scholarly journals Validation of a High Resolution Numerical Weather Prediction Land Surface Scheme using Catchment Water Balances

2021 ◽  
Author(s):  
Daniel Regenass ◽  
Linda Schlemmer ◽  
Oliver Fuhrer ◽  
Jean-Marie Bettems ◽  
Marco Arpagaus ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Land Surface ◽  
Climate Models ◽  
Water Cycle ◽  
Weather Prediction ◽  
The State ◽  
Mass Balances ◽  
Numerical Weather ◽  
Terrestrial Water ◽  
Surface Scheme

AbstractAn adequate representation of the interaction between the land surface and the atmosphere is critical for both numerical weather prediction and climate models. The surface energy and mass balances are tightly coupled to the terrestrial water cycle, mainly through the state of soil moisture. An inadequate representation of the terrestrial water cycle will deteriorate the state of the land surface model and introduce biases to the atmospheric model. The validation of land-surface models is challenging, as there are very few observations and the soil is highly heterogeneous. In this paper, a validation framework for land-surface schemes based on catchment mass balances is presented. The main focus of our development lies in the application to kilometer-resolution numerical weather prediction and climate models, although the approach is scalable in both space and time. The methodology combines information from multiple observation-based datasets. Observational uncertainties are estimated by using independent sets of observations. It is shown that the combination of observation-based datasets and river discharge measurements close the water balance fairly well for the chosen catchments. As a showcase application, the framework is then applied to compare and validate four different versions ofTERRAML, the land-surface scheme of the COSMO numerical weather prediction and climate model over five mesoscale catchments in Switzerland ranging from 105 km2 to 1713 km2. Despite large observational uncertainties, validation results clearly suggest that errors in terrestrial storage changes are closely linked to errors in runoff generation and emphasize the crucial role of infiltration processes.

Development and application of a catchment-based mass balance validation tool for land surface schemes

2020 ◽  
Author(s):  
Daniel Regenass ◽  
Linda Schlemmer ◽  
Oliver Fuhrer ◽  
Jean-Marie Bettems ◽  
Christoph Schär
Keyword(s):  
Water Balance ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Water Cycle ◽  
Weather Prediction ◽  
Water Discharge ◽  
Numerical Weather ◽  
Validation Framework ◽  
Observational Uncertainty ◽  
Terrestrial Water

<p>Land-Atmosphere coupling is a fundamental process of the earth system. From the perspective of atmospheric sciences, a quantitative understanding of the surface energy and mass balances is of vital importance for both numerical weather prediction and climate modeling and needs to be represented adequately in land surface schemes. The partitioning of net radiation into sensible, latent and ground heat fluxes is dependent on the state of the land surface with soil moisture being an important state variable, introducing memory effects of monthly to annual timescales to the coupling. An inadequate representation of the terrestrial water cycle will therefore degrade forecasts and introduce biases in climate simulations. While there exist reasonable estimates for the terrestrial water cycle on subcontinental scales and various observations for evapotranspiration on the point-scale, the validation of land-surface-schemes on the kilometer-scale remains challenging. The fundamental unit on which one can validate all terms of the water balance is the catchment. Here, we present a validation framework based on water balances of mesoscale catchments. The framework incorporates observations of precipitation and streamflow as well as estimates for evapotranspiration from remote sensing data.</p><p>The methodology is applied to five mesoscale catchments in Switzerland ranging from 105 km<sup>2</sup> to 1713 km<sup>2</sup> for the years 2010-2012. Observations include MeteoSwiss operational analyses, hourly discharge measurements provided by the federal office for the environment and the MODIS MOD16A2 evapotranspiration product. While relying on observations, these datasets are subject to substantial uncertainties. We aim to quantify the major part of this observational uncertainty by using data from FLUXNET sites in the Alpine region and a rain-gauge based precipitation dataset from MeteoSwiss (RhiresM).</p><p>As a showcase application, the validation framework is used in order to compare and validate two different parameterizations for soil/ groundwater hydrology in TERRA, the land surface scheme of the COSMO numerical weather prediction and regional climate model. While both versions are based on Richard’s equation, one is implemented with a free drainage boundary condition, while the other one is implemented with a simple parameterization for groundwater. Results from TERRA standalone runs forced with COSMO analysis fields suggest that errors in terrestrial storage change are mostly driven by errors in runoff. In turn, runoff is very sensitive to the parameterization of infiltration and to soil hydraulic parameter. We show that despite large uncertainties in the observations at hand, it is possible to identify respective shortcomings of the two different groundwater formulations and to improve the simulated water balance by introducing a mathematically sound formulation for infiltration and by tuning key parameter associated to ground water discharge.</p>

Intercomparison of terrestrial water budgets in EURO-CORDEX and TSMP evaluation runs

2020 ◽  
Author(s):  
Mohamed Eltahan ◽  
Klaus Goergen ◽  
Carina Furusho-Percot ◽  
Stefan Kollet
Keyword(s):  
Land Surface ◽  
Water Budget ◽  
Climate Models ◽  
Water Cycle ◽  
Weather Prediction ◽  
Regional Climate ◽  
Shallow Groundwater ◽  
Surface Model ◽  
Frequency Distributions ◽  
Terrestrial Water

<p>Water is one of Earth’s most important geo-ecosystem components. Here we present an evaluation of water cycle components using 12 EURO-CORDEX Regional Climate Models (RCMs) and the Terrestrial Systems Modeling Platform (TSMP) from ERA-Interim driven evaluation runs. Unlike the other RCMs, TSMP provides an <span>integrated</span> representation of the terrestrial water cycle by coupling the numerical weather prediction model COSMO, the land surface model CLM and the surface-subsurface hydrological model ParFlow, which simulates shallow groundwater states and fluxes. The study analyses precipitation (P), evapotranspiration (E), runoff (R), and terrestrial water storage (TWS=P-E-R) at a 0.11degree spatial resolution (about 12km) on EUR-11 CORDEX grid from 1996 to 2008. As reference datasets, we use ERA5 reanalysis to <span>represent</span> the complete terrestrial water budget, <span>as well as </span>the E-OBS, GLEAM and E-Run datasets for precipitation, evapotranspiration and runoff, respectively. The terrestrial water budget is investigated for twenty catchments over Europe (Guadalquivir, Guadiana, Tagus, Douro, Ebro, Garonne, Rhone, Po, Seine, Rhine, Loire, Maas, Weser, Elbe, Oder, Vistuala, Danube, Dniester, Dnieper, and Neman). Annual cycles, seasonal variations, empirical frequency distributions, spatial distributions for the water cycle components and budgets over the catchments are assessed. The analysis <span>demonstrates</span> the capability of the RCMs and TSMP to reproduce the overall <span>characteristics of the</span> water cycle over the EURO-CORDEX domain<span>, which is a prerequisite if, e.g., climate change projections with the CORDEX RCMs or TSMP are to be used for vulnerability, impacts, and adaptation studies.</span></p>

scholarly journals Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey

Geosciences ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 489 ◽  
Author(s):  
Jürgen Helmert ◽  
Aynur Şensoy Şorman ◽  
Rodolfo Alvarado Montero ◽  
Carlo De Michele ◽  
Patricia de Rosnay ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Climate Models ◽  
Weather Prediction ◽  
Data Availability ◽  
Observation Data ◽  
Climate Change Scenarios ◽  
Numerical Weather ◽  
The Future

The European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) aims to coordinate efforts in Europe to harmonize approaches to validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation (DA) techniques. One of the key objectives of the action was “Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models and show its benefit for weather and hydrological forecasting as well as other applications.” This paper reviews approaches used for assimilation of snow measurements such as remotely sensed and in situ observations into hydrological, land surface, meteorological and climate models based on a COST HarmoSnow survey exploring the common practices on the use of snow observation data in different modeling environments. The aim is to assess the current situation and understand the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. Based on the responses from the community to the questionnaire and on literature review the status and requirements for the future evolution of conventional snow observations from national networks and satellite products, for data assimilation and model validation are derived and suggestions are formulated towards standardized and improved usage of snow observation data in snow DA. Results of the conducted survey showed that there is a fit between the snow macro-physical variables required for snow DA and those provided by the measurement networks, instruments, and techniques. Data availability and resources to integrate the data in the model environment are identified as the current barriers and limitations for the use of new or upcoming snow data sources. Broadening resources to integrate enhanced snow data would promote the future plans to make use of them in all model environments.

scholarly journals Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast

2017 ◽  
Vol 21 (1) ◽  
pp. 37 ◽  
Author(s):  
Hua Deng ◽  
Yan Li ◽  
Yingchao Zhang ◽  
Hou Zhou ◽  
Peipei Cheng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Least Square ◽  
Support Vector ◽  
Surface Model ◽  
Short Term ◽  
Numerical Weather

The forecast of wind energy is closely linked to the prediction of the variation of winds over very short time intervals. Four wind towers located in the Inner Mongolia were selected to understand wind power resources in the compound plateau region. The mesoscale weather research and forecasting combining Yonsei University scheme and Noah land surface model (WRF/YSU/Noah) with 1-km horizontal resolution and 10-min time resolution were used to be as the wind numerical weather prediction (NWP) model. Three statistical techniques, persistence, back-propagation artificial neural network (BP-ANN), and least square support vector machine (LS-SVM) were used to improve the wind speed forecasts at a typical wind turbine hub height (70 m) along with the WRF/YSU/Noah output. The current physical-statistical forecasting techniques exhibit good skill in three different time scales: (1) short-term (day-ahead); (2) immediate-short-term (6-h ahead); and (3) nowcasting (1-h ahead). The forecast method, which combined WRF/YSU/Noah outputs, persistence, and LS-SVM methods, increases the forecast skill by 26.3-49.4% compared to the direct outputs of numerical WRF/YSU/Noah model. Also, this approach captures well the diurnal cycle and seasonal variability of wind speeds, as well as wind direction. Predicción de vientos en una altiplanicie a la altura del eje con el esquema de la Universidad Yonsei/Modelo Superficie Terrestre Noah y la predicción estadísticaResumenLa estimación de la energía eólica está relacionada con la predicción en la variación de los vientos en pequeños intervalos de tiempo. Se seleccionaron cuatro torres eólicas ubicadas al interior de Mongolia para estudiar los recursos eólicos en la complejidad de un altiplano. Se utilizó la investigación climática a mesoscala y la combinación del esquema de la Universidad Yonsei con el Modelo de Superficie Terrestre Noah (WRF/YSU/Noah), con resolución de 1km horizontal y 10 minutos, como el modelo numérico de predicción meteorológica (NWP, del inglés Numerical Weather Prediction). Se utilizaron tres técnicas estadísticas, persistencia, propagación hacia atrás en redes neuronales artificiales y máquina de vectores de soporte-mínimos cuadrados (LS-SVM, del inglés Least Square Support Vector Machine), para mejorar la predicción de la velocidad del viento en una turbina con la altura del eje a 70 metros y se complementó con los resultados del WRF/YSU/Noah. Las técnicas de predicción físico-estadísticas actuales tienen un buen desempeo en tres escalas de tiempo: (1) corto plazo, un día en adelante; (2) mediano plazo, de seis días en adelante; (3) cercano, una hora en adelante. Este método de predicción, que combina los resultados WRF/YSU/Noah con los métodos de persistencia y LS-SVM incrementa la precisión de predicción entre 26,3 y 49,4 por ciento, comparado con los resultados directos del modelo numérico WRF/YSU/Noah. Además, este método diferencia la variabilidad de las estaciones y el ciclo diurno en la velocidad y la dirección del viento.

Initialisation of Land Surface Variables for Numerical Weather Prediction

2012 ◽  
pp. 607-621
Author(s):  
Patricia de Rosnay ◽  
Gianpaolo Balsamo ◽  
Clément Albergel ◽  
Joaquín Muñoz-Sabater ◽  
Lars Isaksen
Keyword(s):  
Numerical Weather Prediction ◽  
Land Surface ◽  
Weather Prediction ◽  
Numerical Weather

Estimates of the Earth surface influence on the accuracy of numerical prediction of air temperature in Belarus using the WRF model

2021 ◽  
Vol 4 ◽  
pp. 50-68
Author(s):  
S.А. Lysenko ◽  
◽  
P.О. Zaiko ◽  
Keyword(s):  
Land Use ◽  
Air Temperature ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Lead Time ◽  
Weather Prediction ◽  
Wrf Model ◽  
Surface Model ◽  
Numerical Weather ◽  
Global Land

The spatial structure of land use and biophysical characteristics of land surface (albedo, leaf index, and vegetation cover) are updated using the GLASS (Global Land Surface Satellite) and GLC2019 (Global Land Cover, 2019) modern satellite databases for mesoscale numerical weather prediction with the WRF model for the territory of Belarus. The series of WRF-based numerical experiments was performed to verify the influence of the updated characteristics on the forecast quality for some difficult to predict winter cases. The model was initialized by the GFS (Global Forecast System, NCEP) global numerical weather prediction model. It is shown that the use of high-resolution land use data in the WRF and the consideration of the new albedo and leaf index distribution over the territory of Belarus can reduce the root-mean-square error (RMSE) of short-range (to 48 hours) forecasts of surface air temperature by 16–33% as compared to the GFS. The RMSE of the temperature forecast for the weather stations in Belarus for a forecast lead time of 12, 24, 36, and 48 hours decreased on average by 0.40°С (19%), 0.35°С (10%), 0.68°С (23%), and 0.56°С (15%), respectively. The most significant decrease in RMSE of the numerical forecast of temperature (up to 2.1 °С) was obtained for the daytime (for a lead time of 12 and 36 hours), when positive feedbacks between albedo and temperature of the land surface are manifested most. Keywords: numerical weather prediction, WRF, digital land surface model, albedo, leaf area index, forecast model validation

scholarly journals A fast visible-wavelength 3D radiative transfer model for numerical weather prediction visualization and forward modeling

2020 ◽  
Vol 13 (6) ◽  
pp. 3235-3261
Author(s):  
Steven Albers ◽  
Stephen M. Saleeby ◽  
Sonia Kreidenweis ◽  
Qijing Bian ◽  
Peng Xian ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Radiative Transfer ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Weather Prediction ◽  
Lower Boundary ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Numerical Weather ◽  
Visible Wavelength

Abstract. Solar radiation is the ultimate source of energy flowing through the atmosphere; it fuels all atmospheric motions. The visible-wavelength range of solar radiation represents a significant contribution to the earth's energy budget, and visible light is a vital indicator for the composition and thermodynamic processes of the atmosphere from the smallest weather scales to the largest climate scales. The accurate and fast description of light propagation in the atmosphere and its lower-boundary environment is therefore of critical importance for the simulation and prediction of weather and climate. Simulated Weather Imagery (SWIm) is a new, fast, and physically based visible-wavelength three-dimensional radiative transfer model. Given the location and intensity of the sources of light (natural or artificial) and the composition (e.g., clear or turbid air with aerosols, liquid or ice clouds, precipitating rain, snow, and ice hydrometeors) of the atmosphere, it describes the propagation of light and produces visually and physically realistic hemispheric or 360∘ spherical panoramic color images of the atmosphere and the underlying terrain from any specified vantage point either on or above the earth's surface. Applications of SWIm include the visualization of atmospheric and land surface conditions simulated or forecast by numerical weather or climate analysis and prediction systems for either scientific or lay audiences. Simulated SWIm imagery can also be generated for and compared with observed camera images to (i) assess the fidelity and (ii) improve the performance of numerical atmospheric and land surface models. Through the use of the latter in a data assimilation scheme, it can also (iii) improve the estimate of the state of atmospheric and land surface initial conditions for situational awareness and numerical weather prediction forecast initialization purposes.

scholarly journals The Plant–Craig Stochastic Convection Scheme in ICON and Its Scale Adaptivity

2014 ◽  
Vol 71 (9) ◽  
pp. 3404-3415 ◽  
Author(s):  
Richard J. Keane ◽  
George C. Craig ◽  
Christian Keil ◽  
Günther Zängl
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Climate Models ◽  
Weather Prediction ◽  
Precipitation Amount ◽  
Precipitation Variability ◽  
Convection Scheme ◽  
Numerical Weather ◽  
Multiple Resolutions ◽  
Input Variables

Abstract The emergence of numerical weather prediction and climate models with multiple or variable resolutions requires that their parameterizations adapt correctly, with consistent increases in variability as resolution increases. In this study, the stochastic convection scheme of Plant and Craig is tested in the Icosahedral Nonhydrostatic GCM (ICON), which is planned to be used with multiple resolutions. The model is run in an aquaplanet configuration with horizontal resolutions of 160, 80, and 40 km, and frequency histograms of 6-h accumulated precipitation amount are compared. Precipitation variability is found to increase substantially at high resolution, in contrast to results using two reference deterministic schemes in which the distribution is approximately independent of resolution. The consistent scaling of the stochastic scheme with changing resolution is demonstrated by averaging the precipitation fields from the 40- and 80-km runs to the 160-km grid, showing that the variability is then the same as that obtained from the 160-km model run. It is shown that upscale averaging of the input variables for the convective closure is important for producing consistent variability at high resolution.

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