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- Author or Editor: Rogier van der Velde x
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Abstract
This study assesses the impact of assimilating satellite-observed snow albedo on the Noah land surface model (LSM)-simulated fluxes and snow properties. A direct insertion technique is developed to assimilate snow albedo into Noah and is applied to three intensive study areas in North Park (Colorado) that are part of the 2002/03 Cold Land Processes Field Experiment (CLPX). The assimilated snow albedo products are 1) the standard Moderate Resolution Imaging Spectrometer (MODIS) product (MOD10A1) and 2) retrievals from MODIS observations with the recently developed Pattern-Based Semiempirical (PASS) approach. The performance of the Noah simulations, with and without assimilation, is evaluated using the in situ measurements of snow albedo, upward shortwave radiation, and snow depth. The results show that simulations with albedo assimilation agree better with the measurements. However, because of the limited impact of snow albedo updates after subsequent snowfall, the mean (or seasonal) error statistics decrease significantly for only two of the three CLPX sites. Though the simulated snow depth and duration for the snow season benefit from the assimilation, the greatest improvements are found in the simulated upward shortwave radiation, with root mean squared errors reduced by about 30%. As such, this study demonstrates that assimilation of satellite-observed snow albedo can improve LSM simulations, which may positively affect the representation of hydrological and surface energy budget processes in runoff and numerical weather prediction models.
Abstract
This study assesses the impact of assimilating satellite-observed snow albedo on the Noah land surface model (LSM)-simulated fluxes and snow properties. A direct insertion technique is developed to assimilate snow albedo into Noah and is applied to three intensive study areas in North Park (Colorado) that are part of the 2002/03 Cold Land Processes Field Experiment (CLPX). The assimilated snow albedo products are 1) the standard Moderate Resolution Imaging Spectrometer (MODIS) product (MOD10A1) and 2) retrievals from MODIS observations with the recently developed Pattern-Based Semiempirical (PASS) approach. The performance of the Noah simulations, with and without assimilation, is evaluated using the in situ measurements of snow albedo, upward shortwave radiation, and snow depth. The results show that simulations with albedo assimilation agree better with the measurements. However, because of the limited impact of snow albedo updates after subsequent snowfall, the mean (or seasonal) error statistics decrease significantly for only two of the three CLPX sites. Though the simulated snow depth and duration for the snow season benefit from the assimilation, the greatest improvements are found in the simulated upward shortwave radiation, with root mean squared errors reduced by about 30%. As such, this study demonstrates that assimilation of satellite-observed snow albedo can improve LSM simulations, which may positively affect the representation of hydrological and surface energy budget processes in runoff and numerical weather prediction models.
Abstract
Understanding the sources of uncertainty that cause deviations between simulated and satellite-observed states can facilitate optimal usage of these products via data assimilation or calibration techniques. A method is presented for separating uncertainties following from (i) scale differences between model grid and satellite footprint, (ii) residuals inherent to imperfect model and retrieval applications, and (iii) biases in the climatologies of simulations and retrievals. The method is applied to coarse (10 km) soil moisture simulations by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)–Noah regional climate model and 2.5 years of high-resolution (100 m) retrievals from the Advanced Synthetic Aperture Radar (ASAR) data collected over central Tibet. Suppression of the bias is performed via cumulative distribution function (CDF) matching. The other deviations are separated by taking the variance of the ASAR soil moisture at the coarse MM5 model grid as measure for the deviations caused by scale differences. Via decomposition of the uncertainty sources it is shown that the bias and the spatial-scale difference explain the majority (>70%) of the deviations between the two products, whereas the contribution of model–observation residuals is less than 30% on a monthly basis. Consequently, this study demonstrates that accounting for uncertainties caused by bias as well as spatial-scale difference is imperative for meaningful assimilation of high-resolution soil moisture products. On the other hand, the large uncertainties following from spatial-scale differences suggests that high-resolution soil moisture products have a potential of providing observation-based input for the subgrid spatial variability parameterizations within large-scale models.
Abstract
Understanding the sources of uncertainty that cause deviations between simulated and satellite-observed states can facilitate optimal usage of these products via data assimilation or calibration techniques. A method is presented for separating uncertainties following from (i) scale differences between model grid and satellite footprint, (ii) residuals inherent to imperfect model and retrieval applications, and (iii) biases in the climatologies of simulations and retrievals. The method is applied to coarse (10 km) soil moisture simulations by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)–Noah regional climate model and 2.5 years of high-resolution (100 m) retrievals from the Advanced Synthetic Aperture Radar (ASAR) data collected over central Tibet. Suppression of the bias is performed via cumulative distribution function (CDF) matching. The other deviations are separated by taking the variance of the ASAR soil moisture at the coarse MM5 model grid as measure for the deviations caused by scale differences. Via decomposition of the uncertainty sources it is shown that the bias and the spatial-scale difference explain the majority (>70%) of the deviations between the two products, whereas the contribution of model–observation residuals is less than 30% on a monthly basis. Consequently, this study demonstrates that accounting for uncertainties caused by bias as well as spatial-scale difference is imperative for meaningful assimilation of high-resolution soil moisture products. On the other hand, the large uncertainties following from spatial-scale differences suggests that high-resolution soil moisture products have a potential of providing observation-based input for the subgrid spatial variability parameterizations within large-scale models.
ABSTRACT
Current land surface models still have difficulties with producing reliable surface heat fluxes and skin temperature (T sfc) estimates for high-altitude regions, which may be addressed via adequate parameterization of the roughness lengths for momentum (z 0m) and heat (z 0h) transfer. In this study, the performance of various z 0h and z 0m schemes developed for the Noah land surface model is assessed for a high-altitude site (3430 m) on the northeastern part of the Tibetan Plateau. Based on the in situ surface heat fluxes and profile measurements of wind and temperature, monthly variations of z 0m and diurnal variations of z 0h are derived through application of the Monin–Obukhov similarity theory. These derived values together with the measured heat fluxes are utilized to assess the performance of those z 0m and z 0h schemes for different seasons. The analyses show that the z 0m dynamics are related to vegetation dynamics and soil water freeze–thaw state, which are reproduced satisfactorily with current z 0m schemes. Further, it is demonstrated that the heat flux simulations are very sensitive to the diurnal variations of z 0h. The newly developed z 0h schemes all capture, at least over the sparse vegetated surfaces during the winter season, the observed diurnal variability much better than the original one. It should, however, be noted that for the dense vegetated surfaces during the spring and monsoon seasons, not all newly developed schemes perform consistently better than the original one. With the most promising schemes, the Noah simulated sensible heat flux, latent heat flux, T sfc, and soil temperature improved for the monsoon season by about 29%, 79%, 75%, and 81%, respectively. In addition, the impact of T sfc calculation and energy balance closure associated with measurement uncertainties on the above findings are discussed, and the selection of the appropriate z 0h scheme for applications is addressed.
ABSTRACT
Current land surface models still have difficulties with producing reliable surface heat fluxes and skin temperature (T sfc) estimates for high-altitude regions, which may be addressed via adequate parameterization of the roughness lengths for momentum (z 0m) and heat (z 0h) transfer. In this study, the performance of various z 0h and z 0m schemes developed for the Noah land surface model is assessed for a high-altitude site (3430 m) on the northeastern part of the Tibetan Plateau. Based on the in situ surface heat fluxes and profile measurements of wind and temperature, monthly variations of z 0m and diurnal variations of z 0h are derived through application of the Monin–Obukhov similarity theory. These derived values together with the measured heat fluxes are utilized to assess the performance of those z 0m and z 0h schemes for different seasons. The analyses show that the z 0m dynamics are related to vegetation dynamics and soil water freeze–thaw state, which are reproduced satisfactorily with current z 0m schemes. Further, it is demonstrated that the heat flux simulations are very sensitive to the diurnal variations of z 0h. The newly developed z 0h schemes all capture, at least over the sparse vegetated surfaces during the winter season, the observed diurnal variability much better than the original one. It should, however, be noted that for the dense vegetated surfaces during the spring and monsoon seasons, not all newly developed schemes perform consistently better than the original one. With the most promising schemes, the Noah simulated sensible heat flux, latent heat flux, T sfc, and soil temperature improved for the monsoon season by about 29%, 79%, 75%, and 81%, respectively. In addition, the impact of T sfc calculation and energy balance closure associated with measurement uncertainties on the above findings are discussed, and the selection of the appropriate z 0h scheme for applications is addressed.
Abstract
This study evaluates the Noah land surface model (LSM) in its ability to simulate water and heat exchanges over frozen ground in a Tibetan meadow ecosystem. A comprehensive dataset including in situ micrometeorological and soil moisture–temperature profile measurements collected between November and March is utilized, and analyses of the measurements reveal that the measured soil freezing characteristics are better captured by 1) modifying the parameter b l implemented in the current Noah LSM that constrains the shape parameter of soil water retention curve utilized by the water potential freezing point depression equation to produce appropriate liquid water content θ liq under subzero temperature conditions and 2) neglecting the ice effect on soil-specific surface and thus matric potential via setting the empirical parameter that accounts for the effect of increase in specific surface of soil particles and ice–liquid water c k to zero. The numerical experiments performed with the Noah model run show that in comparison to the default Noah LSM, adoption of c k = 0 and site-specific b l values reduces the overestimation of θ liq across the soil profile. Implementation of augmentations such as the parameterization of diurnally varying thermal roughness length resolves the overestimation of daytime turbulent heat fluxes and underestimation of surface temperature. Further adoption of a new heat conductivity parameterization reduces the overestimation of nighttime surface temperature. An appropriate treatment of phase change efficiency that accounts for changing freezing rate with varying liquid water contents is also needed to reduce the temperature underestimation across soil profiles.
Abstract
This study evaluates the Noah land surface model (LSM) in its ability to simulate water and heat exchanges over frozen ground in a Tibetan meadow ecosystem. A comprehensive dataset including in situ micrometeorological and soil moisture–temperature profile measurements collected between November and March is utilized, and analyses of the measurements reveal that the measured soil freezing characteristics are better captured by 1) modifying the parameter b l implemented in the current Noah LSM that constrains the shape parameter of soil water retention curve utilized by the water potential freezing point depression equation to produce appropriate liquid water content θ liq under subzero temperature conditions and 2) neglecting the ice effect on soil-specific surface and thus matric potential via setting the empirical parameter that accounts for the effect of increase in specific surface of soil particles and ice–liquid water c k to zero. The numerical experiments performed with the Noah model run show that in comparison to the default Noah LSM, adoption of c k = 0 and site-specific b l values reduces the overestimation of θ liq across the soil profile. Implementation of augmentations such as the parameterization of diurnally varying thermal roughness length resolves the overestimation of daytime turbulent heat fluxes and underestimation of surface temperature. Further adoption of a new heat conductivity parameterization reduces the overestimation of nighttime surface temperature. An appropriate treatment of phase change efficiency that accounts for changing freezing rate with varying liquid water contents is also needed to reduce the temperature underestimation across soil profiles.
Abstract
This is the first part of a study focusing on evaluating the performance of the Noah land surface model (LSM) in simulating surface water and energy budgets for the high-elevation source region of the Yellow River (SRYR). A comprehensive dataset is utilized that includes in situ micrometeorological and profile soil moisture and temperature measurements as well as laboratory soil property measurements of samples collected across the SRYR. Here, the simulation of soil water flow is investigated, while Part II concentrates on the surface heat flux and soil temperature simulations. Three augmentations are proposed: 1) to include the effect of organic matter on soil hydraulic parameterization via the additivity hypothesis, 2) to implement the saturated hydraulic conductivity 
Abstract
This is the first part of a study focusing on evaluating the performance of the Noah land surface model (LSM) in simulating surface water and energy budgets for the high-elevation source region of the Yellow River (SRYR). A comprehensive dataset is utilized that includes in situ micrometeorological and profile soil moisture and temperature measurements as well as laboratory soil property measurements of samples collected across the SRYR. Here, the simulation of soil water flow is investigated, while Part II concentrates on the surface heat flux and soil temperature simulations. Three augmentations are proposed: 1) to include the effect of organic matter on soil hydraulic parameterization via the additivity hypothesis, 2) to implement the saturated hydraulic conductivity
Abstract
This is the second part of a study on the assessment of the Noah land surface model (LSM) in simulating surface water and energy budgets in the high-elevation source region of the Yellow River. Here, there is a focus on turbulent heat fluxes and heat transport through the soil column during the monsoon season, whereas the first part of this study deals with the soil water flow. Four augmentations are studied for mitigating the overestimation of turbulent heat flux and underestimation of soil temperature measurements: 1) the muting effect of vegetation on the thermal heat conductivity 
Abstract
This is the second part of a study on the assessment of the Noah land surface model (LSM) in simulating surface water and energy budgets in the high-elevation source region of the Yellow River. Here, there is a focus on turbulent heat fluxes and heat transport through the soil column during the monsoon season, whereas the first part of this study deals with the soil water flow. Four augmentations are studied for mitigating the overestimation of turbulent heat flux and underestimation of soil temperature measurements: 1) the muting effect of vegetation on the thermal heat conductivity
Abstract
Current land surface models (LSMs) tend to largely underestimate the daytime land surface temperature 
Abstract
Current land surface models (LSMs) tend to largely underestimate the daytime land surface temperature
Abstract
The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.
Abstract
The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.
