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Terrestrial Snow Two Perspectives: NOAA Weather and Water Operations NASA Earth Science Research

Cryosphere Theme. Terrestrial Snow Two Perspectives: NOAA Weather and Water Operations NASA Earth Science Research. Don Cline. National Operational Hydrologic Remote Sensing Center. Office of Climate, Water and Weather Services, National Weather Service, NOAA.

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Terrestrial Snow Two Perspectives: NOAA Weather and Water Operations NASA Earth Science Research

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  1. Cryosphere Theme Terrestrial Snow Two Perspectives: NOAA Weather and Water Operations NASA Earth Science Research Don Cline National Operational Hydrologic Remote Sensing Center Office of Climate, Water and Weather Services, National Weather Service, NOAA

  2. Operational Drivers: NOAA’s Four Mission Goals • Protect, Restore, and Manage the Use of Coastal and Ocean Resources Through an Ecosystem Approach to Management • Snowmelt is an important component of freshwater input to oceans • Understand Climate Variability and Change to Enhance Society’s Ability to Plan and Respond • Terrestrial snow is a sensitive indicator of climate change, a significant storage component of the global water cycle, and affects weather and climate through several surface energy and mass exchange mechanisms • Several socioeconomic sectors linked to terrestrial snow • Serve Society’s Needs for Weather and Water Information • Snow is a major component of water resources and contributor to flooding • Support the Nation’s Commerce with Information for Safe, Efficient, and Environmentally Sound Transportation • Terrestrial snow directly impacts land transportation

  3. Operational Drivers: Snow Economics “The Value of Snow and Snow Information Services” (2004) - Dr. Rodney Weiher, Chief Economist, National Oceanic and Atmospheric Administration, U.S. Dept. of Commerce Economic Benefits of Snow Economic Costs of Snow

  4. Operational Drivers: Snow Economics “The Value of Snow and Snow Information Services” (2004) - Dr. Rodney Weiher, Chief Economist, National Oceanic and Atmospheric Administration, U.S. Dept. of Commerce Economic Benefits of Snow Economic Costs of Snow “… improved snow information and services have potential benefits greater than $1.3 billion annually.” “… investments that make only modest improvements in snow information will have substantial economic payoffs.”

  5. Snow is critically important to the U.S. 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 Top 20 most significant floods of 20th century (USGS; 9 of 20 were snow-related) Snowmelt flooding affects thousands of lives and causes billions of dollars in damages. Region:Michigan Cause:Rain on snow, Frozen Soils Damage:1 Death, $40 Million Region:New England Cause:Snowmelt, Rain on snow Damage:150+ Deaths, $3.9 Billion Region:Pacific Northwest Cause:Rain on snow Damage:47 Deaths, $2.4 Billion Region:Colorado River Basin Cause:Record Snow Packs Damage:$1.1 Billion Region:Northeast U.S. Cause:Rapid Snowmelt Damage:33 Deaths, $1.7 Billion 1996: Harrisburg, PA 1964: Oregon City, OR 1904: Grand Rapids, MI 1936: Pittsburgh, PA 1983 Salt Lake City, UT 1907: Wheeling, WV 1927: Cairo, IL 1979: Fargo, ND 1997: Grand Forks, ND 1951: Mankato, MN Region:West Virginia Cause:Heavy Rain on Snow Damage:$1.9 Billion Region:Mississippi River Cause:Saturated Soils & Snowmelt Damage:$2.4 Billion Region:Southern Minnesota Cause:Rain on Snow, Rapid Snowmelt Damage: $21 Million Region:Red River of the North Cause:Snowmelt Damage:$96 Million Region:Red River of the North Cause:Snowmelt, Frozen Soil Damage:$5.1 Billion (Damages in 2002 Dollars) Major snow-related flood

  6. Existing Obs. Systems, Data Sources, S&T New Obs. Systems, Data Sources, S&T Conceptual Evolution of Operational Observing Systems Comprehensive Analyses and Data Assimilation(including quality control, multisensor estimation and 4DDA) Requirements Evolutionary infusion of new observing systems, data sources, science, and technology. Products Updated Requirements Systematic Evaluation and Customer Feedback Data and Information Gap Analysis(e.g. high space-time resolution)

  7. Ground-based Snow Data METAR, SNOTEL, CADWR, HADS, NWS Co-op, Mesonets NOHRSC Database Management System Data ingest, quality control, pre-processing Airborne Snow Water Equivalent Satellite Snow Cover Data GOES, AVHRR, SSM/I NEXRAD Radar Data Numerical Weather Model Data Eta, RUC2 NOAA/NWS/NOHRSC National Snow Analyses (NSA) Product Generation and Distribution Elements: Daily National Snow Analyses: • Water Equivalent • Snow Depth, • Snow Temperature • Sublimation • Condensation • Snow Melt Formats: • Interactive Maps • Time-series Plots • Text Discussions • Alphanumeric and Gridded products Distribution: NOHRSC Web Site, AWIPS, direct FTP, NSIDC, NCDC Data and Product Archive NOHRSC Snow Data Assimilation System Energy-and-mass-balance snow modeling and observed snow data assimilation 1-km, Hourly Continental U.S.

  8. User Interactive Mapping on Internet • Comprehensive snow hydrologic information products • Snow water equivalent, depth, wetness, temperature, melt, sublimation losses • GIS-based interactive information distribution on the Internet • Overlay administrative and basin boundaries, rivers, roads, cities • Zoom to full 1-km resolution • Query stations for time- series history • Export text data summaries for each basin • Up to 300,000 hits a day during peak season

  9. Time-series History Queries on Internet User can query any of 40,000 stations shown on interactive map. • SWE, Depth, Density, and Melt • e.g. Washington DC (Reagan National Airport) • Jan 15 - Feb 15, 2004 • Dark blue line show modeled SWE • Light blue line shows modeled snow depth • Light blue points show observed snow depth • Assimilation of observed snow depth on Jan 27 corrected for underestimated snow precipitation

  10. Hourly SWE Analysis, Oct 1 2003 – May 23 2004

  11. Science Drivers for Improved Snow Observations • We lack sufficient understanding of the magnitude and variability of snow water storage and of the fluxesand feedbacks that relate it to the atmosphere and climate necessary to reliably predict local-regional consequences of climate variability and change. • Snow water content is poorly measured by sparse and inconsistent ground networks. • Current remote sensing observing systems are unable to provide process-oriented measurements of snow hydrologic properties required to test and constrain today’s predictive models. • Fundamental questions such as how much water is stored locally, regionally, or globally in seasonal snow packs remain unanswered.

  12. In Situ Snow Observations

  13. Spaceborne Passive Microwave 100000 10000 1000 100 10 1 Space-Time Scales of Snow Processes 10 Years Interannual Variability in Snow Accumulation (Variation in Synoptic Climate) 3 Years 2 Years 1 Year Snow Metamorphism Effects on Structural Proeprties and Radiative Transfer Intraseasonal Variability in Snow Accumulation (Variation in Individual Storm Tracks) 1 Month Snowmelt Floods Snow Melt Effects on Water Balance, Surface Energy Balance and Microwave Radiative Transfer Temporal Scale Temporal Scale (Hours) 1 Week Orographic Precipitation Effects on Snow Accumulation Wind-redistribution of Snow Accumulation on the Ground 3 Days Synoptic Storm Systems (Snow Precipitation and Accumulation) 1 Day Enhanced Boundary-layer Stability over Snow Effects of Snow Cover on Heat and Moisture Exchanges with Advecting Airmasses 1 Hour 10m 100m 1km 10km 100km 1000km Spatial Scale

  14. Comparison of Continental-scale Water Storage

  15. Existing Obs. Systems, Data Sources, S&T New Obs. Systems, Data Sources, S&T Conceptual Evolution of Operational Observing Systems Comprehensive Analyses and Data Assimilation(including quality control, multisensor estimation and 4DDA) Requirements Evolutionary infusion of new observing systems, data sources, science, and technology. Products Updated Requirements Systematic Evaluation and Customer Feedback Data and Information Gap Analysis(e.g. high space-time resolution)

  16. NOAA Operational Observing Requirements • Specified in NOAA Observing System Architecture (NOSA) • http://nosa.noaa.gov • Four snow observation requirements: • Snow Cover • Snow Depth • Shallow, Deep • Snow Water Equivalent (on ground) • Shallow, Deep • Snowfall Water Equivalent (precip rate) • Two spatial domains: • North America • Global • Two levels of requirements for each • Threshold (Minimal acceptable requirement) • Objective

  17. NOAA Operational Observing Requirements Snow Cover Current Operational: GOES and AVHRR (Neither meets T spatial requirements) Current Experimental: MODIS (Meets T spatial and measurement, but not temporal) Planned Operational: GOES-R, VIIRS (Will meet all current T requirements)

  18. NOAA Operational Observing Requirements Snow Depth Current Operational: SSM/I (Doesn’t meet T spatial or measurement requirements) Current Experimental: AMSR (Doesn’t meet T spatial or measurement requirements) Planned Operational: CMIS (Won’t meet T spatial or measurement requirements)

  19. NOAA Operational Observing Requirements Snow Water Equivalent Current Operational: SSM/I, AMSU (Doesn’t meet T spatial or meas. requirements) Current Experimental: AMSR (Doesn’t meet T spatial or measurement requirements) Planned Operational: CMIS (Won’t meet T spatial or measurement requirements)

  20. NOAA Operational Observing Requirements Snowfall Water Equivalent Current Operational: None Current Experimental: Ground-based Doppler Radar (Neither spatial or measurement) Planned Experimental: GPM (TBD)

  21. Existing Obs. Systems, Data Sources, S&T New Obs. Systems, Data Sources, S&T Conceptual Evolution of Operational Observing Systems Comprehensive Analyses and Data Assimilation(including quality control, multisensor estimation and 4DDA) Requirements Evolutionary infusion of new observing systems, data sources, science, and technology. Products Updated Requirements Systematic Evaluation and Customer Feedback Data and Information Gap Analysis(e.g. high space-time resolution)

  22. NASA Cold Land Processes Working Group • Sponsored by Terrestrial Hydrology Program • Identify and implement the relevant science, technology, and application infrastructure necessary to support a future remote sensing mission focused on Cold Land Processes. • Snow on land, ice sheets and sea ice • 15 workshops since 2000 (next Mar 23-24, 2005; Seattle) • Science framework for mission • Technology development for experimental and operational missions • Algorithm development • Model development • Land surface (snow) • Radiative transfer (microwave)

  23. Full global measurement of snow water equivalent and snow wetness Cold Land Processes Roadmap 2008 2010 2012 2014 2016 2002 2004 2006 Cold Land Processes Measurement (CLPM) Mission OPER CLPM MISSION CLP Measurement Technology Development Funded Unfunded • Improved measurement accuracy and precision • Various technology development needs detailed in ESTO database to support multi-frequency SAR, higher-resolution radiometers, larger data volumes, etc. = Field Campaign • Higher spatial and temporal resolution to resolve precipitation from individual storms • Quantification of high latitude precipitation, fresh water stored in seasonal snowpacks, controls on variability of storage, snowpack feedback effects on weather and climate CLPX V Validation CLPP Airborne Simulator Cold Land Processes Pathfinder (CLPP) Mission CLPP Education and Outreach CLPP Technology Devel. CLPP Proposal CLPP Technology Development CLPP MISSION Enterprise Goals: Understand distribution of snowpack water storage and melt state (wetness) Models capable of predicting the water cycle, including floods and droughts, down to 10’s of km Routine probabilistic forecasts of snow water storage and snowfall accurate enough to support economic decisions Improve winter storm hazard forecasting at local scales to support mitigation CLPP Applications Development CLPX III • Narrow-swath sampling of global snow water equivalent and snow wetness Increased coordination and collaboration with polar regions and sea ice communities • Data collection as needed to support advanced CLPP preparations – algorithm refinement, ground system testing, science data processing tests, etc. Development of International Partnerships • Test and refine new active/passive algorithms with augmented Ku-band AIRSAR Knowledge Base • “Routine” modeled estimates of global SWE and snow wetness, largely unconstrained by observations Airborne Imaging Ku SAR CLPX II • Test and refine improved models and data assimilation • Examine key questions unresolved by CLPX I – e.g. dynamics, untested snowpack regimes, polar regions, sea ice, etc. • New algorithms for active/passive SWE and wetness retrieval CLPX I • Improved strategies for assimilation of snow information in models • Progress in microwave radiative transfer models for snow Data Analyses • Improved representation of fundamental cold land processes in regional-global models • Evaluation of regional-global snow models, AMSR-E snow products • Improved general understanding of cold land processes Cold Land Processes Working Group • Continuation of 30-year baseline of global monitoring of snow cover and depth (dry-snow only, coarse resolution passive microwave) SSM/I AMSR-E CMIS (NPOESS) NRA NRA NRA NRA NRA NRA NRA NRA NRA NRA NRA GAPP GAPP GAPP GAPP GAPP GAPP TODAY: • Global variations in areal extent of snow cover well quantified • Poor understanding of how local-scale processes “scale up” • Poor understanding of snow feedbacks to atmosphere • Models don’t account for sub-grid scale snow distributions • Winter precipitation poorly observed, esp. in high latitudes • Paucity of observations of snow water content or melt state

  24. CLPP Baseline Mission Concept • Two-frequency Ku-band SAR • Ku-Band (13 and 17 GHz) • 100 m Resolution (60 looks) • Swath: 35km • 100 W Peak Transmit Power • Incidence angle: ~30 degrees • Polarization: VV, VH • K/Ka-band Radiometer • 7/4 km resolution • Swath: 45km (K-band), 40km (Ka-band) • Polarization: H • Orbit/Launch/Spacecraft • Sun-synchronous, 510km, 5-6 pm ascending • 6-day repeat • Ball 2000 or SA200HP or Equivalent • Peacekeeper L/V 1.95 m pushbroom reflector with offset feeds 6-Day Repeat Swaths Feb 2004 Snow Extent

  25. 100000 10000 1000 100 10 1 CLPP Fills Important Gap in Observation of Processes 30-Year Legacy of Passive Microwave Remote Sensing of Snow Cold Land Processes Pathfinder Measurements 10 Years Interannual Variability in Snow Accumulation (Variation in Synoptic Climate) 3 Years 2 Years 1 Year Intraseasonal Variability in Snow Accumulation (Variation in Individual Storm Tracks) Snow Metamorphism Effects on Microwave and Optical Radiative Transfer 1 Month Snow Melt Effects on Water Balance, Surface Energy Balance and Microwave Radiative Transfer Temporal Scale Temporal Scale (Hours) 1 Week Orographic Precipitation Effects on Snow Accumulation 3 Days Wind-redistribution of Snow Accumulation on the Ground Synoptic Storm Systems (Snow Precipitation and Accumulation) 1 Day Snowmelt Floods Enhanced Boundary-layer Stability over Snow Effects of Snow Cover on Heat and Moisture Exchanges with Advecting Airmasses 1 Hour 10m 100m 1km 10km 100km 1000km Spatial Scale

  26. CLPP Investigation Pathway Preparatory Science/Application Investigations Preparatory Science Investigations Major Science and Application Investigations Science Investigation Level 1 Products Building Block Building Block Level 4 Products Level 2 & 3 Products Active Microwave Algorithm Near Real Time During Flight Global (synoptic) Snow Analyses(Uncoupled Modeling/Assim) Radar FVV FHV Ku FVV FHV Ku Analysis of Local-Global Snow Water Storage, Fluxes, and Variability Global SWE Wetness Depth Grain Size Density Snowmelt Snow Temp Fluxes Runoff CLPP Swaths Only SWE Wetness Depth Grain Size Density Roughness* (*TBD) Ancillary Data (Vegetation, Topography) FUSION Quantify Ice-Sheet Snow Accum. & Melt Characteristics Uncertainty Assessment Passive Microwave Model-based Estimation Near Real Time and Post-flight Snow/Land Modeling & Assimilation (Coupled & Uncoupled) Exploration of Snow Cover on Sea Ice Radiometer TbH19 TbH37 Algorithm Validation Model Validation Operational Demonstration Hydrological Analysis and Forecasting Level 3 Gridded Backscatter and Brightness Data Benefit: NPOESS/CMIS Risk Reduction (Snow Cover Depth and SWE EDRs)

  27. Cryosphere Theme Terrestrial Snow Two Perspectives: NOAA Weather and Water Operations NASA Earth Science Research Don Cline National Operational Hydrologic Remote Sensing Center Office of Climate, Water and Weather Services, National Weather Service, NOAA

  28. Science Drivers • Snow is a significant storage component of the fresh water cycle1, affects weather and climate2, is a critical fresh water resource in many mountainous regions and surrounding lowlands3, and is frequently responsible for loss of life and property due to flooding4. • Snow water storage is highly variable in space and time, but appears to be changing in significant ways, including increasing snow accumulation at different times and locations, in contrast to some climate change hypotheses. • 1On one day in Feb 2004, NWS model analyses indicated the volume of water stored in snow across the CONUS was 11% of the U.S. total annual renewable fresh water resources (258 km3; 59% of estimated U.S. total annual freshwater withdrawal). • 2In addition to the well-known ice-albedo feedback, snow cover depresses overlying air temperatures, which decreases atmospheric thickness, and in turn steers cyclonic activity which affects subsequent snowfall. Persistence of these effects depends on mass of snow (water) present. • 3E.g., in the western U.S between 80-90% of total annual streamflow originates as snow . • 4Eight of the top 20 floods of the 20th century were related to snowmelt (USGS). Three caused over $1B each in damages (2002 dollars).

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