Rain Rate Research Articles

The Extreme Rainfall Events of the 2020 Typhoon Season in Vietnam as Seen by Seven Different Precipitation Products

A series of typhoons and tropical storms have produced extreme precipitation events in Vietnam during the first part of the 2020 monsoon season: events of this magnitude pose significant challenges to remote sensing Quantitative Precipitation Estimation (QPE) techniques. The weather-monitoring needs of modern human activities require that these challenges be overcome. In order to address this issue, in this work, seven precipitation products were validated with high spatial and temporal detail against over 1200 rain gauges in Vietnam during six case studies tailored around the most intense events of 2020. The data sources included the Vietnamese weather radar network, IMERG Early run and Final run, the South Korean GEO-KOMPSAT-2A and Chinese FengYun-4A geostationary satellites, DPR on board the GPM-Core Observatory, and European ERA5-Land reanalysis. All products were resampled to a standardized 0.02° grid and compared at hourly scale with ground stations measurements. The results indicated that the radars product was the most capable of reproducing the information collected by the rain gauges during the selected extreme events, with a correlation coefficient of 0.70 and a coefficient of variation of 1.38. However, it exhibited some underestimation, approximately 30%, in both occurrence and intensity. Conversely, geostationary products tended to overestimate moderate rain rates (FY-4A) and areas with low precipitation (GK-2A). More complex products such as ERA5-Land and IMERG failed to capture the highest intensities typical of extreme events, while GPM-DPR showed promising results in detecting the highest rain rates, but its capability to observe isolated events was limited by its intermittent coverage.

Evaluating the Effects of Raindrop Motion on the Accuracy of the Precipitation Inversion Algorithm by X-SAR

Precipitation has a profound impact on both human life and the natural environment. X-band synthetic aperture radar (X-SAR) utilizes high-resolution microwave remote-sensing technology, providing opportunities for global precipitation measurements. The current precipitation inversion algorithms from X-SAR measurements assume that precipitation particles remain relatively stationary with the ground. However, the motion of raindrops could potentially reduce the accuracy of these algorithms. In this study, we first established a functional relationship between raindrop motion and SAR echoes based on the standard deviation of the raindrop Doppler velocity spectrum. Secondly, an exploratory algorithm was proposed to retrieve rainfall distribution under the raindrop motion error model (RMM) and quantitatively calculate the precipitation inversion error caused by raindrop motion. In comparison to conditions where the atmosphere is stationary, when the standard deviation of the Doppler velocity spectrum of raindrops is 1.1 m/s, the relative error of the retrieved surface rain rate increases from 2.1% to 35.8%. Numerical simulations show that SAR echoes are sensitive to changes in the standard deviation of the Doppler velocity spectrum, and the impact of raindrop motion on the accuracy of X-SAR precipitation measurements cannot be neglected.

Comparison and Synthesis of Precipitation Data from CloudSat CPR and GPM KaPR

Employing different bands of radar to detect precipitation information in identical regions enables the acquisition of a more comprehensive precipitation cloud structure, thereby refining the continuity and completeness of precipitation measurements. This study first compared the coincident data from CloudSat W-band cloud profiling radar (CPR) and Global Precipitation Measurement Mission (GPM) Ka-band precipitation radar (KaPR) from 2014 to 2017, and then a synthesis of the radar reflectivity from CPR and KaPR was attempted to obtain a complete cloud and precipitation structure. The findings of the reflectivity comparisons indicated that the echo-top height identified by CPR is on average 3.6 to 4.2 km higher than that from KaPR, due to the higher sensitivity. Because of strong attenuation of CPR by liquid-phase particles, the reflectivity below the height of the melting layer usually shows an opposite gradient to KaPR with decreasing altitude. The difference in the near-surface rain rates of CPR and KaPR was found to be related to reflectivity gradients in the vertical direction, and the larger the reflectivity gradients, the greater the differences in near-surface rain rates. For better representing the complete vertical structure of precipitation clouds and improving the consistency of the reflectivity and precipitation rate, the radar reflectivity was weighted, synthesized from CPR and KaPR based on the gradient difference of the reflectivity from the two radars. We presented the synthesis results for a stratiform cloud and a deep convective case, and Spearman’s rank correlation coefficient (rs) between the GPM combined radiometer precipitation rate and the radar reflectivity was utilized to analyze the performance of the synthesis. The consistency between synthesized reflectivity and precipitation rate in the non-liquid phase was improved compared with KaPR, and the rs of the ice and mixed phases was increased by about 12% and 10%, respectively.

Comprehensive Evaluation of Near-Real-Time Satellite-Based Precipitation: PDIR-Now over Saudi Arabia

In the past decade, Saudi Arabia has witnessed a surge in flash floods, resulting in significant losses of lives and property. This raises a need for accurate near-real-time precipitation estimates. Satellite products offer precipitation data with high spatial and temporal resolutions. Among these, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Dynamic Infrared Rain Rate near-real-time (PDIR-Now) stands out as a novel, global, and long-term resource. In this study, a rigorous comparative analysis was conducted from 2017 to 2022, contrasting PDIR-Now with rain gauge data. This analysis employs six metrics to assess the accuracy of PDIR-Now across various daily rainfall rates and four yearly extreme precipitation indices. The findings reveal that PDIR-Now slightly underestimates light precipitation but significantly underestimates heavy precipitation. Challenges arise in regions characterized by orographic rainfall patterns in the southwestern area of Saudi Arabia, emphasizing the importance of spatial resolution and topographical considerations. While PDIR-Now successfully captures annual maximum 1-day and 5-day precipitation measurements across rain gauge locations, it exhibits limitations in the length of wet and dry spells. This research highlights the potential of PDIR-Now as a valuable tool for precipitation estimation, offering valuable insights for hydrological, climatological, and water resource management studies.

Evaluation of rainfall rates and rain-induced signal attenuation for satellite communication in the South-South Region of Nigeria

Rain fade causes signal attenuation which can relatively results in signal quality performance degradation, increased pathloss and coverage area reduction in satellite communication networks, most especially in the tropics and at higher operational frequencies above 10GHz. Analysis of rain attenuation provides useful insight for satellite communication engineers for efficient network planning and design within the region of study. Hence this paper presents threeyear analysis of signal-induced attenuation in the South-South States of Nigeria for the cities of Port-Harcourt, Uyo, Calabar and Eket, whose terrain characteristics could be generalized to represent urban, sub-urban and rural terrains in Nigeria. The International Telecommunications Union Radio-wave propagation (ITU-R P618-13) model was used to estimate the long-term rain attenuation at 10GHz, 20GHz and 30GHz for satellite communication applications. Results obtained at 10GHz, 20GHz and 30GHz operational frequencies show that at high frequency, rain fade can cause signal attenuation levels in the south-south region of Nigeria. In all, Calabar recorded the highest rain rate and corresponding highest attenuation level of 96.13dB at 30GHz compared to 50.29dB at 20GHz for the period under review.

Using high resolution climate models to explore future changes in post-tropical cyclone precipitation

One of the most costly effects of climate change will be its impact on extreme weather events, including tropical cyclones (TCs). Understanding these changes is of growing importance, and high resolution global climate models are providing potential for such studies, specifically for TCs. Beyond the difficulties associated with TC behavior in a warming climate, the extratropical transition (ET) of TCs into post-tropical cyclones (PTCs) creates another challenge when understanding these events and any potential future changes. PTCs can produce excessive rainfall despite losing their original tropical characteristics. The present study examines the representation of PTCs and their precipitation in three high resolution (25–50 km) climate models: CNRM, MRI, and HadGEM. All three of these models agree on a simulated decrease in TC and PTC events in the future warming scenario, yet they lack consistency in simulated regional patterns of these changes, which is further evident in regional changes in PTC-related precipitation. The models also struggle with their represented intensity evolution of storms during and after the ET process. Despite these limitations in simulating intensity and regional characteristics, the models all simulate a shift toward more frequent rain rates above 10 mm h−1 in PTCs. These high rain rates become 4%–12% more likely in the warmer climate scenario, resulting in a 5%–12% increase in accumulated rainfall from these rates.

Climatic Drivers of Ice Slabs and Firn Aquifers in Greenland

AbstractRecent observations revealed the existence of ice slabs and aquifers on the Greenland ice sheet (GrIS). Both affect the ice sheet's hydrology: ice slabs facilitate runoff and aquifers modulate drainage to the bed. However, their climatic drivers and history remain unclear, as most observations cover only two decades. Here, we present a model simulation of the evolution of GrIS ice slabs and aquifers (1980–2020), evaluated using radar measurements. The results show that accumulation, melt and rain rates are good predictors for the spatial distribution of ice slabs and aquifers. Both features were already present in the late 1980s, and their extent remained relatively constant until the beginning of this century, after which increased melt led to their expansion. We show that almost any transect from the coast to the ice‐sheet interior will cross either an ice slab region, or an aquifer, or both.

Relating Tropical Cyclone Intensification Rate to Precipitation and Convective Features in the Inner Core

Abstract Using Tropical Rainfall Measuring Mission Microwave Imager observations of global tropical cyclones (TCs) from 1998 to 2013, relationships between TC intensification rate and inner-core convective and precipitation parameters are examined by decoupling the dependency of these parameters on TC intensity and that on TC intensification rate. A total of 16 TC intensity change–intensity categories are categorized based on the initial intensity and 24-h future intensity change. The results show that the TC inner-core mean rain rate, convective intensity, and stratiform rain occurrence, and axisymmetric index of convective intensity increase significantly with TC intensification rate for each TC intensity category. The symmetry of rain rate and stratiform rainfall occurrence also increase significantly with TC intensification rate for each intensity category, except from slowly intensifying (SI) to rapidly intensifying (RI) group when the initial intensity is major hurricane. The RI major hurricanes have significantly more asymmetric rainfall distribution and distribution of stratiform rainfall occurrence than those of SI major hurricanes. For TCs with initial intensity in tropical depression, tropical storm, and major hurricane categories, the RI group has a significantly more asymmetric pattern of shallow precipitation/convection occurrence in the inner core than the SI group, while it has a significantly more symmetric pattern of deep convection occurrence than the SI group. The inner-core size, as quantified by the radius of maximum azimuthal mean rainfall decreases with both TC intensification rate and TC intensity.

Micro Rain Radar and Radiometric Measurements to Unravel Contrasting Features of Rain Microstructure Below and Above the Boundary Layer

AbstractKa‐band Micro rain Doppler radar is an effective tool to investigate the profiles of precipitation microstructure in terms of the raindrop size distribution (DSD). The DSD parameters that vary appreciably with height are indicative of the associated atmospheric phenomena. Hence the present investigation endeavors to put light on the underlying physical processes responsible for the evolution of varied rain microstructure profiles using micro rain radar (MRR), and radiometric measurements complemented with re‐analysis outputs over an urban tropical location, Kolkata (22.57°N, 88.37°E), India. MRR unravels the prevalence of significant biases in the typical power law relationship (Dm = aRb) between rain rate (R) and mass‐weighted mean drop diameter (Dm) along the rain height, especially during intense convective rain events, above the atmospheric boundary layer (ABL). Consequently, an alternative empirical relation appropriate to account for the R‐Dm variability above the ABL is proposed. Further, radiometric measurements and re‐analysis outputs reveal that the presence of atmospheric instabilities coupled with wind shear impacts above the ABL contributes to the enhanced breakup of raindrops and the deviations in the usual R‐Dm relationship. Thus, the present study intends to highlight the applicability of ground‐based radar measurements over the tropics to devise quantitative precipitation algorithms for reliable rain estimates.

Spectroscopic Observations of Coronal Rain Formation and Evolution Following an X2 Solar Flare

A significant impediment to solving the coronal heating problem is that we currently only observe active region loops in their cooling phase. Previous studies showed that the evolution of cooling loop densities and apex temperatures is insensitive to the magnitude, duration, and location of energy deposition. Still, potential clues to how energy is released are encoded in the properties of the cooling phase. The appearance of coronal rain, one of the most spectacular phenomena of the cooling phase, occurs when plasma has cooled below 1 MK, which sets constraints on the heating frequency, for example. Most observations of coronal rain have been made by imaging instruments. Here we report rare Hinode/EUV Imaging Spectrometer (EIS) observations of a loop arcade where coronal rain forms following an X2.1 limb flare. A bifurcation in plasma composition measurements between photospheric at 1.5 MK and coronal at 3.5 MK suggests that we are observing postflare-driven coronal rain. Increases in nonthermal velocities and densities with decreasing temperature (2.7–0.6 MK) suggest that we are observing the formation and subsequent evolution of the condensations. Doppler velocity measurements imply that a 10% correction of apparent flows in imaging data is reasonable. Emission measure analysis at 0.7 MK shows narrow temperature distributions, indicating coherent behavior reminiscent of that observed in coronal loops. The limitations on spatio-temporal resolution of EIS suggest that we are observing the largest features or rain showers. These observations provide insights into the heating rate, source, turbulence, and collective behavior of coronal rain from observations of the loop cooling phase.

Early Diagenetic Controls on Sedimentary Iodine Release and Iodine‐To‐Organic Carbon Ratios in the Paleo‐Record

AbstractIodine cycling in the ocean is closely linked to productivity, organic carbon export, and oxygenation. However, iodine sources and sinks at the seafloor are poorly constrained, which limits the applicability of iodine as a biogeochemical tracer. We present pore water and solid phase iodine data for sediment cores from the Peruvian continental margin, which cover a range of bottom water oxygen concentrations, organic carbon rain rates and sedimentation rates. By applying a numerical reaction‐transport model, we evaluate how these parameters determine benthic iodine fluxes and sedimentary iodine‐to‐organic carbon ratios (I:Corg) in the paleo‐record. Iodine is delivered to the sediment with organic material and released into the pore water as iodide (I−) during early diagenesis. Under anoxic conditions in the bottom water, most of the iodine delivered is recycled, which can explain the presence of excess dissolved iodine in near‐shore anoxic seawater. According to our model, the benthic I− efflux in anoxic areas is mainly determined by the organic carbon rain rate. Under oxic conditions, pore water dissolved I− is oxidized and precipitated at the sediment surface. Much of the precipitated iodine re‐dissolves during early diagenesis and only a fraction is buried. Particulate iodine burial efficiency and I:Corg burial ratios do increase with bottom water oxygen. However, multiple combinations of bottom water oxygen, organic carbon rain rate and sedimentation rate can lead to identical I:Corg, which limits the utility of I:Corg as a quantitative oxygenation proxy. Our findings may help to better constrain the ocean's iodine mass balance, both today and in the geological past.

Assessing Nowcast Models in the Central Mexico Region Using Radar and GOES-16 Satellite Data

In this study, the nowcast models provided by the Python pySTEPS library were evaluated using radar derived rain rate data and the satellite product Split-Window Difference (SWD) based on GOES-16 data, focusing on central Mexico. Initially, we obtained a characterization of the rainfall that occurred in the region using the radar rain rate and the SWD. Subsequently the nowcasts were evaluated using both variables. Two nowcast models were employed from pySTEPS: Extrapolation and S-PROG. The results indicate that average SWD is below 2.5 K, 90 min before the onset of rainfall events, and, on average, the SWD is 2 K during rainfall events. The results from both nowcast models were accurate and produced similar results. The nowcasts performed better when SWD data were used as input, having an average Probability of Detection (PoD) above 70% and a False Alarm Rate (FAR) reaching 40% for the 15-min prediction. The nowcasts were less accurate using the radar rain rate as input for the 15-min forecast, where the PoD was maximum 70% and FAR reaching 40%. However, these nowcasts were more reliable during well-organized precipitation events. In this work, it was determined that the nowcast models provided by pySTEPS can provide valuable rain forecasts using GOES-16 satellite and radar data for the central Mexico region.

Improvements in deep learning-based precipitation nowcasting using major atmospheric factors with radar rain rate

Recently, deep learning-based precipitation nowcasting has been investigated and its usefulness has been recognized. However, existing approaches have treated precipitation nowcasting as a spatiotemporal sequence prediction problem and have mainly used only radar images. Radar images show the distribution of water or ice droplets, but are limited in providing information about the dynamic or thermodynamic processes involved in the development of the rainfall system. In this study, we introduced a deep neural network that can utilize atmospheric factors that play major roles in the development of rainfall systems over the Korean Peninsula, including divergence at 925 hPa and total column water vapor, in addition to radar images. We also proposed a loss function based on mean categorical scores (e.g., critical success index and false alarm ratio) to minimize the performance error. In a quantitative evaluation of 1- to 6-hour forecast results, our deep learning models outperformed the conventional approach based on radar echo extrapolation and produced higher equitable threat scores (ETSs) for moderate to severe rain (i.e., 5, 10, and 20 mm h−1 thresholds). By applying the proposed loss function and using the divergence at 925 hPa as an additional input, the deep-learning model not only obtained the highest ETS values, but also revealed its potential to predict new developments of some heavy rainfall systems. The relationship between the presented deep learning-based precipitation nowcasting and the distribution features of the additional atmospheric factors is discussed further in the case study.

Classification of tropical cyclone rain patterns using convolutional autoencoder

Heavy rainfall produced by tropical cyclones (TCs) frequently causes wide-spread damage. TCs have different patterns of rain depending on their development stage, geographical location, and surrounding environmental conditions. However, an objective system for classifying TC rain patterns has not yet been established. This study objectively classifies rain patterns of North Atlantic TCs using a Convolutional Autoencoder (CAE). The CAE is trained with 11,991 images of TC rain rates obtained from satellite precipitation estimates during 2000−2020. The CAE consists of an encoder which compresses the original TC rain image into low-dimensional features and a decoder which reconstructs an image from the compressed features. Then, TC rain images are classified by applying a k-means method to the compressed features from the CAE. We identified six TC rain patterns over the North Atlantic and confirmed that they exhibited unique characteristics in their spatial patterns (e.g., area, asymmetry, dispersion) and geographical locations. Furthermore, the characteristics of rain patterns in each cluster were closely related to storm intensity and surrounding environmental conditions of moisture supply, vertical wind shear, and land interaction. This classification of TC rain patterns and further investigations into their evolution and spatial variability can improve forecasts and help mitigate damage from these systems.

Improving Simulations of Warm Rain in a Bulk Microphysics Scheme

Abstract Current bulk microphysical parameterization schemes underpredict precipitation intensities and drop size distributions (DSDs) during warm rain periods, particularly upwind of coastal terrain. To help address this deficiency, this study introduces a set of modifications, called RCON, to the liquid-phase (warm rain) parameterization currently used in the Thompson–Eidhammer microphysical parameterization scheme. RCON introduces several model modifications, motivated by evaluating simulations from a bin scheme, which together result in more accurate precipitation simulations during periods of warm rain. Among the most significant changes are 1) the use of a wider cloud water DSD of lognormal shape instead of the gamma DSD used by the Thompson–Eidhammer parameterization and 2) enhancement of the cloud-to-rain autoconversion parameterization. Evaluation of RCON is performed for two warm rain events and an extended period during the Olympic Mountains Experiment (OLYMPEX) field campaign of winter 2015/16. We show that RCON modifications produce more realistic precipitation distributions and rain DSDs than the default Thompson–Eidhammer configuration. For the multimonth OLYMPEX period, we show that rain rates, rainwater mixing ratios, and raindrop number concentrations were increased relative to the Thompson–Eidhammer microphysical parameterization, while concurrently decreasing raindrop diameters in liquid-phase clouds. These changes are consistent with an increase in simulated warm rain. Finally, real-time evaluation of the scheme from August 2021 to August 2022 demonstrated improved precipitation prediction over coastal areas of the Pacific Northwest. Significance Statement Although the accurate simulation of warm rain is critical to forecasting the hydrology of coastal areas and windward slopes, many warm rain parameterizations underpredict precipitation in these locations. This study introduces and evaluates modifications to the Thompson–Eidhammer microphysics parameterization scheme that significantly improve the accuracy of rainfall prediction in those regions.

Retrieval of Hurricane Rain Rate From SAR Images Based on Artificial Neural Network

Retrieval of Hurricane Rain Rate From SAR Images Based on Artificial Neural Network

Propagacija milimetarskih talasa na osnovu NYUSIM modela kanala uzimajući u obzir kišu u tropskim oblastima

The impact of atmospheric attenuation on wireless communication links is much more severe and complicated in tropical regions. That is due to the extreme temperatures, intense humidity, foliage and higher precipitation rain rates. This paper investigates the propagation of mm waves at 38 GHz. Key propagation channel parameters such as the path loss, path loss exponent, Rician K-factor, root mean square, delay spread and received power have been investigated considering the rain attenuation. The NYUSIM simulator was used. The analysis results have been classified considering rain attenuation, antenna setup, link distances, antenna height and antenna gain.

Influences of Summer Precipitation Occurrence Time on Raindrop Spectrum Characteristics over the Northeastern Tibetan Plateau

The impact of unique terrain on the microphysics of nighttime precipitation on the Tibetan Plateau (TP) has not been fully appreciated, due to a lack of observation. In this study, we used three raindrop spectrometers deployed in the northeastern TP to analyze the characteristics of the raindrop spectrum during two types of summer precipitation. These two types are classified according to their occurrence times: one starting in the daytime and lasting into the night (DP), while the other started at night and continuing into the daytime (NP). The results show that precipitation with a rain rate ranging from 1.0 to 5.0 mm h−1 contributes the most to the total precipitation, with this contribution rate being higher in the NP than in the DP. All the raindrop spectra follow a single-peak distribution pattern, and the logarithm of the generalized intercept parameter (lgNw) rises with the rain rate. The spectral widths of the DP-n (the nighttime part of the DP) are broader than those of the DP-d (the daytime part of the DP). Moreover, the average lgNw and mass-weighted mean diameter (Dm) over the northeastern TP were 2.65 mm−1 mm−3 and 1.04 mm, respectively, both of which are smaller than their equivalents in the plains. In addition, the gamma distribution can better fit the raindrop size distributions of the two types of precipitation. It is found that precipitation is more likely to occur over the TP at night. The characteristics of NP are reflected in two aspects. First, the sample size of the precipitation at the rain rate of 1.0–5.0 mm h−1 is higher in the NP-n (the nighttime part of the NP), and the precipitation at this rain rate contributes the most to the total precipitation. Second, for the same rain rate, the precipitation particles in the NP-n are larger.

Late Pleistocene to modern precipitation changes at the Paranal clay pan, central Atacama Desert

The Atacama Desert in Chile is known to be one of the driest deserts on Earth, with dominating hyperaridity at least since the Miocene. During recent times, however, especially the southern part of the Atacama repeatedly experienced exceptional precipitation events, like in 2015 and 2017. Locally, these events with high rainfall rates caused catastrophic floods with significant destruction and human fatalities. Although the meteorological drivers of these heavy rains are widely understood, only little is known about the frequency and amplitude of similar events on geological timescales. Here we present the results of a study on an endorheic clay pan at the southern edge of the hyperarid core of the Atacama, an area with a mean precipitation of approx. 5 mm per year. A modern ground-truthing approach combining sediment data, remote-sensing and meteorological data as well as climate-modelling was applied. Our observations indicate that the clay pan reacted very sensitively to local precipitation during the past 30 years, with four events >20 mm total rain causing sufficient surface run-off in the catchment to partially flood the basin. Comparative analyses of the four events illustrate that the amount of run-off is dependent on the maximum rain rate during the events rather than the total rain sum. A 1.88-m long sediment core recovered from the centre of the clay pan records the local hydrological and -environmental history since the Late Pleistocene. Sedimentological, mineralogical, geochemical, and biological core analyses imply strong variations in the amplitude of the recorded rainfall, with a clear shift from enhanced alluvial activity caused by higher-amplitude rain events during the Late Pleistocene to lower-amplitude Holocene events. The Holocene background sedimentation is superimposed by seven severe “Millennial-scale rain events”, which imply precipitation maxima on sub-orbital timescales that are potentially driven by changes in the El Niño Southern Oscillation (ENSO). The results of the study shed new light on the glacial-interglacial but also the sub-orbital precipitation variability in the Coastal Cordillera of the Atacama Desert and its potential driving mechanisms, and provide perspectives of the future precipitation development in the region under progressive global warming.

Impact of Types of Aerosol and Meteorological Conditions on Rain Rate Over India

Impact of Types of Aerosol and Meteorological Conditions on Rain Rate Over India