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Abstract
A scale-aware convective parameterization based on the Tiedtke scheme is developed and tested in the Weather Research and Forecasting (WRF) Model and the Model for Prediction Across Scales (MPAS) for a few convective cases at grid sizes in the ranges of 1.5–4.5 km. These tests demonstrate that the scale-aware scheme effectively reduces the outcome of deep convection by decreasing the convective portion of the total surface precipitation. When compared to the model runs that use microphysics without the cumulus parameterization at these grid sizes, the modified Tiedtke scheme is shown to improve some aspects of the precipitation forecasts. When the scheme is applied on a variable mesh in MPAS, it handles the convection across the mesh transition zones smoothly.
Significance Statement
Representing convection accounting for variations in the size of grid mesh is crucial in numerical models with variable resolutions, and in precipitation events where convection is not well depicted even by a model mesh of a few kilometers. Many convective parameterizations have already considered this grid-size dependency. This paper fills a gap by applying the same concept to a different convective parameterization, and evaluating it in a few precipitation forecast scenarios.
Abstract
A scale-aware convective parameterization based on the Tiedtke scheme is developed and tested in the Weather Research and Forecasting (WRF) Model and the Model for Prediction Across Scales (MPAS) for a few convective cases at grid sizes in the ranges of 1.5–4.5 km. These tests demonstrate that the scale-aware scheme effectively reduces the outcome of deep convection by decreasing the convective portion of the total surface precipitation. When compared to the model runs that use microphysics without the cumulus parameterization at these grid sizes, the modified Tiedtke scheme is shown to improve some aspects of the precipitation forecasts. When the scheme is applied on a variable mesh in MPAS, it handles the convection across the mesh transition zones smoothly.
Significance Statement
Representing convection accounting for variations in the size of grid mesh is crucial in numerical models with variable resolutions, and in precipitation events where convection is not well depicted even by a model mesh of a few kilometers. Many convective parameterizations have already considered this grid-size dependency. This paper fills a gap by applying the same concept to a different convective parameterization, and evaluating it in a few precipitation forecast scenarios.
Abstract
This is the second part of a study investigating the 1991 severe precipitation event over the Yangtze–Huai River valley (YHRV) in China using both observations and regional model simulations. While Part I reported on the Mei-yu front and its association with large-scale circulation, this study documents the biases associated with the treatment of the lateral boundary in the regional model. Two aspects of the biases were studied: the driving field, which provides large-scale boundary forcing, and the coupling scheme, which specifies how the forcing is adopted by the model. The former bias is defined as model uncertainty because it is not related to the model itself, while the latter bias (as well as those biases attributed to other sources) is referred to as model error. These two aspects were examined by analyzing the regional model simulations of the 1991 summer severe precipitation event over YHRV using different driving fields (ECMWF–TOGA objective analysis, ECMWF reanalysis, and NCEP–NCAR reanalysis) and coupling scheme (distribution function of the nudging coefficient and width of the buffer zone). Spectral analysis was also used to study the frequency distribution of the bias.
The analyses suggest that the 200-hPa winds, 500-hPa geopotential height, and 850-hPa winds and water vapor mixing ratio, which have dominant influences on Mei-yu evolution, are sensitive to large-scale boundary forcing. In particular the 500-hPa geopotential height, and 850-hPa water vapor mixing ratio near the Tibetan Plateau and over the western Pacific Oceans are highly dependent on the driving field. On the other hand, the water vapor in the lower troposphere, wind at all levels, and precipitation pattern are much more affected by the treatment of nudging in the coupling scheme. It is interesting to find that the two commonly used coupling schemes, the lateral boundary coupling and the spectral coupling, provide similar large-scale information to the simulation domain when the former scheme used a wider buffer zone and stronger nudging coefficient. Systematical model errors, existing in the north of the simulation domain, are caused by the overprediction of low-level inversion stratiform clouds.
The analyses further indicate that the model mesoscale signal is not significantly influenced by the different treatments of the nudging procedure. However, it is also shown that the model performance, especially the monthly mean precipitation and its spatial pattern, is substantially improved with the increase of buffer zone width and nudging coefficient.
Abstract
This is the second part of a study investigating the 1991 severe precipitation event over the Yangtze–Huai River valley (YHRV) in China using both observations and regional model simulations. While Part I reported on the Mei-yu front and its association with large-scale circulation, this study documents the biases associated with the treatment of the lateral boundary in the regional model. Two aspects of the biases were studied: the driving field, which provides large-scale boundary forcing, and the coupling scheme, which specifies how the forcing is adopted by the model. The former bias is defined as model uncertainty because it is not related to the model itself, while the latter bias (as well as those biases attributed to other sources) is referred to as model error. These two aspects were examined by analyzing the regional model simulations of the 1991 summer severe precipitation event over YHRV using different driving fields (ECMWF–TOGA objective analysis, ECMWF reanalysis, and NCEP–NCAR reanalysis) and coupling scheme (distribution function of the nudging coefficient and width of the buffer zone). Spectral analysis was also used to study the frequency distribution of the bias.
The analyses suggest that the 200-hPa winds, 500-hPa geopotential height, and 850-hPa winds and water vapor mixing ratio, which have dominant influences on Mei-yu evolution, are sensitive to large-scale boundary forcing. In particular the 500-hPa geopotential height, and 850-hPa water vapor mixing ratio near the Tibetan Plateau and over the western Pacific Oceans are highly dependent on the driving field. On the other hand, the water vapor in the lower troposphere, wind at all levels, and precipitation pattern are much more affected by the treatment of nudging in the coupling scheme. It is interesting to find that the two commonly used coupling schemes, the lateral boundary coupling and the spectral coupling, provide similar large-scale information to the simulation domain when the former scheme used a wider buffer zone and stronger nudging coefficient. Systematical model errors, existing in the north of the simulation domain, are caused by the overprediction of low-level inversion stratiform clouds.
The analyses further indicate that the model mesoscale signal is not significantly influenced by the different treatments of the nudging procedure. However, it is also shown that the model performance, especially the monthly mean precipitation and its spatial pattern, is substantially improved with the increase of buffer zone width and nudging coefficient.
Abstract
The summer Mei-yu event over eastern China, which is strongly influenced by large-scale circulation, is an important aspect of East Asian climate; for example, the Mei-yu frequently brings heavy precipitation to the Yangtze–Huai River valley (YHRV). Both observations and a regional model were used to study the Mei-yu front and its relation to large-scale circulation during the summer of 1991 when severe floods occurred over YHRV. This study has two parts: the first part, presented here, analyzes the association between heavy Mei-yu precipitation and relevant large-scale circulation, while the second part, documented by W. Gong and W.-C. Wang, examines the model biases associated with the treatment of lateral boundary conditions (the objective analyses and coupling schemes) used as the driving fields for the regional model.
Observations indicate that the Mei-yu season in 1991 spans 18 May–14 July, making it the longest Mei-yu period during the last 40 yr. The heavy precipitation over YHRV is found to be intimately related to the western Pacific subtropical high, upper-tropospheric westerly jet at midlatitudes, and lower-tropospheric southwest wind and moisture flux. The regional model simulates reasonably well the regional mean surface air temperature and precipitation, in particular the precipitation evolution and its association with the large-scale circulation throughout the Mei-yu season. However, the model simulates smaller precipitation intensity, which is due partly to the colder and drier model atmosphere resulting from excessive low-level clouds and the simplified land surface process scheme used in the present study.
Abstract
The summer Mei-yu event over eastern China, which is strongly influenced by large-scale circulation, is an important aspect of East Asian climate; for example, the Mei-yu frequently brings heavy precipitation to the Yangtze–Huai River valley (YHRV). Both observations and a regional model were used to study the Mei-yu front and its relation to large-scale circulation during the summer of 1991 when severe floods occurred over YHRV. This study has two parts: the first part, presented here, analyzes the association between heavy Mei-yu precipitation and relevant large-scale circulation, while the second part, documented by W. Gong and W.-C. Wang, examines the model biases associated with the treatment of lateral boundary conditions (the objective analyses and coupling schemes) used as the driving fields for the regional model.
Observations indicate that the Mei-yu season in 1991 spans 18 May–14 July, making it the longest Mei-yu period during the last 40 yr. The heavy precipitation over YHRV is found to be intimately related to the western Pacific subtropical high, upper-tropospheric westerly jet at midlatitudes, and lower-tropospheric southwest wind and moisture flux. The regional model simulates reasonably well the regional mean surface air temperature and precipitation, in particular the precipitation evolution and its association with the large-scale circulation throughout the Mei-yu season. However, the model simulates smaller precipitation intensity, which is due partly to the colder and drier model atmosphere resulting from excessive low-level clouds and the simplified land surface process scheme used in the present study.
Abstract
A parameterization for the absorption of solar radiation as a function of the amount of water vapor in the earth's atmosphere is obtained. Absorption computations are based on the Goody band model and the near-infrared absorption band data of Ludwig et al. A two-parameter Curtis-Godson approximation is used to treat the inhomogeneous atmosphere. Heating rates based on a frequently used one-parameter pressure-scaling approximation are also discussed and compared with the present parameterization.
Abstract
A parameterization for the absorption of solar radiation as a function of the amount of water vapor in the earth's atmosphere is obtained. Absorption computations are based on the Goody band model and the near-infrared absorption band data of Ludwig et al. A two-parameter Curtis-Godson approximation is used to treat the inhomogeneous atmosphere. Heating rates based on a frequently used one-parameter pressure-scaling approximation are also discussed and compared with the present parameterization.
Abstract
This paper presents a comparative study of high-resolution methods for high-frequency radar current mapping. A z-domain transformation and auxiliary z-domain manipulation of the autoregressive method is proposed for this comparison. A Weibull distribution test is recommended to justify the Rayleigh distribution of the sea clutter for quality control. Upon the power spectrum estimation, a conventional centroid method and a new symmetric-peak-sum method for the identification of current Doppler shift are proposed as another comparison. HF radar data were collected over the period from November 2012 to August 2013 at Placentia Bay, Newfoundland, Canada, and were compared with measurements from an acoustic Doppler current meter. This comparison is used to study the utility of high-resolution spectrum estimation and Bragg identification methods for surface current mapping. Results show promising use of these methods in different current scenarios and suggest combined applications to improve accuracy.
Abstract
This paper presents a comparative study of high-resolution methods for high-frequency radar current mapping. A z-domain transformation and auxiliary z-domain manipulation of the autoregressive method is proposed for this comparison. A Weibull distribution test is recommended to justify the Rayleigh distribution of the sea clutter for quality control. Upon the power spectrum estimation, a conventional centroid method and a new symmetric-peak-sum method for the identification of current Doppler shift are proposed as another comparison. HF radar data were collected over the period from November 2012 to August 2013 at Placentia Bay, Newfoundland, Canada, and were compared with measurements from an acoustic Doppler current meter. This comparison is used to study the utility of high-resolution spectrum estimation and Bragg identification methods for surface current mapping. Results show promising use of these methods in different current scenarios and suggest combined applications to improve accuracy.
Abstract
Water resources are an essential part of the ecosystem in the extremely arid northwestern part of China. Previous studies revealed a dry-to-wet climate change since the late 1980s in this region, which suggested a relief from the drought condition. However, the analysis in this study using the updated data shows that the arid situation has continued and even intensified in the past decade. This is reflected by the fact that the low-level air relative humidity and deep soil relative humidity have decreased in the past decade. Examination of the standardized precipitation evapotranspiration index (SPEI) and self-calibrating Palmer drought severity index (sc-PDSI) indicates that the severity and spatial extent of aridity and drought have increased substantially in northwestern China in the most recent decade. It is shown that the drought intensification in northwestern China is mainly caused by the increase of evaporation that results from the continuous rise in temperature, which will pose a continuous threat to the ecosystem and economic development in this region, especially under the background of global warming.
Abstract
Water resources are an essential part of the ecosystem in the extremely arid northwestern part of China. Previous studies revealed a dry-to-wet climate change since the late 1980s in this region, which suggested a relief from the drought condition. However, the analysis in this study using the updated data shows that the arid situation has continued and even intensified in the past decade. This is reflected by the fact that the low-level air relative humidity and deep soil relative humidity have decreased in the past decade. Examination of the standardized precipitation evapotranspiration index (SPEI) and self-calibrating Palmer drought severity index (sc-PDSI) indicates that the severity and spatial extent of aridity and drought have increased substantially in northwestern China in the most recent decade. It is shown that the drought intensification in northwestern China is mainly caused by the increase of evaporation that results from the continuous rise in temperature, which will pose a continuous threat to the ecosystem and economic development in this region, especially under the background of global warming.
Abstract
Fengyun-4A (FY-4A) is a geostationary meteorological satellite with four advanced payloads, which can be used to quantitatively detect Earth’s atmospheric system with multispectral and high spatial and temporal resolution. However, the applicable model limits the application of the FY-4A satellite data. In this paper, the empirical statistical model developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor is extended for FY-4A Advanced Geosynchronous Radiation Imager (AGRI), and it is applied to observed data to evaluate the applicability of the model for AGRI measurements. To improve the accuracy of radiation estimation, the artificially intelligent particle swarm optimization (PSO) algorithm was used for model optimizing. Results show that the estimated radiation has diurnal variation that is in accord with the characteristics of radiation variation. The estimated net surface shortwave radiation (Sn) and observed values show good correlation. However, large deviations from observations are found in the estimated values when the empirical model based on MODIS is directly used to process AGRI data. Thus, the empirical statistical model based on MODIS can be applied to AGRI data, but the empirical parameters need to be revised. Optimization of the empirical statistical model by the PSO algorithm can effectively improve the accuracy of the radiation estimate. The mean absolute percentage error (MAPE) of Sn estimated by optimized models is reduced to 15%. The MAPE of the net surface longwave radiation (Ln) estimated by optimized models is reduced to 31%, and the MAPE of the net radiation (Rn) estimated by optimized models is reduced to 27%. However, for the uncertainty caused by error accumulation effect, the influence of PSO optimization on Rn is not as obvious as that of Ln. However, the analysis of error distribution shows that PSO optimization does improve the estimation results of Rn. Based on AGRI data, the surface radiation can be estimated simply, and the regional or larger-scale surface radiation retrieval can quickly be realized by this method, which has large application potential and popularization value.
Abstract
Fengyun-4A (FY-4A) is a geostationary meteorological satellite with four advanced payloads, which can be used to quantitatively detect Earth’s atmospheric system with multispectral and high spatial and temporal resolution. However, the applicable model limits the application of the FY-4A satellite data. In this paper, the empirical statistical model developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor is extended for FY-4A Advanced Geosynchronous Radiation Imager (AGRI), and it is applied to observed data to evaluate the applicability of the model for AGRI measurements. To improve the accuracy of radiation estimation, the artificially intelligent particle swarm optimization (PSO) algorithm was used for model optimizing. Results show that the estimated radiation has diurnal variation that is in accord with the characteristics of radiation variation. The estimated net surface shortwave radiation (Sn) and observed values show good correlation. However, large deviations from observations are found in the estimated values when the empirical model based on MODIS is directly used to process AGRI data. Thus, the empirical statistical model based on MODIS can be applied to AGRI data, but the empirical parameters need to be revised. Optimization of the empirical statistical model by the PSO algorithm can effectively improve the accuracy of the radiation estimate. The mean absolute percentage error (MAPE) of Sn estimated by optimized models is reduced to 15%. The MAPE of the net surface longwave radiation (Ln) estimated by optimized models is reduced to 31%, and the MAPE of the net radiation (Rn) estimated by optimized models is reduced to 27%. However, for the uncertainty caused by error accumulation effect, the influence of PSO optimization on Rn is not as obvious as that of Ln. However, the analysis of error distribution shows that PSO optimization does improve the estimation results of Rn. Based on AGRI data, the surface radiation can be estimated simply, and the regional or larger-scale surface radiation retrieval can quickly be realized by this method, which has large application potential and popularization value.
Abstract
Two 100-yr equilibrium simulations from the NCAR Community Climate Model coupled to a nondynamic slab ocean are used to investigate the activity of northern winter extratropical cyclones and anticyclones under a greenhouse warming scenario. The first simulation uses the 1990 observed CO2, CH4, N2O, CFC-11, and CFC-12 concentrations, and the second adopts the year 2050 concentrations according to the Intergovernmental Panel on Climate Change business-as-usual scenario. Variables that describe the characteristic properties of the cyclone-scale eddies, such as surface cyclone and anticyclone frequency and the bandpassed root-mean-square of 500-hPa geopotential height, along with the Eady growth rate maximum, form a framework for the analysis of the cyclone and anticyclone activity.
Objective criteria are developed for identifying cyclone and anticyclone occurrences based on the 1000-hPa geopotential height and vorticity fields and tested using ECMWF analyses. The potential changes of the eddy activity under the greenhouse warming climate are then examined. Results indicate that the activity of cyclone-scale eddies decreases under the greenhouse warming scenario. This is not only reflected in the surface cyclone and anticyclone frequency and in the bandpassed rms of 500-hPa geopotential height, but is also discerned from the Eady growth rate maximum. Based on the analysis, three different physical mechanisms responsible for the decreased eddy activity are discussed: 1) a decrease of the extratropical meridional temperature gradient from the surface to the midtroposphere, 2) a reduction in the land–sea thermal contrast in the east coastal regions of the Asian and North American continents, and 3) an increase in the eddy meridional latent heat fluxes. Uncertainties in the results related to the limitations of the model and the model equilibrium simulations are discussed.
Abstract
Two 100-yr equilibrium simulations from the NCAR Community Climate Model coupled to a nondynamic slab ocean are used to investigate the activity of northern winter extratropical cyclones and anticyclones under a greenhouse warming scenario. The first simulation uses the 1990 observed CO2, CH4, N2O, CFC-11, and CFC-12 concentrations, and the second adopts the year 2050 concentrations according to the Intergovernmental Panel on Climate Change business-as-usual scenario. Variables that describe the characteristic properties of the cyclone-scale eddies, such as surface cyclone and anticyclone frequency and the bandpassed root-mean-square of 500-hPa geopotential height, along with the Eady growth rate maximum, form a framework for the analysis of the cyclone and anticyclone activity.
Objective criteria are developed for identifying cyclone and anticyclone occurrences based on the 1000-hPa geopotential height and vorticity fields and tested using ECMWF analyses. The potential changes of the eddy activity under the greenhouse warming climate are then examined. Results indicate that the activity of cyclone-scale eddies decreases under the greenhouse warming scenario. This is not only reflected in the surface cyclone and anticyclone frequency and in the bandpassed rms of 500-hPa geopotential height, but is also discerned from the Eady growth rate maximum. Based on the analysis, three different physical mechanisms responsible for the decreased eddy activity are discussed: 1) a decrease of the extratropical meridional temperature gradient from the surface to the midtroposphere, 2) a reduction in the land–sea thermal contrast in the east coastal regions of the Asian and North American continents, and 3) an increase in the eddy meridional latent heat fluxes. Uncertainties in the results related to the limitations of the model and the model equilibrium simulations are discussed.
Abstract
A comparison study of four cumulus parameterization schemes (CPSs), the Anthes–Kuo, Betts–Miller, Grell, and Kain–Fritsch schemes, is conducted using The Pennsylvania State University–National Center for Atmospheric Research mesoscale model. Performance of these CPSs is examined using six precipitation events over the continental United States for both cold and warm seasons. Grid resolutions of 36 and 12 km are chosen to represent current mesoscale research models and future operational models. The key parameters used to evaluate skill include precipitation, sea level pressure, wind, and temperature predictions. Precipitation is evaluated statistically using conventional skill scores (such as threat and bias scores) for different threshold values based on hourly rainfall observations. Rainfall and other mesoscale features are also evaluated by careful examination of analyzed and simulated fields, which are discussed in the context of timing, evolution, intensity, and structure of the precipitation systems.
It is found that the general 6-h precipitation forecast skill for these schemes is fairly good in predicting four out of six cases examined in this study, even for higher thresholds. The forecast skill is generally higher for cold-season events than for warm-season events. There is an increase in the forecast skill in the 12-km model, and the gain is most obvious in predicting heavier rainfall amounts. The model’s precipitation forecast skill is better in rainfall volume than in either the areal coverage or the peak amount. The scheme with the convective available potential energy–based closure assumption (Kain–Fritsch scheme) appears to perform better. Some systematic behaviors associated with various schemes are also noted wherever possible.
The partition of rainfall into subgrid scale and grid scale is sensitive to the particular parameterization scheme chosen, but relatively insensitive to either the model grid sizes or the convective environments.
The prediction of mesoscale surface features in warm-season cases, such as mesoscale pressure centers, wind-shift lines (gust fronts), and temperature fields, strongly suggests that the CPSs with moist downdrafts are able to predict these surface features more accurately.
Abstract
A comparison study of four cumulus parameterization schemes (CPSs), the Anthes–Kuo, Betts–Miller, Grell, and Kain–Fritsch schemes, is conducted using The Pennsylvania State University–National Center for Atmospheric Research mesoscale model. Performance of these CPSs is examined using six precipitation events over the continental United States for both cold and warm seasons. Grid resolutions of 36 and 12 km are chosen to represent current mesoscale research models and future operational models. The key parameters used to evaluate skill include precipitation, sea level pressure, wind, and temperature predictions. Precipitation is evaluated statistically using conventional skill scores (such as threat and bias scores) for different threshold values based on hourly rainfall observations. Rainfall and other mesoscale features are also evaluated by careful examination of analyzed and simulated fields, which are discussed in the context of timing, evolution, intensity, and structure of the precipitation systems.
It is found that the general 6-h precipitation forecast skill for these schemes is fairly good in predicting four out of six cases examined in this study, even for higher thresholds. The forecast skill is generally higher for cold-season events than for warm-season events. There is an increase in the forecast skill in the 12-km model, and the gain is most obvious in predicting heavier rainfall amounts. The model’s precipitation forecast skill is better in rainfall volume than in either the areal coverage or the peak amount. The scheme with the convective available potential energy–based closure assumption (Kain–Fritsch scheme) appears to perform better. Some systematic behaviors associated with various schemes are also noted wherever possible.
The partition of rainfall into subgrid scale and grid scale is sensitive to the particular parameterization scheme chosen, but relatively insensitive to either the model grid sizes or the convective environments.
The prediction of mesoscale surface features in warm-season cases, such as mesoscale pressure centers, wind-shift lines (gust fronts), and temperature fields, strongly suggests that the CPSs with moist downdrafts are able to predict these surface features more accurately.
