Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature

Based on a 2-layer land surface model, a rather general variational data assimilation framework for estimatingmodel state variables is developed. The method minimizes the error of surface soil temperature predictionssubject to constraints imposed by the prediction model. Retrieval experiments for soil prognostic variables areperformed and the results verified against model simulated data as well as real observations for the OklahomaAtmospheric Surface layer Instrumentation System (OASIS). The optimization scheme is robust with respect toa wide range of initial guess errors in surface soil temperature (as large as 30 K) and deep soil moisture (withinthe range between wilting point and saturation). When assimilating OASIS data, the scheme can reduce theinitial guess error by more than 90%, while for Observing Simulation System Experiments (OSSEs), the initialguess error is usually reduced by over four orders of magnitude.Using synthetic data, the robustness of the retrieval scheme as related to information content of the data andthe physical meaning of the adjoint variables and their use in sensitivity studies are investigated. Throughsensitivity analysis, it is confirmed that the vegetation coverage and growth condition determine whether ornot the optimally estimated initial soil moisture condition leads to an optimal estimation of the surface fluxes.This reconciles two recent studies.With the real data experiments, it is shown that observations during the daytime period are the most effectivefor the retrieval. Longer assimilation windows result in more accurate initial condition retrieval, underlining theimportance of information quantity, especially for schemes assimilating noisy observations

SHORT-WAVELENGTH TECHNOLOGY AND THE POTENTIAL FOR DISTRIBUTED NETWORKS OF SMALL RADAR SYSTEMS

Dense networks of short-range radars capable of mapping storms and detecting atmospheric hazards are described. Composed of small X-band (9.4 GHz) radars spaced tens of kilometers apart, these networks defeat the Earth curvature blockage that limits today\u27s long-range weather radars and enables observing capabilities fundamentally beyond the operational state-of-the-art radars. These capabilities include multiple Doppler observations for mapping horizontal wind vectors, subkilometer spatial resolution, and rapid-update (tens of seconds) observations extending from the boundary layer up to the tops of storms. The small physical size and low-power design of these radars permits the consideration of commercial electronic manufacturing approaches and radar installation on rooftops, communications towers, and other infrastructure elements, leading to cost-effective network deployments. The networks can be architected in such a way that the sampling strategy dynamically responds to changing weather to simultaneously accommodate the data needs of multiple types of end users. Such networks have the potential to supplement, or replace, the physically large long-range civil infrastructure radars in use today

Sensitivity of an ecological model to soil moisture simulations from two different hydrological models

Although advanced land surface schemes have beendeveloped in the past decade, many biosphere models stilluse the simple bucket model, partly due to its efficiencywhen it is coupled with an CGCM model. In this paper,we use a sophisticated land surface model, the Simulatorfor Hydrology and Energy Exchange at the Land Surface(SHEELS), including an explicit vegetation canopy and itsphysiological control on evapotranspiration and multiple,interactive subsurface soil layers. It is found that this modelhas potential for improving the carbon cycling descriptionof a widely used biosphere model, the Carnegie-Ames-Stanford approach (CASA), especially for multiple seasonalintegrations.Verifying with observations from Oklahoma AtmosphericSurface-layer Instrumentation System (OASIS) stations, weshow that a bucket model as implemented in the CASAproduces good simulations of the seasonal cycle of soil moisturecontent, but only for the upper 15-cm soil depth, nomatter how it is initialized. This is partly due to its inabilityto include vegetation characteristics other than a fixed wiltingpoint. Although only approximate, the soil depth towhich CASA simulates storage of below-ground carbon isassumed to be about 30 cm depth, significantly deeper thanthe 15 cm depth. The bucket model cannot utilize the soilprofile measurements that have recently been made widelyavailable.A major finding of this study is that carbon fluxes aresensitive to the soil moisture simulations, especially the soil water content of the upper 30cm. The SHEELS exhibitspotential for simulating soil moisture, and hence the totalsoil water amount, accurately at every level. For the NetPrimary Production (NPP) parameter, the differences betweentwo hydrological schemes occur primarily duringthe growing seasons, when the land surface processes aremore important for climate. However, soil microbial respirationis found to be sensitive all year round to soil moisturesimulations at our 7 selected Oklahoma Mesonet stations.These suggest that for future implementing of interactiverepresentation of soil carbon in CGCMs, the accompanyinghydrological scheme should not be over-simplified