Atmósfera

Regional characterization of ENSO effects on the seasonal rainfall of Sinaloa, Mexico

2024-10-23T17:09:56+00:00

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.

Impact assessment of 3D-var data assimilation on simulation of tropical cyclones using WRF

2024-09-19T19:17:03+00:00

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.

A case study of the sea/land breeze diurnal cycle in the Peninsula and Gulf of Nicoya, Costa Rica: Interactions with local and regional processes

2024-10-11T15:54:00+00:00

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.

Performance evaluation of random forest and boosted tree in rainfall-runoff process modeling for sub-basins of Lake Urmia

2024-10-11T16:07:23+00:00

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.

Changes in surface air temperature for Mediterranean climate in Turkey

2024-08-19T16:53:55+00:00

Local climate influences of inland water bodies, complex topography, and surrounding seas cause temperate, arid, and continental climate properties to prevail with local variations in different parts of Turkey. The intra-regional variability of environmental factors creates uncertainties and challenges in climate modeling. Multi-model ensemble analysis is suggested to be used to characterize the uncertainties and minimize the generalization error in projections. This study is part of a research on climate change impacts in Turkey, focusing on the impacts on surface air temperature through a multi-model ensemble analysis of high-resolution climate models. The ensemble set comprises 12 EURO-CORDEX RCMs and two models from the Japan Meteorological Research Institute (MRI). Firstly, historical model data are validated with temperature records from 59 meteorological stations. Furthermore, changes in temperature climatology in the future in short- (2020-2030), medium- (2031-2050), and long-term (2051-2100) horizons are analyzed and compared with the precipitation changes. In the ensemble, two MRI models (MRI-AGCM, NHRCM) and two CORDEX RCMs nested in the HadGEM2-ES (RCA4 and CCLM4-8-17) perform best to replicate the spatial variability of climatology. The 14-member ensemble projects a gradual increase in the temperature up to 4.5 and 6.6 ºC under RCP4.5 and RCP8.5 scenarios, respectively. The projections agree on an inverse relationship between temperature and precipitation changes. More substantial impacts are projected in inland compared to coastal regions.

Air quality in the Metropolitan Zone of the Valley of Puebla: Comparative evaluation of CAMS and persistence forecasts

2024-09-25T18:48:30+00:00

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 (PM_2.5) represent a health risk. Puebla’s automatic air quality monitoring system measures PM₁₀ and PM_2.5at 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 PM_2.5. However, using the CAMS forecast for a preliminary evaluation of the prediction of PM_2.5is 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.

Satellite precipitation product assessment and correction technique selection at sub-basin scale for maximum annual events. Case study: Acaponeta River basin

2024-09-11T19:01:51+00:00

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.

Diurnal to seasonal meteorological cycles along an equatorial Andean elevational gradient

2024-06-28T20:42:50+00:00

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.

Satellite-based analysis of climate oscillations: Implications for precipitation in an arid watershed in Mexico

2024-04-30T18:04:54+00:00

Climate oscillations are known to have an important influence on weather patterns across the world. While the impact of El Niño Southern Oscillation (ENSO) has been well documented, there is a scarcity of studies examining the effects of the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO). This study uses satellite data to confirm that ENSO significantly influences precipitation in the Nazas-Aguanaval watershed from October to March, as evidenced by Spearman correlation coefficients. In contrast, the PDO influence is registered during specific months (January, March, November and December), while AMO impacts precipitations during April-June, November, and December. These results were corroborated using ANOVA, reinforcing the influence of ENSO and indicating a limited impact of PDO and AMO on this watershed. Finally, a linear model was developed to estimate monthly precipitation anomalies based on the phase of these three indices for the different sub-basins. Notably, monthly precipitation anomalies ranged between 140% and –78% in dry months. Our results demonstrate the influence of climate oscillations in precipitation in the Nazas-Aguanaval watershed and the usefulness of satellite data for conducting these analyses. Likewise, we set a starting point for investigating the implications of climate oscillation phases for water management and drought disaster prevention.