Terrestrial Snow Don Cline Office of Climate, Water and Weather Services, National Weather Service,...

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, 2006 2Third 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

AgricultureAgriculture

DisastersDisasters

ClimateClimate

EcologyEcology

EnergyEnergy

HealthHealth

OceansOceans

WaterWater

WeatherWeather

TransportationTransportation

EngineeringEngineeringCommerceCommerce

SOCIETY

Role of Snow in Cryosphere, Science & Society

Water and EnergyFluxes

Water and EnergyFluxes

BiogeochemicalFluxes

BiogeochemicalFluxes

InsulationInsulation

Mechanics &Dynamics

Mechanics &Dynamics

Radiative TransferRadiative Transfer

SNOW SCIENCE

AtmosphericDynamics

AtmosphericDynamics

Freshwater Inputto Oceans

Freshwater Inputto Oceans

EcosystemDynamics

EcosystemDynamics

FloodsFloods

AvalanchesAvalanches

Soil Freeze/ThawPermafrost

Soil Freeze/ThawPermafrost

Snowmelt RunoffSnowmelt Runoff

Aquifer RechargeAquifer Recharge

Water QualityWater Quality TrafficabilityTrafficability

Carbon CycleCarbon Cycle

Soil MoistureSoil Moisture

Habitat/ForageHabitat/Forage

EARTH SCIENCE

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SNOW PROPERTIES


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.



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 & NutrientConstituents

Required to determine net carbon balance of snow-covered ecosystems.Single most important is CO2

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

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Past Present Future

SnowScience

EarthScience

Applications

EarthSystemScience

Snow Cover, AlbedoWater Equivalent, Depth

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Various Uncoupled,Independent

PhysicsProcesses

Observation

Coupled, Integrated,Linked to

Observing Systems

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Past Present Future

SnowScience

EarthScience

Applications

EarthSystemScience

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Various Uncoupled,Independent

Coupled, Integrated,Linked to

Observing Systems

PhysicsProcesses

Observation


• 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


• 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


• 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

CTO

Measurement Range

Measurement Accuracy

Resolution

Comment / Principal Driver

Spatial Temporal

L H U V U V U V U

Snow Cover

C 20 100 % 15-20 % 1 km day e.g. MODIS

T 0 100 % 10 % 0.5 km 1 dayHydromet

O 0 100 % 5 % 0.1 km 12 hr

Snow Water Equivalent (Shallow)

C 0 0.2 m 2-10 cm 25 km 1 day e.g. AMSR-E

T 0 0.3 m 3 cm 0.5 km 6 dayHydromet

O 0 0.3 m 2 cm 0.1 km 12 hr

Snow Water Equivalent (Deep)

C None --- --- --- --- --- --- --- --- Need HF SAR

T 0.3 3 m 10 % 0.5 km 6 dayHydromet

O 0.3 3 m 7 % 0.1 km 12 hr

Snow Depth (Shallow)

C 0 ~0.7 m 6-35 cm 25 km 1 day e.g. AMSR-E

T 0 1 m 10 cm 0.5 km 6 day Hydromet

O 0 1 m 6 cm 0.1 km 1 hr Transportation

Snow Depth (Mod-Deep)

C None --- --- --- --- --- --- --- --- Need HF SAR

T 1 10 m 10 % 0.5 km 6 day Hydromet

O 1 10 m 6 % 0.1 km 1 hr Transportation

Snow Albedo (Broadband)

C 30 100 % 7 % 1 km 1 day e.g. MODIS

T 0 100 % 1 % 8 km 1 hrHydromet

O 0 100 % 0.5 % 5 km 30 min

Snow Surface TemperatureC TBD

T 200 275 K 1 K 1 km 1 hrHydromet

O 200 275 K 0.1 K 0.1 km 30 min

Parameter

CTO

Measurement Range


Resolution


Spatial Temporal

L H U V U V U V U

Snow Cover

C 20 100 % 15-20 % 1 km day e.g. MODIS


O 0 100 % 5 % 0.1 km 12 hr




O 0 0.3 m 2 cm 0.1 km 12 hr


C None --- --- --- --- --- --- --- --- Need HF SAR


O 0.3 3 m 7 % 0.1 km 12 hr





Snow Depth (Deep)

C None --- --- --- --- --- --- --- --- Need HF SAR




C 30 100 % 7 % 1 km 1 day e.g. MODIS


O 0 100 % 0.5 % 5 km 30 min



O 200 275 K 0.1 K 0.1 km 30 min

• 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

Enhancing

Enabling


• 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…


• 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.



• 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


• 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.



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

CTO

Measurement Range


Resolution


Spatial Temporal

L H U V U V U V U

Snow Cover

C 20 100 % 15-20 % 1 km day e.g. MODIS


O 0 100 % 5 % 0.1 km 12 hr




O 0 0.3 m 2 cm 0.1 km 12 hr


C None --- --- --- --- --- --- --- --- Need HF SAR


O 0.3 3 m 7 % 0.1 km 12 hr





Snow Depth (Deep)

C None --- --- --- --- --- --- --- --- Need HF SAR




C 30 100 % 7 % 1 km 1 day e.g. MODIS


O 0 100 % 0.5 % 5 km 30 min



O 200 275 K 0.1 K 0.1 km 30 min

C = Current Capability

T = Threshold Requirement (Minimum needed)

O = Objective Requirement (Increased benefit can be realized

by achieving these requirements)

Parameter

CTO

Measurement Range


Resolution


Spatial Temporal

L H U V U V U V U

Snow Cover

C 20 100 % 15-20 % 1 km day e.g. MODIS


O 0 100 % 5 % 0.1 km 12 hr




O 0 0.3 m 2 cm 0.1 km 12 hr


C None --- --- --- --- --- --- --- --- Need HF SAR


O 0.3 3 m 7 % 0.1 km 12 hr





Snow Depth (Deep)

C None --- --- --- --- --- --- --- --- Need HF SAR




C 30 100 % 7 % 1 km 1 day e.g. MODIS


O 0 100 % 0.5 % 5 km 30 min



O 200 275 K 0.1 K 0.1 km 30 min

Terrestrial Snow Don Cline Office of Climate, Water and Weather Services, National Weather Service,...

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