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Abstract
The variability of crop and soil states due to uncertain climatic inputs and soil properties is quantified using a mathematical representation of the physiological, biochemical, hydrological, and physical processes related to plant growth. The components of the state-space model of the soil-crop-climate interactions are a plant growth, a moisture transport, and a solute transport model. A linear model for the perturbations of the state and the inputs around the nominal (first-order mean) values is derived. The linear model is used for second-moment uncertainty propagation due to fluctuations of the climatic forcing in time and due to the spatial variability of the soil properties. The most important climatic variables affecting crop production are identified in a case study. Correlation of climatic inputs between days is found to increase the crop yield variance. Significant variance reduction is found in transforming random soil properties to soil-state variables and then to plant-state variables.
Abstract
The variability of crop and soil states due to uncertain climatic inputs and soil properties is quantified using a mathematical representation of the physiological, biochemical, hydrological, and physical processes related to plant growth. The components of the state-space model of the soil-crop-climate interactions are a plant growth, a moisture transport, and a solute transport model. A linear model for the perturbations of the state and the inputs around the nominal (first-order mean) values is derived. The linear model is used for second-moment uncertainty propagation due to fluctuations of the climatic forcing in time and due to the spatial variability of the soil properties. The most important climatic variables affecting crop production are identified in a case study. Correlation of climatic inputs between days is found to increase the crop yield variance. Significant variance reduction is found in transforming random soil properties to soil-state variables and then to plant-state variables.
Abstract
Estimates of the net convergence of atmospheric moisture flux over the Amazon Basin, [C], derived using data products from three global reanalyses, the NCEP–NCAR reanalysis (NCEP-1), the NCEP/Department of Energy reanalysis (NCEP-2), and the 40-yr ECMWF Re-Analysis (ERA-40), are compared. Two types of uncertainty in these [C] estimates are distinguished and quantified: “model-associated uncertainty,” which necessarily arises from imperfections in the numerical weather models or data assimilation algorithms, and “postprocessing uncertainty” introduced by operations performed on the original reanalysis data products to compute [C], particularly the finite-difference approximation of divergence. Model-associated uncertainty is found to overwhelm the postprocessing error. A closer look at the time series of this field extending over the period 1980–2001, and their comparison to basin-averaged precipitation and runoff data, reveals the signatures of two potential sources of model-associated errors. 1) ERA-40 estimates of [C] exhibit an artificial shift in 1987, possibly produced by the start of assimilation of Special Sensor Microwave Imager (SSM/I) data. The estimates preceding 1988 are negatively biased relative to the remaining time series, and hence subsequent analysis is limited to the 14-yr period 1988–2001. 2) NCEP-1 and NCEP-2 estimates of [C] show a negative bias over the period 1992–98, which likely originates in biased Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) data assimilated by these reanalyses. A measure of the random error in the [C] time series produced by each reanalysis, computed using river discharge data as reference, indicates that ERA-40 gives the most accurate estimates of net atmospheric moisture flux convergence for the aforementioned 14-yr period.
Abstract
Estimates of the net convergence of atmospheric moisture flux over the Amazon Basin, [C], derived using data products from three global reanalyses, the NCEP–NCAR reanalysis (NCEP-1), the NCEP/Department of Energy reanalysis (NCEP-2), and the 40-yr ECMWF Re-Analysis (ERA-40), are compared. Two types of uncertainty in these [C] estimates are distinguished and quantified: “model-associated uncertainty,” which necessarily arises from imperfections in the numerical weather models or data assimilation algorithms, and “postprocessing uncertainty” introduced by operations performed on the original reanalysis data products to compute [C], particularly the finite-difference approximation of divergence. Model-associated uncertainty is found to overwhelm the postprocessing error. A closer look at the time series of this field extending over the period 1980–2001, and their comparison to basin-averaged precipitation and runoff data, reveals the signatures of two potential sources of model-associated errors. 1) ERA-40 estimates of [C] exhibit an artificial shift in 1987, possibly produced by the start of assimilation of Special Sensor Microwave Imager (SSM/I) data. The estimates preceding 1988 are negatively biased relative to the remaining time series, and hence subsequent analysis is limited to the 14-yr period 1988–2001. 2) NCEP-1 and NCEP-2 estimates of [C] show a negative bias over the period 1992–98, which likely originates in biased Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) data assimilated by these reanalyses. A measure of the random error in the [C] time series produced by each reanalysis, computed using river discharge data as reference, indicates that ERA-40 gives the most accurate estimates of net atmospheric moisture flux convergence for the aforementioned 14-yr period.
Abstract
Spatially averaged evapotranspiration [ET] over the Amazon Basin is computed as the residual of the basin’s atmospheric water balance equation, at the monthly time scale and for the period 1988–2001. Basin-averaged rainfall [P] is obtained from the Global Precipitation Climatology Project (GPCP) dataset, and alternative estimates of the net convergence of atmospheric water vapor flux over the basin [C] are derived from three global reanalyses: the NCEP–NCAR and NCEP–Department of Energy (DOE) reanalyses and the 40-yr ECMWF Re-Analysis (ERA-40). Additionally, a best estimate of [C] is obtained by taking a weighted average of data from these three sources, in which the weight factors are based on the random error attributed to each reanalysis’ [C] estimates by comparison to river discharge data. The resulting time series is dominated by ERA-40’s contribution, which was found to be the most accurate over the study period. Data products from the three reanalyses are also employed to compute the monthly tendencies of total precipitable water over the basin. While the seasonal signature of this “accumulation term” provides important insight into the Amazon Basin’s hydrological cycle, its magnitude is found to be negligible relative to the other components of the water budget. The value of mean annual [ET] presented in this work is significantly lower than other published estimates that are based on simulations by various land surface models. Furthermore, when the best estimate of [C] is used, the resulting [ET] time series exhibits a seasonal cycle that is in phase with that of basin-averaged surface net radiation, suggesting that Amazonian evapotranspiration is prevalently limited by energy availability. In contrast, most land surface models, including that of the NCEP–NCAR reanalysis, simulate water-limited evapotranspiration in the Amazon Basin. The analysis presented here supports the hypothesis that most Amazonian trees sustain elevated evapotranspiration rates during the dry season through deep roots, which tap into large reservoirs of soil water that are replenished during the following wet season.
Abstract
Spatially averaged evapotranspiration [ET] over the Amazon Basin is computed as the residual of the basin’s atmospheric water balance equation, at the monthly time scale and for the period 1988–2001. Basin-averaged rainfall [P] is obtained from the Global Precipitation Climatology Project (GPCP) dataset, and alternative estimates of the net convergence of atmospheric water vapor flux over the basin [C] are derived from three global reanalyses: the NCEP–NCAR and NCEP–Department of Energy (DOE) reanalyses and the 40-yr ECMWF Re-Analysis (ERA-40). Additionally, a best estimate of [C] is obtained by taking a weighted average of data from these three sources, in which the weight factors are based on the random error attributed to each reanalysis’ [C] estimates by comparison to river discharge data. The resulting time series is dominated by ERA-40’s contribution, which was found to be the most accurate over the study period. Data products from the three reanalyses are also employed to compute the monthly tendencies of total precipitable water over the basin. While the seasonal signature of this “accumulation term” provides important insight into the Amazon Basin’s hydrological cycle, its magnitude is found to be negligible relative to the other components of the water budget. The value of mean annual [ET] presented in this work is significantly lower than other published estimates that are based on simulations by various land surface models. Furthermore, when the best estimate of [C] is used, the resulting [ET] time series exhibits a seasonal cycle that is in phase with that of basin-averaged surface net radiation, suggesting that Amazonian evapotranspiration is prevalently limited by energy availability. In contrast, most land surface models, including that of the NCEP–NCAR reanalysis, simulate water-limited evapotranspiration in the Amazon Basin. The analysis presented here supports the hypothesis that most Amazonian trees sustain elevated evapotranspiration rates during the dry season through deep roots, which tap into large reservoirs of soil water that are replenished during the following wet season.
Abstract
Climate studies and effective environmental management require unbiased climate datasets. This study develops a new bias correction approach using a three-layer feedforward neural network to reduce the biases of climate variables (temperature and precipitation) over northern South America. Air and skin temperature, specific humidity, and net longwave and shortwave radiation are used as inputs to the network for bias correction of 6-hourly temperature. Inputs to the network for bias correction of monthly precipitation are precipitation at lag 0, 1, 2, and 3 months, and also the standard deviation of precipitation from 3 × 3 neighbors around the pixel of interest. The climate model data are provided by the Community Climate System Model, version 3 (CCSM3). Results show that the trained artificial neural network (ANN) can improve the estimation error and correlation of the variables for both calibration and validation periods even when there is a low temporal consistency between the time series of the model data and targets. The developed model is also able to modify the probabilistic structure of the variables although the quantile-based information is not directly considered in the network. The ANN model outperforms linear regression, which is used for comparison purposes. The new method can be used to produce bias-corrected climate variables that can be used as forcing to hydrological and ecological models.
Abstract
Climate studies and effective environmental management require unbiased climate datasets. This study develops a new bias correction approach using a three-layer feedforward neural network to reduce the biases of climate variables (temperature and precipitation) over northern South America. Air and skin temperature, specific humidity, and net longwave and shortwave radiation are used as inputs to the network for bias correction of 6-hourly temperature. Inputs to the network for bias correction of monthly precipitation are precipitation at lag 0, 1, 2, and 3 months, and also the standard deviation of precipitation from 3 × 3 neighbors around the pixel of interest. The climate model data are provided by the Community Climate System Model, version 3 (CCSM3). Results show that the trained artificial neural network (ANN) can improve the estimation error and correlation of the variables for both calibration and validation periods even when there is a low temporal consistency between the time series of the model data and targets. The developed model is also able to modify the probabilistic structure of the variables although the quantile-based information is not directly considered in the network. The ANN model outperforms linear regression, which is used for comparison purposes. The new method can be used to produce bias-corrected climate variables that can be used as forcing to hydrological and ecological models.
Abstract
An extremum hypothesis of turbulent transport in the atmospheric surface layer is postulated. The hypothesis has led to a unique solution of Monin–Obukhov similarity equations in terms of simple expressions linking shear stress (momentum flux) and heat flux to mean wind shear and temperature gradient. The extremum solution is consistent with the well-known asymptotic properties of the surface layer. Validation of the extremum solution has been made by comparison to field measurements of momentum and heat fluxes. Furthermore, a modeling test of predicting surface heat fluxes using the results of this work is presented. A critical reexamination of the interpretation of the Obukhov length is given.
Abstract
An extremum hypothesis of turbulent transport in the atmospheric surface layer is postulated. The hypothesis has led to a unique solution of Monin–Obukhov similarity equations in terms of simple expressions linking shear stress (momentum flux) and heat flux to mean wind shear and temperature gradient. The extremum solution is consistent with the well-known asymptotic properties of the surface layer. Validation of the extremum solution has been made by comparison to field measurements of momentum and heat fluxes. Furthermore, a modeling test of predicting surface heat fluxes using the results of this work is presented. A critical reexamination of the interpretation of the Obukhov length is given.
Abstract
The benefits of short-term (1–6 h), distributed quantitative precipitation forecasts (DQPFs) are well known. However, this area is acknowledged to be one of the most challenging in hydrometeorology. Previous studies suggest that the “state of the art” methods can be enhanced by exploiting relevant information from radar and numerical weather prediction (NWP) models, using process physics and data-dictated tools where each fits best. Tests indicate that improved results are obtained by decomposing the overall problem into component processes, and that each process may require alternative tools ranging from simple interpolation to statistical time series models and artificial neural networks (ANNs). A new hybrid modeling strategy is proposed for DQPF that utilizes measurements from radar [Weather Surveillance Radar-1998 Doppler (WSR-88D) network: 4 km, 1 h] and outputs from NWP models (48-km Eta Model: 48 km, 6 h). The proposed strategy improves distributed QPF over existing methods like radar extrapolation or NWP-based QPF by themselves, as well as combinations of radar extrapolation and NWP-based QPF.
Abstract
The benefits of short-term (1–6 h), distributed quantitative precipitation forecasts (DQPFs) are well known. However, this area is acknowledged to be one of the most challenging in hydrometeorology. Previous studies suggest that the “state of the art” methods can be enhanced by exploiting relevant information from radar and numerical weather prediction (NWP) models, using process physics and data-dictated tools where each fits best. Tests indicate that improved results are obtained by decomposing the overall problem into component processes, and that each process may require alternative tools ranging from simple interpolation to statistical time series models and artificial neural networks (ANNs). A new hybrid modeling strategy is proposed for DQPF that utilizes measurements from radar [Weather Surveillance Radar-1998 Doppler (WSR-88D) network: 4 km, 1 h] and outputs from NWP models (48-km Eta Model: 48 km, 6 h). The proposed strategy improves distributed QPF over existing methods like radar extrapolation or NWP-based QPF by themselves, as well as combinations of radar extrapolation and NWP-based QPF.
Abstract
There have been numerous attempts to detect the presence of deterministic chaos by estimating the correlation dimension. The values of reported correlation dimension for various geophysical time series vary between 1.3 and virtually infinity (i.e., no saturation). It is pointed out that analyzing variables that depend on physical constraints and thresholds, like precipitation, may lead to underestimation of the correlation dimension of the underlying dynamical system.
Abstract
There have been numerous attempts to detect the presence of deterministic chaos by estimating the correlation dimension. The values of reported correlation dimension for various geophysical time series vary between 1.3 and virtually infinity (i.e., no saturation). It is pointed out that analyzing variables that depend on physical constraints and thresholds, like precipitation, may lead to underestimation of the correlation dimension of the underlying dynamical system.
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.
