In this study, class A pan coefficient (Kp) values were simulated via the M5 tree model, by using daily meteorological data of four stations in the East Azerbaijan province, which has arid and cold climate in the northwest of Iran. Firstly, the FAO-24 and FAO-56 methods, which are commonly used to calculate Kp values, were taken into consideration in the study. The Kp values calculated in the second stage were assumed to be observed values and were taken as the outputs of the M5 model. Four different training datasets consisting of 66, 70, 75 and 80% of the original data were tested. The best results were obtained when 70% of the data was used for training and 30% for testing. Results indicated that a Kp value was easily simulated with simple linear equations with high accuracy rate (R2 = 0.99) in all the stations. Furthermore, the Kp value was easily simulated using only two meteorological variables (relative humidity and wind speed), without the need for complex tables and equations. The most important finding of this study was the easy estimation of the Kp with a number of linear functions obtained from the M5 model; as a result, the simulated Kp can help us to calculate evapotranspiration accurately for more effective irrigation planning. The proposed method offers advantages as it is simpler and easier than the existing approaches in the literature.

Rainfall is recognized as the most important driving force of the hydrologic cycle. To accurately represent the spatio-temporal rainfall variability continues to be an enormous hydrological task when using commonly sparse, if available, rain gauges networks. Therefore, the present study devoted a special effort to analyze the robustness of some satellite rainfall products, notably the datasets hereafter named as (i) CHIRP (Climate Hazards Group InfraRed Precipitation), (ii) CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), (iii) 3B42, and (iv) 3B42RT of the Tropical Rainfall Measuring Mission (TRMM), to adequately represent the pluviometric regime in the Madeira river basin. To assess the accuracy of acquired remotely sensed rainfall products, comparisons to observational available rain gauges usually taken as ground-truth in the literature, despite their well-known limitations, were performed. Wavelet analysis was also used to validate the performance of the referred satellite products by means of extracting the corresponding cycles, frequencies, and tendencies along the available time series across the studied basin. The results showed that the data sources CHIRPS and CHIRP better represent the pluviometric phenomenon by means of their monthly accumulated rainfall in the Madeira river basin when compared to the 3B42 and 3B42RT products taking into account rain gauges as baseline information. The CHIRPS product performed the best among the selected rainfall estimators for the Madeira river basin. Further analysis brought up also another very interesting result related to non-rainfall periods, which is usually not reported. However, such evaluation is quite important in hydrology when examining run sequences of droughts and consequent effects in the water balance at the watershed scale. Highly accurate estimates in the sense of identifying non-rainfall periods by remotely sensed information was achieved, which represents an additional and valuable asset of satellite rainfall products. It is worthwhile to say that this perspective deserves to receive much more attention in the literature in order to deeply discuss the water-energy-food nexus.

Mexico is vulnerable to extreme climatic events; however, their impact is not uniform in all the country. This study presents an analysis of extreme temperatures in 12 Mexican cities, modeled under the assumption of a non-stationary climate. Temporal trends were estimated from an available climatological base of maximum and minimum temperatures with the non-parametric tests of Mann-Kendall and Sen’s slope method, and a generalized extreme value (GEV) distribution was used to model both temperatures. A likelihood ratio test and Akaike and Bayesian information criteria were used to evaluate the optimal model choice with incorporation of a covariate. Using the best model, return levels and confidence intervals for future scenarios were estimated. A trend towards urban warming was detected from both the non-parametric tests and the GEV distribution, although with heterogeneous behavior. In the series of the maximum temperatures, half of the cities analyzed were non-stationary, and of those, the city of Guadalajara, located in the center-west of the country had a negative trend. The trend for minimum temperatures was more uniform, as 90% of the cities were non-stationary with a positive trend, and only 10%, in an urban area to the east of the metropolitan area of the Valley of Mexico (Milpa Alta) and a coastal city of the Gulf of Mexico (Veracruz), showed stationary series. It is therefore concluded that return periods of thermal extremes estimated in a changing climate temporarily showed a significant variation, so statistical modeling must consider this behavior due to its importance for risk assessments and adaptation purposes.

This study analyzes changes in the long-term (1901-2015) monthly values of potential evapotranspiration (PET), precipitation, and minimum (Tmin) and maximum (Tmax) temperatures across Africa to quantify trends and assess covariability between these climatic variables. Both warming and drying trends were observed across the continent. The 1979-2015 warming was stronger than that from 1901 to 1940. Some cooling occurred from 1941 to the mid-1970s. The 1901-2015 annual Tmax, Tmin, and PET averaged over Africa exhibited increasing or drying trends across the continent at rates of 0.18 ºC, 0.22 ºC, and 3.5 mm per decade, respectively. The 1961-1990 annual precipitation averaged over the whole continent showed that Africa experienced drying at a rate of about –28 mm per decade. When considering the period 1961-2015, the rate of precipitation decrease was about –8 mm per decade. From 1901 to 1915, areas around Lake Victoria in East Africa and along the western coastline south of the equator experienced wetting rates of up to 36 mm per decade. Significant (p < 0.01) warming trends occurred in Sudan, Southern and Northern Africa. Positive PET trends were significant (p < 0.01) in the warm Mediterranean climate, and the western part of South Africa. Long-term temperature increase and precipitation decrease across northern Africa possibly indicated the Sahara Desert expansion over time. Except in the warm desert climate, the continent exhibited high precipitation variability. Equatorial climate experienced low temperature and PET variability. The strongest coherence between precipitation and temperature existed at multiple scales (6-8 years). Correlations between precipitation and PET (or temperature) were mostly negative and weak (p > 0.01). Because the sensitivity of Tmin to local influences is higher than that of Tmax, areas with strong negative correlation were larger in coverage for Tmax than those of Tmin. These results call for planned measures to tackle food insecurity in sub-Saharan Africa.

Future climate analogues were identified for six capital cities in western South America using a novel nonparametric technique and ensemble experiments. We applied the MRI-AGCM3.2H model with a horizontal resolution of approximately 60 km, three convection schemes, four sea surface temperature distributions, and two initial conditions. All ensemble experiments were conducted under scenario A1B of the Special Report on Emissions Scenarios, in which cumulative emissions are similar to those of the RCP 6.0 scenario. The majority of future analogue cities were at lower latitudes than their respective target cities. In general, all analogues of target cities had similarity scores of 0.1-0.3. Of the six analogues, four were located in central and southern Africa, whereas the remaining two were located in western South America. Projected seasonal variations in surface air temperature and precipitation in Santiago, Chile are similar to the current climate in Cape Town, South Africa, and the climate analogue for La Paz, Bolivia, is found in Oruro, Bolivia. The non-parametric method used in this study can be applied to a variety of impact assessments under a changing global climate.

This study uses a two-year dataset (January 2016-December 2017) on global solar radiation to model the clearness and cloudiness indices at Ile-Ife, Nigeria, a tropical location. Analysis of the daily variations showed that most days at the location are uniform in cloudiness, with scarcely any day either extremely cloudy or extremely clear. On a monthly basis, the clearness index ranged from 0.27 to 0.51, while cloudiness index ranged from 0.44 to 0.72, confirming that the variations of both ratios are influenced by changes in the position of the sun, turbidity and clouds. The seasonal variations of the two parameters were observed to be inverse, so that increases in the clearness index induced by cloudless sky and dry months correspond to decreases in the cloudiness index values and vice versa. The minimum values (0.10 and 0.28) of the clearness index at the location are observed to be higher than the minimum value of 0.05 stipulated for the clearness index in cloudy conditions. Two empirical equations for estimating the maximum clearness index in terms of the average clearness index and the cloudiness index in terms of the clearness index are developed and recommended for other locations with climatological conditions similar to those of the study site. It was furthermore observed from the evaluation of formulated empirical equations that the Hollands and Huget and the Saunier et al. models performed well and improved by about 23.62 and 3.66%, respectively, after calibration, to make the models suitable for their application at other tropical areas. 

Due to its geographical location, Mexico is one of the most vulnerable countries to climate change. However, we currently ignore the exact magnitude and particularities of past climate change in the Mexican territory and are missing a country-level spatially explicit analysis based on observed data. To fill this gap, I analyzed how temperature, precipitation and the water balance of Mexico changed over 1951-2017 at interannual and seasonal scales. My results show a clear national increment in temperature (+0.71 ºC) but no modification in annual mean precipitation. At the seasonal scale, the wet season (June-November) had higher rainfall (+31 mm) and no change was detectable on the dry season (December-May). However, when the full water balance was seasonally accounted for (precipitation minus potential evapotranspiration), the trend resulted in a wetter wet season and a much drier dry season across the country. Regionally, seasonal changes in water balance were larger in the area surrounding the Gulf of Mexico and positive in the Yucatan Peninsula and the central highlands. My results help explaining the recent increase in drought, storms and intense rainfall across Mexico and suggest even more extreme seasonal weather in the future if climate change exacerbates. 

Meteorological data assimilation from surface observations (PREPBUFR) and satellite radiance (BUFR) provided by the National Centers for Environmental Prediction (NCEP) is carried out to determine their possible influence on chemical variables concentrations such as ozone (O3), obtained from air quality modeling over central Mexico using the photochemical Weather Research and Forecasting Model with Chemistry (WRF-Chem) during a bad-pollution event due to high O3 concentrations in the Mexico City Metropolitan Area on May 1-4, 2013. For this, the Weather Research and Forecasting Data Assimilation (WRFDA) module was adapted to run with WRF-Chem, and the 3DVAR assimilation technique (which is implemented in the WRFDA) was selected. Six study cases were defined taking into account the combination of the data source type with the assimilation process start times (00:00 and 12:00 UTC). Results indicate that independently of these factors, data assimilation modifies in general the meteorological variables (temperature and wind) initial conditions to obtain a better agreement between model simulations and observations, although statistics results are even higher when the process starts at 12:00 UTC using a combination of PREPBUFR and BUFR data (PB+RD cases). It was also verified that there is an influence on O3 concentrations since the statistical metrics obtained for the different experiments carried out are modified; however, it is insufficient to considerably improve the chemical variable model performance.

The present study focuses on the investigation of both near-surface and vertical variability of carbon monoxide (CO) concentrations and their potential sources obtained from both in situ and satellite Measurements of Pollution in the Troposphere (MOPITT) over a semiarid region (Anantapur, India) from January 2016 to December 2017. The diurnal variation of CO shows sharp morning (07:00-09:00 LT) and evening (07:00-09:00 LT) peaks associated to local anthropogenic activities as well as the impact of the mixed layer height, and low concentrations during daytime (12:00-15:00 LT). The low levels during afternoon hours may be due to the increase of the mixed layer height and the decrease of anthropogenic sources. The seasonal mean CO showed no obvious variation, with highest levels observed in winter (329 ± 52 ppbv), followed by the pre-monsoon (327 ± 57 ppbv), post-monsoon (234 ± 36 ppbv) and monsoon (192 ± 22 ppbv). The high levels of CO during the winter are attributed to increased emissions from anthropogenic sources and a shallow mixed layer height. The vertical distribution of CO showed secondary peaks at high-pressure levels (300-200 hPa) during winter, pre-monsoon, and post-monsoon, which indicates CO transport from different source regions. These findings are reasonably confirmed through the air mass Concentrated Weighted Trajectory (CWT) analysis obtained from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. This study suggests that except for the monsoon, air masses transported from Indo-Gangetic Basin region also contribute to the enhancement of CO concentrations at the receptor site.

The optical characteristics of atmospheric aerosol are vital in the determination of regional climate trends. Biomass burning is typically known to influence aerosol optical characteristics. Following the incessant biomass burning and the recent drop in precipitation over Western Cape, aerosol optical properties with a focus on the impact of biomass burning are studied over Cape Town using data from the Aerosol Robotic Network (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS). In general terms, measurements from both platforms significantly agree on the estimates of aerosol optical depth (AOD) and water vapor content. The mean AOD (0.075 ± 0.022) and the Ångström exponent (0.63 ± 0.19) derived from AERONET demonstrate the dominance of coarse mode aerosol typical of maritime aerosol. Similarly, aerosol particle size distributions display the predominance of coarse mode particles. However, the derived refractive index is more representative of urban-industrial aerosol. Also, estimated back-trajectories show that more than 70% of the aerosol particles over the region originate over the ocean. Atmospheric vapor increases from winter to summer, mainly influenced by air temperature, supersaturation level, and absorbing aerosol. Furthermore, two significant sources accounted for biomass burning related to high AOD values: local biomass burning and regionally transported aged smoke majorly from elsewhere in Sothern Africa.

A typical practice in air quality modeling assessment is the intercomparison between different dispersion models results and air quality measurements at different atmospheric conditions. In this study, a comparison between the results of two Lagrangian dispersion models, the Lagrangian Particle Dispersion Model FLEXPART and the Lagrangian Puff Model CALPUFF (regulatory model), coupled to the same meteorological fields produced by the Weather Research and Forecasting (WRF) model, was done. As a case study, atmospheric dispersion of anthropogenic nitrogen oxides (NOx) emissions (considered as a passive tracer) was considered, during a typical case of severe pollution over the densely populated area of the Guadeloupe archipelago (West French Indies), including complex terrain and, of course, coastal influence. Even though Lagrangian models usually provide better results of plume dispersion under strong winds, in this case study weak trade winds are dominant, in order to check both models under non-ideal atmospheric conditions. As a result, compared to NOx ground level concentration (glc) observations, FLEXPART shows better agreement than CALPUFF. However, as a regulatory model, CALPUFF overestimates both glc observations and FLEXPART maximum NOx glc results, with higher values when a higher horizontal resolution is applied. Also, differences between models results arise in the spatial distribution of NOx over a 1 × 1 km2 horizontal resolution grid domain, showing quite homogenous isopleths with smooth contours for CALPUFF vs. fragmented isopleths with irregular contours for FLEXPART.

Carbon-driven emissions are on the rise and much work remains to be done to benchmark seasonal carbon increase and ensure its prompt reduction. There is a great need of new methods for validating seasonal trends of carbon dioxide (CO2). We obtained CO2, temperature and normalized difference vegetation index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra satellite from 2003 to 2008 over Africa (Nigeria, Mauritania, Congo, Sudan), Europe (France, Finland, Turkey, Ukraine), and Asia (China, Mongolia, India, Afghanistan). For the first time, seasonal index analysis was used to validate vegetation index. The seasonality in carbon dioxide was determined dividing the monthly average by the annual mean. Additionally, the percentage difference correlation of the NDVI and CO2 relationship was calculated to investigate the underlying influence of both parameters and validate the seasonal change resulting from the solar activity cycle. By grouping years based on solar activity maximum (2003-2004), intermediate (2005-2006) and very low activity (2007-2008), the results expanded the physical interpretation that seasonal fluctuation of NDVI corresponds to the terrestrial sink of regional CO2, mostly occurring during equinoctial months. Our results demonstrate that seasonal variation of CO2 depends on geographic latitude and the solar activity cycle. This result is essential in studying the future trend relationship between NDVI and CO2.

On September 3, 1880 a probable tornado caused significant damage in the city of Talavera de la Reina (Spain). In this work, we analyze the original description made by an observer of that time about this phenomenon and the meteorological conditions for that day. The potential formation of a tornado was possible according to our analysis. This would be the first record of a tornado in the region of Talavera de la Reina prior to 1975. According to the original description, we could estimate the intensity of this tornado as EF2 (in the Enhanced Fujita Scale).

Vortex shedding occurs downstream of Lantau Island over the Hong Kong International Airport and can be hazardous to aircraft operating from that airport. An in-depth analysis of a vortex shedding structure is conducted using surface observations and Ground-Based Velocity Track Display (GBVTD) analysis of the Doppler Light Detection and Ranging (LIDAR) data. This in-depth examination of the surface data indicates that brief wind anomalies were observed contemporaneously with the passage of the vortices. Winds simulated using an idealized vortex model constructed using LIDAR-estimated properties of each vortex fit reasonably well with the weather buoy wind and pressure observations. GBVTD retrievals indicate radial convergence and upward motion at most times throughout the lifecycle of the vortices. This paper aims to document the three-dimensional structure of such vortices shedded from the mountains, which may be useful for aviation safety involving wind shear detection, avoidance and recovery at airports with similar terrain features nearby.

 This paper analyzes the empirical relationship between carbon dioxide (CO2) emissions per-capita and economic growth in a panel of 20 Latin American and Caribbean countries over the period 1971-2011. This empirical relationship, known in the economic literature as the Environmental Kuznets Curve (EKC) hypothesis, suggests that the relationship between these variables, in the long run, follows an inverse U-shape, that is, from a certain level of per-capita income, an increased economic growth would be accompanied by improvements in environmental quality. Although this hypothesis has been studied since the 1990s, its empirical validity has recently been questioned on the basis of, among other things, the lack of diagnosis of the stationarity properties of the variables, and in a panel data context, the presence of cross-sectional dependence. Taking into account both criticisms, we use recent unit root tests and cointegration techniques that are robust to the presence of cross-sectional dependence. We find contradictory results depending on the assumption of cross-dependence. Under the assumption of cross-independence, the existence of an EKC with a realistic turning point is confirmed. However, this assumption is subsequently rejected, and because of the presence of cross-dependence in the panel, a long-run equilibrium relationship between the variables cannot be established, and we reject the existence of an EKC.