Precipitation Observations Research Articles

Soil and Water Assessment Tool (SWAT)-Informed Deep Learning for Streamflow Forecasting with Remote Sensing and In Situ Precipitation and Discharge Observations

In order to anticipate residual errors and improve accuracy while reducing uncertainties, this work integrates the long short-term memory (LSTM) with the Soil and Water Assessment Tool (SWAT) to create a deep learning (DL) model that is guided by physics. By forecasting the residual errors of the SWAT model, the SWAT-informed LSTM model (LSTM-SWAT) differs from typical LSTM approaches that predict the streamflow directly. Through numerical tests, the performance of the LSTM-SWAT was evaluated with both LSTM-only and SWAT-only models in the Upper Heihe River Basin. The outcomes showed that the LSTM-SWAT performed better than the other models, showing higher accuracy and a lower mean absolute error (MAE = 3.13 m3/s). Sensitivity experiments further showed how the quality of the training dataset affects the performance of the LSTM-SWAT. The results of this study demonstrate how the LSTM-SWAT may improve streamflow prediction greatly by remote sensing and in situ observations. Additionally, this study emphasizes the need for detailed consideration of specific sources of uncertainty to further improve the predictive capabilities of the hybrid model.

Intercomparison of reanalysis and satellite precipitation products in endorheic and exorheic basins on the Tibetan Plateau

Study Region: Endorheic and exorheic basins of the Tibetan Plateau (TP). Study Focus: Reanalysis and satellite precipitation products provide alternatives for regions of sparse ground precipitation observation, but pose a tough task to select a suitable one for the TP. This study conducts a multi-scale evaluation of six reanalysis and satellite precipitation products in endorheic and exorheic basins using water balance and extended triple collocation (ETC) methods. The reanalysis precipitation products include ECMWF Re-Analysis version 5 (ERA5-Land), China Meteorological Forcing Dataset (CMFD), Global Land Data Assimilation Systems (GLDAS), and High-resolution Precipitation dataset for the Third Pole region (TPHiPr). The satellite precipitation data include Global Precipitation Measurements (GPM) and Tropical Rainfall Measuring Mission (TRMM) products. New Hydrological Insights for the Region: The precipitation products vary in accuracy from basin to basin, with better performance in exorheic than endorheic basins. Reanalysis-based ERA5-land, TPHiPr, and CMFD perform well in most basins at annual scale, among which TPHiPr performs best at daily scale. At regional scale, GPM performs well in endorheic region, and ERA5-land in exorheic region. While all the products increase significantly in accuracy from basin to regional scale in endorheic region, ERA5-land shows best performance at annual and multi-year scales in the entire region. Our findings provide valuable supports for precipitation product selection in the Tibetan endorheic and exorheic basins.

A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks

Our understanding of the impact of climate change on water availability and natural hazards in high-mountain regions is limited due to the spatial and temporal scarcity of ground observations of precipitation and snow. Freely available, satellite-based information about the snowpack is currently mainly limited to indirect measurements of snow-covered area or very coarse-scale snow water equivalent (SWE), but only for flat areas in lowlands without vegetation cover. Novel space-based laser altimeters, such as ICESat-2, have the potential to provide high-resolution snow depth data in worldwide mountain regions where no ground observations exist. However, these space-based laser altimeters come with spatial gaps between ground tracks, obtained without repetition at a give location. To overcome these drawbacks, here, we present a combined probabilistic data assimilation and deep learning approach to reconstruct spatio-temporal SWE from observations of snow depth along ground tracks, imitating ICESat-2 tracks in view of a potential future global application.Our approach is based on assimilating SWE and snow cover information in a degree-day model with an iterative ensemble smoother (IES) which allows temporally reconstructing SWE along hypothetical ground tracks separated by 3 km. As input, the degree-day model uses daily precipitation and downscaled air temperature from the ERA5 reanalysis. A feedforward neural network (FNN) is then used for spatial propagation of the daily mean and standard deviation of the updated SWE ensemble members obtained from the IES. The combined IES-FNN approach provides uncertainty-aware spatio-temporally continuous estimates of SWE.We tested our approach in the alpine Dischma valley (Switzerland) using high-resolution snow depth maps obtained from photogrammetric techniques mounted on airplanes and unmanned aerial system observations. Our results show that the IES-FNN model provides reliable estimates at a resolution of approximately 100 m. Even assimilating only one SWE observation during the year (combined with satellite-based melt-out date estimates) produces satisfying results when evaluating the IES-FNN SWE reconstructions on independent dates and smaller (<4 km2) areas: mean absolute error of 86 mm (78 mm) at Schürlialp (Latschüelfurgga) for average SWE of 180 mm (254 mm), and average spatial linear correlation with the reference SWE of 0.51 (0.48). However, the assimilated SWE observation must not be too early in the accumulation season or too late in the melt season when the snowpack is starting or ending to accumulate or melt, respectively. Smaller distances between ground tracks (1500 m and 500 m) show improved performance of the IES-FNN approach in space, with no significant improvement in terms of temporal reconstruction.Applying the IES-FNN approach to e.g., real ICESat-2 data, remains challenging due to the higher uncertainties associated with these data. However, the approach we propose remains potentially very helpful in addressing the problem of scarcity of ground observations of precipitation and snow in high-mountain regions.

X-Band Radar and Surface-Based Observations of Cold-Season Precipitation in Western Colorado’s Complex Terrain

Abstract Hydrologic processes associated with intermountain cold-season precipitation in the Upper Colorado River basin have important impacts on avalanche forecasting and water resource management. However, traditional weather radar networks struggle with observations in this complex terrain. Data collected during the Study of Precipitation, the Lower Atmosphere, and the Surface for Hydrometeorology (SPLASH) and its sister campaign, Surface Atmosphere Integrated Field Laboratory (SAIL) in the East River watershed of western Colorado, are used to examine a multistorm period from 23 December 2021 to 1 January 2022 that contributed 35% of the total winter precipitation in this watershed. Dual-polarization X-band radar and disdrometer measurements show ∼30-mm differences in precipitation amount at two sites in proximity over four distinct storm events within the period. Wind patterns, synoptic forcings, microphysical characteristics of precipitation, and surface meteorology are analyzed to explain the observed spatial variability of cold-season precipitation in complex mountainous terrain. Analysis shows that differences over time within this event are mainly accounted for by synoptic forcings, such as frontal passages; differences between sites are accounted for by the impact of variations in local wind patterns on precipitation microphysics. Patterns of surface precipitation intensity are compared and found to be correlated with X-band radar signatures; a relationship between a strong dendritic growth stage and intense low-density surface precipitation is reinforced by this study. This relationship demonstrates the importance of particle growth mechanisms on surface snowfall patterns in high-altitude complex terrain, underscoring the importance of realistic microphysical parameterizations. Significance Statement The amount and density of snowpack from western Colorado winter storms have significant impacts on water resources in the Upper Colorado River basin. Snowpack characteristics are affected by small-scale differences in how snow forms in the atmosphere. These differences are hard to study in the complex terrain of the Rockies, but data from the SPLASH and SAIL field campaigns allows us to investigate how snow crystal formation and mountain-driven wind patterns affect snow near the surface. Our study finds that snow crystal growth varies over small space and time scales and is likely controlled by the terrain beneath a given location and resultant local wind patterns. These results imply that predicting snowpack in the Rockies requires properly representing local wind patterns and crystal growth processes in models.

Analyzing the heavy rainfall event of July 2011 in Niigata using ground, satellite and radar rainfalls

In recent years the application of radar and satellite precipitation observations has become increasingly useful in poorly gauged areas. However, understanding the relationships between these data sources and ground observations is vital to correct the datasets and improve their application in hydrological studies. In this study we analyzed the spatial and temporal relationships between Global Satellite Mapping of Precipitation-Near Real Time (GSMaP_NRT) data set with radar and ground observations at downstream of Shinano River, Japan. GSMap_NRT observation showed better relationship with ground observations for longer-duration observations (eg. 3, 6, 12 and 18 hours) than hourly observations. The GSMap_NRT showed excellent relationship with ground rainfall at satellite observation time compared to non-observation time. Comparison of radar observations at various time scales and spatial resolutions showed that radar estimates at smaller-time-interval ratio and lower-spatial-scale ratio are better than longer-time-interval ratio and higher-spatial-scale ratio. We also observed that radar precipitation estimate well-represent the areal averaged precipitation of ground observations. Results from this study showed that radar precipitation estimates could serve as very important input to improve merging of ground and satellite precipitation as well satellite rainfall improvement systems.

Using Satellite Observations of Lightning and Precipitation to Diagnose the Behavior of Deep Convection in Tropical Cyclones Traversing the Midlatitudes

AbstractThis study uses a unique combination of geostationary and low‐Earth orbiting satellite‐based lightning and precipitation observations, respectively, to examine the evolution of deep convection during the tropical cyclone (TC) lifecycle. The study spans the 2018–2021 Atlantic Basin hurricane seasons and is unique as it provides the first known analysis of total lightning (intra‐cloud and cloud‐to‐ground) observed in TCs through their extratropical transition and post‐tropical cyclone (PTC) phases. We consider the TC lifecycle stage, geographic location (e.g., land, coast, and ocean), shear strength, and quadrant relative to the storm motion and environmental shear vectors. Total lightning maxima are found in the forward right quadrant relative to storm motion and downshear of the TC center, consistent with previous studies using mainly cloud‐to‐ground lightning. Increasing environmental shear focuses the lightning maxima to the downshear right quadrant with respect to the shear vector in tropical storm phases. Vertical profiles of radar reflectivity from the Global Precipitation Measurement mission show that super‐electrically active convective precipitation features (&gt;75 flashes) within the PTC phase of TCs have deeper mixed phase depths and higher reflectivity at −10°C than other phases, indicating the presence of more intense convection. Differences in the net convective behavior observed throughout TC evolution manifest in both the TC‐scale frequency of lightning‐producing cells and the intensity variations amongst individual convective cells. The combination of continuous lightning observations and precipitation snapshots improves our understanding of convective‐scale processes in TCs, especially in PTC phases, as they traverse the tropics and mid‐latitudes.

Precipitation measurements experiment using microwave links in Eastern China from October 2020 to March 2022

Precipitation is an important piece of information needed in many areas such as transportation, military and agriculture, and microwave links have proven to be an effective means of acquiring it and can be used as a complementary means for professional precipitation measurement instruments such as rain gauges, weather radar, rain measuring satellites, etc. In this paper, two microwave links (at 26 GHz, both vertically polarised) located across a river in eastern China and a nearby OTT PARSIVEL disdrometer were used to carry out a precipitation observation experiment from October 2020 to March 2022 (March to November 2021 for liquid precipitation, others for possible non-liquid precipitation). The applicability of the dry and rainy period identification methods (standard deviation method and correlation coefficient method) based on this link data is analyzed first. In addition, a wet antenna attenuation correction model based on a Long Short-Term Memory (LSTM) neural network was applied to non-winter precipitation inversion. The results show that the rain rate and cumulative rainfall calculated from the two links are in good agreement with that calculated by the disdrometer data. Furthermore, quantitative inversion of winter precipitation using two microwave links is performed, and the variation characteristics of the link levels during non-liquid precipitation periods are also analyzed.

Experimental investigations on influence of processing parameters on the microstructure and properties of AM-printed stainless steel

ABSTRACT The current study has investigated the mechanical properties and microstructure of the DMLS processed 316L experimentally by varying process parameters and solution annealing as a post-processing technique. The feedstock material characteristics, laser power input, layer thickness, scanning velocity, hatch spacing, and building direction are important factors to consider. The modified microstructure not only affects mechanical performance and corrosion resistance but also alters the specimen's properties. An analysis of the powder feedstock and density measurement has been conducted. The samples were subjected to tensile testing, hardness testing, and microstructural analysis. According to the Taguchi analysis findings, the most significant parameter affecting product quality was layer thickness. The observation of grain boundary precipitation in a few samples could be reversed through solution annealing. The results of this study will assist in determining the optimal process parameters for achieving the desired mechanical properties.

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.

Emergence of multivariate climate change signals

Abstract The emergence of a climate change signal relative to background variability is a useful metric for understanding local changes and their consequences. Studies have identified emergent signals of climate change, particularly in temperature-based indices with weaker signals found for precipitation metrics. In this study, we adapt climate analogue methods to examine multivariate climate change emergence over the historical period. We use seasonal temperature and precipitation observations and apply a sigma dissimilarity method to demonstrate that large local climate changes may already be identified, particularly in low-latitude regions. The multivariate methodology brings forward the time of emergence by several decades in many areas relative to analysing temperature in isolation. We observed particularly large departures from an early-20th century climate in years when the global warming signal is compounded by an El Niño-influence. The latitudinal dependence in the emergent climate change signal means that lower-income nations have experienced earlier and stronger emergent climate change signals than the wealthiest regions. Analysis based on temperature and precipitation extreme indices finds weaker signals and less evidence of emergence but is hampered by lack of long-running observations in equatorial areas. The framework developed here may be extended to attribution and projections analyses.

Evaluation of global seamless soil moisture products over China: A perspective of soil moisture sensitivity to precipitation

Accurate and timely information about soil moisture (SM) is not only critical to the study of climate change, but is also significant for agricultural production, drought monitoring, and hydrometeorological predictions. With the development of satellite remote sensing techniques, land surface models, and data assimilation techniques, a number of global seamless soil moisture products have been released with highly variable accuracy. A comprehensive evaluation of these products is necessary to select a suitable SM product for use. In this study, we provided such an evaluation of four global seamless SM products (SMAP-L4, ERA5-Land, GLDAS-Noah, and GLEAM) over China based on the stations from the automatic soil moisture observation (ASMO) system. Compared to previous studies that focused on a direct comparison between SM products and in situ SM observations, we specifically evaluated these products from the perspective of SM sensitivity to precipitation. This is because in situ precipitation observations are more spatially representative than in situ SM observations. Precipitation is a regional event, therefore in situ precipitation observations can be better scale-matched with coarse resolution SM products compared to in situ SM observations. Further, in addition to surface soil moisture (SSM), root zone soil moisture (RZSM) was evaluated in this study. The combination of all statistics illustrates that SMAP-L4 and ERA5-Land generally performed better than GLDAS-Noah and GLEAM. Regardless of the SSM or RZSM estimates, SMAP-L4 had the lowest median bias and RMSE of the four products, and ERA5-Land had the lowest median correlation coefficient (R) value. The lowest median ubRMSE was obtained by SMAP-L4 in SSM evaluation and ERA5-Land performed better than other RZSM products with the lowest median ubRMSE. From the perspective of SM sensitivity to precipitation, SMAP-L4 can capture daily precipitation dynamics most effectively, closely followed by ERA5-Land. This partially explains the superiority of SMAP-L4 and ERA5-Land over GLDAS-Noah and GLEAM for SM monitoring. The results of the metrics under different precipitation conditions varied considerably among the products. But while considering of ubRMSE alone, all products performed better under arid and humid conditions than to semi-arid and semi-humid conditions. This may be due to the fact that SM products had a limited ability to capture the SM dynamics under moderate precipitation. These findings may provide new insights into the selection of appropriate SM products and SM estimation in the future.

Unique Seasonal Variation in Rainfall Diurnal Features on the Yunnan–Guizhou Plateau

Based on hourly precipitation observations, the diurnal variation in precipitation and its seasonal evolution over the Yunnan–Guizhou Plateau (YGP) were analyzed. The results indicate that the seasonal variation in hourly rainfall in the western part of the YGP is unique. The rainfall reaches its hourly maximum during the late afternoon in spring (March–April) and during nighttime in summer (July–August), which contrasts with the pattern in most of eastern China. By further classifying the rainfall into short-duration (1–3 h) and long-duration (more than 6 h) events, the unique seasonal variations in the western YGP are found to mainly be comprised of short-duration rainfall. The long-duration rainfall shares similar diurnal peaks year-round for both the western and eastern parts of the YGP. The short-duration rainfall in the western part of the YGP shows a year-round afternoon peak, which is different from that of the eastern YGP, which peaks from midnight to early morning in spring and in the late afternoon in summer. The surface maximum daily temperature and low-level instabilities reach their annual maximum in spring over the western YGP and are also higher than in other parts of the YGP, together providing favorable conditions for convection to be triggered in spring afternoons over the western YGP.

Variations in the Thermal Low-Pressure Location Index over the Qinghai–Tibet Plateau and Its Relationship with Summer Precipitation in China

The thermal and dynamic effects of the special topography of the Qinghai–Tibet Plateau have a significant impact on rainfall in China. Utilizing NCEP/NCAR monthly reanalysis data alongside precipitation observations from 1936 monitoring stations across China spanning from 1966 to 2022, this study establishes a location index for the thermal low-pressure center situated over the Qinghai–Tibet Plateau. Temporal variations in the location index and summer (July) precipitation patterns in China were studied. Over the past six decades, thermal low-pressure centers have been predominantly positioned near 90° E and 32.5° N within a geopotential height of 4360 gpm, with their distribution extending from east to west rather than from south to north. The longitudinal and latitudinal position indices showed the same linear trend, with a negative trend before the 21st century, and then began to turn positive. Mutation analysis highlights pronounced weakening mutations occurring in 1981 and 1973, with the longitudinal index transitioning from an interannual cycle of approximately 6–8 years, while the latitudinal index displays quasi-cyclic oscillations of 5 and 8 and 12–14 years. Strong negative correlations are evident between the location indices and precipitation along the southeastern edge of the Qinghai–Tibet Plateau and in southern China, contrasting with the positive correlations observed in the central-eastern plateau, northwest, north, and the Huang-Huai region of China. The center of the thermal low is located to the east and north, corresponding to the deeper surface thermal low in most areas east of China, and the stronger transport of warm and wet air from the southwest wind, leading to greater convergence of southwest wind and northwest wind in China’s northern region. The south of the Yangtze River is controlled by the strengthening West Pacific subtropical high and South Asia high, resulting in a significant decrease in precipitation, and the warm and humid air from the southwest on the west side of the West Pacific subtropical high is also transported to the north, increasing the precipitation in most parts of the north.

A statistical study of precipitation on the eastern Antarctic plateau (Dome-C) using remote sensing and in-situ instrumentation.

Studying precipitation at very high latitudes is a challenge, particularly during the polar winter. Direct monitoring of ice habit and size in high latitude precipitation is crucial for validating the algorithms used to derive precipitation from radar, and for improving the climatological modeling of polar areas. The high plateau lacks long-term direct observations of precipitation. In this work, carried out at Concordia Station (Dome-C (DC), -75°S, 123°E, 3233 m a.m.s.l), the use of a depolarization LIDAR, a flatbed scanner (ICECAMERA), a microwave profiler (HAMSTRAD) and meteorological instrumentation made possible the study, over the period 2014-2021, of shape, size, height and temperature of formation of precipitation. The precipitation sources were classified into four types: ice fogs, liquid fogs, mixed-phase clouds, and cirrus. Ten representative ice habits for Dome-C were chosen. The size distribution for every habit was calculated, allowing for the estimation of the corresponding radar reflectivity. The use of W-band radars, such as CLOUDSAT, with a sensitivity of -28dB, resulted in capturing all the crystals observed in Concordia. A positive trend was observed between grain size and height in ice habits that are typical of cloud precipitation. North West (NW) and North East (NE) winds at cloud height, blowing from coastal regions, caused the majority of precipitation from clouds. The study also examined the height trend of the ice habit composition of precipitation. The ice habit composition for each of the four types of precipitation source was analyzed, and the possibility of determining the source by simply observing the precipitation was explored. This work marks the first comprehensive investigation of precipitation on the eastern Antarctic plateau.

Effect of simulated extreme rainfall on the vegetative phenology of perennial and annual herbaceous plants from a Brazilian dry forest.

Detecting changes in the phenological responses of herbaceous species as a function of predicted climate change is important for forecasting future scenarios for the functioning of dry tropical forests, especially when predicting an increase in the frequency and intensity of extreme droughts. Because of the sensitivity of plants to water availability, our study hypothesizes that if years become drier or wetter, herbaceous plants will synchronously change the onset, duration, and intensity of their vegetative phenophases. We used a historical series of 60 years of precipitation observations for the Caatinga vegetation to define daily average of precipitation for rainy (Twet), median (Tcontrol), and dry (Tdry) years. We simulated past average daily rainfall (Twet, Tcontrol, and Tdry) while growing two herbaceous perennials and two herbaceous annuals. We monitored plant growth and measured the activity (absence or presence) and intensity of vegetative phenophases. We used circular statistical analysis to assess differences between treatments. Our results revealed that leaf production was seasonal but relatively uniform for perennial species and highly seasonal (wet season) for annual species. Simulated dry years induced lower leaf emergence concentrated over a few months in annual species, but this effect was more strongly significant in one of the two perennial species. Both annual and perennial species can experience delayed and less intense leaf abscission during the rainy season in years with below-average precipitation. In contrast, large voluminous rains in years with above-average precipitation can accelerate and intensify the process of leaf renewal. If future precipitation reductions occur, the changes in phenological response indicate that the cover of annual and perennial herbaceous species in this study will likely decrease, altering the landscape and functioning of dry tropical forests. However, the potential trade-offs observed may help populations of these species to persist during years of severe drought in the Caatinga.

Leading role of Saharan dust on tropical cyclone rainfall in the Atlantic Basin.

Tropical cyclone rainfall (TCR) extensively affects coastal communities, primarily through inland flooding. The impact of global climate changes on TCR is complex and debatable. This study uses an XGBoost machine learning model with 19-year meteorological data and hourly satellite precipitation observations to predict TCR for individual storms. The model identifies dust optical depth (DOD) as a key predictor that enhances performance evidently. The model also uncovers a nonlinear and boomerang-shape relationship between Saharan dust and TCR, with a TCR peak at 0.06 DOD and a sharp decrease thereafter. This indicates a shift from microphysical enhancement to radiative suppression at high dust concentrations. The model also highlights meaningful correlations between TCR and meteorological factors like sea surface temperature and equivalent potential temperature near storm cores. These findings illustrate the effectiveness of machine learning in predicting TCR and understanding its driving factors and physical mechanisms.

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.

IMERG BraMaL: An improved gridded monthly rainfall product for Brazil based on satellite‐based IMERG estimates and machine learning techniques

AbstractPrecipitation is one of the main components of the hydrological cycle and its precise quantification is fundamental to providing information for the understanding and prediction of physical processes. Precipitation observations based on ground‐based devices (manual and automatic rain gauges) are highly accurate but have limited spatial coverage. On the other hand, remote sensing products cover large areas but with lower accuracy. In this context, this study aims to provide a more accurate monthly precipitation estimating product, with lower latency than other products but without directly relying on field data. The methodology consists of applying a machine learning method (k‐nearest neighbours algorithm) to satellite‐based remote sensing data (IMERG Early Run product) and re‐analysis‐based (MERRA‐2) variables with a particular connection to precipitation. The method was applied over the Brazilian territory, which features a large range of precipitation regimes. This methodology resulted in the development of an adjusted IMERG product (IMERG BraMaL). Compared with the original IMERG products (Early Run and Final Run), IMERG BraMaL has improved the evaluated metrics between ground‐based and satellite data in almost all analyses. For instance, KGE (Kling‐Gupta efficiency) went from lower values (0.70 and 0.82 for Early and Late Run, respectively) to values above 0.86 in the IMERG BraMaL. The adjusted product also presented superior performance statistics compared with other global precipitation products (CHIRPS, PERSIANN‐CDR, and MSWEP). The main advantages of IMERG BraMaL compared with IMERG Final Run are (i) much faster availability to the end‐users; (ii) non‐dependency on any field data, allowing its application in areas where rain gauge data is unavailable or of low quality; (iii) the non‐relationship of errors to local features; and (iv) the much‐improved estimations in regions in Brazil where, historically, satellite‐based products usually underestimate the observed data.

Projection of Changes in Rainfall and Drought Based on CMIP6 Scenarios on the Ca River Basin, Vietnam

In this study, future precipitation and drought in the Ca river basin, Vietnam, were projected based on an ensemble of 27 CMIP6 models for four climate change scenarios. The impact of climate change on precipitation and drought was investigated. Monthly precipitation observation data were adjusted using the bias correction method. To detect drought events, the Standard Precipitation Index (SPI) was employed. Changes in drought were assessed using SPI3, SPI6, and SPI12. Although the amount of annual total precipitation slightly increased, the drought events may become more severe. There is a high likelihood of increased drought intensity and severity in Vietnam due to climate change. The frequency of droughts is likely to change depending on the location and climate change scenario. We found that the frequency and severity of droughts may be altered depending on the window size of SPI. The short-term drought events will be more frequent and severe, and long-term drought events will become more severe in the Ca river basin.

Mechanisms of early and late summer precipitation in Southwest China: dynamic and thermodynamic processes

This study investigates dynamic and thermodynamic components of moisture flux convergence in Southwest China (SW-MFC) and their underlying physical mechanisms during early and late summer. Using precipitation observation and CRA-40 reanalysis datasets from 1979 to 2023, the results show that both dynamic and thermodynamic processes modulate the SW-MFC in early summer (May-June), with dynamics playing a pivotal role. In contrast, the precipitation anomaly in late summer (July-August) is predominantly driven by the dynamic factors. Meanwhile, the large-scale circulation over the northern Indian Peninsula significantly modulates the SW-MFC. In early summer, anomalous convection around the Maritime Continent with the tripole sea surface temperature (SST) mode in the tropical Indo-Pacific can trigger the formation of “double ring” vertical zonal circulation cells. A large-scale westerly anomaly at the lower troposphere over the northern Arabian Sea foster cyclone strengthening over the northern Indian Peninsula, enhancing southerly moisture transport and increasing precipitation over Southwest China. During the late summer, large-scale dipole SST pattern between the subtropical central-eastern Pacific and the Indo-Pacific warm pool generates significant easterly anomalies towards the Maritime Continent. The SST gradient stimulates an extensive anticyclonic shear zone over the western equatorial Pacific, with an intensified low-pressure zone to its north. This atmospheric pattern over Southwest China and Indian Peninsula can form a vertical circulation circle that largely intensifies widespread precipitation. Numerical model experiments can reproduce the mechanisms of tropical Indo-Pacific joint effects on the Southwest precipitation in both early and late summer, providing a theoretical basis for understanding and forecasting summer precipitation over Southwest China.