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Abstract
Gridded climate datasets are used by researchers and practitioners in many disciplines, including forest ecology, agriculture, and entomology. However, such datasets are generally unable to account for microclimatic variability, particularly within sites or among individual trees. One such dataset is a recent climatology of extreme minimum temperatures in the U.S. Great Lakes region, based on the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) gridded temperature dataset. Development of this climatology was motivated by interest in the spatiotemporal variability of winter temperatures potentially lethal to the hemlock woolly adelgid (HWA) (Adelges tsugae Annand) (Hemiptera: Adelgidae), an invasive insect that causes mortality of eastern hemlock (Tsuga canadensis). In this study, cold-season daily minimum temperatures were monitored at six Michigan sites varying in latitude, elevation, Great Lakes proximity, and HWA infestation status, to address two objectives. First, we documented the spatiotemporal variability in daily minimum air temperatures recorded at multiple aspects and heights on selected hemlock trees. Second, this variability was characterized in the context of the PRISM extreme minimum temperature climatology. Tree-sensor air temperatures exhibited minimal relationships with aspect but considerable sensitivity to height. Daily minimum temperatures were higher for some tree sensors positioned ≤ 0.2 m above ground level during some time periods, with overall muted temporal variability, relative to an adjacent ambient sensor. This phenomenon was attributed to the insulating effects of snow cover, because the tree–ambient sensor temperature difference was positively correlated with snow depth. Overall, results indicate that such unresolved variability warrants consideration by gridded climate dataset users.
Abstract
Gridded climate datasets are used by researchers and practitioners in many disciplines, including forest ecology, agriculture, and entomology. However, such datasets are generally unable to account for microclimatic variability, particularly within sites or among individual trees. One such dataset is a recent climatology of extreme minimum temperatures in the U.S. Great Lakes region, based on the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) gridded temperature dataset. Development of this climatology was motivated by interest in the spatiotemporal variability of winter temperatures potentially lethal to the hemlock woolly adelgid (HWA) (Adelges tsugae Annand) (Hemiptera: Adelgidae), an invasive insect that causes mortality of eastern hemlock (Tsuga canadensis). In this study, cold-season daily minimum temperatures were monitored at six Michigan sites varying in latitude, elevation, Great Lakes proximity, and HWA infestation status, to address two objectives. First, we documented the spatiotemporal variability in daily minimum air temperatures recorded at multiple aspects and heights on selected hemlock trees. Second, this variability was characterized in the context of the PRISM extreme minimum temperature climatology. Tree-sensor air temperatures exhibited minimal relationships with aspect but considerable sensitivity to height. Daily minimum temperatures were higher for some tree sensors positioned ≤ 0.2 m above ground level during some time periods, with overall muted temporal variability, relative to an adjacent ambient sensor. This phenomenon was attributed to the insulating effects of snow cover, because the tree–ambient sensor temperature difference was positively correlated with snow depth. Overall, results indicate that such unresolved variability warrants consideration by gridded climate dataset users.
Abstract
As the understanding of decadal variability in climate systems deepens, there is a growing interest in investigating the decadal variability of seasonal-mean or monthly mean variables. This study aims to understand the seasonality observed in the amplitude of decadal variability. To accomplish this, we analyze the decadal variability of the monthly mean North Atlantic Oscillation (NAO) index and North Pacific index (NPI) over the past decades using two different calculating processes: the full smoothing (F) process and the seasonal-specific (SS) process. Our findings suggest that the F process only captures the decadal variability of annual-mean variables, whereas the SS process is suited for capturing the seasonality of decadal variability. We find that the seasonality in decadal variability aligns with the seasonality in interannual variability. Additionally, we explore the seasonality in decadal variability in atmospheric and oceanic variables. The seasonality in oceanic decadal variability, including sea surface temperature and salinity, is found to be weak and small. The amplitude of decadal variability in the Pacific decadal oscillation (PDO) is similar across different months, indicating weak seasonality in the PDO. On the other hand, decadal variability of lower-tropospheric atmospheric circulation, including horizontal wind, geopotential height, and surface air temperature, exhibits significant seasonality in the extratropics, with the strongest decadal variability occurring in winter. Moreover, the significant seasonality in decadal variability of precipitation is observed in the tropics, with the strongest decadal variability occurring in summer. Our study provides insights into understanding the seasonality of decadal variability, which can aid in the improvement of decadal prediction of climate variability.
Significance Statement
The amplitude of decadal variability of seasonal-mean or monthly mean variables exhibits seasonality. Our results show that the seasonality in decadal variability is consistent with the seasonality in interannual variability. We also identified that the seasonality in oceanic decadal variability is weak and smaller than that in atmospheric decadal variability. Decadal variability of lower-tropospheric atmospheric circulation exhibits significant seasonality in the extratropics, with the strongest decadal variability occurring in winter. However, the significant seasonality in decadal variability of precipitation occurs in the tropics, with the strongest decadal variability occurring in summer. Our study provides insights into the seasonality of decadal variability in climate systems.
Abstract
As the understanding of decadal variability in climate systems deepens, there is a growing interest in investigating the decadal variability of seasonal-mean or monthly mean variables. This study aims to understand the seasonality observed in the amplitude of decadal variability. To accomplish this, we analyze the decadal variability of the monthly mean North Atlantic Oscillation (NAO) index and North Pacific index (NPI) over the past decades using two different calculating processes: the full smoothing (F) process and the seasonal-specific (SS) process. Our findings suggest that the F process only captures the decadal variability of annual-mean variables, whereas the SS process is suited for capturing the seasonality of decadal variability. We find that the seasonality in decadal variability aligns with the seasonality in interannual variability. Additionally, we explore the seasonality in decadal variability in atmospheric and oceanic variables. The seasonality in oceanic decadal variability, including sea surface temperature and salinity, is found to be weak and small. The amplitude of decadal variability in the Pacific decadal oscillation (PDO) is similar across different months, indicating weak seasonality in the PDO. On the other hand, decadal variability of lower-tropospheric atmospheric circulation, including horizontal wind, geopotential height, and surface air temperature, exhibits significant seasonality in the extratropics, with the strongest decadal variability occurring in winter. Moreover, the significant seasonality in decadal variability of precipitation is observed in the tropics, with the strongest decadal variability occurring in summer. Our study provides insights into understanding the seasonality of decadal variability, which can aid in the improvement of decadal prediction of climate variability.
Significance Statement
The amplitude of decadal variability of seasonal-mean or monthly mean variables exhibits seasonality. Our results show that the seasonality in decadal variability is consistent with the seasonality in interannual variability. We also identified that the seasonality in oceanic decadal variability is weak and smaller than that in atmospheric decadal variability. Decadal variability of lower-tropospheric atmospheric circulation exhibits significant seasonality in the extratropics, with the strongest decadal variability occurring in winter. However, the significant seasonality in decadal variability of precipitation occurs in the tropics, with the strongest decadal variability occurring in summer. Our study provides insights into the seasonality of decadal variability in climate systems.
Abstract
The Met Office’s atmospheric dispersion model Numerical Atmospheric-Dispersion Modeling Environment (NAME) is validated against controlled tracer release experiments, considering the impact of the driving meteorological data and choices in the parameterization of unresolved motions. The Cross-Appalachian Tracer Experiment (CAPTEX) and Across North America Tracer Experiment (ANATEX) were long-range dispersion experiments in which inert tracers were released and the air concentrations measured across North America in the 1980s. NAME simulations of the experiments have been driven by both reanalysis meteorological data from European Centre for Medium-Range Weather Forecasts (ECMWF) and data from the Advanced Research version of the Weather Research and Forecasting (WRF) Model. NAME predictions of air concentrations are assessed against the experimental measurements, using a ranking method composed of four statistical parameters. Differences in the performance of NAME according to this ranking method are compared when driven by different meteorological sources. The effect of changing parameter values in NAME for the unresolved mesoscale motions parameterization is also considered, in particular, whether the parameter values giving the best performance rank are consistent with values typically used. The performance ranks are compared with analyses in the literature for other particle dispersion models, namely, Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), Stochastic Time-Inverted Lagrangian Transport (STILT), and Flexible Particle (FLEXPART). It is found that NAME performance is comparable to the other dispersion models considered, with the different models responding similarly to differences in driving meteorological data.
Abstract
The Met Office’s atmospheric dispersion model Numerical Atmospheric-Dispersion Modeling Environment (NAME) is validated against controlled tracer release experiments, considering the impact of the driving meteorological data and choices in the parameterization of unresolved motions. The Cross-Appalachian Tracer Experiment (CAPTEX) and Across North America Tracer Experiment (ANATEX) were long-range dispersion experiments in which inert tracers were released and the air concentrations measured across North America in the 1980s. NAME simulations of the experiments have been driven by both reanalysis meteorological data from European Centre for Medium-Range Weather Forecasts (ECMWF) and data from the Advanced Research version of the Weather Research and Forecasting (WRF) Model. NAME predictions of air concentrations are assessed against the experimental measurements, using a ranking method composed of four statistical parameters. Differences in the performance of NAME according to this ranking method are compared when driven by different meteorological sources. The effect of changing parameter values in NAME for the unresolved mesoscale motions parameterization is also considered, in particular, whether the parameter values giving the best performance rank are consistent with values typically used. The performance ranks are compared with analyses in the literature for other particle dispersion models, namely, Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), Stochastic Time-Inverted Lagrangian Transport (STILT), and Flexible Particle (FLEXPART). It is found that NAME performance is comparable to the other dispersion models considered, with the different models responding similarly to differences in driving meteorological data.
Abstract
Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently produced global precipitation climate data record. Generated from intercalibrated observations collected by a constellation of passive microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMW precipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 5° scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager (GMI) sensor, while sampling- and morphology-related uncertainties are quantified using GPM’s dual-frequency precipitation radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product’s uncertainty. It is found that at 5° spatial scale monthly mean precipitation uncertainties in tropics can exceed 10%.
Abstract
Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently produced global precipitation climate data record. Generated from intercalibrated observations collected by a constellation of passive microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMW precipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 5° scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager (GMI) sensor, while sampling- and morphology-related uncertainties are quantified using GPM’s dual-frequency precipitation radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product’s uncertainty. It is found that at 5° spatial scale monthly mean precipitation uncertainties in tropics can exceed 10%.
Abstract
To reduce the amount of nonclimatic biases of air temperature in each weather station’s record by comparing it with neighboring stations, global land surface air temperature datasets are routinely adjusted using statistical homogenization to minimize such biases. However, homogenization can unintentionally introduce new nonclimatic biases due to an often-overlooked statistical problem known as “urban blending” or “aliasing of trend biases.” This issue arises when the homogenization process inadvertently mixes urbanization biases of neighboring stations into the adjustments applied to each station record. As a result, urbanization biases of the original unhomogenized temperature records are spread throughout the homogenized data. To evaluate the extent of this phenomenon, the homogenized temperature data for two countries (Japan and the United States) are analyzed. Using the Japanese stations in the widely used Global Historical Climatology Network (GHCN) dataset, it is first confirmed that the unhomogenized Japanese temperature data are strongly affected by urbanization bias (possibly ∼60% of the long-term warming). The U.S. Historical Climatology Network (USHCN) dataset contains a relatively large amount of long, rural station records and therefore is less affected by urbanization bias. Nonetheless, even for this relatively rural dataset, urbanization bias could account for ∼20% of the long-term warming. It is then shown that urban blending is a major problem for the homogenized data for both countries. The IPCC’s estimate of urbanization bias in the global temperature data based on homogenized temperature records may have been low as a result of urban blending. Recommendations on how future homogenization efforts could be modified to reduce urban blending are discussed.
Significance Statement
Most weather station–based global land temperature datasets currently use a process called “statistical homogenization” to reduce the amount of nonclimatic biases. However, using temperature data from two countries (Japan and the United States), we show that the homogenization process unintentionally introduces new nonclimatic biases into the data as a result of an “urban blending” problem. Urban blending arises when the homogenization process inadvertently mixes the urbanization (warming) bias of the neighboring stations into the adjustments applied to each station record. As a result, the urbanization biases of the unhomogenized temperature records are spread throughout all of the homogenized data. The net effect tends to artificially add warming to rural stations and subtract warming from urban stations until all stations have about the same amount of urbanization bias.
Abstract
To reduce the amount of nonclimatic biases of air temperature in each weather station’s record by comparing it with neighboring stations, global land surface air temperature datasets are routinely adjusted using statistical homogenization to minimize such biases. However, homogenization can unintentionally introduce new nonclimatic biases due to an often-overlooked statistical problem known as “urban blending” or “aliasing of trend biases.” This issue arises when the homogenization process inadvertently mixes urbanization biases of neighboring stations into the adjustments applied to each station record. As a result, urbanization biases of the original unhomogenized temperature records are spread throughout the homogenized data. To evaluate the extent of this phenomenon, the homogenized temperature data for two countries (Japan and the United States) are analyzed. Using the Japanese stations in the widely used Global Historical Climatology Network (GHCN) dataset, it is first confirmed that the unhomogenized Japanese temperature data are strongly affected by urbanization bias (possibly ∼60% of the long-term warming). The U.S. Historical Climatology Network (USHCN) dataset contains a relatively large amount of long, rural station records and therefore is less affected by urbanization bias. Nonetheless, even for this relatively rural dataset, urbanization bias could account for ∼20% of the long-term warming. It is then shown that urban blending is a major problem for the homogenized data for both countries. The IPCC’s estimate of urbanization bias in the global temperature data based on homogenized temperature records may have been low as a result of urban blending. Recommendations on how future homogenization efforts could be modified to reduce urban blending are discussed.
Significance Statement
Most weather station–based global land temperature datasets currently use a process called “statistical homogenization” to reduce the amount of nonclimatic biases. However, using temperature data from two countries (Japan and the United States), we show that the homogenization process unintentionally introduces new nonclimatic biases into the data as a result of an “urban blending” problem. Urban blending arises when the homogenization process inadvertently mixes the urbanization (warming) bias of the neighboring stations into the adjustments applied to each station record. As a result, the urbanization biases of the unhomogenized temperature records are spread throughout all of the homogenized data. The net effect tends to artificially add warming to rural stations and subtract warming from urban stations until all stations have about the same amount of urbanization bias.
Abstract
Global warming has been accelerating the frequency and intensity of climate extremes, and has had an immense influence on the economy and society, but attention is seldom paid to future Antarctic temperature extremes. This study investigates five surface extreme temperature indices derived from the multimodel ensemble mean (MMEM) based on 14 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) under the shared socioeconomic pathways (SSPs) of SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. In Antarctica, the variations in extreme temperature indices exhibit regional and seasonal differences. The diurnal temperature range (DTR) usually illustrates a downward trend, particularly for the Antarctic Peninsula and Antarctic coast, and the strongest change occurs in austral summer. In all cases, the annual highest minimum/maximum temperature (TNx/TXx) increases faster in inland Antarctica. Antarctic amplification of extreme temperature indices is detected and is strongest at the lowest maximum temperature (TXn). At the Antarctic Peninsula, TXx amplification only appears in winter. Great DTR amplification appears along the Antarctic coast and is strongest in summer and weakest in winter. The changes in extreme temperature indices indicate the accelerated Antarctic warming in future scenarios.
Abstract
Global warming has been accelerating the frequency and intensity of climate extremes, and has had an immense influence on the economy and society, but attention is seldom paid to future Antarctic temperature extremes. This study investigates five surface extreme temperature indices derived from the multimodel ensemble mean (MMEM) based on 14 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) under the shared socioeconomic pathways (SSPs) of SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. In Antarctica, the variations in extreme temperature indices exhibit regional and seasonal differences. The diurnal temperature range (DTR) usually illustrates a downward trend, particularly for the Antarctic Peninsula and Antarctic coast, and the strongest change occurs in austral summer. In all cases, the annual highest minimum/maximum temperature (TNx/TXx) increases faster in inland Antarctica. Antarctic amplification of extreme temperature indices is detected and is strongest at the lowest maximum temperature (TXn). At the Antarctic Peninsula, TXx amplification only appears in winter. Great DTR amplification appears along the Antarctic coast and is strongest in summer and weakest in winter. The changes in extreme temperature indices indicate the accelerated Antarctic warming in future scenarios.
Abstract
Understanding the growth of tropical convective clouds (TCCs) is of vital importance for the early detection of heavy rainfall. This study explores the properties of TCCs that can cause them to develop into clouds with a high probability of precipitation. Remotely sensed cloud properties, such as cloud-top temperature (CTT), cloud optical thickness (COT), and cloud effective radius (CER) as measured by a geostationary satellite are trained by a neural network. First, the image segmentation algorithm identifies TCC objects with different cloud properties. Second, a self-organizing map (SOM) algorithm clusters TCC objects with similar cloud microphysical properties. Third, the precipitation probability (PP) for each cluster of TCCs is calculated based on the proportion of precipitating TCCs among the total number of TCCs. Precipitating TCCs can be distinguished from nonprecipitating TCCs using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement precipitation data. Results show that SOM clusters with a high PP (>70%) satisfy a certain range of cloud properties: CER ≥ 20 μm and CTT < 230 K. PP generally increases with increasing COT, but COT cannot be a clear cloud property to confirm a high PP. For relatively thin clouds (COT < 30), however, CER should be much larger than 20 μm to have a high PP. More importantly, these TCC conditions associated with a PP ≥ 70% are consistent across regions and periods. We expect our results will be useful for satellite nowcasting of tropical precipitation using geostationary satellite cloud properties.
Significance Statement
We aim to identify the properties of tropical convective clouds (TCCs) that have a high precipitation probability. We designed a two-step framework to identify TCC objects and the conditions of cloud properties for TCCs to have a high precipitation probability. The TCCs with a precipitation probability > 70% tend to have a low cloud-top temperature and a cloud particle effective radius ≥ 20 μm. Cloud optical thicknesses are distributed over a wide range, but thinning requires a particle radius larger than 20 μm. These conditions of cloud properties appear to be unchanged under various spatial–temporal conditions over the tropics. This important observational finding advances our understanding of the cloud–precipitation relationship in TCCs and can be applied to satellite nowcasting of precipitation in the tropics, where numerical weather forecasts are limited.
Abstract
Understanding the growth of tropical convective clouds (TCCs) is of vital importance for the early detection of heavy rainfall. This study explores the properties of TCCs that can cause them to develop into clouds with a high probability of precipitation. Remotely sensed cloud properties, such as cloud-top temperature (CTT), cloud optical thickness (COT), and cloud effective radius (CER) as measured by a geostationary satellite are trained by a neural network. First, the image segmentation algorithm identifies TCC objects with different cloud properties. Second, a self-organizing map (SOM) algorithm clusters TCC objects with similar cloud microphysical properties. Third, the precipitation probability (PP) for each cluster of TCCs is calculated based on the proportion of precipitating TCCs among the total number of TCCs. Precipitating TCCs can be distinguished from nonprecipitating TCCs using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement precipitation data. Results show that SOM clusters with a high PP (>70%) satisfy a certain range of cloud properties: CER ≥ 20 μm and CTT < 230 K. PP generally increases with increasing COT, but COT cannot be a clear cloud property to confirm a high PP. For relatively thin clouds (COT < 30), however, CER should be much larger than 20 μm to have a high PP. More importantly, these TCC conditions associated with a PP ≥ 70% are consistent across regions and periods. We expect our results will be useful for satellite nowcasting of tropical precipitation using geostationary satellite cloud properties.
Significance Statement
We aim to identify the properties of tropical convective clouds (TCCs) that have a high precipitation probability. We designed a two-step framework to identify TCC objects and the conditions of cloud properties for TCCs to have a high precipitation probability. The TCCs with a precipitation probability > 70% tend to have a low cloud-top temperature and a cloud particle effective radius ≥ 20 μm. Cloud optical thicknesses are distributed over a wide range, but thinning requires a particle radius larger than 20 μm. These conditions of cloud properties appear to be unchanged under various spatial–temporal conditions over the tropics. This important observational finding advances our understanding of the cloud–precipitation relationship in TCCs and can be applied to satellite nowcasting of precipitation in the tropics, where numerical weather forecasts are limited.
Abstract
In a future world where most of the energy must come from intermittent renewable energy sources such as wind or solar energy, it would be more efficient if, for each demand area, we could determine the locations for which the output of an energy source would naturally match the demand fluctuations from that area. In parallel, meteorological weather systems such as midlatitude cyclones are often organized in a way that naturally shapes where areas of greater energy need (e.g., regions with more cold air) are with respect to windier or sunnier areas, and these are generally not collocated. As a result, the best places to generate renewable energy may not be near consumption sites; these may be determined, however, by common meteorological patterns. Using data from a reanalysis of six decades of past weather, we determined the complementarity between different sources of energy as well as the relationships between renewable supply and demand at daily averaged time scales for several North American cities. In general, demand and solar power tend to be slightly positively correlated at nearby locations away from the Rocky Mountains; however, wind power often must be obtained from greater distances and at altitude for energy production to be better timed with consumption.
Significance Statement
Weather patterns such as high and low pressure systems shape where and when energy is needed for warming or cooling; they also shape how much renewable energy from winds and the sun can be produced. Hence, they determine the regions where more energy is likely to be available in periods of unusually high need for each demand location. Finding where those areas are may result in more timely renewable energy production in the future to help reduce fossil fuel use for energy production.
Abstract
In a future world where most of the energy must come from intermittent renewable energy sources such as wind or solar energy, it would be more efficient if, for each demand area, we could determine the locations for which the output of an energy source would naturally match the demand fluctuations from that area. In parallel, meteorological weather systems such as midlatitude cyclones are often organized in a way that naturally shapes where areas of greater energy need (e.g., regions with more cold air) are with respect to windier or sunnier areas, and these are generally not collocated. As a result, the best places to generate renewable energy may not be near consumption sites; these may be determined, however, by common meteorological patterns. Using data from a reanalysis of six decades of past weather, we determined the complementarity between different sources of energy as well as the relationships between renewable supply and demand at daily averaged time scales for several North American cities. In general, demand and solar power tend to be slightly positively correlated at nearby locations away from the Rocky Mountains; however, wind power often must be obtained from greater distances and at altitude for energy production to be better timed with consumption.
Significance Statement
Weather patterns such as high and low pressure systems shape where and when energy is needed for warming or cooling; they also shape how much renewable energy from winds and the sun can be produced. Hence, they determine the regions where more energy is likely to be available in periods of unusually high need for each demand location. Finding where those areas are may result in more timely renewable energy production in the future to help reduce fossil fuel use for energy production.
Abstract
This paper presents work using a machine learning model to diagnose Antarctic blowing snow (BLSN) properties with the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), data. We adopt the random forest classifier for BLSN identification and the random forest regressor for BLSN optical depth and height diagnosis. BLSN properties observed from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are used as the truth for training the model. Using MERRA-2 fields such as snow age, surface elevation and pressure, temperature, specific humidity, and temperature gradient at the 2-m level, and wind speed at the 10-m level as input, reasonable results are achieved. Hourly blowing snow property diagnostics are generated with the trained model. Using 2010 as an example, it is shown that the Antarctic BLSN frequency is much higher over East than West Antarctica. High-frequency months are from April to September, during which BLSN frequency exceeds 20% over East Antarctica. For May 2010, the BLSN snow frequency in the region is as high as 37%. Due to the suppression by strong surface-based inversions, larger values of BLSN height and optical depth are usually limited to the coastal regions, wherein the strength of surface-based inversions is weaker.
Abstract
This paper presents work using a machine learning model to diagnose Antarctic blowing snow (BLSN) properties with the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), data. We adopt the random forest classifier for BLSN identification and the random forest regressor for BLSN optical depth and height diagnosis. BLSN properties observed from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are used as the truth for training the model. Using MERRA-2 fields such as snow age, surface elevation and pressure, temperature, specific humidity, and temperature gradient at the 2-m level, and wind speed at the 10-m level as input, reasonable results are achieved. Hourly blowing snow property diagnostics are generated with the trained model. Using 2010 as an example, it is shown that the Antarctic BLSN frequency is much higher over East than West Antarctica. High-frequency months are from April to September, during which BLSN frequency exceeds 20% over East Antarctica. For May 2010, the BLSN snow frequency in the region is as high as 37%. Due to the suppression by strong surface-based inversions, larger values of BLSN height and optical depth are usually limited to the coastal regions, wherein the strength of surface-based inversions is weaker.
