Spatial Variability Of Precipitation Research Articles

Harnessing the Radio Frequency Power Level of Cellular Terminals for Weather Parameter Sensing

In light of recent extreme weather events, it is imperative to explore innovative methodologies for promptly and accurately measuring various meteorological parameters. The high spatial and temporal variability in precipitation often surpasses the resolution capabilities of traditional rain gauge measurements and satellite estimation algorithms. Therefore, exploring alternative methods to capture this variability is crucial. Research on the correlation between signal attenuation and precipitation could offer valuable insights into these alternative approaches. This study investigates (a) the feasibility of the classification of precipitation rate using signal power measurements in cellular terminals and (b) the impact of atmospheric humidity as well as other meteorological parameters on the signal. Specifically, signal power data were collected remotely through a specialized Android application designed for this research. During the time of analysis, the power data were processed alongside meteorological parameters obtained from the meteorological station of the Physics Department at the University of Ioannina gathered over one semester. Having in mind the radio refractivity of the air as a fascinating concept affecting the way radio waves travel through the atmosphere, the processed results revealed a correlation with signal attenuation, while a correlation between the latter and absolute humidity was also observed. Moreover, a precipitation rate classification was attained with an overall accuracy exceeding 88%.

Spatial and temporal variability of precipitation based on marginal and apportionment entropy disorder indices in Iran

Spatial and temporal variability of precipitation based on marginal and apportionment entropy disorder indices in Iran

How well do 13 satellite-derived precipitation products capture the temporal and spatial variations of precipitation over Chinese mainland?

ABSTRACTSatellite observations of precipitation have greatly improved our understanding of its temporal and spatial distribution. In view of the high spatiotemporal heterogeneity and severely skewed distribution characteristics of precipitation, it is necessary and of considerable application value to understand the ability of satellite-derived precipitation products (SPPs) to characterize the variations of precipitation in the time and space dimensions separately. However, to date, research concerning this is scarce. In this study, we explore the ability of 13 SPPs to characterize the temporal and spatial variations of precipitation based on observations from more than 2400 meteorological stations in Chinese mainland from 2001 to 2018. The results show that (1) SPPs tend to perform better in identifying the temporal than the spatial variation of precipitation. Most SPPs can reliably monitor the temporal and spatial variation of monthly and seasonal precipitation in Chinese mainland, although the identification of the spatial variation of daily precipitation involves larger uncertainty; (2) CMORPH-B, IMERG-F, 3B42, and MSWEP generally had better performance in identifying the temporal and spatial variations of precipitation, while PERSIANN and GSMaP-N performed poorly. However, no single SPP outperformed others in all scenarios; (3) with respect to monitoring the temporal variation of daily precipitation, SPPs performed better in southern China than in the north, with three-quarters and half of median KGE values above 0.5 In comparison, no significant spatial difference was observed in their ability to monitor the spatial variation of daily precipitation; and (4) SPPs had large uncertainties in capturing the temporal and spatial variations of precipitation in winter, while they performed best in identifying the temporal and spatial variations of daily precipitation in summer. The results of this paper provide an important reference for data users needing to select suitable SPPs and for satellite developers desiring to further improve data quality.

Establishing SMMS approach to accurately mine the characteristics of regional precipitation trends

Under the joint influence of climate change and human activities, the temporal and spatial variability and trend of precipitation have changed. The trend of precipitation investigated using traditional methods is quite different, which may be opposite of that at adjacent stations. This contradiction may lead to erroneous decision making in regional water resources planning and management, and may cause safety hazard. In order to evaluate the variation in temporal and spatial characteristics of regional precipitation and avoid different trends of regional precipitation time series, this study developed System clustering - Maximum entropy - Multi window sliding trend - Same frequency trend (SMMS) approach, which leads to the method for analyzing regional precipitation changes, trends, and laws. The local microclimate zones (LMZs) are redefined, so that the general law of precipitation variation will not be significantly different from the actual situation and the SMMS method is more accurate and comprehensive in identifying regional precipitation trends compared to traditional methods. Making use of the SMMS approach, the trend analysis of regional representative stations method can not only reasonably divide the study area and reduce computational complexity, but also analyze it according to the typical representative station with the maximum regional information. Furthermore, the impact caused by the length of data can be avoided, and the trend of precipitation intensity can be identified. It is necessary to fully understand the new characteristics of temporal and spatial changes of precipitation and make early preparations to mitigate the potential impact of climate or weather disasters.

Characterizing CONUS-wide spatio-temporal changes in daily precipitation, flow, and variability of extremes

The hydrological cycle strongly relies on the spatial and temporal variability of precipitation, which has been considerably affected by climate change. Focusing on CONUS, this research extended the analysis by evaluating the variability of daily concentrations of various components of the hydrological cycle in terms of the Gini index and its potential effects on floods and droughts by integrating historical data with an existing database of extreme events. Differences were found in the geographical location of significant correlations among the hydrological variables investigated, implying that the impacts of variability are neither spatially nor temporally uniform. The work also showed the importance of selecting thresholds of precipitation and flow to define “extreme” events. We identified a precipitation deficit threshold at the 5th percentile and an extreme precipitation threshold at the 95th percentile as the most representative. Moreover, for flood, the 95th percentile and for hydrological drought, the 5th percentile were the most representative. Spatially the study showed that, the concentration of precipitation deficit and evapotranspiration had a major impact on the occurrence of hydrological hazards, such as droughts and floods.

Performance Evaluation of GPM IMERG Precipitation Products over the Tropical Oceans Using Buoys

Abstract The majority of global precipitation falls in tropical oceans. Nonetheless, due to the lack of in situ precipitation measurements, the number of studies over the tropical oceans remains limited. Similarly, the performance of IMERG products over the tropical oceans is unknown. In this context, this study quantitatively evaluates the 20 years (2001–20) of IMERG V06 Early, Late, and Final products against the in situ buoys’ estimates using the pixel–point approach at a daily scale across the tropical oceans. Results show that IMERG represents well the mean spatial pattern and spatial variation of precipitation, though significant differences exist in the magnitude of precipitation amount. Overall, IMERG notably overestimates precipitation across the tropical ocean, with maxima over the western Pacific and Indian Oceans, while it performs better over the eastern Pacific and Atlantic Oceans. Moreover, irrespective of the region, IMERG sufficiently detects precipitation events (i.e., >0.1 mm day−1) for high-precipitation regions, though it significantly overestimates the magnitude. Despite IMERG’s detection issues of precipitation events over the regions with lower precipitation, it is in good agreement with the buoys in total precipitation estimation. The positive hit bias and false alarm bias are the major contributions to the overall total positive bias. Furthermore, the detection capability of IMERG tends to decline with increasing precipitation rates. In terms of IMERG runs, the IMERG Final product performs slightly better than the Early and Late runs. More detailed studies over the tropical oceans are required to better characterize the biases and their sources.

Spatially coherent variability in modern orographic precipitation produces asymmetric paleo-glacier extents in flowline models: Olympic Mountains, USA

Abstract. Glaciers are sensitive to temporal climate variability. Glacier sensitivity to spatial variability in climate has been much less studied. The Olympic Mountains of Washington state, USA, experience a pronounced orographic precipitation gradient, with modern annual precipitation ranging between ∼6500 and ∼500 mm water equivalent. In the Quinault valley, on the wet side of the range, a glacier extended onto the coastal plain, reaching a maximum position during the Early Wisconsin glaciation. On the dry side of the range, in the Elwha valley, there is no evidence of a large paleo-glacier during the Wisconsin glaciation. We hypothesize that asymmetry in the past glacier extent was driven by spatial variability in precipitation. To evaluate this hypothesis, we constrain the past precipitation gradient and model the glacier extent. We explore variability in observed and modeled precipitation gradients over timescales from 6 h to ∼100 yr. Across three datasets, basin-averaged precipitation in the Elwha is 54 % of that in the Quinault. Our analysis overwhelmingly indicates spatially coherent variability in precipitation across the peninsula. We conclude that the past precipitation gradient was likely similar to the modern gradient. We use a one-dimensional glacier flowline model, driven by sea level summer temperature and annual precipitation to approximate the glacier extent in the Quinault and Elwha valleys. We find several equilibrium states for the Quinault glacier at the mapped maximum position within paleoclimate constraints for cooling and drying, relative to present-day conditions. Assuming stable precipitation gradients, we model the Elwha glacier extent for the climates of these equilibria. At the warm end of the paleoclimate constraint (July average sea level temperature of 10.5 ∘C), a small valley glacier occurs in the high headwaters of the Elwha valley. Yet, for the cooler end of the allowable paleoclimate (July average sea level temperature of 7 ∘C), the Elwha glacier advances to a narrow notch in the valley, thickens, and rapidly extends far beyond the likely true maximum extent. Therefore, we suggest that the Early Wisconsin period was more likely to have been relatively warm because our models of the glacial extent are consistent with the past record of glaciation in both the Quinault valley and Elwha valley for warm conditions but inconsistent for cooler conditions. Alternatively, spatially variable drivers of ablation, including differences in cloudiness, could have contributed to past asymmetry in the glacier extent. Future research to constrain past precipitation gradients and evaluate their impact on glacier dynamics is needed to better interpret the climatic significance of past glaciation and to predict the future response of glaciers to climate change.

Extreme Droughts and Their Relationship with the Interdecadal Pacific Oscillation in the Peruvian Altiplano Region over the Last 100 Years

The Peruvian Altiplano Region (RAP) is a high plateau area surrounded by the Western and Eastern Andes mountain ranges. This study examines the relationship between extreme droughts in the region and the interdecadal Pacific Oscillation (IPO) over the past century. Previous research has shown that precipitation patterns in the region follow a decreasing trend, with systematic increases in precipitation on the western slope and decreases in the eastern, southern, and central parts. The temporal and spatial variability of precipitation in the Altiplano region is influenced by the easterly moisture flux and the interaction between the El Niño Southern Oscillation (ENSO) and below-average values. The study utilizes water level data for Lake Titicaca and IPO data from 1914 to 2015. The analysis employs wavelet transform and empirical orthogonal function (EOF) techniques to identify the relationship between water levels and IPO. The results indicate multidecadal variability in water levels associated with El Niño/La Niña events and the IPO. The negative phase of the IPO aligns with extreme drought periods, suggesting a connection between the IPO climate index and drought events. The EOF analysis shows a moderate positive correlation between water levels and IPO. The findings highlight the importance of considering IPO and its interaction with ENSO in understanding drought patterns in the Altiplano region. However, other atmospheric conditions also influence precipitation in the region. The study contributes to a better understanding of the factors affecting water levels and droughts in the Peruvian Altiplano, with implications for water resource management in the region.

Hydrological and Precipitation Extremes and Trends over the Paraiba do Sul River Basin, Brazil

The Paraiba do Sul River Basin (PSRB) is a vital source of water resources in Brazil, providing water for human consumption, industry, agriculture, and hydroelectric energy generation. As part of one of the most developed areas of the country, in the Southeast of Brazil, the region is vulnerable to the impacts of climate change, with evidence of extreme events such as droughts and floods affecting the availability and quality of water. Hence, this study analyzes precipitation and streamflow rates data from the PSRB between 1939–2020 to investigate the spatial variability of average patterns and extreme events, trends, and their relationship with urban growth and socioeconomic development. The analysis reveals significant spatial variations in precipitation and runoff rates, with higher altitude areas, such as the Serra da Mantiqueira, exhibiting higher average values. Moreover, the Mann–Kendall trend results showed in most of the sites no significant trend regarding precipitation data; however, about 50% of the sites in the PSRB presented a decreasing trend of runoff rates. Since the precipitation does not explain identified changes in the hydrological patterns, the evaluation of the area’s urban growth and socioeconomic development throughout the decades suggested that human activities, such as those associated with urbanization, have played a significant role in altering the runoff patterns in the basin. These findings highlight the importance of sustainable land-use planning and water resource-management practices in the PSRB to mitigate the negative impacts of urbanization on the hydrological cycle and to enhance the resilience of the region’s water resources.

Assessment of performance of the regional climate model (RegCM4.6) to simulate winter rainfall in the north of Morocco: The case of Tangier-Tétouan-Al-Hociema Region

We examine the performance of the regional climate model RegCM v4.6 to simulate spatial variability of precipitation in the northwestern region of Morocco during the winter of 2009–2010. Simulations cover 24 months from 2009 to 2010 with 30 km as a horizontal grid. We use NCEP reanalysis as forcing data and for better comparison of results, observed precipitations derived from CRU, CHIRPS, and CMORPH data. Results indicate that, on the whole, the RegCM4 model represents appropriate regional aspects of rainfall over the study area but underestimates precipitations over mountainous and Mediterranean regions of the study area (Case of Tangier-Tétouan-Al-Hociema Region) which is probably due to poor representation of orography in the Model and some aspects of local Mediterranean climate. Projected precipitations are also examined in this work in comparison with the reference period of 1970–2005, with simulations performed by RegCM 4.6 regional model for the period 2023–2099 under scenarios RCP4.5 and RCP8.5, forced by HadGEM2-ES General Circulation Model. Results show a decrease in precipitations mean for (2023–2099) for both RCP4.5 and RCP8.5 scenarios over the study area in comparison with the historical period (1970–2005), with a significant decrease under RCP8.5 scenarios. This work proves that the RegCM v4.6 model can be used for regional climate prediction, particularly for the spatial distribution of precipitation, but for sectorial applications and impact studies, the Model outputs should be bias corrected.

Understanding Nevada’s Current Drought in Historical and Paleoclimatic Context

Nevada lies in one of the driest parts of North America. The region has long grappled with droughts and also with extended periods in which the already dry climate aridified for decades or centuries. Here we place Nevada’s most recent statewide drought (2020 – present) in the context of historical, archaeological and paleoclimate records dating back through the Mid-Holocene. The current drought is distinct from historical droughts that impacted the state in the 1930s, 1950s, and 1970’s and 1980s. It is notably warmer than droughts of the 20th century and more spatially extensive than any of these, although the 1930s drought also impacted much of the West. The early 2020s drought is also embedded within a two-decade long dry period, raising important questions about whether it is (1) drought, a relatively short period of abnormal dryness; (2) megadrought, a longer and typically more severe period of unusually dry conditions; (3) the manifestation of aridification, a shift toward a generally drier climate because of long-term precipitation declines and/or warming; or (4) some combination of the three. A broader view suggests that at least some aspects of drought since 2020 may not be unique. Paleoclimate evidence from cave deposits, lake and meadow sediments, animal middens, dead wood and tree rings indicate that Nevada and much of the western United States experienced decadal and centennial long dry periods in the Middle Holocene (5000 – 7000 years ago), the Late Holocene, (1800 – 3100 years ago), during the Medieval Climate Anomaly (1000 – 650 years ago) and again 600 – 500 years ago. The archaeological record shows that Indigenous Nevadans responded by repeatedly adapting to changing paleoenvironmental conditions. However, some key questions about fine-scale temporal and spatial variability in the experience of drought remain. Limitations in the spatial, seasonal and temporal resolutions of climate reconstructions—and in the observational record—may obscure the evidence for short or seasonally specific wet periods within longer dry periods or spatial variability in precipitation. Nonetheless, proxy and archaeological evidence from the last 10,000 years shows that understanding and responding to drought requires viewing drought on these time spans with an appreciation for the details that appear in modern observations.

New homogenized precipitation database for Hungary from 1901

AbstractPrecipitation is a highly variable meteorological element. Similarly, to other meteorological elements (e.g. temperature), the changes in the measurement practices or in the environment can cause inhomogeneities in the precipitation data series. Therefore, homogenization of precipitation data series is necessary before studying the long‐term climate change. In this paper we present the main features of the MASH procedure, which we use to produce homogenized climate datasets for Hungary at the Climate Department of the Hungarian Meteorological Service (OMSZ). Due to the increasing number of discontinued precipitation stations, the number of missing data has increased significantly; therefore the station networks used for precipitation homogenization have been completely renewed. With this renewal, the percentage of missing data has been minimized, the number of data series has been increased and a denser station network is now used in mountainous areas where the spatial variability of precipitation is the highest. As a result, a new, more homogeneous, representative precipitation database of Hungary from 1901 to the present has been created. In the paper, we also examine the main characteristics of the detected inhomogeneities, with examples of the main types of inhomogeneities for some stations.

An investigation of the changing patterns of rainfall in the Indravathi subbasin utilizing the Mann-Kendall and Sen’s slope methods

In hydro-meteorological studies, precipitation is an important parameter that is utilized in irrigation system design and management, as well as agricultural planning. Under the effects of climate change, precipitation is predicted to alter, which will have an impact on sustainable development. Using rainfall data for the Indravathi subbasin, Godavari basin, from 1998 to 2016, the spatial variability and temporal trend of precipitation were examined over the region, which has a humid tropical climate. Using non-parametric tests like Mann-Kendall (MK) and Sen’s slope approach, analysis for trend detection was conducted. The test data were loaded into ArcGIS software, which then performed monthly, seasonal, and annual analyses of the spatial and temporal trends of rainfall. According to an analysis of the monthly rainfall trends, February had the largest spatiotemporal declining trend in rainfall, while September had the highest spatiotemporal increasing trend. An analysis of seasonal rainfall data revealed a considerable increase in the tendency for summer rainfall over nearly 80% of the basin. Data on annual precipitation revealed an increase in annual precipitation for the basin’s central eastern sections. The findings of this research may help the decision makers and stakeholders make the most efficient use of hydrological resources by providing insight on the effects of climate change and climatic changes on precipitation patterns in the Indravathi subbasin.

Evaluation of CMIP6 Models and Multi-Model Ensemble for Extreme Precipitation over Arid Central Asia

Simulated historical extreme precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices of 33 Global Circulation Models (GCMs) are evaluated against corresponding indices with observations from the Global Climate Center Precipitation Dataset (GPCC V2020) over five sub-regions across Arid Central Asia (ACA), using the Taylor diagram, interannual variability skill score (IVS) and comprehensive rating index (MR). Moreover, we compare four multi-model ensemble approaches: arithmetic average multi-model ensemble (AMME), median multi-model ensemble (MME), pattern performance-based multi-model ensemble (MM-PERF) and independence weighted mean (IWM). The results show that CMIP6 models have a certain ability to simulate the spatial distribution of extreme precipitation in ACA and the best ability to simulate simple daily intensity (SDII), but it is difficult to capture the spatial bias of consecutive wet days (CWD). Almost all models represent different degrees of wet bias in the southern Xinjiang (SX). Most GCMs are generally able to capture extreme precipitation trends, but to reproduce the performance of interannual variability for heavy precipitation days (R10mm), SDII and CWD need to be improved. The four multi-model ensemble methods can reduce the internal system bias and variability within individual models and outperform individual models in capturing the spatial and temporal variability of extreme precipitation. However, significant uncertainties remain in the simulation of extreme precipitation indices in SX and Tianshan Mountain (TM). Comparatively, IWM simulations of extreme precipitation in the ACA and its sub-regions are more reliable. The results of this study can provide a reference for the application of GCMs in ACA and sub-regions and can also reduce the uncertainty and increase the reliability of future climate change projections through the optimal multi-model ensemble method.

Chemical weathering evidence for East Asian Summer Monsoon rainfall variability in the upper reaches of the Yellow River since the Early Pleistocene

The East Asian Summer Monsoon (EASM) is critically important for determining the spatial variability of precipitation in the continental interior of East Asia. Hence, knowledge of past EASM variability may help predict the response of monsoon rainfall to ongoing global climate change, with important implications for the living environment of the large human population of East Asia. In this study, we obtained several geochemical records from the Hetao Basin in the upper reaches of the Yellow River (UYR), which we use to reconstruct variations in chemical weathering intensity since 1.68 Ma. Our results suggest that chemical weathering was significantly intensified during the interval of ∼0.47–0.18 Ma, evidenced by lower Rb/Sr and αAlSr values, and higher LEF Mg/Al values, within the lacustrine sediments during this period. This enhanced chemical weathering was closely linked to increased monsoon rainfall associated with a strong EASM. The long-term trends of EASM rainfall in the UYR were caused by variations in the atmospheric circulation over the Pacific Ocean, such as the ENSO cycle and the PDO phase transition, which may act as testable factors in the prediction of future EASM rainfall.

Field methods and example applications for the Min‐Trap® mineral sampler

AbstractImportant reactive minerals are commonly created during in situ groundwater remediation activities; for example, iron sulfides formed during enhanced reduction approaches can abiotically degrade many chlorinated solvents. However, cost‐effective tools to evaluate these treatment processes in field applications are limited and the collection of samples to evaluate in situ mineral formation is costly due to drilling requirements. The new passive Min‐Trap sampler is a simple and cost‐effective tool that can directly measure the formation of reactive minerals in situ without the need for additional drilling or soil core collection. The methods presented here describe how Min‐Traps deployed in conventional monitoring wells can measure reactive minerals and how these minerals can be identified through commercially available analytical methods. Several examples are presented that show how Min‐Traps can be used to characterize the rate and spatial variability of reactive mineral precipitation and these data may support operation and optimization decisions.

A Multiplicative-Exponential function to correct precipitation for distributed hydrological modeling in Poorly-gauged basins

Given the greater spatial and temporal variability in precipitation and the absence of rain gauges at mountainous regions, it is challenging to accurately estimate precipitation, consequently impeding the predictive capability of hydrological models. Here, we developed a nonlinear precipitation correction method in the SWAT (Soil and Water Assessment Tool) model to correct gridded (remote sensing or reanalysis) precipitation data for better distributed hydrological modeling. The precipitation correction module is featured by a multiplicative-exponential equation with two parameters, a multiplicative coefficient (PCP_COE) and an exponent (PCP_EXP). We proposed three configurations to determine PCP_COE and PCP_EXP, i.e., curve fitting between gridded and gauge precipitation data pairs (Configuration C1) or using gridded precipitation from multi-grids within an elevation range of the gauge (Configuration C2), and the calibration of the SWAT model against observed streamflow (Configuration C3). We applied the proposed method to a typical poorly-gauged alpine inland basin, the Xiangride River Basin of Northwest China, with only one rain gauge and streamflow station. Results show that Configuration C1 outperformed C2 and C3 by significantly improving streamflow modeling in both model calibration and validation, particularly when the GPM (Global Precipitation Measurement) precipitation datasets were used. We also argue that Configuration C3 is promising in ungauged basins with only streamflow available, since this configuration does not require gauge precipitation. This study puts forward a feasible method to derive more accurate spatiotemporal precipitation estimate based on gridded precipitation products, hence improving distributed hydrological modeling in poorly-gauged and ungauged basins.

Spatial coherence patterns of extreme winter precipitation in the U.S.

Extreme precipitation events have a significant impact on life and property. The U.S. experiences huge economic losses due to severe floods caused by extreme precipitation. With the complex terrain of the region, it becomes increasingly important to understand the spatial variability of extreme precipitation to conduct a proper risk assessment of natural hazards such as floods. In this work, we use a complex network-based approach to identify distinct regions exhibiting spatially coherent precipitation patterns due to various underlying climate mechanisms. To quantify interactions between event series of different locations, we use a nonlinear similarity measure, called the edit-distance method, which considers not only the occurrence of the extreme events but also their intensity, while measuring similarity between two event series. Using network measures, namely, degree and betweenness centrality, we are able to identify the specific regions affected by the landfall of atmospheric rivers in addition to those where the extreme precipitation due to storm track activity is modulated by different mountain ranges such as the Rockies and the Appalachians. Our approach provides a comprehensive picture of the spatial patterns of extreme winter precipitation in the U.S. due to various climate processes despite its vast, complex topography.

Long-term spatiotemporal variability of precipitation and its linkages with atmospheric teleconnections in the Yellow River Basin, China

AbstractPrecipitation is widely considered as a crucial index toward the apprehension of global climate change. Hence, it becomes imperative to explore spatiotemporal patterns and the interlinked factors of precipitation in the basin. In the study, the spatial and temporal variability of precipitation and the individual or integrated effects of various atmospheric teleconnections on precipitation variations are explored in the Yellow River Basin. The total precipitation showed a slightly declining tendency during 1950–2019 and the dependence relationship of precipitation gradient on latitude and longitude is different in various seasons and elevations. The spatiotemporal variability of precipitation is more sensitive to the latitude gradient. For each 1-degree increase in longitude and latitude, the average annual precipitation increases/decreases by 10.73 and 57.24 mm, respectively. Moreover, the precipitation spatiotemporal patterns could be interpreted by four empirical orthogonal functions (EOFs) modes about 71.9% of precipitation variations. The strength of the linkages between various circulation factors and precipitation varied at different time scales. The integrated effects of multiple factors should be taken into consideration in explaining precipitation variability at all time scales. It is expected that the study can be helpful for understanding the internal mechanism of the hydrological cycle in the YRB.

A multiple-step scheme for the improvement of satellite precipitation products over the Tibetan Plateau from multisource information

Precipitation data with high accuracy and spatial resolution characteristics play significant roles in the regional hydrological and eco-environmental system applications. Thus, satellite-based precipitation products (SPPs) with high spatial resolution and high accuracy must be developed. This study proposed a multiple-step scheme to improve the global precipitation measurement (GPM) at daily and monthly timescales over the Tibetan Plateau (TP) from 2014 to 2017. First, combined with the geographically weighted regression (GWR) method using geographic and topographic factors, the gamma-distribution mapping and local intensity scaling (GDM-LOCI) method is applied to effectively merge the observed data attributes and the spatial representation of SPPs at the daily scale by correcting precipitation volumes and frequencies. Second, the areal merged precipitation, normalized difference vegetation index (NDVI), and reanalyzed atmospheric data are used to improve the spatial resolution of monthly GPM with a random forest (RF) model that uses the 17 land cover types to establish the local downscaling model windows. The results show that daily merged precipitation can better reflect the spatial and temporal variability of precipitation than can satellite estimates, and the correlation coefficient (R), and critical success index (CSI) increased by 0.12 and 0.26, respectively. In the merged downscaling model, the merged precipitation factor can weaken the negative effect of the other auxiliary predictors due to its spatial autocorrelation in precipitation estimation. Most importantly, by using the land cover types to establish local model windows for the downscaling model, not only the spatial resolution of the GPM product is downscaled to 1 km, but also the spatial structure of the downscaled products is enhanced, with less deviation and a higher spatial correlation. The R, the root mean square error (RMSE) and the relative bias (BIAS) were 0.89, 50.19 mm and 0.57, respectively. This study presents a promising scheme for generating high-quality precipitation data for regional hydrometeorological research in data-scarce regions.