Yuqiong Liu

The Ensemble Verification System (EVS): A software tool for verifying ensemble forecasts of hydrometeorological and hydrologic variables at discrete locations

Ensemble forecasting is widely used in meteorology and, increasingly, in hydrology to quantify and propagate uncertainty. In practice, ensemble forecasts cannot account for every source of uncertainty, and many uncertainties are difficult to quantify accurately. Thus, ensemble forecasts are subject to errors, which may be correlated in space and time and may be systematic. Ensemble verification is necessary to quantify these errors, and to better understand the sources of predictive error and skill in particular modeling situations. The Ensemble Verification System (EVS) is a flexible, user-friendly, software tool that is designed to verify ensemble forecasts of numeric variables, such as temperature, precipitation and streamflow. It can be applied to forecasts from any number of discrete locations, which may be issued with any frequency and lead time. The EVS can also produce and verify forecasts that are aggregated in time, such as daily precipitation totals based on hourly forecasts, and can aggregate verification statistics across several discrete locations. This paper is separated into four parts. It begins with an overview of the EVS and the structure of the Graphical User Interface. The verification metrics available in the EVS are then described. These include metrics that verify the forecast probabilities and metrics that verify the ensemble mean forecast. Several new verification metrics are also presented. Following a description of the Application Programming Interface, the procedure for adding a new metric to the EVS is briefly outlined. Finally, the EVS is illustrated with two examples from the National Weather Service (NWS), one focusing on ensemble forecasts of precipitation from the NWS Ensemble Pre-Processor and one focusing on ensemble forecasts of streamflow from the NWS Ensemble Streamflow Prediction system. The conclusions address future enhancements to, and applications of, the EVS.

Constraining Land Surface and Atmospheric Parameters of a Locally Coupled Model Using Observational Data

Abstract In coupled land surface–atmosphere modeling, the possibility and benefits of constraining model parameters using observational data bear investigation. Using the locally coupled NCAR Single-column Community Climate Model (NCAR SCCM), this study demonstrates some feasible, effective approaches to constrain parameter estimates for coupled land–atmosphere models and explores the effects of including both land surface and atmospheric parameters and fluxes/variables in the parameter estimation process, as well as the value of conducting the process in a stepwise manner. The results indicate that the use of both land surface and atmospheric flux variables to construct error criteria can lead to better-constrained parameter sets. The model with “optimal” parameters generally performs better than when a priori parameters are used, especially when some atmospheric parameters are included in the parameter estimation process. The overall conclusion is that, to achieve balanced, reasonable model performance ...

Impacts of a Parameterization Deficiency on Offline and Coupled Land Surface Model Simulations

Surface water and energy balance plays an important role in land surface models, especially in coupled land surface‐atmospheric models due to the complicated interactions between land surfaces and the overlying atmosphere. The primary purpose of this paper is to demonstrate the significant negative impacts that a minor deficiency in the parameterization of canopy evaporation may have on offline and coupled land surface model simulations. In this research, using the offline NCAR Land Surface Model (LSM) and the locally coupled NCAR Single-column Community Climate Model (SCCM) as examples, intensive effort has been focused on the exploration of the mechanisms involved in the activation of unrealistically high canopy evaporation and thus unreasonable surface energy partitions because of a minor deficiency in the parameterization of canopy evaporation. The main causes responsible for exacerbating the impacts of the deficiency of the land surface model through the coupling of the two components are analyzed, along with possible impacts of land surface parameters in triggering the problems. Results from experimental runs show that, for a large number of randomly generated physically realistic land surface parameter sets, this model deficiency has caused the occurrences of negative canopy water with a significantly high frequency for both the offline NCAR LSM and the coupled NCAR SCCM, suggesting that land surface parameters are not the only important factors in triggering the problems associated with the model deficiency. In addition, the concurrence of intense solar radiation and enough precipitation is identified to be mainly responsible for exacerbating the negative impacts of the parameterization deficiency. Finally, a simple adjustment has been made in this study to effectively prevent the occurrences of negative canopy water storages, leading to significantly improved model performances.

Formal Scenario Development for Environmental Impact Assessment Studies

Abstract Scenario analysis is a process of evaluating possible future events through the consideration of alternative plausible, though not equally likely, states (scenarios). The analysis is designed to enable improved decision making and assessment through a more rigorous evaluation of possible outcomes and their implications. For environmental impact and integrated assessment studies, the process of scenario development typically involves making explicit and/or implicit assumptions about potential future conditions, such as climate change, land cover and land use changes, population growth, economic development and technological changes. Realistic assessment of scenario impacts often requires complex integrated modelling frameworks that represent environmental and socioeconomic systems to the best of our knowledge, including assumptions about plausible future conditions. In addition, scenarios have to be developed in a context relevant to the stakeholders involved, and include estimation and communication of uncertainties, to establish transparency, credibility and relevance of scenario results among the stakeholders. This paper reviews the state of the art of scenario development and analysis, proposes a formal approach to scenario development in environmental studies and discusses existing issues. Major recommendations for future research in this area include proper consideration of uncertainty involved in scenario studies, construction of scenarios of a more variable nature, and sharing of information and resources among the scenario development research community.

Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods

ABSTRACT This paper comparatively assesses the performance of five data assimilation techniques for three-parameter Muskingum routing with a spatially lumped or distributed model structure. The assimilation techniques used include direct insertion (DI), nudging scheme (NS), Kalman filter (KF), ensemble Kalman filter (EnKF) and asynchronous ensemble Kalman filter (AEnKF), which are applied to river reaches in Texas and Louisiana, USA. For both lumped and distributed routing, results from KF, EnKF and AEnKF are sensitive to the error specification. As expected, DI outperformed the other models in the case of lumped modelling, while in distributed routing, KF approaches, particularly AEnKF and EnKF, performed better than DI or nudging, reflecting the benefit of updating distributed states through error covariance modelling in KF approaches. The results of this work would be useful in setting up data assimilation systems that employ increasingly abundant real-time observations using distributed hydrological routing models.