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Abstract
A stationary mountain wave, embedded in southwesterly flow over Mont Blanc in the Alps, was observed simultaneously by three research aircraft and three types of remote sensing: GPS dropsondes, airborne light detecting and ranging (lidar), and rapid-scan satellite imagery. These observations provide a basis for testing linear and nonlinear theories of how mountain waves over complex terrain are controlled by the ambient wind profile, especially the effects of a low-level stagnant layer and the jet stream aloft. The layer of blocked flow near the ground reduced the amplitude of the wave generation. The strong wind and weak stability in the upper troposphere forced the wave into a decaying “evanescent” state. In spite of this evanescent condition, no lee waves were observed. The authors resolve this paradox by demonstrating that the stagnant layer below 3 km played an additional role. It was able to absorb downward reflected waves, preventing the formation of a resonant cavity. Linear theory, including this low-level absorption, predicts the observed wave structure quite well and captures the wave absorption process found in the fully nonlinear Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) model. In spite of wave decay through the upper troposphere, there is evidence from satellite images and model simulation that the waves reached the uppermost troposphere.
Abstract
A stationary mountain wave, embedded in southwesterly flow over Mont Blanc in the Alps, was observed simultaneously by three research aircraft and three types of remote sensing: GPS dropsondes, airborne light detecting and ranging (lidar), and rapid-scan satellite imagery. These observations provide a basis for testing linear and nonlinear theories of how mountain waves over complex terrain are controlled by the ambient wind profile, especially the effects of a low-level stagnant layer and the jet stream aloft. The layer of blocked flow near the ground reduced the amplitude of the wave generation. The strong wind and weak stability in the upper troposphere forced the wave into a decaying “evanescent” state. In spite of this evanescent condition, no lee waves were observed. The authors resolve this paradox by demonstrating that the stagnant layer below 3 km played an additional role. It was able to absorb downward reflected waves, preventing the formation of a resonant cavity. Linear theory, including this low-level absorption, predicts the observed wave structure quite well and captures the wave absorption process found in the fully nonlinear Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) model. In spite of wave decay through the upper troposphere, there is evidence from satellite images and model simulation that the waves reached the uppermost troposphere.
A Temporal Gauge Quality Control Algorithm as a Method for Identifying Potential Instrumentation MalfunctionsAbstract
There are multiple reasons as to why a precipitation gauge would report erroneous observations. Systematic errors relating to the measuring apparatus or resulting from observational limitations due to environmental factors (e.g., wind-induced undercatch or wetting losses) can be quantified and potentially corrected within a gauge dataset. Other challenges can arise from instrumentation malfunctions, such as clogging, poor siting, and software issues. Instrumentation malfunctions are challenging to quantify as most gauge quality control (QC) schemes focus on the current observation and not on whether the gauge has an inherent issue that would likely require maintenance of the gauge. This study focuses on the development of a temporal QC scheme to identify the likelihood of an instrumentation malfunction through the examination of hourly gauge observations and associated QC designations. The analyzed gauge performance resulted in a temporal QC classification using one of three categories: GOOD, SUSP, and BAD. The temporal QC scheme also accounts for and provides an additional designation when a significant percentage of gauge observations and associated hourly QC were influenced by meteorological factors (e.g., the inability to properly measure winter precipitation). Findings showed a consistent percentage of gauges that were classified as BAD through the running 7-day (2.9%) and 30-day (4.4%) analyses. Verification of select gauges demonstrated how the temporal QC algorithm captured different forms of instrumental-based systematic errors that influenced gauge observations. Results from this study can benefit the identification of degraded performance at gauge sites prior to scheduled routine maintenance.
Significance Statement
This study proposes a scheme that quality controls rain gauges based on its performance over a running history of hourly observational data and quality control flags to identify gauges that likely have an instrumentation malfunction. Findings from this study show the potential of identifying gauges that are impacted by issues such as clogging, software errors, and poor gauge siting. This study also highlights the challenges of distinguishing between erroneous gauge observations based on an instrumentation malfunction versus erroneous observations that were the result of an environmental factor that influence the gauge observation or its quality control classification, such as winter precipitation or virga.
Abstract
There are multiple reasons as to why a precipitation gauge would report erroneous observations. Systematic errors relating to the measuring apparatus or resulting from observational limitations due to environmental factors (e.g., wind-induced undercatch or wetting losses) can be quantified and potentially corrected within a gauge dataset. Other challenges can arise from instrumentation malfunctions, such as clogging, poor siting, and software issues. Instrumentation malfunctions are challenging to quantify as most gauge quality control (QC) schemes focus on the current observation and not on whether the gauge has an inherent issue that would likely require maintenance of the gauge. This study focuses on the development of a temporal QC scheme to identify the likelihood of an instrumentation malfunction through the examination of hourly gauge observations and associated QC designations. The analyzed gauge performance resulted in a temporal QC classification using one of three categories: GOOD, SUSP, and BAD. The temporal QC scheme also accounts for and provides an additional designation when a significant percentage of gauge observations and associated hourly QC were influenced by meteorological factors (e.g., the inability to properly measure winter precipitation). Findings showed a consistent percentage of gauges that were classified as BAD through the running 7-day (2.9%) and 30-day (4.4%) analyses. Verification of select gauges demonstrated how the temporal QC algorithm captured different forms of instrumental-based systematic errors that influenced gauge observations. Results from this study can benefit the identification of degraded performance at gauge sites prior to scheduled routine maintenance.
Significance Statement
This study proposes a scheme that quality controls rain gauges based on its performance over a running history of hourly observational data and quality control flags to identify gauges that likely have an instrumentation malfunction. Findings from this study show the potential of identifying gauges that are impacted by issues such as clogging, software errors, and poor gauge siting. This study also highlights the challenges of distinguishing between erroneous gauge observations based on an instrumentation malfunction versus erroneous observations that were the result of an environmental factor that influence the gauge observation or its quality control classification, such as winter precipitation or virga.
Abstract
A thermodynamic energy budget analysis is applied to the lowest model level of the ERA5 dataset to investigate the mechanisms that drive the growth and decay of extreme positive surface air temperature (SAT) events. Regional and seasonal variation of the mechanisms are investigated. For each grid point on Earth’s surface, a separate composite analysis is performed for extreme SAT events, which are days when temperature anomaly exceeds the 95th percentile. Among the dynamical terms, horizontal temperature advection of the climatological temperature by the anomalous wind dominates SAT anomaly growth over the extratropics, while nonlinear horizontal temperature advection is a major factor over high-latitude regions and the adiabatic warming is important over major mountainous regions. During the decay period, advection of the climatological temperature by the anomalous wind sustains the warming while nonlinear advection becomes the dominant decay mechanism. Among diabatic heating processes, vertical mixing contributes to the SAT anomaly growth over most locations while longwave radiative cooling hinders SAT anomaly growth, especially over the ocean. However, over arid regions during summer, longwave heating largely contributes to SAT anomaly growth while the vertical mixing dampens the SAT anomaly growth. During the decay period, both longwave cooling and vertical mixing contribute to SAT anomaly decay with more pronounced effects over the ocean and land, respectively. These regional and seasonal characteristics of the processes that drive extreme SAT events can serve as a benchmark for understanding the future behavior of extreme weather.
Abstract
A thermodynamic energy budget analysis is applied to the lowest model level of the ERA5 dataset to investigate the mechanisms that drive the growth and decay of extreme positive surface air temperature (SAT) events. Regional and seasonal variation of the mechanisms are investigated. For each grid point on Earth’s surface, a separate composite analysis is performed for extreme SAT events, which are days when temperature anomaly exceeds the 95th percentile. Among the dynamical terms, horizontal temperature advection of the climatological temperature by the anomalous wind dominates SAT anomaly growth over the extratropics, while nonlinear horizontal temperature advection is a major factor over high-latitude regions and the adiabatic warming is important over major mountainous regions. During the decay period, advection of the climatological temperature by the anomalous wind sustains the warming while nonlinear advection becomes the dominant decay mechanism. Among diabatic heating processes, vertical mixing contributes to the SAT anomaly growth over most locations while longwave radiative cooling hinders SAT anomaly growth, especially over the ocean. However, over arid regions during summer, longwave heating largely contributes to SAT anomaly growth while the vertical mixing dampens the SAT anomaly growth. During the decay period, both longwave cooling and vertical mixing contribute to SAT anomaly decay with more pronounced effects over the ocean and land, respectively. These regional and seasonal characteristics of the processes that drive extreme SAT events can serve as a benchmark for understanding the future behavior of extreme weather.
Abstract
Soil moisture observations from seven observational networks (spanning portions of seven states) with different biome and climate conditions were used in this study to evaluate multimodel simulated soil moisture products. The four land surface models, including Noah, Mosaic, Sacramento soil moisture accounting (SAC), and the Variable Infiltration Capacity model (VIC), were run within phase 2 of the North American Land Data Assimilation System (NLDAS-2), with a ⅛° spatial resolution and hourly temporal resolution. Hundreds of sites in Alabama, Colorado, Michigan, Nebraska, Oklahoma, West Texas, and Utah were used to evaluate simulated soil moisture in the 0–10-, 10–40-, and 40–100-cm soil layers. Soil moisture was spatially averaged in each state to reduce noise. In general, the four models captured broad features (e.g., seasonal variation) of soil moisture variations in all three soil layers in seven states, except for the 10–40-cm soil layer in West Texas and the 40–100-cm soil layer in Alabama, where the anomaly correlations are weak. Overall, Mosaic, SAC, and the ensemble mean have the highest simulation skill and VIC has the lowest simulation skill. The results show that Noah and VIC are wetter than the observations while Mosaic and SAC are drier than the observations, mostly likely because of systematic errors in model evapotranspiration.
Abstract
Soil moisture observations from seven observational networks (spanning portions of seven states) with different biome and climate conditions were used in this study to evaluate multimodel simulated soil moisture products. The four land surface models, including Noah, Mosaic, Sacramento soil moisture accounting (SAC), and the Variable Infiltration Capacity model (VIC), were run within phase 2 of the North American Land Data Assimilation System (NLDAS-2), with a ⅛° spatial resolution and hourly temporal resolution. Hundreds of sites in Alabama, Colorado, Michigan, Nebraska, Oklahoma, West Texas, and Utah were used to evaluate simulated soil moisture in the 0–10-, 10–40-, and 40–100-cm soil layers. Soil moisture was spatially averaged in each state to reduce noise. In general, the four models captured broad features (e.g., seasonal variation) of soil moisture variations in all three soil layers in seven states, except for the 10–40-cm soil layer in West Texas and the 40–100-cm soil layer in Alabama, where the anomaly correlations are weak. Overall, Mosaic, SAC, and the ensemble mean have the highest simulation skill and VIC has the lowest simulation skill. The results show that Noah and VIC are wetter than the observations while Mosaic and SAC are drier than the observations, mostly likely because of systematic errors in model evapotranspiration.
Abstract
In this second part of a two-part paper, the impacts of soil texture and vegetation type misclassification and their combined effect on soil moisture, evapotranspiration, and total runoff simulation are investigated using the Noah model. The results show that these impacts are significant for most regions and soil layers, although they vary depending on soil texture classification, vegetation type, and season. The use of site-observed soil texture classification and vegetation type in the model does not necessarily improve anomaly correlations and reduce mean absolute error for soil moisture simulations. Instead, results are mixed when examining all regions and soil layers. This is attributed to the compensation effects (e.g., effect of ill-calibrated model parameters), as Noah has been more or less calibrated with model-specified soil texture classification and vegetation type. The site-based analysis shows that Noah can reasonably simulate the variation of daily evapotranspiration, soil moisture, and total runoff when soil texture classification (vegetation type) is corrected from loam (forest) to clay (grasslands) or vice versa. This suggests that the performance of Noah can be further improved by tuning model parameters when site-observed soil texture and vegetation type are used.
Abstract
In this second part of a two-part paper, the impacts of soil texture and vegetation type misclassification and their combined effect on soil moisture, evapotranspiration, and total runoff simulation are investigated using the Noah model. The results show that these impacts are significant for most regions and soil layers, although they vary depending on soil texture classification, vegetation type, and season. The use of site-observed soil texture classification and vegetation type in the model does not necessarily improve anomaly correlations and reduce mean absolute error for soil moisture simulations. Instead, results are mixed when examining all regions and soil layers. This is attributed to the compensation effects (e.g., effect of ill-calibrated model parameters), as Noah has been more or less calibrated with model-specified soil texture classification and vegetation type. The site-based analysis shows that Noah can reasonably simulate the variation of daily evapotranspiration, soil moisture, and total runoff when soil texture classification (vegetation type) is corrected from loam (forest) to clay (grasslands) or vice versa. This suggests that the performance of Noah can be further improved by tuning model parameters when site-observed soil texture and vegetation type are used.
Abstract
This study presents an assessment of the impact of a March 2006 change in the Met Office operational global numerical weather prediction model through the introduction of a nonlocal momentum mixing scheme. From comparisons with satellite observations of surface wind speed and sea surface temperature (SST), it is concluded that the new parameterization had a relatively minor impact on SST-induced changes in sea surface wind speed in the Met Office model in the September and October 2007 monthly averages over the Agulhas Return Current region considered here. The performance of the new parameterization of vertical mixing was evaluated near the surface layer and further through comparisons with results obtained using a wide range of sensitivity of mixing parameterization to stability in the Weather Research and Forecasting (WRF) Model, which is easily adapted to such sensitivity studies. While the new parameterization of vertical mixing improves the Met Office model response to SST in highly unstable (convective) conditions, it is concluded that significantly enhanced vertical mixing in the neutral to moderately unstable conditions (nondimensional stability 
Abstract
This study presents an assessment of the impact of a March 2006 change in the Met Office operational global numerical weather prediction model through the introduction of a nonlocal momentum mixing scheme. From comparisons with satellite observations of surface wind speed and sea surface temperature (SST), it is concluded that the new parameterization had a relatively minor impact on SST-induced changes in sea surface wind speed in the Met Office model in the September and October 2007 monthly averages over the Agulhas Return Current region considered here. The performance of the new parameterization of vertical mixing was evaluated near the surface layer and further through comparisons with results obtained using a wide range of sensitivity of mixing parameterization to stability in the Weather Research and Forecasting (WRF) Model, which is easily adapted to such sensitivity studies. While the new parameterization of vertical mixing improves the Met Office model response to SST in highly unstable (convective) conditions, it is concluded that significantly enhanced vertical mixing in the neutral to moderately unstable conditions (nondimensional stability
Abstract
A composite-based statistical model utilizing Northern Hemisphere teleconnection patterns is developed to predict East Asian wintertime surface air temperature for lead times out to 6 weeks. The level of prediction is determined by using the Heidke skill score. The prediction skill of the statistical model is compared with that of hindcast simulations by a climate model, Global Seasonal Forecast System, version 5. When employed individually, three teleconnections (i.e., the east Atlantic/western Russian, Scandinavian, and polar/Eurasian teleconnection patterns) are found to provide skillful predictions for lead times beyond 4–5 weeks. When information from the teleconnections and the long-term linear trend are combined, the statistical model outperforms the climate model for lead times beyond 3 weeks, especially during those times when the teleconnections are in their active phases.
Abstract
A composite-based statistical model utilizing Northern Hemisphere teleconnection patterns is developed to predict East Asian wintertime surface air temperature for lead times out to 6 weeks. The level of prediction is determined by using the Heidke skill score. The prediction skill of the statistical model is compared with that of hindcast simulations by a climate model, Global Seasonal Forecast System, version 5. When employed individually, three teleconnections (i.e., the east Atlantic/western Russian, Scandinavian, and polar/Eurasian teleconnection patterns) are found to provide skillful predictions for lead times beyond 4–5 weeks. When information from the teleconnections and the long-term linear trend are combined, the statistical model outperforms the climate model for lead times beyond 3 weeks, especially during those times when the teleconnections are in their active phases.
Abstract
A climatology of the structure of the low-altitude cloud field (tops below 4 km) over the Southern Ocean (40°–65°S) in the vicinity of Australia (100°–160°E) has been constructed with CloudSat products for liquid water and ice water clouds. Averaging over longitude and time, CloudSat produces a roughly uniform cloud field between heights of approximately 750 and 2250 m across the extent of the domain for both winter and summer. This cloud field makes a transition from consisting primarily of liquid water at the lower latitudes to ice water at the higher latitudes. This transition is primarily driven by the gradient in the temperature, which is commonly between 0° and −20°C, rather than by direct physical observation.
The uniform lower boundary is a consequence of the CloudSat cloud detection algorithm being unable to reliably separate radar returns because of the bright surface versus returns due to clouds, in the lowest four range bins above the surface. This is potentially very problematic over the Southern Ocean where the depth of the boundary layer has been observed to be as shallow as 500 m. Cloud fields inferred from upper-air soundings at Macquarie Island (54.62°S, 158.85°E) similarly suggest that the peak frequency lies between 260 and 500 m for both summer and winter. No immediate explanation is available for the uniformity of the cloud-top boundary. This lack of a strong seasonal cycle is, perhaps, remarkable given the large seasonal cycles in both the shortwave (SW) radiative forcing experienced and the cloud condensation nuclei (CCN) concentration over the Southern Ocean.
Abstract
A climatology of the structure of the low-altitude cloud field (tops below 4 km) over the Southern Ocean (40°–65°S) in the vicinity of Australia (100°–160°E) has been constructed with CloudSat products for liquid water and ice water clouds. Averaging over longitude and time, CloudSat produces a roughly uniform cloud field between heights of approximately 750 and 2250 m across the extent of the domain for both winter and summer. This cloud field makes a transition from consisting primarily of liquid water at the lower latitudes to ice water at the higher latitudes. This transition is primarily driven by the gradient in the temperature, which is commonly between 0° and −20°C, rather than by direct physical observation.
The uniform lower boundary is a consequence of the CloudSat cloud detection algorithm being unable to reliably separate radar returns because of the bright surface versus returns due to clouds, in the lowest four range bins above the surface. This is potentially very problematic over the Southern Ocean where the depth of the boundary layer has been observed to be as shallow as 500 m. Cloud fields inferred from upper-air soundings at Macquarie Island (54.62°S, 158.85°E) similarly suggest that the peak frequency lies between 260 and 500 m for both summer and winter. No immediate explanation is available for the uniformity of the cloud-top boundary. This lack of a strong seasonal cycle is, perhaps, remarkable given the large seasonal cycles in both the shortwave (SW) radiative forcing experienced and the cloud condensation nuclei (CCN) concentration over the Southern Ocean.
Abstract
This study evaluates the impacts of sea surface temperature (SST) specification and grid resolution on numerical simulations of air–sea coupling near oceanic fronts through analyses of surface winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The 9 May 2001 change of the boundary condition from the Reynolds SST analyses to the NOAA Real-Time Global (RTG) SST in the ECMWF model resulted in an abrupt increase in mesoscale variance of the model surface winds over the ocean. In contrast, the 21 November 2000 change of the grid resolution resulted in an abrupt increase in mesoscale variability of surface winds over mountainous regions on land but had no significant effect on winds over the ocean.
To further investigate model sensitivity to the SST boundary condition and grid resolution, a series of simulations were made with the Weather Research and Forecasting (WRF) model over a domain encompassing the Agulhas return current (ARC: also called “retroflection”) region in the south Indian Ocean. Results from three WRF simulations with SST measured by the Advanced Microwave Scanning Radiometer on the Earth Observing System Aqua satellite (AMSR-E) and the Reynolds and RTG SST analyses indicate the vital importance of the resolution of the SST boundary condition for accurate simulation of the air–sea coupling between SST and surface wind speed. WRF simulations with grid spacings of 40 and 25 km show that the latter increased energy only on scales shorter than 250 km. In contrast, improved resolution of SST significantly increased the mesoscale variability for scales up to 1000 km.
Further sensitivity studies with the WRF model conclude that the weak coupling of surface wind speeds from the ECMWF model to SST is likely attributable primarily to the weak response of vertical turbulent mixing to SST-induced stability in the parameterization of boundary layer turbulence, with an overestimation of vertical diffusion by about 60% on average in stable conditions and an underestimation by about 40% in unstable conditions.
Abstract
This study evaluates the impacts of sea surface temperature (SST) specification and grid resolution on numerical simulations of air–sea coupling near oceanic fronts through analyses of surface winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The 9 May 2001 change of the boundary condition from the Reynolds SST analyses to the NOAA Real-Time Global (RTG) SST in the ECMWF model resulted in an abrupt increase in mesoscale variance of the model surface winds over the ocean. In contrast, the 21 November 2000 change of the grid resolution resulted in an abrupt increase in mesoscale variability of surface winds over mountainous regions on land but had no significant effect on winds over the ocean.
To further investigate model sensitivity to the SST boundary condition and grid resolution, a series of simulations were made with the Weather Research and Forecasting (WRF) model over a domain encompassing the Agulhas return current (ARC: also called “retroflection”) region in the south Indian Ocean. Results from three WRF simulations with SST measured by the Advanced Microwave Scanning Radiometer on the Earth Observing System Aqua satellite (AMSR-E) and the Reynolds and RTG SST analyses indicate the vital importance of the resolution of the SST boundary condition for accurate simulation of the air–sea coupling between SST and surface wind speed. WRF simulations with grid spacings of 40 and 25 km show that the latter increased energy only on scales shorter than 250 km. In contrast, improved resolution of SST significantly increased the mesoscale variability for scales up to 1000 km.
Further sensitivity studies with the WRF model conclude that the weak coupling of surface wind speeds from the ECMWF model to SST is likely attributable primarily to the weak response of vertical turbulent mixing to SST-induced stability in the parameterization of boundary layer turbulence, with an overestimation of vertical diffusion by about 60% on average in stable conditions and an underestimation by about 40% in unstable conditions.
Abstract
The surface warming in recent decades has been most rapid in the Arctic, especially during the winter. Here, by utilizing global reanalysis and satellite datasets, it is shown that the northward flux of moisture into the Arctic during the winter strengthens the downward infrared radiation (IR) by 30–40 W m−2 over 1–2 weeks. This is followed by a decline of up to 10% in sea ice concentration over the Greenland, Barents, and Kara Seas. A climate model simulation indicates that the wind-induced sea ice drift leads the decline of sea ice thickness during the early stage of the strong downward IR events, but that within one week the cumulative downward IR effect appears to be dominant. Further analysis indicates that strong downward IR events are preceded several days earlier by enhanced convection over the tropical Indian and western Pacific Oceans. This finding suggests that sea ice predictions can benefit from an improved understanding of tropical convection and ensuing planetary wave dynamics.
Abstract
The surface warming in recent decades has been most rapid in the Arctic, especially during the winter. Here, by utilizing global reanalysis and satellite datasets, it is shown that the northward flux of moisture into the Arctic during the winter strengthens the downward infrared radiation (IR) by 30–40 W m−2 over 1–2 weeks. This is followed by a decline of up to 10% in sea ice concentration over the Greenland, Barents, and Kara Seas. A climate model simulation indicates that the wind-induced sea ice drift leads the decline of sea ice thickness during the early stage of the strong downward IR events, but that within one week the cumulative downward IR effect appears to be dominant. Further analysis indicates that strong downward IR events are preceded several days earlier by enhanced convection over the tropical Indian and western Pacific Oceans. This finding suggests that sea ice predictions can benefit from an improved understanding of tropical convection and ensuing planetary wave dynamics.
