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Jet Propulsion Laboratory California Institute of Technology JPL’s California Water Mission A Strawman Consideration for a Virtual Flight Project.

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Presentation on theme: "Jet Propulsion Laboratory California Institute of Technology JPL’s California Water Mission A Strawman Consideration for a Virtual Flight Project."— Presentation transcript:

1 Jet Propulsion Laboratory California Institute of Technology JPL’s California Water Mission A Strawman Consideration for a Virtual Flight Project

2 Jet Propulsion Laboratory California Institute of Technology Supposition Progress on advancing the state of hydrological and land surface models has not kept pace with the need to address critical issues of changing water supply under the pressures of global change and population growing growth. An accelerated effort is required to support research, applications and decision support. We suggest here that the scope of the problem is commensurate with a flight project and also, that a successful effort will only come when we apply the methodologies and rigor of a JPL flight project. Moreover, the bulk of the work is in applied sciences and engineering, and the work paradigm should be consistent with efforts in those disciplines. Enclosed is a notional “project” strategy for a California Water “Mission”

3 Jet Propulsion Laboratory California Institute of Technology Motivation (3 slides) Supposition & Recommendation (1 slide) Mission Goals (1 slide) Mission Objectives (1 slide) Applications Traceability Matrix (1 slides) Project Organization (2 slides) Project Timeline (1 slides) Project Support (1 slide) Additional Considerations (1 slide) Outline

4 Jet Propulsion Laboratory California Institute of Technology External Organizations/Elements Relevant to Water CA Dept of Water Resources, Metropolitan Water District, Local water districts Bureau of Reclamation, USGS Western Governors Association, CDFA, OPR, California agriculture NASA Applied Sciences, Hydrology, MAP programs NOAA and Operational Weather/Climate Prediction Centers California and western U. S. universities Multi-Faceted Stakeholder Needs – Water supply forecasting, reservoir operations, groundwater monitoring, flood/hazard, consumptive use, allocation decisions, etc Quantify and Monitor Water Reserves/Fluxes - Snowpack, Surface Water, Soil Moisture, Groundwater, Evapotranspiration Long-lead Forecasting Information Integrated Hydrologic Data Assimilation & Modeling The science, applied science and decision support demands associated with the CA/regional water problem and its solutions are extraordinary. Science Stakeholders Multi-Mission Modeling Data Assimilation Big Data Motivation for a Virtual Flight Project

5 Jet Propulsion Laboratory California Institute of Technology Internal JPL Organization Elements Relevant to Water 830 – Formulation 870 – Airborne Div32/Sec329, Div33, Div39, etc GCE Climate Center NASA’s “Center” for Snow & Water Availability NSTA – civil, commercial, defense Missions Relevant to CA Water cycle OPERATING AIRS, CloudSat, GPS/COSMIC GRACE, ground-based GPS, InSAR, (SMAP) Airborne e.g. ASO, airSWOT, airMOSS, UAVSAR DEVELOPING MISSIONS (2014-2020) GPS/COSMIC-2 ECOSTRESS, GRACE-FO, SWOT, NISAR The science and programmatic landscape at JPL for addressing water is complex, multi-faceted and has lacked focus & organization. Motivation for a Virtual Flight Project

6 Jet Propulsion Laboratory California Institute of Technology Water is the most intrinsic and pressing problem facing California Are we employing JPL’s absolute best efforts and capabilities to the problem? Motivation for a Virtual Flight Project

7 Jet Propulsion Laboratory California Institute of Technology In order to make significant progress towards advancing the state of hydrological and land surface models for water science and applications, we must recognize that the scope of the problem is commensurate with a flight project and that success will only come when we apply the methodologies and rigor of a JPL flight project*. *This includes project management, specific goals & requirements definition, timeline, reviews, ATM, (applied) science team, product specifications, and start-to-finish stakeholder engagement. Supposition/Recommendation

8 Jet Propulsion Laboratory California Institute of Technology Primary Goal Harnessing the power of JPL water science and engineering to develop state-of-the-art models, data products and data management to support advanced research and sustainable water management for California and the west. Secondary Goals Demonstrate that we can provide an integrated end-to-end, stakeholder-valued solution for societal benefits in water, as done for Natural Hazards/ARIA. Advance the laboratory’s capabilities for using multi-mission support and flight project-level formalism for addressing complex, grand challenge problems. Advance our position for obtaining/leveraging non-NASA support for decision making and increase our attunement to future water mission needs. Advance hydrologic process model components and satellite data assimilation methodologies and export to GMAO/NASA, NCAR and the broader community. Deeper collaborations with NASA/GSFC California Water Mission Goals

9 Jet Propulsion Laboratory California Institute of Technology I.Produce a robust state estimation of California’s freshwater resources. II.Provide subseasonal-to-seasonal forecasts (e.g. 1 week to 2 months) of California’s freshwater resources. Both I & II would include a)snow water equivalent, surface water, soil moisture, groundwater, evaporative demand, flood potential and drought status b)Assimilation or readiness for key water (GRACE, GRACE-FO, SMAP, SWOT) and related missions (e.g. NISAR, GPS, ECOSTRESS) c)spatial coverage over entire state d)daily updates e)spatial resolutions of 10’s kms or less f)quantitative uncertainties. California Water Mission Goals

10 Jet Propulsion Laboratory California Institute of Technology Applicatio n Question Application Concept Application Measurement (or Model) Requirements Applied Sciences Category Potential Host Agency Mission Data Product Projected Mission Performanc e Applicati on Readines s Level Ancillary Measurements What is the current state of CA freshwater resources? Provide state estimates, with quantitative uncertainties, of CA natural freshwater resources, namely snow, surface, soil, and ground water, along with evaporative demand, drought status and flood potential. Measurements of snow, soil moisture, precipitation, surface temperature, surface elevation of land and water, groundwater – with  t of 1 day to < 1 month, and  x of 100’s km or less. Water Management Agriculture Energy Disaster Mitigation NASA & other agencies responsible for in-situ networks Current estimates of SWE, soil moisture, surface water, ground water, with quantitative uncertainties. Spatial Extent: California Spatial Resolution: 10’s km or less Temporal Updates: Daily 5-9 NASA Available MODIS, GRACE, GPM, GPS TBS SMAP, ECOSTRESS, NISAR, SWOT, GRACE-FO DWR Snow pillows, river gauges, soil moisture, reservoir levels, well data 4-5 Data assimilation system capable of providing a hydrologic state estimate and initial conditions for a hydrologic forecast model CA Department of Water Resources How will CA’s freshwater resources change in the coming weeks and seasons? Provide subseasonal-to- seasonal (S2S) forecasts, with quantitative uncertainties, of CA’s freshwater resources – as itemized above. Hydrologic forecast model capable of evolving surface, soil and groundwater forward out to 6 months. Water Management Agriculture Energy Disaster Mitigation CA Department of Water Resources Forecasts of SWE, soil moisture, surface water, ground water, precipitation, evapotranspir ation, and runoff, with quantitative uncertainties. Spatial Extent: California Spatial Resolution: 10’s km or less Temporal Updates: Daily 4-5 Atmospheric Forecasts: Development phase: WCRP/WWRP S2S Project Forecast database (9 Centers’ operational S2S ensemble forecasts, delayed 3 weeks) Operational NCEP’s operational ensemble and/or others available in realtime. Operational atmospheric/S2S forecasts of precipitation, temperature, wind, etc out to 6 months with diurnal resolution. NOAA and/or the consortium of operational S2S agencies 7-9 This is NASA ASP’s format for Traceability. Application Traceability Matrix

11 Jet Propulsion Laboratory California Institute of Technology Applied* Science Team - Div 32 (see next) Deputy Program Managers for Applications (DPAs; see next) Project Manager – Basillo Project Scientist – Famiglietti Business Manger – Daleo Standing Review Board (see next) Capture Input Data Streams “Algorithm” Maintenance Model Hosting & Execution Product Generation Product Archive Product Dissemination Data Systems Div 39 * This is not a research project Project System Engineer - Duren Example/Notional Names Early Adopters Stakeholder Engagement Operational Uptake Project Organization (1/2)

12 Jet Propulsion Laboratory California Institute of Technology Applied* Science Team Hydrology Model- Famiglietti Data Assimilation – Konstantinos Snow/ASO – Painter Snow/Groundwater/GPS – Argus Groundwater/GRACE – Reager, Landerer Groundwater/InSAR – Farr, Liu Surface Water – Konstantinos, David Evapotranspiration – Fisher Precipitation - Behrangi Soil Moisture – Das Hydrologic Extremes - Reager S2S Forecasts – Waliser, Guan Standing Review Board (TBD) All TBD– MWD, SWRCB, OPR USGS, NOAA, BoR D. Lettenmaier – UCLA D. Cayan - UCSD R. Juricich – DWR Systems Expert – JPL Mission Assurance – JPL Deputy Program Managers for Applications (No more than 2) Granger Srinivasan Das Castano * This is not a research project Example/Notional Names Project Organization (2/2)

13 Jet Propulsion Laboratory California Institute of Technology FY’15FY’16FY’17 Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4 Strategic R&TD Consensus on Proposed Mission Objectives, ATM, Products, Organization & Timeline KDP-A KDP-B Mission Concept Details Model, data assimilation and forecasting implementation strategy Data systems scoping Stakeholder & NASA water “Center” planning Pre-Phase APhase APhase BPhase CPhase DPhase E* Component Development Obs and forecast data streams Data assimilation system Hydrology model Run-time environment Product archive/dissemination Early Adopter Activities KDP-C KDP-D Component Test State estimation Hydrologic hindcast Exercise data system Early adopter activities System Integration & Test Near real-time water state estimation Near real-time water forecasts Product generation Delayed product availability Early adopter testing KDP-E Operational Capability* State estimation Hydrologic forecasts Dissemination Stakeholder use *status after FY’17 depends on NASA & stakeholder interest/funding Notional Timelines and Milestones

14 Jet Propulsion Laboratory California Institute of Technology Applied Science Team Current Water R&TD GCE Decision Support Leverage Direct Deputy Program Managers for Applications ASP Water “Center” Project Manager – 0.2 - ? Project Scientist – 0.2 – Water R&TD Business Manger - 0.05 - ? Standing Review Board GCE Decision Support Data Systems Current Big Data R&TD Leverage Direct Project System Engineer – 0.15 - ? How to appropriately leverage sources of support? Funding sources and gaps Project Support

15 Jet Propulsion Laboratory California Institute of Technology Leverages Famiglietti’s UC Center for Hydrologic Modeling, JPL’s Snow & Water “Center”, R&TD Water Initiative, ASO, RHEAS, subsidence & GPS work, our visible profile and momentum on CA, JPL centroid of water missions, S2S connections, etc. By focusing on CA, we avoid a direct/perceived conflict with GMAO and GLDAS, while complementing those efforts via model and data assimilation developments that can be exported to GMAO/GLDAS and broader community; all while enhancing relationships with GSFC Relationship with Snow & Water “Center”: 1) This CA Water Mission is either an uber example of a product/stakeholder development pair, and/or 2) the “Center” is simply perceived as the stakeholder development arm of the Mission. Provides the critical missing framework and foundation for JPL to optimally exploit our/NASA’s multiple water missions – and thus advance our interests in developing multi-mission, project-level architectures to complex, grand challenge (applied) science problems. Synergies and Other Considerations


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