Estimation of daytime downward longwave radiation under clear and cloudy skies conditions over a sub-humid region

Downward longwave radiation (LW _↓ ) is a relevant variable for meteorological and climatic studies. Good estimates of this term are vitally important in correct determining of the net radiation, which, in turn, modulates the magnitude of the terms in the surface energy budget (e.g., evaporation). In remote sensing applications, the determination of daytime LW_↓ is required for estimation of the net radiation using satellite data. LW_↓ is not directly measured in weather stations and then is estimated using models with surface air temperature and humidity as input. In this paper, we identify the best models to estimate daytime downward longwave radiation from meteorological data in the sub-humid Pampean region. Several well-known models to estimate LW_↓ under clear and cloudy skies were tested. We use downward radiation components and meteorological data registered at Tandil (Argentina) from 2006 to 2010 (840 days). In addition, we propose two multiple linear regression models (MLRM-1 and MLRM-2) to estimate LW_↓ at the surface for all sky conditions. The new equations show better performance than the others models tested with root mean square errors between 12 and 16 W m^?2, bias close to zero and best agreements with measured data (r ²?≥?0.85).     

Estimation of hourly values of downward atmospheric radiation under cloudless skies during day- and night-time conditions

Summary Several atmospheric radiation models have been developed which provide methods for evaluating the effective emissivity of the atmosphere. Most of these methods are derived for night-time data utilizing local empirical coefficients. There are several, however, which are thought to be more universal in their predictive ability and some of these were tested on a data set registered in Granada from 1983 to 1985, inclusive. The models evaluated in this study are the Brutsaert model, the Berdahl and Martin model and the method proposed by Idso; in the first two cases the original coefficients were used. All the equations showed a good behaviour for night-time data but consistently overestimated day-time measured radiation. These deviations are explained in terms of day-night differences in the effective emissivity regime due to the differing vertical structure of the atmosphere during day and night. An empirical correction term for these differences improves the estimates of these models. The Idso model with a reduction in its independent term provided the best adjustment. Nevertheless, the Berdahl and Martin model with the original coefficients and the day-night correction term provided good results too, deviations being within the experimental error, indicating the universality of its coefficients.With 2 Figures     

Comparison of Surface Wind Stress Anomalies over the Tropical Pacific Simulated by an AGCM and by a Simple Atmospheric Model

Ｃｏｍｐａｒｉｓｏｎ　ｏｆ　Ｓｕｒｆａｃｅ　Ｗｉｎｄ　Ｓｔｒｅｓｓ　Ａｎｏｍａｌｉｅｓ　ｏｖｅｒ　ｔｈｅ　Ｔｒｏｐｉｃａｌ　Ｐａｃｉｆｉｃ　Ｓｉｍｕｌａｔｅｄ　ｂｙ　ａｎ　ＡＧＣＭ　Ａｎｄ　ｂｙ　ａ　Ｓｉｍｐｌｅ　Ａｔｍｏｓｐｈｅｒｉｃ　ＭｏｄｅｌＮ...     

Regional climate modelling over complex terrain: an evaluation study of COSMO-CLM hindcast model runs for the Greater Alpine Region

In this study the results of the regional climate model COSMO-CLM (CCLM) covering the Greater Alpine Region (GAR, 4°–19°W and 43°–49°N) were evaluated against observational data. The simulation was carried out as a hindcast run driven by ERA-40 reanalysis data for the period 1961–2000. The spatial resolution of the model data presented is approx. 10 km per grid point. For the evaluation purposes a variety of observational datasets were used: CRU TS 2.1, E-OBS, GPCC4 and HISTALP. Simple statistics such as mean biases, correlations, trends and annual cycles of temperature and precipitation for different sub-regions were applied to verify the model performance. Furthermore, the altitude dependence of these statistical measures has been taken into account. Compared to the CRU and E-OBS datasets CCLM shows an annual mean cold bias of ?0.6 and ?0.7 °C, respectively. Seasonal precipitation sums are generally overestimated by +8 to +23 % depending on the observational dataset with large variations in space and season. Bias and correlation show a dependency on altitude especially in the winter and summer seasons. Temperature trends in CCLM contradict the signals from observations, showing negative trends in summer and autumn which are in contrast to CRU and E-OBS.     

The use of a meso-gamma scale model for evaluation of pollution concentration over an industrial region in Israel (Hadera)

A model was developed for pollutant dispersion from a point source simulating the Hadera (Israel) power plant stack. The model is based on the NCAR mesoscale meteorological MM4 model that provides the wind fields and coefficients of turbulent diffusion. The model was implemented using an implicit numerical scheme with changing directions. A comparison between the model calculations and an analytical solution for the advection-diffusion equation shows good agreement. Relatively low numerical diffusion of the adopted advection scheme was noted. Results for the hilly region of central Israel are presented for a summer case.     

Sensitivity evaluation of the different physical parameterizations schemes in regional climate model RegCM4.5 for simulation of air temperature and precipitation over North and West of Iran

In this research the dynamic downscaling method by Regional Climate Model (RegCM4.5) was used to assess the performance and sensitivity of seasonal simulated North and West of Iran (NI&WI) climate factors to different convection schemes, and transforms the large-scale simulated climate variables into land surface states over the North of Iran (NI) and West of Iran (WI). A 30-year (1986–2015) numerical integration simulation of climate over NI&WI was conducted using the regional climate model RegCM4.5 nested in one-way ERA-Interim reanalysis data. The Grell, Kuo and MIT-Emanuel cumulus convection with Holtslag and University of Washington (UW) planetary boundary layer (PBL) parameterization schemes were applied in the running of RegCM4.5 to test their capability in simulating precipitation and temperature in winter-spring (January–April) over NI and WI. The results demonstrated that the RegCM4.5 model has a good potential for simulating the variables and trend of surface temperature over the NI and WI region. Magnitude of the model bias for land surface temperature over different regions of Iran varies by convection parameterization schemes. In most cases, the root mean square error between post-processed simulated seasonal average temperature and observation value was less than 1 °C, but there is a systematic “cold bias”. In general, with respect to land surface temperature simulations, a better performance is obtained when using post-processing model’s data with Holtslag PBL-Grell and Holtslag PBL-Kuo configuration schemes, compared to the other simulations, over the NI&WI region. Also, the UW PBL convection schemes show a relatively excellent spatial correlations and normalized standard deviations closer to 1 for thirty-year seasonal land surface temperature anomalies over the entire NI&WI region. However, the simulation accuracy of model for precipitation is not as optimal as for temperature. The dominant feature in model simulations is a dry bias with the largest average value (∼1.04 mm/day) over NI region, while the lowest mean bias precipitation (∼−0.47 mm/day), mainly located in WI region. In the comparison of six configuration convection schemes, the Emanuel scheme has been proven to be the most accurate for simulating winter-spring seasonal mean precipitation over NI&WI region. The accuracy of the scheme also showed great difference in simulated station interpolation of precipitation, which urges the improvement for the simulation capability of spatial distribution of precipitation. In general, for seasonal variation of precipitation, the Emanuel convection with two (Holtslag, UW) PBL configuration schemes outperforms with a good correlation score between 0.7−0.8 and normalized standard deviations closer to 1.     

Validation for a tropical belt version of WRF: sensitivity tests on radiation and cumulus convection parameterizations

对基本气候态和降水日变化的分析是检验模式模拟性能、理解模式误差来源的重要手段。为了评估出对热带气候模拟效果较好的物理参数化方案组合,本文应用WRF带状区域模式,主要比较了四种积云对流参数化方案:NewTiedtke、Kain-Fritsch、newSAS、Tiedtke,和两种辐射参数化方案:RRTMG和CAM,对热带带状区域的气候模拟结果。研究表明:使用NewTiedtke积云对流参数化方案和RRTMG辐射方案的试验,表现出对气温、降水及降水日变化等综合性最好的模拟性能;NewTiedtke积云对流参数化方案能模拟出较好的降水空间分布和降水日变化位相分布特征;与RRTMG辐射方案相比,CAM辐射方案会使温度模拟偏低,特别是陆地上更明显,这种陆地上的冷偏差可能主要来源于Tmin的模拟偏冷。     

Understanding and simulating the link between African easterly waves and Atlantic tropical cyclones using a regional climate model: the role of domain size and lateral boundary conditions

Using a suite of lateral boundary conditions, we investigate the impact of domain size and boundary conditions on the Atlantic tropical cyclone and african easterly Wave activity simulated by a regional climate model. Irrespective of boundary conditions, simulations closest to observed climatology are obtained using a domain covering both the entire tropical Atlantic and northern African region. There is a clear degradation when the high-resolution model domain is diminished to cover only part of the African continent or only the tropical Atlantic. This is found to be the result of biases in the boundary data, which for the smaller domains, have a large impact on TC activity. In this series of simulations, the large-scale Atlantic atmospheric environment appears to be the primary control on simulated TC activity. Weaker wave activity is usually accompanied by a shift in cyclogenesis location, from the MDR to the subtropics. All ERA40-driven integrations manage to capture the observed interannual variability and to reproduce most of the upward trend in tropical cyclone activity observed during that period. When driven by low-resolution global climate model (GCM) integrations, the regional climate model captures interannual variability (albeit with lower correlation coefficients) only if tropical cyclones form in sufficient numbers in the main development region. However, all GCM-driven integrations fail to capture the upward trend in Atlantic tropical cyclone activity. In most integrations, variations in Atlantic tropical cyclone activity appear uncorrelated with variations in African easterly wave activity.     

The relative importance of ENSO and tropical Atlantic sea surface temperature anomalies for seasonal precipitation over South America: a numerical study

The role of tropical Atlantic sea surface temperature (SST) anomalies during ENSO episodes over northeast Brazil (Nordeste) is investigated using the CPTEC/COLA Atmospheric General Circulation Model (AGCM). Four sets of integrations are performed using SST in El Niño and La Niña (ENSO) episodes, changing the SST of the Atlantic Ocean. A positive dipole (SST higher than normal in the tropical North Atlantic and below normal in the tropical South Atlantic) and a negative dipole (opposite conditions), are set as the boundary conditions of SST in the Atlantic Ocean. The four experiments are performed using El Niño or La Niña SST in all oceans, except in the tropical Atlantic where the two phases of the SST dipole are applied. Five initial conditions were integrated in each case in order to obtain four ensemble results. The positive SST dipole over the tropical Atlantic Ocean and El Niño conditions over the Pacific Ocean resulted in dry conditions over the Nordeste. When the negative dipole and El Niño conditions over the Pacific Ocean were applied, the results showed precipitation above normal over the north of Nordeste. When La Niña conditions over Pacific Ocean were tested together with a negative dipole, positive precipitation anomalies occurred over the whole Nordeste. Using the positive dipole over the tropical Atlantic, the precipitation over Nordeste was below average. During La Niña episodes, the Atlantic Ocean conditions have a larger effect on the precipitation of Nordeste than the Pacific Ocean. In El Niño conditions, only the north region of Nordeste is affected by the Atlantic SST. Other tropical areas of South America show a change only in the intensity of anomalies. Central and southeast regions of South America are affected by the Atlantic conditions only during La Niña conditions, whereas during El Niño these regions are influenced only by conditions in the Pacific Ocean.     

A microscale model for air pollutant dispersion simulation in urban areas: Presentation of the model and performance over a single building

A microscale air pollutant dispersion model system is developed for emergency response purposes. The model includes a diagnostic wind field model to simulate the wind field and a random-walk air pollutant dispersion model to simulate the pollutant concentration through consideration of the influence of urban buildings. Numerical experiments are designed to evaluate the model's performance, using CEDVAL(Compilation of Experimental Data for Validation of Microscale Dispersion Models) wind tunnel experiment data, including wind fields and air pollutant dispersion around a single building. The results show that the wind model can reproduce the vortexes triggered by urban buildings and the dispersion model simulates the pollutant concentration around buildings well. Typically, the simulation errors come from the determination of the key zones around a building or building cluster. This model has the potential for multiple applications; for example, the prediction of air pollutant dispersion and the evaluation of environmental impacts in emergency situations; urban planning scenarios;and the assessment of microscale air quality in urban areas.     

Impact of chemical and meteorological boundary and initial conditions on air quality modeling: WRF-Chem sensitivity evaluation for a European domain

This study evaluates the impact of different chemical and meteorological boundary and initial conditions on the state-of-the-art Weather Research and Forecasting (WRF) model with its chemistry extension (WRF-Chem). The evaluation is done for July 2005 with 50 km horizontal resolution. The effect of monthly mean chemical boundary conditions derived from the chemical transport model LMDZ-INCA on WRF-Chem is evaluated against the effect of the preset idealized profiles. Likewise, the impact of different meteorological initial and boundary conditions (GFS and Reanalysis II) on the model is evaluated. Pearson correlation coefficient between these different runs range from 0.96 to 1.00. Exceptions exists for chemical boundary conditions on ozone and for meteorological boundary conditions on PM₁₀, where coefficients of 0.90 were obtained. Best results were achieved with boundary and initial conditions from LMDZ-INCA and GFS. Overall, the European simulations show encouraging results for observed air pollutant, with ozone being the most and PM₁₀ being the least satisfying.     

Cabin air temperature of parked vehicles in summer conditions: life-threatening environment for children and pets calculated by a dynamic model

In vehicles that are parked, no ventilation and/or air conditioning takes place. If a vehicle is exposed to direct solar radiation, an immediate temperature rise occurs. The high cabin air temperature can threaten children and animals that are left unattended in vehicles. In the USA, lethal heat strokes cause a mean death rate of 37 children per year. In addition, temperature-sensitive goods (e.g. drugs in ambulances and veterinary vehicles) can be adversely affected by high temperatures. To calculate the rise of the cabin air temperature, a dynamic model was developed that is driven by only three parameters, available at standard meteorological stations: air temperature, global radiation and wind velocity. The transition from the initial temperature to the constant equilibrium temperature depends strongly on the configuration of the vehicle, more specifically on insulation, window area and transmission of the glass, as well as on the meteorological conditions. The comparison of the model with empirical data showed good agreement. The model output can be applied to assess the heat load of children and animals as well as temperature-sensitive goods, which are transported and/or stored in a vehicle.     

Soil thermal dynamics of terrestrial ecosystems of the conterminous United States from 1948 to 2008: an analysis with a process-based soil physical model and AmeriFlux data

The spatiotemporal distribution characteristics of soil temperature are a significant, but seldom described signal of climate warming. This study examines the spatiotemporal trends in soil temperature at depths of 10, 20, and 50 cm in the conterminous US during 1948–2008. We find a warming trend of between 0.2 and 0.4 °C at all depths from 1948 to 2008. The lowest soil temperatures are in Colorado and the area where Wyoming, Idaho, and Montana meet. The coastal areas, such as Texas, Florida, and California, experienced the highest soil temperature. In addition, areas that experienced weak cooling in summer soil temperature include Texas, Oklahoma, and Arkansas. Warming was recorded in Arizona, Nevada, and Oregon. In winter, Mississippi, Alabama, and Georgia show a cooling trend, and Montana, North Dakota, and South Dakota have been warming over the 61-year period. Additionally, mix-forest areas experience slightly cooler soil temperature in comparison with air temperature. Shrubland areas experience slightly warmer soil temperature in comparison with air temperature. This study is among the first to analyze the spatiotemporal distribution characteristics of soil temperature in the conterminous US by using multiple site observational data. Improved understanding of the spatially complex responses of soil temperature shall have significant implications for future studies in climate change over the region.