ALBERT

Description: Two 20-day, continental midlatitude cases are simulated with a three-dimensional (3D) cloud-resolving model (CRM) and are compared to Atmospheric Radiation Measurement Program (ARM) data. Surface fluxes from ARM ground stations and a land data assimilation system are used to drive the CRM. This modeling evaluation shows that the model simulates precipitation well but overpredicts clouds, especially in the upper troposphere. The evaluation also shows that the ARM surface fluxes can have noticeable errors in summertime. Theoretical analysis reveals that buoyancy damping is sensitive to spatial smoothers in two-dimensional (2D) CRMs, but not in 3D ones. With this theoretical analysis and the ARM cloud observations as background, 2D and 3D simulations are compared, showing that the 2D CRM has not only rapid fluctuations in surface precipitation but also spurious dehumidification (or a decrease in cloud amount). The present study suggests that the rapid precipitation fluctuation and spurious dehumidification be attributed to the sensitivity of buoyancy damping to dimensionality.

Description: Land–atmosphere interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture states. The degree of coupling between the land surface and PBL in numerical weather prediction and climate models remains largely unexplored and undiagnosed because of the complex interactions and feedbacks present across a range of scales. Furthermore, uncoupled systems or experiments [e.g., the Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS)] may lead to inaccurate water and energy cycle process understanding by neglecting feedback processes such as PBL-top entrainment. In this study, a framework for diagnosing local land–atmosphere coupling is presented using a coupled mesoscale model with a suite of PBL and land surface model (LSM) options along with observations during field experiments in the U.S. Southern Great Plains. Specifically, the Weather Research and Forecasting Model (WRF) has been coupled to the Land Information System (LIS), which provides a flexible and high-resolution representation and initialization of land surface physics and states. Within this framework, the coupling established by each pairing of the available PBL schemes in WRF with the LSMs in LIS is evaluated in terms of the diurnal temperature and humidity evolution in the mixed layer. The coevolution of these variables and the convective PBL are sensitive to and, in fact, integrative of the dominant processes that govern the PBL budget, which are synthesized through the use of mixing diagrams. Results show how the sensitivity of land–atmosphere interactions to the specific choice of PBL scheme and LSM varies across surface moisture regimes and can be quantified and evaluated against observations. As such, this methodology provides a potential pathway to study factors controlling local land–atmosphere coupling (LoCo) using the LIS–WRF system, which will serve as a test bed for future experiments to evaluate coupling diagnostics within the community.

Description: Root-zone soil moisture controls the land–atmosphere exchange of water and energy, and exhibits memory that may be useful for climate prediction at monthly scales. Assimilation of satellite-based surface soil moisture observations into a land surface model is an effective way to estimate large-scale root-zone soil moisture. The propagation of surface information into deeper soil layers depends on the model-specific representation of subsurface physics that is used in the assimilation system. In a suite of experiments, synthetic surface soil moisture observations are assimilated into four different models [Catchment, Mosaic, Noah, and Community Land Model (CLM)] using the ensemble Kalman filter. The authors demonstrate that identical twin experiments significantly overestimate the information that can be obtained from the assimilation of surface soil moisture observations. The second key result indicates that the potential of surface soil moisture assimilation to improve root-zone information is higher when the surface–root zone coupling is stronger. The experiments also suggest that (faced with unknown true subsurface physics) overestimating surface–root zone coupling in the assimilation system provides more robust skill improvements in the root zone compared with underestimating the coupling. When CLM is excluded from the analysis, the skill improvements from using models with different vertical coupling strengths are comparable for different subsurface truths. Last, the skill improvements through assimilation were found to be sensitive to the regional climate and soil types.

Description: This manuscript presents an assessment of daily regional simulations of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model initialized with high-resolution land surface data from the NASA Land Information System (LIS) software versus a control WRF configuration that uses land surface data from the National Centers for Environmental Prediction (NCEP) Eta Model. The goal of this study is to investigate the potential benefits of using the LIS software to improve land surface initialization for regional NWP. Fifty-eight individual nested simulations were integrated for 24 h for both the control and experimental (LISWRF) configurations during May 2004 over Florida and the surrounding areas: 29 initialized at 0000 UTC and 29 initialized at 1200 UTC. The land surface initial conditions for the LISWRF runs came from an offline integration of the Noah land surface model (LSM) within LIS for two years prior to the beginning of the month-long study on an identical grid domain to the subsequent WRF simulations. Atmospheric variables used to force the offline Noah LSM integration were provided by the North American Land Data Assimilation System and Global Data Assimilation System gridded analyses. The LISWRF soil states were generally cooler and drier than the NCEP Eta Model soil states during May 2004. Comparisons between the control and LISWRF runs for one event suggested that the LIS land surface initial conditions led to an improvement in the timing and evolution of a sea-breeze circulation over portions of northwestern Florida. Surface verification statistics for the entire month indicated that the LISWRF runs produced a more enhanced and accurate diurnal range in 2-m temperatures compared to the control as a result of the overall drier initial soil states, which resulted from a reduction in the nocturnal warm bias in conjunction with a reduction in the daytime cold bias. Daytime LISWRF 2-m dewpoints were correspondingly drier than the control dewpoints, again a manifestation of the drier initial soil states in LISWRF. The positive results of the LISWRF experiments help to illustrate the importance of initializing regional NWP models with high-quality land surface data generated at the same grid resolution.

Description: Two 20-day, continental midlatitude cases are simulated with a three-dimensional (3D) cloud-resolving model (CRM) and compared to Atmospheric Radiation Measurement (ARM) data. This evaluation of long-term cloud-resolving model simulations focuses on the evaluation of clouds and surface fluxes. All numerical experiments, as compared to observations, simulate surface precipitation well but over-predict clouds, especially in the upper troposphere. The sensitivity of cloud properties to dimensionality and other factors is studied to isolate the origins of the over prediction of clouds. Due to the difference in buoyancy damping between 2D and 3D models, surface precipitation fluctuates rapidly with time, and spurious dehumidification occurs near the tropopause in the 2D CRM. Surface fluxes from a land data assimilation system are compared with ARM observations. They are used in place of the ARM surface fluxes to test the sensitivity of simulated clouds to surface fluxes. Summertime simulations show that surface fluxes from the assimilation system bring about a better simulation of diurnal cloud variation in the lower troposphere.

Description: Land-atmosphere interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture states. The degree of coupling between the land surface and PBL in numerical weather prediction and climate models remains largely unexplored and undiagnosed due to the complex interactions and feedbacks present across a range of scales. Further, uncoupled systems or experiments (e.g., the Project for Intercomparison of Land Parameterization Schemes, PILPS) may lead to inaccurate water and energy cycle process understanding by neglecting feedback processes such as PBL-top entrainment. In this study, a framework for diagnosing local land-atmosphere coupling is presented using a coupled mesoscale model with a suite of PBL and land surface model (LSM) options along with observations during field experiments in the U. S. Southern Great Plains. Specifically, the Weather Research and Forecasting (WRF) model has been coupled to the Land Information System (LIS), which provides a flexible and high-resolution representation and initialization of land surface physics and states. Within this framework, the coupling established by each pairing of the available PBL schemes in WRF with the LSMs in LIS is evaluated in terms of the diurnal temperature and humidity evolution in the mixed layer. The co-evolution of these variables and the convective PBL is sensitive to and, in fact, integrative of the dominant processes that govern the PBL budget, which are synthesized through the use of mixing diagrams. Results show how the sensitivity of land-atmosphere interactions to the specific choice of PBL scheme and LSM varies across surface moisture regimes and can be quantified and evaluated against observations. As such, this methodology provides a potential pathway to study factors controlling local land-atmosphere coupling (LoCo) using the LIS-WRF system, which will serve as a testbed for future experiments to evaluate coupling diagnostics within the community.

Description: The Land Information System (LIS; http://lis.gsfc.nasa.gov; Kumar et al., 2006; Peters- Lidard et al.,2007) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. As such, LIS represents a step towards the next generation land component of an integrated Earth system model. In recognition of LIS object-oriented software design, use and impact in the land surface and hydrometeorological modeling community, the LIS software was selected ase co-winner of NASA's 2005 Software of the Year award. LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has evolved from two earlier efforts North American Land Data Assimilation System (NLDAS; Mitchell et al. 2004) and Global Land Data Assimilation System (GLDAS; Rodell al. 2004) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of GLDAS and NLDAS now use specific configurations of the LIS software in their current implementations. In addition, LIS was recently transitioned into operations at the US Air Force Weather Agency (AFWA) to ultimately replace their Agricultural Meteorology (AGRMET) system, and is also used routinely by NOAA's National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) for their land data assimilation systems to support weather and climate modeling. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through "plugins,". As described in Kumar et al., 2007, and demonstrated in Case et al., 2008, and Santanello et al., 2009, LIS has been coupled to the Weather Research and Forecasting (WRF) model to support studies of land-atmosphere coupling the enabling ensembles of land surface states to be tested against multiple representations of the atmospheric boundary layer. LIS has also been demonstrated for parameter estimation as described in Peters-Lidard et al. (2008) and Santanello et al. (2007), who showed that the use of sequential remotely sensed soil moisture products can be used to derive soil hydraulic and texture properties given a sufficient dynamic range in the soil moisture retrievals and accurate precipitation inputs. LIS has also recently been demonstrated for multi-model data assimilation (Kumar et al., 2008) using an Ensemble Kalman Filter for sequential assimilation of soil moisture, snow, and temperature. Ongoing work has demonstrated the value of bias correction as part of the filter, and also that of joint calibration and assimilation. Examples and case studies demonstrating the capabilities and impacts of LIS for hydrometeoroogical modeling, assimilation and parameter estimation will be presented as advancements towards the next generation of integrated observation and modeling systems.

Description: The U.S. Bureau of Reclamation identified the remote sensing of evapotranspiration (ET) as an important water flux for study and designated a test site in the Lower Colorado River basin. A consortium of groups will work together with the goal to develop more accurate and cost effective techniques using the enhanced spatial and temporal coverage afforded by remote sensing. ET is a critical water loss flux where improved estimation should lead to better management of Reclamation responsibilities. There are several areas where NASA satellite and modeling data may be useful to meet Reclamation's objectives for improved ET estimation. In this paper we outline one possible contribution to use NASA's data integration capability of the Land Information System (LIS) to provide a merger of observational (in situ and satellite) with physical process models to provide estimates of ET and other water availability outputs (e.g., runoff, soil moisture) retrospectively, in near real-time, and also providing short-term predictions.

Description: Scientists have made great strides in modeling physical processes that represent various weather and climate phenomena. Many modeling systems that represent the major earth system components (the atmosphere, land surface, and ocean) have been developed over the years. However, developing advanced Earth system applications that integrates these independently developed modeling systems have remained a daunting task due to limitations in computer hardware and software. Recently, efforts such as the Earth System Modeling Ramework (ESMF) and Assistance for Land Modeling Activities (ALMA) have focused on developing standards, guidelines, and computational support for coupling earth system model components. In this article, the development of a coupled land-atmosphere hydrometeorological modeling system that adopts these community interoperability standards, is described. The land component is represented by the Land Information System (LIS), developed by scientists at the NASA Goddard Space Flight Center. The Weather Research and Forecasting (WRF) model, a mesoscale numerical weather prediction system, is used as the atmospheric component. LIS includes several community land surface models that can be executed at spatial scales as fine as 1km. The data management capabilities in LIS enable the direct use of high resolution satellite and observation data for modeling. Similarly, WRF includes several parameterizations and schemes for modeling radiation, microphysics, PBL and other processes. Thus the integrated LIS-WRF system facilitates several multi-model studies of land-atmosphere coupling that can be used to advance earth system studies.