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Abstract
Improving estimation of snow water equivalent rate (SWER) from radar reflectivity (Ze), known as a SWER(Ze) relationship, is a priority for NASA’s Global Precipitation Measurement (GPM) mission ground validation program as it is needed to comprehensively validate spaceborne precipitation retrievals. This study investigates the performance of eight operational and four research-based SWER(Ze) relationships utilizing Precipitation Imaging Probe (PIP) observations from the International Collaborative Experiment for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field campaign. During ICE-POP 2018, there were 10 snow events that are classified by synoptic conditions as either cold low or warm low, and a SWER(Ze) relationship is derived for each event. Additionally, a SWER(Ze) relationship is derived for each synoptic classification by merging all events within each class. Two new types of SWER(Ze) relationships are derived from PIP measurements of bulk density and habit classification. These two physically based SWER(Ze) relationships provided superior estimates of SWER when compared to the operational, event-specific, and synoptic SWER(Ze) relationships. For estimates of the event snow water equivalent total, the event-specific, synoptic, and best-performing operational SWER(Ze) relationships outperformed the physically based SWER(Ze) relationship, although the physically based relationships still performed well. This study recommends using the density or habit-based SWER(Ze) relationships for microphysical studies, whereas the other SWER(Ze) relationships are better suited toward hydrologic application.
Abstract
Improving estimation of snow water equivalent rate (SWER) from radar reflectivity (Ze), known as a SWER(Ze) relationship, is a priority for NASA’s Global Precipitation Measurement (GPM) mission ground validation program as it is needed to comprehensively validate spaceborne precipitation retrievals. This study investigates the performance of eight operational and four research-based SWER(Ze) relationships utilizing Precipitation Imaging Probe (PIP) observations from the International Collaborative Experiment for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field campaign. During ICE-POP 2018, there were 10 snow events that are classified by synoptic conditions as either cold low or warm low, and a SWER(Ze) relationship is derived for each event. Additionally, a SWER(Ze) relationship is derived for each synoptic classification by merging all events within each class. Two new types of SWER(Ze) relationships are derived from PIP measurements of bulk density and habit classification. These two physically based SWER(Ze) relationships provided superior estimates of SWER when compared to the operational, event-specific, and synoptic SWER(Ze) relationships. For estimates of the event snow water equivalent total, the event-specific, synoptic, and best-performing operational SWER(Ze) relationships outperformed the physically based SWER(Ze) relationship, although the physically based relationships still performed well. This study recommends using the density or habit-based SWER(Ze) relationships for microphysical studies, whereas the other SWER(Ze) relationships are better suited toward hydrologic application.
Abstract
Parameters of the normalized gamma particle size distribution (PSD) have been retrieved from the Precipitation Image Package (PIP) snowfall observations collected during the International Collaborative Experiment–PyeongChang Olympic and Paralympic winter games (ICE-POP 2018). Two of the gamma PSD parameters, the mass-weighted particle diameter D mass and the normalized intercept parameter NW , have median values of 1.15–1.31 mm and 2.84–3.04 log(mm−1 m−3), respectively. This range arises from the choice of the relationship between the maximum versus equivalent diameter, D mx–D eq, and the relationship between the Reynolds and Best numbers, Re–X. Normalization of snow water equivalent rate (SWER) and ice water content W by NW reduces the range in NW , resulting in well-fitted power-law relationships between SWER/NW and D mass and between W/NW and D mass. The bulk descriptors of snowfall are calculated from PIP observations and from the gamma PSD with values of the shape parameter μ ranging from −2 to 10. NASA’s Global Precipitation Measurement (GPM) mission, which adopted the normalized gamma PSD, assumes μ = 2 and 3 in its two separate algorithms. The mean fractional bias (MFB) of the snowfall parameters changes with μ, where the functional dependence on μ depends on the specific snowfall parameter of interest. The MFB of the total concentration was underestimated by 0.23–0.34 when μ = 2 and by 0.29–0.40 when μ = 3, whereas the MFB of SWER had a much narrower range (from −0.03 to 0.04) for the same μ values.
Abstract
Parameters of the normalized gamma particle size distribution (PSD) have been retrieved from the Precipitation Image Package (PIP) snowfall observations collected during the International Collaborative Experiment–PyeongChang Olympic and Paralympic winter games (ICE-POP 2018). Two of the gamma PSD parameters, the mass-weighted particle diameter D mass and the normalized intercept parameter NW , have median values of 1.15–1.31 mm and 2.84–3.04 log(mm−1 m−3), respectively. This range arises from the choice of the relationship between the maximum versus equivalent diameter, D mx–D eq, and the relationship between the Reynolds and Best numbers, Re–X. Normalization of snow water equivalent rate (SWER) and ice water content W by NW reduces the range in NW , resulting in well-fitted power-law relationships between SWER/NW and D mass and between W/NW and D mass. The bulk descriptors of snowfall are calculated from PIP observations and from the gamma PSD with values of the shape parameter μ ranging from −2 to 10. NASA’s Global Precipitation Measurement (GPM) mission, which adopted the normalized gamma PSD, assumes μ = 2 and 3 in its two separate algorithms. The mean fractional bias (MFB) of the snowfall parameters changes with μ, where the functional dependence on μ depends on the specific snowfall parameter of interest. The MFB of the total concentration was underestimated by 0.23–0.34 when μ = 2 and by 0.29–0.40 when μ = 3, whereas the MFB of SWER had a much narrower range (from −0.03 to 0.04) for the same μ values.
Abstract
Over the past decade, networks of Multifilter Rotating Shadowband Radiometers (MFRSR) and automated sun photometers have been established in the United States to monitor aerosol properties. The MFRSR alternately measures diffuse and global irradiance in six narrow spectral bands and a broadband channel of the solar spectrum, from which the direct normal component for each may be inferred. Its 500-nm channel mimics sun photometer measurements and thus is a source of aerosol optical depth information. Automatic data reduction methods are needed because of the high volume of data produced by the MFRSR. In addition, these instruments are often not calibrated for absolute irradiance and must be periodically calibrated for optical depth analysis using the Langley method. This process involves extrapolation to the signal the MFRSR would measure at the top of the atmosphere (I λ0). Here, an automated clear-sky identification algorithm is used to screen MFRSR 500-nm measurements for suitable calibration data. The clear-sky MFRSR measurements are subsequently used to construct a set of calibration Langley plots from which a mean I λ0 is computed. This calibration I λ0 may be subsequently applied to any MFRSR 500-nm measurement within the calibration period to retrieve aerosol optical depth. This method is tested on a 2-month MFRSR dataset from the Table Mountain NOAA Surface Radiation Budget Network (SURFRAD) station near Boulder, Colorado. The resultant I λ0 is applied to two Asian dust–related high air pollution episodes that occurred within the calibration period on 13 and 17 April 2001. Computed aerosol optical depths for 17 April range from approximately 0.30 to 0.40, and those for 13 April vary from background levels to >0.30. Errors in these retrievals were estimated to range from ±0.01 to ±0.05, depending on the solar zenith angle. The calculations are compared with independent MFRSR-based aerosol optical depth retrievals at the Pawnee National Grasslands, 85 km to the northeast of Table Mountain, and to sun-photometer-derived aerosol optical depths at the National Renewable Energy Laboratory in Golden, Colorado, 50 km to the south. Both the Table Mountain and Golden stations are situated within a few kilometers of the Front Range of the Rocky Mountains, whereas the Pawnee station is on the eastern plains of Colorado. Time series of aerosol optical depth from Pawnee and Table Mountain stations compare well for 13 April when, according to the Naval Aerosol Analysis and Prediction System, an upper-level Asian dust plume enveloped most of Colorado. Aerosol optical depths at the Golden station for that event are generally greater than those at Table Mountain and Pawnee, possibly because of the proximity of Golden to Denver's urban aerosol plume. The dust over Colorado was primarily surface based on 17 April. On that day, aerosol optical depths at Table Mountain and Golden are similar but are 2 times the magnitude of those at Pawnee. This difference is attributed to meteorological conditions that favored air stagnation in the planetary boundary layer along the Front Range, and a west-to-east gradient in aerosol concentration. The magnitude and timing of the aerosol optical depth measurements at Table Mountain for these events are found to be consistent with independent measurements made at NASA Aerosol Robotic Network (AERONET) stations at Missoula, Montana, and at Bondville, Illinois.
Abstract
Over the past decade, networks of Multifilter Rotating Shadowband Radiometers (MFRSR) and automated sun photometers have been established in the United States to monitor aerosol properties. The MFRSR alternately measures diffuse and global irradiance in six narrow spectral bands and a broadband channel of the solar spectrum, from which the direct normal component for each may be inferred. Its 500-nm channel mimics sun photometer measurements and thus is a source of aerosol optical depth information. Automatic data reduction methods are needed because of the high volume of data produced by the MFRSR. In addition, these instruments are often not calibrated for absolute irradiance and must be periodically calibrated for optical depth analysis using the Langley method. This process involves extrapolation to the signal the MFRSR would measure at the top of the atmosphere (I λ0). Here, an automated clear-sky identification algorithm is used to screen MFRSR 500-nm measurements for suitable calibration data. The clear-sky MFRSR measurements are subsequently used to construct a set of calibration Langley plots from which a mean I λ0 is computed. This calibration I λ0 may be subsequently applied to any MFRSR 500-nm measurement within the calibration period to retrieve aerosol optical depth. This method is tested on a 2-month MFRSR dataset from the Table Mountain NOAA Surface Radiation Budget Network (SURFRAD) station near Boulder, Colorado. The resultant I λ0 is applied to two Asian dust–related high air pollution episodes that occurred within the calibration period on 13 and 17 April 2001. Computed aerosol optical depths for 17 April range from approximately 0.30 to 0.40, and those for 13 April vary from background levels to >0.30. Errors in these retrievals were estimated to range from ±0.01 to ±0.05, depending on the solar zenith angle. The calculations are compared with independent MFRSR-based aerosol optical depth retrievals at the Pawnee National Grasslands, 85 km to the northeast of Table Mountain, and to sun-photometer-derived aerosol optical depths at the National Renewable Energy Laboratory in Golden, Colorado, 50 km to the south. Both the Table Mountain and Golden stations are situated within a few kilometers of the Front Range of the Rocky Mountains, whereas the Pawnee station is on the eastern plains of Colorado. Time series of aerosol optical depth from Pawnee and Table Mountain stations compare well for 13 April when, according to the Naval Aerosol Analysis and Prediction System, an upper-level Asian dust plume enveloped most of Colorado. Aerosol optical depths at the Golden station for that event are generally greater than those at Table Mountain and Pawnee, possibly because of the proximity of Golden to Denver's urban aerosol plume. The dust over Colorado was primarily surface based on 17 April. On that day, aerosol optical depths at Table Mountain and Golden are similar but are 2 times the magnitude of those at Pawnee. This difference is attributed to meteorological conditions that favored air stagnation in the planetary boundary layer along the Front Range, and a west-to-east gradient in aerosol concentration. The magnitude and timing of the aerosol optical depth measurements at Table Mountain for these events are found to be consistent with independent measurements made at NASA Aerosol Robotic Network (AERONET) stations at Missoula, Montana, and at Bondville, Illinois.
CLOUDS AND MORE: ARM Climate Modeling Best Estimate Data
A New Data Product for Climate Studies
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Northern Eurasia, the largest landmass in the northern extratropics, accounts for ~20% of the global land area. However, little is known about how the biogeochemical cycles, energy and water cycles, and human activities specific to this carbon-rich, cold region interact with global climate. A major concern is that changes in the distribution of land-based life, as well as its interactions with the environment, may lead to a self-reinforcing cycle of accelerated regional and global warming. With this as its motivation, the Northern Eurasian Earth Science Partnership Initiative (NEESPI) was formed in 2004 to better understand and quantify feedbacks between northern Eurasian and global climates. The first group of NEESPI projects has mostly focused on assembling regional databases, organizing improved environmental monitoring of the region, and studying individual environmental processes. That was a starting point to addressing emerging challenges in the region related to rapidly and simultaneously changing climate, environmental, and societal systems. More recently, the NEESPI research focus has been moving toward integrative studies, including the development of modeling capabilities to project the future state of climate, environment, and societies in the NEESPI domain. This effort will require a high level of integration of observation programs, process studies, and modeling across disciplines.
Northern Eurasia, the largest landmass in the northern extratropics, accounts for ~20% of the global land area. However, little is known about how the biogeochemical cycles, energy and water cycles, and human activities specific to this carbon-rich, cold region interact with global climate. A major concern is that changes in the distribution of land-based life, as well as its interactions with the environment, may lead to a self-reinforcing cycle of accelerated regional and global warming. With this as its motivation, the Northern Eurasian Earth Science Partnership Initiative (NEESPI) was formed in 2004 to better understand and quantify feedbacks between northern Eurasian and global climates. The first group of NEESPI projects has mostly focused on assembling regional databases, organizing improved environmental monitoring of the region, and studying individual environmental processes. That was a starting point to addressing emerging challenges in the region related to rapidly and simultaneously changing climate, environmental, and societal systems. More recently, the NEESPI research focus has been moving toward integrative studies, including the development of modeling capabilities to project the future state of climate, environment, and societies in the NEESPI domain. This effort will require a high level of integration of observation programs, process studies, and modeling across disciplines.
