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- Author or Editor: Thomas H. Painter x
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Abstract
It is well established that episodic deposition of dust on mountain snow reduces snow albedo and impacts snow hydrology in the western United States, particularly in the Colorado Rockies, which are headwaters for the Colorado River. Until recently the snow observations needed to physically quantify radiative forcing (RF) by dust on snow were lacking, and analysis of impacts used a semiempirical relationship between snow optical properties and observed surface reflectance. Here, we present a physically based daily time series of RF by dust and black carbon (BC) in snow at Senator Beck Basin Study Area, Colorado. Over the 2013 ablation season (March–May), a snow–aerosol radiative transfer model was forced with near daily measured snow property inputs (density, effective grain size, and dust/BC concentrations) and validated with coincidentally measured spectral albedo. Over the measurement period, instantaneous RF by dust and BC in snow ranged from 0.25 to 525 W m−2, with daily averages ranging from 0 to 347 W m−2. Dust dominated particulate mass, accounting for more than 90% of RF. The semiempirical RF values, which constitute the continuous long-term record, compared well to the physically based RF values; over the full time series, daily reported semiempirical RF values were 8 W m−2 higher on average, with a root-mean-square difference of 16 W m−2.
Abstract
It is well established that episodic deposition of dust on mountain snow reduces snow albedo and impacts snow hydrology in the western United States, particularly in the Colorado Rockies, which are headwaters for the Colorado River. Until recently the snow observations needed to physically quantify radiative forcing (RF) by dust on snow were lacking, and analysis of impacts used a semiempirical relationship between snow optical properties and observed surface reflectance. Here, we present a physically based daily time series of RF by dust and black carbon (BC) in snow at Senator Beck Basin Study Area, Colorado. Over the 2013 ablation season (March–May), a snow–aerosol radiative transfer model was forced with near daily measured snow property inputs (density, effective grain size, and dust/BC concentrations) and validated with coincidentally measured spectral albedo. Over the measurement period, instantaneous RF by dust and BC in snow ranged from 0.25 to 525 W m−2, with daily averages ranging from 0 to 347 W m−2. Dust dominated particulate mass, accounting for more than 90% of RF. The semiempirical RF values, which constitute the continuous long-term record, compared well to the physically based RF values; over the full time series, daily reported semiempirical RF values were 8 W m−2 higher on average, with a root-mean-square difference of 16 W m−2.
Abstract
Episodic dust events cause hazardous air quality along Utah’s Wasatch Front and dust loading of the snowpack in the adjacent Wasatch Mountains. This paper presents a climatology of episodic dust events of the Wasatch Front and adjoining region that is based on surface weather observations from the Salt Lake City International Airport (KSLC), Geostationary Operational Environmental Satellite (GOES) imagery, and additional meteorological datasets. Dust events at KSLC—defined as any day [mountain standard time (MST)] with at least one report of a dust storm, blowing dust, and/or dust in suspension with a visibility of 10 km or less—average 4.3 per water year (WY: October–September), with considerable interannual variability and a general decline in frequency during the 1930–2010 observational record. The distributions of monthly dust-event frequency and total dust flux are bimodal, with primary and secondary maxima in April and September, respectively. Dust reports are most common in the late afternoon and evening. An analysis of the 33 most recent (2001–10 WY) events at KSLC indicates that 11 were associated with airmass convection, 16 were associated with a cold front or baroclinic trough entering Utah from the west or northwest, 4 were associated with a stationary or slowly moving front or baroclinic trough west of Utah, and 2 were associated with other synoptic patterns. GOES imagery from these 33 events, as well as 61 additional events from the surrounding region, illustrates that emission sources are located primarily in low-elevation Late Pleistocene–Holocene alluvial environments in southern and western Utah and southern and western Nevada.
Abstract
Episodic dust events cause hazardous air quality along Utah’s Wasatch Front and dust loading of the snowpack in the adjacent Wasatch Mountains. This paper presents a climatology of episodic dust events of the Wasatch Front and adjoining region that is based on surface weather observations from the Salt Lake City International Airport (KSLC), Geostationary Operational Environmental Satellite (GOES) imagery, and additional meteorological datasets. Dust events at KSLC—defined as any day [mountain standard time (MST)] with at least one report of a dust storm, blowing dust, and/or dust in suspension with a visibility of 10 km or less—average 4.3 per water year (WY: October–September), with considerable interannual variability and a general decline in frequency during the 1930–2010 observational record. The distributions of monthly dust-event frequency and total dust flux are bimodal, with primary and secondary maxima in April and September, respectively. Dust reports are most common in the late afternoon and evening. An analysis of the 33 most recent (2001–10 WY) events at KSLC indicates that 11 were associated with airmass convection, 16 were associated with a cold front or baroclinic trough entering Utah from the west or northwest, 4 were associated with a stationary or slowly moving front or baroclinic trough west of Utah, and 2 were associated with other synoptic patterns. GOES imagery from these 33 events, as well as 61 additional events from the surrounding region, illustrates that emission sources are located primarily in low-elevation Late Pleistocene–Holocene alluvial environments in southern and western Utah and southern and western Nevada.
Abstract
To provide ground validation data for satellite precipitation products derived from the Global Precipitation Measurement (GPM) mission, such as IMERG, in cold seasons and where orographic factors exert strong controls on precipitation, the Olympic Mountain Experiment (OLYMPEX) was conducted during winter 2015/16. By utilizing multiple observational resources from OLYMPEX, estimates of daily and finer-scale precipitation are constructed at 1/32° spatial resolution over the OLYMPEX domain. The estimates are based on NOAA WSR-88D and gauge estimates as incorporated in NOAA’s National Severe Storms Laboratory (NSSL) Q3GC product, augmented with an additional 120 gauges available during OLYMPEX. Few stations are located in the interior of the Olympic Peninsula at elevations higher than about 500 m, and in this part of the domain the Variable Infiltration Capacity (VIC) hydrology model is used to invert the snow water equivalent (SWE) estimates, derived from two NASA JPL Airborne Snow Observatory (ASO) snow depth maps on 8–9 February 2016 and 29–30 March 2016, for precipitation through adjustment of the precipitation-weighting factor on a grid cell by grid cell basis. In comparison with this composite product, both IMERG (version 04A) and its Japanese counterpart GSMaP’s (version 04B) satellite-only products tend to underestimate winter precipitation, by 41% and 28%, respectively, over the entire domain from 1 October 2015 to 30 April 2016. The underestimation is more pronounced for the orographically enhanced mountainous interior of the OLYMPEX domain, by 57% and 48%, respectively. In contrast, IMERG and GSMaP storm interarrival time statistics are quite similar to those estimated from gridded observations.
Abstract
To provide ground validation data for satellite precipitation products derived from the Global Precipitation Measurement (GPM) mission, such as IMERG, in cold seasons and where orographic factors exert strong controls on precipitation, the Olympic Mountain Experiment (OLYMPEX) was conducted during winter 2015/16. By utilizing multiple observational resources from OLYMPEX, estimates of daily and finer-scale precipitation are constructed at 1/32° spatial resolution over the OLYMPEX domain. The estimates are based on NOAA WSR-88D and gauge estimates as incorporated in NOAA’s National Severe Storms Laboratory (NSSL) Q3GC product, augmented with an additional 120 gauges available during OLYMPEX. Few stations are located in the interior of the Olympic Peninsula at elevations higher than about 500 m, and in this part of the domain the Variable Infiltration Capacity (VIC) hydrology model is used to invert the snow water equivalent (SWE) estimates, derived from two NASA JPL Airborne Snow Observatory (ASO) snow depth maps on 8–9 February 2016 and 29–30 March 2016, for precipitation through adjustment of the precipitation-weighting factor on a grid cell by grid cell basis. In comparison with this composite product, both IMERG (version 04A) and its Japanese counterpart GSMaP’s (version 04B) satellite-only products tend to underestimate winter precipitation, by 41% and 28%, respectively, over the entire domain from 1 October 2015 to 30 April 2016. The underestimation is more pronounced for the orographically enhanced mountainous interior of the OLYMPEX domain, by 57% and 48%, respectively. In contrast, IMERG and GSMaP storm interarrival time statistics are quite similar to those estimated from gridded observations.
Abstract
This paper describes satellite data collected as part of the 2002/03 Cold Land Processes Experiment (CLPX). These data include multispectral and hyperspectral optical imaging, and passive and active microwave observations of the test areas. The CLPX multispectral optical data include the Advanced Very High Resolution Radiometer (AVHRR), the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Multi-angle Imaging Spectroradiometer (MISR). The spaceborne hyperspectral optical data consist of measurements acquired with the NASA Earth Observing-1 (EO-1) Hyperion imaging spectrometer. The passive microwave data include observations from the Special Sensor Microwave Imager (SSM/I) and the Advanced Microwave Scanning Radiometer (AMSR) for Earth Observing System (EOS; AMSR-E). Observations from the Radarsat synthetic aperture radar and the SeaWinds scatterometer flown on QuikSCAT make up the active microwave data.
Abstract
This paper describes satellite data collected as part of the 2002/03 Cold Land Processes Experiment (CLPX). These data include multispectral and hyperspectral optical imaging, and passive and active microwave observations of the test areas. The CLPX multispectral optical data include the Advanced Very High Resolution Radiometer (AVHRR), the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Multi-angle Imaging Spectroradiometer (MISR). The spaceborne hyperspectral optical data consist of measurements acquired with the NASA Earth Observing-1 (EO-1) Hyperion imaging spectrometer. The passive microwave data include observations from the Special Sensor Microwave Imager (SSM/I) and the Advanced Microwave Scanning Radiometer (AMSR) for Earth Observing System (EOS; AMSR-E). Observations from the Radarsat synthetic aperture radar and the SeaWinds scatterometer flown on QuikSCAT make up the active microwave data.
Abstract
Runoff from mountain snowpack is an important freshwater supply for many parts of the world. The deposition of aeolian dust on snow decreases snow albedo and increases the absorption of solar irradiance. This absorption accelerates melting, impacting the regional hydrological cycle in terms of timing and magnitude of runoff. The Moderate Resolution Imaging Spectroradiometer (MODIS) Dust Radiative Forcing in Snow (MODDRFS) satellite product allows estimation of the instantaneous (at time of satellite overpass) surface radiative forcing caused by dust. While such snapshots are useful, energy balance modeling requires temporally resolved radiative forcing to represent energy fluxes to the snowpack, as modulated primarily by varying cloud cover. Here, the instantaneous MODDRFS estimate is used as a tie point to calculate temporally resolved surface radiative forcing. Dust radiative forcing scenarios were considered for 1) clear-sky conditions and 2) all-sky conditions using satellite-based cloud observations. Comparisons against in situ stations in the Rocky Mountains show that accounting for the temporally resolved all-sky solar irradiance via satellite retrievals yields a more representative time series of dust radiative effects compared to the clear-sky assumption. The modeled impact of dust on enhanced snowmelt was found to be significant, accounting for nearly 50% of the total melt at the more contaminated station sites. The algorithm is applicable to regional basins worldwide, bearing relevance to both climate process research and the operational management of water resources.
Abstract
Runoff from mountain snowpack is an important freshwater supply for many parts of the world. The deposition of aeolian dust on snow decreases snow albedo and increases the absorption of solar irradiance. This absorption accelerates melting, impacting the regional hydrological cycle in terms of timing and magnitude of runoff. The Moderate Resolution Imaging Spectroradiometer (MODIS) Dust Radiative Forcing in Snow (MODDRFS) satellite product allows estimation of the instantaneous (at time of satellite overpass) surface radiative forcing caused by dust. While such snapshots are useful, energy balance modeling requires temporally resolved radiative forcing to represent energy fluxes to the snowpack, as modulated primarily by varying cloud cover. Here, the instantaneous MODDRFS estimate is used as a tie point to calculate temporally resolved surface radiative forcing. Dust radiative forcing scenarios were considered for 1) clear-sky conditions and 2) all-sky conditions using satellite-based cloud observations. Comparisons against in situ stations in the Rocky Mountains show that accounting for the temporally resolved all-sky solar irradiance via satellite retrievals yields a more representative time series of dust radiative effects compared to the clear-sky assumption. The modeled impact of dust on enhanced snowmelt was found to be significant, accounting for nearly 50% of the total melt at the more contaminated station sites. The algorithm is applicable to regional basins worldwide, bearing relevance to both climate process research and the operational management of water resources.
Abstract
This paper describes the airborne data collected during the 2002 and 2003 Cold Land Processes Experiment (CLPX). These data include gamma radiation observations, multi- and hyperspectral optical imaging, optical altimetry, and passive and active microwave observations of the test areas. The gamma observations were collected with the NOAA/National Weather Service Gamma Radiation Detection System (GAMMA). The CLPX multispectral optical data consist of very high-resolution color-infrared orthoimagery of the intensive study areas (ISAs) by TerrainVision. The airborne hyperspectral optical data consist of observations from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Optical altimetry measurements were collected using airborne light detection and ranging (lidar) by TerrainVision. The active microwave data include radar observations from the NASA Airborne Synthetic Aperture Radar (AIRSAR), the Jet Propulsion Laboratory’s Polarimetric Ku-band Scatterometer (POLSCAT), and airborne GPS bistatic radar data collected with the NASA GPS radar delay mapping receiver (DMR). The passive microwave data consist of observations collected with the NOAA Polarimetric Scanning Radiometer (PSR). All of the airborne datasets described here and more information describing data collection and processing are available online.
Abstract
This paper describes the airborne data collected during the 2002 and 2003 Cold Land Processes Experiment (CLPX). These data include gamma radiation observations, multi- and hyperspectral optical imaging, optical altimetry, and passive and active microwave observations of the test areas. The gamma observations were collected with the NOAA/National Weather Service Gamma Radiation Detection System (GAMMA). The CLPX multispectral optical data consist of very high-resolution color-infrared orthoimagery of the intensive study areas (ISAs) by TerrainVision. The airborne hyperspectral optical data consist of observations from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Optical altimetry measurements were collected using airborne light detection and ranging (lidar) by TerrainVision. The active microwave data include radar observations from the NASA Airborne Synthetic Aperture Radar (AIRSAR), the Jet Propulsion Laboratory’s Polarimetric Ku-band Scatterometer (POLSCAT), and airborne GPS bistatic radar data collected with the NASA GPS radar delay mapping receiver (DMR). The passive microwave data consist of observations collected with the NOAA Polarimetric Scanning Radiometer (PSR). All of the airborne datasets described here and more information describing data collection and processing are available online.
Abstract
Forest canopies influence the proportion of the land surface that is visible from above, or the viewable gap fraction (VGF). The VGF limits the amount of information available in satellite data about the land surface, such as snow cover in forests. Efforts to recover fractional snow cover from the spectral mixture analysis model Moderate Resolution Imaging Spectroradiometer (MODIS) snow-covered area and grain size (MODSCAG) indicate the importance of view angle effects in forested landscapes. The VGF can be estimated using both hemispherical photos and forest canopy models. For a set of stands in the Cold Land Field Processes Experiment (CLPX) sites in the Fraser Experimental Forest in Colorado, the convergence of both measurements and models of the VGF as a function of view angle supports the idea that VGF can be characterized as a function of forest properties. A simple geometric optical (GO) model that includes only between-crown gaps can capture the basic shape of the VGF as a function of view zenith angle. However, the GO model tends to underestimate the VGF compared with estimates derived from hemispherical photos, particularly at high view angles. The use of a more complicated geometric optical–radiative transfer (GORT) model generally improves estimates of the VGF by taking into account within-crown gaps. Small footprint airborne lidar data are useful for mapping forest cover and height, which makes the parameterization of the GORT model possible over a landscape. Better knowledge of the angular distribution of gaps in forest canopies holds promise for improving remote sensing of snow cover fraction.
Abstract
Forest canopies influence the proportion of the land surface that is visible from above, or the viewable gap fraction (VGF). The VGF limits the amount of information available in satellite data about the land surface, such as snow cover in forests. Efforts to recover fractional snow cover from the spectral mixture analysis model Moderate Resolution Imaging Spectroradiometer (MODIS) snow-covered area and grain size (MODSCAG) indicate the importance of view angle effects in forested landscapes. The VGF can be estimated using both hemispherical photos and forest canopy models. For a set of stands in the Cold Land Field Processes Experiment (CLPX) sites in the Fraser Experimental Forest in Colorado, the convergence of both measurements and models of the VGF as a function of view angle supports the idea that VGF can be characterized as a function of forest properties. A simple geometric optical (GO) model that includes only between-crown gaps can capture the basic shape of the VGF as a function of view zenith angle. However, the GO model tends to underestimate the VGF compared with estimates derived from hemispherical photos, particularly at high view angles. The use of a more complicated geometric optical–radiative transfer (GORT) model generally improves estimates of the VGF by taking into account within-crown gaps. Small footprint airborne lidar data are useful for mapping forest cover and height, which makes the parameterization of the GORT model possible over a landscape. Better knowledge of the angular distribution of gaps in forest canopies holds promise for improving remote sensing of snow cover fraction.
Abstract
Numerical simulations of snow water equivalent (SWE) in mountain systems can be biased, and few SWE observations have existed over large domains. New approaches for measuring SWE, like NASA’s ultra-high-resolution Airborne Snow Observatory (ASO), offer an opportunity to improve model estimates by providing a high-quality validation target. In this study, a computationally efficient snow data assimilation (DA) approach over the western United States at 1.75-km spatial resolution for water years (WYs) 2001–17 is presented. A local ensemble transform Kalman filter implemented as a batch smoother is used with the VIC hydrology model to assimilate the remotely sensed daily MODIS fractional snow-covered area (SCA). Validation of the high-resolution SWE estimates is done against ASO SWE data in the Tuolumne basin (California), Uncompahgre basin (Colorado), and Olympic Peninsula (Washington). Results indicate good performance in dry years and during melt, with DA reducing Tuolumne basin-average SWE percent differences from −68%, −92%, and −84% in open loop to 0.6%, 25%, and 3% after DA for WYs 2013–15, respectively, for ASO dates and spatial extent. DA also improved SWE percent difference over the Uncompahgre basin (−84% open loop, −65% DA) and Olympic Peninsula (26% open loop, −0.2% DA). However, in anomalously wet years DA underestimates SWE, likely due to an inadequate snow depletion curve parameterization. Despite potential shortcomings due to VIC model setup (e.g., water balance mode) or parameterization (snow depletion curve), the DA framework implemented in this study shows promise in overcoming some of these limitations and improving estimated SWE, in particular during drier years or at higher elevations, when most in situ observations cannot capture high-elevation snowpack due to lack of stations there.
Abstract
Numerical simulations of snow water equivalent (SWE) in mountain systems can be biased, and few SWE observations have existed over large domains. New approaches for measuring SWE, like NASA’s ultra-high-resolution Airborne Snow Observatory (ASO), offer an opportunity to improve model estimates by providing a high-quality validation target. In this study, a computationally efficient snow data assimilation (DA) approach over the western United States at 1.75-km spatial resolution for water years (WYs) 2001–17 is presented. A local ensemble transform Kalman filter implemented as a batch smoother is used with the VIC hydrology model to assimilate the remotely sensed daily MODIS fractional snow-covered area (SCA). Validation of the high-resolution SWE estimates is done against ASO SWE data in the Tuolumne basin (California), Uncompahgre basin (Colorado), and Olympic Peninsula (Washington). Results indicate good performance in dry years and during melt, with DA reducing Tuolumne basin-average SWE percent differences from −68%, −92%, and −84% in open loop to 0.6%, 25%, and 3% after DA for WYs 2013–15, respectively, for ASO dates and spatial extent. DA also improved SWE percent difference over the Uncompahgre basin (−84% open loop, −65% DA) and Olympic Peninsula (26% open loop, −0.2% DA). However, in anomalously wet years DA underestimates SWE, likely due to an inadequate snow depletion curve parameterization. Despite potential shortcomings due to VIC model setup (e.g., water balance mode) or parameterization (snow depletion curve), the DA framework implemented in this study shows promise in overcoming some of these limitations and improving estimated SWE, in particular during drier years or at higher elevations, when most in situ observations cannot capture high-elevation snowpack due to lack of stations there.
