Performance of site-specific parameterizations of longwave radiation

Abstract

Abstract. In this work 10 algorithms for estimating downwelling longwave atmospheric radiation (L↓) and 1 for upwelling longwave radiation (L↑) are integrated into the JGrass-NewAge modelling system. The algorithms are tested against energy flux measurements available for 24 sites in North America to assess their reliability. These new JGrass-NewAge model components are used (i) to evaluate the performances of simplified models (SMs) of L↓, as presented in literature formulations, and (ii) to determine by automatic calibration the site-specific parameter sets for L↓ in SMs. For locations where calibration is not possible because of a lack of measured data, we perform a multiple regression using on-site variables, i.e. mean annual air temperature, relative humidity, precipitation, and altitude. The regressions are verified through a leave-one-out cross validation, which also gathers information about the possible errors of estimation. Most of the SMs, when executed with parameters derived from the multiple regressions, give enhanced performances compared to the corresponding literature formulation. A sensitivity analysis is carried out for each SM to understand how small variations of a given parameter influence SM performance. Regarding the L↓ simulations, the Brunt (1932) and Idso (1981) SMs, in their literature formulations, provide the best performances in many of the sites. The site-specific parameter calibration improves SM performances compared to their literature formulations. Specifically, the root mean square error (RMSE) is almost halved and the Kling–Gupta efficiency is improved at all sites. Also in this case, Brunt (1932) and Idso (1981) SMs provided the best performances. The L↑ SM is tested by using three different temperatures (surface soil temperature, air temperature at 2 m elevation, and soil temperature at 4 cm depth) and model performances are then assessed. Results show that the best performances are achieved using the surface soil temperature and the air temperature.

Bridging Thermal Infrared Sensing and Physically‐Based Evapotranspiration Modeling: From Theoretical Implementation to Validation Across an Aridity Gradient in Australian Ecosystems

“…Estimation of T R was based on the observed upwelling longwave radiation ( R L↑ ) and the Stefan‐Boltzmann equation (Formetta et al, ; Park et al, ; Sun & Pinker, ) ( ɛ is the infrared surface emissivity, δ is the Stefan‐Boltzmann constant). Upwelling longwave radiation was directly measured with pyrgeometer in all the study sites.…”

A Comparative Study of Bulk Parameterization Schemes for Estimating Cloudy-Sky Surface Downward Longwave Radiation

“…The accuracy of predicted SDLR is improved by incorporating the total precipitable water (TPW) [50,51]. Other factors, such as uncertainties in the cloud fraction, aerosol loads, heatwaves in hot summers, and temperature inversions in the lower atmospheric levels during cold winter night, also affect the predictability of the bulk formulae [27,34,50,52]. The positive BIAS over climate types Af and Cf might result from strong unstable air temperatures with high humidifies, and negative BIAS over climate type ET might relate to dry atmospheric conditions and strong inversions; In addition, the negative BIAS over land cover type bare land may be incurred by strong atmospheric inversions.…”

The design, deployment, and testing of kriging models in GEOframe with SIK-0.9.8

“…The various theoretical semivariogram models or kriging types to be chosen at run time were encapsulated by using the Simple Factory class (Freeman et al, 2004). In this way, adding a new type of variogram to, or deleting an obsolete one from, the SIK-TV is straightforward and requires few changes, which are confined to just a class.…”

“…To make the code more flexible, easily extensible, and maintainable, SIK was completely refactored and a systematic use of design patterns (DPs) (Gamma, 1994;Freeman et al, 2004) was introduced.…”