Precipitation Analysis Research Articles

METEOROLOGICAL DROUGHT RISK ASSESSMENT USING SPI NUMERICAL MODEL: A CASE STUDY OF HELMAND RIVER BASIN, AFGHANISTAN

Meteorological droughts, which result from insufficient precipitation, can cause significant economic damage. While preventing meteorological droughts is impossible, their harmful effects can be reduced through close monitoring. This study aims to evaluate the meteorological drought in the Helmand River Basin using the Standardized Precipitation Index (SPI) model. The hydrometeorological data used for this analysis were collected from the Ministry of Energy and Water (MEW) in Afghanistan. The precipitation data collected from MEW covers a 40-year period from 1979 to 2021. The SPI analysis of precipitation shows that 1990, 1991, and 1992 were moderately wet, while 1982, 1983, 1995-1998, 2005, 2014, and 2015 were nearly normal. However, moderately dry conditions were observed in 2000, 2001, 2018, and 2021. Among the sampled stations, Waras and Gardez consistently had low drought levels, while Tarnak, Shila-i-charkha, and Khwabgah stations experienced moderate-level drought. Meanwhile, Lashkargah and Adraskan stations exhibited relatively high levels of drought. In conclusion, this research on the HRB, using the SPI method, has provided valuable knowledge for understanding drought dynamics in the region. The findings underscore the importance of conducting region-specific analyses, the necessity of implementing sustainable water management strategies, and the global significance of addressing drought as a pressing environmental challenge.

Assessments of various precipitation product performances and disaster monitoring utilities over the Tibetan Plateau

The Tibetan Plateau, often referred to as Asia’s water tower, is a focal point for studying spatiotemporal changes in water resources amidst global warming. Precipitation is a crucial water resource for the Tibetan Plateau. Precipitation information holds significant importance in supporting research on the Tibetan Plateau. In this study, we estimate the performance and applicability of Climate Prediction Center Merged Analysis of Precipitation (CMAP), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Land Data Assimilation System (GLDAS), and Global Precipitation Climatology Project (GPCP) precipitation products for estimating precipitation and different disaster scenarios (including extreme precipitation, drought, and snow) across the Tibetan Plateau. Extreme precipitation and drought indexes are employed to describe extreme precipitation and drought conditions. We evaluated the performance of various precipitation products using daily precipitation time series from 2000 to 2014. Statistical metrics were used to estimate and compare the performances of different precipitation products. The results indicate that (1) Both CMAP and IMERG showed higher fitting degrees with gauge precipitation observations in daily precipitation. Probability of detection, False Alarm Ratio, and Critical Success Index values of CMAP and IMERG were approximately 0.42 to 0.72, 0.38 to 0.56, and 0.30 to 0.42, respectively. Different precipitation products presented higher daily average precipitation amount and frequency in southeastern Tibetan Plateau. (2) CMAP and GPCP precipitation products showed relatively great and poor performance, respectively, in predicting daily and monthly precipitation on the plateau. False alarms might have a notable impact on the accuracy of precipitation products. (3) Extreme precipitation amount could be better predicted by precipitation products. Extreme precipitation day could be badly predicted by precipitation products. Different precipitation products showed that the bias of drought estimation increased as the time scale increased. (4) GLDAS series products might have relatively better performance in simulating (main range of RMSE: 2.0–4.5) snowfall than rainfall and sleet in plateau. G-Noah demonstrated slightly better performance in simulating snowfall (main range of RMSE: 1.0–2.1) than rainfall (main range of RMSE: 2.0–3.8) and sleet (main range of RMSE: 1.5–3.8). This study’s findings contribute to understanding the performance variations among different precipitation products and identifying potential factors contributing to biases within these products. Additionally, the study sheds light on disaster characteristics and warning systems specific to the Tibetan Plateau.

Two decades of research trends in microbial-induced carbonate precipitation for heavy metal removal: a bibliometric review and literature review.

Amidst the increasing significance of innovative solutions for bioremediation of heavy metal removal, this paper offers a thorough bibliometric analysis of microbial-induced carbonate precipitation (MICP) for heavy metal removal, as a promising technology to tackle this urgent environmental issue. This study focused on articles published from 1999 to 2022 in the Scopus database. It assesses trends, participation, and key players within the MICP for heavy metal sequestration. Among the 930 identified articles, 74 countries participated in the field, with China being the most productive. Varenyam Achal, the Chinese Academy of Sciences, and Chemosphere are leaders in the research landscape. Using VOSviewer and R-Studio, keyword hotspots like "MICP", "urease", and "heavy metals" underscore the interdisciplinary nature of MICP research and its focus on addressing a wide array of environmental and soil-related challenges. VOSviewer emphasises essential terms like "calcium carbonate crystal", while R-Studio highlights ongoing themes such as "soil" and "organic" aspects. These analyses further showcase the interdisciplinary nature of MICP research, addressing a wide range of environmental challenges and indicating evolving trends in the field. This review also discusses the literature concerning the potential of MICP to immobilise contaminants, the evolution of the research outcome in the last two decades, MICP treatment techniques for heavy metal removal, and critical challenges when scaling from laboratory to field. Readers will find this analysis beneficial in gaining valuable insights into the evolving field and providing a solid foundation for future research and practical implementation.

Remote Sensing-Based Analysis of Precipitation Events: Spatiotemporal Characterization across China

Precipitation occurs in individual events, but the event characteristics of precipitation are often neglected. This work seeks to identify the precipitation events on both spatial and temporal scales, explore the event characteristics of precipitation, and reveal the relationships between the different characteristics of precipitation events. To do this, we combined the Forward-in-Time (FiT) algorithm with the gridded hourly precipitation product to detect precipitation events in time and space over China. The identified precipitation events were analyzed to determine their characteristics. The results indicate that precipitation events can be detected and identified in time and space scales based on the FiT algorithm and the gridded hourly precipitation product. The precipitation total, duration, and intensity of these events decrease gradually from the southern (eastern) coastal regions to northern (western) inland areas of China. The event precipitation totals are strongly correlated with event duration and event maximum intensity; the totals are more strongly correlated with event maximum intensity and event intensity in the regions with lower precipitation than the regions with higher precipitation. More than 90% of precipitation events are shorter than 6 h, and events with long duration normally occur in temperate monsoon (TM) and subtropical/tropical monsoon (ST) climate zones. Heavy precipitation events with a duration longer than 7 h generally occur more than seven times per year in TM and ST climate zones. Our results suggest that precipitation analyses should sufficiently consider the characteristics of events across different regions.

Assessment of a downscaling using quantiles mapping with MIROC Model in Guadalquivir Basin, Bolivia

In this study historical and combined GS precipitation products were assessed versus climate change scenarios at Guadalquivir basin. The purpose of this study was to evaluate the downscaling procedure using Quantiles Downscaling Mapping (QDM). This scheme is based in statistical analysis where precipitation and temperature are adjusted using observed datasets. Moreover, simulated river discharge was obtained using HYDROBID tool. It was identified underestimation of combined GS precipitation product as well as simulated river discharge. Three climate change scenarios from MIROC CORDEX were used namely: RCP2.6, RCP4.5 and RCP8.5. The precipitation and temperature analyses were performed at daily time step. The results of QDM application showed more efficiency with RCP 8.5 and combined product GS for the 230 subbasins. About hydrological modeling using CORDEX variables, it was observed perturbed climate change using combined product GS with lower values against rain gauges. In this sense, precipitation and discharge were underestimated. Temperature showed the best performance at all cases. Finally, discharge series were integrated into volumes, where a decrease in water availability at the basins was noted as for near and far future. It is recommended to use perturbed scenarios with combined precipitation product since it is the most critical and takes into account the spatial variability with 230 syntactical rain gauges.

Optimizing Ridge–Furrow Rainwater-Harvesting Strategies for Potato Cultivation in the Drylands of Northwestern China: A Regional Approach

The arid and semi-arid region of Northwest China plays a significant role in potato production, yet yields are often hampered by drought due to limited precipitation and irrigation water. The ridge–furrow rainwater-harvesting technology is an efficient and widely used technique to relieve drought impact and improve crop yield by changing the micro-topography to harvest rainwater to meet the water demand of crops. An analysis of precipitation, water demand, and runoff data spanning 30 years guided the selection of suitable rainwater-harvesting methods tailored to meteorological conditions. The results showed that potato water demand exceeded precipitation in the region. The mulching approach performed best in the western arid region with the most significant increase in yield and water use efficiency (WUE) and was suitable for the western semi-arid region and the agro-pastoral ecotone. In the potato dryland farming areas, the water deficit increased from southeast to northwest. Specifically, northern Gansu, northern Ningxia, and midwestern Inner Mongolia experienced a water deficit of over 200 mm, and rainwater harvesting combined with irrigation was recommended. Conversely, regarding deficits below 200 mm in southern Gansu, Ningxia, and central Inner Mongolia, a 1:1 or 2:1 pattern of ridges could be applied, and mulching was needed only in the necessary areas. For the southern Qinghai, Shaanxi, and eastern Inner Mongolia regions, ridge–furrow rainwater harvesting could be replaced by flat potato cropping. In summary, rainwater harvesting addresses water deficits, aiding climate adaptation in Northwest China’s arid and semi-arid regions. The implementation of mulching and ridge–furrow technology must be location-specific.

Revisiting the Use of the Gumbel Distribution: A Comprehensive Statistical Analysis Regarding Modeling Extremes and Rare Events

The manuscript presents the applicability of the Gumbel distribution in the frequency analysis of extreme events in hydrology. The advantages and disadvantages of using the distribution are highlighted, as well as recommendations regarding its proper use. A literature review was also carried out regarding the methods for estimating the parameters of the Gumbel distribution in hydrology. Thus, for the verification of the methods, case studies are presented regarding the determination of the maximum annual flows and precipitations using nine methods for estimating the distribution parameters. The influence of the variability of the observed data lengths on the estimation of the statistical indicators, the estimation of the parameters, and the quantiles corresponding to the field of small exceedance probabilities (p < 1%) is also highlighted. In each case, the results are analyzed compared to those obtained with the Generalized Extreme Value distribution, the four-parameter Burr distribution, and the five-parameter Wakeby distribution estimated using the L-moments method. The results of the case studies highlight and reaffirm the statistical, mathematical, and hydrological recommendations regarding the avoidance of applying the Gumbel distribution in flood frequency analysis and its use with reservations in the case of maximum precipitation analysis, especially when the statistical indicators of the analyzed data are not close to the characteristic ones and unique to the distribution.

Rainfall analysis of the May 2021 southeastern Texas and southern Louisiana flood

ABSTRACT This paper analyzes the heavy rainfall on 16–20 May 2021 across southeastern Texas and southern Louisiana. In Louisiana, five fatalities were attributed to the event and > $1 billion in losses. The rainfall was caused by two slow-moving, mesoscale convective vortices that concentrated precipitation south of Port Arthur, TX, Lake Charles, LA, and Baton Rouge, LA, in 6 to 12-hour periods. Using calibrated radar data, the Storm Precipitation Analysis System estimated 6-h rainfall totals of 254–362 mm south of Lake Charles, surpassing 200–500-year average recurrence intervals (ARI) for those locations, with an isolated area receiving 367.5 mm, equating to a > 800-year ARI. In East Baton Rouge Parish, LA, 203–268 mm was estimated in 6-h (200–500-year ARI), with an isolated area receiving 274 mm, equivalent to a 750-year ARI. A comparison of the May 2021 and August 2016 events, which both affected southern Louisiana, is made using a depth-area-duration analysis. While the forcing mechanisms differed, 1–6 h rainfall depths were similar at 1 –25,900 km2. The May 2021 event exceeded rainfall depths produced by the August 2016 event for 2–6 h durations for area sizes of 1–388 km2, highlighting the precipitation intensity produced by the May 2021 event.

Cross-Validation Methods for Multisource Precipitation Datasets over the Sparse-Gauge Region: Applicability and Uncertainty

Abstract Evaluating the accuracy of various precipitation datasets over ungauged or even sparse-gauge areas is a challenging task. Cross-validation methods can evaluate three or more datasets based on the error independence from input data, without relying on ground observations. Here, the triple collocation (TC) method is employed to evaluate multisource precipitation datasets: China Gauge-based Daily Precipitation Analysis (CGDPA), model-based ERA5, and satellite-derived IMERG-Early, IMERG-Late, GSMaP in near–real time (GSMaP-NRT), and GSMaP moving vector with Kalman filter (GSMaP-MVK) over the Tibetan Plateau (TP). TC-based results show that ERA5 has better performances than satellite-only precipitation products over mountainous regions with complex terrains. For purely satellite-derived products, IMERG products outperform GSMaP products. Considering the potential existence of error dependencies among input datasets, caution should be exercised. Thus, it is necessary to introduce an alternative cross-validation method (generalized three-cornered hat) and explore the applicability of cross validation from the perspective of error independence. We found that cross-validation methods have high applicability in most TP regions with sparse-gauge density (accounting for about 80.1% of the total area). Additionally, we conducted simulation experiments to discuss the applicability and robustness of TC. The simulation results substantiated that cross validation can serve as a robust evaluation method over sparse-gauge regions. Although it is generally known that the cross-validation methods can be served in sparse-gauge regions, the application condition of different evaluation methods for precipitation products is identified quantitatively in TP now. Significance Statement Cross validation is a powerful assessment method for multisource precipitation datasets. This method is widely used by hydrologists in sparse-gauge or ungauged regions, like the African continent and the Tibetan Plateau (TP). However, as an indirect assessment method, the inherent uncertainty in cross validation warrants emphasis. Here, two cross-validation methods (the triple collocation method and the generalized three-cornered hat method) are employed to evaluate multisource precipitation datasets: China Gauge-based Daily Precipitation Analysis (CGDPA), model-based ERA5, and satellite-derived IMERG-Early, IMERG-Late, GSMaP in near–real time (GSMaP-NRT), and GSMaP moving vector with Kalman filter (GSMaP-MVK) over the TP. In this study, we not only assessed the current mainstream six precipitation datasets but also analyzed their uncertainties by combining these two cross-validation methods.

Trend analysis of precipitation and drought characteristics over Churu district of northeast Rajasthan, India

ABSTRACT Drought is one of the most destructive natural disasters, becoming more extreme and less predictable due to climate change. Drought directly affects a region's water resources, leading to inadequate water availability and harming crops, animals, and humans. The objective of the present study is to assess the long-term trends of precipitation and drought characteristics for the Churu district of northeast Rajasthan, India. For this purpose, mean monthly precipitation data are collected through the India-WRIS from 1901 to 2022. The trend analysis uses statistical methods (M-K test & amp; SS estimator), and the results are compared with a graphical method (innovative trend analysis). Drought events are found using precipitation-based drought indices, i.e., standardized precipitation index, Z-score index, and percentage of normal precipitation index. Interestingly, the results of the statistical method are well matched with the results of the graphical method. The study concludes that during the annual precipitation a rising trend is observed, which can help to mitigate the severity of drought. Also, it is found that moderately dry droughts occur most frequently compared with other drought events. This study will help policy-makers and local administrators to take necessary action to mitigate the severity of drought.

Use of proxy observations to evaluate the accuracy of precipitation spatial gridding

AbstractA WRF‐based high‐resolution reanalysis of the Irish climate (1981–2010) is used to create proxy daily precipitation observations at the locations of climatological sites used for precipitation monitoring; the data are statistically representative of the real precipitation climate both for mean (over monthly, seasonal and annual periods) and extreme values. The proxy observations are spatially interpolated to the original WRF grid using a typical gridding package and compared against the original data to assess gridding errors. The errors are more complex than the estimates provided by the gridding software; systematic biases are evident which by the inclusion of strategically placed additional observing sites are shown to be greatly reduced. There is also evidence of systematic differences in trend analyses of extreme precipitation over the period. The method provides independent estimates of the errors that arise from actual gridding applications. It also facilitates the testing of the optimality of a network by highlighting possible inadequacies in an existing station layout and suggesting new observing site locations to fill gaps. Uncertainties regarding the errors in real precipitation observations, and possible spurious impacts linked to temporal changes in the real observing network, are avoided by this method.

Beyond the Surface: Non-Invasive Low-Field NMR Analysis of Microbially-Induced Calcium Carbonate Precipitation in Shale Fractures

AbstractMicrobially-induced calcium carbonate precipitation (MICP) is a biological process in which microbially-produced urease enzymes convert urea and calcium into solid calcium carbonate (CaCO3) deposits. MICP has been demonstrated to reduce permeability in shale fractures under elevated pressures, raising the possibility of applying this technology to enhance shale reservoir storage safety. For this and other applications to become a reality, non-invasive tools are needed to determine how effectively MICP seals shale fractures at subsurface temperatures. In this study, two different MICP strategies were tested on 2.54 cm diameter and 5.08 cm long shale cores with a single fracture at 60 ℃. Flow-through, pulsed-flow MICP-treatment was repeatedly applied to Marcellus shale fractures with and without sand (“proppant”) until reaching approximately four orders of magnitude reduction in apparent permeability, while a single application of polymer-based “immersion” MICP-treatment was applied to an Eagle Ford shale fracture with proppant. Low-field nuclear magnetic resonance (LF-NMR) and X-Ray computed microtomography (micro-CT) techniques were used to assess the degree of biomineralization. With the flow-through approach, these tools revealed that while CaCO3 precipitation occurred throughout the fracture, there was preferential precipitation around proppant. Without proppant, the same approach led to premature sealing at the inlet side of the core. In contrast, immersion MICP-treatment sealed off the fracture edges and showed less mineral precipitation overall. This study highlights the use of LF-NMR relaxometry in characterizing fracture sealing and can help guide NMR logging tools in subsurface remediation efforts.

Effect of NO2 on SO2–SO42- conversion in atmosphere: Evidence from long-term precipitation

Sulfate aerosol (SO42−) is an important factor leading to acid rain and aggravating atmospheric particulate pollution. SO42− is mainly converted from gaseous SO2, in which the oxidant gas NO2 may play a key role. In real atmosphere with complexity and nonlinearity, the impact of NO2 on SO2–SO42- conversion exhibits scale dependence due to the combined effect of meteorology, atmospheric chemistry, and pollution emission. Here, based on the long-term atmospheric precipitation data from 2014 to 2021 of Nanchong City in the Sichuan Basin, the Extended Ensemble Empirical Mode Decomposition (EEMD) and Multifractal Detrended Cross-Correlation Analysis (MF-DCCA) methods have been employed to analyze the nonlinear correlations between gaseous SO2 and SO42− in rainwater at different time scales. A multifractal parameter Δh has been described as the scaling features of SO2–SO42- conversion efficiency in the real atmosphere. Then, the impact of NO2 on SO2–SO42- conversion in cold and warm seasons has been quantified, using sliding window analysis and Pearson correlation analysis. The results show that NO2 significantly promoted SO2–SO42- conversion in the cold season, but had little effect on it in the warm season. As direct evidence from atmospheric precipitation analysis, this finding has provided a new theoretical basis for evaluating the impact of NO2 on secondary aerosol generation.

Assessing uncertainty in bioclimatic modelling: a comparison of two high-resolution climate datasets in northern Patagonia

Climate change is reshaping forest ecosystems, presenting urgent and complex challenges that demand attention. In this context, research that quantifies interactions between climate and forests is substantial. However, modelling at a spatial resolution relevant for ecological processes presents a significant challenge, especially given the diverse geographical contexts in which it is applied. In our study, we aimed to assess the effects of applying CHELSA v.2.1 and WorldClim v2.1 data on bioclimatic analysis within the Río Puelo catchment area in northern Patagonia. To achieve this, we inter-compared and evaluated present and future bioclimates, drawing on data from both climate datasets. Our findings underscore substantial consistency between both datasets for temperature variables, confirming the reliability of both for temperature analysis. However, a strong contrast emerges in precipitation predictions, with significant discrepancies highlighted by minimal overlap in bioclimatic classes, particularly in steep and elevated terrains. Thus, while CHELSA and WorldClim provide valuable temperature data for northern Patagonia, their use for precipitation analysis requires careful consideration of their limitations and potential inaccuracies. Nevertheless, our bioclimatic analyses of both datasets under different scenarios reveal a uniform decline in mountain climates currently occupied by N. pumilio, with projections suggesting a sharp decrease in their coverage under future climate scenarios.

Spatiotemporal trend analysis of precipitation, temperature, and soil moisture in selected agrarian locations within Nigeria—a comparative study

Spatiotemporal trend analysis of precipitation, temperature, and soil moisture in selected agrarian locations within Nigeria—a comparative study

Spatial and conventional verifications of hurricanes Dorian and Fiona using the Canadian precipitation analysis & integrated multi-satellite retrievals for GPM products

Reanalysis and satellite-based rainfall packages are useful for monitoring hydroclimatic extremes. These advanced tools can be used as an early-warning system for decision-making during extreme events. Hurricane Dorian and Fiona impacted the North Atlantic from September 6–9, 2019 and September 22–25, 2022, respectively. This study evaluated the Canadian Precipitation Analysis (CaPA) in conjunction with integrated multi-satellite retrievals for the Global Precipitation Measurement Mission (GPM) in capturing these events at Early, Late and Final run stages (IMERG-E, IMERG-L & IMERG-F, respectively). Statistical verification was conducted through continuous, categorical and spatial methods to answer multiple research questions. The results show that CaPA outperformed all the IMERG products across various intensities throughout the duration of the extremes compared to station observations; therefore, CaPA can be a proxy for gauged observations. When CaPA was used as the reference for spatial verification, IMERG products correctly captured the spatial evolution of the hurricane from day to day, indicating they are reliable for hydroclimatic extreme applications. In addition, there are no striking statistical differences between the IMERG products despite the fact that IMERG-E and IMERG-L had no adjustment or gauge observation. In general, this study shows that IMERG-E has many potential applications such as CaPA assimilation, a hazard early warning system, and hurricane tracking and prediction. Further, including IMERG-E into CaPA will leverage its high resolution and latency. This study will benefit those involved in hydroclimatic studies and decision-making in the North Atlantic.

Spatio-temporal analysis of precipitation in eastern India for last three decades

Spatio-temporal analysis of precipitation in eastern India for last three decades

Utilization of satellite data for landslide disaster mitigation in extreme event: a case study June 18, 2016, landslide in Banjarnegara Regency, Indonesia

Abstract Understanding the characteristics of landslides produced by rainfall and soil humidity is critical for reducing the occurrence of landslides. Extreme rain, as an external impact, has been suggested to play a role in landslide development. Many people were killed in a landslide that occurred in the Banjarnegara Regency on June 18, 2016, which was unexpected because June is not Indonesia’s rainy season. El Niño and La Niña events were observed in Indonesia in 2015-2016. The initial hypothesis is that such extreme rain conditions caused this landslide. This study aims to apply an alternative approach to detecting landslides by monitoring soil moisture and rainfall with remote sensing data during extreme events. An analysis of precipitation and moisture data has been carried out before the event and is associated with the El Niño and La Niña phenomena. The findings indicate that the natural environment of Banjarnegara Regency is unique. Landslides occur even during the extreme dry season. Rain still drops, and soil moisture is high. The impact of local variables is more significant than regional conditions. They are still at risk of a landslide during the El Niño phase. During that stage, Banjarnegara Regency’s potential landslide area would continue to be observed. Monitoring needs to be carried out rapidly, with a high level of spatial and temporal resolution. It is possible to resolve it using IMERG and SMAP data.

Impact of Assimilating Radar Solid Precipitation Data in the Canadian Precipitation Analysis System

Abstract The Canadian Precipitation Analysis (CaPA) system provides near-real-time precipitation analyses over Canada by combining observations with short-term numerical weather prediction forecasts. CaPA’s snowfall estimates suffer from the lack of accurate solid precipitation measurements to correct the first-guess estimate. Weather radars have the potential to add precipitation measurements to CaPA in all seasons but are not assimilated in winter due to radar snowfall estimate imprecision and lack of precipitation gauges for calibration. The main objective of this study is to assess the impact of assimilating Canadian dual-polarized radar-based snowfall data in CaPA to improve precipitation estimates. Two sets of experiments were conducted to evaluate the impact of including radar snowfall retrievals, one set using the high-resolution CaPA (HRDPA) with the currently operational quality control configuration and another increasing the number of assimilated surface observations by relaxing quality control. Experiments spanned two winter seasons (2021 and 2022) in central Canada, covering part of the entire CaPA domain. The results showed that the assimilation of radar-based snowfall data improved CaPA’s precipitation estimates 81.75% of the time for 0.5-mm precipitation thresholds. An increase in the probability of detection together with a decrease in the false alarm ratio suggested an improvement of the precipitation spatial distribution and estimation accuracy. Additionally, the results showed improvements for both precipitation mass and frequency biases for low precipitation amounts. For larger thresholds, the frequency bias was degraded. The results also indicated that the assimilation of dual-polarization radar data is beneficial for the two CaPA configurations tested in this study.

IMERG in the Canadian Precipitation Analysis (CaPA) System for Winter Applications

IMERG in the Canadian Precipitation Analysis (CaPA) System for Winter Applications