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Abstract
The relative contributions of El Niño–Southern Oscillation (ENSO), long-term tropical Pacific variations, and the Arctic oscillation (AO) to the explained variance of U.S. precipitation and surface air temperature are investigated. The time variability of monthly precipitation in the tropical Pacific basin is separated into high-pass and low-pass filtered components. The leading EOFs of the high-pass and low-pass filtered data capture ENSO cycle–related interannual variability and ENSO-like interdecadal variability, respectively. The dominant mode of variability in the extratropics is the AO, which has been implicated in some of the secular variability of climate in the Northern Hemisphere extratropics.
ENSO produces large, reasonably reproducible spatial and temporal shifts in tropical precipitation. The tropical interdecadal variability produces more subtle, but still significant, shifts in tropical precipitation that contribute significantly to the explained variance and to trends in the North Pacific sector, over the United States, and extending into the North Atlantic sector. Consistent with previous studies, the largest and most significant AO-related contributions are during the cold season (October–March), particularly over the eastern half of the United States, the North Atlantic sector, Eurasia, and the polar cap.
The results indicate that a significant portion of the skill of climate forecast models will likely arise from an ability to forecast the temporal and spatial variability of the interdecadal shifts in tropical precipitation as well as the associated teleconnection patterns into midlatitudes. Because the AO encompasses the North Atlantic oscillation, it appears that additional increases in skill over portions of North America require forecasts of the AO.
Abstract
The relative contributions of El Niño–Southern Oscillation (ENSO), long-term tropical Pacific variations, and the Arctic oscillation (AO) to the explained variance of U.S. precipitation and surface air temperature are investigated. The time variability of monthly precipitation in the tropical Pacific basin is separated into high-pass and low-pass filtered components. The leading EOFs of the high-pass and low-pass filtered data capture ENSO cycle–related interannual variability and ENSO-like interdecadal variability, respectively. The dominant mode of variability in the extratropics is the AO, which has been implicated in some of the secular variability of climate in the Northern Hemisphere extratropics.
ENSO produces large, reasonably reproducible spatial and temporal shifts in tropical precipitation. The tropical interdecadal variability produces more subtle, but still significant, shifts in tropical precipitation that contribute significantly to the explained variance and to trends in the North Pacific sector, over the United States, and extending into the North Atlantic sector. Consistent with previous studies, the largest and most significant AO-related contributions are during the cold season (October–March), particularly over the eastern half of the United States, the North Atlantic sector, Eurasia, and the polar cap.
The results indicate that a significant portion of the skill of climate forecast models will likely arise from an ability to forecast the temporal and spatial variability of the interdecadal shifts in tropical precipitation as well as the associated teleconnection patterns into midlatitudes. Because the AO encompasses the North Atlantic oscillation, it appears that additional increases in skill over portions of North America require forecasts of the AO.
Abstract
The Simple Biosphere Model (SiB) as described in Sellers et al. is a bio-physically based model of land surface-atmosphere interaction. For some general circulation model (GCM) climate studies, further simplifications are desirable to have greater computation efficiency, and more important, to consolidate the parametric representation. Three major reductions in the complexity of SiB have been achieved in the present study.
The diurnal variation of surface albedo is computed in SiB by means of a comprehensive yet complex calculation. Since the diurnal cycle is quite regular for each vegetation type, this calculation can be simplified considerably. The effect of root zone soil moisture on stomatal resistance is substantial, but the computation in SiB is complicated and expensive. We have developed approximations, which simulate the effects of reduced soil moisture more simply, keeping the essence of the biophysical concepts used in SiB.
The surface stress and the fluxes of heat and moisture between the top of the vegetation canopy and an atmospheric reference level have been parameterized in an off-line version of SiB based upon the studies by Businger et al. and Paulson. We have developed a linear relationship between Richardson number and aero-dynamic resistance. Finally, the second vegetation layer of the original model does not appear explicitly after simplification. Compared to the model of Sellers et al., we have reduced the number of input parameters from 44 to 21. A comparison of results using the reduced parameter biosphere with those from the original formulation in a GCM and a zero-dimensional model shows the simplified version to reproduce the original results quite closely. After simplification, the computational requirement of SiB was reduced by about 55%.
Abstract
The Simple Biosphere Model (SiB) as described in Sellers et al. is a bio-physically based model of land surface-atmosphere interaction. For some general circulation model (GCM) climate studies, further simplifications are desirable to have greater computation efficiency, and more important, to consolidate the parametric representation. Three major reductions in the complexity of SiB have been achieved in the present study.
The diurnal variation of surface albedo is computed in SiB by means of a comprehensive yet complex calculation. Since the diurnal cycle is quite regular for each vegetation type, this calculation can be simplified considerably. The effect of root zone soil moisture on stomatal resistance is substantial, but the computation in SiB is complicated and expensive. We have developed approximations, which simulate the effects of reduced soil moisture more simply, keeping the essence of the biophysical concepts used in SiB.
The surface stress and the fluxes of heat and moisture between the top of the vegetation canopy and an atmospheric reference level have been parameterized in an off-line version of SiB based upon the studies by Businger et al. and Paulson. We have developed a linear relationship between Richardson number and aero-dynamic resistance. Finally, the second vegetation layer of the original model does not appear explicitly after simplification. Compared to the model of Sellers et al., we have reduced the number of input parameters from 44 to 21. A comparison of results using the reduced parameter biosphere with those from the original formulation in a GCM and a zero-dimensional model shows the simplified version to reproduce the original results quite closely. After simplification, the computational requirement of SiB was reduced by about 55%.
Abstract
Accurate representations of lake–ice–atmosphere interactions in regional climate modeling remain one of the most critical and unresolved issues for understanding large-lake ecosystems and their watersheds. To date, the representation of the Great Lakes two-way interactions in regional climate models is achieved with one-dimensional (1D) lake models applied at the atmospheric model lake grid points distributed spatially across a 2D domain. While some progress has been made in refining 1D lake model processes, such models are fundamentally incapable of realistically resolving a number of physical processes in the Great Lakes. In this study, a two-way coupled 3D lake-ice–climate modeling system [Great Lakes–Atmosphere Regional Model (GLARM)] is developed to improve the simulation of large lakes in regional climate models and accurately resolve the hydroclimatic interactions. Model results are compared to a wide variety of observational data and demonstrate the unique skill of the coupled 3D modeling system in reproducing trends and variability in the Great Lakes regional climate, as well as in capturing the physical characteristics of the Great Lakes by fully resolving the lake hydrodynamics. Simulations of the climatology and spatiotemporal variability of lake thermal structure and ice are significantly improved over previous coupled, 1D simulations. At seasonal and annual time scales, differences in model results are primarily observed for variables that are directly affected by lake surface temperature (e.g., evaporation, precipitation, sensible heat flux) while no significant differences are found in other atmospheric variables (e.g., solar radiation, cloud cover). Underlying physical mechanisms for the simulation improvements using GLARM are also discussed.
Abstract
Accurate representations of lake–ice–atmosphere interactions in regional climate modeling remain one of the most critical and unresolved issues for understanding large-lake ecosystems and their watersheds. To date, the representation of the Great Lakes two-way interactions in regional climate models is achieved with one-dimensional (1D) lake models applied at the atmospheric model lake grid points distributed spatially across a 2D domain. While some progress has been made in refining 1D lake model processes, such models are fundamentally incapable of realistically resolving a number of physical processes in the Great Lakes. In this study, a two-way coupled 3D lake-ice–climate modeling system [Great Lakes–Atmosphere Regional Model (GLARM)] is developed to improve the simulation of large lakes in regional climate models and accurately resolve the hydroclimatic interactions. Model results are compared to a wide variety of observational data and demonstrate the unique skill of the coupled 3D modeling system in reproducing trends and variability in the Great Lakes regional climate, as well as in capturing the physical characteristics of the Great Lakes by fully resolving the lake hydrodynamics. Simulations of the climatology and spatiotemporal variability of lake thermal structure and ice are significantly improved over previous coupled, 1D simulations. At seasonal and annual time scales, differences in model results are primarily observed for variables that are directly affected by lake surface temperature (e.g., evaporation, precipitation, sensible heat flux) while no significant differences are found in other atmospheric variables (e.g., solar radiation, cloud cover). Underlying physical mechanisms for the simulation improvements using GLARM are also discussed.
Abstract
Three different land surface schemes that are designed for use in atmospheric general circulation models are compared. They were run in offline mode with identical atmospheric forcing values that were observed at Cabauw. This procedure allows one to analyze differences in the simulations that are not caused by different atmospheric conditions and to relate them to certain model characteristics. The intercomparison shows that the models produced similar results for surface temperature and total net radiation, which are also in good agreement with the observations. But they underestimate latent heat flux and overestimate sensible heat flux in summer. Differences in the components of energy and hydrological cycle as simulated by the schemes can be related to differences in model structures. The calculation of the surface temperature is of major importance, particularly on a diurnal timescale. Depending on the scheme chosen, the simulated surface temperature is closer to the observed radiative surface temperature or the observed soil temperature at a depth of a few centimeters. If a land surface scheme is going to be coupled to an atmospheric model, this needs to be considered. The simulation of the surface energy fluxes can be improved by careful calibration of the relevant parameters according to the conditions at the observational site. The stomatal resistance was found to be an essential parameter in determining the evolution of evapotranspiration for the Cabauw simulations.
Abstract
Three different land surface schemes that are designed for use in atmospheric general circulation models are compared. They were run in offline mode with identical atmospheric forcing values that were observed at Cabauw. This procedure allows one to analyze differences in the simulations that are not caused by different atmospheric conditions and to relate them to certain model characteristics. The intercomparison shows that the models produced similar results for surface temperature and total net radiation, which are also in good agreement with the observations. But they underestimate latent heat flux and overestimate sensible heat flux in summer. Differences in the components of energy and hydrological cycle as simulated by the schemes can be related to differences in model structures. The calculation of the surface temperature is of major importance, particularly on a diurnal timescale. Depending on the scheme chosen, the simulated surface temperature is closer to the observed radiative surface temperature or the observed soil temperature at a depth of a few centimeters. If a land surface scheme is going to be coupled to an atmospheric model, this needs to be considered. The simulation of the surface energy fluxes can be improved by careful calibration of the relevant parameters according to the conditions at the observational site. The stomatal resistance was found to be an essential parameter in determining the evolution of evapotranspiration for the Cabauw simulations.
Abstract
Experiments were conducted with an electrodynamic levitation system to study the kinetics of droplet evaporation under chemically rich conditions. Single solution droplets of known composition (HNO3/H2O or H2SO4/HNO3/H2O) were introduced into an environmentally controlled cubic levitation cell. The gaseous environment was set intentionally out of equilibrium with the droplet properties, thus permitting the HNO3 mass accommodation coefficient to be determined. Measurements were performed at room temperature and various pressures (200–1000 hPa). Droplet sizes (initial radii in the range 12–26 μm) were measured versus time to high precision (±0.03 μm) via Mie scattering and compared with sizes computed by different models for mass and heat transfer in the transition regime. The best agreement between the theoretical calculations and experimental results was obtained for an HNO3 mass accommodation coefficient of 0.11 ± 0.03 at atmospheric pressure, 0.17 ± 0.05 at 500 hPa, and 0.33 ± 0.08 at 200 hPa. The determination of the mass accommodation coefficient was not sensitive to the transport model used. The results show that droplet evaporation is strongly limited by HNO3 and occurs in two stages, one characterized by rapid H2O mass transfer and the other by HNO3 mass transfer. The presence of a nonvolatile solute (SO2− 4) affects the activities of the volatile components (HNO3 and H2O) and prevents complete evaporation of the solution droplets. These findings validate recent attempts to include the effects of soluble trace gases in cloud models, as long as suitable model parameters are used.
Abstract
Experiments were conducted with an electrodynamic levitation system to study the kinetics of droplet evaporation under chemically rich conditions. Single solution droplets of known composition (HNO3/H2O or H2SO4/HNO3/H2O) were introduced into an environmentally controlled cubic levitation cell. The gaseous environment was set intentionally out of equilibrium with the droplet properties, thus permitting the HNO3 mass accommodation coefficient to be determined. Measurements were performed at room temperature and various pressures (200–1000 hPa). Droplet sizes (initial radii in the range 12–26 μm) were measured versus time to high precision (±0.03 μm) via Mie scattering and compared with sizes computed by different models for mass and heat transfer in the transition regime. The best agreement between the theoretical calculations and experimental results was obtained for an HNO3 mass accommodation coefficient of 0.11 ± 0.03 at atmospheric pressure, 0.17 ± 0.05 at 500 hPa, and 0.33 ± 0.08 at 200 hPa. The determination of the mass accommodation coefficient was not sensitive to the transport model used. The results show that droplet evaporation is strongly limited by HNO3 and occurs in two stages, one characterized by rapid H2O mass transfer and the other by HNO3 mass transfer. The presence of a nonvolatile solute (SO2− 4) affects the activities of the volatile components (HNO3 and H2O) and prevents complete evaporation of the solution droplets. These findings validate recent attempts to include the effects of soluble trace gases in cloud models, as long as suitable model parameters are used.
Abstract
This paper describes a methodology for coupling the Simplified Simple Biosphere Model (SSiB) to the regional Eta Model of the National Centers for Environmental Prediction (NCEP), and presents the application of the coupled system in regional simulation studies. The coupled Eta–SSiB model is used to study the impact of land surface processes and land surface initialization on the regional water and energy cycle in an extreme climate event, by comparing the results from the Eta–SSiB with those from the Eta–bucket model. Simulations from both models spanned 3 months via a succession of 48-hr simulations over June, July, and August 1993, a summer of heavy flooding in the United States. The monthly and seasonal means from the simulations in both model runs are compared.
The Eta–SSiB model produces more realistic monthly mean precipitation over the United States and the flood areas. The improvements are mainly manifested in the intensity of the heavy rainfall and its spatial distribution. The results demonstrate that even with a short-term simulation, a more realistic representation of land surface processes and land surface initialization improves the monthly and seasonal means of the simulated regional precipitation for the summer of 1993. In addition to precipitation, the simulations of surface air temperature are also evaluated and they show that the Eta–SSiB model produces reasonable results over most of the United States, with the exception of a cold bias at night in the mountainous western region of the United States.
To understand the mechanisms of land surface–atmosphere interactions and the causes for the differences in the Eta–SSiB and the Eta–bucket simulations, the water cycle in the atmosphere–land system and the energy balance at the land surface are analyzed. The changes in (a) spatial distribution and diurnal cycle of surface latent and sensible heat, and (b) low-level moisture flux convergence (MFC) in response to these changes in surface heating are the primary factors for the improvement in the precipitation simulation. That is, the different surface models of SSiB and bucket, and their different soil moisture initializations, produce different energy partitioning in the surface heat fluxes of the Eta Model. The changes in both the daily mean and the diurnal variation at the land surface lead to different boundary layer evolutions and atmospheric stability conditions. In response to these differences, the Eta–SSiB model and the Eta–bucket model produce different low-level MFC in the heavy rainfall area. Strong and persistent MFC was one of the major forces that produced the heavy rainfall in the summer of 1993.
In the above experiments, the Eta–SSiB model used the global reanalysis of the NCEP–NCAR (National Center for Atmospheric Research) 40-year Reanalysis Project (NNRP) for its initial soil moisture, whereas the Eta–bucket model used a tuned annual-mean fixed field of initial soil moisture as employed in the then-operational Eta Model. Because of this important initialization difference, a further set of simulations was performed in which the Eta–bucket was initialized with the NNRP reanalysis soil moisture employed in the Eta–SSiB. Results show that with similarly derived initial soil moisture states, the differences between the Eta–SSiB and the Eta–bucket are reduced but still evident, suggesting that improved representation of vegetation in the SSiB is at least partially responsible for the overall improvements in the simulations.
Given that the NCEP–NCAR reanalysis is used for initial conditions and lateral and lower boundary conditions in these experiments, this study shows that a coupled atmosphere–biosphere regional model imbedded in a global reanalysis has the potential to provide a more realistic simulation of precipitation in extreme climate events.
Abstract
This paper describes a methodology for coupling the Simplified Simple Biosphere Model (SSiB) to the regional Eta Model of the National Centers for Environmental Prediction (NCEP), and presents the application of the coupled system in regional simulation studies. The coupled Eta–SSiB model is used to study the impact of land surface processes and land surface initialization on the regional water and energy cycle in an extreme climate event, by comparing the results from the Eta–SSiB with those from the Eta–bucket model. Simulations from both models spanned 3 months via a succession of 48-hr simulations over June, July, and August 1993, a summer of heavy flooding in the United States. The monthly and seasonal means from the simulations in both model runs are compared.
The Eta–SSiB model produces more realistic monthly mean precipitation over the United States and the flood areas. The improvements are mainly manifested in the intensity of the heavy rainfall and its spatial distribution. The results demonstrate that even with a short-term simulation, a more realistic representation of land surface processes and land surface initialization improves the monthly and seasonal means of the simulated regional precipitation for the summer of 1993. In addition to precipitation, the simulations of surface air temperature are also evaluated and they show that the Eta–SSiB model produces reasonable results over most of the United States, with the exception of a cold bias at night in the mountainous western region of the United States.
To understand the mechanisms of land surface–atmosphere interactions and the causes for the differences in the Eta–SSiB and the Eta–bucket simulations, the water cycle in the atmosphere–land system and the energy balance at the land surface are analyzed. The changes in (a) spatial distribution and diurnal cycle of surface latent and sensible heat, and (b) low-level moisture flux convergence (MFC) in response to these changes in surface heating are the primary factors for the improvement in the precipitation simulation. That is, the different surface models of SSiB and bucket, and their different soil moisture initializations, produce different energy partitioning in the surface heat fluxes of the Eta Model. The changes in both the daily mean and the diurnal variation at the land surface lead to different boundary layer evolutions and atmospheric stability conditions. In response to these differences, the Eta–SSiB model and the Eta–bucket model produce different low-level MFC in the heavy rainfall area. Strong and persistent MFC was one of the major forces that produced the heavy rainfall in the summer of 1993.
In the above experiments, the Eta–SSiB model used the global reanalysis of the NCEP–NCAR (National Center for Atmospheric Research) 40-year Reanalysis Project (NNRP) for its initial soil moisture, whereas the Eta–bucket model used a tuned annual-mean fixed field of initial soil moisture as employed in the then-operational Eta Model. Because of this important initialization difference, a further set of simulations was performed in which the Eta–bucket was initialized with the NNRP reanalysis soil moisture employed in the Eta–SSiB. Results show that with similarly derived initial soil moisture states, the differences between the Eta–SSiB and the Eta–bucket are reduced but still evident, suggesting that improved representation of vegetation in the SSiB is at least partially responsible for the overall improvements in the simulations.
Given that the NCEP–NCAR reanalysis is used for initial conditions and lateral and lower boundary conditions in these experiments, this study shows that a coupled atmosphere–biosphere regional model imbedded in a global reanalysis has the potential to provide a more realistic simulation of precipitation in extreme climate events.
Abstract
For long-range predictions (e.g., seasonal), it is a common practice for retrospective forecasts (also referred to as the hindcasts) to accompany real-time predictions. The necessity for the hindcasts stems from the fact that real-time predictions need to be calibrated in an attempt to remove the influence of model biases on the predicted anomalies. A fundamental assumption behind forecast calibration is the long-term stationarity of forecast bias that is derived based on hindcasts.
Hindcasts require specification of initial conditions for various components of the prediction system (e.g., ocean, atmosphere) that are generally taken from a long reanalysis. Trends and discontinuities in the reanalysis that are either real or spurious can arise due to several reasons, for example, the changing observing system. If changes in initial conditions were to persist during the forecast, there is a potential for forecast bias to depend over the period it is computed, making calibration even more of a challenging task. In this study such a case is discussed for the recently implemented seasonal prediction system at the National Centers for Environmental Prediction (NCEP), the Climate Forecast System version 2 (CFS.v2).
Based on the analysis of the CFS.v2 for 1981–2009, it is demonstrated that the characteristics of the forecast bias for sea surface temperature (SST) in the equatorial Pacific had a dramatic change around 1999. Furthermore, change in the SST forecast bias, and its relationship to changes in the ocean reanalysis from which the ocean initial conditions for hindcasts are taken is described. Implications for seasonal and other long-range predictions are discussed.
Abstract
For long-range predictions (e.g., seasonal), it is a common practice for retrospective forecasts (also referred to as the hindcasts) to accompany real-time predictions. The necessity for the hindcasts stems from the fact that real-time predictions need to be calibrated in an attempt to remove the influence of model biases on the predicted anomalies. A fundamental assumption behind forecast calibration is the long-term stationarity of forecast bias that is derived based on hindcasts.
Hindcasts require specification of initial conditions for various components of the prediction system (e.g., ocean, atmosphere) that are generally taken from a long reanalysis. Trends and discontinuities in the reanalysis that are either real or spurious can arise due to several reasons, for example, the changing observing system. If changes in initial conditions were to persist during the forecast, there is a potential for forecast bias to depend over the period it is computed, making calibration even more of a challenging task. In this study such a case is discussed for the recently implemented seasonal prediction system at the National Centers for Environmental Prediction (NCEP), the Climate Forecast System version 2 (CFS.v2).
Based on the analysis of the CFS.v2 for 1981–2009, it is demonstrated that the characteristics of the forecast bias for sea surface temperature (SST) in the equatorial Pacific had a dramatic change around 1999. Furthermore, change in the SST forecast bias, and its relationship to changes in the ocean reanalysis from which the ocean initial conditions for hindcasts are taken is described. Implications for seasonal and other long-range predictions are discussed.
Abstract
In this paper, the authors estimate the uncertainty of the rainfall products from NASA and Japan Aerospace Exploration Agency's (JAXA) Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) so that they may be used in a quantitative manner for applications like hydrologic modeling or merging with other rainfall products. The spatial error structure of TRMM PR surface rain rates and types was systematically studied by comparing them with NOAA/National Severe Storms Laboratory's (NSSL) next generation, high-resolution (1 km/5 min) National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (QPE; NMQ/Q2) over the TRMM-covered continental United States (CONUS). Data pairs are first matched at the PR footprint scale (5 km/instantaneous) and then grouped into 0.25° grid cells to yield spatially distributed error maps and statistics using data from December 2009 through November 2010. Careful quality control steps (including bias correction with rain gauges and quality filtering) are applied to the ground radar measurements prior to considering them as reference data. The results show that PR captures well the spatial pattern of total rainfall amounts with a high correlation coefficient (CC; 0.91) with Q2, but this decreases to 0.56 for instantaneous rain rates. In terms of precipitation types, Q2 and PR convective echoes are spatially correlated with a CC of 0.63. Despite this correlation, PR's total annual precipitation from convection is 48.82% less than that by Q2, which points to potential issues in the PR algorithm's attenuation correction, nonuniform beam filling, and/or reflectivity-to-rainfall relation. Finally, the spatial analysis identifies regime-dependent errors, in particular in the mountainous west. It is likely that the surface reference technique is triggered over complex terrain, resulting in high-amplitude biases.
Abstract
In this paper, the authors estimate the uncertainty of the rainfall products from NASA and Japan Aerospace Exploration Agency's (JAXA) Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) so that they may be used in a quantitative manner for applications like hydrologic modeling or merging with other rainfall products. The spatial error structure of TRMM PR surface rain rates and types was systematically studied by comparing them with NOAA/National Severe Storms Laboratory's (NSSL) next generation, high-resolution (1 km/5 min) National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (QPE; NMQ/Q2) over the TRMM-covered continental United States (CONUS). Data pairs are first matched at the PR footprint scale (5 km/instantaneous) and then grouped into 0.25° grid cells to yield spatially distributed error maps and statistics using data from December 2009 through November 2010. Careful quality control steps (including bias correction with rain gauges and quality filtering) are applied to the ground radar measurements prior to considering them as reference data. The results show that PR captures well the spatial pattern of total rainfall amounts with a high correlation coefficient (CC; 0.91) with Q2, but this decreases to 0.56 for instantaneous rain rates. In terms of precipitation types, Q2 and PR convective echoes are spatially correlated with a CC of 0.63. Despite this correlation, PR's total annual precipitation from convection is 48.82% less than that by Q2, which points to potential issues in the PR algorithm's attenuation correction, nonuniform beam filling, and/or reflectivity-to-rainfall relation. Finally, the spatial analysis identifies regime-dependent errors, in particular in the mountainous west. It is likely that the surface reference technique is triggered over complex terrain, resulting in high-amplitude biases.
Abstract
Two-dimensional simulations of the 11 January 1972 Boulder, Colorado, windstorm, obtained from 11 diverse nonhydrostatic models, are intercompared with special emphasis on the turbulent breakdown of topographically forced gravity waves, as part of the preparation for the Mesoscale Alpine Programme field phase. The sounding used to initialize the models is more representative of the actual lower stratosphere than those applied in previous simulations. Upper-level breaking is predicted by all models in comparable horizontal locations and vertical layers, which suggests that gravity wave breaking may be quite predictable in some circumstances. Characteristics of the breaking include the following: pronounced turbulence in the 13–16-km and 18–20-km layers positioned beneath a critical level near 21-km, a well-defined upstream tilt with height, and enhancement of upper-level breaking superpositioned above the low-level hydraulic jump. Sensitivity experiments indicate that the structure of the wave breaking was impacted by the numerical dissipation, numerical representation of the horizontal advection, and lateral boundary conditions. Small vertical wavelength variations in the shear and stability above 10 km contributed to significant changes in the structures associated with wave breaking. Simulation of this case is ideal for testing and evaluation of mesoscale numerical models and numerical algorithms because of the complex wave-breaking response.
Abstract
Two-dimensional simulations of the 11 January 1972 Boulder, Colorado, windstorm, obtained from 11 diverse nonhydrostatic models, are intercompared with special emphasis on the turbulent breakdown of topographically forced gravity waves, as part of the preparation for the Mesoscale Alpine Programme field phase. The sounding used to initialize the models is more representative of the actual lower stratosphere than those applied in previous simulations. Upper-level breaking is predicted by all models in comparable horizontal locations and vertical layers, which suggests that gravity wave breaking may be quite predictable in some circumstances. Characteristics of the breaking include the following: pronounced turbulence in the 13–16-km and 18–20-km layers positioned beneath a critical level near 21-km, a well-defined upstream tilt with height, and enhancement of upper-level breaking superpositioned above the low-level hydraulic jump. Sensitivity experiments indicate that the structure of the wave breaking was impacted by the numerical dissipation, numerical representation of the horizontal advection, and lateral boundary conditions. Small vertical wavelength variations in the shear and stability above 10 km contributed to significant changes in the structures associated with wave breaking. Simulation of this case is ideal for testing and evaluation of mesoscale numerical models and numerical algorithms because of the complex wave-breaking response.
Abstract
Characterization of the error associated with quantitative precipitation estimates (QPEs) from spaceborne passive microwave (PMW) sensors is important for a variety of applications ranging from flood forecasting to climate monitoring. This study evaluates the joint influence of precipitation and surface characteristics on the error structure of NASA’s Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) surface QPE product (2A12). TMI precipitation products are compared with high-resolution reference precipitation products obtained from the NOAA/NSSL ground radar–based Multi-Radar Multi-Sensor (MRMS) system. Surface characteristics were represented via a surface classification dataset derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). This study assesses the ability of 2A12 to detect, classify, and quantify precipitation at its native resolution for the 2011 warm season (March–September) over the southern continental United States. Decreased algorithm performance is apparent over dry and sparsely vegetated regions, a probable result of the surface radiation signal mimicking the scattering signature associated with frozen hydrometeors. Algorithm performance is also shown to be positively correlated with precipitation coverage over the sensor footprint. The algorithm also performs better in pure stratiform and convective precipitation events, compared to events containing a mixture of stratiform and convective precipitation within the footprint. This possibly results from the high spatial gradients of precipitation associated with these events and an underrepresentation of such cases in the retrieval database. The methodology and framework developed herein apply more generally to precipitation estimates from other passive microwave sensors on board low-Earth-orbiting satellites and specifically could be used to evaluate PMW sensors associated with the recently launched Global Precipitation Measurement (GPM) mission.
Abstract
Characterization of the error associated with quantitative precipitation estimates (QPEs) from spaceborne passive microwave (PMW) sensors is important for a variety of applications ranging from flood forecasting to climate monitoring. This study evaluates the joint influence of precipitation and surface characteristics on the error structure of NASA’s Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) surface QPE product (2A12). TMI precipitation products are compared with high-resolution reference precipitation products obtained from the NOAA/NSSL ground radar–based Multi-Radar Multi-Sensor (MRMS) system. Surface characteristics were represented via a surface classification dataset derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). This study assesses the ability of 2A12 to detect, classify, and quantify precipitation at its native resolution for the 2011 warm season (March–September) over the southern continental United States. Decreased algorithm performance is apparent over dry and sparsely vegetated regions, a probable result of the surface radiation signal mimicking the scattering signature associated with frozen hydrometeors. Algorithm performance is also shown to be positively correlated with precipitation coverage over the sensor footprint. The algorithm also performs better in pure stratiform and convective precipitation events, compared to events containing a mixture of stratiform and convective precipitation within the footprint. This possibly results from the high spatial gradients of precipitation associated with these events and an underrepresentation of such cases in the retrieval database. The methodology and framework developed herein apply more generally to precipitation estimates from other passive microwave sensors on board low-Earth-orbiting satellites and specifically could be used to evaluate PMW sensors associated with the recently launched Global Precipitation Measurement (GPM) mission.
