Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

Satellite precipitation products (SPP) are increasingly being used for detailed hydrological studies due to scarce and discontinuous precipitation observations at different spatial and temporal scales. However, to evaluate its full utility, it is necessary to assess and correct the bias between estimated and observed precipitation (OP). The aim of this paper is to evaluate the CHIRPSv2.0 product for maximum annual events and different climatological conditions based on in-situ observations, using statistical metrics and selecting from linear scaling (LS), local intensity scaling (LOCI) and power transformation (PT) the appropriate bias correction technique (CT), at point and sub-basin scale, improving the maximum annual precipitation records for the period 2001-2020 in the Acaponeta River basin, Mexico. Previous applications of bias CT have focused on broader temporal scales rather than specific maximum events. Differences in the performance of the correction methods were identified between point and sub-basin scales. PT presented a good performance at the point scale, in contrast to percentual bias (PBIAS), which resulted in a great overestimation at the sub-basin scale in the upper zone for the average and dry years, while for the wet year, it overestimated in the lower part. Although LS and LOCI generally observed a good PBIAS reduction at the gauge stations, LS overestimated at the sub-basin scale overall. LOCI showed better SPP corrections in the middle and lower zones and a wider range of overestimation for the upper basins in the middle and wet years. The corrected annual maximum estimated values for the revised period are useful for hydrological analysis in the context of flood risk assessment.

The climate of the Andean equatorial mountains has a pronounced spatiotemporal variability, which, coupled with limited meteorological monitoring, hampers our understanding of the regional and local atmospheric processes that govern this variability. To deepen our understanding of this region’s climate, we analyzed diurnal to seasonal meteorological patterns of the main meteorological variables: precipitation, air temperature, relative humidity, incident solar radiation, and wind speed and direction. We used a unique 10-year high-resolution dataset from March 2013 to February 2023 along an elevation gradient located in southern Ecuador. Our analyses reveal a trimodal regime of precipitation; two wet seasons are associated with convective processes influenced by the position of the Intertropical Convergence Zone (ITCZ) over the study area during the equinoxes, and the less humid season is due to the intensification of the Walker circulation, which produces subsidence over the study area. The relative humidity shows distinct daily and seasonal variations, reaching minimum daily values around noon when the air temperature is the highest, and an annual minimum in November. Incident solar radiation reaches its maximum values around the equinoxes when sunlight is almost perpendicular, which produces greater heating on the surface and, hence, a more humid atmosphere. The meridional displacement of the ITCZ around the year influences the climate, increasing humidity from March to May and wind speed from April to July. Our research reveals significant differences between diurnal and seasonal meteorological cycles, highlighting the importance of altitude, topography, and wind patterns in the climate dynamics of the equatorial Andes.

The presence of sea breeze (SB) is analyzed at nine meteorological stations in the northwest of Costa Rica (Peninsula and Gulf of Nicoya, GF); two from the Ticosonde-NAME experiment, University of Costa Rica, and seven from the National Meteorological Institute, for the period from July 1 to September 16, 2004. An objective detection algorithm for SB is applied to hourly data from the stations and sea surface temperature (SST). The algorithm uses temperature gradient and wind direction. Pinilla and Guacalillo stations show 64% of SB on the 78 days analyzed. Liberia (20 km inland) presents 44.9% of SB associated with weak synoptic winds from the east. Puntarenas presents doubtful cases due to wind errors, while the other stations do not present complete records. Some of the non-SB days are dominated, on one hand, by strong synoptic flow from the northeast associated with the low-level Caribbean jet that in turn coincides with the periods of reduced rainfall or mid-summer drought and, on the other hand, by synoptic flow from the southwest associated with the passage of weather systems in the western Caribbean. The algorithm shows a good ability to detect SB despite the poor spatial resolution of SST. Consistent with a typical SB circulation, precipitation at almost all stations is characterized by coastal convective activity and precipitation in the late afternoon and evening hours. The results are encouraging for their potential application to artisanal fishing, agriculture, tourism, and regional air quality, as there are very active ports in the Gulf of Nicoya (Puntarenas and Caldera), points of intense movement of tourist and commercial ships that negatively impact environmental conditions.

Background on air quality in the Metropolitan Zone of the Valley of Puebla shows that suspended particles smaller than 10 micrometers (PM10) and smaller than 2.5 micrometers (PM2.5) represent a health risk. Puebla’s automatic air quality monitoring system measures PM10 and PM2.5 at five stations in the municipalities of Puebla and Coronango. These measurements allow for determining the Air and Health Index according to the NOM-172-SEMARNAT-2019 standard for these pollutants. The advancement of global pollutant modeling techniques represents an opportunity for air quality management in areas with scarce terrestrial measurements. However, it is necessary to validate global forecasts with ground measurements from georeferenced monitoring stations to reduce uncertainties and determine reliability. The Copernicus Atmospheric Monitoring Service (CAMS) forecast allows atmospheric pollution exploration processes in the study region. This study presents an analysis of the CAMS forecast against the Persistence forecast. The results show that the persistence forecast performs better than the CAMS forecast in general, both for PM10 and for PM2.5. However, using the CAMS forecast for a preliminary evaluation of the prediction of PM2.5 is feasible due to its acceptable values in the comparison criteria of the dichotomous statistics ACCURACY, probability of detection (POD), false alarm rate (FAR), probability of false detection (POFD), success ratio (SR), threat score (TS), equitable threat score (ETS), Heidke skill score (HSS), and odds ratio skill score (ORSS). This work provides valuable insights to both the population and decision-makers, aiding in the enhancement of air quality management and public health strategies.

The study analyzed the influence of the Atlantic Niño on precipitation anomalies in Maranhão from 1980 to 2020. Using the Atlantic Niño index, 20 events were identified, predominantly with 2- to 8-year scales. A significant reduction was noted from the 2000s, likely due to the weakening of the Bjerknes feedback and the Atlantic Multidecadal Oscillation. However, events in 2019 and 2021 suggested that the negative phase of the Atlantic Multidecadal Oscillation may have contributed to the reactivation of the Atlantic Niño after 19 inactive years. The negative early termination event was “non-canonical”, with positive sea surface temperature anomalies in the North Tropical Atlantic and positive east winds. A relationship was found between the Atlantic Niño and the Atlantic Meridional Mode, influencing elements like sea surface temperature and interhemispheric winds, which in turn affected precipitation patterns in Maranhão. These findings highlight the complex climate interactions in the region, emphasizing the need to consider multiple factors, including local and remote climate modes, for understanding precipitation variability. The study underscores the importance of continuous monitoring and research on the Atlantic Niño and the Atlantic Meridional Mode to anticipate impacts on rainfall volume and distribution in Maranhão, aiding regional strategic planning.

In agriculture, the wavelengths of interest are UV A + B radiation and photosynthetically active radiation. Different techniques can be used by farmers to enhance radiation distribution on crops, with one alternative being the installation of polypropylene anti-weed nets. The analysis of the radiation balance can be performed using different predictive methods, which are a function of solar geometry, climate, and weather variables. The objective of this research was to develop multiple regression models for comparison with the Holt-Winters model in time series to analyze and estimate incident radiation and its reflectance on surfaces covered with white and black polypropylene anti-weed nets and soil without cover. The results indicate an increase in radiation and temperature between Julian days 116 and 273, decreasing significantly with cloud cover. The white polypropylene anti-weed nets reflected a higher amount of solar radiation. On the other hand, the multiple regression models presented better accuracy for the prediction of incident solar radiation and its reflectance compared to the Holt Winters time series model. However, each model provides a different analysis of radiation, so that they can be complementary in decision making for agricultural purposes.

Rainfall seasonality is of paramount relevance for the northwestern Mexican ecosystems. Among other factors, it is annually driven by the North American Monsoon. An outstanding yet irregular and changing factor that affects rainfall seasonality is the El Niño Southern Oscillation (ENSO) and its two phases, El Niño and La Niña, which can change the seasonal rainfall patterns. Here, we characterized spatially seasonal rainfall patterns of three physiographic regions of Sinaloa and adjacent states in northwestern Mexico. The covariances between El Niño and La Niña phases and their respective summer and winter rainfall amounts were estimated in each station within their regions. The magnitude of covariance was also differentiated among regions and characterized spatially. A multivariate analysis was performed to attain a simultaneous perspective of the rainfall-related variables. We detected differences among regions for the measured rainfall-related variables; altitude and longitude explained most of its spatial variation. Winter rainfall increased in all stations of El Niño and La Niña occurrence. El Niño decreased rainfall in most stations for summer, whilst La Niña increased rainfall in summer. Summer rainfall covariance with El Niño and La Niña was differentiated among regions. Latitude and longitude were correlated with the covariation between El Niño and La Niña and winter rainfall. Altitude correlated to the interaction of summer rainfall and La Niña and El Niño. Multivariate analysis segregated regions on the variation of winter, annual rainfall, number of rainfall events, and rainfall seasonality.  

The combination of data from the Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS) satellites provide measurements in frequency channels 23-183 GHz, which allow the estimation of vertical profiles of atmospheric temperature and humidity. These measurements play a significant role in numerical weather prediction models, improving initial conditions during tropical cyclone development. In the present study, measurements from AMSU-A and MHS have been assimilated in the Weather Research and Forecasting (WRF) model through the 3D-variational (3D-var) data assimilation technique using the Gridpoint Statistical Interpolation (GSI) analysis system. The assimilation impact has been assessed on super cyclonic storm Amphan and severe cyclonic storm Nisarga, which formed over the Bay of Bengal (BoB) and the Arabian Sea (AS), respectively. To investigate their impact, a series of experiments are conducted with and without assimilation of AMSU-A and MHS observations from each day’s initial condition for both cyclones. The track and landfall errors of all the experiments are computed against the best track position provided by the India Meteorological Department (IMD). The results indicate that the assimilation of AMSU-A and MHS observations led to an improvement in track errors of about 11 to 35% for Amphan and 6 to 20% for Nisarga for 12 to 72 h lead times. Furthermore, the assimilation of AMSU-A and MHS observations helped to improve the simulation of landfall position and time. The evaluation of maximum sustained surface wind, central pressure, and rainfall against the observations demonstrates the positive impact of the assimilated observations on the performance of the WRF model.

This study aimed to develop rainfall-runoff (P-Q) modeling using machine learning models in the sub-basins of Lake Urmia, Iran. In this research, chronological records of hydrological parameters and meteorological inputs at a regional scale were analyzed using Random Forest (RF) and Boosted Tree (BT) heuristic methods. This study compared the performance of these two models for the Urmia Basin over the period from 1976 to 2019. The results showed that the RF model provided better estimates in Akhula, Daryan, and Ghermez Gol stations in the eastern sub-basin and Miandoab, Pole Ozbak, Abajalu Sofla, Nezam Abad, and Pole Bahramlu stations in the western sub-basin. In contrast, the BT model performed better at Pole Senikh, Shishvan, Gheshlagh Amir, Shirin Kandi, and Khormazard stations in the eastern sub-basin and Babarud, Keshtiban, and Yalghoz Aghaj stations in the western sub-basin. Additionally, the time series analysis showed changes in yearly rainfall frequency and a decreasing trend in flow discharge in most years. These findings highlight a significant reduction in inflow to Lake Urmia over the past 43 years, with a particularly sharp decline in recent years.

Although climate change is evidenced by a steady increase in global temperature, several indicators have been defined and are represented by mathematical expressions called indices, which are identified, recorded, and compared to demonstrate variations in climate change. However, using these indices requires: (1) timely evaluation, and (2) determining their spatial variation over a given region. However, there are only a few studies on the spatial trend of these indices, which is important considering that the impacts of climate change, as well as the factors that determine them, are not spatially homogeneous. Therefore, information from the historical series (1969-2009) of 16 meteorological stations, distributed in Chiapas, Mexico, was used. To determine the spatial variation of the climatic indices, each index was associated with 25 environmental variables through multiple linear regressions defined by the stepwise procedure. According to the results, the environmental variables with the greatest significant influence (p < 0.001) were mean annual temperature, mean annual runoff, real evapotranspiration, mean minimum temperature, and mean annual isotherms. On the other hand, the variables not used in the models were: highest insolation in May, soil moisture regimes, hydrogeology, biotic provinces, and physiographic provinces. The results of multiple linear regression models defined high R2 values (from 0.72 to 0.97), and the resulting mapping shows that each index defined a particular spatial variation. We conclude that, for the purpose of evidencing climate change, the process followed in this work can be used to determine the variation of this type of index in other regions.