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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
The accuracy and uncertainty of radar echo top heights estimated by ground-based radars remains largely unknown despite their critical importance for applications ranging from aviation weather forecasting to severe weather diagnosis. As the vantage point of space is more suited for the estimation of echo top heights than ground-based radars, the use of space-borne radar observations is explored as an external reference for cross-comparison. An investigation has been carried out across the conterminous United States by comparing the National Oceanic and Atmospheric Administration (NOAA)/National Severe Storms Laboratory Multi-Radar Multi-Sensor (MRMS) system to the space-based radar onboard the NASA/JAXA Global Precipitation Measurement satellite platform. No major bias was assessed between the two products. An annual cycle of differences is found, driven by an underestimation of the stratiform cloud echo top heights and an overestimation of the convective ones. The investigation of the systematic biases for different radar volume coverage pattern (VCP) shows that scanning strategies with fewer tilts and greater voids as VCP 21/121/221 contribute to overestimations observed for high MRMS tops. For VCP 12/212, the Automated Volume Scan Evaluation and Termination (AVSET) function increases the radar cone of silence, causing overestimations when the echo top lies above the highest elevation scan. However, it seems that for low echo tops, the shorter refresh rates contribute to mitigate underestimations, especially in stratiform cases.
Abstract
The accuracy and uncertainty of radar echo top heights estimated by ground-based radars remains largely unknown despite their critical importance for applications ranging from aviation weather forecasting to severe weather diagnosis. As the vantage point of space is more suited for the estimation of echo top heights than ground-based radars, the use of space-borne radar observations is explored as an external reference for cross-comparison. An investigation has been carried out across the conterminous United States by comparing the National Oceanic and Atmospheric Administration (NOAA)/National Severe Storms Laboratory Multi-Radar Multi-Sensor (MRMS) system to the space-based radar onboard the NASA/JAXA Global Precipitation Measurement satellite platform. No major bias was assessed between the two products. An annual cycle of differences is found, driven by an underestimation of the stratiform cloud echo top heights and an overestimation of the convective ones. The investigation of the systematic biases for different radar volume coverage pattern (VCP) shows that scanning strategies with fewer tilts and greater voids as VCP 21/121/221 contribute to overestimations observed for high MRMS tops. For VCP 12/212, the Automated Volume Scan Evaluation and Termination (AVSET) function increases the radar cone of silence, causing overestimations when the echo top lies above the highest elevation scan. However, it seems that for low echo tops, the shorter refresh rates contribute to mitigate underestimations, especially in stratiform cases.
Abstract
We use dual-polarization C-band data collected in the Southern Ocean to examine the properties of snow observed during a voyage in the austral summer of 2018. Using existing forward modeling formalisms based on an assumption of Rayleigh scattering by soft spheroids, an optimal estimation algorithm is implemented to infer snow properties from horizontally polarized radar reflectivity, the differential radar reflectivity, and the specific differential phase. From the dual-polarization observables, we estimate ice water content qi , the mass-mean particle size Dm , and the exponent of the mass–dimensional relationship bm that, with several assumptions, allow for evaluation of snow bulk density, and snow number concentration. Upon evaluating the uncertainties associated with measurement and forward model errors, we determine that the algorithm can retrieve qi , Dm , and bm within single-pixel uncertainties conservatively estimated in the range 120%, 60%, and 40%, respectively. Applying the algorithm to open-cellular convection in the Southern Ocean, we find evidence for secondary ice formation processes within multicellular complexes. In stratiform precipitation systems we find snow properties and infer processes that are distinctly different from the shallow convective systems with evidence for riming and aggregation being common. We also find that embedded convection within the frontal system produces precipitation properties consistent with graupel. Examining 5 weeks of data, we show that snow in open-cellular cumulus has higher overall bulk density than snow in stratiform precipitation systems with implications for interpreting measurements from space-based active remote sensors.
Abstract
We use dual-polarization C-band data collected in the Southern Ocean to examine the properties of snow observed during a voyage in the austral summer of 2018. Using existing forward modeling formalisms based on an assumption of Rayleigh scattering by soft spheroids, an optimal estimation algorithm is implemented to infer snow properties from horizontally polarized radar reflectivity, the differential radar reflectivity, and the specific differential phase. From the dual-polarization observables, we estimate ice water content qi , the mass-mean particle size Dm , and the exponent of the mass–dimensional relationship bm that, with several assumptions, allow for evaluation of snow bulk density, and snow number concentration. Upon evaluating the uncertainties associated with measurement and forward model errors, we determine that the algorithm can retrieve qi , Dm , and bm within single-pixel uncertainties conservatively estimated in the range 120%, 60%, and 40%, respectively. Applying the algorithm to open-cellular convection in the Southern Ocean, we find evidence for secondary ice formation processes within multicellular complexes. In stratiform precipitation systems we find snow properties and infer processes that are distinctly different from the shallow convective systems with evidence for riming and aggregation being common. We also find that embedded convection within the frontal system produces precipitation properties consistent with graupel. Examining 5 weeks of data, we show that snow in open-cellular cumulus has higher overall bulk density than snow in stratiform precipitation systems with implications for interpreting measurements from space-based active remote sensors.
Abstract
With the launch of the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) in 2014, renewed interest in retrievals of snowfall in the atmospheric column has occurred. The current operational GPM-DPR retrieval largely underestimates surface snowfall accumulation. Here, a neural network (NN) trained on data that are synthetically derived from state-of-the-art ice particle scattering models and measured in situ particle size distributions (PSDs) is used to retrieve two parameters of the PSD: liquid equivalent mass-weighted mean diameter
Abstract
With the launch of the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) in 2014, renewed interest in retrievals of snowfall in the atmospheric column has occurred. The current operational GPM-DPR retrieval largely underestimates surface snowfall accumulation. Here, a neural network (NN) trained on data that are synthetically derived from state-of-the-art ice particle scattering models and measured in situ particle size distributions (PSDs) is used to retrieve two parameters of the PSD: liquid equivalent mass-weighted mean diameter
Abstract
Two power-law relations linking equivalent radar reflectivity factor Z e and snowfall rate S are derived for a K-band Micro Rain Radar (MRR) and for a W-band cloud radar. For the development of these Z e –S relationships, a dataset of calculated and measured variables is used. Surface-based video-disdrometer measurements were collected during snowfall events over five winters at the high-latitude site in Hyytiälä, Finland. The data from 2014 to 2018 include particle size distributions (PSD) and their fall velocities, from which snowflake masses were derived. The K- and W-band Z e values are computed using these surface-based observations and snowflake scattering properties as provided by T-matrix and single-particle scattering tables, respectively. The uncertainty analysis shows that the K-band snowfall-rate estimation is significantly improved by including the intercept parameter N 0 of the PSD calculated from concurrent disdrometer measurements. If N 0 is used to adjust the prefactor of the Z e –S relationship, the RMSE of the snowfall-rate estimate can be reduced from 0.37 to around 0.11 mm h−1. For W-band radar, a Z e –S relationship with constant parameters for all available snow events shows a similar uncertainty when compared with the method that includes the PSD intercept parameter. To demonstrate the performance of the proposed Z e –S relationships, they are applied to measurements of the MRR and the W-band microwave radar for Arctic clouds at the Arctic research base operated by the German Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI) and the French Polar Institute Paul Emile Victor (IPEV) (AWIPEV) in Ny-Ålesund, Svalbard, Norway. The resulting snowfall-rate estimates show good agreement with in situ snowfall observations while other Z e –S relationships from literature reveal larger differences.
Abstract
Two power-law relations linking equivalent radar reflectivity factor Z e and snowfall rate S are derived for a K-band Micro Rain Radar (MRR) and for a W-band cloud radar. For the development of these Z e –S relationships, a dataset of calculated and measured variables is used. Surface-based video-disdrometer measurements were collected during snowfall events over five winters at the high-latitude site in Hyytiälä, Finland. The data from 2014 to 2018 include particle size distributions (PSD) and their fall velocities, from which snowflake masses were derived. The K- and W-band Z e values are computed using these surface-based observations and snowflake scattering properties as provided by T-matrix and single-particle scattering tables, respectively. The uncertainty analysis shows that the K-band snowfall-rate estimation is significantly improved by including the intercept parameter N 0 of the PSD calculated from concurrent disdrometer measurements. If N 0 is used to adjust the prefactor of the Z e –S relationship, the RMSE of the snowfall-rate estimate can be reduced from 0.37 to around 0.11 mm h−1. For W-band radar, a Z e –S relationship with constant parameters for all available snow events shows a similar uncertainty when compared with the method that includes the PSD intercept parameter. To demonstrate the performance of the proposed Z e –S relationships, they are applied to measurements of the MRR and the W-band microwave radar for Arctic clouds at the Arctic research base operated by the German Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI) and the French Polar Institute Paul Emile Victor (IPEV) (AWIPEV) in Ny-Ålesund, Svalbard, Norway. The resulting snowfall-rate estimates show good agreement with in situ snowfall observations while other Z e –S relationships from literature reveal larger differences.
Abstract
Few statistical downscaling applications have provided gridded products that can provide downscaled values for a no-gauge area as is done by dynamical downscaling. In this study, a gridded statistical downscaling scheme is presented to downscale summer precipitation to a dense grid that covers North China. The main innovation of this scheme is interpolating the parameters of single-station models to this dense grid and assigning optimal predictor values according to an interpolated predictand–predictor distance function. This method can produce spatial dependence (spatial autocorrelation) and transmit the spatial heterogeneity of predictor values from the large-scale predictors to the downscaled outputs. Such gridded output at no-gauge stations shows performances comparable to that at the gauged stations. The area mean precipitation of the downscaled results is comparable to other products. The main value of the downscaling scheme is that it can obtain reasonable outputs for no-gauge stations.
Abstract
Few statistical downscaling applications have provided gridded products that can provide downscaled values for a no-gauge area as is done by dynamical downscaling. In this study, a gridded statistical downscaling scheme is presented to downscale summer precipitation to a dense grid that covers North China. The main innovation of this scheme is interpolating the parameters of single-station models to this dense grid and assigning optimal predictor values according to an interpolated predictand–predictor distance function. This method can produce spatial dependence (spatial autocorrelation) and transmit the spatial heterogeneity of predictor values from the large-scale predictors to the downscaled outputs. Such gridded output at no-gauge stations shows performances comparable to that at the gauged stations. The area mean precipitation of the downscaled results is comparable to other products. The main value of the downscaling scheme is that it can obtain reasonable outputs for no-gauge stations.
Abstract
Large hail is a primary contributor to damages and loss around the world, in both agriculture and infrastructure. The sensitivity of passive microwave radiometer measurements to scattering by hail led to the development of proxies for severe hail, most of which use brightness temperature thresholds from 37-GHz and higher-frequency microwave channels on board weather satellites in low-Earth orbit. Using 16+ years of data from the Tropical Rainfall Measuring Mission (TRMM; 36°S–36°N), we pair TRMM brightness temperature–derived precipitation features with surface hail reports in the United States to train a hail retrieval on passive microwave data from the 10-, 19-, 37-, and 85-GHz channels based on probability curves fit to the microwave data. We then apply this hail retrieval to features in the Global Precipitation Measurement (GPM) domain (from 69°S to 69°N) to develop a nearly global passive microwave–based climatology of hail. The extended domain of the GPM satellite into higher latitudes requires filtering out features that we believe are over icy and snowy surface regimes. We also normalize brightness temperature depression by tropopause height in an effort to account for differences in storm depth between the tropics and higher latitudes. Our results show the highest hail frequencies in the region of northern Argentina through Paraguay, Uruguay, and southern Brazil; the central United States; and a swath of Africa just south of the Sahel. Smaller hot spots include Pakistan, eastern India, and Bangladesh. A notable difference between these results and many prior satellite-based studies is that central Africa, while still active in our climatology, does not rival the aforementioned regions in retrieved hailstorm frequency.
Abstract
Large hail is a primary contributor to damages and loss around the world, in both agriculture and infrastructure. The sensitivity of passive microwave radiometer measurements to scattering by hail led to the development of proxies for severe hail, most of which use brightness temperature thresholds from 37-GHz and higher-frequency microwave channels on board weather satellites in low-Earth orbit. Using 16+ years of data from the Tropical Rainfall Measuring Mission (TRMM; 36°S–36°N), we pair TRMM brightness temperature–derived precipitation features with surface hail reports in the United States to train a hail retrieval on passive microwave data from the 10-, 19-, 37-, and 85-GHz channels based on probability curves fit to the microwave data. We then apply this hail retrieval to features in the Global Precipitation Measurement (GPM) domain (from 69°S to 69°N) to develop a nearly global passive microwave–based climatology of hail. The extended domain of the GPM satellite into higher latitudes requires filtering out features that we believe are over icy and snowy surface regimes. We also normalize brightness temperature depression by tropopause height in an effort to account for differences in storm depth between the tropics and higher latitudes. Our results show the highest hail frequencies in the region of northern Argentina through Paraguay, Uruguay, and southern Brazil; the central United States; and a swath of Africa just south of the Sahel. Smaller hot spots include Pakistan, eastern India, and Bangladesh. A notable difference between these results and many prior satellite-based studies is that central Africa, while still active in our climatology, does not rival the aforementioned regions in retrieved hailstorm frequency.
Abstract
Global Precipitation Measurement (GPM) Microwave Imager (GMI) brightness temperatures (BTs) were simulated over a case of severe convection in Texas using ground-based S-band radar and the Atmospheric Radiative Transfer Simulator. The median particle diameter D o of a normalized gamma distribution was varied for different hydrometeor types under the constraint of fixed radar reflectivity to better understand how simulated GMI BTs respond to changing particle size distribution parameters. In addition, simulations were conducted to assess how low BTs may be expected to reach from realistic (although extreme) particle sizes or concentrations. Results indicate that increasing D o for cloud ice, graupel, and/or hail leads to warmer BTs (i.e., weaker scattering signature) at various frequencies. Channels at 166.0 and 183.31 ± 7 GHz are most sensitive to changing D o of cloud ice, channels at ≥89.0 GHz are most sensitive to changing D o of graupel, and at 18.7 and 36.5 GHz they show the greatest sensitivity to hail D o . Simulations contrasting BTs above high concentrations of small (0.5-cm diameter) and low concentrations of large (20-cm diameter) hailstones distributed evenly across a satellite pixel showed much greater scattering using the higher concentration of smaller hailstones with BTs as low as ~110, ~33, ~22, ~46, ~100, and ~106 K at 10.65, 18.7, 36.5, 89.0, 166.0, and 183.31 ± 7 GHz, respectively. These results suggest that number concentration is more important for scattering than particle size given a constant S-band radar reflectivity.
Abstract
Global Precipitation Measurement (GPM) Microwave Imager (GMI) brightness temperatures (BTs) were simulated over a case of severe convection in Texas using ground-based S-band radar and the Atmospheric Radiative Transfer Simulator. The median particle diameter D o of a normalized gamma distribution was varied for different hydrometeor types under the constraint of fixed radar reflectivity to better understand how simulated GMI BTs respond to changing particle size distribution parameters. In addition, simulations were conducted to assess how low BTs may be expected to reach from realistic (although extreme) particle sizes or concentrations. Results indicate that increasing D o for cloud ice, graupel, and/or hail leads to warmer BTs (i.e., weaker scattering signature) at various frequencies. Channels at 166.0 and 183.31 ± 7 GHz are most sensitive to changing D o of cloud ice, channels at ≥89.0 GHz are most sensitive to changing D o of graupel, and at 18.7 and 36.5 GHz they show the greatest sensitivity to hail D o . Simulations contrasting BTs above high concentrations of small (0.5-cm diameter) and low concentrations of large (20-cm diameter) hailstones distributed evenly across a satellite pixel showed much greater scattering using the higher concentration of smaller hailstones with BTs as low as ~110, ~33, ~22, ~46, ~100, and ~106 K at 10.65, 18.7, 36.5, 89.0, 166.0, and 183.31 ± 7 GHz, respectively. These results suggest that number concentration is more important for scattering than particle size given a constant S-band radar reflectivity.
Abstract
Satellite retrieval algorithms and model microphysical parameterizations require guidance from observations to improve the representation of ice-phase microphysical quantities and processes. Here, a parameterization for ice-phase particle size distributions (PSDs) is developed using in situ measurements of cloud microphysical properties collected during the Global Precipitation Measurement (GPM) Cold-Season Precipitation Experiment (GCPEx). This parameterization takes advantage of the relation between the gamma-shape parameter μ and the mass-weighted mean diameter D m of the ice-phase PSD sampled during GCPEx. The retrieval of effective reflectivity Z e and ice water content (IWC) from the reconstructed PSD using the μ–D m relationship was tested with independent measurements of Z e and IWC and overall leads to a mean error of 8% in both variables. This represents an improvement when compared with errors using the Field et al. parameterization of 10% in IWC and 37% in Z e . Current radar precipitation retrieval algorithms from GPM assume that the PSD follows a gamma distribution with μ = 3. This assumption leads to a mean overestimation of 5% in the retrieved Z e , whereas applying the μ–D m relationship found here reduces this bias to an overestimation of less than 1%. Proper selection of the a and b coefficients in the mass–dimension relationship is also of crucial importance for retrievals. An inappropriate selection of a and b, even from values observed in previous studies in similar environments and cloud types, can lead to more than 100% bias in IWC and Z e for the ice-phase particles analyzed here.
Abstract
Satellite retrieval algorithms and model microphysical parameterizations require guidance from observations to improve the representation of ice-phase microphysical quantities and processes. Here, a parameterization for ice-phase particle size distributions (PSDs) is developed using in situ measurements of cloud microphysical properties collected during the Global Precipitation Measurement (GPM) Cold-Season Precipitation Experiment (GCPEx). This parameterization takes advantage of the relation between the gamma-shape parameter μ and the mass-weighted mean diameter D m of the ice-phase PSD sampled during GCPEx. The retrieval of effective reflectivity Z e and ice water content (IWC) from the reconstructed PSD using the μ–D m relationship was tested with independent measurements of Z e and IWC and overall leads to a mean error of 8% in both variables. This represents an improvement when compared with errors using the Field et al. parameterization of 10% in IWC and 37% in Z e . Current radar precipitation retrieval algorithms from GPM assume that the PSD follows a gamma distribution with μ = 3. This assumption leads to a mean overestimation of 5% in the retrieved Z e , whereas applying the μ–D m relationship found here reduces this bias to an overestimation of less than 1%. Proper selection of the a and b coefficients in the mass–dimension relationship is also of crucial importance for retrievals. An inappropriate selection of a and b, even from values observed in previous studies in similar environments and cloud types, can lead to more than 100% bias in IWC and Z e for the ice-phase particles analyzed here.
Abstract
Retrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth’s atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global Precipitation Measurement (GPM) Core Observatory satellite and CloudSat’s Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow–rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow–rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM’s Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)–snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR–DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z–S approach.
Abstract
Retrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth’s atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global Precipitation Measurement (GPM) Core Observatory satellite and CloudSat’s Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow–rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow–rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM’s Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)–snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR–DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z–S approach.
