Cold Land Processes Pathfinder Measurement Objectives and Science

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers The CLPP will provide the first globally-sampled high-resolution measurements of snow water equivalent and snow wetness. These are the two most important snow parameters for understanding: 1) water cycle variability, 2) the effect of snow cover dynamics on Earth’s climate, and 3) the influence of snow-covered regions on weather predictability. Frequent reliable measurements of snow water equivalent and snow wetness, even within the limited swaths of this pathfinder, will comprise an improvement in snow measurement several orders of magnitude better than provided by existing ground observation networks.

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers Scientific Merit and Impact • Variability – How are global precipitation, evaporation, and the cycling of water changing? • Response – What effects do clouds and surface hydrologic processes have on Earth’s climate? • Consequences – How do variations in local weather, precipitation and water resources relate to global climate variation? • Prediction – How can we improve weather forecast duration and reliability with new space-based observations, data assimilation, and modeling? 12/22/2021 3

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers DIMENSIONS OF THE DISCUSSION PRECIP OBSERVATION STORAGE MODELS CONTROLS PROCESS FEEDBACKS

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers Scientific Merit and Impact This breakthrough will enable advances in four cross-cutting science areas: PRECIP STORAGE CONTROLS FEEDBACKS 12/22/2021 5

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers Scientific Merit and Impact 1. Snowfall Distribution (PRECIP). What intraseasonal and interannual, distributions characterize snowfall? How many snowfall events contribute to the formation of snowpacks and the world’s fresh water storage? • In many high-latitude and mountainous regions of the world more snow falls than rain, and the frequency of snowfall events varies widely. In some regions, snowpacks develop from a high frequency of light-snowfall events. Elsewhere, fewer but larger events form the snowpack. In areas such as the western U. S. , a single storm can often make the difference between wellbelow normal and well-above normal snowpacks. Understanding the frequency of winter precipitation is essential to understanding weather and climate variability. • Despite its role as a global precipitation input to the hydrological cycle, we do a poor job measuring snow. Precipitation gages, designed for rainfall, do not capture snowfall effectively. Undercatch commonly amounts to 25 -50%, depending on wind conditions and the type of gage. Furthermore, high latitudes and elevations typically have sparse networks of gages. Planned precipitation measurement missions will not measure light snowfall anywhere and will not measure any high-latitude precipitation. Consequently, the distribution of global snowfall is poorly known. • This mission will address this gap, providing the first regular estimate of global snowfall by frequent measurement of accumulated snow water equivalent. Temporal resolution of one week or less will help resolve precipitation from individual storm events, which is necessary for evaluating and improving predictive models for these regions. 12/22/2021 6

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers Scientific Merit and Impact 2. Snowpack Water Storage (STORAGE). What role does seasonal snow storage play in the global fresh water budget? What is the intraseasonal and interannual variability in the amount of fresh water stored in seasonal snowpacks (i. e. snow water equivalent) at local, regional, continental, and global scales? 12/22/2021 • Seasonal snow covers store a major component of the global water cycle. The location and amount of water stored can change dramatically in a matter of days. On average, over 60% of the northern hemisphere land surface has snow cover in mid-winter, and over 30% of Earth’s total land surface has seasonal snow. • The 27 -year passive microwave legacy of SMMR and SSM/I, now carried forward by AMSR and eventually by NPOESS sensors, provides our historical “baseline” for estimating this storage term. However, due to limitations of coarse-resolution radiometry, substantial uncertainty follows, associated with this historical baseline. Hydrometeorological models now provide estimates of snow water equivalent (SWE), but because of the high uncertainty of available observations these estimates remain largely unconstrained. • This mission will provide the first regular global observations of SWE with sufficient resolution to reduce uncertainty to levels enabling assessment of variability in fresh-water storage, and improve predictive water, weather and climate models. Furthermore, the 30 km swath width of the CLPP corresponds with the nominal resolution of the legacy sensors. Calibration of the traditional passive microwave measurements with respect to physical properties observed by the CLPP’s higher resolution measurements will reduce, or at least define, the uncertainty of the historical record, improving understanding of changes to snowpack water storage over the last three decades. 7

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers Scientific Merit and Impact 3. Controls on Snowpack Dynamics (CONTROLS). What governs the magnitude and variability of snow water storage and snow melt – atmospheric controls, or surface controls? • Complex interactions between moderate- to large-scale atmospheric phenomena and relatively small-scale terrain features of the land surface control the extent and distribution of the Earth’s snow covers. While snowpack formation obviously begins with snowfall from the atmosphere, land surface characteristics and interactions with wind exercise a strong control on the distribution of snow on the ground. As a result of these interactions, snowpacks exhibit a high degree of spatial and temporal variability at scales from meters to a few kilometers, and from hours to days. • Wind redistributes snow during and after snowfall, resulting in complex depositional patterns controlled by topography and vegetation, as well as by the amount of snow available for redistribution. This dramatically changes both the mass and energy regimes of the snowpack by moving snow preferentially to different slopes, aspects, and land covers. Complete snow removal from some areas, exposing bare ground with markedly lower albedos, influence subsequent energy exchanges with the atmosphere and snow melt, and in turn affect feedbacks to the atmosphere that change local, and possible larger-scale weather. • This mission will make the first global measurements of snow water equivalent and snow wetness measurements with resolution sufficient to support understanding of winter orographic precipitation and redistribution of snow on the ground, and the affects these processes have on water storage and melt. Poor representation of these processes in current water, weather and climate models has been widely accepted as a major deficiency. This mission will enable the model representation and validation of these processes. 12/22/2021 8

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers Scientific Merit and Impact 4. Feedbacks on Weather and Climate (FEEDBACKS). At what spatial and temporal scales do the extent, variability, and evolution of snow water equivalent and snow wetness affect weather and climate through controls on fluxes, storage, and transformations of water and energy? How significant are these effects, and what are the implications for improved prediction? • Snow covers affect the atmosphere through their obvious and dramatic modification of the surface albedo, and by their less obvious but equally important affects on stability of the boundary layer, surface roughness, and latent and sensible energy and mass exchanges. The significant energy required to melt seasonal snow covers makes them an important energy sink in the global energy budget. As air masses advect over snow cover, their characteristics can change markedly depending on the characteristics of each. Thus snow covers exert a control on weather both locally and downstream. • The nonlinear behavior of these cold land processes results in dramatic shifts in behavior that depend to a first order on snow water equivalent (i. e. mass of ice and water present) and snow wetness (i. e. amount of liquid water present, and an indication of temperature and melt state of the snowpack). These characteristics change constantly and often rapidly, producing highly variable effects on weather. • This mission will provide global measurements of these two first-order snowpack variables, which for the first time will enable understanding of how small-scale interactions between the snowpack and the atmosphere translate to larger scales of weather and climate. 12/22/2021 9

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers Scientific Merit and Impact • The current state of knowledge of water, weather, and climate processes in cold land regions is largely fundamental. • While we consider most first-order physical processes operating at short temporal and spatial scales well-understood, the paucity of process-oriented observations has severely limited gains in understanding these processes in larger-scale contexts of weather and climate. • Models lack adequate representation of these processes, in large part because of insufficient observations to properly constrain and evaluate them. • These deficiencies severely limit predictive capabilities in cold regions. • This mission directly addresses these deficiencies by providing the necessary observations to discover the effects of these processes over larger scales and to represent these processes effectively in predictive models. 12/22/2021 10

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers DIMENSIONS OF THE DISCUSSION PRECIP ion s OBSERVATION d. M iss STORAGE CONTROLS PROCESS Cu rre nt &P lan ne MODELS FEEDBACKS

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers Scientific Merit and Impact Improving our quantitative understanding of the changes and consequences of cold land processes will require synergistic advancements in three areas: • PROCESS UNDERSTANDING – Understanding how cold land processes, most comprehensively understood at local scales, extend to larger scales • MODEL REPRESENTATION – Improved representation of cold land processes in coupled and uncoupled land surface models • OBSERVATIONS – A breakthrough in large-scale observation of hydrologic characteristics, in order of priority: 1) snow characteristics, 2) soil moisture, 3) extent of frozen soils and transition between frozen and thawed soils. 12/22/2021 12

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers Scientific Merit and Impact Process Understanding We understand cold land processes, such as snowpack transformations, snowatmosphere interactions, and snowmelt most comprehensively at local or hillslope scales, associated with limited ground observations of snow characteristics and micrometeorological flux towers used for energy balance studies. • We know very little about the aggregate behavior of these local-scale processes, their variability, or their consequences at larger scales. • Measurements from this mission will provide, for the first time, the ability to examine process-oriented snowpack characteristics over a continuous range of scales, starting from the local-process scale and extending to the scale of weather and climate processes. • This information about snow water equivalent and snow wetness will be fundamentally important, and will enable several important cold land processes to be either directly determined or inferred, including: • – – – 12/22/2021 Snowfall frequency and distribution Orographic Precipitation Wind-redistribution of snow Snow-atmosphere energy exchanges Snowmelt 13

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers Scientific Merit and Impact Model Representation • Typically, snow accumulation and melt processes in water, weather, and climate simulations apply simple energy and mass balance accounting procedures to a uniform snowpack. • Currently, atmospheric and land-surface hydrology models do not account for subgrid-scale snow distributions (resulting principally from wind-redistribution of snow and orographic influence on solid precipitation) on the evolution of atmospheric and hydrologic processes. – These nonuniform snowfall variations result from differences in local wind fields, atmospheric stability, and moisture distributions due to interactions with topography. – These factors, in turn, influence cloud microphysical-processes associated with precipitation. – The lack of subgrid-scale snow representation has been acknowledged as a major deficiency in snowcover-evolution and atmospheric-interaction simulations. 12/22/2021 14

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers Scientific Merit and Impact Model Representation, cont. • During blowing snow events 10 to 50% of the snowcover returns to the atmosphere by sublimation of wind-borne snow particles. This fraction represents a significant portion of the winter surface moisture budget, currently not adequately accounted for by regional and global climate models. • Snow distribution mechanisms also operate within forested landscapes. Trees affect wind-redistribution processes, and forest canopies intercept snowfall. Canopy-intercepted snow can lose mass by sublimation before the snow falls from the branches and accumulates on the ground. Most current land-surface models do not account for this important winter energy and mass transfer process. • The measurements from this mission will provide, for the first time, global subgrid-scale observations of snow water equivalent, which will reveal the results of orographic effects, wind-redistribution, and other cold land processes. • This will enable new implementation of these processes in land-surface and atmospheric models, where the observations will allow evaluation of new model representations and constraint of the models through data assimilation. 12/22/2021 15

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers Scientific Merit and Impact Observations • The majority of the world’s surface snow observations are of snow depth, which is of great popular interest but does not address fundamental issues related to water and energy. Snow water equivalent (SWE, the total water contained in a snowpack) is far more important in this regard, but there are relatively few SWE observations. • Passive microwave sensors and microwave scatterometers have demonstrated sensitivity to snow characteristics, but currently have resolutions too coarse to address hydrologic modeling of all but the largest river basins, or to address subgrid-scale variability issues in atmospheric models. • More importantly, recovery of SWE from measurements from current passive sensors and scatterometers is confounded by severe mixed pixel effects (e. g. forest cover). The natural variation of snow properties affecting microwave response at these resolutions also complicates the signal. 12/22/2021 16

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers Scientific Merit and Impact Observations, cont. • The combination of high-resolution SAR with moderate-resolution radiometry in this mission will provide the first global observations of SWE with temporal and spatial resolution sufficient to support significant advancement in process-understanding, modeling and prediction. – These observations will reduce mixed-pixel problems will by several orders of magnitude, which will enable physically based retrievals instead of simple empirical approaches – Higher resolution and near-nadir viewing angles dramatically increase the opportunities for snow measurement in forest gaps and clearings. • Frequent reliable measurements of SWE and snow wetness, even within the limited swaths of this pathfinder, will comprise an improvement in snow measurement several orders of magnitude better than provided by existing ground observation networks. – For example, predictive hydrometeorologic models operating in the western U. S. now rely on a relatively data-rich network of only 640 SNOw TELemetry (SNOTEL) sites for daily snow water equivalent observations to update model states through data assimilation. Each CLPP swath in the U. S. between Canada and Mexico will have roughly three times this number of observations from passive radiometry alone, and hundreds more from SAR. 12/22/2021 17

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers Conceptual Measurement Specifications Measurement Snow Water Equivalent (SWE) Accuracy Precision Spatial Resolution <= 100 -m Active 5 -cm Absolute 1 -cm <= 5 -km Passive Temporal Resolution <7 -Day Exact Repeat <= 100 -m Active Snow Wetness (SW) 2% Absolute 1% <= 5 -km Passive <7 -Day Exact Repeat 18

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers Measurement Uncertainties & Mitigation • Terrain Effects • Well-known layover effect due to terrain occurs more frequently for radar observations with small incidence. Where available, we will use SRTM-derived and other terrain elevation data to provide pixel-based incidence angles for improved retrieval and geometric correction for products. Absence of local incidence angle information from an accurate DEM, makes absolute radiometric calibration more difficult. To reduce measurement uncertainty caused by terrain effects, the retrieval algorithms will incorporate the ratio of the dual-frequency-polarization measurements, unaffected by the absolute radiometric calibration. • Forest Effects • We do not fully understand physical interactions between snow and forest vegetation. Similarly, microwave remote sensing of snow is more complicated in forested areas, so measurement uncertainty is higher in forested areas. Importantly, the CLPP measurements will help improve understanding of this problem. To address this uncertainty in the near-term, CLPP products will identify pixels where forests or other significant vegetation substantially increase the uncertainty. • Undersampling • Although minor biases may arise from undersampling the snow field with narrow swaths, the major source of bias is introduced by undersampling the temporal dynamics. This cannot be removed by increasing the cross-track data (Salby and Callaghan, 1997). To reduce this uncertainty, and to meet several related science objectives, the CLPP concept trades continuous spatial coverage for an improved suite of measurements with shorter repeat cycles. 19

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers Measurement Validation • The validation strategy will be based on relevant aspects of the successful Cold Land Processes Field Experiment (CLPX) in 2002 and 2003. • The CLPX approach used intensive ground-based observations, airborne remote sensing, and landsurface modeling to comprehensively determine the distribution of snow characteristics within pixels or footprints, and the uncertainty associated with satellite remote sensing measurements. • Sampling strategies and study areas will likely differ from CLPX, but the basic concepts will remain the same. Several study areas will be selected globally that are a) large enough to contain whole pixels or footprints, and b) representative of a wide range of snow characteristics, land cover and terrain. Uniform measurement protocols will be used in each study area to enable comparative analysis. • • The CLPX approach also relied on close ties with the modeling and the broader cold regions science community through many established pathways, including CLP working group. • • This approach enables reliable inference of accuracy and uncertainty at scales ranging from local to global See www. nohrsc. nws. gov/~cline/clpx. html for more information on the CLPX approach The CLPP Science Team includes the principal architects of the CLPX, and substantial expertise in snow science, land-surface and atmospheric modeling. 20

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers – Science Tracability Science drives ESSP missions We must quantify contribution to the state of knowledge toward addressing science questions by means of a sensitivity analysis, which illustrates the anticipated improvements in the state of knowledge/understanding as a result of reductions in uncertainty, and a Science Traceability Matrix. The Science Traceability Matrix presents the mapping between scientific objectives and the measurements required to fulfill these objectives, with individual scientific requirements mapping into functional requirements, which themselves map into higher order engineering requirements. Coherence of the traceability from the proposed objectives, to the measurements required, to the instrument functional requirements and the instrument/mission engineering requirements will be used in evaluating the Science/Applications merit criteria. Evaluation will consider the proposed scientific/applications justification and Science Traceability Matrix as the basis from which overall scientific merit and ESSP/ESE program relevance are assessed. Proposed missions that seek to address a broad variety of scientific/applications issues at various disparate levels, without attempting to resolve a particular issue, get scored lower than focused missions, which articulate a well-defined scientific justification by means of a sensitivity analysis and Science Traceability Matrix.

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers – Science Tracability Science Objectives (1) Precipitation: Determine intraseason and interannual frequency and distribution of snowfall over land. (2) Storage: Determine intraseason and interannual variability of fresh water stored in seasonal snowpacks. (3) Controls: Determine governing controls (atmospheric or surface) on magnitude and variability of snow water storage and snow melt. (4) Feedbacks: Quantify the effects of extent, variability, and evolution of snow water equivalent and snow wetness on weather and climate through controls on fluxes, storage, and transformations of water and energy. Scientific Measurement Requirements Global Snow Water Equivalent: (supports objectives 1 -4) 5 -cm accuracy 100 meter resolution Instrument Functional Requirements Data archiving and distribution. Mission Functional Requirements Integration of CLPP data products with contemporary and historical products from on -going and planned missions, enabling diagnostic capabilities and improvements in SWE retrieval from other sources. Global Snow Wetness: (supports objectives 3 and 4) 2% accuracy 100 meter resolution Field validation program. Sample diurnal cycle at consistent time of day (6 am/6 pm) at 6 -day repeat time scales. Swath Width: 30 km Orbit: 500 -700 km , circular, polar, sunsynchronous, ~6 am/pm equator crossing Observation over three annual cycles Three-year mission life. Three-year baseline mission

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers – Science Tracability QUESTIONS/ISSUES: • We need to re-visit accuracy requirements for SWE and snow wetness. • We say we want SWE at 100 m (active) and better than 5 -km passive. We need one value that states what resolution we need, independent of instrument. • Do we need GLOBAL SWE at 100 meters? We need to state the global requirement, and then trace this to our instrument measurement/functional requirement. Do the pathfinder-class of measurements actually enable 100 meter SWE and snow wetness globally? If we are to calibrate/improve SWE derived from other (low-res) sources, are we really getting 100 meter SWE globally? • Regarding sampling time: 6 AM sampling optimum for SWE monitoring? • 6 AM/6 PM sampling optimum for snow wetness monitoring? Else do we need the time in PM when we expect max snow wetness diurnally? • For instrument performance, do we need of relative accuracy, or stability? • In the charts, we state that synergy with HYDROS will enable additional estimation of snow density – but needed anyway for SWE estimates? •

Cold Land Processes Pathfinder Spatial and Temporal Scales of Cold Land Processes Phenomena and Remote Sensing Measurements Cold Land Processes Pathfinder Measurements 30 -Year Legacy of Passive Microwave Remote Sensing of Snow 100000 10 Years Interannual Variability in Snow Accumulation 3 Years 2 Years 1 Month 10000 Snow Metamorphism Effects on Microwave and Optical Radiative Transfer Intraseasonal Variability in Snow Accumulation (Variation in Individual Storm Tracks) 1000 Snow Melt Effects on Water Balance, Surface Energy Balance and Microwave Radiative Transfer 1 Week 3 Days 1 Day Wind-redistribution of Snow Accumulation on the Ground Orographic Precipitation Effects on Snow Accumulation Synoptic Storm Systems Enhanced Boundary-layer Stability over Snowmelt Floods (Snow Precipitation and Accumulation) 100 10 Effects of Snow Cover on Heat and Moisture Exchanges with Advecting Airmasses 1 1 Hour 10 m 100 m 1 km 10 km Spatial Scale 100 km 1000 km Temporal Scale (Hours) Temporal Scale 1 Year 6 Months (Variation in Synoptic Climate)

Cold Land Processes Pathfinder Spatial and Temporal Scales of Cold Land Processes

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers • • • Who likes snow? Who likes field work? Who likes modeling? Who likes high tech? Who likes research on all this stuff? We want to get NASA to pay us to do what we like.

Cold Land Processes Pathfinder Measurement Objectives and Science Drivers Ideas to Keep in Mind: • NASA ESE Payoff – With changing intensity of measurement (spatial/temporal), and type of measurements, driven by equipment and therefore budget, how does the ability to deliver a timely, high-quality “product” change? • Unique Niche in answering ESE questions – Does the R&D investment in CLPP and associated CLPXs provide a critical solution in areas where we see little incentive for other programs to compete? Do we compliment other programs? • NASA Mission & Technical Excellence – will the R&D investment in Cold Land Processes and the Pathfinder Mission provide value to NASA’s mission to support its core competencies - VISION • Technical Certainty – How certain is the science/technology, and enabling technologies required for success (engineering, algorithms, models)? • Technical Approach – How robust does the plan, tactical and strategic appear? • Transition – Do the products of CLP have proponency for long term operational support?