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- Author or Editor: Wade T. Crow x
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Abstract
Rain/no-rain detection error is a key source of uncertainty in regional and global precipitation products that propagates into offline hydrological and land surface modeling simulations. Such detection error is difficult to evaluate and/or filter without access to high-quality reference precipitation datasets. For cases where such access is not available, this study proposes a novel approach for improved rain/no-rain detection. Based on categorical triple collocation (CTC) and a probabilistic framework, a weighted merging algorithm (CTC-M) is developed to combine noisy, but independent, precipitation products into an optimal binary rain/no-rain time series. Compared with commonly used approaches that directly apply the best parent product for rain/no-rain detection, the superiority of CTC-M is demonstrated analytically and numerically using spatially dense precipitation measurements over Europe. Our analysis also suggests that CTC-M is tolerant to a range of cross-correlated rain/no-rain detection errors and detection biases of the parent products. As a result, CTC-M will benefit global precipitation estimation by improving the representation of precipitation occurrence in gauge-based and multisource merged precipitation products.
Abstract
Rain/no-rain detection error is a key source of uncertainty in regional and global precipitation products that propagates into offline hydrological and land surface modeling simulations. Such detection error is difficult to evaluate and/or filter without access to high-quality reference precipitation datasets. For cases where such access is not available, this study proposes a novel approach for improved rain/no-rain detection. Based on categorical triple collocation (CTC) and a probabilistic framework, a weighted merging algorithm (CTC-M) is developed to combine noisy, but independent, precipitation products into an optimal binary rain/no-rain time series. Compared with commonly used approaches that directly apply the best parent product for rain/no-rain detection, the superiority of CTC-M is demonstrated analytically and numerically using spatially dense precipitation measurements over Europe. Our analysis also suggests that CTC-M is tolerant to a range of cross-correlated rain/no-rain detection errors and detection biases of the parent products. As a result, CTC-M will benefit global precipitation estimation by improving the representation of precipitation occurrence in gauge-based and multisource merged precipitation products.
Abstract
Due to recent advances in the development of land data assimilation systems (LDAS) and the availability of high-quality, satellite-based surface soil moisture (SSM) retrieval products, we now have unambiguous evidence that the assimilation of SSM retrievals, or their proxy, can improve the precision (i.e., correlation versus truth) of surface state estimates provided by a land surface model (LSM). However, this clarity does not yet extend to the estimation of LSM surface water fluxes that are key to hydrologic and numerical weather forecasting applications. Here, we hypothesize that a key obstacle to extrapolating realized improvements in water state precision into comparable improvements in water flux accuracy (i.e., mean absolute error) is the presence of water-state/water-flux coupling strength biases existing in LSMs. To test this hypothesis, we conduct a series of synthetic fraternal twin data assimilation experiments where realistic levels of state/flux coupling strength bias - involving both evapotranspiration and runoff - are systematically introduced into an assimilation LSM. Results show that the accuracy of the resulting water flux analysis is sharply reduced by the presence of such bias – even in cases where the precision of soil moisture state estimates (e.g., SSM) is improved. The re-scaling of SSM observations prior to their assimilation (i.e., the most common approach for addressing systematic differences between LSMs and assimilated observations) is not always a robust strategy for addressing these errors and can, in certain circumstances, degrade water flux accuracy. Overall, results underscore the critical need to assess, and correct for, LSM water-state/water-flux coupling strength biases during the operation of an LDAS.
Abstract
Due to recent advances in the development of land data assimilation systems (LDAS) and the availability of high-quality, satellite-based surface soil moisture (SSM) retrieval products, we now have unambiguous evidence that the assimilation of SSM retrievals, or their proxy, can improve the precision (i.e., correlation versus truth) of surface state estimates provided by a land surface model (LSM). However, this clarity does not yet extend to the estimation of LSM surface water fluxes that are key to hydrologic and numerical weather forecasting applications. Here, we hypothesize that a key obstacle to extrapolating realized improvements in water state precision into comparable improvements in water flux accuracy (i.e., mean absolute error) is the presence of water-state/water-flux coupling strength biases existing in LSMs. To test this hypothesis, we conduct a series of synthetic fraternal twin data assimilation experiments where realistic levels of state/flux coupling strength bias - involving both evapotranspiration and runoff - are systematically introduced into an assimilation LSM. Results show that the accuracy of the resulting water flux analysis is sharply reduced by the presence of such bias – even in cases where the precision of soil moisture state estimates (e.g., SSM) is improved. The re-scaling of SSM observations prior to their assimilation (i.e., the most common approach for addressing systematic differences between LSMs and assimilated observations) is not always a robust strategy for addressing these errors and can, in certain circumstances, degrade water flux accuracy. Overall, results underscore the critical need to assess, and correct for, LSM water-state/water-flux coupling strength biases during the operation of an LDAS.
Abstract
Relative to other geophysical variables, soil moisture (SM) estimates derived from land surface models (LSMs) and land data assimilation systems (LDAS) are difficult to transfer between platforms and applications. This difficulty stems from the highly model-dependent nature of LSM SM estimates and differences in the vertical support of discretized SM values. As a result, operational SM estimates generated by one LSM (or LDAS) cannot generally be directly applied to a hydrologic monitoring or forecast system designed around a second LSM. This lack of transferability is particularly problematic for LDAS applications, where the time, expertise, and computational resources required to generate an operational LDAS analysis cannot be practically duplicated for every LSM-specific application. Here, we develop a set of simple regression tools for translating SM estimates between LSMs and multiple LDAS analyses. Results demonstrate that simple multivariate linear regression—utilizing independent variables based on multilayer and temporally lagged SM estimates—can significantly improve upon baseline transformation approaches using direct percentile matching. The proposed regression approaches are effective for both the LSM-to-LSM and LDAS-to-LDAS transformation of multilayer SM percentiles. Application of this approach will expand the utility of existing, high-quality (but LSM-specific) operational sources of SM information like the NASA Soil Moisture Active Passive Level-4 Soil Moisture product.
Abstract
Relative to other geophysical variables, soil moisture (SM) estimates derived from land surface models (LSMs) and land data assimilation systems (LDAS) are difficult to transfer between platforms and applications. This difficulty stems from the highly model-dependent nature of LSM SM estimates and differences in the vertical support of discretized SM values. As a result, operational SM estimates generated by one LSM (or LDAS) cannot generally be directly applied to a hydrologic monitoring or forecast system designed around a second LSM. This lack of transferability is particularly problematic for LDAS applications, where the time, expertise, and computational resources required to generate an operational LDAS analysis cannot be practically duplicated for every LSM-specific application. Here, we develop a set of simple regression tools for translating SM estimates between LSMs and multiple LDAS analyses. Results demonstrate that simple multivariate linear regression—utilizing independent variables based on multilayer and temporally lagged SM estimates—can significantly improve upon baseline transformation approaches using direct percentile matching. The proposed regression approaches are effective for both the LSM-to-LSM and LDAS-to-LDAS transformation of multilayer SM percentiles. Application of this approach will expand the utility of existing, high-quality (but LSM-specific) operational sources of SM information like the NASA Soil Moisture Active Passive Level-4 Soil Moisture product.
Abstract
This study aims to identify the impact of vertical support on the information content of soil moisture (SM) for latent heat flux estimation. This objective is achieved via calculation of the mutual information (MI) content between multiple soil moisture variables (with different vertical supports) and current/future evaporative fraction (EF) using ground-based soil moisture and latent/sensible heat flux observations acquired from the AmeriFlux network within the contiguous United States. Through the intercomparison of MI results from different SM–EF pairs, the general value (for latent heat flux estimation) of superficial soil moisture observations 
Abstract
This study aims to identify the impact of vertical support on the information content of soil moisture (SM) for latent heat flux estimation. This objective is achieved via calculation of the mutual information (MI) content between multiple soil moisture variables (with different vertical supports) and current/future evaporative fraction (EF) using ground-based soil moisture and latent/sensible heat flux observations acquired from the AmeriFlux network within the contiguous United States. Through the intercomparison of MI results from different SM–EF pairs, the general value (for latent heat flux estimation) of superficial soil moisture observations
Abstract
The rapid development of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) precipitation product provides new opportunities for a wide range of Earth system and natural hazard applications. Spatiotemporal averaging is a common method for IMERG users to acquire suitable resolutions specific to their research or application purpose and has a direct impact on the overall quality of IMERG precipitation estimates. Here, three different IMERG, version 06 (V06), latency run products (i.e., early, late, and final) are assessed against a ground-based benchmark along a continuous series of spatiotemporal resolutions over the Huai River basin (HuaiRB) between June 2014 and May 2017. In general, IMERG products better capture the spatial pattern of precipitation, and demonstrate better reliability, in the southern portion of the HuaiRB relative to its northern region. Furthermore, the degradation of spatiotemporal resolution is associated with better rain/no-rain determination and the consistent improvement of rainfall product performance metrics. This improvement is more pronounced for IMERG products at fine spatiotemporal resolution. However, due to the presence of autocorrelated errors, the performance improvement associated with the degradation of spatiotemporal resolution is less than theoretical expectations assuming purely uncorrelated errors. Component analysis indicates that while both temporal and spatial aggregation do not mitigate temporally autocorrelated errors, temporal averaging can remove spatially autocorrelated error. Hence, temporal averaging is found to be more effective than spatial averaging for improving the quality of IMERG products. These results will inform users of the reliability of IMERG products at different spatiotemporal scales and assist in unifying former disparate validation assessments applied at different scales within the literature.
Abstract
The rapid development of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) precipitation product provides new opportunities for a wide range of Earth system and natural hazard applications. Spatiotemporal averaging is a common method for IMERG users to acquire suitable resolutions specific to their research or application purpose and has a direct impact on the overall quality of IMERG precipitation estimates. Here, three different IMERG, version 06 (V06), latency run products (i.e., early, late, and final) are assessed against a ground-based benchmark along a continuous series of spatiotemporal resolutions over the Huai River basin (HuaiRB) between June 2014 and May 2017. In general, IMERG products better capture the spatial pattern of precipitation, and demonstrate better reliability, in the southern portion of the HuaiRB relative to its northern region. Furthermore, the degradation of spatiotemporal resolution is associated with better rain/no-rain determination and the consistent improvement of rainfall product performance metrics. This improvement is more pronounced for IMERG products at fine spatiotemporal resolution. However, due to the presence of autocorrelated errors, the performance improvement associated with the degradation of spatiotemporal resolution is less than theoretical expectations assuming purely uncorrelated errors. Component analysis indicates that while both temporal and spatial aggregation do not mitigate temporally autocorrelated errors, temporal averaging can remove spatially autocorrelated error. Hence, temporal averaging is found to be more effective than spatial averaging for improving the quality of IMERG products. These results will inform users of the reliability of IMERG products at different spatiotemporal scales and assist in unifying former disparate validation assessments applied at different scales within the literature.
Abstract
Despite considerable interest in the application of land surface data assimilation systems (LDASs) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, this paper evaluates an LDAS for agricultural drought monitoring by benchmarking individual components of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model, and a sequential data assimilation filter) against a series of linear models that perform the same function (i.e., have the same basic input/output structure) as the full system component. Benchmarking is based on the calculation of the lagged rank cross correlation between the normalized difference vegetation index (NDVI) and soil moisture estimates acquired for various components of the system. Lagged soil moisture/NDVI correlations obtained using individual LDAS components versus their linear analogs reveal the degree to which nonlinearities and/or complexities contained within each component actually contribute to the performance of the LDAS system as a whole. Here, a particular system based on surface soil moisture retrievals from the Land Parameter Retrieval Model (LPRM), a two-layer Palmer soil water balance model, and an ensemble Kalman filter (EnKF) is benchmarked. Results suggest significant room for improvement in each component of the system.
Abstract
Despite considerable interest in the application of land surface data assimilation systems (LDASs) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, this paper evaluates an LDAS for agricultural drought monitoring by benchmarking individual components of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model, and a sequential data assimilation filter) against a series of linear models that perform the same function (i.e., have the same basic input/output structure) as the full system component. Benchmarking is based on the calculation of the lagged rank cross correlation between the normalized difference vegetation index (NDVI) and soil moisture estimates acquired for various components of the system. Lagged soil moisture/NDVI correlations obtained using individual LDAS components versus their linear analogs reveal the degree to which nonlinearities and/or complexities contained within each component actually contribute to the performance of the LDAS system as a whole. Here, a particular system based on surface soil moisture retrievals from the Land Parameter Retrieval Model (LPRM), a two-layer Palmer soil water balance model, and an ensemble Kalman filter (EnKF) is benchmarked. Results suggest significant room for improvement in each component of the system.
Abstract
The validation of satellite surface soil moisture products requires comparisons between point-scale ground observations and footprint-scale (>100 km2) retrievals. In regions containing a limited number of measurement sites per footprint, some of the observed difference between the retrievals and ground observations is attributable to spatial sampling error and not the intrinsic error of the satellite retrievals themselves. Here, a triple collocation (TC) approach is applied to footprint-scale soil moisture products acquired from passive microwave remote sensing, land surface modeling, and a single ground-based station with the goal of the estimating (and correcting for) spatial sampling error in footprint-scale soil moisture estimates derived from the ground station. Using these three soil moisture products, the TC approach is shown to estimate point-to-footprint soil moisture sampling errors to within 0.0059 m3 m−3 and enhance the ability to validate satellite footprint-scale soil moisture products using existing low-density ground networks.
Abstract
The validation of satellite surface soil moisture products requires comparisons between point-scale ground observations and footprint-scale (>100 km2) retrievals. In regions containing a limited number of measurement sites per footprint, some of the observed difference between the retrievals and ground observations is attributable to spatial sampling error and not the intrinsic error of the satellite retrievals themselves. Here, a triple collocation (TC) approach is applied to footprint-scale soil moisture products acquired from passive microwave remote sensing, land surface modeling, and a single ground-based station with the goal of the estimating (and correcting for) spatial sampling error in footprint-scale soil moisture estimates derived from the ground station. Using these three soil moisture products, the TC approach is shown to estimate point-to-footprint soil moisture sampling errors to within 0.0059 m3 m−3 and enhance the ability to validate satellite footprint-scale soil moisture products using existing low-density ground networks.
Abstract
Hydrologic data assimilation has become an important tool for improving hydrologic model predictions by using observations from ground, aircraft, and satellite sensors. Among existing data assimilation methods, the ensemble Kalman filter (EnKF) provides a robust framework for optimally updating nonlinear model predictions using observations. In the EnKF, background prediction uncertainty is obtained using a Monte Carlo approach where state variables, parameters, and forcing data for the model are synthetically perturbed to explicitly simulate the error-prone representation of hydrologic processes in the model. However, it is shown here that, owing to the nonlinear nature of these processes, an ensemble of model forecasts perturbed by mean-zero Gaussian noise can produce biased background predictions. This ensemble perturbation bias in soil moisture states can lead to significant mass balance errors and degrade the performance of the EnKF analysis in deeper soil layers. Here, a simple method of bias correction is introduced in which such perturbation bias is corrected using an unperturbed model simulation run in parallel with the EnKF analysis. The proposed bias-correction scheme effectively removes biases in soil moisture and reduces soil water mass balance errors. The performance of the EnKF is improved in deeper layers when the filter is applied with the bias-correction scheme. The interplay of nonlinear hydrologic processes is discussed in the context of perturbation biases, and implications of the bias correction for real-data assimilation cases are presented.
Abstract
Hydrologic data assimilation has become an important tool for improving hydrologic model predictions by using observations from ground, aircraft, and satellite sensors. Among existing data assimilation methods, the ensemble Kalman filter (EnKF) provides a robust framework for optimally updating nonlinear model predictions using observations. In the EnKF, background prediction uncertainty is obtained using a Monte Carlo approach where state variables, parameters, and forcing data for the model are synthetically perturbed to explicitly simulate the error-prone representation of hydrologic processes in the model. However, it is shown here that, owing to the nonlinear nature of these processes, an ensemble of model forecasts perturbed by mean-zero Gaussian noise can produce biased background predictions. This ensemble perturbation bias in soil moisture states can lead to significant mass balance errors and degrade the performance of the EnKF analysis in deeper soil layers. Here, a simple method of bias correction is introduced in which such perturbation bias is corrected using an unperturbed model simulation run in parallel with the EnKF analysis. The proposed bias-correction scheme effectively removes biases in soil moisture and reduces soil water mass balance errors. The performance of the EnKF is improved in deeper layers when the filter is applied with the bias-correction scheme. The interplay of nonlinear hydrologic processes is discussed in the context of perturbation biases, and implications of the bias correction for real-data assimilation cases are presented.
Abstract
The treatment of aerodynamic surface temperature in soil–vegetation–atmosphere transfer (SVAT) models can be used to classify approaches into two broad categories. The first category contains models utilizing remote sensing (RS) observations of surface radiometric temperature to estimate aerodynamic surface temperature and solve the terrestrial energy balance. The second category contains combined water and energy balance (WEB) approaches that simultaneously solve for surface temperature and energy fluxes based on observations of incoming radiation, precipitation, and micrometeorological variables. To date, few studies have focused on cross comparing model predictions from each category. Land surface and remote sensing datasets collected during the 2002 Soil Moisture–Atmosphere Coupling Experiment (SMACEX) provide an opportunity to evaluate and intercompare spatially distributed surface energy balance models. Intercomparison results presented here focus on the ability of a WEB-SVAT approach [the TOPmodel-based Land–Atmosphere Transfer Scheme (TOPLATS)] and an RS-SVAT approach [the Two-Source Energy Balance (TSEB) model] to accurately predict patterns of turbulent energy fluxes observed during SMACEX. During the experiment, TOPLATS and TSEB latent heat flux predictions match flux tower observations with root-mean-square (rms) accuracies of 67 and 63 W m−2, respectively. TSEB predictions of sensible heat flux are significantly more accurate with an rms accuracy of 22 versus 46 W m−2 for TOPLATS. The intercomparison of flux predictions from each model suggests that modeling errors for each approach are sufficiently independent and that opportunities exist for improving the performance of both models via data assimilation and model calibration techniques that integrate RS- and WEB-SVAT energy flux predictions.
Abstract
The treatment of aerodynamic surface temperature in soil–vegetation–atmosphere transfer (SVAT) models can be used to classify approaches into two broad categories. The first category contains models utilizing remote sensing (RS) observations of surface radiometric temperature to estimate aerodynamic surface temperature and solve the terrestrial energy balance. The second category contains combined water and energy balance (WEB) approaches that simultaneously solve for surface temperature and energy fluxes based on observations of incoming radiation, precipitation, and micrometeorological variables. To date, few studies have focused on cross comparing model predictions from each category. Land surface and remote sensing datasets collected during the 2002 Soil Moisture–Atmosphere Coupling Experiment (SMACEX) provide an opportunity to evaluate and intercompare spatially distributed surface energy balance models. Intercomparison results presented here focus on the ability of a WEB-SVAT approach [the TOPmodel-based Land–Atmosphere Transfer Scheme (TOPLATS)] and an RS-SVAT approach [the Two-Source Energy Balance (TSEB) model] to accurately predict patterns of turbulent energy fluxes observed during SMACEX. During the experiment, TOPLATS and TSEB latent heat flux predictions match flux tower observations with root-mean-square (rms) accuracies of 67 and 63 W m−2, respectively. TSEB predictions of sensible heat flux are significantly more accurate with an rms accuracy of 22 versus 46 W m−2 for TOPLATS. The intercomparison of flux predictions from each model suggests that modeling errors for each approach are sufficiently independent and that opportunities exist for improving the performance of both models via data assimilation and model calibration techniques that integrate RS- and WEB-SVAT energy flux predictions.
Abstract
Over land, remotely sensed surface soil moisture and rainfall accumulation retrievals contain complementary information that can be exploited for the mutual benefit of both product types. Here, a Kalman filtering–based tool is developed that utilizes a time series of spaceborne surface soil moisture retrievals to enhance short-term (2- to 10-day) satellite-based rainfall accumulation products. Using ground rain gauge data as a validation source, and a soil moisture product derived from the Advanced Microwave Scanning Radiometer aboard the NASA Aqua satellite, the approach is evaluated over the contiguous United States. Results demonstrate that, for areas of low to moderate vegetation cover density, the procedure is capable of improving short-term rainfall accumulation estimates extracted from a variety of satellite-based rainfall products. The approach is especially effective for correcting rainfall accumulation estimates derived without the aid of ground-based rain gauge observations. Special emphasis is placed on demonstrating that the approach can be applied in continental areas lacking ground-based observations and/or long-term satellite data records.
Abstract
Over land, remotely sensed surface soil moisture and rainfall accumulation retrievals contain complementary information that can be exploited for the mutual benefit of both product types. Here, a Kalman filtering–based tool is developed that utilizes a time series of spaceborne surface soil moisture retrievals to enhance short-term (2- to 10-day) satellite-based rainfall accumulation products. Using ground rain gauge data as a validation source, and a soil moisture product derived from the Advanced Microwave Scanning Radiometer aboard the NASA Aqua satellite, the approach is evaluated over the contiguous United States. Results demonstrate that, for areas of low to moderate vegetation cover density, the procedure is capable of improving short-term rainfall accumulation estimates extracted from a variety of satellite-based rainfall products. The approach is especially effective for correcting rainfall accumulation estimates derived without the aid of ground-based rain gauge observations. Special emphasis is placed on demonstrating that the approach can be applied in continental areas lacking ground-based observations and/or long-term satellite data records.
