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

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

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

Earth Sciences Research Journal ◽

10.15446/esrj.v21n1.63004 ◽

2017 ◽

Vol 21 (1) ◽

pp. 37 ◽

Cited By ~ 1

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.

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Evaluation of a NOAH-MP Land Surface Model Ensemble over the Mississippi Basin

10.5194/egusphere-egu2020-21890 ◽

2020 ◽

Author(s):

Jonas Rothermel ◽

Maike Schumacher

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Numerical Weather Prediction ◽

Land Surface ◽

Hydrological Cycle ◽

Weather Prediction ◽

Surface Model ◽

Empirical Parameter ◽

Numerical Weather ◽

Parameter Values

Physical-based Land Surface Models (LSMs) have deepened the understanding of the hydrological cycle and serve as the lower boundary layer in atmospheric models for numerical weather prediction. As any numerical model, they are subject to various sources of uncertainty, including simplified model physics, unknown empirical parameter values and forcing errors, particularly precipitation. Quantifying these uncertainties is important for assessing the predictive power of the model, especially in applications for environmental hazard warning. Data assimilation systems also benefit from realistic model error estimates.In this study, the LSM NOAH-MP is evaluated over the Mississippi basin by running a large ensemble of model configurations with suitably perturbed forcing data and parameter values. For this, sensible parameter distributions are obtained by performing a thorough sensitivity analysis, identifying the most informative parameters beforehand by a screening approach. The ensemble of model outputs is compared against various hydrologic and atmospheric feedback observations, including SCAN soil moisture data, GRACE TWS anomaly data and AmeriFlux evapotranspiration measurements. The long-term aim of this study is to improve land-surface states via data assimilation and to investigate their influence on short- to midterm numerical weather prediction. Thus, the uncertainty of the simulated model states, such as snow, soil moisture in various layers, and groundwater are thoroughly studied to estimate the relative impact of possible hydrologic data sets in the assimilation.

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Comparisons of Diurnal Variations of Land Surface Temperatures from Numerical Weather Prediction Analyses, Infrared Satellite Estimates and In Situ Measurements

Remote Sensing ◽

10.3390/rs12030583 ◽

2020 ◽

Vol 12 (3) ◽

pp. 583 ◽

Cited By ~ 2

Author(s):

Xiaoni Wang ◽

Catherine Prigent

Keyword(s):

Numerical Weather Prediction ◽

Land Surface ◽

Weather Prediction ◽

In Situ Measurements ◽

The Other ◽

Surface Model ◽

Night Time ◽

Barren Land ◽

Numerical Weather

This study evaluates the diurnal cycle of Land Surface Temperature (LST) from Numerical Weather Prediction (NWP) reanalyses (ECMWF ERA5 and ERA Interim), as well as from infrared satellite estimates (ISCCP and SEVIRI/METEOSAT), with in situ measurements. Data covering a full seasonal cycle in 2010 are studied. Careful collocations and cloud filtering are applied. We first compare the reanalysis and satellite products at continental and regional scales, and then we concentrate on comparisons with the in situ observations, under a large variety of environments. SEVIRI shows better agreement with the in situ measurements than the other products, with bias often less than ±2K and correlation of 0.99. Over snow or arid surface, ISCCP tends to have more systematic errors than the other products. ERA5 agrees better to the in situ over barren land than ERA Interim, particularly at night time, thanks to the new surface model. However, over vegetated surfaces, both reanalyses tend to have higher/lower temperature at night/day time than the in situ measurements, probably related to the surface processes and its interactions with atmosphere in the NWP model.

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Verification of Forecast Weather Surface Variables over Vietnam Using the National Numerical Weather Prediction System

Advances in Meteorology ◽

10.1155/2016/8152413 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Tien Du Duc ◽

Lars Robert Hole ◽

Duc Tran Anh ◽

Cuong Hoang Duc ◽

Thuy Nguyen Ba

Keyword(s):

Numerical Weather Prediction ◽

Weather Prediction ◽

Model Performance ◽

Wrf Model ◽

Horizontal Resolution ◽

Prediction System ◽

Regional Models ◽

Global Spectral Model ◽

Numerical Weather ◽

The Us

The national numerical weather prediction system of Vietnam is presented and evaluated. The system is based on three main models, namely, the Japanese Global Spectral Model, the US Global Forecast System, and the US Weather Research and Forecasting (WRF) model. The global forecast products have been received at 0.25- and 0.5-degree horizontal resolution, respectively, and the WRF model has been run locally with 16 km horizontal resolution at the National Center for Hydro-Meteorological Forecasting using lateral conditions from GSM and GFS. The model performance is evaluated by comparing model output against observations of precipitation, wind speed, and temperature at 168 weather stations, with daily data from 2010 to 2014. In general, the global models provide more accurate forecasts than the regional models, probably due to the low horizontal resolution in the regional model. Also, the model performance is poorer for stations with altitudes greater than 500 meters above sea level (masl). For tropical cyclone performance validations, the maximum wind surface forecast from global and regional models is also verified against the best track of Joint Typhoon Warning Center. Finally, the model forecast skill during a recent extreme rain event in northeast Vietnam is evaluated.

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The Met Office Unified Model Global Atmosphere 4.0 and JULES Global Land 4.0 configurations

Geoscientific Model Development ◽

10.5194/gmd-7-361-2014 ◽

2014 ◽

Vol 7 (1) ◽

pp. 361-386 ◽

Cited By ~ 121

Author(s):

D. N. Walters ◽

K. D. Williams ◽

I. A. Boutle ◽

A. C. Bushell ◽

J. M. Edwards ◽

...

Keyword(s):

Land Surface ◽

Weather Prediction ◽

Regional Climate ◽

Land Surface Model ◽

Unified Model ◽

Surface Model ◽

Development Cycle ◽

Global Land ◽

Overall Performance ◽

Ongoing Development

Abstract. We describe Global Atmosphere 4.0 (GA4.0) and Global Land 4.0 (GL4.0): configurations of the Met Office Unified Model and JULES (Joint UK Land Environment Simulator) community land surface model developed for use in global and regional climate research and weather prediction activities. GA4.0 and GL4.0 are based on the previous GA3.0 and GL3.0 configurations, with the inclusion of developments made by the Met Office and its collaborators during its annual development cycle. This paper provides a comprehensive technical and scientific description of GA4.0 and GL4.0 as well as details of how these differ from their predecessors. We also present the results of some initial evaluations of their performance. Overall, performance is comparable with that of GA3.0/GL3.0; the updated configurations include improvements to the science of several parametrisation schemes, however, and will form a baseline for further ongoing development.

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Background Error in WRF Model

WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT ◽

10.37394/232015.2020.16.63 ◽

2020 ◽

Vol 16 ◽

Keyword(s):

Data Assimilation ◽

Physical Properties ◽

Covariance Matrix ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Wrf Model ◽

Background Error ◽

Numerical Weather ◽

Error Covariance ◽

First Time

WRF model have been tuned and tested over Georgia’s territory for years. First time in Georgia theprocess of data assimilation in Numerical weather prediction is developing. This work presents how forecasterror statistics appear in the data assimilation problem through the background error covariance matrix – B, wherethe variances and correlations associated with model forecasts are estimated. Results of modeling of backgrounderror covariance matrix for control variables using WRF model over Georgia with desired domain configurationare discussed and presented. The modeling was implemented in two different 3DVAR systems (WRFDA andGSI) and results were checked by pseudo observation benchmark cases using also default global and regional BEmatrixes. The mathematical and physical properties of the covariances are also reviewed.

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Spin-up time from switching the microphysics scheme in the assimilation cycle and impacts on the precipitation forecast quality

10.5194/ems2021-379 ◽

2021 ◽

Author(s):

Sven Ulbrich ◽

Christian Welzbacher ◽

Kobra Khosravianghadikolaei ◽

Michael Hoff ◽

Alberto de Lozar ◽

...

Keyword(s):

Numerical Weather Prediction ◽

Lead Time ◽

Weather Prediction ◽

Radar Data ◽

Precipitation Forecast ◽

Additional Measure ◽

Microphysics Scheme ◽

Numerical Weather ◽

Complementary Approach ◽

Forecast Quality

The SINFONY project at Deutscher Wetterdienst (DWD) aims to produce seamless precipitation forecast products from minutes up to 12 hours, with particular focus on convective events. While the near future predictions are typically from nowcasting procedures using radar data, the numerical weather prediction (NWP) aims at longer time scales. The lead-time in the latest available forecast is usually too long for merging both the nowcasting and NWP output to produce reliable seamless predictions.At DWD, the current forecasts are produced by the short range numerical weather prediction (SRNWP) making use of a continuous assimilation cycle with relatively long cutoff times and using 1-moment microphysics. In order to reduce the differences in the precipitation to the nowcasting on the NWP side, we use two different approaches. First, we reduce the lead-time from the model start by running 1-hourly forecasts based on an assimilation cycle with shorter data cutoff. Secondly, we use new observational systems in the assimilation cycle, such as radar or satellite data to capture and represent strong convective activity. This procedure is called Rapid Update Cycle (RUC). As an additional measure, we introduce a 2-Moment microphysics scheme into the numerical model, resulting in a better representation of the radar reflectivities. In order to keep the model state similar to that of the SRNWP, the RUC is a time limited assimilation cycle starting from forecasts of the SRNWP at pre-defined times.The introduction of the 2-Moment scheme leads to a spin-up affecting both the assimilation cycle and the short forecasts. The resulting effects are analysed by comparison with the corresponding assimilation cycle using the 1-Moment scheme. As a complementary approach for the analysis, the routine cycle is run with the 2-Moment scheme. The forecast quality is used as a measure to compare the results with respect to precipitation and additional observed parameters. It is shown in how far the resulting improvements are related to the assimilation and momentum scheme, or to the higher frequency of forecasts.

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Initialisation of Land Surface Variables for Numerical Weather Prediction

The Earth's Hydrological Cycle - Space Sciences Series of ISSI ◽

10.1007/978-94-017-8789-5_7 ◽

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

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The Use of a Numerical Weather Prediction Model to Simulate Near-Field Volcanic Plumes

Atmosphere ◽

10.3390/atmos11060594 ◽

2020 ◽

Vol 11 (6) ◽

pp. 594

Author(s):

Ralph R. Burton ◽

Mark J. Woodhouse ◽

Alan M. Gadian ◽

Stephen D. Mobbs

Keyword(s):

Numerical Weather Prediction ◽

Near Field ◽

Weather Prediction ◽

Wrf Model ◽

Integral Model ◽

Eruption Column ◽

Volcanic Plumes ◽

Numerical Weather ◽

Essential Components ◽

Nwp Model

In this paper, a state-of the art numerical weather prediction (NWP) model is used to simulate the near-field plume of a Plinian-type volcanic eruption. The NWP model is run at very high resolution (of the order of 100 m) and includes a representation of physical processes, including turbulence and buoyancy, that are essential components of eruption column dynamics. Results are shown that illustrate buoyant gas plume dynamics in an atmosphere at rest and in an atmosphere with background wind, and we show that these results agree well with those from theoretical models in the quiescent atmosphere. For wind-blown plumes, we show that features observed in experimental and natural settings are reproduced in our model. However, when comparing with predictions from an integral model using existing entrainment closures there are marked differences. We speculate that these are signatures of a difference in turbulent mixing for uniform and shear flow profiles in a stratified atmosphere. A more complex implementation is given to show that the model may also be used to examine the dispersion of heavy volcanic gases such as sulphur dioxide. Starting from the standard version of the weather research and forecasting (WRF) model, we show that minimal modifications are needed in order to model volcanic plumes. This suggests that the modified NWP model can be used in the forecasting of plume evolution during future volcanic events, in addition to providing a virtual laboratory for the testing of hypotheses regarding plume behaviour.

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