Rainfall Rate Research Articles

The 1996 Mid-Atlantic Winter Flood: Exploring Climate Risk through a Storyline Approach

Abstract This article explores the application of thermodynamic perturbations to a historical midlatitude, wintertime, rain-on-snow flood event to evaluate how similar events may evolve under different climate forcings. In particular, we generate a hindcast of the 1996 Mid-Atlantic flood using an ensemble of 14-km variable-resolution simulations completed with the U.S. Department of Energy’s global Energy Exascale Earth System Model (E3SM). We show that the event is skillfully reproduced over the Susquehanna River Basin (SRB) by E3SM when benchmarked against in situ observational data and high-resolution reanalyses. In addition, we perform five counterfactual experiments to simulate the flood under preindustrial conditions and four different levels of warming as projected by the Community Earth System Model Large Ensemble. We find a nonlinear response in simulated surface runoff and streamflow as a function of atmospheric warming. This is attributed to changing contributions of liquid water input from a shallower initial snowpack (decreased snowmelt), increased surface temperatures and rainfall rates, and increased soil water storage. Flooding associated with this event peaks from around +1 to +2 K of global average surface warming and decreases with additional warming beyond this. There are noticeable timing shifts in peak runoff and streamflow associated with changes in the flashiness of the event. This work highlights the utility of using storyline approaches for communicating climate risk and demonstrates the potential nonlinearities associated with hydrologic extremes in areas that experience ephemeral snowpack, such as the SRB.

A storyline analysis of Hurricane Irma’s precipitation under various levels of climate warming

Understanding how extreme weather, such as tropical cyclones, will change with future climate warming is an interesting computational challenge. Here, the hindcast approach is used to create different storylines of a particular tropical cyclone, Hurricane Irma (2017). Using the community atmosphere model, we explore how Irma’s precipitation would change under various levels of climate warming. Analysis is focused on a 48 h period where the simulated hurricane tracks reasonably represent Irma’s observed track. Under future scenarios of 2 K, 3 K, and 4 K global average surface temperature increase above pre-industrial levels, the mean 3-hourly rainfall rates in the simulated storms increase by 3–7% K−1 compared to present. This change increases in magnitude for the 95th and 99th percentile 3-hourly rates, which intensify by 10–13% K−1 and 17–21% K−1, respectively. Over Florida, the simulated mean rainfall accumulations increase by 16–26% K−1, with local maxima increasing by 18–43% K−1. All percent changes increase monotonically with warming level.

Extreme Gradent Boosting Method Forecasting Rainfall in Lembang District, West Java Province

Lembang is a notable regional tourism destination that bears considerable significance within the urban area of Bandung. Lembang is widely recognized for its flourishing agricultural sector, which supports a significant community of farmers engaged in the cultivation of fruits, vegetables, and ornamental plants, in addition to its intrinsic scenic beauty. Therefore, the acquisition of precipitation data is of considerable significance for individuals live in the area to maintain their economic endeavors. This study employs daily historical data from the period of 2018 to 2021, wherein approximately 70% of the data is categorized as sparse. This discourse aims to examine the utilization of the Extreme Gradient Boosting (XGboost) technique for predicting rainfall in the Lembang region, specifically emphasizing its effectiveness in handling limited data. The findings indicate that the model, when trained and tested using a 7:3 data split ratio, achieved a mean absolute error (MAE) of 1.834 for training and 4.473 for testing. Additionally, the root mean square error (RMSE) was calculated to be 3.319 for training and 7.637 for testing. The optimal hyperparameters consist of a learning rate of 0.005, a max_depth value of 10, and the utilization of 300 decision trees as n_estimators. The model effectively captures the pattern of sparse time series data and non-rainy days data, as evidenced by its low error metrics. However, it slightly underestimates the rainfall rate on the days with intense precipitation

Twelve years of rain attenuation statistics of Earth–space propagation experiment at Ka band in Toulouse

SummarySince 2009, ONERA has been running Ka band propagation experiments in Toulouse (France, latitude 43.57°N, longitude 1.47°E). A rain gauge was also deployed on site to collect rainfall rate measurements concurrently to beacon data. Since April 2011, the beacon receiver has been recording the 20.2 GHz (vertical polarisation) Astra 3B beacon signal along a slant path of 35.1° of elevation angle. All years of the experiment had excellent data availability, hence giving 12 years of data at Ka band. First, the propagation experiment and the data processing methodology are described. Second, a statistical analysis of rain attenuation and rainfall rate is conducted. Comparisons are then performed with the prediction methods of Recommendations ITU‐R P.837‐7 (rainfall rate), P.618‐13 (rain attenuation) and P.678‐3 (variability of propagation phenomena).

Spatial and Temporal Variations of the Precipitation Structure in Jiangsu Province from 1960 to 2020 and Its Potential Climate-Driving Factors

This study investigated the temporal and spatial variations of precipitation duration and intensity in Jiangsu Province from 1960 to 2020 using the IDW spatial interpolation method and Kendall’s tau trend test, based on daily precipitation data collected from 22 meteorological stations. Additionally, a Pearson correlation analysis was conducted to examine the correlations between the occurrence rate and contribution rate of precipitation with different durations and grades, as well as five large-scale climate indices. The results indicated the following trends: (1) An increase in the precipitation duration corresponded to a decrease in the occurrence rates, while the contribution rates initially increased and then decreased. The province was predominantly characterized by 1–3 days of light rainfall, with a higher probability of short-duration heavy rainfall in northern Jiangsu. (2) From 1960 to 2020, most stations experienced decreasing trends in the precipitation duration occurrence and contribution rates, but heavy rainfall increased, suggesting a shift to short-duration heavy precipitation. (3) The Arctic Oscillation (AO) notably negatively correlates with the 9-day occurrence rate of precipitation (9dOR), while it positively correlates significantly with the occurrence rate of moderate rainfall (MROR). The North Atlantic Oscillation (NAO) exhibits a significant positive correlation with the 2-day occurrence rate of precipitation (2dOR) and a notable negative correlation with the 9-day occurrence rate of precipitation (9dOR). The PDO (Pacific Decadal Oscillation) has shown significant positive correlations with the 2-day precipitation occurrence rate (2dOR) and contribution rate (2dCR), a negative correlation with the light rainfall occurrence rate (LROR), and significant positive correlations with both the moderate and heavy rainfall occurrence rates (MROR and HROR, respectively). The AO, NAO, and PDO are potential climate factors that influence changes in the precipitation structure in Jiangsu Province. These research findings offer valuable insights for regional water resource management, flood risk assessment, and predicting future precipitation trends under climate change scenarios.

How does humidity data impact land surface modeling of hydrothermal regimes at a permafrost site in Utqiaġvik, Alaska?

Humidity is a basic and crucial meteorological indicator commonly measured in several forms, including specific humidity, relative humidity, and absolute humidity. These different forms can be inter-derived based on the saturation vapor pressure (SVP). In past decades, dozens of formulae have been developed to calculate the SVP with respect to, and in equilibrium with, liquid water and solid ice surfaces, but many prior studies use a single function for all temperature ranges, without considering the distinction between over the liquid water and ice surfaces. These different approaches can result in humidity estimates that may impact our understanding of surface-subsurface thermal-hydrological dynamics in cold regions. In this study, we compared the relative humidity (RH) downloaded and calculated from four data sources in Alaska based on five commonly used SVP formulas. These RHs, along with other meteorological indicators, were then used to drive physics-rich land surface models at a permafrost-affected site. We found that higher values of RH (up to 40 %) were obtained if the SVP was calculated with the over-ice formulation when air temperatures were below freezing, which could lead to a 30 % maximum difference in snow depths. The choice of whether to separately calculate the SVP over an ice surface in winter also produced a significant range (up to 0.2 m) in simulated annual maximum thaw depths. The sensitivity of seasonal thaw depth to the formulation of SVP increases with the rainfall rate and the height of above-ground ponded water, while it diminishes with warmer air temperatures. These results show that RH variations based on the calculation of SVP with or without over-ice calculation meaningfully impact physically-based predictions of snow depth, sublimation, soil temperature, and active layer thickness. Under particular conditions, when severe flooding (inundation) and cool air temperatures are present, care should be taken to evaluate how humidity data is estimated for land surface and earth system modeling.

Notifications of Leptospirosis in the State of Ceará - Brazil, from 2013 to 2019: Profile of Cases and Factors Associated with the Incidence

Objective: This study proposes an epidemiological analysis and distribution of human cases of leptospirosis reported in the State of Ceará, from 2013 to 2019.
 Methods: Descriptive study, with data on leptospirosis accidents reported in the Notifiable Diseases Information System between 2013 and 2019.
 Results: 344 cases and 24 deaths were reported, with emphasis on the second two months (41%), corresponding to an average annual incidence of 0.54/105 inhabitants and a fatality rate equal to 2.2% per year, directly related to rainfall rates. Of those affected by the disease, 72% were aged 20 to 59, 76% were men, 88% considered themselves black, 39% were residents of urban areas and 59% did not report their education; 87% were diagnosed using clinical-laboratory criteria and 76% were cured.
 Conclusion: There was an increase in reports of leptospirosis associated with rainfall, mainly affecting the economically active population, most of whom live on the social margins.

Data-driven clay-fouled ballast permeability assessment using analytical-numerical and machine learning approaches

The occurrence of ballast contamination or fouling frequently results in a sudden decline in the capacity of railway ballasted tracks. Considering the various sources of ballast fouling, clay is the most severe one for causing a drastic reduction in the drainage capacity of the ballast layer. In the current study, we utilized a large-scale flume test to measure the water height along the cross-section of the clay-fouled ballast. Subsequently, an analytical–numerical (A-N) approach was developed to simulate the movement of water through porous media under steady-state conditions, while also considering the flow regime. This A-N approach was validated using the results of flume tests. Finally, the validated A-N approach was employed to generate a dataset and develop machine learning models for predicting water height. The characterized machine learning models included random forest regression (RFR), support vector machine (SVM), and extreme gradient boosting (XGBoost). Various variables, such as ballast gradation, fouling ratio, bed slope, rainfall rate, and water height on the side ditch, were incorporated into the machine learning models to reveal the contribution of each individual variable. Results show that for clean ballast, the incorporation of a nonlinear model between flow velocity and hydraulic gradient in the A-N approach is crucial to properly estimate the experimental measurements. However, a comparison of the water height measured via the flume test and the water level estimated based on the A-N approach confirms the suitability of the linear model, i.e., Darcy's law, for the water flow regime through clay-fouled ballast. According to the machine learning results, particularly those from the XGBoost model, which was characterized as the elite model, the rainfall rate and the fouling index emerged as the most influential variables affecting the water height in the clay-fouled ballast layer of the railway track.

Comparisons of Rainfall Microphysical Characteristics between the Southeastern Tibetan Plateau and Low-Altitude Areas

Abstract The low air pressure and density over the Tibetan Plateau may have an impact on the microphysical features of rainfall. Using a two-dimensional video disdrometer (2DVD), a Micro Rain Radar (MRR), and a microwave radiometer (MWR), the features of the raindrop size distribution (DSD) on the southeastern Tibetan Plateau (SETP) are explored and compared with those in low-altitude regions. The falling speed of raindrops on the SETP is higher than that in low-altitude areas. Under different rainfall-rate categories, the number concentration and the maximum diameter of raindrops on the SETP are smaller than those in low-altitude regions. The convective rainfall on the SETP is more maritime-like because the South Asian summer monsoon brings water vapor from the ocean here. For stratiform and convective rainfall, the SETP has more small-sized raindrops than low-altitude locations. The mass-weighted mean diameters (Dm) on the SETP are the smallest among six sites. The generalized intercept parameter (Nw) of stratiform rainfall is balanced at a low rainfall rate, while that of convective rainfall is balanced at a high rainfall rate. Furthermore, for a given μ (the shape parameter of gamma distribution) value, the λ (the slope parameter of gamma distribution) value on the SETP is the highest of the six sites. Significance Statement For the occurrence and progression of rainfall, microphysical processes (for instance, collision, fragmentation, coalescence, and evaporation) that take place when rainfall particles descend are crucial. A key factor in the microphysical features of rainfall that varies with rainfall rates and types is the raindrop size distribution (DSD). The southeastern Tibetan Plateau (SETP)’s unique terrain ensures that there is enough moisture for rain to fall there, but little is known about the microphysical aspects of this type of precipitation. There has not been enough research done on how the high altitude affects the microphysical features of rainfall. The microphysical features of rainfall in this area must thus be studied.

Evaluation of GPM IMERG and error sources for tropical cyclone precipitation over eastern China

Long-term precipitation data with high temporal-spatial resolution and high precision is crucial for the monitoring of tropical cyclone (TC) precipitation and the understanding of its response to global climate change. The performance of the Final run of Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) V06B product to characterize the TC precipitation is evaluated during the TC season (May to September) from 2014 to 2019 against a dense network of gauges over eastern China, considering the impact of land cover types (LCTs), different techniques (morphing) and instruments, i.e., passive microwave (PMW) and infrared (IR) sensors. Overall, IMERG captures well the spatial pattern and temporal variations of mean TC rainfall rate (RR) over eastern China but shows underestimations of mean TC RR compared to gauge observations, with the mean error (ME) being −0.78 mm/h, which are mainly contributed by the underestimations of heavy TC RR (ME: −12.1 mm/h). Moreover, the IMERG performance shows dependence on the LCTs and IMERG components (i.e., morph, IR + morph, and PMW sensors). The overestimation/underestimation of light/heavy TC precipitation events is most obvious over water/evergreen broadleaf forests, with the bias being 1.2/0.5. Differently, IMERG even slightly underestimates light TC precipitation events over grasslands (bias = 0.88). Among four IMERG components, Microwave Humidity Sounder (MHS) performs the best in characterizing all TC precipitation events, followed by morph, Special Sensor Microwave Imager/Sounder (SSMIS), and finally IR + morph, indicating that IR algorithms might need to be further improved. The mean TC RR is slightly overestimated by MHS over most regions of eastern China with positive ME exceeding 2.8 mm/h, while is largely underestimated by IR + morph among four components with most MEs lower than −1.2 mm/h. This study may provide an observational reference for the continued development and future improvement of the IMERG product.

Intra-event evolution of elemental and ionic concentrations in wet deposition in an urban environment

Abstract. A measurement campaign was conducted in the Paris region, focusing on the evolution of chemical composition of wet deposition during rainfall events from sequential sampling. A total of eight rain events were documented and characterized by varying meteorological conditions, atmospheric dynamics, and aerosol particle concentrations representative of urban conditions and influenced by long-range mineral dust transport. The intra-event evolution of the chemical composition of wet deposition revealed the predominant role of meteorological parameters and local sources in the observed mass concentration variability. From selected case studies, the washout ratios (WRs) and scavenging coefficients were quantified by conducting simultaneous measurements of aerosol particle composition and wet deposition. The results highlighted a variability of the WR and scavenging coefficients depending on the rainfall rate and on the chemical species. Scavenging coefficients estimated from WR ranged from 5.4×10-8 to 1.1×10-5 s−1 for chemical elements, and they are within the range of values reported in the literature for 0.2–2 µm particle diameters. Our results pointed out that the scavenging coefficient increases with rainfall rate according to a power law, as previously shown in the literature, indicating a stronger removal of particles from the atmosphere with greater precipitation intensity. Quantitative analysis of the data allowed us to estimate the relative contributions of in-cloud scavenging (ICS) for selected rain events. The ICS relative contributions ranged on average from 23 % to 62 % depending on the rain events, and they varied according to the chemical species within the same rain event. This highlights the variability and complexity of the wet deposition process and the influence of specific factors on the contribution of ICS, such as aerosol particle size and hygroscopicity. Overall, this study highlights the variability of wet deposition and its chemical composition and the need to consider the specificities of each event to fully understand the underlying mechanisms.

Rainfall from Tropical Storms Might Be on the Downswing

Two decades’ worth of satellite data suggest that the rainfall rates of tropical cyclones might be decreasing relative to background levels.

Az édesvízhiány története: Namíbia vízkészletei

This article introduces the various water resources available to Namibia, an arid country in southern Africa. With low and extremely variable annual rainfall and high evaporation rates, surface water is scarce. Permanent rivers are only found along the southern and northern borders. Ephemeral rivers flow for short periods after thundershowers and few reach the ocean. Many ephemeral rivers sink into the gravelly and sandy riverbeds, where they feed alluvial aquifers. Groundwater is the only water source for about 80% of the territory. Dams were built in ephemeral rivers to create artificial lakes that play an important role in water provision for urban areas, irrigation schemes and mines. Despite the extreme aridity, Namibia has a variety of wetland habitats, including five Ramsar sites.

Vegetation communities and summer net ecosystem CO2 exchange on western Axel Heiberg Island, Canadian High Arctic

Climate change is expected to result in the Arctic transitioning from a carbon sink to a carbon source environment, with models predicting half of the carbon stock of the upper 3 m soil layer to be released by the year 2300 (van Huissteden and Dolman 2012). However, uncertainty in latitudinal warming and changes in Arctic ecosystem functions, such as gross carbon ecosystem exchange (GEE), are poorly understood, in part a reflection of a high variability in vascular plant community diversity that is dependent upon and sensitive to physiographic controls, such as soil moisture, topography, and seasonal active layer depth (Walker et al. 2005). This heterogeneity complicates assessments of carbon fluxes on a landscape scale and how they will change in the future (Shaver et al. 2007), especially given their sensitivity to local changes in climate, such as warming and higher rates of rainfall (Bintanja 2018, Bintanja and Andry 2017). As part of the creation of a long-term ecological and environmental monitoring program at the McGill Arctic Research Station at Expedition Fiord, western Axel Heiberg Island, field-based studies in 2021-2022 of plant surveys and summer net ecosystem CO2 exchange monitoring were undertaken to: define the major vegetation communities; quantify and investigate CO2 fluxes with chambers and their analogous biophysical variables; and upscale plot level CO2 measurements to the landscape scale using high spatial resolution remote sensing data. define the major vegetation communities; quantify and investigate CO2 fluxes with chambers and their analogous biophysical variables; and upscale plot level CO2 measurements to the landscape scale using high spatial resolution remote sensing data. The Expedition Fiord area is recognized as a polar oasis, with high plant species richness existing within an environment of heterogeneous physiography. At the moment, five vegetation communities have been identified (xeric dwarf shrub barren, xeric-mesic dwarf shub barren, mesic dwarf shrub tundra, cassiope heath, and sedge meadow) that varied as a function of species diversity, percent cover, soil moisture, and net ecosystem carbon exchange. Barren vegetation communities having stronger respiration fluxes (i.e., carbon source environments) while more vegetated communities have stronger photosynthesis fluxes (i.e., carbon sink environments). Landcover classification revealed with high accuracy (79.3%) that barren ground and barren vegetation communities cover a much larger area compared to wetter habitats. Upscaling summer season measured carbon fluxes based on the landcover map revealed that Expedition Fiord is a carbon source environment, with an average efflux of +94.6 g CO2/day. Ongoing work focuses on the expansion of carbon flux and subsurface monitoring locations, as well as studies of soil carbon and microbial diversity across the different land cover classifications, which will help to better resolve how soil microorganisms, plant detritus, labile organic carbon, soil moisture, slope, aspect, and bedrock geology influence CO2 fluxes throughout the summer season in this high Arctic setting.

Microphysics-based rainfall energy estimation using remote sensing and reanalysis data

Rainfall plays a critical role in soil erosion by detaching and transporting soil particles, making it a key factor in soil erosion models. Its potential effect on soil erosion is reflected by rainfall erosivity, which considers the interaction between raindrops and soil, primarily driven by the kinetic energy carried by raindrops. Traditional approaches rely on empirical relationships between rainfall rate and kinetic energy, but they often overlook regional differences in rainfall microphysical characteristics. This study aims to compare the performance of conventional fixed and disdrometer-derived local microphysics-based unit rainfall kinetic energy–rain rate (KE–I) relationships for estimating rainfall energy using the IMERG and ERA5-land precipitation datasets. The findings reveal that conventional method using the fixed KE–I relationship do not account for regional differences in rainfall microphysical characteristics and tend to overestimate KE values for high rainfall intensities in the UK. Disdrometer-derived microphysics-based KE–I relationships improve estimation correlations, with the fully fitting formula exhibiting the highest correlation coefficient. The findings highlight the need to consider local rainfall characteristics for improved estimation accuracy and demonstrate that ERA5-land exhibits higher correlation and lower error compared to IMERG for estimating rainfall energy, but it generally underestimates rainfall intensity. This study will provide valuable insights into the applicability of using gridded precipitation datasets for large-scale soil erosion estimation and emphasizes the significance of accounting for regional variations in rainfall characteristics.

Further development and testing of a rainfall test rig

Changes made recently by The Ministry of Health New Zealand, to the internal noise design requirements for Preschools, has required that the design of the roof reduces rainfall noise to Ministry of Education, Design Quality Learning Spaces Acoustics (DQLS) Version 3.0, for rainfall noise. Previously Canterbury Acoustic Testing Services (CATS) had developed a rainfall noise test rig, in accordance with ISO 10140-1: 2016 [3] and ISO 10140-5:2010/Amd 1:2014 and carried out testing on a series of warm roof/ceiling constructions. Preschools tend to use cold roof systems where the thermal insulation sits on the ceiling, rather than in a warm roof, where the insulation is part of the roof structure. So there has been no tested performance data for rainfall noise acoustic design purpose, using a cold roof construction. As part of the testing program, there was a need to recommission the rig, to verify rain drop size and rainfall rate. A testing program was established, to include the base roof structure (roofing iron only) to a full buildup of roof and ceiling, to get a better understanding of how each component in the system contributes to the control of the rainfall noise.

Assessing canopy rainfall partitioning by Mediterranean dryland shrubs under extreme rainfall

AbstractIntense rain events have become more frequent in some regions due to climate change, and this trend is particularly concerning in dryland regions where the ecological and geomorphological impacts of rainfall are intimately tied to its intensity. The interception of rainfall by vegetation is a critical process in the water balance of drylands; thus, this study estimated the canopy interception capacity and interception rates as well as stemflow of three typical Mediterranean shrub species (Rosmarinus officinalis, Thymus vulgaris, and Macrochloa tenacissima) of three size classes in Spain under a simulated extreme rainfall rate (~8 mm min−1, historical return period of >100 years). Given that these plants' canopy structures markedly differ from taller woody plants (i.e., trees), a novel method was developed to assess the stemflow fraction. Results showed significant differences in interception amount, rates, and storage capacity among the shrub species, with variations in plant morphology, such as shrub height and canopy diameter, being the key factors determining interception capacity. R. officinalis had the highest interception fraction per unit canopy area, or ‘specific interception,’ (18.4%). In contrast, the lowest specific interception fraction was measured for M. tenacissima (6.5%). Thymus vulgaris was characterized by the highest stemflow yields per unit canopy area (4.85 mm) and fraction (up to 29.6% of rainfall), which was the lowest for M. tenacissima (1.09 mm, ~1%–4%). Strong linear correlations were found between canopy interception and shrub canopy diameter (|r| > −0.51, max = −0.90), when observations were grouped for size class. These linear correlations between shrub morphology and partitioning enabled multiple‐regression linear models to be developed that predicted canopy interception and stemflow with good accuracy (r2 > 0.64, with a maximum of 0.82) from shrub height, canopy diameter, dry biomass, size class, and species. Despite these measurements being conducted under one extreme storm depth and intensity, the results provide: (i) values of rainfall partitioning for important shrub species in Mediterranean dryland environments; and (ii) a simple but reliable model that may be further developed (e.g., embedding variable rainfall values as weather input or incorporating other morphological parameters) and may be integrated into complex hydrological models.

Path Difference Optimization of 5G Millimeter Wave Communication Networks in Malaysia

The development of intelligent transport systems, mobile cellular networks, microwave links, and vehicle communications has accelerated with the use of wireless connections as a communication channel in 5G wireless technology. Weather, including rain, fog, snow, sand, and dust, impacts wireless communication channels in various ways. These effects are more pronounced at the high frequencies of millimeter-wave bands. Recently, the 5G network has made it possible to support a variety of applications with fast speeds and high-quality content. To facilitate the use of high-millimeter-wave frequencies, a recent study investigated how sand and dust affect the 5G communication channel. In this paper, we consider the impact of frequent and heavy rainfall on millimeter-wave propagation and cross-polarization of the wave at various points along the propagation path caused by rainfall in urban and highway scenarios in Malaysia. We estimate rainfall attenuation, path loss, and link margin at various millimeter-wave frequencies. From our simulation results, it is evident that rainfall attenuation, path loss, and link margin depend on the operating frequency, path difference, and rainfall rate. In this paper, we estimate and compare the optimal path difference values under urban and highway scenarios both with and without rainfall attenuation.

Espresso: A Global Deep Learning Model to Estimate Precipitation from Satellite Observations

Estimating precipitation is of critical importance to climate systems and decision-making processes. This paper presents Espresso, a deep learning model designed for estimating precipitation from satellite observations on a global scale. Conventional methods, like ground-based radars, are limited in terms of spatial coverage. Satellite observations, on the other hand, allow global coverage. Combined with deep learning methods, these observations offer the opportunity to address the challenge of estimating precipitation on a global scale. This research paper presents the development of a deep learning model using geostationary satellite data as input and generating instantaneous rainfall rates, calibrated using data from the Global Precipitation Measurement Core Observatory (GPMCO). The performance impact of various input data configurations on Espresso was investigated. These configurations include a sequence of four images from geostationary satellites and the optimal selection of channels. Additional descriptive features were explored to enhance the model’s robustness for global applications. When evaluated against the GPMCO test set, Espresso demonstrated highly accurate precipitation estimation, especially within equatorial regions. A comparison against six other operational products using multiple metrics indicated its competitive performance. The model’s superior storm localization and intensity estimation were further confirmed through visual comparisons in case studies. Espresso has been incorporated as an operational product at Météo-France, delivering high-quality, real-time global precipitation estimates every 30 min.

Seasonal variation of microphysical characteristics for different rainfall types in the Tianshan Mountains of China

Three-year (2020−2022) raindrop size distribution (DSD) measurements from a second-generation PARSIVEL disdrometer deployed at Zhaosu, which is located in the Tianshan Mountains of China, are analyzed to investigate the seasonal variability of microphysical characteristics of two types of rainfall. Rainfall at Zhaosu is classified as stratiform rainfall and convective rainfall in the normalized intercept parameter–median volume diameter (log10Nw–D0) and log10Nw–mass-weighted mean diameter (log10Nw–Dm) spaces, respectively. Both stratiform rainfall and convective rainfall samples in the log10Nw–D0 space are more than three times those in the log10Nw–Dm space, accompanied by smaller mean rainfall rate (R). Rainfall in summer (spring) has the lowest (highest) concentration of small-size raindrops, while rainfall in fall (summer) has the lowest (highest) concentration of mid- and large-size raindrops, accompanied by the largest (smallest) mean Dm and D0 values and the smallest (largest) mean log10Nw values in summer (spring). Convective rainfall exhibits more significant seasonal variation in microphysical parameters and DSD than stratiform rainfall, whether in the log10Nw–D0 space or the log10Nw–Dm space. The convective rainfall in spring is more like maritime cluster, whereas that in summer is more like continental cluster in the log10Nw–Dm space. Furthermore, in the log10Nw–D0 and log10Nw–Dm spaces, localized relationships of radar reflectivity factor – R (Z–R) and shape parameter – slope parameter (μ–∧) are derived across seasons for different rainfall types. The results indicate that there are significant seasonal variations in the microphysical characteristics of rainfall in the Tianshan Mountains of China for the two rainfall types. This study further emphasizes that different classification schemes for rainfall types can lead to significant differences in the characteristics of DSD, Z–R, and μ–∧ relationships for different rainfall types.