Dr. Paul Houser - A vision for an ultra-high resolution integrated water cycle observation and prediction system.

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Dr. Paul Houser - A vision for an ultra-high resolution integrated water cycle observation and prediction system.

  1. 1. A vision for an ultra high resolution integrated water cycle observation and prediction system Paul R. Houser, George Mason University “Science exists to serve human welfare. It’s wonderful to have the opportunity given us by society to do basic research, but in return, we have a very important moral responsibility to apply that research to benefiting humanity.” Walter Orr Roberts phouser@gmu.edu Paul R. Houser,25 February 2011, Page 1
  2. 2. Presentation Outline• Climate Change and the Global Water Cycle• Solution Strategies: – Water Cycle Science Integration • Advanced Land Modeling Vision • Water Cycle Mission Vision • Community of Practice – Science making a difference • Case Studies Paul R. Houser, February 25, 2011, Page 2
  3. 3. The Water and Energy CycleWater in the climate system Role of the Water & Energy Cyclefunctions on all time scales: in the Climate System: From hours to centuries •Water exists in all three phases in the climate system; its phase transitions regulate global and regional energy balances •Water vapor in the atmosphere is the principal greenhouse gas; clouds represent both positive and negative feedbacks in climate system response •Water is the ultimate solvent which mediates the biogeochemical and element cycles •Water directly impacts and constraint human society and its well-being. The Energy and Water Cycle is tightly intertwined •Solar radiation drives and feedbacks with the water cycle •Energy is transferred through water movement and phase change Paul R. Houser, February 25, 2011, Page 3
  4. 4. “Warming is Unequivocal” IPCC AR4 Report Rising atmospheric temperatureRising sea level Reductions in NH snow cover Paul R. Houser, February 25, 2011, Page 4
  5. 5. Land Precipitation is changing significantly over broad areasSmoothed annual anomalies for precipitation (%) over land from 1900 to 2005; otherregions are dominated by variability. Paul R. Houser, February 25, 2011, Page 5
  6. 6. Water Vapor FeedbackWater vapor responds tochanges in climate, but itdoesn’t drive changes inclimate. It’s a major feedbackthat amplifies global climatechange.IPCC (2007):Observed trends thatdemonstrate the trend, in boththe upper troposphere and atthe surface. Paul R. Houser, February 25, 2011, Page 6
  7. 7. Multi-model ensemble mean change from IPCC GCMs Change in (P-E) for 2100 minus 2000“Dry regions get drier, wet regions get wetter” Held and Soden (2006) “Thermodynamic” component (P-E) mm/day Paul R. Houser, February 25, 2011, Page 7 Vecchi and Soden (2007)
  8. 8. The importance of Water “The Grim Arithmetic of Water”---Official Discussing Emerging Freshwater Crisis---Source: September 2002 National Geographic Population is dramatically increasing Ultimately, a limited water supply will meet limited Paul R. Houser, February 25, 2011, Page 8 needs
  9. 9. Water World: Two thirds of our planet is covered bywater. 97.5% of the water is saltwater. The majority offreshwater is beyond our reach, locked into polar snowand ice.Water to Drink: At just 2% dehydration your performancedecreases by around 20%.Water Diseases: 80% of all illness in developingcountries is caused by water related diseases. 90% ofwastewater in developing countries is discharged directlyinto rivers and streams without treatment.Clean Water: 1.1 billion people in the world still do nothave access to safe water. This is nearly 20% of thepopulation.Water Future: The UN estimates that by 2025, 75% ofthe world population won’t have reliable, clean water.Water Impacts all sectors: Agriculture, industry,transportation, urban development, insurance, health,diaster, etc. Paul R. Houser,25 February 2011, Page 9
  10. 10. Conduct research that addresses end-user needs, and nurture the transition Challenge: of these research results into straightforward end-user solutions.• Information about hydrologic conditions is of critical importance to real-world applications.• A vast array of high-resolution global hydrologic data are becoming available from the next generation instruments and models.• Water management practitioners are increasingly inundated with observations and model output in disparate formats and locations.• We know science and technology has the potential to improve water management….• So, why doesn’t research and technology advances always improve applications? – Inadequate application understanding produces non-optimal science/technology investment. – Inadequate technology (lack of useful water resource observations). – Inadequate integration of information (lack of informative predictions, or bottlenecks in software/hardware engineering).• This leads to a paradigm lock where new science results are isolated by a lack of proven utility, and water management is isolated by legal and professional precedence• So, what can we do about this?• Improved prediction of consequences is the key. – Define research priorities based on needs – Observe key environmental factors – Integrate information from diverse sensors – Assess the current environmental conditions – Predict future environmental possibilities – Link to decision and operation support systems• Move from observation to predict consequences: Integrated environmental information systems adapt advanced sensor webs, high-performance prediction systems, and decision support tools to minimize uncertainties Paul R. Houser, February 25, 2011, Page 10
  11. 11. Linking Science to Consequences End-to-end coordination enabling understanding and prediction of the Earth system: Research driven by the needs of society Use the adequate tool for the job… To deliver social, economic and environmental benefit to stakeholders throughsustainable and appropriate use of water by directing towards improved integrated water system management Paul R. Houser, February 25, 2011, Page 11
  12. 12. Water Cycle Questions What are the causes of water cycle variations? Are variations in the global and regional water cycle predictable? How are water and nutrient cycles linked?Objective: Quantify and predict water cycle consequences of earth system variability and change. – Integrate research across traditional disciplines – An end-to-end program that transitions theoretical research to academic/public education and real-world application, – Cultivate partnerships with universities, government, and international agencies.• Focused investments towards better water cycle prediction: Observation Understanding Models Prediction Consequences Process Assimilation Assessment Resolution Applications Data Initialization Synthesis Coverage Education Proficiency Diagnosis Analysis Coupling Linkages Prognosis Solutions Observation Validation Modeling Paul R. Houser, February 25, 2011, Page 12
  13. 13. Water Cycle Prediction Components • Modeling: Diagnose state-of-the-art “operational” Earth system models; conduct sensitivity and predictability experiments; infuse process-scale• Observation: Quantify understanding to predict water cycle extremes, long-term water cycle enhance prediction through observational trends & variability; constraints; explore limits of water cycle progress toward a predictability coordinated water cycle observation system; extract knowledge and understanding from diverse observations. • Solutions: Enhance operational decision support tools; Engage in public and research community education; link to other earth system components Paul R. Houser, February 25, 2011, Page 13
  14. 14. Climate Change Consequences delivered through Water & Energy Cycle Global Water and Energy Cycle Modeling and ComputingKey Earth Science Questionsrequiring Advances in EarthSystem ModelingVariabilityHow are global precipitation,evaporation and the cycling ofwater changing?ResponsesWhat are the effects of cloudsand hydrologic processes onglobal climate?PredictabilityHow are variations in weather,precipitation, and water Grand Challenge: interpret observations to instillresources related to globalclimate change? understanding and information into global predictionLink to Biogeochemistry systems to determine the predictable water and energyHow do ecosystems respond to, cycle variations and trends, particularly precipitation andand affect global environmental replenishment of water resources that are associated with climate changes.change and the carbon cycle?Enabling PredictionHow can weather forecasts be •Explicitly simulate weather systems in climate models.improved by new space-basedobservations, data assimilation, •Assimilate and initialize models using water cycle observations.and modeling? •More effective representations of sub-grid scale physical processes. Use global observations for analysis and monitoring of the Earth-system and validation, initialization and assimilation in global prediction systems Paul R. Houser, February 25, 2011, Page 14
  15. 15. Can climate models effectively represent water and energy cycle processes? Use global satellite data to: •Advance understanding and model physics •Improve initialization and parameterization •Diagnose and identify predictable changes •Predict consequences & develop solutions Global Precipitation 0.125 mm/d Integrate disparate 0 mm/d observations PREDICTED Process-scale modeling or “Super-parameterizations” 2.8 mm/d 2.5 mm/d OBSERVED Weather resolving climate models? Paul R. Houser, February 25, 2011, Page 15
  16. 16. Advance Understanding and Model PhysicsClimate models’ grid-box representation of Earth’s processes... Each grid-box can only represent the However, controlling processes of the “average” conditions of its area. water cycle (e.g. precipitation) vary over much smaller areas. How can climate models effectively represent the controlling processes of the global water cycle? “Conventional” approach: make the model grid-boxes smaller (increase resolution) •Slow progress: may take ~50 years to be computationally feasible Breakthrough approach: Simulate a sample of the small-scale physics and dynamics using high resolution process-resolving models within each climate model grid-box •“Short-cut” the conventional approach (~10 years to implement) Paul R. Houser, February 25, 2011, Page 16
  17. 17. State of the Water and Energy CycleVariable ↓ Sphere → Ocean Terrestrial Atmosphere upper ocean currents (I/S) topography/elevation (I/S) land cover (I/S) wind (I/S ) sea surface temperature (I/S) leaf area index (I) upper air temperature (I/S) sea level/surface topography (I/S) soil moisture/wetness (I/S) surface air temperature (I/S) sea surface salinity (I/S) soil structure/type (I/S) sea level pressure (I) sea ice (I/S) permafrost (I) upper air water vapor (I/S) Internal or State wave characteristics (I/S) vegetation/biomass vigor (I/S) surface air humidity (I/S) Variable mid- and deep-ocean currents (I) water runoff (I/S) precipitation (I/S) subsurface thermal structure (I) surface temperature (I/S) clouds (I/S) subsurface salinity structure (I) snow/ice cover (I/S) liquid water content (I/S) ocean biomass/phytoplankton (I/S) subsurface temperature (I/S) subsurface carbon(I), nutrients(I) subsurface moisture (I/S) subsurface chemical tracers(I) soil carbon, nitrogen, phosphorus, nutrients (I) ocean surface wind & stress (I/S) incoming SW radiation (I/S) sea surface temperature (II/S ) incoming SW radiation (I/S) incoming LW radiation (I/S) surface soil moisture (I/S) surface soil incoming LW radiation (I/S) PAR radiation temperature (I/S) surface air temperature (I/S) surface winds (I) surface topography (I/S) surface air humidity (I/S) surface air temperature (I/S) land surface vegetation (I/S) precipitation (I/S) surface humidity (I/S) CO2 & other greenhouse gases, ozone & Forcing or Feedback evaporation (I/S) albedo (I/S) chemistry, aerosols (I/S) Variable fresh water flux (I/S) evapotranspiration (I/S) evapotranspiration (I/S) air-sea CO2 flux (I) precipitation (I/S) snow/ice cover (I/S) geothermal heat flux (I) land use (I/S) SW and LW surface radiation budget organic & inorganic effluents (I/S) deforestation (I/S) (I/S) biomass and standing stock (I/S) land degradation (I/S) solar irradiance (S) biodiversity (I) sediment transport (I/S) human impacts-fishing (I) air-land CO2 flux (I) BLUE=Water Cycle Variable; RED=Energy Cycle Variable; GREEN=Carbon/Chemistry Variable; BLACK=Boundary condition d Q  E  P dt Paul R. Houser, February 25, 2011, Page 17
  18. 18. Multi-Scale Information Remote SensingSoil Moisture Variability Data DEM Vis/IR Vegetation Microwave Soil Microwave Moisture Precipitation Total Variability Land Cover-Induced Precipitation- Topography- Induced Induced 1m 10 m 100 m 1 km 10 km 100 km Scale Paul R. Houser, February 25, 2011, Page 18
  19. 19. Water & Energy Cycle Prediction Strategy Global Warming Scenarios Operational Climate Models (GFDL, NCAR, NCEP) Global Water& Energy Cycle Prediction System Next-generationIntegrated Water-Cycle Advance Understanding and Model Physics Observation System: Ground- and Improve Initialization & Assimilation Space-Based Observing Programs Diagnose and Identify Predictable Changes Water & Energy Cycle Prediction Developing Advanced Process-Resolving Models Useful prediction is critical – it is the link to stakeholders. We must move towards a new paradigm of climate models that produce useful weather-scale, process-scale, and application-scale prediction of local extremes (not just mean states). We must more fully constrain climate models with observations, to improve their realism and believability. Paul R. Houser, February 25, 2011, Page 19
  20. 20. Terrestrial hydrologic cycle: many coupled processesWeather generating processes Biogeochemical cycles (N, C) Water resources Paul R. Houser, February 25, 2011, Page 20
  21. 21. Yet it is usually simulated with disconnected models Land Surface ModelGroundwater/Vadose Model Atmospheric Model Surface Water Model Paul R. Houser, February 25, 2011, Page 21
  22. 22. Land Model Developments•Addition of carbon-nitrogen cycle model•Dynamic Vegetation Modeling•Urban model•Organic soil / deeper soil column / Groundwater•Subgrid/Fine mesh: high res land and downscaling•Ice sheet model•Irrigation/Landuse/Agriculture•Integrated global crop model•Dynamic wetlands•Lake Model•River Routing and Diversions•Isotopes•Multi-Layer Snow Modeling•Radiance Forward Model Coupling•Sensor Webs•Implicit Land-Atmosphere Coupling Paul R. Houser, February 25, 2011, Page 22
  23. 23. Paul R. Houser, February 25, 2011, Page 23
  24. 24. Paul R. Houser, February 25, 2011, Page 24
  25. 25. GLASS “LOcal-COupled” Project:“LoCo” Overarching goal:Accurately understand, model and predict the role of local TOA Radiationland – atmosphere coupling in the evolution of land-atmosphere fluxes and state variables, including clouds. Single Horizontal Column Science Questions: Flux Model Divergences•Are the results of PILPS, GSWP, or data assimilationexperiments affected by the lack of land surface-atmosphere coupling? Land•Can we explain the physical mechanisms leading to the Surfacecoupling strength differences found in GLACE or other Modelcoupled NWP/climate experiments? (www.arm.gov) A Single Column Model•Is there an observable diagnostic that quantifies the role oflocal land-atmosphere coupling? Paul R. Houser, February 25, 2011, Page 25
  26. 26. Objective: A 1/8 degree (~10km) global land modeling and assimilation system that uses all relevant observed forcing,storages, and validation. Expand the current N. American LDAS to the globe. 1km global resolution goalBenefits: Enable improved land-atmosphere understanding, hydrological and climate prediction, transfer research toapplication, and enable consistent inter-site comparison (I.e. GEWEX).Open Collaboration: Encourage international participation through code and data access, and cooperative evaluation.Coupled Connections: GLDAS is the off-line land-surface development strategy for DAO, NCEP, NCAR, and NSIPP.GLDAS Data Sources: DAO(GEOS), NCEP(GDAS), and ECMWF NWP “base” forcing.Force with Observations: IR, TRMM, and SSM/I Precipitation, Geostationary Insolation.Assimilation and Validation: GTS, IR derived Surface Temperature, AMSR/TRMM Soil Moisture, MODIS Snow Cover, GRACEtotal water storage change. Consistent Global Intercomparison Land Data Observed Forcing Assimilation DataInsertion of Datainto the Model n t io ra t eg l In de Mo Improved products, Obs 4DDA Model CEOP predictions, understanding Paul R. Houser, February 25, 2011, Page 26
  27. 27. Vision: A near-real time “patched” Global LDASAction: Overlay high-res regional LDAS model forcing and output over baseline low-res GLDAS model for best local informationAdvantage: Share land-hydrology data/forcing globally in a Hydrologic “GTS” framework Issues: Global consistency studies Paul R. Houser, February 25, 2011, Page 27
  28. 28. Future of Hyper-Resolution Land ModelingFuture Land Modeling Development Topics: •Dynamic water-table parameterization (i.e. TOPMODEL concepts). •Dynamic vegetation (including human management). •Nitrogen & carbon dynamics. •Runoff routing, including anthropogenic abstractions. •Land Data Assimilation - Adjoint development?Progress toward a fully process-scale resolving model of land surface hydrology, atmospheric dynamics, andcloud processes over the global domain.Integrate all obviously interdependent land-atmosphere processes into a common ultra-resolution (100’s ofmeters) framework for Earth system modeling, through fusion of traditional land surface hydrology moduleswith boundary-layer turbulence and cloud process modules.Envision two, eventually convergent paths toward global land-atmosphere coupling: 1) Implement traditional cloud parameterization and atmospheric turbulence schemes and implicitly couple those to patch-based land models at highest possible resolution; 2) Develop true global process-resolving coupled land-atmosphere models in a phased approach: (a) off-line land-cloud process resolving studies (b) land-cloud super-parameterizations based on sampling the relevant process scales (c) nested land-cloud resolving models in a GCM framework (d) true global ultra-high-resolution global cloud-land process resolving model Paul R. Houser, February 25, 2011, Page 28
  29. 29. Global Water and Energy Cycle: Observation Strategy Future: Water Cycle Mission Observation of water molecules through the atmosphere and land surface using an active/passive hyper spectral microwave instrument. -2 - O H +1 H +1 + ICEsat Quantity Spatial Temporal Frequency Aquarius Jason Resolution Resolution NPOESS Groundwater 50 km 2 weeks 100 MHz? SMOS Soil Moisture 10 km 3 days 1.4 GHz Landsat/SPOT Salinity 50 km 2 weeks 1.4 GH Geostationary Freeze/thaw 1 km 1 day 1.2 GHz DMSP Rain 5 km 3 hour 10-90 GHz NOAA Falling Snow 5 km 3 hour 150 GHz Hydro Altimetry Snow 1-5 km 1 day 10-90 GHz Etc. TPW 10 km (sea) 3 hour 6-37 GHz (land) 3 hour 183 GHz Need a strategy to compare and Temperature 10 km Primary missing global integrate and make sense of (sea) 3 hour 6-37 GHzobservations: Precipitation, Soil existing observations (land) 3 hour 6-37 GHz ET (4DDA) 5 km 3 hour 1.4-90 GHz Moisture, Snow Paul R. Houser, February 25, 2011, Page 29
  30. 30. Water Cycle Mission:Microwave radiation is modified strongly by the dipole of water molecules• Through the earth’s surface and in the atmosphere• Dependent on the microwave radiation source and frequency and on the water phase and concentration.• soil moisture, rainfall, snowfall, snow cover, water vapor, total precipitable water, soil freeze-thaw, ocean salinity, vegetation water-content, surface inundation, streamflowThere is the potential of developing a water cycle mission:• high-resolution, active-passive, multi-frequency microwave mission• make simultaneous observations of almost every critical water-cycle process, and bring water-cycle science to a more compelling level.• This mission could be built around a single, elegant, highly-integrated large aperture (10’s of meters in size) multi-frequency active/passive microwave antenna• Could be deployed in a geostationary orbit, or as part of a polar orbiting constellationIf we want to achieve this goal, we will need to take decisive, calculated steps:• Focused experimental ground, air, and space based instruments (i.e. TRMM,GPM,HYDROS,AQUARIUS).• Development of robust microwave radiative-transfer algorithms to derive the desired quantities.• Develop mission concept options in the near-term.Non-microwave water cycle observations involving visible and infrared derived snow cover, surfacetemperature, and cloud top temperature, as well as lidar or radar altimetry derived river and lake levelswould further increase the relevance of a potential “water cycle” mission. Paul R. Houser, February 25, 2011, Page 30
  31. 31. Water Cycle Mission: Options? Quantity Spatial Temporal Frequency Resolution Resolution Groundwater 50 km 2 weeks 100 MHz? Soil Moisture 10 km 3 days 1.4 GHz Salinity 50 km 2 weeks 1.4 GH Freeze/thaw 1 km 1 day 1.2 GHz Rain 5 km 3 hour 10-90 GHz Falling Snow 5 km 3 hour 150 GHz Snow 1-5 km 1 day 10-90 GHz TPW 10 km (sea) 3 hour 6-37 GHz (land) 3 hour 183 GHz Temperature 10 km (sea) 3 hour 6-37 GHz (land) 3 hour 6-37 GHz ET (4DDA) 5 km 3 hour 1.4-90 GHz Paul R. Houser, February 25, 2011, Page 31
  32. 32. Water Cycle Mission: Demonstration?Challenge: progress from single-variable water-cycle instruments to multivariable integrated instruments.Vision: dedicated high-resolution water-cycle microwave-based satellite mission may be possible based on large-aperture antenna technology that can harvest the synergy that would be afforded by simultaneous multichannel activeand passive microwave measurements.Demonstration: A partial demonstration of these ideas can be realized with existing microwave satellite observationsto support advanced multivariate retrieval methods that can exploit the totality of the microwave spectral information.Impact: Simultaneous multichannel active and passive microwave retrieval would allow improved-accuracy retrievalsthat are not possible with isolated measurements. Paul R. Houser, February 25, 2011, Page 32
  33. 33. Observation and prediction tools are advancing ? Grid Computing Decision Support Systems Can we link these advanced tools Prediction Models reduce uncertainties in end-uses? Critical ApplicationSatellite Sensor Web A vision for the future: Integrated environmental information systems • Integration of • Advanced/smart sensor webs (in-situ, airborne, and space-based) • High-performance prediction systems (Global-scale, locally relevant) • Decision support tools (planning, management, operations) • Enables system adaptation to minimize uncertainties • Sensing, monitoring, prediction, and application at global, regional and local. • Collaborative, coherent, consistent and consolidated environmental data collection, fusion, prediction and distribution. • What does it do? • interoperable: different kinds of sensors, models, and DSS can be linked; • intelligent: components communicate with each other; • dynamic: components are "position aware" and mobile; • flexible: components handle various modes of data transmission; • expandable: new components will need to be easily added. Paul R. Houser, February 25, 2011, Page 33
  34. 34. Today: Tomorrow: Large space-based Observatories Integrated environmental information system Smart Sensor Web SWARM Adaptive Resource ManagerRM C2 Single sensor retrievals Coordination for distributed monitoring,Spatial/temporal inconsistency processing, and decision makingParameter-driven requirements Easy deployment of technology and scalability Multiple sensor retrievals Spatial/temporal consistency Integrated cross-sensor calibration System-driven requirements Reconfigurable ground and space information systems Paul R. Houser, February 25, 2011, Page 34
  35. 35. Earth Science Vision Distributed Information-System-in-the-Sky Commercial Optical CrosslinkCommunication Network Ka Crosslink Optical Interoperating Measurement Crosslink Systems (Air / Spacecraft / In-situ) with modeling systems Flexible Measurement Network Architecture Direct Distribution of Derived Active Products Optical Ka Network Computing-in-the-Sky Ka Ka Passive Optical In-situ User PC Based GS Comm Gateway Digital Library Metadata Warehouse Paul R. Houser, February 25, 2011, Page 35
  36. 36. 4DDA Value- Added Products Prediction Models Airborne Sensors Decision Support Systems Integrated environmental information system The “internet” of Earth InformationSatellite Sensor Web Critical Application Surface Remote-Sensing Data Mining In-Situ Sensors Grid Computing Paul R. Houser, February 25, 2011, Page 36
  37. 37. Linking Science to Consequences End-to-end coordination enabling understanding and prediction of the Earth system: Research driven by the needs of society To deliver social, economic and environmental benefit to stakeholders throughsustainable and appropriate use of water by directing towards improved integrated water system management Paul R. Houser, February 25, 2011, Page 37
  38. 38. WaterNet: A Water Cycle Community of Practice Demonstration• Advances are currently available to develop solutions to the global watercrisis, but they remain largely unused. Effective communication is the criticaltask to enable using these advances to develop solutions and set futureresearch priorities.•WCCoP (Water Cycle Community of Practice) • has knowledge of both the decision support needs and the cutting-edge research results • can formulate a broad array of water cycle partnerships and solutions.Questions remain: •Is a WCCoP really needed? •How do we enable the formulation of a WCCoP? •What partners & elements already exist? •What tools & services are available? •If we build it, will they come? Paul R. Houser, February 25, 2011, Page 38
  39. 39. Objective: A 1/4 degree (and other) global land modeling and assimilation system that uses all relevant observed forcing, storages, and validation. Expand the current N. American LDAS to the globe. 1km global resolution goal Consistent Global Intercomparison Soil SW down Moisture ET (May 2001) Observed Forcing U.MD AVHRR- Tsurface Veg Cover Land Data Assimilation Data CEOPInsertion of Datainto the Model on at i gr te l In de Mo Merged Ppt Snow WE Forcing Improved products, Obs 4DDA Model predictions, understanding Paul R. Houser, February 25, 2011, Page 39
  40. 40. Coupled Model Forecast: 1988 Midwestern U.S. Drought (JJA precipitation anomalies, in mm/day) Observations Predicted: AMIP Without soil moisture initialization With soil moisture initialization 10 Predicted: LDAS Predicted: Scaled LDAS 3. 1. 0.5 0.2 0 -0.2 -0.5 -1. -3. -10 Koster et al., 2004 Paul R. Houser, February 25, 2011, Page 40
  41. 41. Land Information System http://lis.gsfc.nasa.gov Co-PIs: P. Houser, C. Peters-Lidard 2005 NASA SOY co-winner!! Summary: LIS is a high performance set of land surface modeling (LSM) assimilation tools. Applications: Weather and climate model initialization and coupled modeling, Flood and water resources, precision agriculture, Mobility assessment … External LIS Internal Memory Wallclock time CPU time (MB) (minutes) (minutes) LDAS 3169 116.7 115.8 LIS 313 22 21.8 reduction factor 10.12 5.3 5.3 200 Node “LIS” ClusterOptimized I/O, GDS Servers Paul R. Houser, February 25, 2011, Page 41
  42. 42. LIS Future: Enabling Process-Resolving Earth System ModelsLIS uses interoperability standards:•The Earth System Modeling Framework (ESMF)•Assistance for Land Modeling Activities (ALMA)•GrADS Data Server (GDS) Atmos.•Open-source Project for a Network Data Access Protocol (OPeNDAP) ModelsEnables LIS integration Oceanwith other components: LIS• Weather Research and Forecasting (WRF) model Models• Goddard Cumulous Ensemble (GCE) model• etc.LIS Impact Example: Coupling LIS to a Weather Model Observed Rainfall With Without LIS LIS 12-Hours Ahead Atmospheric Model Forecasts Paul R. Houser, February 25, 2011, Page 42
  43. 43. Global Scale Initiative as an Solution Network DemonstrationLinks Geophysics of H20, Governance, Vulnerability, Supply Limitation Imposed by Pollution, Ecosystem Flow Requirements (Links to NASA NEWS, GWSP, GEWEX/HAP, IGWCO, ICSU Socioeconomics Group) Meta-data & Link Archive Integrative GWSP Models Value-addedExternal GWSP And Outputs Indicator Generators Data ATLAS(e.g. NASA,ESA, JAXA) Evaluate & Improve Internal Geospatial Archive Paul R. Houser,25 February 2011, Page 45
  44. 44. The Coral Reef Early Warning System (CREWS) Network: marine environmental monitoring to support research and marine sanctuary management A CREWS Station is a "smart" meteorological and oceanographic monitoring platform installed near coral reef areas, software-configured to ensure the gathering of high quality data and the eliciting of automated alerts when specified environmental conditions occur (e.g., those thought to be conducive to coral bleaching) ...and information synthesis products Surface-truth for satellite products, coral bleaching alerts, data quality alerts; and matching patterns as proscribed by biologists, oceanographers and the public (fish & invertebrate spawning, migration, bloom conditions, good fishing and/or diving conditions, etc.) Paul R. Houser, February 25, 2011, Page 46
  45. 45. CNRFC Demonstration Project Goals:• Demonstrate value added to National Weather Service Decision Support Tools (DSTs) by various remote sensing and land surface modeling research applications to improve floodforecasting, estimation of snow pack and rainfall runoff, stream flow and water supply forecasts. • Evaluate impacts of improved forecasts on water management Decision Support Tools • Focus future NASA resources on gaps identified Paul R. Houser, February 25, 2011, Page 47
  46. 46. Bureau of Reclamation Study Soil Moisture Analysis Snow Water Equivalent Integration of Land Products: Land Cover, Snow,Evapotranspiration, Streamflow, Soil Moisture, Other Goal to produce successful demonstration of these applications- based studies using satellite data for applications such as Hydro- energy management. Paul R. Houser, February 25, 2011, Page 48
  47. 47. A vision for making a differenceApply state-of-the-art capabilities in water cycle observation, analysis, and modeling to reach new frontiers in earth system prediction, and enabling real benefit to society. – Integrate research across traditional disciplines – An end-to-end program that transitions theoretical research to academic/public education and real-world application, – Cultivate universities, government, and international partnerships...• Focused investments towards better water cycle solutions: Observation Understanding Models Prediction Consequences Process Assimilation Assessment Resolution Applications Data Initialization Synthesis Coverage Education Proficiency Diagnosis Analysis Coupling Linkages Prognosis Validation Paul R. Houser, February 25, 2011, Page 49

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