Browse
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.
Abstract
In this study, we design a statistical method to couple observations with a physics-based tropical cyclone (TC) rainfall model (TCR) and engineered-synthetic storms for assessing TC rainfall hazard. We first propose a bias-correction method to minimize the errors induced by TCR via matching the probability distribution of TCR-simulated historical TC rainfall with gauge observations. Then we assign occurrence probabilities to engineered-synthetic storms to reflect local climatology, through a resampling method that matches the probability distribution of a newly proposed storm parameter named rainfall potential (POT) in the synthetic dataset with that in the observation. POT is constructed to include several important storm parameters for TC rainfall such as TC intensity, duration, and distance and environmental humidity near landfall, and it is shown to be correlated with TCR-simulated rainfall. The proposed method has a satisfactory performance in reproducing the rainfall hazard curve in various locations in the continental United States; it is an improvement over the traditional joint probability method (JPM) for TC rainfall hazard assessment.
Abstract
In this study, we design a statistical method to couple observations with a physics-based tropical cyclone (TC) rainfall model (TCR) and engineered-synthetic storms for assessing TC rainfall hazard. We first propose a bias-correction method to minimize the errors induced by TCR via matching the probability distribution of TCR-simulated historical TC rainfall with gauge observations. Then we assign occurrence probabilities to engineered-synthetic storms to reflect local climatology, through a resampling method that matches the probability distribution of a newly proposed storm parameter named rainfall potential (POT) in the synthetic dataset with that in the observation. POT is constructed to include several important storm parameters for TC rainfall such as TC intensity, duration, and distance and environmental humidity near landfall, and it is shown to be correlated with TCR-simulated rainfall. The proposed method has a satisfactory performance in reproducing the rainfall hazard curve in various locations in the continental United States; it is an improvement over the traditional joint probability method (JPM) for TC rainfall hazard assessment.
Abstract
Vertically pointing Ka-band radar measurements are used to derive fall velocity–reflectivity factor (
Significance Statement
Frozen hydrometeor fall velocities influence cloud life cycles and the moisture transport in the atmosphere. The knowledge of these velocities is also needed to enhance remote sensing of snowfall parameters. In this study, the relations between fall velocities and radar reflectivities of snowflakes of different types and shapes are quantitively analyzed using observations with vertically pointing radars.
Abstract
Vertically pointing Ka-band radar measurements are used to derive fall velocity–reflectivity factor (
Significance Statement
Frozen hydrometeor fall velocities influence cloud life cycles and the moisture transport in the atmosphere. The knowledge of these velocities is also needed to enhance remote sensing of snowfall parameters. In this study, the relations between fall velocities and radar reflectivities of snowflakes of different types and shapes are quantitively analyzed using observations with vertically pointing radars.
Abstract
In West Africa, climatic data issues, especially availability and quality, remain a significant constraint to the development and application of distributed hydrological modeling. As alternatives to ground-based observations, reanalysis products have received increasing attention in recent years. This study aims to evaluate three reanalysis products, namely, ERA5, Water and Global Change (WATCH) Forcing Data (WFD) ERA5 (WFDE5), and MERRA-2, from 1981 to 2019 to determine their ability to represent four hydrological climates variables over a range of space and time scales in Benin. The variables from the reanalysis products are compared with point station databased metrics Kling–Gupta efficiency (KGE), mean absolute error (MAE), correlation, and relative error in precipitation annual (REPA). The results show that ERA5 presents a better correlation for annual mean temperature (between 0.74 and 0.90) than do WFDE5 (0.63–0.78) and MERRA-2 (0.25–0.65). Both ERA5 and WFDE5 are able to reproduce the observed upward trend of temperature (0.2°C decade−1) in the region. We noted a systematic cold bias of ∼1.3°C in all reanalyses except WFDE5 (∼0.1°C). On the monthly time scale, the temperature of the region is better reproduced by ERA5 and WFDE5 (KGE ≥ 0.80) than by MERRA-2 (KGE < 0.5). At all time scales, WFDE5 produces the best MAE scores for longwave (LW) and shortwave (SW) radiation, followed by ERA5. WFDE5 also provides the best estimates for the annual precipitation (REPA ∈ ]−25, 25[ and KGE ≥ 50% at most stations). ERA5 produces similar results, but MERRA-2 performs poorly in all the metrics. In addition, ERA5 and WFDE5 reproduce the bimodal rainfall regime in southern Benin, unlike MERRA-2, but all products have too many small rainfall events.
Abstract
In West Africa, climatic data issues, especially availability and quality, remain a significant constraint to the development and application of distributed hydrological modeling. As alternatives to ground-based observations, reanalysis products have received increasing attention in recent years. This study aims to evaluate three reanalysis products, namely, ERA5, Water and Global Change (WATCH) Forcing Data (WFD) ERA5 (WFDE5), and MERRA-2, from 1981 to 2019 to determine their ability to represent four hydrological climates variables over a range of space and time scales in Benin. The variables from the reanalysis products are compared with point station databased metrics Kling–Gupta efficiency (KGE), mean absolute error (MAE), correlation, and relative error in precipitation annual (REPA). The results show that ERA5 presents a better correlation for annual mean temperature (between 0.74 and 0.90) than do WFDE5 (0.63–0.78) and MERRA-2 (0.25–0.65). Both ERA5 and WFDE5 are able to reproduce the observed upward trend of temperature (0.2°C decade−1) in the region. We noted a systematic cold bias of ∼1.3°C in all reanalyses except WFDE5 (∼0.1°C). On the monthly time scale, the temperature of the region is better reproduced by ERA5 and WFDE5 (KGE ≥ 0.80) than by MERRA-2 (KGE < 0.5). At all time scales, WFDE5 produces the best MAE scores for longwave (LW) and shortwave (SW) radiation, followed by ERA5. WFDE5 also provides the best estimates for the annual precipitation (REPA ∈ ]−25, 25[ and KGE ≥ 50% at most stations). ERA5 produces similar results, but MERRA-2 performs poorly in all the metrics. In addition, ERA5 and WFDE5 reproduce the bimodal rainfall regime in southern Benin, unlike MERRA-2, but all products have too many small rainfall events.
Abstract
Meteorological Cooperation on Operational Numeric Weather Prediction (MetCoOp) is a northern European collaboration on operational numerical weather prediction based on a common limited-area, kilometer-scale ensemble system. The initial states of this model are produced using a three-dimensional, variational, data assimilation scheme utilizing a large number of observations from conventional in situ measurements, weather radars, global navigation satellite systems, advanced scatterometer data, and satellite radiances. Since 2019, the MetCoOp system was enhanced by utilization of observations based on selective mode (Mode-S) enhanced surveillance (EHS) reports that are broadcast by aircraft in response to interrogation from air traffic control radar. These observations, obtained from the European Meteorological Aircraft Derived Data Center, are used to derive indirect information of atmospheric wind speed and temperature. The use of these observations compensated for the considerably reduced number of direct aircraft observations encountered as an effect of the COVID-19 pandemic. The MetCoOp observation handling system is described, with emphasis on Mode-S EHS data. The quality of these observations is evaluated, and we show that they are suitable to be used in MetCoOp data assimilation. The impact on average forecast verification scores of the additional Mode-S EHS data is slightly positive. The benefit of using Mode-S EHS was demonstrated for an individual case and also a demonstration of utilizing the Stockholm Arlanda receiver data in assimilation has been performed.
Abstract
Meteorological Cooperation on Operational Numeric Weather Prediction (MetCoOp) is a northern European collaboration on operational numerical weather prediction based on a common limited-area, kilometer-scale ensemble system. The initial states of this model are produced using a three-dimensional, variational, data assimilation scheme utilizing a large number of observations from conventional in situ measurements, weather radars, global navigation satellite systems, advanced scatterometer data, and satellite radiances. Since 2019, the MetCoOp system was enhanced by utilization of observations based on selective mode (Mode-S) enhanced surveillance (EHS) reports that are broadcast by aircraft in response to interrogation from air traffic control radar. These observations, obtained from the European Meteorological Aircraft Derived Data Center, are used to derive indirect information of atmospheric wind speed and temperature. The use of these observations compensated for the considerably reduced number of direct aircraft observations encountered as an effect of the COVID-19 pandemic. The MetCoOp observation handling system is described, with emphasis on Mode-S EHS data. The quality of these observations is evaluated, and we show that they are suitable to be used in MetCoOp data assimilation. The impact on average forecast verification scores of the additional Mode-S EHS data is slightly positive. The benefit of using Mode-S EHS was demonstrated for an individual case and also a demonstration of utilizing the Stockholm Arlanda receiver data in assimilation has been performed.
Abstract
An objective detection and tracking algorithm based on relative vorticity at 850 hPa using National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) Reanalysis-1 data was applied to track cyclones in the Southern Hemisphere during austral winters from 1948 to 2017. The climatological characteristics of extratropical cyclones, including track density, frequency, intensity, lifetime, and their related variabilities, are discussed. The frequency and average lifetime of cyclones have substantially decreased. The average maximum intensity of cyclones has shown an increasing trend over the 70-yr study period. The cyclone track density shows a decreasing trend in lower latitudes, consistent with the region where the upper-troposphere zonal wind weakens. Baroclinicity can explain the increase in cyclone intensity: when a cyclone moves to higher latitudes and enters the region with greater baroclinicity, it strengthens. As there is no discernible increase in cyclogenesis in the medium latitudes (45°–70°S), but significantly less cyclogenesis in lower and higher latitudes, it is hypothesized that there is no clear poleward cyclogenesis shift over the Southern Hemisphere.
Significance Statement
While much is known about the Northern Hemisphere cyclones, few studies have examined how extratropical cyclones have changed in the Southern Hemisphere. We used an automatic tracking algorithm to study the climatological characteristics of extratropical cyclones over the past 70 years and found that the frequency of winter extratropical cyclones has decreased significantly in most parts of the Southern Hemisphere, with the number of intense cyclones increasing. Under global warming conditions, variability in regional low-level baroclinicity and a weakened upper-troposphere jet are likely to be responsible for this change. Future studies may focus on how the increasing autumn sea ice around Antarctica affects polar cyclone activities.
Abstract
An objective detection and tracking algorithm based on relative vorticity at 850 hPa using National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) Reanalysis-1 data was applied to track cyclones in the Southern Hemisphere during austral winters from 1948 to 2017. The climatological characteristics of extratropical cyclones, including track density, frequency, intensity, lifetime, and their related variabilities, are discussed. The frequency and average lifetime of cyclones have substantially decreased. The average maximum intensity of cyclones has shown an increasing trend over the 70-yr study period. The cyclone track density shows a decreasing trend in lower latitudes, consistent with the region where the upper-troposphere zonal wind weakens. Baroclinicity can explain the increase in cyclone intensity: when a cyclone moves to higher latitudes and enters the region with greater baroclinicity, it strengthens. As there is no discernible increase in cyclogenesis in the medium latitudes (45°–70°S), but significantly less cyclogenesis in lower and higher latitudes, it is hypothesized that there is no clear poleward cyclogenesis shift over the Southern Hemisphere.
Significance Statement
While much is known about the Northern Hemisphere cyclones, few studies have examined how extratropical cyclones have changed in the Southern Hemisphere. We used an automatic tracking algorithm to study the climatological characteristics of extratropical cyclones over the past 70 years and found that the frequency of winter extratropical cyclones has decreased significantly in most parts of the Southern Hemisphere, with the number of intense cyclones increasing. Under global warming conditions, variability in regional low-level baroclinicity and a weakened upper-troposphere jet are likely to be responsible for this change. Future studies may focus on how the increasing autumn sea ice around Antarctica affects polar cyclone activities.
Abstract
Low-level jets (LLJs) are an important nocturnal source of wind energy in the U.S. Great Plains. An August 2017 lidar-based field-measurement campaign [the Land–Atmosphere Feedback Experiment (LAFE)] studied LLJs over the central SGP site in Oklahoma and found nearly equal occurrences of the usual southerly jets and postfrontal northeasterly jets—typically rare during this season—for an opportunity to compare the two types of LLJs during this month. Southerly winds were stronger than the northeasterlies by more than 4 m s−1 on average, reflecting a significantly higher frequency of winds stronger than 12 m s−1. The analysis of this dataset has been expanded to other SGP Doppler-lidar sites to quantify the variability of winds and LLJ properties between sites of different land use. Geographic variations of winds over the study area were noted: on southerly wind nights, the winds blew stronger at the highest, westernmost sites by 2 m s−1, whereas on the northeasterly flow nights, the easternmost sites had the strongest wind speeds. Lidar measurements at 5 sites during August 2017, contrasted to the 2016–21 summertime data, revealed unusual wind and LLJ conditions. Temporal hodographs using hourly averaged winds at multiple heights revealed unorganized behavior in the turbulent stable boundary layer (SBL) below the jet nose. Above the nose, some nights showed veering qualitatively similar to inertial oscillation (IO) behavior, but at amplitudes much smaller than expected for an IO, whereas other nights showed little veering. Vertical hodographs had a linear shape in the SBL, indicating little directional shear there, and veering above, resulting in a hook-shaped hodograph with height.
Significance Statement
Doppler-lidar measurements at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) were used to quantify the variability of winds and low-level jet (LLJ) properties between five sites of different land use and wind regimes across this area. Knowledge of wind and LLJ structure and dynamics is important for many applications, and strong southerly LLJ winds at night are an important resource for wind energy. The analysis of multiyear (2016–21) summertime LLJ parameters provided insight into the LLJ climatology in this part of the Great Plains.
Abstract
Low-level jets (LLJs) are an important nocturnal source of wind energy in the U.S. Great Plains. An August 2017 lidar-based field-measurement campaign [the Land–Atmosphere Feedback Experiment (LAFE)] studied LLJs over the central SGP site in Oklahoma and found nearly equal occurrences of the usual southerly jets and postfrontal northeasterly jets—typically rare during this season—for an opportunity to compare the two types of LLJs during this month. Southerly winds were stronger than the northeasterlies by more than 4 m s−1 on average, reflecting a significantly higher frequency of winds stronger than 12 m s−1. The analysis of this dataset has been expanded to other SGP Doppler-lidar sites to quantify the variability of winds and LLJ properties between sites of different land use. Geographic variations of winds over the study area were noted: on southerly wind nights, the winds blew stronger at the highest, westernmost sites by 2 m s−1, whereas on the northeasterly flow nights, the easternmost sites had the strongest wind speeds. Lidar measurements at 5 sites during August 2017, contrasted to the 2016–21 summertime data, revealed unusual wind and LLJ conditions. Temporal hodographs using hourly averaged winds at multiple heights revealed unorganized behavior in the turbulent stable boundary layer (SBL) below the jet nose. Above the nose, some nights showed veering qualitatively similar to inertial oscillation (IO) behavior, but at amplitudes much smaller than expected for an IO, whereas other nights showed little veering. Vertical hodographs had a linear shape in the SBL, indicating little directional shear there, and veering above, resulting in a hook-shaped hodograph with height.
Significance Statement
Doppler-lidar measurements at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) were used to quantify the variability of winds and low-level jet (LLJ) properties between five sites of different land use and wind regimes across this area. Knowledge of wind and LLJ structure and dynamics is important for many applications, and strong southerly LLJ winds at night are an important resource for wind energy. The analysis of multiyear (2016–21) summertime LLJ parameters provided insight into the LLJ climatology in this part of the Great Plains.
Abstract
The new-generation multisatellite precipitation algorithm, namely, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG), version 6, provides a high resolution and large spatial extent and can be used to offset the lack of surface observations. This study aimed to evaluate the precipitation detection capability of GPM-IMERG V06 Final Run products in different climatic and topographical regions of China for the 2014–20 period. This study showed that 1) GPM-IMERG could capture the spatial and temporal precipitation distributions in China. At the annual scale, GPM-IMERG performed well, with a correlation coefficient R >0.95 and a relative bias ratio (RBias) between 15.38% and 23.46%. At the seasonal scale, GPM-IMERG performed best in summer. At the monthly scale, GPM-IMERG performed better during the wet season (April–September) (RBias = 7.41%) than during the dry season (RBias = 13.65%). 2) GPM-IMERG performed well in terms of precipitation estimation in Southwest China, Central China, East China, and South China, followed by Northeast China and North China, but it performed poorly in Northwest China and Tibet. 3) The climate zone, followed by elevation, played a leading role in the GPM-IMERG accuracy in China, and the main sources of GPM-IMERG deviation in arid and semiarid regions were missed precipitation and false precipitation. However, the influences of missed precipitation and false precipitation gradually increased with increasing elevation. Despite the obvious differences between the GPM-IMERG and surface precipitation estimates, the study results highlight the potential of GPM-IMERG as a valuable resource for monitoring high-resolution precipitation information that is lacking in many parts of the world.
Significance Statement
The purpose of this study was to better understand the capability of GPM-IMERG for precipitation estimation and the causes of errors. GPM-IMERG performed well when estimating precipitation in Southwest China, Central China, East China, and South China, followed by Northeast China and North China, but it performed poorly in Northwest China and Tibet. Climate, followed by elevation, played a leading role in GPM-IMERG accuracy in China. Our results could provide a greater understanding of the accuracy of GPM-IMERG precipitation estimation in the different regions of China and can be applied to water resource management, afforestation (or reforestation) projects, and so on, in areas worldwide where meteorological stations are scarce.
Abstract
The new-generation multisatellite precipitation algorithm, namely, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG), version 6, provides a high resolution and large spatial extent and can be used to offset the lack of surface observations. This study aimed to evaluate the precipitation detection capability of GPM-IMERG V06 Final Run products in different climatic and topographical regions of China for the 2014–20 period. This study showed that 1) GPM-IMERG could capture the spatial and temporal precipitation distributions in China. At the annual scale, GPM-IMERG performed well, with a correlation coefficient R >0.95 and a relative bias ratio (RBias) between 15.38% and 23.46%. At the seasonal scale, GPM-IMERG performed best in summer. At the monthly scale, GPM-IMERG performed better during the wet season (April–September) (RBias = 7.41%) than during the dry season (RBias = 13.65%). 2) GPM-IMERG performed well in terms of precipitation estimation in Southwest China, Central China, East China, and South China, followed by Northeast China and North China, but it performed poorly in Northwest China and Tibet. 3) The climate zone, followed by elevation, played a leading role in the GPM-IMERG accuracy in China, and the main sources of GPM-IMERG deviation in arid and semiarid regions were missed precipitation and false precipitation. However, the influences of missed precipitation and false precipitation gradually increased with increasing elevation. Despite the obvious differences between the GPM-IMERG and surface precipitation estimates, the study results highlight the potential of GPM-IMERG as a valuable resource for monitoring high-resolution precipitation information that is lacking in many parts of the world.
Significance Statement
The purpose of this study was to better understand the capability of GPM-IMERG for precipitation estimation and the causes of errors. GPM-IMERG performed well when estimating precipitation in Southwest China, Central China, East China, and South China, followed by Northeast China and North China, but it performed poorly in Northwest China and Tibet. Climate, followed by elevation, played a leading role in GPM-IMERG accuracy in China. Our results could provide a greater understanding of the accuracy of GPM-IMERG precipitation estimation in the different regions of China and can be applied to water resource management, afforestation (or reforestation) projects, and so on, in areas worldwide where meteorological stations are scarce.
Abstract
The accuracy and uncertainty of radar echo-top heights estimated by ground-based radars remain largely unknown despite their critical importance for applications ranging from aviation weather forecasting to severe weather diagnosis. Because the vantage point of space is more suited than that of ground-based radars for the estimation of echo-top heights, the use of spaceborne 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 NOAA/National Severe Storms Laboratory Multi-Radar Multi-Sensor (MRMS) system with the space-based radar on board 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 patterns (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 remain largely unknown despite their critical importance for applications ranging from aviation weather forecasting to severe weather diagnosis. Because the vantage point of space is more suited than that of ground-based radars for the estimation of echo-top heights, the use of spaceborne 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 NOAA/National Severe Storms Laboratory Multi-Radar Multi-Sensor (MRMS) system with the space-based radar on board 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 patterns (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.
