Terrestrial Snow Don Cline Office of Climate, Water and Weather Services, National Weather Service, NOAA National Operational Hydrologic Remote Sensing Center Cryosphere Theme

October 16-18, Third Cryosphere Theme Workshop, Noordwijk, The Netherlands Role of Snow in the Cryosphere, Science & Society Snow Observation Needs Status of Observing Systems Current Remote Sensing Capabilities and Requirements Preliminary Recommendations Remaining Tasks Outline

Agriculture Disasters Climate Ecology Energy Health Oceans Water Weather Transportation Engineering Commerce SOCIETY Role of Snow in Cryosphere, Science & Society Water and Energy Fluxes Water and Energy Fluxes Biogeochemical Fluxes Biogeochemical Fluxes Insulation Mechanics & Dynamics Mechanics & Dynamics Radiative Transfer SNOW SCIENCE Atmospheric Dynamics Atmospheric Dynamics Freshwater Input to Oceans Freshwater Input to Oceans Ecosystem Dynamics Ecosystem Dynamics Floods Avalanches Soil Freeze/Thaw Permafrost Soil Freeze/Thaw Permafrost Snowmelt Runoff Aquifer Recharge Water Quality Trafficability Carbon Cycle Soil Moisture Habitat/Forage EARTH SCIENCE kg h CO 2 pH N d R0R0 SNOW PROPERTIES

October 16-18, Third Cryosphere Theme Workshop, Noordwijk, The Netherlands Snow Observation Needs Snow Cover Extent Used by many models as fundamental control on hydrologic, atmospheric, and ecological processes. First-order inference Mass and energy information Snow Water Equivalent Volume of water storage. Establishes “memory” and first-order predictability. Complements precipitation observation. Greatest popular interest Used in atmospheric models for surface roughness. Key factor in determining insulative properties for soil/ecosystems. Snow Depth Varies widely between 35-95% Required to accurately determine radiation and energy budgets. Sensitive to difficult-to-model factors (e.g. dust) Snow Albedo Models sensitive to forcing errors Snow Temperature Needed for radiation and energy budgets. Constrains memory-propagation of forcing errors in subsequent fluxes.

October 16-18, Third Cryosphere Theme Workshop, Noordwijk, The Netherlands Snow Observation Needs Required to asses biogeochemical fluxes and estimate potential impacts of release from storage (through snowmelt) on water quality and ecosystems. pH, conductance, alkalinity, base cations, strong mineral acids, silica, ammonium, dissolved organic carbon, total and dissolved organic nitrogen, total phosphorous. Chemical & Nutrient Constituents Required to determine net carbon balance of snow-covered ecosystems. Single most important is CO 2 Trace Gas Fluxes Density, Liquid Water Content, Grain Size, Grain Shape, Stratigraphic Structure, Strength, Stability. Required to asses avalanche hazards. Model development, parameterization and testing, process understanding, and advancement of observing systems are fundamentally dependent. Microphysical & Mechanical Properties

kg h CO 2 pH N d R0R0 Snow Observation Needs PastPresentFuture Snow Science Earth Science Applications Earth System Science Snow Cover, Albedo Water Equivalent, Depth kg h CO 2 pH N d R0R0 kg h CO 2 pH N d R0R0 kg h CO 2 pH N d R0R0 kg h CO 2 pH N d R0R0 Snow Cover, Albedo Water Equivalent, Depth Snow Cover, Albedo Water Equivalent, Depth Snow Cover, Albedo Water Equivalent, Depth kg h CO 2 pH N d R0R0 kg h CO 2 pH N d R0R0 kg h CO 2 pH N d R0R0 Various Uncoupled, Independent Physics Processes Observation Coupled, Integrated, Linked to Observing Systems

kg h CO 2 pH N d R0R0 Snow Observation Needs PastPresentFuture Snow Science Earth Science Applications Earth System Science Snow Cover, Albedo Water Equivalent, Depth kg h CO 2 pH N d R0R0 kg h CO 2 pH N d R0R0 kg h CO 2 pH N d R0R0 kg h CO 2 pH N d R0R0 Snow Cover, Albedo Water Equivalent, Depth Snow Cover, Albedo Water Equivalent, Depth Snow Cover, Albedo Water Equivalent, Depth kg h CO 2 pH N d R0R0 kg h CO 2 pH N d R0R0 kg h CO 2 pH N d R0R0 Various Uncoupled, Independent Physics Processes Observation Coupled, Integrated, Linked to Observing Systems

kg h CO 2 pH N d R0R0 Snow Observation Needs PastPresentFuture Snow Science Earth Science Applications Earth System Science kg h CO 2 pH N d R0R0 kg h CO 2 pH N d R0R0 kg h CO 2 pH N d R0R0 kg h CO 2 pH N d R0R0 Snow Cover, Albedo Water Equivalent, Depth Snow Cover, Albedo Water Equivalent, Depth Snow Cover, Albedo Water Equivalent, Depth kg h CO 2 pH N d R0R0 kg h CO 2 pH N d R0R0 kg h CO 2 pH N d R0R0 Various Uncoupled, Independent Coupled, Integrated, Linked to Observing Systems Physics Processes Observation

October 16-18, Third Cryosphere Theme Workshop, Noordwijk, The Netherlands Snow properties are observed using wide variety of instruments on surface, airborne, and satellite platforms In Situ – graduated probes and stakes – acoustic snow-depth sounders – snow coring devices – snow pillows (pressure transducers) – excavation of snow pits with detailed sampling using various instruments Remote Sensing – terrestrial gamma radiation from low-flying aircraft – visible and infrared radiance from aircraft and both low Earth-orbit (LEO) and geostationary (GEO) satellites – microwave emissions and backscatter using passive and active sensors on aircraft and LEO satellites These observations are used and combined in many different ways depending on the application, required scales and accuracies, and the types of observations available. Status of Snow Observing Systems

October 16-18, Third Cryosphere Theme Workshop, Noordwijk, The Netherlands Worldwide, networks have diminished or been lost completely. Point observations are only representative of small area, problematic in context of high spatial variability of snow properties. Observations often are not representative of surrounding area – may be intended as indices or be otherwise intentionally biased. SWE is essential, but SWE observations are sparse and in many areas completely absent. Observation of other snow properties is comparatively rare and occurs in context of specific projects/applications. Data are often unavailable. Value is reduced by large inconsistencies in measurement methods, observation frequency, and reporting times and standards. Many in situ snow observations lack adequate metadata. Often little more than a coordinate and a value is available. Effects of changing site conditions (e.g. vegetation growth) on snow accumulation is becoming a major problem. With lack of metadata, climatological interpretation of long-term records is difficult. Shortcomings of In Situ Observing Systems

October 16-18, Third Cryosphere Theme Workshop, Noordwijk, The Netherlands Resolution of most snow and land-surface models has far surpassed that of microwave sensors with significant capability to observe SWE or depth. Skill of SWE and snow depth retrievals using current passive microwave sensors is generally insufficient. Due in part to sensor resolution and to limitations of the algorithmic approach in general use, which is under-determined Regional tuning of algorithms has had some success but not widely adopted Limited to maximum SWE of ~20 cm  therefore both algorithm and resolution not suited for mountainous areas Snow cover observation is reasonably mature, but snow/cloud discrimination remains a problem Spectral resolution, bandwidth, and dynamic range are fundamental constraints Broadband snow-albedo observation-accuracy is sufficient for many applications, but, increasingly, greater accuracy and multi-spectral albedo are needed Snow surface temperature could be useful for constraining models, but current accuracy is insufficient Shortcomings of Remote Sensing Systems

Parameter CTOCTO Measurement Range Measurement Accuracy Resolution Comment / Principal Driver SpatialTemporal LHUVUVUVU Snow Cover C20100%15-20%1kmdaye.g. MODIS T0100%10%0.5km1day Hydromet O0100%5%0.1km12hr Snow Water Equivalent (Shallow) C00.2m2-10cm25km1day e.g. AMSR-E T00.3m3cm0.5km6day Hydromet O00.3m2cm0.1km12hr Snow Water Equivalent (Deep) CNone--- Need HF SAR T0.33m10%0.5km6day Hydromet O0.33m7%0.1km12hr Snow Depth (Shallow) C0~0.7m6-35cm25km1daye.g. AMSR-E T01m10cm0.5km6dayHydromet O01m6cm0.1km1hrTransportation Snow Depth (Mod-Deep) CNone--- Need HF SAR T110m %0.5km6dayHydromet O110m6%0.1km1hrTransportation Snow Albedo (Broadband) C30100%7%1km1day e.g. MODIS T0100%1%8km1hr Hydromet O0100%0.5%5km30min Snow Surface Temperature CTBD T200275K1K1km1hr Hydromet O200275K0.1K km30min

Parameter CTOCTO Measurement Range Measurement Accuracy Resolution Comment / Principal Driver SpatialTemporal LHUVUVUVU Snow Cover C20100%15-20%1kmdaye.g. MODIS T0100%10%0.5km1day Hydromet O0100%5%0.1km12hr Snow Water Equivalent (Shallow) C00.2m2-10cm25km1day e.g. AMSR-E T00.3m3cm0.5km6day Hydromet O00.3m2cm0.1km12hr Snow Water Equivalent (Deep) CNone--- Need HF SAR T0.33m10%0.5km6day Hydromet O0.33m7%0.1km12hr Snow Depth (Shallow) C0~0.7m6-35cm25km1daye.g. AMSR-E T01m10cm0.5km6dayHydromet O01m6cm0.1km1hrTransportation Snow Depth (Deep) CNone--- Need HF SAR T110m %0.5km6dayHydromet O110m6%0.1km1hrTransportation Snow Albedo (Broadband) C30100%7%1km1day e.g. MODIS T0100%1%8km1hr Hydromet O0100%0.5%5km30min Snow Surface Temperature CTBD T200275K1K1km1hr Hydromet O200275K0.1K km30min Snow Cover Improve detection of < 20% snow cover Improve sensor capabilities to enhance snow-cloud discrimination Improve spatial resolution of high-temporal repeat sensors Shallow SWE (< 20 cm) (Similar for depth) Improve spatial resolution of sensors Improve accuracy of algorithms Mod-Deep SWE (> 20 cm) (Similar for depth) Need high-frequency SAR Albedo Improve sensor capabilities to improve accuracy Surface Temperature Improved sensors and algorithms needed Enhancing Enabling Enhancing Enabling

October 16-18, Third Cryosphere Theme Workshop, Noordwijk, The Netherlands A coordinated plan for in situ snow observation networks should be developed, first at national then at international levels. Should address: Needs for improved consistency in observing methods and reporting standards Need for observation of other snow parameters beyond SWE, depth The capability of satellite observations should be improved. Improved passive and active microwave instruments with higher spatial and spectral resolution are needed. High-frequency SAR should be considered a priority, especially for mountainous regions. Priority be given to research, development and validation of algorithms and new sensors to measure SWE under a wide range of vegetation conditions. Moreover, it may be possible to improve algorithms to more effectively use existing sensors. Recommendations (Preliminary) Continued…

October 16-18, Third Cryosphere Theme Workshop, Noordwijk, The Netherlands Targeted field projects be conducted that deal directly with the measurement of snow in multiple environments. These should seek to advance coordinated remote sensing of snow cover, albedo, and temperature (optical measurements) together with snow water equivalent and depth (microwave measurements). Study areas for intensive campaigns should be established with long-term plans to maintain them for “Super Sites” to provide reliable reference targets for multi-sensor remote sensing and modeling applications. Integrated multi-sensor data fusion and global analysis systems that blend snow observation from all sources should be improved. Improved algorithms for the objective, optimal combination of snow observations from multiple disparate sources must be developed. The ideal global snow observing system will use observations from all relevant sources in coherent, consistent, high-resolution analyses of (at a minimum): Snow cover, snow depth, snow water equivalent, and snow wetness. Recommendations (Preliminary)

October 16-18, Third Cryosphere Theme Workshop, Noordwijk, The Netherlands Collective requirements for non-satellite observations? Can we differentiate needed improvements by sensor type? This is how technical elements of space agencies are organized Strategic planning for implementation Can the ideal snow observing system be realized? Remaining Tasks (Theme Report)

Terrestrial Snow Don Cline Office of Climate, Water and Weather Services, National Weather Service, NOAA National Operational Hydrologic Remote Sensing Center Cryosphere Theme

October 16-18, Third Cryosphere Theme Workshop, Noordwijk, The Netherlands A coordinated plan for surface-based snow-observation networks be developed, first at the national, then at the international level. The plan should address the needs for improved consistency in observation methods and reporting standards and for improved exchange of data. It should address current and emerging needs for measurement of other snow properties besides snow depth and SWE. A consistent approach to compiling and facilitation of using considerably improved metadata for snow observations are needed. The capability of satellite observations be improved. The development/validation of satellite remote sensing techniques, including the validation of existing products, support of new systems (e.g., E-GPM/CGPM and CloudSat for solid precipitation), and support of algorithm development is required. High-frequency active and passive microwave observations, which are uniquely well-suited to observing SWE and snow depth, have low spatial and spectral resolution. Improved instruments with higher spatial and spectral resolution are required. High- frequency (Ku, X-band) SAR should be considered a priority for global SWE observation. Priority be given to research and development of algorithms and new sensors to measure SWE, under a wide range of vegetation conditions. Furthermore, it may be possible to design improved algorithms to more effectively use existing data sources. Further research is necessary to realize the retrieval of SWE from SAR data, with their higher spatial resolution. E.g., SAR is the only instrument capable of mapping wet snow cover at the fine spatial resolution required in mountainous terrain (where the hydrology is dominated by the melting snow pack). Techniques are developed to merge in situ measurements and satellite retrievals. Targeted field projects should be conducted to deal directly with the measurement of snow in multiple environments. These should seek to advance coordinated remote sensing of snow albedo and surface temperature (i.e. optical measurements) together with SWE and snow depth (i.e. microwave measurements). Study areas for intensive field campaigns should be established with long-term plans to maintain them as “Super Sites” to improve knowledge of snow processes and to provide reference targets for multi-sensor remote sensing and modeling applications. Integrated multi-sensor data fusion and global analysis systems that blend snow observations from all sources be improved. The ideal global snow observing system will use observations from all relevant sources in coherent, consistent high-resolution analyses of (at a minimum): 1) the extent of snow cover, 2) snow depth, 3) SWE, and 4) snow wetness. No current system provides global coverage, and a more complete system would include snow albedo and temperature, microphysical properties, and chemical constituents. Improved algorithms for the objective, optimal combination of snow observations from widely disparate sources must be developed. These must address both mass and energy considerations of snow models. Recommendations (Preliminary)

October 16-18, Third Cryosphere Theme Workshop, Noordwijk, The Netherlands Vision Ideal Global Snow Observing System Will use observations from all relevant sources in coherent, consistent, high-resolution global analyses of (at a minimum): extent of snow cover, snow water equivalent, snow depth, snow wetness. Multi-sensor Data Fusion

Parameter CTOCTO Measurement Range Measurement Accuracy Resolution Comment / Principal Driver SpatialTemporal LHUVUVUVU Snow Cover C20100%15-20%1kmdaye.g. MODIS T0100%10%0.5km1day Hydromet O0100%5%0.1km12hr Snow Water Equivalent (Shallow) C00.2m2-10cm25km1day e.g. AMSR-E T00.3m3cm0.5km6day Hydromet O00.3m2cm0.1km12hr Snow Water Equivalent (Deep) CNone--- Need HF SAR T0.33m10%0.5km6day Hydromet O0.33m7%0.1km12hr Snow Depth (Shallow) C0~0.7m6-35cm25km1daye.g. AMSR-E T01m10cm0.5km6dayHydromet O01m6cm0.1km1hrTransportation Snow Depth (Deep) CNone--- Need HF SAR T110m %0.5km6dayHydromet O110m6%0.1km1hrTransportation Snow Albedo (Broadband) C30100%7%1km1day e.g. MODIS T0100%1%8km1hr Hydromet O0100%0.5%5km30min Snow Surface Temperature CTBD T200275K1K1km1hr Hydromet O200275K0.1K km30min C = Current Capability T = Threshold Requirement (Minimum needed) O = Objective Requirement (Increased benefit can be realized by achieving these requirements)

Parameter CTOCTO Measurement Range Measurement Accuracy Resolution Comment / Principal Driver SpatialTemporal LHUVUVUVU Snow Cover C20100%15-20%1kmdaye.g. MODIS T0100%10%0.5km1day Hydromet O0100%5%0.1km12hr Snow Water Equivalent (Shallow) C00.2m2-10cm25km1day e.g. AMSR-E T00.3m3cm0.5km6day Hydromet O00.3m2cm0.1km12hr Snow Water Equivalent (Deep) CNone--- Need HF SAR T0.33m10%0.5km6day Hydromet O0.33m7%0.1km12hr Snow Depth (Shallow) C0~0.7m6-35cm25km1daye.g. AMSR-E T01m10cm0.5km6dayHydromet O01m6cm0.1km1hrTransportation Snow Depth (Deep) CNone--- Need HF SAR T110m %0.5km6dayHydromet O110m6%0.1km1hrTransportation Snow Albedo (Broadband) C30100%7%1km1day e.g. MODIS T0100%1%8km1hr Hydromet O0100%0.5%5km30min Snow Surface Temperature CTBD T200275K1K1km1hr Hydromet O200275K0.1K km30min

Parameter CTOCTO Measurement Range Measurement Accuracy Resolution Comment / Principal Driver SpatialTemporal LHUVUVUVU Snow Cover C20100%15-20%1kmdaye.g. MODIS T0100%10%0.5km1day Hydromet O0100%5%0.1km12hr Snow Water Equivalent (Shallow) C00.2m2-10cm25km1day e.g. AMSR-E T00.3m3cm0.5km6day Hydromet O00.3m2cm0.1km12hr Snow Water Equivalent (Deep) CNone--- Need HF SAR T0.33m10%0.5km6day Hydromet O0.33m7%0.1km12hr Snow Depth (Shallow) C0~0.7m6-35cm25km1daye.g. AMSR-E T01m10cm0.5km6dayHydromet O01m6cm0.1km1hrTransportation Snow Depth (Deep) CNone--- Need HF SAR T110m %0.5km6dayHydromet O110m6%0.1km1hrTransportation Snow Albedo (Broadband) C30100%7%1km1day e.g. MODIS T0100%1%8km1hr Hydromet O0100%0.5%5km30min Snow Surface Temperature CTBD T200275K1K1km1hr Hydromet O200275K0.1K km30min

Parameter CTOCTO Measurement Range Measurement Accuracy Resolution Comment / Principal Driver SpatialTemporal LHUVUVUVU Snow Cover C20100%15-20%1kmdaye.g. MODIS T0100%10%0.5km1day Hydromet O0100%5%0.1km12hr Snow Water Equivalent (Shallow) C00.2m2-10cm25km1day e.g. AMSR-E T00.3m3cm0.5km6day Hydromet O00.3m2cm0.1km12hr Snow Water Equivalent (Deep) CNone--- Need HF SAR T0.33m10%0.5km6day Hydromet O0.33m7%0.1km12hr Snow Depth (Shallow) C0~0.7m6-35cm25km1daye.g. AMSR-E T01m10cm0.5km6dayHydromet O01m6cm0.1km1hrTransportation Snow Depth (Deep) CNone--- Need HF SAR T110m %0.5km6dayHydromet O110m6%0.1km1hrTransportation Snow Albedo (Broadband) C30100%7%1km1day e.g. MODIS T0100%1%8km1hr Hydromet O0100%0.5%5km30min Snow Surface Temperature CTBD T200275K1K1km1hr Hydromet O200275K0.1K km30min

Parameter CTOCTO Measurement Range Measurement Accuracy Resolution Comment / Principal Driver SpatialTemporal LHUVUVUVU Snow Cover C20100%15-20%1kmdaye.g. MODIS T0100%10%0.5km1day Hydromet O0100%5%0.1km12hr Snow Water Equivalent (Shallow) C00.2m2-10cm25km1day e.g. AMSR-E T00.3m3cm0.5km6day Hydromet O00.3m2cm0.1km12hr Snow Water Equivalent (Deep) CNone--- Need HF SAR T0.33m10%0.5km6day Hydromet O0.33m7%0.1km12hr Snow Depth (Shallow) C0~0.7m6-35cm25km1daye.g. AMSR-E T01m10cm0.5km6dayHydromet O01m6cm0.1km1hrTransportation Snow Depth (Deep) CNone--- Need HF SAR T110m %0.5km6dayHydromet O110m6%0.1km1hrTransportation Snow Albedo (Broadband) C30100%7%1km1day e.g. MODIS T0100%1%8km1hr Hydromet O0100%0.5%5km30min Snow Surface Temperature CTBD T200275K1K1km1hr Hydromet O200275K0.1K km30min