scholarly journals Contribution to the development of DOE ARM Climate Best Estimate Data (ARMBE) products: Land Surface Forcing and Evaluation Data Set (ARMBE-LAND) from the ARM SGP site

2013 ◽  
Author(s):  
M Jin
Keyword(s):  
Land Surface ◽  
Data Set ◽  
Evaluation Data ◽  
Best Estimate

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 17 (7) ◽  
pp. 2781-2796 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Data Set ◽  
Model Based ◽  
Snow Water

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

scholarly journals Continuous high-resolution midlatitude-belt simulations for July–August 2013 with WRF

2017 ◽  
Vol 10 (5) ◽  
pp. 2031-2055 ◽  
Author(s):  
Thomas Schwitalla ◽  
Hans-Stefan Bauer ◽  
Volker Wulfmeyer ◽  
Kirsten Warrach-Sagi
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Temperature Fields ◽  
Surface Model ◽  
Limited Area ◽  
Data Set ◽  
Latitude Belt ◽  
Simulated Precipitation ◽  
The Impact

Abstract. Increasing computational resources and the demands of impact modelers, stake holders, and society envision seasonal and climate simulations with the convection-permitting resolution. So far such a resolution is only achieved with a limited-area model whose results are impacted by zonal and meridional boundaries. Here, we present the setup of a latitude-belt domain that reduces disturbances originating from the western and eastern boundaries and therefore allows for studying the impact of model resolution and physical parameterization. The Weather Research and Forecasting (WRF) model coupled to the NOAH land–surface model was operated during July and August 2013 at two different horizontal resolutions, namely 0.03 (HIRES) and 0.12° (LOWRES). Both simulations were forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis data at the northern and southern domain boundaries, and the high-resolution Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) data at the sea surface.The simulations are compared to the operational ECMWF analysis for the representation of large-scale features. To analyze the simulated precipitation, the operational ECMWF forecast, the CPC MORPHing (CMORPH), and the ENSEMBLES gridded observation precipitation data set (E-OBS) were used as references.Analyzing pressure, geopotential height, wind, and temperature fields as well as precipitation revealed (1) a benefit from the higher resolution concerning the reduction of monthly biases, root mean square error, and an improved Pearson skill score, and (2) deficiencies in the physical parameterizations leading to notable biases in distinct regions like the polar Atlantic for the LOWRES simulation, the North Pacific, and Inner Mongolia for both resolutions.In summary, the application of a latitude belt on a convection-permitting resolution shows promising results that are beneficial for future seasonal forecasting.

scholarly journals EAGLE 2006 – Multi-purpose, multi-angle and multi-sensor in-situ and airborne campaigns over grassland and forest

2009 ◽  
Vol 13 (6) ◽  
pp. 833-845 ◽  
Author(s):  
Z. Su ◽  
W. J. Timmermans ◽  
C. van der Tol ◽  
R. Dost ◽  
R. Bianchi ◽  
...  
Keyword(s):  
The Netherlands ◽  
Land Surface ◽  
Information Science ◽  
Microwave Radiometer ◽  
European Space Agency ◽  
International Institute ◽  
Data Set ◽  
Field Campaign ◽  
Land Surface Parameter ◽  
Satellite Missions

Abstract. EAGLE2006 – an intensive field campaign for the advances in land surface hydrometeorological processes – was carried out in the Netherlands from 8th to 18th June 2006, involving 16 institutions with in total 67 people from 16 different countries. In addition to the acquisition of multi-angle and multi-sensor satellite data, several airborne instruments – an optical imaging sensor, an imaging microwave radiometer, and a flux airplane – were deployed and extensive ground measurements were conducted over one grassland site at Cabauw and two forest sites at Loobos and Speulderbos in the central part of the Netherlands. The generated data set is both unique and urgently needed for the development and validation of models and inversion algorithms for quantitative land surface parameter estimation and land surface hydrometeorological process studies. EAGLE2006 was led by the Department of Water Resources of the International Institute for Geo-Information Science and Earth Observation (ITC) and originated from the combination of a number of initiatives supported by different funding agencies. The objectives of the EAGLE2006 campaign were closely related to the objectives of other European Space Agency (ESA) campaign activities (SPARC2004, SEN2FLEX2005 and especially AGRISAR2006). However, one important objective of the EAGLE2006 campaign is to build up a data base for the investigation and validation of the retrieval of bio-geophysical parameters, obtained at different radar frequencies (X-, C- and L-Band) and at hyperspectral optical and thermal bands acquired simultaneously over contrasting vegetated fields (forest and grassland). As such, all activities were related to algorithm development for future satellite missions such as the Sentinels and for validation of retrievals of land surface parameters with optical and thermal and microwave sensors onboard current and future satellite missions. This contribution describes the campaign objectives and provides an overview of the airborne and field campaign dataset. This dataset is available for scientific investigations and can be accessed on the ESA Principal Investigator Portal http://eopi.esa.int/.

scholarly journals A New Global Storage‐Area‐Depth Data Set for Modeling Reservoirs in Land Surface and Earth System Models

2018 ◽  
Vol 54 (12) ◽  
Author(s):  
Wondmagegn Yigzaw ◽  
Hong‐Yi Li ◽  
Yonas Demissie ◽  
Mohamad I. Hejazi ◽  
L. Ruby Leung ◽  
...  
Keyword(s):  
Land Surface ◽  
Earth System ◽  
Data Set ◽  
System Models ◽  
Depth Data ◽  
Storage Area ◽  
Earth System Models

scholarly journals Derivation and analysis of a high-resolution estimate of global permafrost zonation

2011 ◽  
Vol 5 (3) ◽  
pp. 1547-1582
Author(s):  
S. Gruber
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Permafrost Region ◽  
Large Area ◽  
Data Set ◽  
Regional Patterns ◽  
Actual Surface ◽  
Interesting Insight ◽  
Air Temperatures ◽  
Discontinuous Permafrost

Abstract. Permafrost underlies much of Earths' surface and interacts with climate, eco-systems and human systems. It is a complex phenomenon controlled by climate and (sub-) surface properties and reacts to change with variable delay. Heterogeneity and sparse data challenge the modeling of its spatial distribution. Currently, there is no data set to adequately inform global studies of permafrost. The available data set for the Northern Hemisphere is frequently used for model evaluation, but its quality and consistency are difficult to assess. A global model of permafrost extent and dataset of permafrost zonation are presented and discussed, extending earlier studies by including the Southern Hemisphere, by consistent data and methods, and most importantly, by attention to uncertainty and scaling. Established relationships between air temperature and the occurrence of permafrost are re-formulated into a model that is parametrized using published estimates. It is run with a high-resolution (<1 km) global elevation data and air temperatures based on the NCAR-NCEP reanalysis and CRU TS 2.0. The resulting data provides more spatial detail and a consistent extrapolation to remote regions, while aggregated values resemble previous studies. The estimated uncertainties affect regional patterns and aggregate number, but provide interesting insight. The permafrost area, i.e. the actual surface area underlain by permafrost, north of 60° S is estimated to be 13–18 × 106 km2 or 9–14 % of the exposed land surface. The global permafrost area including Antarctic and sub-sea permafrost is estimated to be 16–21 × 106 km2. The global permafrost region, i.e. the exposed land surface below which some permafrost can be expected, is estimated to be 22 ± 3 × 106 km2. A large proportion of this exhibits considerable topography and spatially-discontinuous permafrost, underscoring the importance of attention to scaling issues and heterogeneity in large-area models.

scholarly journals Evaluation of the snow regime in dynamic vegetation land surface models using field measurements

2013 ◽  
Vol 7 (3) ◽  
pp. 2333-2372
Author(s):  
E. Kantzas ◽  
M. Lomas ◽  
S. Quegan ◽  
E. Zakharova
Keyword(s):  
Land Surface ◽  
Field Data ◽  
Climate Models ◽  
Optimization Techniques ◽  
The Arctic ◽  
Surface Model ◽  
Land Surface Models ◽  
Snow Density ◽  
Data Set ◽  
Surface Models

Abstract. An increasing number of studies have demonstrated the significant climatic and ecological changes occurring in the northern latitudes over the past decades. As coupled, earth-system models attempt to describe and simulate the dynamics and complex feedbacks of the Arctic environment, it is important to reduce their uncertainties in short-term predictions by improving the description of both the systems processes and its initial state. This study focuses on snow-related variables and extensively utilizes a historical data set (1966–1996) of field snow measurements acquired across the extend of the Former Soviet Union (FSU) to evaluate a range of simulated snow metrics produced by a variety of land surface models, most of them embedded in IPCC-standard climate models. We reveal model-specific issues in simulating snow dynamics such as magnitude and timings of SWE as well as evolution of snow density. We further employ the field snow measurements alongside novel and model-independent methodologies to extract for the first time (i) a fresh snow density value (57–117 kg m–3) for the region and (ii) mean monthly snowpack sublimation estimates across a grassland-dominated western (November–February) [9.2, 6.1, 9.15, 15.25] mm and forested eastern sub-sector (November–March) [1.53, 1.52, 3.05, 3.80, 12.20] mm; we subsequently use the retrieved values to assess relevant model outputs. The discussion session consists of two parts. The first describes a sensitivity study where field data of snow depth and snow density are forced directly into the surface heat exchange formulation of a land surface model to evaluate how inaccuracies in simulating snow metrics affect important modeled variables and carbon fluxes such as soil temperature, thaw depth and soil carbon decomposition. The second part showcases how the field data can be assimilated with ready-available optimization techniques to pinpoint model issues and improve their performance.

scholarly journals Climate, snow, and soil moisture data set for the Tuolumne and Merced river watersheds, California, USA

2019 ◽  
Vol 11 (1) ◽  
pp. 101-110 ◽  
Author(s):  
James W. Roche ◽  
Robert Rice ◽  
Xiande Meng ◽  
Daniel R. Cayan ◽  
Michael D. Dettinger ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Sierra Nevada ◽  
Land Surface ◽  
Forest Canopy ◽  
Snow Water Equivalent ◽  
Process Models ◽  
Series Data ◽  
Climate Data ◽  
Data Set ◽  
Data Repositories

Abstract. We present hourly climate data to force land surface process models and assessments over the Merced and Tuolumne watersheds in the Sierra Nevada, California, for the water year 2010–2014 period. Climate data (38 stations) include temperature and humidity (23), precipitation (13), solar radiation (8), and wind speed and direction (8), spanning an elevation range of 333 to 2987 m. Each data set contains raw data as obtained from the source (Level 0), data that are serially continuous with noise and nonphysical points removed (Level 1), and, where possible, data that are gap filled using linear interpolation or regression with a nearby station record (Level 2). All stations chosen for this data set were known or documented to be regularly maintained and components checked and calibrated during the period. Additional time-series data included are available snow water equivalent records from automated stations (8) and manual snow courses (22), as well as distributed snow depth and co-located soil moisture measurements (2–6) from four locations spanning the rain–snow transition zone in the center of the domain. Spatial data layers pertinent to snowpack modeling in this data set are basin polygons and 100 m resolution rasters of elevation, vegetation type, forest canopy cover, tree height, transmissivity, and extinction coefficient. All data are available from online data repositories (https://doi.org/10.6071/M3FH3D).

Hydrological functioning of irrigated maize crops in southwest France using Eddy Covariance measurements and a land surface model

2021 ◽  
Author(s):  
Oluwakemi Dare-Idowu ◽  
Lionel Jarlan ◽  
Aurore Brut ◽  
Valerie Le-Dantec ◽  
Vincent Rivalland ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Land Surface ◽  
Sap Flow ◽  
Heat Fluxes ◽  
Added Value ◽  
Surface Model ◽  
Data Set ◽  
Flow Measurements ◽  
Ground Heat Flux

&lt;p&gt;This study aims to analyze the main components of the energy and hydric budgets of irrigated maize in southwestern France. To this objective, the ISBA-A-gs (&lt;span&gt;Interactions between Soil, Biosphere, and Atmosphere) &lt;/span&gt;is run over six maize growing seasons. As a preliminary step, the ability of the ISBA-A-gs model to predict the different terms of the energy and water budgets is assessed thanks to a large database of &lt;em&gt;in situ&lt;/em&gt; measurements by comparing the single budget version of the model with the new Multiple Energy Balance version solving an energy budget separately for the soil and the vegetation. The &lt;em&gt;in situ&lt;/em&gt; data set acquired at the Lamasquere site (43.48&lt;sup&gt;o&lt;/sup&gt; N, 1.249&lt;sup&gt;o&lt;/sup&gt; E) includes half-hourly measurements of sensible (H) and latent heat fluxes (LE) estimated by an Eddy Covariance system. Measurements also include net radiation (Rn), ground heat flux (G), plant transpiration with sap flow sensors, meteorological variables, and 15-days measurements of vegetation characteristics. The seasonal dynamics of the turbulent fluxes were properly reproduced by both configurations of the model with an R&amp;#178; ranging from 0.66 to 0.89, and a root mean square error lower than 48 W m&lt;sup&gt;-2&lt;/sup&gt;. Statistical metrics showed that H was better predicted by MEB with R&amp;#178; of 0.80 in comparison to ISBA-Ags (0.73). However, the difference between the RMSE of ISBA-Ags and MEB during the well-developed stage of the plants for both H and LE does not exceed 8 W m&lt;sup&gt;-2&lt;/sup&gt;. This implies that MEB only has a significant added value over ISBA-Ags when the soil and the canopy are not fully coupled, and over a heterogeneous field. Furthermore, this study made a comparison between the sap flow measurements and the transpiration simulated by ISBA-A-gs and MEB. A good dynamics was reproduced by ISBA-A-gs and MEB, although, MEB (R&amp;#178;= 0.91) provided a slightly more realistic estimation of the vegetation transpiration. Consequently, this study investigated the dynamics of the water budget during the growing maize seasons. Results indicated that drainage is almost null on the site, while the observed values of cumulative evapotranspiration that was higher than the water inputs are related to a shallow ground table that provides supplement water to the crop. This work provides insight into the modeling of water and energy exchanges over maize crops and opens perspectives for better water management of the crop in the future.&lt;/p&gt;

Library book intelligent recommendation system based on artificial intelligence

2021 ◽  
pp. 1-6
Author(s):  
Lu Pang
Keyword(s):  
Artificial Intelligence ◽  
Recommendation System ◽  
Absolute Error ◽  
Recall Rate ◽  
Data Set ◽  
Evaluation Data ◽  
Artificial Intelligence Technology ◽  
Collaborative Filtering Algorithm ◽  
Book Evaluation ◽  
Evaluation Matrix

In order to improve the accuracy of intelligent recommendation of library books, an intelligent recommendation system of library books based on artificial intelligence was designed. The system uses artificial intelligence technology to clean up and normalize the data, automatically extracts the user’s historical evaluation data of books, divides the whole user space into several similar user clusters through the similar user clustering module, constructs the user book evaluation matrix according to the historical evaluation data, and uses the hybrid collaborative filtering algorithm which integrates user based and project-based to predict each user a book evaluation matrix of similar user clusters was used to realize the intelligent recommendation of library books, and the recommendation results were displayed to users through the user interface module. The results show that the average absolute error and root mean square error of the system are always the lowest, and the recommendation accuracy is high. When the control parameter is 0.4, the best intelligent book recommendation effect can be obtained; the recommended recall rate is not affected by the sparse density of the data set, and the stability is strong.

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