Sparse Rain Gauge Research Articles

Evaluation of ERA5 and CHIRPS rainfall estimates against observations across Ethiopia

Satellite-based precipitation estimates and global reanalysis products bear the promise of supporting the development of accurate and timely climate information for end users in sub-Sharan Africa. The accuracy of these global models, however, may be reduced in data-scarce regions and should be carefully evaluated. This study evaluates the performance of ERA5 reanalysis data and CHIRPS precipitation data against ground-based measurements from 167 rain gauges in Ethiopia, a region with complex topography and diverse climates. Focusing over a 38-year period (1981–2018), our study utilizes a point-to-pixel analysis to compare daily, monthly, seasonal, and annual precipitation data, conducting an evaluation based on continuous and categorical metrics. Our findings indicate that over Ethiopia CHIRPS generally outperforms ERA5, particularly in high-altitude areas, demonstrating a better capability in detecting high-intensity rainfall events. Both datasets, however, exhibit lower performance in Ethiopia's lowland regions, possibly the influence of sparse rain gauge networks informing gridded datasets. Notably, both CHIRPS and ERA5 were found to underestimate rainfall variability, with CHIRPS displaying a slight advantage in representing the erratic nature of Ethiopian rainfall. The study’s results highlight considerable performance differences between CHIRPS and ERA5 across varying Ethiopian landscapes and climatic conditions. CHIRPS’ effectiveness in high-altitude regions, especially for daily rainfall estimation, emphasizes its suitability in similar geographic contexts. Conversely, the lesser performance of ERA5 in these areas suggests a need for refined calibration and validation processes, particularly for complex terrains. These insights are essential for the application of satellite-based and reanalysis of rainfall data in meteorological, agricultural, and hydrological contexts, particularly in topographically and climatically diverse regions.

Performance evaluation of various techniques in estimating precipitation record of a sparsely gauged mountainous watershed.

Comprehensive precipitation data is essential for hydrological, agricultural, and climatological studies. Yet, gaps and sparse rain gauge distribution pose challenges, requiring imputation algorithms to fill data gaps. The aim of this research is to evaluate the performance of several approaches for estimating incomplete precipitation data in the Upper Indus Basin (UIB). Eight various imputation approaches were used on sparsely gauged mountainous UIB on a monthly time series of twenty-four meteorological observatories. Following that, the estimation approaches were evaluated using a rank-based approach comprising four different statistical indicators. The results indicate that multiple linear regression is the best-performing strategy for most of the stations regardless of season or orography, followed by the arithmetic average method and inverse distance weighing method.

Assessing the Improvement of a Sparse Rain Gauge Network in a Landslide Hotspot in Kerala, India—A Multi-Criteria Approach

Assessing the Improvement of a Sparse Rain Gauge Network in a Landslide Hotspot in Kerala, India—A Multi-Criteria Approach

Optimal selection of daily satellite precipitation product based on structural similarity index at 1 km resolution for the Pra catchment, Ghana

Thirteen satellite precipitation products (SPPs), re-gridded to 1 km resolution, were evaluated in terms of the structural similarity index (SSI) over the Pra catchment in Ghana. Three SPP scenarios were considered: Scenario one (S1) was the original SPPs; Scenario two (S2) was bias-corrected SPPs; and Scenario three (S3) was the better of S1 and S2 for each wet day. For each scenario, the best SPP was selected to constitute the 14th SPP referred to as the BEST SPP. Each SPP was evaluated in terms of SSI against the rain gauge rainfield for each wet day. For S1, the top three SPPs were TMPA, GSMAP and CMORPH; for S2, CMORPH, PERCCS and MSWEP were the top three; and for S3, CMORPH, PERCCS and TMPA came out on top in order of decreasing performance. Bias correction led to improvement in the overall SSI measure (SSIM) for 73% of wet days. The BEST SPP increased the SSIM of the best individual SPP by over 50% for S1, and over 30% for both S2 and S3. Comparing the BEST SPP of the three scenarios, S2 increased the SSIM statistic by 20% over that for S1, and SSIM was further improved by 4% for S3. It is highly recommended to use BEST SPP (S3) to generate the required 1 km × 1 km rainfields for the Pra, or other catchments around the world with a sparse rain gauge network, through conditional merging with rain gauge data as demonstrated.

Applicabilty of TRMM Precipitation for Hydrological Modeling Using HEC-HMS in Karnali River Basin in Nepal

This study aims to validate the HEC-HMS model in the Karnali river basin of Nepal, while also physically verifying the model parameters for the area. The HEC-HMS model is a semi-distributed physically based model that requires site-specific information about the weather, as well as spatial information on soil and land use. Using Arc-GIS, HEC-HMS, and HEC DSS, a hydrological rainfall runoff model was developed for the Karnali River basin at Asaraghat (DHM station 240) for the period of 1998-2005. Calibration for the time period of 1998-2001 resulted in an NSE value of 73.2% and an R2 value of 84%. Validation for the time period of 2003-2005 resulted in an NSE value of 72.4% and an R2 value of 82.02%. To evaluate the performance of the TRMM 3B42V7 rainfall product, statistical indicators such as NSE and R2 were used. Results showed that the TRMM rainfall data was reliable and had good precision in application to the Karnali River Basin. This study concludes that the TRMM rainfall data can be used as an alternative data source for basins with sparse rain gauge stations in Nepal. The results also suggest that the HEC-HMS modeling tool is good enough to relate rainfall and runoff. The model parameters for each sub-basin and reaches were analyzed with the physical characteristics of the watershed. Minor variations were found within calibrated and calculated Initial abstraction and Muskingum K, x, leading to the verification of the model parameters physically.

A Gridded Monthly Precipitation Merged Rain Gauge and Satellite Analysis Dataset for the Tian Shan Range between 1981 and 2019

Abstract An accurate and reliable precipitation product on regular grids is essential for understanding trends and variability within climate studies, for weather forecasting, and in hydrology and agrometeorology applications. However, the construction of high-resolution spatiotemporal precipitation grid products is challenging for complex terrain with sparse rain gauge networks and when only coarse spatial resolutions of satellite data are available. The objective of this study was to consequently provide a practical method to create a grid precipitation product by merging accurate quantitative observations from weather stations with continuous spatial information and from a satellite-based estimate product. The new gridded precipitation product exhibits a monthly temporal resolution and a spatial resolution of 0.01° for the Tian Shan range, extending back to 1981. To overcome the limitation of low densities and sparse distributions of meteorological stations in the complex terrain of the Tian Shan, a suitable interpolation of Australian National University Spline (ANUSPLIN) was used to interpolate grid precipitation based on in situ data. The interpolation grid precipitation was then merged with the satellite precipitation product developed by the U.S. Geological Survey and the Climate Hazards Group. After evaluation and validation using withheld stations and comparison to reference datasets, the result indicated that the merged product exhibits considerable promise for application in complex terrain. The method can be widely applied and is expected to construct precipitation products with high spatial and temporal resolution by merging multiple precipitation data sources. Significance Statement The purpose of this study is to construct a gridded precipitation dataset by merging the interpolation precipitation based on in situ observation and a satellite precipitation product in arid mountain regions. This is important because gridded precipitations are essential to the evaluation of climate model output and detection of trends in mean climate and climate extremes. Our results present a guide on merging frameworks to construct precipitation datasets used multisource data sources for regions with complex topography, precipitation scarcity, and ungauged and sparse collection.

Toward a better understanding of uncertainty for satellite precipitation products over complex terrain with sparse rain gauge data

Satellite precipitation product quality is critical for various meteorological and hydrological applications. Products are subjected to more uncertainties in sparsely-gauged regions than in densely-gauged regions due to insufficient error correction. However, ground-truth-based validation cannot accurately quantify the spatially distributed uncertainty of satellite precipitation products over complex terrain with sparse rain gauge data. In this study, we explored a gauge-independent method, called the Three-Cornered Hat (TCH), to overcome the quantification issue and better understand the uncertainty of satellite precipitation products in such situations. Firstly, the reliability of the TCH method was explored over a topographically complex area. Then, the performance of the Soil Moisture TO RAIN-Advanced SCATterometer (SM2RAIN-ASCAT) and Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) in the Chuan-Yu region was evaluated using TCH, with the help of the China Merged Precipitation Analysis (CMPA) precipitation product as the third input. We found that: (1) The high consistency between error standard deviation (ESD) estimated by TCH and root mean square error (RMSE) estimated by ground-truth-based validation demonstrated that TCH is reliable for evaluating precipitation datasets over complex terrain; (2) The ESDs of SM2RAIN-ASCAT and IMERG had different spatial distributions, and the quality of IMERG was overall higher than that of SM2RAIN-ASCAT over a mountainous area; (3) IMERG performed better than SM2RAIN-ASCAT both at low (under 3000 m) and high elevations (over 3000 m) in most years, as well as for most main land cover types (woody savannas, mixed forests, and grasslands) in the study area. This method and these findings provide a new insight for better use of satellite precipitation products over complex terrain.

Assessment of the Urban Extreme Precipitation by Satellite Estimates over Mainland China

The accurate estimation of urban extreme precipitation is essential for urban design and risk management, which is hard for developing countries, due to the fast urbanization and sparse rain gauges. Satellite precipitation products (SPPs) have emerged as a promising solution. Not only near real-time SPPs can provide critical information for decision making, but post-processed SPPs can also offer essential information for climate change adaption, risk management strategy development, and related fields. However, their ability in urban extreme precipitation estimation has not been examined in detail. This study presents a comprehensive evaluation of four recent SPPs that are post-processed, including IMERG, GSMaP_Gauge, MSWEP, and CMFD, for their ability to capture urban extreme precipitation in mainland China at the national, city, and inner-city scales. The performance of the four SPPs was assessed using daily observations from the 821 urban gauges from 2001 to 2018. The assessment includes: (1) the extreme precipitation estimates from the four SPPs in the total urbanized areas of mainland China were evaluated using correlation coefficients (CC), absolute deviation (AD), relative deviation (RB), and five extreme precipitation indices; (2) The extreme precipitation estimates over 21 Chinese major cities were assessed with the two most important extreme indices, namely the 99th percentile of daily precipitation on wet days (R99) and total precipitation when daily precipitation exceeding R99 (R99TOT); and (3) Bivariate Moran’s I (BMI) was adopted to assess the inner-city spatial correlation of R99 and R99TOT between SPPs and gauge observations in four major cities with most gauges. The results indicate that MSWEP has the highest CC of 0.79 and the lowest AD of 1.61 mm at the national scale. However, it tends to underestimate urban precipitation, with an RB of −8.5%. GSMaP_Gauge and IMERG performed better in estimating extreme values, with close extreme indices with gauge observations. According to the 21 major cities, GSMaP_Gauge also shows high accuracy in estimating R99 and R99TOT values, with the best RB and AD in these cities, while CMFD and MSWEP exhibit the highest CC values for R99 and R99TOT, respectively, indicating a strong correlation between their estimates and those obtained from gauge observations. At the inner-city scale, MSWEP shows advantages in monitoring the spatial distribution of urban extreme precipitation in most of cities. The study firstly provided the multiscale assessment of urban extreme precipitation by SPPs over mainland China, which is useful for their applications.

Investigating the feasibility of using precipitation measurements from weather RaDAR to estimate potential recharge in regional aquifers: the Majella massif case study in Central Italy

Rain gauge spatial sparsity and temporal discontinuity of data represent one of the major issues for reliable recharge estimations. In the past decades, the use of ground-based microwave weather RaDAR has dramatically improved quantitative rainfall estimation by providing spatially continuous estimates of rainfall over an area of more than 400 km2 every 10 minutes. Furthermore, weather RaDAR data have also proved relatively reliable in mountainous areas. These paramount features of RaDAR-derived precipitation data could improve the estimation of potential recharge of aquifers, which rely on geospatializations (e.g., Thiessen polygons) of rainfall data collected by a sparse rain gauge network which often shows lacking at high altitude (i.e., recharge areas), introducing additional uncertainty in the inflow volumes. Weather RaDAR rainfall estimation is also affected by various sources of error, which can be reduced by proper post-processing; however, uncertainties remain, especially for surface rain rate estimations. Despite the currently necessary complex numerical processing, the purpose of the study is to evaluate the use of the weather RaDAR data as an alternative or in addition to meteorological data. Based on the above considerations, the feasibility of using RaDAR-based precipitation data to estimate aquifer potential recharge and calculate a detailed water budget in the areas characterized by high elevations, such as the Majella massif in the central Apennines, has been evaluated. To address this objective, the water budget has been calculated in the 2017-2018 period using both RaDAR-based precipitation data and rain gauge data, as well as adopting different methods (i.e., Turc and Thornthwaite). Although intrinsically uncertain, the RaDAR-based precipitation data provided solid results, pointed out by comparing it with water budget obtained by rain gauge data, and especially with experimental literature data. This interdisciplinary work may pave the way for continuous monitoring of aquifer potential recharge at extremely high temporal and spatial resolution.

Challenges of modeling rainfall triggered landslides in a data-sparse region: A case study from the Western Ghats, India

• Modelling rainfall triggered landslides in a data sparse region. • Comparison of satellite rainfall measure with rain gauge observations. • Conditional merging as a method to gauge satellite and real rainfall. • Comparison of rain gauge and satellite observed rainfall thresholds. • Satellite rainfall has better spatial variability than rain gauges. Accurate rainfall estimates are required to forecast the spatio-temporal distribution of rain-triggered landslides. In this study, a comparison between rain gauge and satellite rainfall data for assessing landslide distribution in a data-sparse region, the mountainous district of Idukki, along the Western Ghats of southwestern India, is carried out. Global Precipitation Mission Integrated Multi-satellitE Retrievals for GPM-Late (GPM IMERG-L) rainfall products were compared with rain gauge measurements, and it was found that the satellite rainfall observations were underpredicting the actual rainfall. A conditional merging algorithm was applied to develop a product that combines the accuracy of rain gauges and the spatial variability of satellite precipitation data. Correlation Coefficient (CC) and Root Mean Squared Error (RMSE) were used to check the performance of the conditional merging process. An example from a station with the least favorable statistics shows the CC increasing from 0.589 to 0.974 and the RMSE decreasing from 65.22 to 20.01. A case scenario was considered that evaluated the performance of a landslide prediction model by relying solely on a sparse rain gauge network. Rainfall thresholds computed from both the conditionally merged GPM IMERG-L and the rain gauge data were compared and the differences indicated that relying solely on a discrete, sparse rain gauge network would create false predictions. A total of 18.7% of landslide predictions only were identified as true positives, while 60.7% was the overall false-negative rate, and the remaining were false-positives. This pointed towards the need of having a continuous data that is both accurate in measurement and efficient in capturing spatial variability of rainfall.

Evaluation of multiple satellite precipitation products for rainfed maize production systems over Vietnam

High-resolution reliable rainfall datasets are vital for agricultural, hydrological, and weather-related applications. The accuracy of satellite estimates has a significant effect on simulation models in particular crop simulation models, which are highly sensitive to rainfall amounts, distribution, and intensity. In this study, we evaluated five widely used operational satellite rainfall estimates: CHIRP, CHIRPS, CPC, CMORPH, and GSMaP. These products are evaluated by comparing with the latest improved Vietnam-gridded rainfall data to determine their suitability for use in impact assessment models. CHIRP/S products are significantly better than CMORPH, CPC, and GsMAP with higher skill, low bias, showing a high correlation coefficient with observed data, and low mean absolute error and root mean square error. The rainfall detection ability of these products shows that CHIRP outperforms the other products with a high probability of detection (POD) scores. The performance of the different rainfall datasets in simulating maize yields across Vietnam shows that VnGP and CHIRP/S were capable of producing good estimates of average maize yields with RMSE ranging from 536 kg/ha (VnGP), 715 kg/ha (CHIRPS), 737 kg/ha (CHIRP), 759 kg/ha (GsMAP), 878 kg/ha (CMORPH) to 949 kg/ha (CPC). We illustrated that there is a potential for use of satellite rainfall estimates to overcome the issues of data scarcity in regions with sparse rain gauges.

Evaluation of GPM-IMERG rainfall estimates at multiple temporal and spatial scales over Greece

Accurate precipitation data in space and time are among the most important variables for analyzing, modelling and forecasting many hydrometeorological applications and hazards. For countries like Greece, which have a complex terrain and great spatial variability of precipitation, satellite precipitation products (SPPs) are an alternative source of precipitation information that could complement the sparse rain gauge network. The objective of this study is to evaluate the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06B Final-run product over Greece for a 5-year period (January 2015 to December 2019) using as reference measurements from 263 rain gauges. The evaluation of IMERG is performed at multiple temporal (annual, monthly, daily) and spatial (country, region, grid) scales by employing several statistical and graphical methods. The rainfall detection capability of IMERG is examined with categorical verification metrics and the accuracy of the estimated rainfall amounts with continuous verification metrics. In addition, the performance of IMERG under different rainfall intensities, elevations and seasons is examined.On the monthly and annual scale evaluation the intercomparison between the calibrated (IMERGcal) and uncalibrated (IMERGuncal) IMERG estimates revealed the superiority of IMERGcal, but on the same time it highlighted the weakness of the GPCC gauge-based calibration product over Greece. The monthly IMERGcal estimates could reproduce the annual cycle of precipitation with a slight overestimation of monthly precipitation (RBIAS = 5.1%). On a daily scale IMERGcal performed well on detecting rainfall events (POD = 0.77), but it also produced many false alarm events (FAR = 0.40) which are primarily found over the islands of Aegean and Ionian Sea and along the coastline of mainland Greece. The assessment of daily rainfall intensities by IMERGcal showed a moderate correlation across Greece (0.49), with overestimation of light and moderate precipitation events (0.4–20 mm/day) and underestimation of heavy rainfall events (>20 mm/day). On a regional scale, IMERGcal tends to underestimate the daily rainfall over the rainfall-prone western part of Greece because in addition to the smoothing of high rainfall values by the GPCC calibration, satellite sensors fail to detect the magnitude of orographic rainfall. The complete opposite situation was found over the Aegean and Ionian Sea and many areas of the coastline, with great overestimation of rainfall. The results of our study highlight the potentials and limitations of IMERG rainfall estimates over Greece and they can be very helpful for future users of IMERG in Greece.

A GWR downscaling method to reconstruct high-resolution precipitation dataset based on GSMaP-Gauge data: A case study in the Qilian Mountains, Northwest China

Accurate precipitation data are crucial for hydrological, meteorological, and ecological research. However, it is difficult to obtain high-precision and high-resolution spatiotemporal distributions of precipitation in remote mountain regions with complex topography and sparse rain gauges. In addition, the spatial resolutions of existing satellite precipitation products are too coarse to apply them in the mountain regions with great spatiotemporal heterogeneity. To overcome the disadvantage, downscaling satellite precipitation products has been an effectively method to develop high-resolution precipitation data. In this study, a geographically weighted regression (GWR) model, coupling with topographical and water vapor source variables filtered by stepwise regression analysis (SRA), is applied to downscale the GSMaP-Gauge precipitation products (0.1° × 0.1°) to obtain high-resolution (1 km × 1 km) precipitation from 2000 to 2020 at annual, seasonal, and monthly scales over the Qilian Mountains. Besides, the accuracy of the downscaled precipitation based on all meteorological stations and the stations at high altitude (i.e., over 3000 m) are validated. Furtherly, the spatiotemporal variations of precipitation are analyzed. The results show that: (1) the accuracy after downscaling has been improved comparing with that of the original data. The accuracy of precipitation simulated at high-altitude stations is lower than that at all stations; (2) The trend of precipitation before and after downscaling is consistent in space. The spatial distributions of precipitation at annual, spring, summer, autumn, and months from March to November are decreased from the southeast to the northwest; (3) The spatial variations of precipitation show an increasing trend in most areas (>50%) at different time scales, except for March and September. Along with the time, the annual precipitation shows an increasing trend with a slope of 3.83 over the last 20 years. These findings suggest that the GWR method can be applied effectively to downscale annual, seasonal, and monthly precipitation of GSMaP-Gauge products in the Qilian Mountains.

Using high-resolution regional climate models to estimate return levels of daily extreme precipitation over Bavaria

Abstract. Extreme daily rainfall is an important trigger for floods in Bavaria. The dimensioning of water management structures as well as building codes is based on observational rainfall return levels. In this study, three high-resolution regional climate models (RCMs) are employed to produce 10- and 100-year daily rainfall return levels and their performance is evaluated by comparison to observational return levels. The study area is governed by different types of precipitation (stratiform, orographic, convectional) and a complex terrain, with convective precipitation also contributing to daily rainfall levels. The Canadian Regional Climate Model version 5 (CRCM5) at a 12 km spatial resolution and the Weather and Forecasting Research (WRF) model at a 5 km resolution both driven by ERA-Interim reanalysis data use parametrization schemes to simulate convection. WRF at a 1.5 km resolution driven by ERA5 reanalysis data explicitly resolves convectional processes. Applying the generalized extreme value (GEV) distribution, the CRCM5 setup can reproduce the observational 10-year return levels with an areal average bias of +6.6 % and a spatial Spearman rank correlation of ρ=0.72. The higher-resolution 5 km WRF setup is found to improve the performance in terms of bias (+4.7 %) and spatial correlation (ρ=0.82). However, the finer topographic details of the WRF-ERA5 return levels cannot be evaluated with the observation data because their spatial resolution is too low. Hence, this comparison shows no further improvement in the spatial correlation (ρ=0.82) but a small improvement in the bias (2.7 %) compared to the 5 km resolution setup. Uncertainties due to extreme value theory are explored by employing three further approaches. Applied to the WRF-ERA5 data, the GEV distributions with a fixed shape parameter (bias is +2.5 %; ρ=0.79) and the generalized Pareto (GP) distributions (bias is +2.9 %; ρ=0.81) show almost equivalent results for the 10-year return period, whereas the metastatistical extreme value (MEV) distribution leads to a slight underestimation (bias is −7.8 %; ρ=0.84). For the 100-year return level, however, the MEV distribution (bias is +2.7 %; ρ=0.73) outperforms the GEV distribution (bias is +13.3 %; ρ=0.66), the GEV distribution with fixed shape parameter (bias is +12.9 %; ρ=0.70), and the GP distribution (bias is +11.9 %; ρ=0.63). Hence, for applications where the return period is extrapolated, the MEV framework is recommended. From these results, it follows that high-resolution regional climate models are suitable for generating spatially homogeneous rainfall return level products. In regions with a sparse rain gauge density or low spatial representativeness of the stations due to complex topography, RCMs can support the observational data. Further, RCMs driven by global climate models with emission scenarios can project climate-change-induced alterations in rainfall return levels at regional to local scales. This can allow adjustment of structural design and, therefore, adaption to future precipitation conditions.

Evaluation of CHIRPS Satellite Gridded Dataset as an Alternative Rainfall Estimate for Localized Modelling over Uganda

The Ugandan economy is largely dependent on rural-based and rain-fed agriculture. This creates a critical need to understand the rainfall dynamics at the local scale. However, the country has a sternly sparse and unreliable rain gauge network. This research, therefore, sets out to evaluate the use of the CHIRPS satellite gridded dataset as an alternative rainfall estimate for local modelling of rainfall in Uganda. Complete, continuous and reliable in situ station observations for the period between 2012 and 2020 were used for the comparison with CHIRPS satellite data models in the same epoch. Rainfall values within the minimum 5 km and maximum 20 km radii from the in situ stations were extracted at a 5 km interval from the interpolated in situ station surface and the CHIRPS satellite data model for comparison. Results of the 5 km radius were adopted for the evaluation as it’s closer to the optimal rain gauge coverage of 25 km2. They show the R2 = 0.91, NSE = 0.88, PBias = -0.24 and RSR = 0.35. This attests that the CHIRPS satellite gridded datasets provide a good approximation and simulation of in situ station data with high collinearity and minimum deviation. This tallies with related studies in other regions that have found CHIRPS datasets superior to interpolation surfaces and sparse rain gauge data in the comprehensive estimation of rainfall. With a 0.05° * 0.05° (Latitude, longitude) spatial resolution, CHIRPS satellite gridded rainfall estimates are therefore able to provide a comprehensive rainfall estimation at a local scale. Essentially these results reward research science in regions like Uganda that have sparse rain gauges networks characterized by incomplete, inconsistent and unreliable data with an empirically researched alternative source of rainfall estimation data. It further provides a platform to scientifically interrogate the rainfall dynamics at a local scale in order to infuse local policy with evidence-based formulation and application.

Evaluation of the Impacts of Rain Gauge Density and Distribution on Gauge-Satellite Merged Precipitation Estimates

The capacity of combined gauge-satellite precipitation estimates largely depends on the characteristics of the input data such as the number, location and reliability of rain gauges, and satellite-derived precipitation quality. The objective of this study is to examine the influence of rain gauge network configuration including density and spatial distribution on the performance of the gauge-satellite merging estimation at monthly and ten-day temporal scales. Dense rain gauge observations and satellite-derived precipitation data (i.e., TMPA 3B42 Version 7 and Version 06 IMERG Final Run) in two provinces of China are used. A two-stage downscaling-integration approach is applied in the gauge-satellite precipitation estimation. Various scenarios of rain gauge density and combination are designed and their corresponding merged precipitation estimates are evaluated using statistical indices. The merged results using the TMPA and IMERG precipitation product, respectively, are compared. The results show that: 1) the influence of rain gauge network configuration on the gauge-satellite merged precipitation estimates gradually decreases with the increase in rain gauge density, and the gauge-satellite merged precipitation estimates are more sensitive to the rain gauge network density in wet season and ten-day temporal scale than in dry season and monthly scale, respectively and 2) the merged precipitation estimation using the IMERG precipitation data generally outperforms the estimation using TMPA precipitation data in the low gauge density scenarios, and the gap decreases with the increase in the rain gauge network density. In the areas with sparse rain gauges, improving the quality of satellite precipitation data would significantly improve the performance of the gauge-satellite merging estimation.

Spatial-Temporal Assessment of Satellite-Based Rainfall Estimates in Different Precipitation Regimes in Water-Scarce and Data-Sparse Regions

Accurate precipitation measurement is very important for socio-hydrological resilience in the face of frequent extreme weather events such as cyclones. This study evaluates the performance of two satellite products: the Tropical Rainfall Measuring Mission (TRMM 3B43V7) Multi-satellite Precipitation Analysis (TMPA, hereafter: TRMM) and the Integrated Multi-satellite Retrievals for GPM (IMERG, Final Run V06, hereafter: GPM) in the Sultanate of Oman. Oman is an arid country that generally has few rainy days, but has experienced significant flash floods, tropical storms and cyclones recently, leading to the loss of lives and millions of dollars in damage. Accurate precipitation analysis is crucial in flood monitoring, hydrologic modeling, and the estimation of the water balance of any basin, and the lack of a sufficient weather monitoring network is a barrier to accurate precipitation measurement. Satellite rainfall estimates can be a reliable option in sparse network areas, especially in arid and semi-arid countries. This study evaluated monthly rainfall (hereafter: OBSERVED) levels at 77 meteorological stations from January 2016 to December 2018. The capacity of the TRMM and GPM satellite products to detect monthly rainfall amounts at varying precipitation thresholds was also evaluated. Findings included (1) overall and across the 11 Governorates of Oman, both satellite products show different spatial variability and performance to the OBSERVED at the monthly, seasonal, and annual temporal scales; (2) from the perspective of precipitation detection and frequency bias, GPM showed a similar performance to TRMM at detecting low precipitation (2 mm/month) but was poorer at detecting high precipitation (>30 mm/month) across the entire country as well as in the Northern, Interior, and Dhofar regions; (3) both products show similarities to the OBSERVED through the partitioning of their seasonal time series into a distinct number of homogenous segments; and (4) both products had difficulty reproducing OBSERVED levels in the Dhofar and Interior regions, which is consistent with studies conducted in mountainous and coastal regions. With the aim of reproducing the spatial and temporal structure of OBSERVED in a rugged terrain, the study shows that both satellite products can be used in areas of sparse rain gauges or as additional observation for studies of extreme weather events. Overall, this study suggests that for Oman, both satellite products can be used as proxies for OBSERVED with appropriate bias corrections and GPM is also a reliable replacement for TRMM as a precipitation satellite product. The findings will be useful to the country’s flood resilience and mitigation efforts, especially in areas where there is sparse rain gauge coverage.

Comprehensive evaluation of latest GPM era IMERG and GSMaP precipitation products over mainland China

Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, the latest GPM era high-resolution satellite precipitation products (SPPs), namely Integrated Multi-satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP), have been released. A critical evaluation of these newly released SPPs is very important for both end users and data developers. This study aims to evaluate the performance of the IMERG and GSMaP products over mainland China from 1 January 2015 to 31 December 2017, using daily precipitation data from 778 meteorological stations. The SPPs are validated adopting three widely used statistical metrics (CC, RMSE and RB). The assessment also considers the precipitation detection capability (POD, FAR and CSI), probability density function (PDF) of precipitation intensities, and elevation factor. Results show that GSMaP_G performs better in daily, monthly and seasonal scales than other SPPs. In terms of CC and RMSE, all SPPs share similar spatial patterns, with high values in southeastern China and low values in northwestern China. In addition, all SPPs underestimate the precipitation amount in southern China, but overestimate that in northern China, especially in northwestern China. Obviously, the precipitation detection ability of all products is good in southern China, except the Sichuan Basin. All SPPs can better reproduce the PDF in terms of precipitation intensity. Compared with other SPPs, the performance of GSMaP_G is better at all elevations. However, significant elevation-dependent errors exist in the GSMaP_N. Overall, the gauge-corrected GSMaP_G and IMERG_F perform better than other SPPs in reducing various errors, especially the former. This study will be valuable for both algorithm developers and users, as well as for associated products, and it will also better support disaster risk reduction and hydrological applications, especially in areas with a sparse rain gauge network.

Bias Correction of Satellite-Based Precipitation Estimations Using Quantile Mapping Approach in Different Climate Regions of Iran

High-resolution real-time satellite-based precipitation estimation datasets can play a more essential role in flood forecasting and risk analysis of infrastructures. This is particularly true for extended deserts or mountainous areas with sparse rain gauges like Iran. However, there are discrepancies between these satellite-based estimations and ground measurements, and it is necessary to apply adjustment methods to reduce systematic bias in these products. In this study, we apply a quantile mapping method with gauge information to reduce the systematic error of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Due to the availability and quality of the ground-based measurements, we divide Iran into seven climate regions to increase the sample size for generating cumulative probability distributions within each region. The cumulative distribution functions (CDFs) are then employed with a quantile mapping 0.6° × 0.6° filter to adjust the values of PERSIANN-CCS. We use eight years (2009–2016) of historical data to calibrate our method, generating nonparametric cumulative distribution functions of ground-based measurements and satellite estimations for each climate region, as well as two years (2017–2018) of additional data to validate our approach. The results show that the bias correction approach improves PERSIANN-CCS data at aggregated to monthly, seasonal and annual scales for both the calibration and validation periods. The areal average of the annual bias and annual root mean square errors are reduced by 98% and 56% during the calibration and validation periods, respectively. Furthermore, the averages of the bias and root mean square error of the monthly time series decrease by 96% and 26% during the calibration and validation periods, respectively. There are some limitations in bias correction in the Southern region of the Caspian Sea because of shortcomings of the satellite-based products in recognizing orographic clouds.

Performance Analysis of IMD High-Resolution Gridded Rainfall (0.25° × 0.25°) and Satellite Estimates for Detecting Cloudburst Events over the Northwest Himalayas

AbstractThe presence of a sparse rain gauge network in complex terrain like the Himalayas has encouraged the present study for the concerned evaluation of Indian Meteorological Department (IMD) ground-based gridded rainfall data for highly prevalent events like cloudbursts over the northwest Himalayas (NWH). To facilitate the abovementioned task, we intend to evaluate the performance of these observations at 0.25° × 0.25° (latitude–longitude) resolution against a predefined threshold (i.e., 99.99th percentile), thereby initially comprehending the success of IMD data in capturing the cloudburst events reported in media during 2014–16. Further, seven high-resolution satellite products, namely, CMORPH V0.x, PERSIANN-CDR, TMPA 3B42RT V7, IMERG V06B, INSAT-3D multispectral rainfall (IMR), CHIRPS V.2, and PERSIANN-CCS are evaluated against the IMD dataset. The following are our main results. 1) Six out of 18 cloudburst events are detected using IMD gridded data. 2) The contingency statistics at the 99.99th percentile reveal that the probability of detection (POD) of TMPA varies from 19.4% to 53.9% over the geographical stretch of NWH, followed by PERSIANN-CDR (18.6%–48.4%) and IMERG (4.9%–17.8%). 3) A new metric proposed as improved POD (IPOD) has been developed in this work, which takes into account the temporal lag that exists between observed and satellite estimates during an event period. Results show that for an event analysis IPOD provides a better comparison. The IPOD for TMPA is 32.8%–74.4%, followed by PERSIANN-CDR (34.4%–69.11%) and IMERG (15.3%–39.0%). 4) The conclusion stands as precipitation estimates obtained from CHIRPS are most suitable for monitoring cloudburst events over NWH with IPOD of 60.5%–78.6%.