In developing countries, accurate rainfall estimation with adequate spatial distribution is limited due to sparse rain gauge networks. One way to solve this problem is the use of satellite-based precipitation products. These satellite products have significant spatial coverage of rainfall estimates and it is of fundamental importance to investigate their performance across space–time scales and the factors that affect their uncertainties. In the open literature, some studies have already analyzed the ability of satellite-based rain estimation products to estimate average rainfall values. These investigations have found very close agreement between the estimates and observed data. However, further evaluation of the satellite precipitation products is necessary to improve their reliability to estimate extreme values. In this scenario, the main goal of this work is to evaluate the ability of satellite-based precipitation products to capture the characteristics of extreme precipitation over the tropical region of South America. The products evaluated in this investigation were 3B42 RT v7.0, 3B42 RT v7.0 uncalibrated, CMORPH V1.0 RAW, CMORPH V1.0 CRT, GSMAP-NRT-no gauge v6.0, GSMAP-NRT- gauge v6.0, CHIRP V2.0, CHIRPS V2.0, PERSIANN CDR v1 r1, CoSch and TAPEER v1.5 from Frequent Rainfall Observations on GridS (FROGS) database. Some products considered in this investigation are adjusted with rain gauge values and others only with satellite information. In this study, these two sets of products were considered. In addition, gauge-based daily precipitation data, provided by Brazil’s National Institute for Space Research, were used as reference in the analyses. In order to compare gauge-based daily precipitation and satellite-based data for extreme values, statistical techniques were used to evaluate the performance the selected satellite products over the tropical region of South America. According to the results, the threshold for rain to be considered an extreme event in South America presented high variability, ranging from 20 to 150 mm/day, depending on the region and the percentile threshold chosen for analysis. In addition, the results showed that the ability of the satellite estimates to retrieve rainfall extremes depends on the geographical location and large-scale rainfall regimes.

The main objective of this study is to assess the ability of several high-resolution satellite-based precipitation estimates to represent the Precipitation Diurnal Cycle (PDC) over Brazil during the 2014–2018 period, after the launch of the Global Precipitation Measurement satellite (GPM). The selected algorithms are the Global Satellite Mapping of Precipitation (GSMaP), The Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Prediction Center (CPC) MORPHing technique (CMORPH). Hourly rain gauge data from different national and regional networks were used as the reference dataset after going through rigid quality control tests. All datasets were interpolated to a common 0.1° × 0.1° grid every 3 h for comparison. After a hierarchical cluster analysis, seven regions with different PDC characteristics (amplitude and phase) were selected for this study. The main results of this research could be summarized as follow: (i) Those regions where thermal heating produce deep convective clouds, the PDC is better represented by all algorithms (in term of amplitude and phase) than those regions driven by shallow convection or low-level circulation; (ii) the GSMaP suite (GSMaP-Gauge (G) and GSMaP-Motion Vector Kalman (MVK)), in general terms, outperforms the rest of the algorithms with lower bias and less dispersion. In this case, the gauge-adjusted version improves the satellite-only retrievals of the same algorithm suggesting that daily gauge-analysis is useful to reduce the bias in a sub-daily scale; (iii) IMERG suite (IMERG-Late (L) and IMERG-Final (F)) overestimates rainfall for almost all times and all the regions, while the satellite-only version provide better results than the final version; (iv) CMORPH has the better performance for a transitional regime between a coastal land-sea breeze and a continental amazonian regime. Further research should be performed to understand how shallow clouds processes and convective/stratiform classification is performed in each algorithm to improve the representativity of diurnal cycle.

The Anomaly hotSpots of Agricultural Production (ASAP) Decision Support System was launched operationally in 2017 for providing timely early warning information on agricultural production based on Earth Observation and agro-climatic data in an open and easy to use online platform. Over the last three years, the system has seen several methodological improvements related to the input indicators and to system functionalities. These include: an improved dataset of rainfall estimates for Africa; a new satellite indicator of biomass optimised for near-real-time monitoring; an indicator of crop and rangeland water stress derived from a water balance accounting scheme; the inclusion of seasonal precipitation forecasts; national and sub-national crop calendars adapted to ASAP phenology; and a new interface for the visualisation and analysis of high spatial resolution Sentinel and Landsat data. In parallel to these technical improvements, stakeholders and users uptake was consolidated through the set up of regionally adapted versions of the ASAP system for Eastern Africa in partnership with the Intergovernmental Authority on Development (IGAD) Climate Prediction and Applications Centre (ICPAC), for North Africa with the Observatoire du Sahara et du Sahel (OSS), and through the collaboration with the Angolan National Institute of Meteorology and Geophysics (INAMET), that used the ASAP system to inform about agricultural drought. Finally, ASAP indicators have been used as inputs for quantitative crop yield forecasting with machine learning at the province level for Algeria’s 2021 and 2022 winter crop seasons that were affected by drought.

This paper presents the Intertropical Convergence Zone (ITCZ) as the possible source mechanism of the medium-scale traveling ionospheric disturbances (MSTIDs) propagating to the southeast direction over the South American region. Using the data collected by the GNSS dual-frequency receivers network from January 2014 to December 2019, detrended TEC maps were generated to identify and characterize 144 MSTIDs propagating southeastward over the South American low-latitude and equatorial region. We also used images from the Geostationary Operational Environmental Satellite (GOES) 13 and 16 in the infrared (IR) and water vapor (WV) channel, and reanalisys data from the National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA) to study the daily features and seasonal migration of ITCZ. In the winter, when ITCZ migrates to the northern hemisphere around 10–15° N, 20 MSTIDs propagated southeastward. During summer, when the ITCZ lies within the continent, around 0–5° S 80 MSTIDs were observed to propagate southeastward; in the equinoxes (spring and fall), 44 MSTIDs were observed. Again, the MSTIDs propagating southeastward showed a clear seasonality of their local time dependence; in summer, the MSTIDs occurred frequently in the evening hours, whereas those in winter occurred during the daytime. We also found for the first time that the day-to-day observation of ITCZ position and MSTIDs propagation directions were consistent. With regard to these new findings, we report that the MSTIDs propagating southeastward over the South American region are possibly induced by the atmospheric gravity waves, which are proposed as being generated by the ITCZ in the troposphere. The mean distribution of the horizontal wavelength, period, and phase velocity are 698 ± 124 km, 38 ± 8 min, and 299 ± 89 m s⁻¹, respectively. For the first time, we were able to use MSTID propagation directions as a proxy to study the source region.

The knowledge of the diurnal cycle of precipitation is of extreme relevance to understanding the physical/dynamic processes associated with the spatial and temporal distribution of precipitation. The main difficulty of this task is the lack of surface precipitation information over certain regions on an hourly time scale and the low spatial representativeness of these data (normally surface gauges). In order to overcome these difficulties, the main objective of this study is to create a 3-h precipitation accumulation database from the gauge-adjusted daily regional precipitation products to resolve the diurnal cycle properly. This study also proposes to evaluate different methodologies for partitioning gauge-adjusted daily precipitation products, i.e., a product made by the combination of satellite estimates and surface gauge observations, into 3-h precipitation accumulation. Two methodologies based on the calculation of a conversion factor F between a daily gauge-adjusted product, combined scheme (CoSch, hereafter), and a non-gauge-adjusted one, the integrated multi-satellite retrievals for GPM (IMERG)-Early (IMERG, hereafter) were tested for this research. Hourly rain gauge stations for the period of 2015–2018 over Brazil were used to assess the performance of the proposed methodologies over the whole region and five sub-regions with homogeneous precipitation regimes. Standard statistical metrics and categorical indices related with the capability to detect rainfall events were used to compare the ability of each product to represent the diurnal cycle. The results show that the new 3-h CoSch products show better agreement with rainfall gauge stations when compared with IMERG, better capturing the diurnal cycle of precipitation. The biggest improvement was over northeastern region close to the coast, where IMERG was not able to capture the diurnal cycle properly. One of the proposed methodologies (CoSchB) performed better on the critical success index and equitable threat score metrics, suggesting that this is the best product over the two. The downside, when compared with the other methodology (CoSchA), was a slight increase in the values of bias and mean absolute error, but still at acceptable levels.

O nevoeiro é um fenômeno associado a redução da visibilidade, causando prejuízos em diversos sectores socioeconômicos no estado do Rio Grande do Sul, influenciando principalmente no encerramento das atividades aeroportuárias. Os objetivos principais são: 1) classificar os tipos de nevoeiros em Porto Alegre em um período de 2 anos (2008-2009) a partir da análise dos processos sinóticos e termodinâmicos e 2) avaliar o comportamento de produtos de reanálise do NCEP, reanalise (CFSR-1) e previsão (CFSR-2) do CFSR nas situações de formação de nevoeiro de radiação. Variáveis meteorológicas da estação de superfície do aeroporto foram utilizadas para: 1) o estudo de frequência de nevoeiros e 2) a análise das condições de formação dos nevoeiros. Os sistemas sinóticos atuantes durante os eventos de nevoeiros foram analisados utilizando diversos produtos de reanálise do NCEP e do CFSR e imagens de satélite GOES-12. As condições termodinâmicas foram analisadas usando dados de radiossondagem e perfis plotados com o produto CFSR-1 e CFSR-2. Foram encontrados 82 casos de nevoeiros com duração entre 10 minutos e 11 horas, ocorrendo principalmente no outono e no inverno. Um único caso ocorreu com chuva. A análise sinotica foi elaborada para os 23 casos que ocorreram no horário de observação de radiossondagem. O estudo dos processos sinóticos mostrou que 13 eventos foram nevoeiros de radiação e 10 frontais. Os perfis do CFSR-1 mostraram camada úmida entre 1000-950hPa, com umidade de 86-89%. Os perfis do CFSR-2 mostraram camada úmida entre a altura 2m até o nível de 950hPa com umidade de 80-96.9%. No geral, os perfis de toda a troposfera acima desses níveis foram típicos para os casos de nevoeiros radiação, com baixa umidade e inversões de subsidência. Concluímos que 1) a situação sinóptica foi apresentada semelhante pelo NCEP e o CFSR, 2) os perfis do CFSR mostram a estrutura da troposfera típica para os eventos de nevoeiro de radiação, excluindo a camada superficial.