scholarly journals A High-Resolution National-Scale Hydrologic Forecast System from a Global Ensemble Land Surface Model

2016 ◽  
Vol 52 (4) ◽  
pp. 950-964 ◽  
Author(s):  
Alan D. Snow ◽  
Scott D. Christensen ◽  
Nathan R. Swain ◽  
E. James Nelson ◽  
Daniel P. Ames ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
National Scale ◽  
Forecast System ◽  
Hydrologic Forecast

scholarly journals ORCHIDEE-ROUTING: A new river routing scheme using a high resolution hydrological database

2018 ◽  
Author(s):  
Trung Nguyen-Quang ◽  
Jan Polcher ◽  
Agnès Ducharne ◽  
Thomas Arsouze ◽  
Xudong Zhou ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
River Flow ◽  
Anthropogenic Impacts ◽  
Computational Cost ◽  
Land Surface Model ◽  
Surface Model ◽  
Routing Scheme ◽  
Wide Range ◽  
River Routing

Abstract. This study presents a revised river routing scheme (RRS) for the Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) land surface model. The revision is carried out to benefit from the high resolution topography provided the Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales (HydroSHEDS), processed to a resolution of approximately 1 kilometer. The RRS scheme of the ORCHIDEE uses a unit-to-unit routing concept which allows to preserve as much of the hydrological information of the HydroSHEDS as the user requires. The evaluation focuses on 12 rivers of contrasted size and climate which contribute freshwater to the Mediterranean Sea. First, the numerical aspect of the new RRS is investigated, to identify the practical configuration offering the best trade-off between computational cost and simulation quality for ensuing validations. Second, the performance of the revised scheme is evaluated against observations at both monthly and daily timescales. The new RRS captures satisfactorily the seasonal variability of river discharges, although important biases come from the water budget simulated by the ORCHIDEE model. The results highlight that realistic streamflow simulations require accurate precipitation forcing data and a precise river catchment description over a wide range of scales, as permitted by the new RRS. Detailed analyses at the daily timescale show promising performances of this high resolution RRS for replicating river flow variation at various frequencies. Eventually, this RRS is well adapted for further developments in the ORCHIDEE land surface model to assess anthropogenic impacts on river processes (e.g. damming for irrigation operation).

scholarly journals ORCHIDEE-ROUTING: revising the river routing scheme using a high-resolution hydrological database

2018 ◽  
Vol 11 (12) ◽  
pp. 4965-4985 ◽  
Author(s):  
Trung Nguyen-Quang ◽  
Jan Polcher ◽  
Agnès Ducharne ◽  
Thomas Arsouze ◽  
Xudong Zhou ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
River Flow ◽  
Anthropogenic Impacts ◽  
Computational Cost ◽  
Land Surface Model ◽  
Surface Model ◽  
Routing Scheme ◽  
Wide Range ◽  
River Routing

Abstract. The river routing scheme (RRS) in the Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) land surface model is a valuable tool for closing the water cycle in a coupled environment and for validating the model performance. This study presents a revision of the RRS of the ORCHIDEE model that aims to benefit from the high-resolution topography provided by the Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales (HydroSHEDS), which is processed to a resolution of approximately 1 km. Adapting a new algorithm to construct river networks, the new RRS in ORCHIDEE allows for the preservation of as much of the hydrological information from HydroSHEDS as the user requires. The evaluation focuses on 12 rivers of contrasting size and climate which contribute freshwater to the Mediterranean Sea. First, the numerical aspect of the new RRS is investigated, in order to identify the practical configuration offering the best trade-off between computational cost and simulation quality for ensuing validations. Second, the performance of the new scheme is evaluated against observations at both monthly and daily timescales. The new RRS satisfactorily captures the seasonal variability of river discharge, although important biases stem from the water budget simulated by the ORCHIDEE model. The results highlight that realistic streamflow simulations require accurate precipitation forcing data and a precise river catchment description over a wide range of scales, as permitted by the new RRS. Detailed analyses at the daily timescale show the promising performance of this high-resolution RRS with respect to replicating river flow variation at various frequencies. Furthermore, this RRS may also eventually be well adapted for further developments in the ORCHIDEE land surface model to assess anthropogenic impacts on river processes (e.g. damming for irrigation operation).

scholarly journals Near-Surface and Land Surface Forecast System of the Vancouver 2010 Winter Olympic and Paralympic Games

2011 ◽  
Vol 12 (4) ◽  
pp. 508-530 ◽  
Author(s):  
Natacha B. Bernier ◽  
Stéphane Bélair ◽  
Bernard Bilodeau ◽  
Linying Tong
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Snow Depth ◽  
Land Surface Model ◽  
Surface Model ◽  
Near Surface ◽  
Elevation Changes ◽  
Forecast Models ◽  
Operational Models ◽  
Paralympic Games

Abstract A high-resolution 2D near-surface and land surface model was developed to produce snow and temperature forecasts over the complex alpine region of the Vancouver 2010 Winter Olympic and Paralympic Games. The model is driven by downscaled operational outputs from the Meteorological Service of Canada’s regional and global forecast models. Downscaling is applied to correct forcings for elevation differences between the operational forecast models and the high-resolution surface model. The high-resolution near-surface and land surface model is then used to further refine the forecasts. The model was validated against temperature and snow depth observations. The largest improvements were found in regions where low-resolution (i.e., on the order of 10 km or more) operational models typically lack the spatial resolution to capture rapid elevation changes. The model was found to better reproduce the intermittent snow cover at low-lying stations and to reduce snow depth error by as much as 3 m at alpine stations.

scholarly journals Understanding the Impacts of Soil Moisture Initial Conditions on NWP in the Context of Land–Atmosphere Coupling

2019 ◽  
Vol 20 (5) ◽  
pp. 793-819 ◽  
Author(s):  
Joseph A. Santanello Jr. ◽  
Patricia Lawston ◽  
Sujay Kumar ◽  
Eli Dennis
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Initial Conditions ◽  
Land Surface Model ◽  
Surface Model ◽  
Coupled Models ◽  
Wide Range ◽  
Soil Moisture Initialization

Abstract The role of soil moisture in NWP has gained more attention in recent years, as studies have demonstrated impacts of land surface states on ambient weather from diurnal to seasonal scales. However, soil moisture initialization approaches in coupled models remain quite diverse in terms of their complexity and observational roots, while assessment using bulk forecast statistics can be simplistic and misleading. In this study, a suite of soil moisture initialization approaches is used to generate short-term coupled forecasts over the U.S. Southern Great Plains using NASA’s Land Information System (LIS) and NASA Unified WRF (NU-WRF) modeling systems. This includes a wide range of currently used initialization approaches, including soil moisture derived from “off the shelf” products such as atmospheric models and land data assimilation systems, high-resolution land surface model spinups, and satellite-based soil moisture products from SMAP. Results indicate that the spread across initialization approaches can be quite large in terms of soil moisture conditions and spatial resolution, and that SMAP performs well in terms of heterogeneity and temporal dynamics when compared against high-resolution land surface model and in situ soil moisture estimates. Case studies are analyzed using the local land–atmosphere coupling (LoCo) framework that relies on integrated assessment of soil moisture, surface flux, boundary layer, and ambient weather, with results highlighting the critical role of inherent model background biases. In addition, simultaneous assessment of land versus atmospheric initial conditions in an integrated, process-level fashion can help address the question of whether improvements in traditional NWP verification statistics are achieved for the right reasons.

An investigation into the influence of high-resolution land-surface heterogeneity on atmospheric dynamics

2020 ◽  
Author(s):  
Jason Simon ◽  
Khaled Ghannam ◽  
Gabriel Katul ◽  
Paul Dirmeyer ◽  
Kirsten Findell ◽  
...  
Keyword(s):  
Boundary Layer ◽  
High Resolution ◽  
Planetary Boundary Layer ◽  
Land Surface ◽  
Climate Modeling ◽  
Land Surface Model ◽  
Surface Heterogeneity ◽  
Atmospheric Dynamics ◽  
Surface Model ◽  
Land Surface Heterogeneity

<p>Land-surface heterogeneity is known to play an important role in land surface hydrology and thus the boundary conditions for numerical weather prediction (NWP) and climate modeling. For this reason, there have been considerable efforts over the past two decades to improve its representation in large scale models. However, to date, the inclusion of sub-grid heterogeneity in modeling land-atmosphere interactions in regional and global models has been limited to sub-grid spatial means and thus have almost entirely disregarded its multi-scale impact on the simulated atmospheric dynamics. To begin to address this challenge, here we use large-eddy simulations (LES) coupled to a land-surface model to gain a more complete understanding of its role in the coupled land-atmosphere system. In this work, we illustrate its impact over the Southern Great Plains (SGP) site in the United States and present a path forward for using these modeling experiments to guide the development of a complementary coupling parameterization within climate models.</p><p>More specifically, over the SGP site, we use high-resolution LES to investigate the impact of SGS land heterogeneity under different atmospheric and surface conditions to inform the development of land-surface and planetary boundary layer (PBL) parameterizations for coarser, operational-scale weather and climate modeling efforts. The experiment methodology uses a high-resolution land-surface model (WRF-Hydro), spun-up over multiple years using reanalysis data, which is then coupled to the Weather Research and Forecasting (WRF) model for high-resolution LES. Cases are considered using both the fully heterogeneous land model as well as using a homogeneous surface with domain-averaged flux values at all grid points, allowing the dynamical effects of land-surface heterogeneity on the atmosphere to be isolated, and the land/atmospheric conditions under which land-surface heterogeneity plays a role to be studied. Results are evaluated primarily by the differences in the development of the planetary boundary layer and the extent, duration and intensity of developing rainfall events.</p>

scholarly journals Land–Atmosphere Coupling Strength in the Global Forecast System

2011 ◽  
Vol 12 (1) ◽  
pp. 147-156 ◽  
Author(s):  
Li Zhang ◽  
Paul A. Dirmeyer ◽  
Jiangfeng Wei ◽  
Zhichang Guo ◽  
Cheng-Hsuan Lu
Keyword(s):  
Coupling Strength ◽  
Land Surface ◽  
Surface Soil ◽  
Land Surface Model ◽  
Atmospheric Model ◽  
Surface Model ◽  
Global Forecast System ◽  
Forecast System ◽  
Subsurface Soil ◽  
Soil Wetness

Abstract The operational coupled land–atmosphere forecast model from the National Centers for Environmental Prediction (NCEP) is evaluated for the strength and characteristics of its coupling in the water cycle between land and atmosphere. Following the protocols of the Global Land–Atmosphere Coupling Experiment (GLACE) it is found that the Global Forecast System (GFS) atmospheric model coupled to the Noah land surface model exhibits extraordinarily weak land–atmosphere coupling, much as its predecessor, the GFS–Oregon State University (OSU) coupled system. The coupling strength is evaluated by the ability of subsurface soil wetness to affect locally the time series of precipitation. The surface fluxes in Noah are also found to be rather insensitive to subsurface soil wetness. Comparison to another atmospheric model coupled to Noah as well as a different land surface model show that Noah is responsible for some of the lack of sensitivity, primarily because its thick (10 cm) surface layer dominates the variability in surface latent heat fluxes. Noah is found to be as responsive as other land surface models to surface soil wetness and temperature variations, suggesting the design of the GLACE sensitivity experiment (based only on subsurface soil wetness) handicapped the Noah model. Additional experiments, in which the parameterization of evapotranspiration is altered, as well as experiments where surface soil wetness is also constrained, isolate the GFS atmospheric model as the principal source of the weak sensitivity of precipitation to land surface states.

scholarly journals Incoming Solar and Infrared Radiation Derived from METEOSAT: Impact on the Modeled Land Water and Energy Budget over France

2012 ◽  
Vol 13 (2) ◽  
pp. 504-520 ◽  
Author(s):  
D. Carrer ◽  
S. Lafont ◽  
J.-L. Roujean ◽  
J.-C. Calvet ◽  
C. Meurey ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Infrared Radiation ◽  
Land Surface Model ◽  
Added Value ◽  
Surface Model ◽  
Radiation Fluxes ◽  
Satellite Applications ◽  
The Impact

Abstract The Land Surface Analysis Satellite Applications Facility (LSA SAF) project radiation fluxes, derived from the Meteosat Second Generation (MSG) geostationary satellite, were used in the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model (LSM), which is a component of the Surface Externalisée (SURFEX) modeling platform. The Système d’Analyze Fournissant des Renseignements Atmosphériques à la Neige (SAFRAN) atmospheric analysis provides high-resolution atmospheric variables used to drive LSMs over France. The impact of using the incoming solar and infrared radiation fluxes [downwelling surface shortwave (DSSF) and longwave (DSLF), respectively] from either SAFRAN or LSA SAF, in ISBA, was investigated over France for 2006. In situ observations from the Flux Network (FLUXNET) were used for the verification. Daily differences between SAFRAN and LSA SAF radiation fluxes averaged over the whole year 2006 were 3.75 and 2.61 W m−2 for DSSF and DSLF, respectively, representing 2.5% and 0.8% of their average values. The LSA SAF incoming solar radiation presented a better agreement with in situ measurements at six FLUXNET stations than the SAFRAN analysis. The bias and standard deviation of differences were reduced by almost 50%. The added value of the LSA SAF products was assessed with the simulated surface temperature, soil moisture, and the water and energy fluxes. The latter quantities were improved by the use of LSA SAF satellite estimates. As many areas lack a high-resolution meteorological analysis, the LSA SAF radiative products provide new and valuable information.

scholarly journals Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES

2021 ◽  
Author(s):  
Evan Baker ◽  
Anna Harper ◽  
Daniel Williamson ◽  
Peter Challenor
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Global Climate ◽  
Computational Cost ◽  
Land Surface Model ◽  
Parameter Tuning ◽  
Surface Model ◽  
Climate Projections ◽  
Land Surface Models ◽  
Surface Models

Abstract. Land surface models are typically integrated into global climate projections, but as their spatial resolution increases the prospect of using them to aid in local policy decisions becomes more appealing. If these complex models are to be used to make local decisions, then a full quantification of uncertainty is necessary, but the computational cost of running just one simulation at high resolution can hinder proper analysis. Statistical emulation is an increasingly common technique for developing fast approximate models in a way that maintains accuracy but also provides comprehensive uncertainty bounds for the approximation. In this work, we develop a statistical emulation framework for land surface models which acknowledges the forcing data fed into the model, providing predictions at a high resolution. We use The Joint UK Land Environment Simulator (JULES) as a case study for this strategy, and perform initial sensitivity analysis and parameter tuning to showcase its capabilities. JULES is perhaps one of the most complex land surface models, and so our success here suggests incredible gains can be made for all types of land surface model.

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