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Globally, a variety of numerical weather prediction (NWP) models are employed by national agencies. However, the derived outputs from these models are generally inadequate for precise cultivation management due to bias error. This study uses observational data acquired over several months to develop an equation for correcting surface air temperature (SAT) error in NWP model outputs. With the proposed method, model outputs are converted into potential temperature to cancel the influence of differences between computational elevation and real elevation. The potential temperature errors in the NWP model outputs are corrected using a linear regression equation in which a radiative cooling scale (RCS) serves as the independent variable. The RCS is computed using air temperature observed at a nearby meteorological observation station, together with upper‐level pressure data from the NWP model. Versions of the correction models are developed for specific situations: day, night, and upper‐level meteorology, classified into six groups. In an application of the proposed method, the root mean square error (RMSE) of the corrected SAT output from a global spectral model operated by the Japan Meteorological Agency (JMA) was 1.2–1.6 K; the RMSE of the corrected SAT output from a nonhydrostatic model operated by the JMA was 1.2–1.5 K. The proposed method corrected the NWP model SAT output errors to a level of accuracy similar to that of data corrected using the Kalman filtering technique without the requirement for constant observation at the target site. [ABSTRACT FROM AUTHOR]

Urban development exacerbates urban heat island effects of cities and thermal discomfort of the residents worldwide. However, such urban effects vary with the geographical and climatic conditions of the respective cities. This study is the first to quantitatively estimate the impact of past (1878), present (2012), and future (2050) urbanization on surface air temperatures and human thermal comfort in Sofia, the largest city of Bulgaria. We used the state-of-the-art Weather Research and Forecasting (WRF) climate model, with a 1-km horizontal resolution for three cases: LU1878, LU2012, and LU2050. We first validated the results of the control simulation (case LU2012) against actual observations. The model satisfactorily reproduced the diurnal variation of the observed monthly mean surface air temperatures for July (2011-2013) with a mean bias of ±0.6°C. We then estimated the potential impacts of past and future urbanization on surface temperature. Our results suggest that because of urbanization of Sofia, the surface air temperature at 0600 Local Standard Time (LST) has increased approximately 4.0°C in the past (1878-2012) and approximately 0.4°C in future (2012-2050). We also evaluated the impact of the urban thermal environment on human comfort levels. Considering the LU2050 case in the central part of Sofia, the temperature-humidity index during mid-afternoon (1400-1700 LST) is estimated to be 24-26, indicating that approximately 50% of the residents could feel perceptibly uncomfortable. [ABSTRACT FROM AUTHOR]

In climate change research, accurate temperature data are often demanded. However, affected by many factors, especially solar radiation, the accuracy of environmental air temperature measurement can be greatly reduced, since there is a difference in temperature between the environmental air and the related temperature measured by the sensor accommodated inside the radiation shield. In the paper, the term “temperature error” refers to the temperature difference described above. To improve the accuracy of the temperature data, a temperature errorcorrection method is proposed. First, a computational fluid dynamics (CFD) method is adopted to quantify the temperature errors accurately. A neural network algorithm is then applied to form a universal correction equation by fitting temperature errors calculated using the CFD method. Finally, to validate the correction equation, field observation experiments are performed. The root mean square error (RMSE) and the mean absolute error (MAE) between the temperature errors obtained experimentally using a sensor inside the DTR503A shield and the corresponding temperature errors determined by using the proposed correction method are 0.043 and 0.038°C, respectively. The RMSE and MAE for the DTR13 radiation shield are 0.049 and 0.044°C, respectively. This method may reduce the error of the temperature data to 0.05°C. If the environmental factors corresponding to the temperature data can be quantified accurately, the factors influencing the temperature error can be added to the correction method continuously. The accuracy of this correction method may be furtherly improved. [ABSTRACT FROM AUTHOR]

ABSTRACT: The correction of heat‐biased temperatures measured by radiosondes during the daytime is affected by other environmental parameters such as ventilation, air temperature and pressure. Among them, the effect of low temperature on temperature sensors under irradiation is not well known. This paper studies the combined effect of low temperature and low pressure on the air‐temperature measurement under irradiation to obtain a correction formula using dual thermistors with different emissivities. The temperature variation of both thermistors under irradiation (about 1500 W m–2) is increased as temperature and pressure are decreased from 25 to −80 °C and from 1000 to 10 hPa, respectively. The observed behaviour is explained using heat‐transfer equations. In order to calculate the irradiance with no pyranometer, the temperature difference between dual thermistors is formulated as a function of irradiance, temperature and pressure through chamber‐based experiments. After the relationship between parameters is established, the temperature difference between thermistors is solely used to calculate the irradiance. The uncertainty of the calculated irradiance using dual thermistors is 6.2% (94 W m−2) at the coverage factor k = 1. The calculated irradiance is used for the correction of the heat‐biased temperatures measured by the thermistors. The combined uncertainty of corrected temperature is 0.79 K (k = 1), which includes uncertainties owing to the irradiance, pressure and temperature sensors as well as modelling. The dual thermistor‐based technique using ground‐calibration facilities can provide the traceability to the International System of Units (SI) in the measurement of irradiance and temperature in the upper air. [ABSTRACT FROM AUTHOR]