Ally M. Toure

Assessment and Enhancement of MERRA Land Surface Hydrology Estimates

AbstractThe Modern-Era Retrospective Analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides, in addition to atmospheric fields, global estimates of soil moisture, latent heat flux, snow, and runoff for 1979–present. This study introduces a supplemental and improved set of land surface hydrological fields (“MERRA-Land”) generated by rerunning a revised version of the land component of the MERRA system. Specifically, the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameter values in the rainfall interception model, changes that effectively correct for known limitations in the MERRA surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ECMWF...

Water Balance in the Amazon Basin from a Land Surface Model Ensemble

AbstractDespite recent advances in land surface modeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-of-the-art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 1° spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to match monthly Global Precipitation Climatology Project (GPCP) and Global Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l’Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simu...

A Case Study of Using a Multilayered Thermodynamical Snow Model for Radiance Assimilation

A microwave radiance assimilation (RA) scheme for the retrieval of snow physical state variables requires a snowpack physical model (SM) coupled to a radiative transfer model. In order to assimilate microwave brightness temperatures (Tbs) at horizontal polarization (h-pol), an SM capable of resolving melt-refreeze crusts is required. To date, it has not been shown whether an RA scheme is tractable with the large number of state variables present in such an SM or whether melt-refreeze crust densities can be estimated. In this paper, an RA scheme is presented using the CROCUS SM which is capable of resolving melt-refreeze crusts. We assimilated both vertical (v) and horizontal (h) Tbs at 18.7 and 36.5 GHz. We found that assimilating Tb at both h-pol and vertical polarization (v-pol) into CROCUS dramatically improved snow depth estimates, with a bias of 1.4 cm compared to -7.3 cm reported by previous studies. Assimilation of both h-pol and v-pol led to more accurate results than assimilation of v-pol alone. The snow water equivalent (SWE) bias of the RA scheme was 0.4 cm, while the bias of the SWE estimated by an empirical retrieval algorithm was -2.9 cm. Characterization of melt-refreeze crusts via an RA scheme is demonstrated here for the first time; the RA scheme correctly identified the location of melt-refreeze crusts observed in situ.

Assimilation of MODIS snow cover through the Data Assimilation Research Testbed and the Community Land Model version 4

To improve snowpack estimates in Community Land Model version 4 (CLM4), the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) was assimilated into the Community Land Model version 4 (CLM4) via the Data Assimilation Research Testbed (DART). The interface between CLM4 and DART is a flexible, extensible approach to land surface data assimilation. This data assimilation system has a large ensemble (80-member) atmospheric forcing that facilitates ensemble-based land data assimilation. We use 40 randomly chosen forcing members to drive 40 CLM members as a compromise between computational cost and the data assimilation performance. The localization distance, a parameter in DART, was tuned to optimize the data assimilation performance at the global scale. Snow water equivalent (SWE) and snow depth are adjusted via the ensemble adjustment Kalman filter, particularly in regions with large SCF variability. The root-mean-square error of the forecast SCF against MODIS SCF is largely reduced. In DJF (December-January-February), the discrepancy between MODIS and CLM4 is broadly ameliorated in the lower-middle latitudes (23°–45°N). Only minimal modifications are made in the higher-middle (45°–66°N) and high latitudes, part of which is due to the agreement between model and observation when snow cover is nearly 100%. In some regions it also reveals that CLM4-modeled snow cover lacks heterogeneous features compared to MODIS. In MAM (March-April-May), adjustments to snow move poleward mainly due to the northward movement of the snowline (i.e., where largest SCF uncertainty is and SCF assimilation has the greatest impact). The effectiveness of data assimilation also varies with vegetation types, with mixed performance over forest regions and consistently good performance over grass, which can partly be explained by the linearity of the relationship between SCF and SWE in the model ensembles. The updated snow depth was compared to the Canadian Meteorological Center (CMC) data. Differences between CMC and CLM4 are generally reduced in densely monitored regions.

Evaluation of the Snow Simulations from the Community Land Model, Version 4 (CLM4)

AbstractThis paper evaluates the simulation of snow by the Community Land Model, version 4 (CLM4), the land model component of the Community Earth System Model, version 1.0.4 (CESM1.0.4). CLM4 was run in an offline mode forced with the corrected land-only replay of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-Land) and the output was evaluated for the period from January 2001 to January 2011 over the Northern Hemisphere poleward of 30°N. Simulated snow-cover fraction (SCF), snow depth, and snow water equivalent (SWE) were compared against a set of observations including the Moderate Resolution Imaging Spectroradiometer (MODIS) SCF, the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover, the Canadian Meteorological Centre (CMC) daily snow analysis products, snow depth from the National Weather Service Cooperative Observer (COOP) program, and Snowpack Telemetry (SNOTEL) SWE observations. CLM4 SCF was converted into snow-cover extent (SCE) to compare with MODIS...

Estimating Snow Water Storage in North America Using CLM4, DART, and Snow Radiance Data Assimilation

AbstractThis paper addresses continental-scale snow estimates in North America using a recently developed snow radiance assimilation (RA) system. A series of RA experiments with the ensemble adjustment Kalman filter are conducted by assimilating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature TB at 18.7- and 36.5-GHz vertical polarization channels. The overall RA performance in estimating snow depth for North America is improved by simultaneously updating the Community Land Model, version 4 (CLM4), snow/soil states and radiative transfer model (RTM) parameters involved in predicting TB based on their correlations with the prior TB (i.e., rule-based RA), although degradations are also observed. The RA system exhibits a more mixed performance for snow cover fraction estimates. Compared to the open-loop run (0.171 m RMSE), the overall snow depth estimates are improved by 1.6% (0.168 m RMSE) in the rule-based RA whereas the default RA (without a rule) resu...

Error Characterization of Coupled Land Surface-Radiative Transfer Models for Snow Microwave Radiance Assimilation

Snow microwave radiance assimilation (RA) or brightness temperature data assimilation (DA) has shown promise for improving snow water equivalent (SWE) estimation. A successful RA study requires, however, an analysis of the error characteristics of coupled land surface-radiative transfer models (LSM/RTMs). This paper focuses on the Community Land Model version 4 (CLM4) as the land-surface model and on the microwave emission model for layered snowpacks (MEMLS) and the dense media radiative transfer multilayer (DMRT-ML) model as RTMs. Using the National Aeronautics and Space Administration Cold Land Processes Field Experiment (CLPX) data sets and through synthetic experiments, the errors of the coupled CLM4/DMRT-ML and CLM4/MEMLS are characterized by: 1) evaluating the CLM4 snowpack state simulations; 2) assessing the performance of RTMs in simulating the brightness temperature (T_B); and 3) analyzing the correlations between the SWE error (ε_SWE) and the T_B error (ε_T_B) from the RA perspective. The results using the CLPX data sets show that, given a large error of the snow grain radius (ε_r_e) under dry snowpack conditions (along with a small error of the snow temperature (ε_T_snow)), the correlations between ε_SWE and ε_T_B are mainly determined by the relationship between ε_r_e and the snow depth error (ε_d_snow) or the snow density error (ε_ρ_snow). The synthetic experiments were carried out for the CLPX region (shallow snowpack conditions) and the Rocky Mountains (deep snowpack conditions) using the atmospheric ensemble reanalysis produced by the coupled DA Research Testbed/Community Atmospheric Model (CAM4). The synthetic experiments support the results from the CLPX data sets and show that the errors of soil (the water content and the temperature), snow wetness, and snow temperature mostly result in positive correlations between ε_SWE and ε_T_B. CLM4/DMRT-ML and CLM4/MEMLS tend to produce varying RA performance, with more positive and negative correlations between ε_SWE and ε_T_B, respectively. These results suggest the necessity of using multiple snowpack RTMs in RA to improve the SWE estimation at the continental scale. The results in this paper also show that the magnitude of ε_r_e and its relationship to ε_SWE are important for the RA performance. Most of the SWE estimations in RA are improved when ε_SWE and ε_r_e show a high positive correlation (greater than 0.5).

Improving the Radiance Assimilation Performance in Estimating Snow Water Storage across Snow and Land-Cover Types in North America

AbstractContinental-scale snow radiance assimilation (RA) experiments are conducted in order to improve snow estimates across snow and land-cover types in North America. In the experiments, the ensemble adjustment Kalman filter is applied and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature TB observations are assimilated into an RA system composed of the Community Land Model, version 4 (CLM4); radiative transfer models (RTMs); and the Data Assimilation Research Testbed (DART). The performance of two snowpack RTMs, the Dense Media Radiative Transfer–Multi-Layers model (DMRT-ML), and the Microwave Emission Model of Layered Snowpacks (MEMLS) in improving snow depth estimates through RA is compared. Continental-scale snow estimates are enhanced through RA by using AMSR-E TB at the 18.7- and 23.8-GHz channels [3% (DMRT-ML) and 2% (MEMLS) improvements compared to the cases using the 18.7- and 36.5-GHz channels] and by considering the vegetation single-scatte...

Assimilation of MODIS Snow Cover Fraction Observations into the NASA Catchment Land Surface Model

The NASA Catchment land surface model (CLSM) is the land model component used for the Modern-Era Retrospective Analysis for Research and Applications (MERRA). Here, the CLSM versions of MERRA and MERRA-Land are evaluated using snow cover fraction (SCF) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Moreover, a computationally-efficient empirical scheme is designed to improve CLSM estimates of SCF, snow depth, and snow water equivalent (SWE) through the assimilation of MODIS SCF observations. Results show that data assimilation (DA) improved SCF estimates compared to the open-loop model without assimilation (OL), especially in areas with ephemeral snow cover and mountainous regions. A comparison of the SCF estimates from DA against snow cover estimates from the NOAA Interactive Multisensor Snow and Ice Mapping System showed an improvement in the probability of detection of up to 28% and a reduction in false alarms by up to 6% (relative to OL). A comparison of the model snow depth estimates against Canadian Meteorological Centre analyses showed that DA successfully improved the model seasonal bias from −0.017 m for OL to −0.007 m for DA, although there was no significant change in root-mean-square differences (RMSD) (0.095 m for OL, 0.093 m for DA). The time-average of the spatial correlation coefficient also improved from 0.61 for OL to 0.63 for DA. A comparison against in situ SWE measurements also showed improvements from assimilation. The correlation increased from 0.44 for OL to 0.49 for DA, the bias improved from −0.111 m for OL to −0.100 m for DA, and the RMSD decreased from 0.186 m for OL to 0.180 m for DA.

Comparison of in situ and grace estimated groundwater in the Canadian prairies

Groundwater is an important component of the hydrological cycle. In Canada, more than 30% of the population relies on groundwater as the main source of water for domestic use. However, its measurement and monitoring remain challenging at large spatial scales. In this study, we examined the relationship between in situ groundwater data extracted from existing wells, and those derived from the Gravity Recovery And Climate Experiment (GRACE) mission terrestrial water storage data. The other intervening water components, such as soil moisture, were extracted from the Global Land Data Assimilation System (GLDAS).