Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable ...for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change.
In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage.
Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne.
Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable ...for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change.
In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage.
Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne.
This paper presents a comprehensive spatiotemporal analysis of precipitation patterns over the territory of the Czech Republic for the 1961–2019 period. Monthly, seasonal and annual series of ...precipitation totals and numbers of precipitation days were calculated for four altitudinal groups and the entire Czech Republic, based upon the daily precipitation totals recorded by 531 rain‐gauge stations run by the Czech Hydrometeorological Institute. Analysis of series of monthly, seasonal and annual precipitation totals revealed relatively stable fluctuations, while linear trends remained largely insignificant. However, wavelet analysis indicated significant interannual variability on a timescale of 4–8‐years in seasonal and annual series. The minimum in annual variation tended to appear in February (also in January and, at higher altitudes in particular, in April) with the maximum favouring July (but also June and August). The relative proportions of annual totals taken up by winter precipitation increased with altitude, while the proportions for summer precipitation decreased with altitude. Linear trends in the numbers of precipitation days exhibited the most pronounced decreases from April to June, reflected in negative precipitation trends in April–June and positive in July–September. The results obtained are also presented in the broader (central) European context.
April–June (AMJ) and July–September (JAS) precipitation totals over the territory of the Czech Republic in the 1961–2019 period: (a, b) fluctuations in totals (1) smoothed by 5‐year Gaussian filter (2) and linear trends (3); (c, d) spatial distribution (triangles) of relative linear trends (%) and stations with statistically significant positive and negative trends (0.05 significance level).
The high‐altitude Indus basin is one of the most complex and inadequately explored mountain terrains in the World, where reliable observations of precipitation are highly lacking. Therefore, ...spatially distributed precipitation products developed at global/regional scale are often used in several scientific disciplines. However, large uncertainties in precipitation estimates of such precipitation data sets often lead to suboptimal outcomes. In this study, performance of 27 widely used gridded precipitation products belonging to three different categories of gauge‐based, reanalysis and merged products is evaluated with respect to high‐quality reference climatologies of mean monthly precipitation. Widely used statistical measures and quantitative analysis techniques are used to analyse the spatial patterns and quantitative distribution of mean monthly, seasonal and annual precipitation at sub‐regional scale. Mean annual precipitation estimates of the gridded data sets are cross validated with the corresponding adjusted streamflows using Turc‐Budyko non‐dimensional analysis. Results reveal poor to moderately good performance of the gridded data sets. Marked differences in spatiotemporal and quantitative distribution of precipitation are found among the data sets. All data sets are consistent in their patterns showing negative or dry bias in wet areas and positive or wet bias in dry areas, although considerable differences in the magnitudes of the biases are noticed at sub‐regional scale. None of the data sets is equally good for all sub‐regions due to very high spatiotemporal variability in their performance at sub‐regional scale. Gauge‐based and merged products performed better in dry regions and during monsoon season, while reanalysis products provided better estimates in wet areas and during winter months. GPCC V8, ERA5 and MSWEP2.2 are found better than their counter‐grouped data sets. Overall, ERA5 is found most acceptable for all sub‐regions, particularly at higher‐altitudes, in wet areas and during winter months.
This study evaluated 27 gridded precipitation products for the high‐altitude Indus basin and observed high variability in their quantitative and spatio‐temporal precipitation estimates. All data sets are consistent in their patterns showing negative or dry bias in wet areas and positive or wet bias in dry areas, but none of the data sets is equally good for all sub‐regions. Gauge‐based and merged products performed better in dry regions and during monsoon season, while reanalyses products provided better estimates in wet areas and during winter months. Overall, ERA5 data set is found most acceptable for all sub‐regions. Turc‐Budyko representation of runoff ratio (Q/P) and aridity index (P/PET) from various data sets for study area and its five sub‐regions.
We investigate the global distribution of hourly precipitation and its connections with the El Niño–Southern Oscillation (ENSO) using both satellite precipitation estimates and the global sub-daily ...rainfall gauge dataset. Despite limited moisture availability over continental surfaces, we find that the highest mean and extreme hourly precipitation intensity (HPI) values are mainly located over continents rather than over oceans, a feature that is not evident in daily or coarser resolution data. After decomposing the total precipitation into the product of the number of wet hours (NWH) and HPI, we find that ENSO modulates total precipitation mainly through the NWH, while its effects on HPI are more limited. The contrasting responses to ENSO in NWH and HPI is particularly apparent at the rising branches of the Pacific and Atlantic Walker Circulations, and is also notable over land-based gauges in Australia, Malaysia, the USA, Japan and Europe across the whole distribution of hourly precipitation (i.e. extreme, moderate and light precipitation). These results provide new insights into the global precipitation distribution and its response to ENSO forcing.
Review of GPM IMERG performance: A global perspective Pradhan, Rajani K.; Markonis, Yannis; Vargas Godoy, Mijael Rodrigo ...
Remote sensing of environment,
January 2022, 2022-01-00, 20220101, Volume:
268
Journal Article
Peer reviewed
•A comprehensive review and analysis of IMERG validation studies from 2016 to 2019.•There is robust representation of spatio-temporal patterns of precipitation.•Discrepancies can be found in extreme ...and light precipitation, and the winter season.•The 30-min scale has not yet been sufficiently evaluated.•Using IMERG in hydrological simulation results to high variance in their performance.
Accurate, reliable, and high spatio-temporal resolution precipitation data are vital for many applications, including the study of extreme events, hydrological modeling, water resource management, and hydroclimatic research in general. In this study, we performed a systematic review of the available literature to assess the performance of the Integrated Multi-Satellite Retrievals for GPM (IMERG) products across different geographical locations and climatic conditions around the globe. Asia, and in particular China, are the subject of the largest number of IMERG evaluation studies on the continental and country level. When compared to ground observational records, IMERG is found to vary with seasons, as well as precipitation type, structure, and intensity. It is shown to appropriately estimate and detect regional precipitation patterns, and their spatial mean, while its performance can be improved over mountainous regions characterized by orographic precipitation, complex terrains, and for winter precipitation. Furthermore, despite IMERG's better performance compared to other satellite products in reproducing spatio-temporal patterns and variability of extreme precipitation, some limitations were found regarding the precipitation intensity. At the temporal scales, IMERG performs better at monthly and annual time steps than the daily and sub-daily ones. Finally, in terms of hydrological application, the use of IMERG has resulted in significant discrepancies in streamflow simulation. However, and most importantly, we find that each new version that replaces the previous one, shows substantial improvement in almost every spatiotemporal scale and climatic condition. Thus, despite its limitations, IMERG evolution reveals a promising path for current and future applications.
This study inter-compares the spatiotemporal features of precipitation extremes at global and regional scales within six daily precipitation datasets, i.e., gauge-based (Global Precipitation ...Climatology Center dataset, GPCC), satellite-retrieval (Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record, PERSIANN-CDR), three reanalysis datasets (ERA-Interim, ERAI; the National Center for Atmospheric Research Reanalysis 2, NCEP2; and the WATCH-Forcing-Data-ERA-Interim, WFDEI), and products merged from the three type datasets (the Multi-Source Weighted-Ensemble Precipitation, MSWEP). All datasets reproduce similar spatial patterns of both annual and seasonal precipitation extremes over the period from 1979 to 2017. Compared to the reference dataset gauge-based GPCC, the reanalysis WFDEI outperforms among six products with spatial correlation coefficients of 0.89 and 0.80 for the annual extreme indices (i.e., annual total amount of 95th precipitation and maximum 1-day precipitation), respectively. The satellite-based product PERSIANN-CDR performs better than reanalyses and merged datasets in capturing the temporal variability of the intensity and amount of precipitation extremes with similar changing tendencies and magnitudes of about 45 mm day
−1
and 230 mm at the global scale, respectively. The reanalyses and merged products underestimate the intensity of precipitation extremes. The selected six datasets behave differently in various regions. For the percentile-based frequency of precipitation extremes, NCEP2 performs well in regions of the Southeast Asian (SEA) and Amazon (AMZ), while WFDEI better matches GPCC over East North America (ENA) and North Australia (NAU) in both spatial patterns and temporal changes with correlation coefficients of 0.84 and 0.90, respectively. For the intensity features of annual precipitation extremes, NCEP2 performs better than other four datasets over regions of SEA, AMZ and West Africa (WAF). ERAI and WFDEI are consistent with GPCC in ENA and NAU with correlations coefficients of the intensity between ERAI (WFDEI) and GPCC are 0.82 (0.77) and 0.78 (0.64) for ENA and NAU, respectively. For the intensity of seasonal precipitation extremes, GPCC shows the highest estimates in regions of SEA, AMZ, ENA and WAF. ERAI and WFDEI perform better in reproducing the spatial patterns of seasonal precipitation extremes in all regions. NCEP2 (ERAI and WFDEI) show(s) consistent temporal variability of seasonal precipitation extremes with GPCC in regions of AMZ and WAF (ENA and NAU). Overall, there are large discrepancies in the absolute values of daily precipitation among datasets, and performances of non-gauged-based precipitation datasets in capturing the spatiotemporal variability of precipitation extremes are dependent on seasons, regions, and time periods.
Quantifying rates of climate change in mountain regions is of considerable interest, not least because mountains are viewed as climate “hotspots” where change can anticipate or amplify what is ...occurring elsewhere. Accelerating mountain climate change has extensive environmental impacts, including depletion of snow/ice reserves, critical for the world's water supply. Whilst the concept of elevation‐dependent warming (EDW), whereby warming rates are stratified by elevation, is widely accepted, no consistent EDW profile at the global scale has been identified. Past assessments have also neglected elevation‐dependent changes in precipitation. In this comprehensive analysis, both in situ station temperature and precipitation data from mountain regions, and global gridded data sets (observations, reanalyses, and model hindcasts) are employed to examine the elevation dependency of temperature and precipitation changes since 1900. In situ observations in paired studies (using adjacent stations) show a tendency toward enhanced warming at higher elevations. However, when all mountain/lowland studies are pooled into two groups, no systematic difference in high versus low elevation group warming rates is found. Precipitation changes based on station data are inconsistent with no systematic contrast between mountain and lowland precipitation trends. Gridded data sets (CRU, GISTEMP, GPCC, ERA5, and CMIP5) show increased warming rates at higher elevations in some regions, but on a global scale there is no universal amplification of warming in mountains. Increases in mountain precipitation are weaker than for low elevations worldwide, meaning reduced elevation‐dependency of precipitation, especially in midlatitudes. Agreement on elevation‐dependent changes between gridded data sets is weak for temperature but stronger for precipitation.
Plain Language SummaryMountains cover a large part of the Earth's surface and harbor distinct ecosystems, hold most of snow and ice outside the polar regions, and provide water for billions of people. This research looks at recent climate changes in mountains and compares them with simultaneous changes in lowland regions using weather station data, large global data sets, and climate models. We examine changes since 1900, but also concentrate on the last 40 years since this is when many changes have started to accelerate. Nearly all regions of the globe are getting warmer. When we make local comparisons, mountain sites are usually warming faster than lower areas nearby. However, when we average data from all global mountains and compare them with those from all lowland areas, there is no significant difference. Rainfall/snowfall on the other hand is decreasing in some areas, and increasing in others. In nearly all cases the strongest increase is occurring in the lowland areas, with increases in the mountains being more subdued (if at all). One consequence of our findings is that stores of mountain snow and ice may decline even faster than previously assumed due to the combination of enhanced mountain warming and reduced elevation dependency of rainfall/snowfall.
Key PointsUsing station and gridded data sets, we compare global precipitation and temperature trends by elevationLocal comparisons of paired stations and regional comparisons using gridded data often show faster mountain than lowland warmingPrecipitation differences between mountains and adjacent lowlands are reducing, often driven by stronger precipitation increase in lowlands
The precipitation concentration index (PCI) is a powerful indicator for temporal precipitation distribution and is also very useful for the assessment of seasonal precipitation changes. The primary ...objectives of this study are to investigate and analyse the temporal–spatial variability patterns of annual and seasonal PCI values based on monthly precipitation data. These data were collected from 597 meteorological stations located throughout China, for the time period of 1960–2016, and were used to assess the impacts of geographical parameters (latitude, longitude, and altitude) on the PCI. Additionally, the possible teleconnection with the large‐scale circulation pattern was investigated. Our results reveal that the variation trend of annual PCI values has decreased significantly at a rate of −.234/10 year (α = .01) in China over the past 57 years. For all studied station records, 434 (72.7%) stations showed decreasing trends of PCI values, and these stations are distributed over large areas in China. On an annual scale, the average PCI value ranged from 11 in Hunan province to 44 in Qinghai province. The precipitation concentration in China can be described as strongly irregular in the western and northern parts of the northwest and in the northern region of the Tibetan Plateau, while it is irregular in the southwest and the north of China, and moderately irregular in some parts of the middle‐lower regions of the Yangtze River and southern China. The regularity of the annual precipitation pattern significantly decreased in spring, autumn, and winter from southeastern to northwestern China, and was the most in winter. However, the summer precipitation dispersion and the pattern in the considered period were more regular than those of the other seasons. Furthermore, changes in the PCI appear to be rather complex and possibly related to global atmospheric characteristics as well as geographical factors (latitude, longitude, and altitude). The results presented in this study indicate that the PCI is an essential feature for water resource planning, prediction of risk due to droughts or floods, and the management of natural resources.
Various characteristics of precipitation concentration index and its possible teleconnection with atmospheric circulation patterns in China between 1960 and 2016.
The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) produces the latest generation of satellite precipitation estimates and has been widely used since its release ...in 2014. IMERG V06 provides global rainfall and snowfall data beginning from 2000. This study comprehensively analyzes the quality of the IMERG product at daily and hourly scales in China from 2000 to 2018 with special attention paid to snowfall estimates. The performance of IMERG is compared with nine satellite and reanalysis products (TRMM 3B42, CMORPH, PERSIANN-CDR, GSMaP, CHIRPS, SM2RAIN, ERA5, ERA-Interim, and MERRA2). Results show that the IMERG product outperforms other datasets, except the Global Satellite Mapping of Precipitation (GSMaP), which uses daily-scale station data to adjust satellite precipitation estimates. The monthly-scale station data adjustment used by IMERG naturally has a limited impact on estimates of precipitation occurrence and intensity at the daily and hourly time scales. The quality of IMERG has improved over time, attributed to the increasing number of passive microwave samples. SM2RAIN, ERA5, and MERRA2 also exhibit increasing accuracy with time that may cause variable performance in climatological studies. Even relying on monthly station data adjustments, IMERG shows good performance in both accuracy metrics at hourly time scales and the representation of diurnal cycles. In contrast, although ERA5 is acceptable at the daily scale, it degrades at the hourly scale due to the limitation in reproducing the peak time, magnitude and variation of diurnal cycles. IMERG underestimates snowfall compared with gauge and reanalysis data. The triple collocation analysis suggests that IMERG snowfall is worse than reanalysis and gauge data, which partly results in the degraded quality of IMERG in cold climates. This study demonstrates new findings on the uncertainties of various precipitation products and identifies potential directions for algorithm improvement. The results of this study will be useful for both developers and users of satellite rainfall products.
•GPM IMERG and nine precipitation products are evaluated from 2000 to 2018.•IMERG shows higher accuracy by years due to increasing passive microwave samples.•Some new findings of various precipitation datasets are demonstrated and discussed.•The accuracy, distributions, and trends of snowfall in China are revealed.•Reanalysis products exceed IMERG and even gauge data in snowfall estimation.
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