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- Author or Editor: Rafael L. Bras x
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Abstract
The behavior of a numerical cloud model is investigated in terms of its sensitivity to perturbations with two kinds of lateral boundary conditions: 1) with cyclic lateral boundary conditions, the model is sensitive to many aspects of its structure, including a very small potential temperature perturbation at only one grid point, changes in time step, and small changes in parameters such as the autoconversion rate from cloud water to rainwater and the latent heat of vaporization; 2) with prescribed lateral boundary conditions, growth and decay of perturbations are highly dependent on the flow conditions inside the domain. It is shown that under relatively uniform (unidirectional) advection across the domain, the perturbations will decay. On the other hand, convergence, divergence, or, in general, flow patterns with changing directions support error growth. This study shows that it is the flow structure inside the model domain that is important in determining whether the prescribed lateral boundary conditions will result in decaying or growing perturbations. The numerical model is inherently sensitive to initial perturbations, but errors can decay due to advection of information from lateral boundaries across the domain by uniform flow. This result provides one explanation to the reported results in earlier studies showing both error growth and decay.
Abstract
The behavior of a numerical cloud model is investigated in terms of its sensitivity to perturbations with two kinds of lateral boundary conditions: 1) with cyclic lateral boundary conditions, the model is sensitive to many aspects of its structure, including a very small potential temperature perturbation at only one grid point, changes in time step, and small changes in parameters such as the autoconversion rate from cloud water to rainwater and the latent heat of vaporization; 2) with prescribed lateral boundary conditions, growth and decay of perturbations are highly dependent on the flow conditions inside the domain. It is shown that under relatively uniform (unidirectional) advection across the domain, the perturbations will decay. On the other hand, convergence, divergence, or, in general, flow patterns with changing directions support error growth. This study shows that it is the flow structure inside the model domain that is important in determining whether the prescribed lateral boundary conditions will result in decaying or growing perturbations. The numerical model is inherently sensitive to initial perturbations, but errors can decay due to advection of information from lateral boundaries across the domain by uniform flow. This result provides one explanation to the reported results in earlier studies showing both error growth and decay.
Abstract
Rainfall data collected by radar in the vicinity of Darwin, Australia, have been analysed in terms of their mean, variance, autocorrelation of area-averaged rain rate, and diurnal variation. It is found that, when compared with the well-studied GATE (Global Atmospheric Research Program Atlantic Tropical Experiment) data, Darwin rainfall has larger coefficient of variation (CV), faster reduction of CV with increasing area size, weaker temporal correlation, and a strong diurnal cycle and intermittence. The coefficient of variation for Darwin rainfall has larger magnitude and exhibits larger spatial variability over the sea portion than over the land portion within the area of radar coverage. Stationary and nonstationary models have been used to study the sampling errors associated with space-based rainfall measurement. The nonstationary model shows that the sampling error is sensitive to the starting sampling time for some sampling frequencies, due to the diurnal cycle of rain, but not for others. Sampling experiments using data also show such sensitivity. When the errors are averaged over starting time, the results of the experiments and the stationary and nonstationary models match each other very closely. In the small areas for which data are available for both Darwin and GATE, the sampling error is expected to be larger for Darwin due to its larger CV.
Abstract
Rainfall data collected by radar in the vicinity of Darwin, Australia, have been analysed in terms of their mean, variance, autocorrelation of area-averaged rain rate, and diurnal variation. It is found that, when compared with the well-studied GATE (Global Atmospheric Research Program Atlantic Tropical Experiment) data, Darwin rainfall has larger coefficient of variation (CV), faster reduction of CV with increasing area size, weaker temporal correlation, and a strong diurnal cycle and intermittence. The coefficient of variation for Darwin rainfall has larger magnitude and exhibits larger spatial variability over the sea portion than over the land portion within the area of radar coverage. Stationary and nonstationary models have been used to study the sampling errors associated with space-based rainfall measurement. The nonstationary model shows that the sampling error is sensitive to the starting sampling time for some sampling frequencies, due to the diurnal cycle of rain, but not for others. Sampling experiments using data also show such sensitivity. When the errors are averaged over starting time, the results of the experiments and the stationary and nonstationary models match each other very closely. In the small areas for which data are available for both Darwin and GATE, the sampling error is expected to be larger for Darwin due to its larger CV.
Abstract
Persistent and prolonged periods of dry or moist conditions are often evident in the interannual variability of continental-type climates This variability appears as fluctuations around several distinct and preferred moisture states. These fluctuations and transitions between the preferred states are commonly attributed to large-scale changes in atmospheric circulation patterns possibly caused by oceanic influence.
This paper argues that a major contributing factor to the persistent dry or moist behavior could be due to feedback and nonlinear interaction between the components of the hydrologic cycle in both the land and the atmosphere. A model that couples the water balance of continental landmasses and the overlying atmosphere is presented. The large-scale variabilities in atmospheric circulation are introduced by way of simple randomness in key forcing parameters. The result is a multiplicative-noise stochastic differential equation for the water balance dynamics of continental-type climates that includes land surface-atmosphere interaction.
The solution to this differential equation exhibits a bimodal probability distribution function for soil moisture and precipitation. Extended periods of anomalous dry conditions (drought) or alternatively wet conditions (pluvial), with abrupt transitions between them, are present in the model. The statistics of persistent anomalous conditions are analyzed for two climatic classifications. The probability distribution function for transitions out of droughts are developed for the modeled climates.
Abstract
Persistent and prolonged periods of dry or moist conditions are often evident in the interannual variability of continental-type climates This variability appears as fluctuations around several distinct and preferred moisture states. These fluctuations and transitions between the preferred states are commonly attributed to large-scale changes in atmospheric circulation patterns possibly caused by oceanic influence.
This paper argues that a major contributing factor to the persistent dry or moist behavior could be due to feedback and nonlinear interaction between the components of the hydrologic cycle in both the land and the atmosphere. A model that couples the water balance of continental landmasses and the overlying atmosphere is presented. The large-scale variabilities in atmospheric circulation are introduced by way of simple randomness in key forcing parameters. The result is a multiplicative-noise stochastic differential equation for the water balance dynamics of continental-type climates that includes land surface-atmosphere interaction.
The solution to this differential equation exhibits a bimodal probability distribution function for soil moisture and precipitation. Extended periods of anomalous dry conditions (drought) or alternatively wet conditions (pluvial), with abrupt transitions between them, are present in the model. The statistics of persistent anomalous conditions are analyzed for two climatic classifications. The probability distribution function for transitions out of droughts are developed for the modeled climates.
Abstract
Weather radar, in combination with a distributed rainfall-runoff model, promises to significantly improve real-time flood forecasting. This paper investigates the value of radar-derived precipitation in forecasting streamflow in the Sieve River basin, near Florence, Italy. The basin is modeled with a distributed rainfall-runoff model that exploits topographic information available from digital elevation maps. The sensitivity of the flood forecast to various properties of the radar-derived rainfall is studied. It is found that use of the proper radar reflectivity-rainfall intensity (Z-R) relationship is the most crucial factor in obtaining correct food hydrographs. Errors resulting from spatially averaging radar rainfall are acceptable, but the use of discrete point information (i.e., raingage) can lead to serious problems. Reducing the resolution of the 5-min radar signal by temporally averaging over 15 and 30 min does not lead to major errors. Using 3-bit radar data (rather than the usual 8-bit data) to represent intensifies results in significant operational savings without serious problems in hydrograph accuracy.
Abstract
Weather radar, in combination with a distributed rainfall-runoff model, promises to significantly improve real-time flood forecasting. This paper investigates the value of radar-derived precipitation in forecasting streamflow in the Sieve River basin, near Florence, Italy. The basin is modeled with a distributed rainfall-runoff model that exploits topographic information available from digital elevation maps. The sensitivity of the flood forecast to various properties of the radar-derived rainfall is studied. It is found that use of the proper radar reflectivity-rainfall intensity (Z-R) relationship is the most crucial factor in obtaining correct food hydrographs. Errors resulting from spatially averaging radar rainfall are acceptable, but the use of discrete point information (i.e., raingage) can lead to serious problems. Reducing the resolution of the 5-min radar signal by temporally averaging over 15 and 30 min does not lead to major errors. Using 3-bit radar data (rather than the usual 8-bit data) to represent intensifies results in significant operational savings without serious problems in hydrograph accuracy.
Abstract
This study investigates the use of a previously published algorithm for estimating ground heat flux (GHF) at the global scale. The method is based on an analytical solution of the diffusion equation for heat transfer in a soil layer and has been shown to be effective at the point scale. The algorithm has several advantageous properties: 1) it only needs a single-level input of surface (skin) temperature, 2) the time-mean GHF can be derived directly from time-mean skin temperature, 3) it has reduced sensitivity to the variability in soil thermal properties and moisture, 4) it does not requires snow depth, and 5) it is computationally effective. A global map of the necessary thermal inertia parameter is derived using reanalysis data as a function of soil type. These parameter estimates are comparable to values obtained from in situ observations. The new global GHF estimates are generally consistent with the reanalysis GHF output simulated using two-layer soil hydrology models. The authors argue that the new algorithm is more robust and trustworthy in regions where they differ. The proposed algorithm offers potential benefits for direct assimilation of observations of surface temperature as well as GHF into the reanalysis models at various time scales.
Abstract
This study investigates the use of a previously published algorithm for estimating ground heat flux (GHF) at the global scale. The method is based on an analytical solution of the diffusion equation for heat transfer in a soil layer and has been shown to be effective at the point scale. The algorithm has several advantageous properties: 1) it only needs a single-level input of surface (skin) temperature, 2) the time-mean GHF can be derived directly from time-mean skin temperature, 3) it has reduced sensitivity to the variability in soil thermal properties and moisture, 4) it does not requires snow depth, and 5) it is computationally effective. A global map of the necessary thermal inertia parameter is derived using reanalysis data as a function of soil type. These parameter estimates are comparable to values obtained from in situ observations. The new global GHF estimates are generally consistent with the reanalysis GHF output simulated using two-layer soil hydrology models. The authors argue that the new algorithm is more robust and trustworthy in regions where they differ. The proposed algorithm offers potential benefits for direct assimilation of observations of surface temperature as well as GHF into the reanalysis models at various time scales.
Abstract
Using satellite measurements in microwave bands to retrieve precipitation over land requires proper discrimination of the weak rainfall signals from strong and highly variable background Earth surface emissions. Traditionally, land retrieval methods rely on a weak signal of rainfall scattering on high-frequency channels and make use of empirical thresholding and regression-based techniques. Because of the increased surface signal interference, retrievals over radiometrically complex land surfaces—snow-covered lands, deserts, and coastal areas—are particularly challenging for this class of retrieval techniques. This paper evaluates the results by the recently proposed Shrunken Locally Linear Embedding Algorithm for Retrieval of Precipitation (ShARP) using data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The study focuses on a radiometrically complex region, partly covering the Tibetan highlands, Himalayas, and Ganges–Brahmaputra–Meghna River basins, which is unique in terms of its diverse land surface radiation regime and precipitation type, within the TRMM domain. Promising results are presented using ShARP over snow-covered land surfaces and in the vicinity of coastlines, in comparison with the land rainfall retrievals of the standard TRMM 2A12, version 7, product. The results show that ShARP can significantly reduce the rainfall overestimation due to the background snow contamination and markedly improve detection and retrieval of rainfall in the vicinity of coastlines. During the calendar year 2013, compared to TRMM 2A25, it is demonstrated that over the study domain the root-mean-square difference can be reduced up to 38% annually, while the improvement can reach up to 70% during the cold months of the year.
Abstract
Using satellite measurements in microwave bands to retrieve precipitation over land requires proper discrimination of the weak rainfall signals from strong and highly variable background Earth surface emissions. Traditionally, land retrieval methods rely on a weak signal of rainfall scattering on high-frequency channels and make use of empirical thresholding and regression-based techniques. Because of the increased surface signal interference, retrievals over radiometrically complex land surfaces—snow-covered lands, deserts, and coastal areas—are particularly challenging for this class of retrieval techniques. This paper evaluates the results by the recently proposed Shrunken Locally Linear Embedding Algorithm for Retrieval of Precipitation (ShARP) using data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The study focuses on a radiometrically complex region, partly covering the Tibetan highlands, Himalayas, and Ganges–Brahmaputra–Meghna River basins, which is unique in terms of its diverse land surface radiation regime and precipitation type, within the TRMM domain. Promising results are presented using ShARP over snow-covered land surfaces and in the vicinity of coastlines, in comparison with the land rainfall retrievals of the standard TRMM 2A12, version 7, product. The results show that ShARP can significantly reduce the rainfall overestimation due to the background snow contamination and markedly improve detection and retrieval of rainfall in the vicinity of coastlines. During the calendar year 2013, compared to TRMM 2A25, it is demonstrated that over the study domain the root-mean-square difference can be reduced up to 38% annually, while the improvement can reach up to 70% during the cold months of the year.
Abstract
Hydrological applications rely on the availability and quality of precipitation products, especially model- and satellite-based products for use in areas without ground measurements. It is known that the quality of model- and satellite-based precipitation products is complementary: model-based products exhibit high quality during cold seasons while satellite-based products are better during warm seasons. To explore the complementary behavior of the quality of the precipitation products, this study uses 2-m air temperature as auxiliary information to evaluate high-resolution (0.1°/hourly) precipitation estimates from the Weather Research and Forecasting (WRF) Model and from the version 5 Integrated Multisatellite Retrievals for GPM (IMERG) algorithm (i.e., early and final runs). The products are evaluated relative to the reference NCEP Stage IV precipitation estimates over the central United States during August 2015–July 2017. Results show that the IMERG final-run estimates are nearly unbiased, while the IMERG early-run and the WRF estimates are positively biased. The WRF estimates exhibit high correlations with the reference data when the temperature falls below 280 K. The IMERG estimates, both early and final runs, do so when the temperature exceeds 280 K. Moreover, the complementary behavior of the WRF and the IMERG products conditioned on air temperature does not vary with either season or location.
Abstract
Hydrological applications rely on the availability and quality of precipitation products, especially model- and satellite-based products for use in areas without ground measurements. It is known that the quality of model- and satellite-based precipitation products is complementary: model-based products exhibit high quality during cold seasons while satellite-based products are better during warm seasons. To explore the complementary behavior of the quality of the precipitation products, this study uses 2-m air temperature as auxiliary information to evaluate high-resolution (0.1°/hourly) precipitation estimates from the Weather Research and Forecasting (WRF) Model and from the version 5 Integrated Multisatellite Retrievals for GPM (IMERG) algorithm (i.e., early and final runs). The products are evaluated relative to the reference NCEP Stage IV precipitation estimates over the central United States during August 2015–July 2017. Results show that the IMERG final-run estimates are nearly unbiased, while the IMERG early-run and the WRF estimates are positively biased. The WRF estimates exhibit high correlations with the reference data when the temperature falls below 280 K. The IMERG estimates, both early and final runs, do so when the temperature exceeds 280 K. Moreover, the complementary behavior of the WRF and the IMERG products conditioned on air temperature does not vary with either season or location.
Abstract
Mesoscale circulations forced by a random distribution of surface sensible heat flux have been investigated using a three-dimensional numerical model. The complex land surface is modeled as a homogeneous random field characterized by a spectral distribution. Standard deviation and length scale of the sensible heat flux at the surface have been identified as the important parameters that describe the thermal variability of land surface. The form of the covariance of the random surface forcing is not critical in driving the mesoscale circulation. The thermally induced mesoscale circulation is significant and extends up to about 5 km when the atmosphere is neutral. It becomes weak and is suppressed when the atmosphere is stable. The mesoscale momentum flux is much stronger than the corresponding turbulent momentum flux in the neutral atmosphere, while the two are comparable in the stable atmosphere. The mesoscale heat flux has a different vertical profile than turbulent heat flux and may provide a major heat transport mechanism beyond the planetary boundary layer. The impact of synoptic wind on the mesoscale circulations is relatively weak. Nonlinear advection terms are responsible for momentum flux in the absence of synoptic wind.
Abstract
Mesoscale circulations forced by a random distribution of surface sensible heat flux have been investigated using a three-dimensional numerical model. The complex land surface is modeled as a homogeneous random field characterized by a spectral distribution. Standard deviation and length scale of the sensible heat flux at the surface have been identified as the important parameters that describe the thermal variability of land surface. The form of the covariance of the random surface forcing is not critical in driving the mesoscale circulation. The thermally induced mesoscale circulation is significant and extends up to about 5 km when the atmosphere is neutral. It becomes weak and is suppressed when the atmosphere is stable. The mesoscale momentum flux is much stronger than the corresponding turbulent momentum flux in the neutral atmosphere, while the two are comparable in the stable atmosphere. The mesoscale heat flux has a different vertical profile than turbulent heat flux and may provide a major heat transport mechanism beyond the planetary boundary layer. The impact of synoptic wind on the mesoscale circulations is relatively weak. Nonlinear advection terms are responsible for momentum flux in the absence of synoptic wind.
Abstract
A general framework has been developed to study the predictability of space–time averages of mesoscale rainfall in the tropics. A comparative ratio between the natural variability of the rainfall process and the prediction error is used to define the predictability range. The predictability of the spatial distribution of precipitation is quantified by the cross correlation between the control and the perturbed rainfall fields. An upper limit of prediction error, called normalized variability, has been derived as a function of space–time averaging. Irrespective of the type and amplitude of perturbations, a space–time averaging set of 25 km2–15 min (or larger time averaging) is found to be necessary to limit the error growth up to or below the prescribed large-scale mean rainfall.
Abstract
A general framework has been developed to study the predictability of space–time averages of mesoscale rainfall in the tropics. A comparative ratio between the natural variability of the rainfall process and the prediction error is used to define the predictability range. The predictability of the spatial distribution of precipitation is quantified by the cross correlation between the control and the perturbed rainfall fields. An upper limit of prediction error, called normalized variability, has been derived as a function of space–time averaging. Irrespective of the type and amplitude of perturbations, a space–time averaging set of 25 km2–15 min (or larger time averaging) is found to be necessary to limit the error growth up to or below the prescribed large-scale mean rainfall.
Abstract
The existence of analytical solutions for two-dimensional nonlinear semigeostrophic models of moist frontogenesis is investigated. Two different schemes for the modeling of stratiform cloud thermodynamics are taken into account, one based on the assumption of an everywhere cloudy environment, while in the other the atmosphere is considered to be exactly saturated and only condensation effects are relevant. In the first case, an exact analytical solution is derived for arbitrary boundary conditions, which satisfies the requirements for the validation of the semigeostrophic approximation even when the atmosphere is conditionally unstable with respect to slantwise convection. The growth of symmetric instabilities with no short-wave cutoff is predicted for this conditionally unstable atmosphere, even under the approximations of the semigeostrophic theory. An analogous, but approximate, analytical solution is then proposed for the second case. The errors introduced by the approximation are, however, not bigger than the terms neglected in the semigeostrophic approximation itself.
Abstract
The existence of analytical solutions for two-dimensional nonlinear semigeostrophic models of moist frontogenesis is investigated. Two different schemes for the modeling of stratiform cloud thermodynamics are taken into account, one based on the assumption of an everywhere cloudy environment, while in the other the atmosphere is considered to be exactly saturated and only condensation effects are relevant. In the first case, an exact analytical solution is derived for arbitrary boundary conditions, which satisfies the requirements for the validation of the semigeostrophic approximation even when the atmosphere is conditionally unstable with respect to slantwise convection. The growth of symmetric instabilities with no short-wave cutoff is predicted for this conditionally unstable atmosphere, even under the approximations of the semigeostrophic theory. An analogous, but approximate, analytical solution is then proposed for the second case. The errors introduced by the approximation are, however, not bigger than the terms neglected in the semigeostrophic approximation itself.
