a dollar invested (or saved) in one industry has a multiplier effect in others. This method is used extensively to show the benefits of public investment, but has not yet been used 5 by the Weather Enterprise. Using an approach developed in Pollin et al. (2009) a model will be developed for an expanded weather radar market, and also to quantify how improved data can result in jobs. Brenda Philips, Resource Economics, University of Massachusetts Milestones Entrepreneurship Course 6 month Develop student skill assessment tool Market Evaluation Develop network of mentors in DFW. 12 months Develop at least 1 business plan with Student Venture Teams Develop operations model 18 months Develop 2nd business plan with Student Venture Teams. Assess student skill acquisition and confidence 24 months Final assessment of student skill acquisition and confidence. Job Growth, Increased Tax Revenue Develop impact of high resolution data on job creation modeling parameters Complete 1st model Development impact of expanded x-band radar industry on job creation, and increased tax revenue Complete 2"d model • B. Urban Flash Flood Warning Systems Flooding is one of the most common natural hazards in the world. In the US, it is the second leading cause of weather related fatalities, with an average of 92 people losing their lives each year (NWS 2010a). The majority of flood deaths occur from flash floods and 63% of all flash flood deaths are vehicle related (Ashley and Ashley 2008) An estimated average of $7.65 billion in damages is caused by flooding every year in the US (NWS 2010b). Annual flood related damages have increased over the last century due to urbanization and development of the nation's floodplains. The impact of urbanization typically lowers the rainfall intensity and duration necessary to produce a flood particularly flash floods. Flash floods occur over small time scales (-6 hours or less) in response to high -intensity rainfall within relatively short periods of time. This project will demonstrate the benefits of CASA radars for urban flood prediction and management to the Fort Worth Transportation and Public Works Department, Stormwater Management Division (TPWD Storm Water Management). Public works and stormwater departments have been targeted as an important stakeholder for the shared ownership/operations model because of the expected benefits they will see from the CASA system and because of their ability to raise significant funds for water infrastructure and safety through fees to citizens and bond issuance. For example, Dallas Storm Water Management is about to launch a $450 million project to improve the storm water drainage system in the city(DallasNews.com 2011); and the cities of Austin, TX, and Charlotte, NC, spend up to $1 million annually on flood alert systems (PostGazette.com). However, according to a recent University Corporation for Atmospheric Research (UCAR) report, "Flash flood events are still missed by even the most sophisticated warning systems due to science s inability to pinpoint the location and timing of small- scale heavy rain" (UCAR 2010 p.17). CASA radars systems can pinpoint small-scale heavy rain through quantitative precipitation estimation (QPE) and nowcast products(Wang and Chandrasekar 2010; Ruzanski and Chandrasekar 2011); In this project, university researchers will collaborate with TPWD Storm Water Management hydrologists and AECOM personnel to create an operationally viable, cost-effective approach for integrating CASA data into existing and enhanced urban flood risk and management tools. The city's goals are: to i) improve lead times for public works personnel responding to flash flood threats in the City 6 of Fort Worth, ii) improve accuracy for identifying and delineating flood risk areas for warnings and response for the public works personnel; and al) improve their communications strategies to promote timely public response. / The proposed prototype high -resolution flash flood warning system includes the following four principal components QPE, nowcasting hydrologic models and hydraulic models (Figure 3). The QPE component receives input from radar and rain gauge data to produce the best (in some objective statistical sense) multisensor QPE. The nowcasting component receives input from the radar data to produce quantitative precipitation forecast (QPF) out to 30 min. The hydrologic model component .. :. _..:.: receives input from QPE and nowcast to -, n : ` produce high -resolution analysis (i.e. the 11 current conditions) and prediction of soil n... maps at selected locations at the current and future time steps. For flash flood warning, timely and seamless generation of QPE, mu" OPE & Pointed High -Res. Hydro. a flash flood Decision nowcast, hydrologic roducts and inundation w Noeesting Hydrate,.Y* Modeling tning Products Maker products, maps is critical. The large amount of information produced must be organized and presented to the user to allow quick and actionable decision -making. The translational research that needs to occur is to match the scales of CASA data to those of hydrologic and hydraulic models such that the high -resolution QPE information is best translated into high resolution runoff and flow information. The overall approach is to integrate CASA data with high -resolution hydrological models for the whole city and then conduct selective high resolution hydraulic modeling on streams that would benefit from high resolution data the most. At the same time, AECOM will be developing a flood forecasting system for the city of Fort Worth based on current technology, but with an eye towards integrating CASA information and modules of the high-res prototype warning system. Close collaboration among AECOM, university researchers and the City of Fort Worth will ensure the systems have interoperability, and allow for side - by -side comparisons during real time events. moisture, surface runoff and channel flow. Figure 3. CASA Prototype Flash Flood System 4w -.. .. The hydraulic model component receives 1> input from lateral and upstream inflows from the hydrologic models to produce inundation 1. Metrics — Development of relevant metric of success for the City of Fort Worth such as reduced public works personnel costs, increased lead times will be developed for project evaluation* metrics will also be developed for each of the sub -systems (QPE, hydrology hydraulic, public response) as well as metrics for each of the stages in the plan. 2. Comparative evaluation of CASA QPE and nowcasting — In this project, CASA QPE, nowcast, and QPF will be compared to local ground assets and existing QPE products, from the perspective of integration within the flood forecasting system. We will intercompare CASA QPE with products currently available from NOAA over a range of temporal and spatial scales for a variety of precipitation events. NOAA products include: MPE (Multisensor Precipitation Estimator), Q2, and when available, NEXRAD Dual Pol. To measure performance, suite of widely used and accepted error statistics will be used. In addition, CASA nowcasting algorithm will be evaluated to assess gain in effective lead time and geographic specificity of information in comparison to other nowcasting algorithms using a set of standard performance measures. Results will be shared with AECOM and Stormwater Management. Chandrasekar, Colorado State U.; Rees, U. Mass; Seo, U. Texas Arlington. 7 3. Hydrologic modeling - To take full advantage of the high -resolution radar rainfall information, it is necessary to operate hydrologic models at a scale commensurate with the scale of the quantitative precipitation information (QPI). Because the rainfall -runoff processes are highly nonlinear and scale - dependent, and all QPE and hydrologic models are subject to various sources of error (see, e.g , Seo et al. 2006, 2010) the accuracy of runoff prediction is necessarily scale -sensitive (Finnerty et al. 1997). To assess the sensitivity and hence to arrive at the optimal scale of rainfall -runoff modeling given the available QPI, the hydrologic models should offer flexibility for variable -scale operation in addition to an established record of performance and real-time operation (Reed et al 2004 2007). In this task, we will adapt the research version of the National Weather Service (NWS) Hydrology Laboratory's (HL) Distributed Hydrologic Model (HL-RDHM) (Koren et al 2004) for implementation at a fine scale. The hydrologic models in HL-RDHM to be used are SAC-HTET (Koren et al 2010), which is gridded implementation of the heat transfer and enhanced evaporation physics version of the Sacramento soil moisture accounting model (Burnash et al. 1995), and the kinematic wave models for hillslope and channel routing (Koren et al. 2004) Currently, the operational version of HL-RDHM or the Distributed Hydrologic Model (DHM), is used in the NWS on a �4 km grid on which the model parameters already exist nationally (Koren et al. 2000; Koren et al 2003• Anderson et al. 2006). The performance of HL-RDHM is well documented through the Distributed Model Intercomparison Project I, or DMIP1 (Smith et al. 2004; Reed et al 2004), and DMIP2 (Smith et al. 2012). For rainfall -runoff modeling, we will incorporate additional finer -scale physiographic information including the fractional impervious area for implementation at sub-1 km scale. For routing, the model parameters can currently be derived nominally at 1 km (Sean Reed, personal communication). We plan to reduce this to sub-1 km scale (300-500 m) pending availability of observed streamflow data. The routing results will provide a city-wide view of flooding threats which can be drilled down for more detailed information such as inundation maps for selected locations of particular importance where hydraulic models will be implemented (see the hydrologic modeling section) In the beginning of the program as the real time CASA data links are established and as we wait for significant events we plan to prepare ourselves with hydrologic and hydraulic model exercises in two ways: a) use the high resolution CASA data from the Oklahoma test bed, and b) use the NEXRAD observations, to drive the models. For this, we will use the high -resolution (lkm x 1°) NEXRAD data. CASA system itself has used auxiliary input data like this for system validation in the past, and this approach has proven useful. To prescribe the initial conditions for the fine -scale SAC- HTET, we will use the model states obtained from the historical HL-RDHM simulation on a coarse (-4km) grid forced by the MPE (Seo et al. 2010) data produced operationally by the NWS West Gulf River Forecast Center (WGRFC). Performance of the hydrologic models will be evaluated mainly through comparisons with streamflow observations from 14 pressure transducer -based sensors located throughout the City. 4. Hydraulic modeling - Accurately estimating the propagation of stormwater flow is a key element of flash flood monitoring. Rainfall -runoff information having high spatial and temporal resolution offers new potential for modeling that stormwater flow. Runoff rates estimated by hydrologic models provide the necessary input for subsequent hydraulic routing models which compute the propagation and inundation as these flows traveling downstream through drainage systems. The benefit of using high -resolution hydrologic models to drive detailed hydraulic models will not be uniform over large drainage areas. Therefore, it is necessary to identify the flood prone areas and reaches where detailed hydrologic and hydraulic modeling will yield the greatest benefit. This will begin with an assessment of the primary basins within the Fort Worth boundaries A set of 3 to 6 sub -catchments will be selected as candidates for detailed hydraulic modeling using inflow hydrographs from HL-RDHM. The size of land area for these sub -catchments will be part of the assessment and will include sufficient variation to examine sensitivity to that dimension. Sub - catchment selection will also be influenced by the location and availability of physical stream gauges 8 needed for hydraulic model calibration and verification. The extent to which calibration and verification may be carried out depends greatly on the availability of stream data. Using hydrographs provided by HL-RDHM stream data will be needed for that same period of record. As part of the model selection process, we will inventory the available data and determine the appropriate level of model complexity. This will include gauges from the City of Fort Worth ALERT System Tarrant Regional Water District and the US Geological Survey. This effort will also include the selection of the appropriate hydraulic models for analyzing detailed urban flooding. The candidate models include the US Army Corps of Engineers Hydrologic Engineering Center River Analysis System, (HEC-RAS) Version 4.1, HEC-RAS (Brunner 2010) and the US Environmental Protection Agency Storm Water Management Model EPA-SWMM Version 5.0 (Rossman 2010). There are of number of characteristics common to both which make these models preferable for the detailed hydraulics modeling of urban flash flooding HEC-RAS has recently been adopted by the US National Weather Service for unsteady modeling in its flood forecast system, Community Hydrologic Prediction System (CHPS) Both are listed as accepted regulatory models by the US Federal Emergency Management Agency (FEMA) (FEMA 2012). To develop detailed hydraulic models for the selected sub -catchments very specific terrain and drainage course information will be assembled LiDAR survey data will be used to detailed compile cross -sectional information for channelized flow areas Researchers will coordinate with the City of Fort Worth to obtain existing model information and to transfer records from their public works section containing details of as -built drainage structures The historic record of stream flow measurements gathered as part of this task will be used to conduct calibration and then verification of the model. Output from the sub -catchment models will be superimposed upon a GIS framework to generate a mapped display of inundation extent This will be done using ESRI ArcGIS Server Version 10.0. Mcenery, UTA; Cannon, City of Ft. Worth. 5. System integration, off-line testing and evaluation - In this task, we will integrate the principle components and carry out, off line, end -to -end testing and evaluation of the system including data ingest and flow and product generation and display as a state-of-the-art flash flood warning capability and as a robust and reliable integrated system fit for operational use. AECOM and the City of Fort Worth will supply information on their computational resources, so the CASA system will be feasible for implementation by the city. To supplement the real time data we will use high resolution data from the Oklahoma test bed (Wang and Chandrasekar 2010) and the historical archive of high - resolution (1km x 1°) NEXRAD data, for hindcasting experiments for multiple significant events. To prescribe the initial conditions for the fine -scale hydrologic models in these experiments, we will use the historical simulation of HL-RDHM on a coarse grid (--4km) forced by MPE. To evaluate performance for warning in a hindcast mode, we will use the standard measures such as the lead time, probability of detection (POD), false alarm rate (FAR) and critical success index (CSI). Necessarily, verification of flash flood warnings is possible only if the ground truth or sufficiently reliable verifying information is available. For binary (i.e. hit vs. miss) verification, we will use all locally available data sources, including those obtainable via WebEOC®. For quantitative verification in terms of water level, we will use the water stage observations at 14 locations throughout the City. Mcenery, UTA • Cannon, City of Ft. Worth • Seo, UTA; Chandrasekar, CSU; Rees, UlLMass. 6. Real-time testing - In the real-time mode, events that are not encountered in the off-line mode may still arise. In this task, we will test data acquisition ingest and quality control, execution of QPE, nowcast, hydrologic and hydraulic models, generation, publication and distribution of products and management of all data involved in a real-time mode, to enable synchronization. In addition to improving robustness and reliability of the system, we will also test and modify, as necessary, the operations concept as well as the information content and look and feel of the products in a real-time decision -making environment based on the user feedback. We will evaluate the overall system performance in terms of reliability and timeliness of the data flow and product generation and 9 availability to the users. There will be side -by -side comparisons with the tools to be used by AECOM for the system. The CASA flood warning system will run on UTA servers initially, but then it is expected the part of the system will run on the city's servers. 7. Public education/user behavior - In the state of Texas, flash floods were responsible for at least forty-two deaths in 2007, and of these deaths, over 76 percent were vehicle -related (Eblin 2007). Despite the progress made in technological innovation flood mitigation, and warning communication, flood fatalities remain high due to inadequate human behavior in floods (Gruntfest 1977; Montz and Gruntfest 2002). Furthermore, as human activity expands into urbanized areas, an increased social vulnerability and a decreased flood risk tolerance persists (Brilly and Polic 2005). Research shows the public often underestimates their level of risk, resulting in difficulties for drivers to perceive flood danger during their daily activities (Ruin et al. 2007). A closer investigation of the public's response to flooded roadways and to various flood warning systems is needed in order to reduce the vulnerability of motorists on flooded roads. Warning communication must also be addressed for improved response to flash flooding. This project will demonstrate the capability of the CASA system in improving public response to flash floods. To address the current behavior of motorists in the Metroplex focus groups will be conducted with the general public to identify the physical, technological, and social circumstances that pose the highest risk for drivers during flooding conditions. The public will be asked what actions they take during flooding events, and what motivates them to either do the right thing or disregard flood warnings and road barriers when driving in flooding conditions. Furthermore, using cameras and direct observations, this study will also document what actions the public are actually taking at different types of crossings during heavy precipitation events This study will propose tailored public education and communication initiatives addressing how flash flood threats may best be communicated to the public for improved response. Furthermore, this study will make warning policy recommendations to regional emergency management and public works officials that will incorporate how current practices and public response may be improved by incorporating CASA s integrated flood warning system into the decision -making process. Milestones 6 months 12 months 18 month 24 months Milestones 6 months 12 months 18 month QPE Real time product development Validation of real time products and link to Hydrologic models Link QPE nowcast to hydrologic models Validate end to end functionality of QPE/Nowcast/hydrologic and hydraulic model system Hydraulics Assess and select hydraulic model Obtain existing model information, as -built details and topographic information Develop structure of Hydrology Set up the rainfall -runoff component of the hydrologic model Test and validate QPE ingest and generation of rainfall -runoff products Set up the hydrologic routing component of the hydrologic model Test and validate generation of hydrologic routing product Public Response Identify flood -prone areas and warning systems in place Observe driver behavior at flooded City crossings Conduct focus 10 Integrated System/Real time Evaluation Develop interfaces for linking sub -components Off line integration completed Comparison with AECOM System plan Integrated system testing 24 months hydraulic model Calibrate and validate hydraulic model Produce mapped inundation product group/interview research with the public regarding flash flood behaviors Recommend public education initiatives to improve public response to flash floods Real time system implementation Real time testing and validation; side by side C. Productizing MCC A CASA radar network is characterized by an ability to execute scans based on arbitrary geotagged inputs. The inputs may be weather features such as "storm cells" detected by the radar network itself, `cues" based on a detections made by another type of sensor, predictions from a forecast model, warning polygons from NWS forecasters, tornado detections from a human spotters, or locations such as stadiums and race tracks otherwise deemed important to monitor. Unlike radars such as NEXRAD that "sit -and - spin," a CASA network samples each different geotagged input at a rate that depends on the specific attributes of the input. In CASA's Oklahoma testbed, this "collaborative adaptive sensing of the atmosphere' was used to track tornadic storm cells through the network with rapid multi -tilt PPI sector scans and RHI storm top finder scans while at the same time performing periodic 360 degree surveillance scans to search for new storm cells. In the DFW testbed, this capability will be used not only to track storm cells through the Metroplex, but will also allow the network of radars to be "cued" by other sensors such as lightning detectors to explore relationships between lightning discharge and dual-pol radar returns as part of a study on convective storm initiation. This adaptive scanning capability will also be used, for example, for focused scans over specific hydrologic basins and risk zones to obtain high resolution rainfall estimates over those sensitive regions. Because the pencil beams of weather radar require a certain amount of time to scan a given volume and overlapping radars require a certain amount of coordination, as when one of them is heavily attenuated in a certain direction or when dual -Doppler wind vectors need to be synthesized, a challenging resource allocation problem emerges. The middleware that manages this allocation of resources in a CASA radar network is termed the "meteorological command and control" (MCC) The goal of this project is to productize the MCC so that (i) the heterogeneous mix of radars from different vendors that will ultimately make up the DFW testbed network can all be put under collaborative adaptive control, and (ii) so that ultimately the MCC concept can be transferred through licensing to the vendors participating in the DFW testbed and other radar vendors. Productizing the MCC will involve the following activities: (i) Generalization The existing MCC suite of software modules was developed for the purpose of performing research experiments with the CASA prototype radar that was deployed in the "tornado alley" of Oklahoma from 2006-2011. As software for exploratory research using a homogeneous network of radars, it was expeditious to ` hardcode" into the software certain assumptions regarding the radars, the communications network that interconnected them, and the very limited set of geotagged inputs that the network needed to respond to. A first step in productizing the MCC would, therefore, require making these assumptions explicit e.g., through appropriate documentation defining the interfaces between the radars and the MCC, and the development of setup tools to allow the end -user to specify the characteristics of the radars and the networking that interconnects them, the types of inputs the network will need to respond to along with requirements on how each different input needs to be scanned (e.g sample rate, vertical volume, variables of interest, etc.) and the requirements on data storage and archiving. This step is will allow an arbitrary network of radars to be integrated and coordinated under MCC management. 11 (ii) Validation and Testing. Once step (i) is complete, this step will involve the CASA MCC engineering team working with the radar network vendors (Le , Ridgeline Instruments) to understand the needs of both the vendor and the vendor's end -users so as to ensure the MCC is configured to meet those needs. This step will identify the "value proposition" to the vendor and the vendor's end -users by allowing them to compare the performance of the radar network with and without the MCC. (iii) Extensions. The validation and testing may uncover certain missing elements or limitations in the MCC design. Missing elements may include "features" (i.e., geotagged inputs) that cannot be detected using the MCC s current baseline set of convective weather ' feature detectors. ' Internally, the MCC is based on a very flexible framework known as a "blackboard framework." Meeting the requirements of the experiments conducted by CASA with its Oklahoma prototype network exercised only a very limited range of the capabilities of the blackboard framework. It is thus conceivable that a vendor or an end -user may have data collection needs that, although within the capabilities of the radars, cannot be satisfactorily achieved by the existing MCC design. The actions taken should such limitations be exposed, i.e., whether or not the changes are made, would depend on a tradeoff between effort and value. (iv) Automation. In addition to tools for set-up and configuration, a version of an MCC for 24-7 operations would require the creation of modules for status monitoring, built-in test, fault detection and response, and automated operator notification of faults. Included in this would be fail-safe features to avoid equipment damage both when under MCC control and when control is lost, and to ensure personnel safety, as for example when maintenance is being performed on a radar. Such modules were not needed for CASA's research experiments since these were typically of limited duration and performed for under fairly close supervision. Milestones Generalization 6 months Complete an initial version of the MCC interface specifications and configuration dialogs. 12 months Release to a vendor an initial version of the MCC with interface specfications and configuration dialogs. 18 months Iterate the interfaces and usability with the vendors and network operators. 24 months Complete final `shrinkwrapped" MCC for sale and Validation & Testing Perform initial unit testing with the CASA radars. Perform initial and testing by integrating the first vendor radar into the network. Continue to integrate new radars as they are installed in the network. Continued bug checking. 12 Extensions Automation Identify missing `feature detectors" and `input methods," e.g., HMI's for entering geotagged inputs. Begin implementation of missing feature detectors and input methods. Work with radar vendor and DFW system users to identify deficiencies in MCC performance. Test new feature detectors alongside existing ones to ensure proper functioning and no untoward interactions. Finalize the suite of feature detectors and input methods to be Begin design of the automated components. Complete an initial version of the automated components and begin testing with the existing DFW network of radars. Complete a next release based on experiences of one year of DFW operations. Finalize the automation components and bug licensing to radar vendors. packaged with the MCC release check through operation in the DFW network. D. Storm Initiat►on Products Improvements in the detection and forecast of convective initiation (CI) and convective evolution are two major strategic NWS objectives. A new paradigm for detection and prediction of atmospheric phenomena is dynamically merging (fusing) data from multiple observing platforms; such a system optimizes the advantages from a diverse set of observations. The University of Oklahoma's Center for Analysis and Prediction of Storms (CAPS) has several analysis and assimilation tools (e.g., ADAS, 3DVAR and Ensemble Kalman Filter — EnKF) that can be adapted to the test bed region All have the capability to assimilate all available radars, local wind and thermodynamic profile data (Raob, GPS lidar, and radiometers), ACARS and TAMDAR take -off and landing data, any satellite data that have low latency (generally GOES infrared and visible channels), and all surface data (e.g. ASOS, AWOS, MADIS). This project will focus on ADAS. Initially (i.e., spring 2012), a continuous cycling analysis will be produced by ADAS every 30 minutes. The analysis will have 1 km horizontal resolution and a stretched terrain -following vertical grid with 20 m (near ground, first level at 10 m AGL) to 700 m (aloft) vertical resolution. Once gridded fields exist, users can obtain any field or diagnostic they desire. This will provide users with frequent looks at, e.g., moisture convergence, vertical profiles, and sounding calculations. Products that identify areas of likely convective initiation (CI) and related CI products of interest (e.g., moisture convergence, delta fields, wind shear, low-level boundaries, etc.) from the 1 km gridded forecast products will be developed using applying methods already used for the probabilistic forecasts. Milestones 6 month Evaluation/verification of current ADAS analysis of CI variables and storm representation. Identify key, precursor variables to CI. Consult with NWS forecasters on key decision making needs 12 months Evaluate value of different observing sensors to CI analysis. Test statistical, post -processing techniques to best elicit CI precursors. 18 months Develop automated algorithm to identify expected areas of CI (as best determined from analyses and possibly NWP forecasts as time permits). 24 months Complete evaluation and refining of CI algorithm. Test algorithm in real-time operations for evaluation by forecasters for decision - making impacts. • VI. Education There are two distinct educational opportunities provided by CASA-WSII: participation in the Translational Research (TR) projects and involvement in the Student Venture Teams that will be part of the entrepreneurship course offered at the University of North Texas. (See Business Models section for a description of the course.) Through the Translational Research projects, a small group of graduate students (TR students) will experience first hand, how having practitioners involved in the process changes the goals and timelines of a project CASA —WSII will ensure that all TR students are truly an integral part of a practitioner university partnership, attending meetings, presenting and discussing results. TR students will also participate, as part of their research assistantships, in the Student Venture Teams as technical content experts. In this way, TR students are exposed to the technology evaluation and commercialization process. A broader group of graduate students and select undergraduate seniors will participate in the Student Venture Teams to create strategies around warning system deployment or 13 commercialization of component technologies. These students will have access to the translational research teams work through periodic presentations on goals and progress. VII. Assessment Plan The success of the proposed partnership to create an ecosystem that drives innovation will be assessed by evaluating the following: 1. Transfer of domain expertise between the various participants 2. Movement of the technology along the path of commercialization 3. Addition of new stakeholders and infusion of new capital 4. Readiness of the new workforce required to drive economic growth 1. Transfer of domain expertise - Every participant in the ecosystem brings a different piece of disciplinary knowledge that needs to be shared with the other participants. The various academic participants bring an understanding of how weather systems work, how the CASA radar network can enhance our understanding of the weather, how this new information can be linked to existing warning decision technologies, and how users of the weather information respond to what is communicated. The practitioners (National Weather Service City of Fort Worth) understand how procurement of new systems takes place in the weather enterprise, organizational resistance to change (new processes and new technologies), and the risk associated with adoption of new technologies. Finally, the private sector participants (AECOM, Ridgeline) understand the market (commercial and government) for these technologies, competitive factors and politico -economic climate that influences the market. A continuous and vibrant exchange of information thus is critical to the success of the ecosystem. Related metrics: 1. Number of cross -sector exchanges between participants (teleconferences, web conferences, in - person meetings) 2. Number of technical documents developed/exchanged (white papers, specification sheets) 3. Number of joint publications 2. Path to commercialization - CASA technologies have entered the Valley of Death with CASA receiving the last tranche of ERC funding in Year 1 of this proposal. The radar network has been prototyped and demonstrated in an operational environment (severe weather decision making in Oklahoma) and can be considered to be at Technology Readiness Level 7 (TRL 7) in meeting this requirement. However, development of a flood warning system still remains at TRL 3 with system components that yet need to be integrated into the network. Individual components of the system (MC&C, rainfall estimation algorithms, storm initiation models) are at various levels of readiness. Deployment of the system in Dallas Fort Worth — in an operational environment- will help move the technologies forward. Evaluation of CASA technologies (and their readiness) will have to be measured. Related metrics: 1. Technology Readiness Level of various systems and subsystems 2. Number of products put into an operational environment 3. Number of licenses issued/ patents granted 4. Business plans developed (by Student Venture Teams and as reported by industrial participants) 3. Addition of new stakeholders and infusion of new capital - The success of the ecosystem enabled by this proposal will also need to be measured by the additional interest in generates in the weather enterprise. While the current group of partners and investors have made a significant commitment (technical and financial) to the project (technical and financial), the ecosystem should be able to attract new potential users of the weather data, new weather data resellers and sensor manufacturers and system capability enhancers. Accordingly, the following metrics will be measured: 1. New stakeholder groups such as transportation, aviation, utilities, medical emergency management. 2. Additional funding (private, government, academic sectors) 14 3. New system capability enhancers (sensor manufacturers, weather data resellers) 4. New businesses started 4. Readiness of Workforce - A final measure of the success of the ecosystem will be its ability to prepare the next generation of engineers, computer scientists, meteorologists and social scientists to participate in the new industrial sectors spawned by CASA technologies. This requires the active participation of students in this ecosystem, the development of new course material and publishing of findings. The following metrics will be used to measure success: 1. Number of students participating in Student Venture Teams and across the CASA enterprise 2. Number of student -practitioner interactions 3. Surveys of students to see how well they understand the commercialization process (pre- and post - classes) 4. Number of new courses developed 5 Number of related publications in academic and trade journals. The metrics will be tracked on Excel worksheets owned by the Director and maintained with the help of Dr Tony Mendes. There will be at least two enterprise -wide meetings to assess the metrics and results will be presented to NSF at meetings. VIII. Management The Principal Investigator, Brenda Philips, will serve as director of CASA-WSII. She has extensive experience in leading multidisciplinary groups of academic researchers and practitioners, and managing long distance collaborations through her 9 year tenure at the CASA-ERC. The deputy director of the CASA-WSII will be Dr. V. Chandrasekar, a senior faculty member who has multiple patents and consults with the National Weather Service and the private sector for translational research in radar meteorology Each project (Business models flash flood warning, MCC, Storm Initiation) will have co -leads, one from the academic side and the practitioner side. Tony Mendes, entrepreneurship professor, will lead CASA- WSII's educational activities. This group will meet at least monthly for update on progress and sharing of information. Supporting the director, will be a part-time project manager, Apoorva Bajaj, who will address IP issues and has had initial discussions with third -party funders on the appropriate intellectual property policies, including patent disclosures and filings, and coordinate usage of the DFW Urban Demonstration Network. This position is funded through the CASA ERC. The NCTCOG will make available a full time administrator, Amanda Everly, to the project, funded through fees generated by the DFW Urban Demonstration Network. CASA-WSII will establish an advisory board with representatives from all the participating institutions, third party investors we will also target leaders from other urban areas to participate in the steering committee so they can see the progress of the project, as well as representatives from key stakeholder groups The advisory board will meet three times during the two-year grant using web conferencing technology. As of the 2000 census, the racial makeup of the DFW area was 13.9% African American <1% Native American and Pacific Islander, and 21.7% Hispanics/Latinos (Wikipedia). Helping to build a diverse pipeline of students interested in and pursuing STEM fields, including women is of great interest to our project team and will be one of the foci of the entrepreneurship education program and graduate student recruitment for translations research. However, we also understand that to build a diverse pipeline, it is critically important to include role models within the project team. The PI is an African American woman, and the Senior Personnel include a Latino male and two women. In addition, two of the investigators are very involved in diversity efforts on their respective campuses 15 References Anderson, R.M., V.I. Koren, and S.M. Reed. 2006. Using SSURGO data to improve Sacramento Model a priori parameter estimates Journal of Hydrology, 320: 103-116. Ashley, S T. and W S. Ashley. 2008 Flood casualties in the United States. Journal of Applied Meteorology and Climatology, 47(3): 805-818. Bass, E.J., B. Hogan, D. Rude, B. Philips, D. Westbrook, C. League, J. Brotzge, P. Marsh, R. Riley and L. Lemon 2011. A method for investigating real-time distributed weather forecaster -emergency manager Interaction. 2011 IEEE International Conference on Systems, Man, and Cybernetics October 9-12, 2011, Anchorage, Alaska 2809-2815. BostonGlobe.Com 2011. Precision twister tracking. Available from http://articles.boston.com/2011-06-13/business/29653593_1 _radars-doppler-early- warning. (Accessed 25 Feb , 2012). Brewster, K., M. Hu, M. Xue and J. Gao. 2005. Efficient assimilation of radar data at high resolution for short-range numerical weather prediction. World Weather Research Program Symposium on Nowcasting and Very Short -Range Forecasting, WSN05, Tolouse, France, WMO, Symposium CD, Paper 3.06. Brewster, K., K. Thomas, J. Gao, J. Brotzge, M Xue and Y. Wang 2010. A nowcasting system using full physics numerical weather prediction initialized with CASA and NEXRAD radar data. Preprints, 25th Conf. Severe Local Storms Denver CO, Amer. Meteor. Soc., Denver, CO, Paper 9.4 Brilly, M and M Poltc. 2005. Public perception of flood risks, flood forecasting and mitigation. Natural Hazards and Earth System Sciences, 5: 345-355. Brotzge, J., K. Hondl, B. Philips, L. Lemon, E. Bass, D. Rude and D. Andra, Jr. 2010. Evaluation of Distributed Collaborative Adaptive Sensing for detection of low-level circulations and implications for severe weather warning operations Weather and Forecasting 25. 173- 189 Brunner, G. 2010. HEC-RAS river analysis system: hydraulic reference manual. Available from http://www. hec.usace. army.mil/software/hec-ras/documents/HEC- RAS_4.1_Reference_Manual.pdf. (Accessed 25 Feb., 2012). Burnash R.J.C., 1995. The NWS River Forecast System — Catchment Modeling. In: Computer Models of Watershed Hydrology, V.P. Singh (Ed.), Water Resources Publications Littleton, Colorado, 311-366. Cate, G. 2010. Dual polarization is coming to NEXRAD! NEXRADNow, 20:1-2. Costa, J. E 1987. Hydraulics and basin morphometry of the largest flash floods in the continental United States, Journal of Hydrology, 93:313-338 Crippen J.R. 1982 Envelope curves for extreme flood events. American Society of Civil Engineers. 108(HY 10): 1208-1212 Crippen J.R., and C.D. Bue 1977. Maximum floodflows in the conterminous United States: U.S. Geological Survey Water -Supply Paper 1887, 52 p. DallasNews.com. 2011. Dallas officials plan relatively modest bond program for' 12 Available from http://www.dallasnews.com/news/community-news/dallas/headlines/20111231- dallas-officials-plan-relatively-modest-bond-program-for-12 ece (Accessed 25 Feb., 2012). Donner, W R 2007 The political ecology of disaster: An analysis of factors influencing U.S. tornado fatalities and injuries, 1998-2000 Demography, 44(3): 669-685. Eblen, L. 2007. Report: Flash Flood Deaths in 2007. Unpublished email. FEMA. 2012. Numerical models meeting the minimum requirement of NFIP. Available from http://www.fema.gov/plan/prevent/fhm/en_hydra.shtm. (Accessed 25 Feb., 2012). 1 Finnerty, B.D., M.B. Smith, D.-J. Seo, V.I. Koren and G. Moglen. 1997. Space-time scale sensitivity of the Sacramento model to radar -gage precipitation inputs, Journal of Hydrology, 203:21-38. Friday, E W., Jr. 1994. The modernization and associated restructuring of the National Weather Service: An overview. Bulletin of the American Meteorological Society, 75:43-52. Gruntfest, E.C. 1977. What people did during the Big Thompson flood. Natural Hazard Research Working paper 32. Natural Hazards Center, University of Colorado Boulder. Hu, M., M. Xue and K. Brewster. 2006. 3DVAR and cloud analysis with WSR-88D Level -II Data for the prediction of Fort Worth tornadic thunderstorms Part I: Cloud analysis and its impact. Monthly Weather Review 134:675-698. Insurance Information Institute. 2012. Catastrophes: U.S. Available from http://www.iii.org/facts_statistics/catastrophes-us.html (Accessed 25 Feb., 2012). Junyent F. and V. Chandrasekar. 2009. Theory and characterization of weather radar networks. Journal ofAtmospheric and Oceanic Technology, 26:474-491. Koren, V., M. Smith, D. Wang, and Z. Zhang. 2000. Use of soil property data in the derivation of conceptual rainfall -runoff model parameters. Proceedings of the 15th Conference on Hydrology, Long Beach CA., Amer. Meteor. Soc. 10-14 January, 2000, 103-106. Koren , V., M. Smith, Z Cui, B. Cosgrove, K. Werner, and R. Zamora. 2010 Modification of Sacramento soil moisture accounting heat transfer component (SAC -HT) for enhanced evapotranspiration. NOAA Technical Report NWS 53, Oct 2010, 66pp Koren, V., S. Reed M. Smith, Z Zhang, and D.-J. Seo 2004. Hydrology laboratory research modeling system (HL-RMS) of the U S. National Weather Service. Journal of Hydrology, 291: 297-318. Koren, V., M. Smith, and Q Duan. 2003. Use of a priori parameter estimates in the derivation of spatially consistent parameter sets of rainfall -runoff models. In: Calibration of Watershed Models: Water Science and Applications 6, AGU Press, Duan et al., Editors, 239-254. Lazo, J. K., M. Lawson P.H. Larsen and D.M. Waldman. 2011. U.S economic sensitivity to weather variability Bulletin of the American Meteorological Society, 92(6):709-720. League, C.E. W. Diaz, B. Philips, E.J. Bass, K. Kloesel, E. Gruntfest and A. Gessner. 2010 Emergency Manager Decision Making and Tornado Warning Communication. Meteorological Applications, 17(2):163-172. McLaughlin, D., E. Knapp, Y. Wang, V. Chandrasekar. 2007. Distributed weather radar using x- band active arrays. Proceedings of IEEE Radar Conference 2007, Waltham, MA, April 17-20, 2007. McLaughlin, D., D. Pepyne, V. Chandrasekar, B. Philips, J. Kurose, M. Zink, K. Droegemeier, S. Cruz-Pol F. Junyent, J. Brotzge, D. Westbrook N. Bharadwaj, Y. Wang, E. Lyons, K. Hondl, Y Liu, E. Knapp M. Xue A. Hopf, K Kloesel, A DeFonzo, P. Kollias, K. Brewster R. Contreras, T. Djaferis, E Insanic, S. Frasier and F. Carr. 2009. Short - wavelength technology and the potential for distributed networks of small radar systems. Bulletin of the American Meteorological Society, 90(12): 1797-1817. Montz, B. and E.C. Gruntfest. 2002. Flash flood mitigation: recommendations for research and applications. Environmental Hazards, 4:15-22. National Climatic Data Center. 2012. Storm Events. Available from http://www4.ncdc.noaa.gov/cgi-win/wwcgi.dll?wwEventr-Storms (Accessed 25 Feb., 2012). National Weather Service. 2010a. Weather Fatalities. Available from www.weather.gov/os/hazstats.shtml. (Accessed 25 Feb., 2012). National Weather Service. 2010b. United States flood loss report - water year 2010. Available from http://www.nws.noaa.gov/hic/flood_stats/Summaries/WY2010.pdf (Accessed 25 Feb., 2012). 2 Pepyne, D., D. Westbrook, B. Philips, E. Lyons, M. Zink and J. Kurose. 2008. Distributed collaborative adaptive sensor networks for remote sensing applications Proceedings of the 2008 American Control Conference Seattle, Washington USA, 11-13 June 2008. Philips, B., D Westbrook, D. Pepyne, E.J. Bass D.J. Rude and J Brotzge. 2008 User Evaluations Of Adaptive Scanning Patterns in the CASA Spring Experiment 2007. 2008 IEEE Geoscience and Remote Sensing Society July 6-11, Boston, MA. Philips, B. V. Chandrasekar, J Brotzge, M. Zink, H. Rodriguez, C League and W. Diaz. 2010. Performance of the CASA radar network during the May 13 2009 Anadarko tornado Preprints, 15th Symp. Meteor Observ. Instrumentation, Atlanta, GA, 17-21 January 17- 21, 2010. Philips, B , C. League, J. Brotzge and E. J. Bass. 2012. Emergency manager use of high resolution radar data during the May 24, 2011 Oklahoma tornado outbreak: a lesson socio-technical system design American Meteorological Society's 92nd Annual Meeting, 22-26 January, 2012, New Orleans, LA, P.648. Pollin, R., J. Heintz and H. Garrett -Peltier. 2009. The economic benefits of investing in clean energy: how the economic stimulus program and new legislation can boost U.S. economic growth and employment. Department of Economics and Political Economy Research Institute (PERI), University of Massachusettes. Available from http://www.americanprogress.org/issues/2009/06/pdf/peri_report.pdf (Accessed 28 Feb. 2012). PostGazette.com 2011. Flash flood warnings systems measuring water levels are costly. Available from http.//www.post-gazette.com/pg/11240/1170159-53-0.stm (Accessed 25 Feb., 2012). National Research Council (2009). Observing weather and climate from the ground up: a nationwide network of networks. National Academies Press, 250 pp National Research Council (2010). When weather matters: science and services to meet critical societal needs. National Academies Press, 198 pp. Reed, S., J. Schaake, and Z. Zhang. 2007. A distributed hydrologic model and threshold frequency -based method for flash flood forecasting at ungauged locations. Journal of Hydrology 337 402-420. Reed, S., V. Koren M. Smith, Z Zhang, F. Moreda, D.-J. Seo, and DMIP Participants. 2004. Overall distributed model intercomparison project results. Journal of Hydrology, 298 (1- 4): 27-60. Rossman, L.A. 2010. Storm water management model: user s manual, version 5.0. Available from http.//www.epa.gov/nrmrl/pubs/600r05040/600r05040.pdf. (Accessed 25 Feb , 2012). Ruin, I., J.-C. Gaillard and C. Lutoff. 2007. How to get there? Assessing motorists' flash flood risk perception on daily itineraries. Environmental Hazards, 7: 235-244. Ruzanski, E. V. Chandrasekar. 2011. Scale filtering for improved nowcasting performance in a high -resolution x-band radar network. IEEE Transactions on Geoscience and Remote Sensing, 49(6):2296-2307. Seo, D.-J., H. Herr and J. Schaake. 2006. A statistical post -processor for accounting of hydrologic uncertainty in short-range ensemble streamflow prediction, Hydrology and Earth System Sciences Discussions, 3: 1987-2035. Seo, D.-J., A. Seed and G. Delrieu. 2010. Radar -based rainfall estimation, chapter in AGU Book Volume on Rainfall: State of the Science, F. Testik and M. Gebremichael Editors. Smith, J.A. M.L. Baeck, Y Zhang, and C.A. Doswell. 2001. Extreme rainfall and flooding from supercell thunderstorms. Journal of Hydrometeorology, 2(5):469-489. Smith, M B. D.-J Seo V.I. Koren, S. Reed, Z. Zhang, Q.-Y. Duan F Moreda and S Cong. 2004. The distributed model intercomparison project (DMIP): motivation and experiment design. Journal of Hydrology, 298(1-4): 4-26. 3 Smith, M., V. Koren, Z. Zhang, Y. Zhang S. Reed, Z Cui, F. Moreda, B. Cosgrove, N. Mizukami, E. Anderson, and DMIP 2 Participants. 2012. Results from the DMIP 2 Oklahoma Experiments. Journal of Hydrology 418-419: 17-48 Trainor, J.E. 2011. Do false alarm rates lead to desensitization? Presented at Weather Ready Nation. A Vital Conversation, 13 December, 2011 Norman, OK. University Corporation for Atmospheric Research (UCAR) 2010. Flash flood early warning system reference guide. Available from www meted.ucar edu/hazwarnsys/ffewsrg/FF_EWS.pdf. (Accessed 25 Feb., 2012). US Census. 2010 Available from http://2010.census.gov/2010census. (Accessed 25 Feb., 2012). Wang, Y. and V. Chandrasekar. 2010. Quantitative precipitation estimation in the CASA X-band dual -polarization radar network. Journal of Atmospheric and Oceanic Technology, 27: 1665-1676. Zink, M. E. Lyons, D. Westbrook, J. Kurose and D.L Pepyne 2010. Closed loop architecture for distributed collaborative adaptive sensing of the atmosphere. meteorological command and control. International Journal of Sensor Networks. 7(1/2): 4-18. 4 M&C Review Page 1 of 2 COUNCIL ACTION: Approved on 1/15/2013 Official site of the City of Fort Worth, Texas FORT WORT II DATE: 1/15/2013 REFERENCE NO.: **C-26055 LOG NAME 20CASAYRI CODE C TYPE CONSENT PUBLIC HEARING: NO SUBJECT: Authorize Execution of a Memorandum of Understanding with the University of Massachusetts, Amherst and Payment of Grant Matching Funds in the Aggregate Amount of $300,000.00 to Integrate Weather Radar Data into a Flood Warning System (ALL COUNCIL DISTRICTS) --, RECOMMENDATION: It is recommended that the City Council authorize the execution of a Memorandum of Understanding with the University of Massachusetts, Amherst and payment of grant matching funds in the aggregate amount of $300,000 00 to integrate weather radar data into a flood warning system. DISCUSSION: The Memorandum of Understanding (MOU) recommended by this Mayor and Council Communication provides for the City's participation in a regional initiative to dramatically improve early warning capabilities related to hazardous weather events. As an end product of this effort a system will be in place for high resolution, real-time rain intensity data to be automatically fed into and processed by the drainage models for two of the largest watersheds in the city (including parts of Council Districts 3 5, 6, 8 and 9). Both of these watersheds include chronic flash flood areas. The new system will allow 15 minutes or more of advanced warning of impending flash floods in these watersheds. This effort will also provide the software platform for progressively making similar connections for other watershed models throughout the City to eventually develop greater flood warning capability citywide. The primary source of funding for this initiative is a National Science Foundation grant to the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) in the amount of $1,340,000.00. The agency administering the work funded by this grant is the University of Massachusetts, Amherst. By this MOU the City of Fort Worth Stormwater Utility commits to provide a total in the amount of $300,000 00 (two installments of $150,000.00 each) over the next two years in support of the initiative. Other partners include: the City of Fort Worth Emergency Management Office the North Central Texas Council of Governments, and the National Weather Service Office of Science and Technology. The central feature in the flood warning system to be developed as a part of this MOU is the CASA radar network. The radar technology used in the CASA system is significantly more precise than conventional weather radar systems, allowing much more accurate forecasts to be developed much earlier during the development of a storm event. The specific focus of the effort facilitated by the recommended MOU will be the development of software to integrate rainfall estimates generated by the CASA system into existing drainage models. Flash flooding is the most deadly weather hazard in Texas. Since 1986 there have been 17 fatalities in the City of Fort Worth as a result of automobiles being swept off of the road during flood events The work that will be accomplished via this MOU will greatly contribute toward mitigating the risk of future such tragedies in Fort Worth by. improving the lead time for emergency response to flash flood events improving precision for predicting in advance the specific locations where flooding is expected, and developing effective communication strategies for warning the public of impending flooding conditions. http:/lapps.cfwnet org/council_paeketlmc review.asp?ID=17900&councildate=1/15/2013 02/19/2013 M&C Review Page 2 of 2 MWBE - A waiver for the goal for MBE/SBE subcontracting requirements was requested and approved by the MWWBE Office because the purchase of goods or services is from sources where subcontracting or supplier opportunities are negligible This study includes ALL COUNCIL DISTRICTS. FISCAL INFORMATION/CERTIFICATION: The Finance Director certifies that funds are available in the current operating budget, as appropriated, of the Stormwater Utility Fund. TO Fund/Account/Centers FROM Fund/Account/Centers 1) PE69 539120 0209207 $300.000.00 Submitted for City Manaaer's Office by: Fernando Costa (6122) Oriainatina Department Head: Douglas W. Wiersig (7801) Additional Information Contact: Amy Cannon (2289) ATTACHMENTS http://apps.cfwnet org/council_packet/mc_review.asp?ID=-17900&councildate=1 /15/2013 02/19/2013