Longwave Radiation Model Research Articles

All-sky longwave radiation modelling based on infrared images and machine learning

Sky longwave radiation is an important input parameter for heat transfer and radiant energy balance calculations of the building envelope, and it has a significant effect on the accuracy of predictions of the energy consumption of buildings. The ability of the sky to emit longwave radiation needs to be accurately quantified under different weather conditions. In this study, a low-cost sky cloudiness observation platform based on an infrared imager was established, and an algorithm for calculating the cloudiness of the sky dome by stitching and processing infrared images was proposed. A machine learning model was developed by collecting local meteorological data; these data were then input into the synthetic minority oversampling technique algorithm. This model can estimate sky cloudiness based on conventional meteorological parameters. Nine different machine learning algorithms were tested, and a cloudiness prediction model based on the XGBoost algorithm was finally established, which had a prediction accuracy of 88.81%. Furthermore, an all-sky longwave radiation model was developed by introducing cloudiness parameters. The applicability of different longwave radiation models for clear and cloudy skies in subtropical climates was examined, and the coefficient values of the different models were modified. Finally, the formula applicable to subtropical climates was determined via a fitting method.

Single-footprint retrieval of clear-sky surface longwave radiation from hyperspectral AIRS data

Surface longwave radiation (SLR) derived from remotely sensed data facilitates understanding of the SLR in global climate change. Hyperspectral infrared sounders aboard space platforms provide information on the surface and vertical structure of Earth’s atmosphere. However, currently, SLR products estimated from these observations are unavailable, which hampers their application potential for Earth’s radiation budget in the context of global warming. To address this issue, we developed simple and effective SLR model under clear-sky conditions using at-sensor spectral radiances from Atmospheric Infrared Sounder (AIRS). The model was found to be insensitive to AIRS instrument noise, and showed good performances based on a simulation dataset. The AIRS footprint geometrical model was proposed to match the AIRS and Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimate the cloud fraction. Validation against ground-based measurements found that the surface upward longwave radiation model has a bias of 3.18 W/m2, root-mean-square error (RMSE) of 30.51 W/m2, and R2 of 0.84; the surface downward longwave radiation model has a bias of 0.77 W/m2, RMSE of 29.09 W/m2, and R2 of 0.78. The large validation biases at two ground sites reflect the limited spatial representativeness for AIRS footprints. Terrain-induced altitude differences and spatial inhomogeneity can redistribute the spatial distributions of SLR. Moreover, the model performances were weakly dependent on seasonal variation. The results indicate that the proposed model provides a foundation for the further development of operational SLR products.

A microenvironment prediction model for Chinese solar greenhouses based on the bond graph approach.

To improve the prediction accuracy of temperature and humidity in typical Chinese solar greenhouses, this paper proposed a new longwave/shortwave radiation modeling method using bond graph. This model takes into account sun position, useful incoming solar radiation model, sky longwave radiation model, inside longwave, and shortwave radiation model. The approach solves the problems caused by underestimating the effects of longwave radiation on night temperature and relative humidity. The study found that after a period of t = 7.5 h, with the increase of sun altitude angle, the internal temperature was significantly affected by the temperature rise of outside environment on sunny day. The sun altitude angle gradually falls over a period of t = 12.5 h (beginning at 12.30 p.m.). The decline in night temperature steadily slowed after a period of t = 20.5 h. On the other hand, the temperature variation has a multi-peak distribution and the warming rate of the CSG slows down on cloudy days. Furthermore, a good agreement between the experimental and simulation data were obtained, with a maximum temperature deviation of 2°C and maximum humidity deviation of 5%. The developed model is a universal and valuable approach that can be used for greenhouse climate simulation. Furthermore, it can be used as a support system during decision-making processes to help manage Chinese solar greenhouses more efficiently, which provides several control perspectives on the low-energy greenhouse in the future. This work has also provided several control perspectives on the low energy greenhouse in the future.

Evaluation of shortwave and longwave radiation models for mechanistic-empirical pavement analysis

ABSTRACT Pavement temperature is one of the most important factors influencing the performance of flexible pavements. The major components of the Earth's heat balance system are downwelling shortwave radiation (D-SWR) from the sun, downwelling longwave radiation (D-LWR) from the atmosphere, and upwelling longwave radiation (U-LWR) emitted by the warm pavement surface. The Pavement ME Design (PMED) software (AASHTO, 2015. Mechanistic-empirical pavement design guide–a manual of practice. Washington, DC: AASHTO) computes temperature distributions in the pavement over depth and time using algorithms originally proposed by Dempsey et al. (1985. Environmental effects on pavements: theory manual. US Department of Transportation, Federal Highway Administration). These algorithms are largely empirical, depend heavily on imprecise corrections for cloud cover, and in some parts contradict atmospheric physics. Improved models for the major radiation components proposed here include: (1) D-SWR values modelled by the MERRA-2 climate re-analysis product from NASA (Rienecker et al., 2011. MERRA: NASA’s modern-era retrospective analysis for research and applications. Journal of Climate, 24, 3624–3648); (2) D-LWR modelled using the Idso (1981. A set of equations for full spectrum and 8-to 14-μm and 10.5-to 12.5-μm thermal radiation from cloudless skies. Water Resources Research, 17, 295–304) empirical parameterisation with a physics-consistent adjustment for cloud cover; and (3) U-LWR calculations without the physics-inconsistent cloud cover adjustment embedded in PMED. The D-SWR and D-LWR radiation models were evaluated via comparisons against ground-based radiation observations at 21 locations in the Solar Infrared Radiations Stations (SIRS) database. The MERRA-2 estimates of D-SWR, although not perfect, were found to agree substantially better than the current PMED models with the ground truth SIRS. The D-LWR fluxes from the Idso (1981. A set of equations for full spectrum and 8-to 14-μm and 10.5-to 12.5-μm thermal radiation from cloudless skies. Water Resources Research, 17, 295–304) parameterisation were also found to agree substantially better than the current PMED with the ground truth SIRS, with particularly striking improvements for the all-sky (i.e. including clouds) condition. A limited series of flexible pavement performance analyses were conducted to illustrate the potential practical impact of these radiation model changes. The new radiation models produced significantly higher pavement temperatures that lead to substantially higher total rutting, asphalt rutting, and bottom up fatigue cracking. These changes in distress magnitudes must be evaluated in qualitative terms only, as all analyses used the same field calibration coefficients for the distress models. The primary conclusion from this work is that the current radiation models incorporated in PMED are inaccurate and, in some cases, inconsistent with fundamental atmospheric physics. This can have significant impacts on predicted pavement performance. It is recommended that the current PMED radiation models be replaced by the new D-SWR, D-LWR, and U-LWR. New field calibration of the empirical distress models would be required after incorporating the new radiation models.

Evaluation and Calibration of Downward Longwave Radiation Models under Cloudless Sky at Ile-Ife, Nigeria

Downward longwave radiation flux is an important variable for estimating net radiation, in order to investigate the surface energy budget and carry out climatic studies. The flux is usually estimated using empirical models based on the information of meteorological parameters such as relative humidity, surface and air temperature, and water vapor pressure. This paper presents the assessment of 11 widely used empirical models for estimating downward longwave radiation using a ground-based dataset acquired from January 2016 to December 2017 at Ile-Ife, a tropical city in Nigeria. The original Idso and Niemela et al. models performed better than other models with errors less than 5.0% when compared to measured values. The performances of all the models improved greatly after calibration. The Guest model, which gave low errors (MBE = 0.65 Wm–2, RMBE = 0.15%, RMSE = 9.38 Wm–2, RRMSE = 2.14%, MAE = 7.84 Wm–2, RMAE = 1.79%), performed best followed by the Dilley and O’Brien, Idso, Prata, Brutsaert, Garratt, Niemela et al., and Ångström models. The calibrated models presented in this study can be used to estimate the flux under cloudless sky conditions at Ile-Ife and at other places with similar meteorological conditions, where this flux is not measured due to technological problems and the high cost of purchasing and maintaining the needed sensors. The proposed model for estimating the flux showed better performance with lower statistical errors than all the existing empirical models tested, and conform greatly (R2 = 0.88) to the measured data.

Deep learning and regression modelling of cloudless downward longwave radiation

BackgroundThough downward longwave radiation (DLR) models curb the paucity of data, they are mostly location dependent. Therefore, there is a need to evaluate their relevance given the increasing use of machine learning techniques. In this study, cloudless DLR estimates from regression models and soft computing models of neural networks (NN), support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS) were compared. Clear days from September 1992 to August 1994 and July 1995 to March 1998 in Ilorin (8.50 °N, 4.55 °E), Nigeria were considered, while the predictors for the models were water vapour pressure, e and air temperature, T.ResultsA new regression model in relation to the Boltzmann constant, σ: left(1.014left(frac{1.0times {10}^{30}times e}{T^{13}}right)+0.699right)sigma {T}^4 , was better than other regression models and applicable at another location. Between 1 and 8, the sixth degree was the best polynomial kernel function in SVR models’ estimations of cloudless DLR. Though the new regression model was comparable to expert systems, ANFIS was still the best model due to its consistent high correlations and lowest estimation errors.ConclusionsExperience-based computational procedures that combine enough logics with neural networks respond effectively to other data. Furthermore, the analytical relationship between water vapour pressure and air temperature in DLR’s mechanism should be redefined accordingly, while the sixth polynomial should be used as the default setting in SVR systems.

Modeling surface longwave radiation over high-relief terrain

Thermal anisotropy is an important phenomenon in thermal infrared remote sensing as it restricts the retrieval accuracy of surface longwave radiation (SLR). Topography is an essential controlling factor for the directionality of SLR for high-relief regions (e.g., mountain regions) where there is land surface heterogeneity and non-isothermal properties at pixel scales. However, satellite sensors can only receive radiance from a specific surface object at a small number of simultaneous viewing angles, which makes the quantitative modeling of thermal anisotropy challenging. Therefore, we developed the topographic longwave radiation model (TLRM) to describe the directionality of SLR components taking into account the variability of both subpixel topography and thermal anisotropy in high-relief regions. The reliability of TLRM was validated using the Discrete Anisotropic Radiative Transfer (DART) model over two typical geomorphic areas: a valley scene and a peak scene. The preliminary validation shows good agreement in terms of surface upward longwave radiance, which confirms the potential of TLRM for capturing the anisotropic patterns of land surfaces. The radiance values simulated by the DART model were first revised for the spectral mismatch. Then, they are used to correct residual deviation for TLRM using linear regressions. The root mean square error (RMSE) and coefficient of determination (R2) were 0.830 W/(m2 ∙ sr) and 0.746 for the valley scene, respectively, and 0.239 W/(m2 ∙ sr) and 0.711 for the peak scene, respectively. Compared with TLRM, models that do not consider terrain effects generate significant discrepancies in high relief SLR components. The differences in downward longwave radiation can reach −60 W/m2 in valleys without considering terrain effects. Based on the reference of hemispherical upward longwave radiation, surface upward longwave radiation estimated by the direct estimation method had a bias of 11.41 W/m2 and standard deviation (STD) of 7.30 W/m2, while the directional upward longwave radiation had a bias of 5.99 W/m2 and STD of 4.08 W/m2, showing lower estimation variation. The discrepancy between surface net longwave radiation (NLR) and terrain-corrected NLR ranged between 50 and −130 W/m2 with clear negative biases mainly occurring in valleys. With higher spatial resolutions of remotely sensed imagery, the influence of complex terrain on land surface radiative flux has become more significant. This parameterization scheme is expected to better represent the topographic effects on SLR, enhance understanding of thermal anisotropy in non-isothermal mixed pixel areas of high relief, and improve the inversion accuracy of SLR.

Modeling of downward longwave radiation and radiative cooling potential in China

The information on downward longwave (LW) radiation is necessary for the design of the passive cooling system of buildings. Because the measured LW radiation data are not available in most locations, few studies have focused on the modeling of LW radiation in China. In this study, empirical models for estimating LW radiation were proposed under all-sky conditions based on the meteorological parameters such as the ambient dry-bulb temperature, water vapor pressure, and relative humidity, which can be classified into four cases: All day, nighttime, and daytimes with and without considering the cloud modification factor. The proposed models performed are well compared with the existing models. The LW radiation datasets for 351 locations of China were developed using the proposed model for all the day based on the typical meteorological year. Moreover, the distribution map of radiative cooling potential in July was created using the proposed model for the nighttime, which can provide a valuable reference for building cooling design.

Pseudo bond graph tunnel greenhouse model with accurate longwave/shortwave radiations model

ABSTRACT This research work is focused on the construction of an accurate longwave/shortwave radiation model on a tunnel greenhouse pseudo-bond graph model, widely used in Tunisia. This model includes sun position, useful incoming solar radiation model, sky longwave radiation model, inside longwave and shortwave radiation model. The key idea is to use bond graphs allowing a lumped modelling approach which is suitable for control applications. Furthermore, an evaluation of some longwave radiative model components was made, noting that these components are particularly sensitive regarding to the thermal behaviour of the model. Experimental tunnel greenhouse data are used as validation elements for the present model with globally good results. A comparative study was also performed between the present model and a previous bond graph model containing a simplistic radiative model. Practical simulation results show a clear improvement compared with the previous model.

Rule of long-wave radiation in enclosed building space

To study the trend of long-wave radiation in urban spaces surrounded by dense buildings, a calculation model for long-wave radiation was established. A simplified calculation of view factor was built in the model. The model of long-wave radiation formed by direct and reflected components and Kirchhoff's current law was used to calculate the reflected radiation. The accuracy of model was verified using the measured experimental data obtained from three locations in Xi'an, Sanya, and Harbin. The model was used to calculate the long-wave radiation on the external surface of a building in both enclosed and open spaces. Using the long-wave radiation model, the rule of long-wave radiation in an enclosed space was obtained by analyzing different forms of building enclosure.

Evaluation of the atmospheric longwave radiation models estimated in Brasilia – DF

A radiação atmosférica de ondas prolongadas tem uma importância considerável nos estudos meteorológicos, bem como no aquecimento global. Por estar ligado aos gases atmosféricos e à temperatura do ar. Este artigo tem como objetivo avaliar o desempenho de doze modelos para a estimativa da radiação de onda longa atmosférica em Brasília-DF. Os dados meteorológicos utilizados neste estudo foram adquiridos a partir da rede de dados do Sistema de Organização de Dados Ambientais - SONDA. Neste estudo, os dados da temperatura do ar, umidade relativa, radiação global e radiação atmosférica de onda longa foram utilizados para os modelos avaliados. Os cálculos foram processados com médias horárias e diárias. A avaliação dos modelos e a comparação dos resultados estimados e medidos da radiação da onda longa atmosférica foram realizadas utilizando os métodos estatísticos do erro médio quadrado (RMSE), erro absoluto médio (MAE) e erro médio médio relativo (PMRE). O coeficiente de correlação de Pearson (r) e o coeficiente de determinação (R²) também foram aplicados. Os modelos que tiveram excelentes resultados foram Brunt (1932), Prata (1996), Berdahl e Martin (1984), Idso (1982) e Berger et al. (1984), uma vez que apresentaram um bom desempenho, devido aos menores valores de RMSE , MAE e PMRE

Retrieval of Leaf, Sunlit Soil, and Shaded Soil Component Temperatures Using Airborne Thermal Infrared Multiangle Observations

Land surface component temperatures are important inputs in longwave radiation and evapotranspiration estimation models. Most component temperature inversion approaches focus only on two components, namely, soil and leaves, because space-based multiangle observations are lacking. This approach is inconsistent with ground-based measurements, which suggest that the temperatures of sunlit and shaded soil may significantly differ. This paper explores a three-component temperature inversion scheme that uses airborne multiangle thermal infrared observations to decrease the difference between the retrieved data and the actual subpixel temperature distribution. The FR97 model, which is an analytical directional brightness temperature model that was modified by dividing the soil component into sunlit and shaded portions, is adopted to calculate the matrix of component effective emissivity, which links multiangular observations and component temperatures. The new forward model and the inversion scheme are assessed using simulated data sets from the Scattering by Arbitrarily Inclined Leaves (4SAIL) model. The results indicate that the modified FR97 model provides good precision and that the inversion scheme based on the modified FR97 model is appropriate because of the model's simplicity and accuracy and the inversion's low sensitivity to noise. The inversion scheme is validated using airborne data collected by the wide-angle infrared dual-mode line/area array scanner over an area planted with maize and ground measurements collected during the Heihe Watershed Allied Telemetry Experimental Research campaign. The results indicate that the root mean square errors of the component temperatures of the leaves, sunlit soil, and shaded soil were 0.72 °C, 1.55 °C, and 2.73 °C, respectively. Because of the modified FR97's straightforward form and acceptable precision, we recommend this new retrieval scheme as an option for retrieving the component temperatures of leaves, sunlit soil, and shaded soil.

Parameterization of incoming longwave radiation at glacier sites in the Canadian Rocky Mountains

AbstractWe examine longwave radiation fluxes in the Canadian Rocky Mountains based on multiyear observations at glaciers in the southern and northern Rockies. Our main objective is to develop improved parameterizations of incoming longwave radiation for surface energy balance and melt modeling in glaciological studies, in situations where minimal meteorological data are available. We concentrate on the summer melt season, June through August. We test several common parameterizations of mean daily incoming longwave radiation and also explore simple regression‐based models of atmospheric emissivity as a function of near‐surface vapor pressure, relative humidity, and a sky clearness index (i.e., a proxy for cloud cover). Multivariate regressions based on these three variables have the strongest performance at our two sites, with RMS errors of 9–13 W m−2 and biases 1–2 W m−2 when transferred to different time periods or between sites in our study region. We also find good results for all‐sky atmospheric emissivity with a bivariate relation based on vapor pressure and relative humidity. This parameterization requires only screen‐level temperature and humidity as input data, which has value for modeling of incoming longwave radiation and surface energy balance when observational radiation and cloud data are not available.

Topographic radiation modeling and spatial scaling of clear-sky land surface longwave radiation over rugged terrain

Abstract Longwave radiation (5–100 μm) is a critical component of the Earth's radiation budget. Most of the existing satellite-based retrieval algorithms are valid only for flat surfaces without accounting for topographic effects. This causes significant errors. Meanwhile, the fixed spatial resolution of remote sensing data makes it difficult to link the satellite-derived longwave radiation to different land models running on various scales. These deficiencies result in an urgent need for topographic modeling and spatial scaling studies of longwave radiation. In this paper, a longwave topographic radiation model (LWTRM) is proposed that quantifies all possible radiation-affecting factors over rugged terrain. For driving the LWTRM, a hybrid method for simultaneously deriving multiple components of longwave radiation from MODIS data is suggested based on artificial neuron networks (ANN) and the radiative transfer simulation. Topographically corrected longwave radiation is then derived by coupling the ANN outputs and LWTRM. Based on this, a general upscaling strategy for longwave radiation is presented. The results demonstrate that: (1) both the proposed LWTRM and the upscaling strategy are rather effective and work well over rugged areas; (2) the ANN-based retrieval method can produce longwave radiation with better accuracy(RMSE

Assessment of valley cold pools and clouds in a very high-resolution numerical weather prediction model

Abstract. The formation of cold air pools in valleys under stable conditions represents an important challenge for numerical weather prediction (NWP). The challenge is increased when the valleys that dominate cold pool formation are on scales unresolved by NWP models, which can lead to substantial local errors in temperature forecasts. In this study a 2-month simulation is presented using a nested model configuration with a finest horizontal grid spacing of 100 m. The simulation is compared with observations from the recent COLd air Pooling Experiment (COLPEX) project and the model's ability to represent cold pool formation, and the surface energy balance is assessed. The results reveal a bias in the model long-wave radiation that results from the assumptions made about the sub-grid variability in humidity in the cloud parametrization scheme. The cloud scheme assumes relative humidity thresholds below 100 % to diagnose partial cloudiness, an approach common to schemes used in many other models. The biases in radiation, and resulting biases in screen temperature and cold pool properties are shown to be sensitive to the choice of critical relative humidity, suggesting that this is a key area that should be improved for very high-resolution modeling.

Observations and modeling of incoming longwave radiation to snow beneath forest canopies in the west Tianshan Mountains, China

Forest canopy reduces shortwave radiation and increases the incoming longwave radiation to snowpacks beneath forest canopies. Furthermore, the effect of forest canopy may be changed by complex topography. In this paper, we measured and simulated the incoming longwave radiation to snow beneath forest at different canopy openness in the west Tianshan Mountains, China (43°16′N, 84°24′E) during spring 2013. A sensitivity study was conducted to explore the way that terrain influenced the incoming longwave radiation to snow beneath forest canopies. In the simulation model, measurement datasets, including air temperature, incoming shortwave radiation above canopy, and longwave radiation enhanced by adjacent terrain, were applied to calculate the incoming longwave radiation to snow beneath forest canopy. The simulation results were consistent with the measurements on hourly scale and daily scale. The effect of longwave radiation enhanced by terrain was important than that of shortwave radiation above forest canopy with different openness except the 20% canopy openness. The longwave radiation enhanced due to adjacent terrain increases with the slope increase and temperature rise. When air temperature (or slope) is relatively low, the longwave radiation enhanced by adjacent terrain is not sensitive to slope (or air temperature), but the sensitivity increases with the decrease of snow cover area on sunny slope. The effect of longwave radiation is especially sensitive when the snow cover on sunny slope melts completely. The effect of incoming shortwave radiation reflected by adjacent terrain on incoming longwave radiation to snow beneath forest canopies is more slight than that of the enhanced longwave radiation.

Modeling the Downwelling Longwave Radiation over a Groomed Ski Run under Clear Skies

AbstractThe surface radiation budget of a groomed ski run is important to ski racing. Variables such as snow-surface temperature and liquid water content depend upon the surface radiation budget and are crucial to preparing fast skis. This case study focuses on downwelling longwave radiation, measurements of which were made at a point on a ski run on Whistler Mountain, British Columbia, Canada, throughout a 5-day clear-sky intensive observation period. Tall trees often dominate the horizon of a point on a ski run, and so contributions to total downwelling longwave radiation from trees and sky were treated separately. The “LWRAD” longwave radiative flux model estimated the total downwelling longwave radiation by first calculating thermal contributions from the trees, incorporating regressions for tree temperature that use routine meteorological measurements. Contributions from each azimuth direction were determined with horizon-elevation angles from a theodolite survey. Thermal emissions were weighted accordingly and summed. Sky contributions were estimated using the “libRadtran” radiative transfer model with input of local atmospheric profiles of temperature and humidity and were added to tree emissions. Two clear-sky emissivity parameterizations using screen-height measurements were tested for comparison. LWRAD total downwelling longwave radiation varies between 235 and 265 W m−2 and compares well to measurements, with correlation coefficient squared (r2) of 0.96. These results can be used to improve estimates of downwelling longwave radiation for a groomed ski run.

Estimation of clear‐sky land surface longwave radiation from MODIS data products by merging multiple models

The surface longwave radiation budget plays an important role in the Earth's climate system. Remote sensing provides the most practical way to map surface longwave radiation on a large scale and at a high spatial resolution. In this paper, we evaluate both surface downward longwave radiation (DLR) and upwelling longwave radiation (ULR) models under clear‐sky conditions from MODIS data products. There are multiple DLR models available with variable uncertainties, and the Bayesian Model Averaging (BMA) method is incorporated in this study to combine the predictive distribution of these models for better accuracy. The integrated estimates for DLR based on the BMA method have lower root‐mean‐square errors (RMSEs) and higher coefficients of determination (R2) than the best individual model. The RMSEs decreased by approximately 10 W/m2 at two forest sites and by approximately 4 W/m2 at other sites. The R2value increased at each site by more than 0.05. Two models for calculating the surface upwelling longwave radiation (ULR) are also evaluated at 16 sites. The results show that both the land surface temperature (LST)–emissivity method and the direct method, the Wang‐U model underestimate the clear‐sky ULR. The validation results show that the surface net longwave radiation (NLR) estimated using DLR estimates based on the BMA method and ULR estimates based on the LST‐emissivity method is the most accurate.

Simplified modeling of downwelling long-wave radiation over brazilian semi-arid under irrigation conditions

Data collected in a micrometeorological campaign conducted in a banana orchard in a semi-arid land of Brazil in the period from 2005 to 2006 were usedfor a long-wave radiation (LWR) study. The objective of the study was the calibration and determination of a long-wave radiation models. In this sense, two establishedmodels were used for estimating apparent emissivity of the atmosphere locally calibrated. The percentage and root square errors obtained by calibrated equations wereequal to 3.28% and 16.10 Wm-2 for the model of Brutsaert and 2.46% and 12.68 Wm-2 for the model Bastiaanssen. It was found that the errors obtained with thecalibrations performed substantially decreased and that estimates of the radiation incident wave with the same produce results far more accurate than those made withthe calibrations proposed originally. This shows the variability of the coefficients in the regions studied and the importance of local calibration.

Application of WRF weather model to forecast precipitation in Nilwala river basin

The Weather Research and Forecasting (WRF) model is a numerical weather prediction and atmospheric simulation system designed for both research and operational applications. It was fine tuned to forecast precipitation over Nilwala river basin in Sri Lanka by investigating the impacts of its physics options on precipitation forecasting. In model fine tuning, impacts of microphyics schemes, cumulus schemes, land surface schemes, boundary layer schemes and long/shortwave schemes were investigated. Model predictions were compared with observed point rainfall data for three rainfall events to find reasonably good physics combinations. It was seen that the model combination, Ferrier microphysics scheme, Kain-Fritsch cumulus scheme, Rapid Update Curve land surface scheme, Yonsei boundary layer scheme, Rapid Radiative Transfer model longwave radiation scheme and Dudhia shortwave scheme yields good precipitation predictions over the basin when compared with the other physics combinations tested.