Satellite Rainfall Estimates Research Articles

Sensitivity of Simulations of Zambian Heavy Rainfall Events to the Atmospheric Boundary Layer Schemes

Weather forecasting relies on the use of numerical weather prediction (NWP) models, whose resolution is informed by the available computational resources. The models resolve large scale processes, while subgrid processes are parametrized. One of the processes that is parametrized is turbulence which is represented in planetary boundary layer (PBL) schemes. In this study, we evaluate the sensitivity of heavy rainfall events over Zambia to four different PBL schemes in the Weather Research and Forecasting (WRF) model using a parent domain with a 9 km grid length and a 3 km grid spacing child domain. The four PBL schemes are the Yonsei University (YSU), nonlocal first-order medium-range forecasting (MRF), University of Washington (UW) and Mellor–Yamada–Nakanishi–Niino (MYNN) schemes. Simulations were done for three case studies of extreme rainfall on 17 December 2016, 21 January 2017 and 17 April 2019. The use of YSU produced the highest rainfall peaks across all three cases; however, it produced performance statistics similar to UW that are higher than those of the two other schemes. These statistics are not maintained when adjusted for random hits, indicating that the extra events are mainly random rather than being skillfully placed. UW simulated the lowest PBL height, while MRF produced the highest PBL height, but this was not matched by the temperature simulation. The YSU and MYNN PBL heights were intermediate at the time of the peak; however, MYNN is associated with a slower decay and higher PBL heights at night. WRF underestimated the maximum temperature during all cases and for all PBL schemes, with a larger bias in the MYNN scheme. We support further use of the YSU scheme, which is the scheme selected for the tropical suite in WRF. The different simulations were in some respects more similar to one another than to the available observations. Satellite rainfall estimates and the ERA5 reanalysis showed different rainfall distributions, which indicates a need for more ground observations to assist with studies like this one.

Evaluation of RFE Satellite Precipitation and its Use in Streamflow Simulation in Poorly Gauged Basins

The performance of satellite Rainfall Estimates (RFE, version 2.0) at daily resolution was evaluated in comparison with ground-based meteorological datasets (GBD) by applying statistical and hydrological modeling approaches. In-situ daily rainfall observations from 5 stations in and around the periphery of the Nasia river basin in Ghana, covering a period of 15 years (2001–2015), were used in this research. Comparison of the observed rainfall data with satellite-based estimates revealed a strong positive correlation, which yielded the highest correlation coefficient (R2) of 0.74 at the monthly timescale as against the weak positive linear relationship with the highest R2 of 0.41 at the daily timescale. Mean annual precipitation computed from both datasets also showed close correspondence yielding 978.83 mm/annum and 977.12 mm/annum for RFE and GBD, respectively. Calibration at the daily timescale showed that the ground-based data (GBD) performed better in simulating the observed streamflows compared to the satellite-based (RFE) simulations yielding a Nash Sutcliffe Efficiency (NSE) of 0.81 and 0.67 for the GBD and RFE, respectively. At the monthly timescale, the performance of both datasets improved, resulting in an NSE of 0.89 and 0.80 for the GBD and RFE, respectively. Although the RFE-based simulations could not perfectly reproduce the observed discharge, it can be used to supplement traditional in-situ gauge data to address the problem of non-availability of observed rainfall data for hydrological applications such as water resources planning and assessment. Future research into the usability of the RFE in other medium scale river basins could be carried out to compare with these results. • There is a strong correlation between RFE version 2.0 and ground-based monthly rainfall data • RFE-based simulations appear to underestimate the observed hydrographs compared to ground-based data simulations • RFE could be used to supplement in-situ observations of rainfall for hydrological analysis

Assessment of satellite products for filling rainfall data gaps in the Amazon region

AbstractRainfall data series with adequate quality and length are often incomplete or nonexistent. Thus, filling in rainfall gaps becomes necessary to complete databases. This article proposes the use of satellite products (TRMM—Tropical Rainfall Measuring Mission, CHIRPS—Climate Hazards Group InfraRed Precipitation with Stations and CMORPH—CPC Morphing Technique) to fill gaps in the rainfall historical series. The simple regression method, using satellite rainfall estimates, was tested to fill the missing data from 164 rainfall gauge stations in the Amazon region. Large dispersions were observed between rainfall data, with R2 ranging from 0.383 to 0.844, the best results were found in areas with less rainfall. As well, the greatest performance of the products was verified in the dry period, with r and d higher than 0.899 and 0.950, respectively. The product with the best representation in the region was CHIRPS, which had the lowest monthly values of mean absolute error (0.979 mm) and root mean square error (3.656 mm). The results confirm that the satellite estimates satisfactorily represent the seasonal variation of rainfall in the region, despite presenting cases of overestimation and underestimation of data. The higher performance of CHIRPS can be explained by the higher spatial resolution (0.05°), allowing for more accurate weather forecasts. In fact, CHIRPS has the CHPclim model, which adds other factors to the good product performance. These characteristics justify the better performance of the CHIRPS product for filling gaps in daily rainfall data in the Amazon region, favoring the best monthly rainfall estimates for each region state analyzed.Recommendations for Resource Managers Satellite products have been increasingly used for estimating rainfall data in regions with a low number of installed rainfall gauge stations. Thus, the assessment and selection of these products needs to be elaborated for the best decision making of water resource managers. Rainfall data are important to recognize the occurrence patterns for prediction of the climatic behavior of a region. Sectors such as agriculture and disaster prevention (droughts, floods, erosion of watersheds, and river silting) need knowledge of rainfall for planning, management, and mitigation. Knowledge of rainfall behavior is very important in the Amazon region. In this case, the dry season and temperatures have been increasing due to global climate change. These changes establish conditions for more intense fires, which increases the deforestation of the region.

Assessment of GPM-Era Satellite Products’ (IMERG and GSMaP) Ability to Detect Precipitation Extremes over Mountainous Country Nepal

The reliability of satellite precipitation products is important in climatic and hydro-meteorological studies, which is especially true in mountainous regions because of the lack of observations in these areas. Two recent satellite rainfall estimates (SREs) from Global Precipitation Measurement (GPM)-era—Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG-V06) and gauge calibrated Global Satellite Mapping of Precipitation (GSMaP-V07) are evaluated for their spatiotemporal accuracy and ability to capture extreme precipitation events using 279 gauge stations from southern slope of central Himalaya, Nepal, between 2014 and 2019. The overall result suggests that both SREs can capture the spatiotemporal precipitation variability, although they both underestimated the observed precipitation amount. Between the two, the IMERG product shows a more consistent performance with a higher correlation coefficient (0.52) and smaller bias (−2.49 mm/day) than the GSMaP product. It is worth mentioning that the monthly gauge-calibrated IMERG product yields better detection capability (higher probability of detection (POD) values) of daily precipitation events than the daily gauge calibrated GSMaP product; however, they both show similar performance in terms of false alarm ratio (FAR) and critical success index (CSI). Assessment based on extreme precipitation indices revealed that the IMERG product outperforms GSMaP in capturing daily precipitation extremes (RX1Day and RX5Day). In contrast, the GSMaP product tends to be more consistent in capturing the duration and threshold-based precipitation extremes (consecutive dry days (CDD), consecutive wet days (CWD), number of heavy precipitation days (R10mm), and number of extreme precipitation days (R25mm)). Therefore, it is suggested that the IMERG product can be a good alternative for monitoring daily extremes; meanwhile, GSMaP could be a better option for duration-based extremes in the mountainous region.

Performance evaluation of GPM-IMERG early and late rainfall estimates over Lake Hawassa catchment, Rift Valley Basin, Ethiopia

High resolutions of satellite rainfall products have been widely used for hydrometeorological and hydroclimatological studies over the globe. However, the performance of satellite rainfall estimates varies and is affected by topography and atmospheric characteristics. The assessment of satellite rainfall products is important over different regions. In this study, Integrated Multi-SatellitE Retrievals’ performance for the Global Precipitation Mission version 6 (GPM-IMERG v6) was evaluated before and after bias correction, over the Lake Hawassa catchment. A linear scaling bias correction approach was used to correct the bias of GPM-IMERG early and late rainfall products. The satellite rainfall products were also compared with ground observed rainfall data in the Lake Hawassa catchment. Statistical performance assessing methods were used to evaluate both raw and bias-corrected IMERG early and late rainfall products. The percentage of bias (PBIAS) for early and late rainfall estimates was 91.54 and 77.03, respectively, for the entire periods before bias correction. It indicates that GPM-IMERG overestimated rainfall relative to ground-gauged rainfall. The results show that IMERG rainfall products are in good agreement with ground observed rainfall after bias correction. The correlation values (R) for IMERG early and late is 0.86 and 0.85, respectively, indicating a good correlation between IMERG’s estimated rainfall and observed rainfall after bias correction. The performance of IMERG rainfall estimates varies with the seasons. The bias correction for only rainy season shows a good match with observed rainfall compared to all seasons. Bias-correction resulted in a good match between estimated and observed rainfall. Generally, evaluation of GPM-IMERG satellite rainfall products is essential prior to use for hydrological modeling and forecasting in data-scarce areas.

Spatial evaluation of satellite-retrieved extreme rainfall rates in the Upper Awash River Basin, Ethiopia

Understanding the performance of satellite-retrieved extreme rainfall rates is a prerequisite for developing appropriate flood and drought monitoring systems in a region where rain gauge data are sparse and unevenly distributed. In this study, seven satellite rainfall estimates (SREs) that primarily use the infrared (PERSIANN, PERSIANN-CDR, and TAMSATv3) and the microwave (CMORPHv1, TRMM 3B42RT, TRMM 3B42v7 and IMERG-v06B) data were evaluated in the highland and lowland regions of Upper Awash Basin, Ethiopia, from 2003 to 2015. Three categorical error metrics, seven extreme precipitation indices, and the Empirical Cumulative Distribution Function (ECDF) plot were used to compare satellite retrieved extreme rainfall rates against rain gauge observations using a pixel-to-point approach. Results showed that TAMSATv3 outperformed all the remaining SREs in detecting and estimating low rainfall in both the highlands (elevation>2000 m) and lowlands (elevation<2000 m) of the basin. For high rainfall, the IMERG-v06B followed by TRMM 3B42v7 and TRMM 3B42RT products relatively produced the best result in both regions. Overall, the performance of SREs in detecting and estimating extreme rainfall rates was better in the highlands than in the lowlands, and this may be associated with the density of rain gauge networks. Additionally, all seven SREs can correctly detect low rainfall but their performances tend to worsen as the extreme precipitation quantile increases. In both regions, on average, the microwave-based SREs showed the highest result in capturing high rainfall. In contrast, the infrared-based SREs had a better performance for low rainfall. This study suggests that TAMSATv3 product can be used for drought monitoring studies in the basin; however, a lot of effort (e.g. bias-adjustment, and improvements of SREs algorithm) could be needed to integrate SREs into hydrological models for flood forecasting and early warning purposes.

Evaluation of Satellite Rainfall Estimates for Meteorological Drought Analysis over the Upper Blue Nile Basin, Ethiopia

The objective of this study was to evaluate the performance of satellite rainfall estimates (Climate Hazards Group Infrared Precipitation with Stations version 2 (CHIRPSv2) and Multi-Source Weighted-Ensemble Precipitation version 2 (MSWEPv2) from 1981 to 2018 for monthly meteorological drought analysis over the Upper Blue Nile (UBN) basin. The reference for the performance evaluation was rainfall measured in situ selected with reference to the elevation zones of the basin: Highland, midland, and lowland. Both the measured and estimated rainfall datasets were aggregated by month at a spatial resolution of 10 km × 10 km with a temporal coverage of 38 years from 1981 to 2018 and evaluated with respect to raw precipitation statistics and the standardized precipitation index (SPI). The values of SPI were validated with reference to documented meteorological drought records of the country. The mean bias, correlation coefficient, probability of bias (PBias, %), mean error (ME, mm), and root mean square error (RMSE, mm) values across the elevation zones for CHIRPSv2 were found to be 1.07, 0.91, 6.75, 7.74, and 122.34, respectively. The corresponding values were 1.19, 0.87, 18.56, 19.54, and 130.26 for MSWEPv2. Based on this result, CHIRPSv2 was employed to analyze the magnitude of drought in the different elevation zones of the UBN. The magnitude (SPI) of monthly meteorological drought over the entire UBN basin from 1981 to 2018 ranged from 0 to −3.74. The strongest negative SPI value (−3.74) was observed in August 1984 in midland areas. The highest magnitude of drought was −3.0 in July 2015 over the highland and −3.03 in June 2015 over the lowland during 2014–2017. The observed drought was characterized by extreme, severe, and moderate levels. The mean frequency of severe/extreme meteorological drought in the 38-year period over the highland, midland, and lowland parts of the UBN ranged from 7% to 11%. The average of severe/extreme drought events in each of the elevation zones of the basin was 9%, that is, drought occurred almost every 10 years for all elevation zones of the basin. Over the 38-year period, severe/extreme drought occurred at the onset and/or offset time of rainy season over all elevation zones of the basin. The UBN is characterized as a drought-prone basin. However, the frequency and magnitude of drought could neither be described as a decreasing nor as an increasing linear trend. Thus, the farming practices in the basin need to be enhanced with an improved early warning system and drought-resistant seed technologies.

Evaluation of Satellite Precipitation Products in Simulating Streamflow in a Humid Tropical Catchment of India Using a Semi-Distributed Hydrological Model

Precipitation obtained from rain gauges is an essential input for hydrological modelling. It is often sparse in highly topographically varying terrain, exhibiting a certain amount of uncertainty in hydrological modelling. Hence, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. In this study, an attempt was made to evaluate the Tropical Rainfall Measuring Mission (TRMM) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), employing a semi-distributed hydrological model, i.e., Soil and Water Assessment Tool (SWAT), for simulating streamflow and validating them against the flows generated by the India Meteorological Department (IMD) rainfall dataset in the Gurupura river catchment of India. Distinct testing scenarios for simulating streamflow were made to check the suitability of these satellite precipitation data. The TRMM was able to better estimate rainfall than CHIRPS after performing categorical and continuous statistical results with respect to IMD rainfall data. While comparing the performance of model simulations, the IMD rainfall-driven streamflow emerged as the best followed by the TRMM, CHIRPS-0.05, and CHIRPS-0.25. The coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) were in the range 0.63 to 0.86, 0.62 to 0.86, and −14.98 to 0.87, respectively. Further, an attempt was made to examine the spatial distribution of key hydrological signature, i.e., flow duration curve (FDC) in the 30–95 percentile range of non-exceedance probability. It was observed that TRMM underestimated the flow for agricultural water availability corresponding to 30 percent, even though it showed a good performance compared to the other satellite rainfall-driven model outputs.

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 (&gt;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.

Evaluation of spatiotemporal variability of temperature and precipitation over the Karakoram Highway region during the cold season by a Regional Climate Model

Precipitation and temperature are two important factors associated to snow hazards which block the transport infrastructure and cause loss of life and properties in the cold season. The in-situ observations are limited in the alpine with complex topographic characteristics, while coarse satellite rainfall estimates, reanalysis rain datasets, and gridded in-situ rain gauge datasets obscure the understanding of the precipitation patterns in hazardprone areas. Considering the Karakoram Highway (KKH) region as a study area, a double nested Weather Research and Forecasting (WRF) model with the high resolution of a 10-km horizontal grid was performed to investigate the spatial and temporal patterns of temperature and precipitation covering the Karakoram Highway region during the cold season. The results of WRF were compared with the in-situ observations and Multi-Source Weighted-Ensemble Precipitation (MSWEP) datasets. The results demonstrated that the WRF model well reproduced the observed monthly temperature (R = 0.96, mean bias = −3.92°C) and precipitation (R = 0.57, mean bias = 8.69 mm). The WRF model delineated the essential features of precipitation variability and extremes, although it overestimated the wet day frequency and underestimated the precipitation intensity. Two rain bands were exhibited in a northwest-to-southeast direction over the study area. High wet day frequency was found in January, February, and March in the section between Hunza and Khunjerab. In addition, the areas with extreme values are mainly located in the Dasu-Islamabad section in February, March, and April. The WRF model has the potential to compensate for the spatial and temporal gaps of the observational networks and to provide more accurate predictions on the meteorological variables for avoiding common cold-weather hazards in the ungauged and high altitude areas at a regional scale.

Evaluation of the TRMM Product for Monitoring Drought over Paraíba State, Northeastern Brazil: A Statistical Analysis

Drought is a natural phenomenon that originates from the absence of precipitation over a certain period and is capable of causing damage to societal development. With the advent of orbital remote sensing, rainfall estimates from satellites have appeared as viable alternatives to monitor natural hazards in ungauged basins and complex areas of the world; however, the accuracies of these orbital products still need to be verified. Thus, this work aims to evaluate the performance of Tropical Rainfall Measuring Mission (TRMM) satellite rainfall estimates in monitoring the spatiotemporal behavior of droughts at multiple temporal scales over Paraíba State based on the standardized precipitation index (SPI) over 20 years (1998–2017). For this purpose, rainfall data from 78 rain gauges and 187 equally spaced TRMM cell grids throughout the region are used, and accuracy analyses are performed at the single-gauge level and in four mesoregions at eight different time scales based on 11 statistical metrics calculations divided into three different categories. The results show that in the mesoregions close to the coast, the satellite-based product is less accurate in capturing the drought behavior regardless of the evaluated statistical metrics. At the temporal scale, the TRMM is more accurate in identifying the pattern of medium-term droughts; however, there is considerable spatial variation in the accuracy of the product depending on the performance index. Therefore, it is concluded that rainfall estimates from the TRMM satellite are a valuable source of data to identify drought behavior in a large part of Paraíba State at different time scales, and further multidisciplinary studies should be conducted to monitor these phenomena more accurately based on satellite data.

Performance of the CMORPH and GPM IMERG Products over the United Arab Emirates

Satellite-based precipitation products are becoming available at very high temporal and spatial resolutions, which has accelerated their use in various hydro-meteorological and hydro-climatological applications. Because the quantitative accuracy of such products is affected by numerous factors related to atmospheric and terrain properties, validating them over different regions and environments is needed. This study investigated the performance of two high-resolution global satellite-based precipitation products: the climate prediction center MORPHing technique (CMORPH) and the latest version of the Integrated Multi-SatellitE Retrievals for the Global Precipitation Mission (GPM) algorithm (IMERG), V06, over the United Arab Emirates from 2010 through 2018. The estimates of the products and that of 71 in situ rain gauges distributed across the country were compared by employing several common quantitative, categorical, and graphical statistical measures at daily, event-duration, and annual temporal scales, and at the station and study area spatial scales. Both products perform quite well in rainfall detection (above 70%), but report rainfall not observed by the rain gauges at an alarming rate (more than 30%), especially for light rain (lower quartile). However, for moderate and intense (upper quartiles) rainfall rates, performance is much better. Because both products are highly correlated with rain gauge observations (mostly above 0.7), the satellite rainfall estimates can probably be significantly improved by removing the bias. Overall, the CMORPH and IMERG estimates demonstrate great potential for filling spatial gaps in rainfall observations, in addition to improving the temporal resolution. However, further improvement is required, regarding the overestimation and underestimation of small and large rainfall amounts, respectively.

Hydrologic Validation of MERGE Precipitation Products over Anthropogenic Watersheds

Satellite rainfall estimates (SRFE) are a promising alternative for the lack of reliable, densely distributed, precipitation data common in developing countries and remote locations. SRFE may be significantly improved when corrected based on rain gauge data. In the present study the first complete validation of the Tropical Rainfall Measuring Mission (TRMM) 3B42-based MERGE product is performed by means of ground truthing and hydrological modeling-based applications. Four distinct, highly anthropogenic watersheds were selected in the Upper Paraíba do Sul River Basin (UPSRB)—Brazil. The results show that when compared to TRMM Multi-Satellite Precipitation Analysis (TMPA) 3B42V7 at the watershed scale, MERGE has a higher correlation with observed data. Likewise, root mean square errors and bias are significantly lower for MERGE products. When hydrologically validated, MERGE-based streamflow simulations have shown the capacity of reproducing the overall hydrological regime with “good” to “very good” results for the downstream lowland sections. Limitations were observed in the hydrological modeling of the upstream, highly anthropogenic, dammed watersheds. However, such limitations may not be attributed to MERGE precipitation since they were also obtained for the individually calibrated rain gauge-based simulations. The results indicate that the used MERGE dataset as a hydrological model input is better suited for application in the UPSRB than the TMPA 3B42V7.

Improving satellite rainfall estimation from MSG data in Northern Algeria by using a multi-classifier model based on machine learning

The primary focus in this paper is the estimation of precipitation from MSG images (Meteosat Second Generation) using a machine learning-based multi-classifier model. Learning and validation of the multiclass model is performed using the correspondences between MSG satellite data and radar data. To do this, six classifiers were first combined in order to exploit the full potential of each of these classifiers. These are Random Forest (RF1), Artificial Neural Network (ANN), Support Vector Machine (SVM), Naive Bayesian (NB), Weighted k-Nearest Neighbors (WkNN), and the Kmeans ++ algorithm (Kmeans). The application of these classifiers makes it possible to carry out a classification at level 1. A pixel can therefore be assigned to more than one class by the different classifiers. We calculated six certainty coefficients from these classification results. To refine these results, in a second step, a classification at level 2 was performed using the Random Forest classifier (RF2) taking the certainty coefficients as input parameters. Six classes of precipitation intensities are thus obtained: very high precipitation intensities, moderate to high precipitation intensities, moderate precipitation intensities, light to moderate precipitation intensities, light precipitation intensities and no rain. Comparisons between the results of the multi-classifiers model and those obtained by the classifiers used separately show a clear improvement in the quality of classification.Using multiple linear regressions, precipitation rates for the different classes were determined using data from the rain gauges. To validate the model, precipitation amounts were estimated and compared to actual rain gauge data and then compared to the results of a few precipitation estimation methods. The results are very interesting and show superior performance for the elaborated model. Indeed, the coefficient of correlation has a value of 0.93 for developed scheme against 0.46 to 90 for the different techniques presented here for comparison. Also, a better Bias (2.2 mm) and a better RMSD (12 mm) were obtained for developed scheme while the other methods indicate bias between −11 mm to 16 mm and RMSD higher than 14 mm.

A Comparison of the Accuracy of Multi-satellite Precipitation Estimation and Ground Meteorological Records Over Southwestern Nigeria

Multi-satellite rainfall observations are compared with ground-based meteorological stations data in this study. Both ground and satellite meteorological datasets were analysed and synchronized in this study. Satellite rainfall datasets used are Tropical Rainfall Measuring Mission (TRMM), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) and African Rainfall Estimation (RFE 2.0), which were obtained from the archives of the NOAA Climate Prediction Centre. The ground meteorological data were obtained from the Nigerian Meteorological Agency (NIMET) Lagos office in Oshodi, for selected stations in Southwest of Nigeria, for the period 1998–2016. Descriptive and inferential analyses were used to analyse the dataset. Spatial and temporal trends analyses were carried out to compare rainfall estimates from satellite and ground stations. Generally, correlation results show that the relationship between satellite datasets and ground observation (NIMET) appears remarkably high in all stations with R2> 0.70. This relationship is greatly strong between TRMM and NIMET in Abeokuta, Akure, and Osogbo with R2 = 0.99, 0.98 and 0.98, respectively. CHIRPS also show high correlation with NIMET in these three stations with R2> 0.90, but RFE-NIMET relationship appears much more in Osogbo with R2 > 0.90. It is also obvious that TRMM and CHIRPS have a very high relationship in almost all locations with R2> 0.87. The major finding from this study is that though in situ meteorological data may be used for local studies of small areas, satellite rainfall estimates may also be used to augment such data, over a larger area. Besides, satellite rainfall estimates are more useful for areas with larger spatial and temporal coverage.

Applicability of long-term satellite-based precipitation products for drought indices considering global warming

This study evaluates the applicability of using long-term satellite rainfall estimate (SRE) precipitation products in drought monitoring over mainland China under global warming conditions. Two widely used drought indices, the self-calibrating Palmer Drought Severity Index (scPDSI) and the Standardized Precipitation Evapotranspiration Index (SPEI), were selected as study cases; both indices consider global warming but based on different mechanisms. Two popular long-term SREs were selected to calculate the indices: the Precipitation Estimation from Remotely Sensed Information using the Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS). A ground-based gridded observation dataset known as the China monthly Precipitation Analysis Product (CPAP) was used as a reference for the evaluation. Research results showed that on a grid cell scale, the SPEI based on both SREs was consistent with observations in eastern China (correlation coefficient over 0.9), while the scPDSI was much less accurate (correlation coefficient of only 0.5) and its accuracy patterns were highly spatially heterogeneous. However, on a regional scale, after spatial errors were offset by spatial averaging, the performance of the SRE-based scPDSI improved, and it showed the same ability as the SPEI in temporally detecting the timing, intensity, and magnitude of drought. The self-calibrating procedure of the scPDSI was determined as the most probable cause of its poorer performance and high heterogeneity, which would increase instability and enlarge the uncertainty of the SREs. It is thus considered that the SPEI should be the first choice for use in monitoring global-warming related drought, primarily because of the high uncertainty and instability of the scPDSI.

A new perspective in understanding rainfall from satellites over a complex topographic region of India

Present study focuses on rainfall over Western Ghats (WG), a complex topographic region (elevation > 500 m) of India to evaluate and to better understand the satellite behavior in contrast with a flat region (FR) (elevation < 500 m) of central India from 1998 to 2016 using the combinatory data sets of TMPA and IMERG (satellite rainfall estimation). The categorical Intra Seasonal Oscillations (ISO) of Indian summer monsoon (ISM) namely, Madden Julian Oscillation (MJO) and Quasi Bi-Weekly Oscillation (QBWO) are tested in satellite and India Meteorological Department (IMD) gridded rainfall data sets to find out the satellite performance. As the accurate estimation of rainfall from satellites over higher elevation zones is challenging, here we propose a new perspective to select the rainfall products of satellite for better comparison with ground measurements. Considering the satellite’s best capability in detecting the cold clouds resulting from deep convection and its coupling with higher-level circulation, we show that the rainfall from satellites yield fruitful comparison with ground measurements when moist static stability, tropical easterly jet is above the climatological values.

Evaluation of remotely sensed precipitation estimates using PERSIANN-CDR and MSWEP for spatio-temporal drought assessment over Iran

Satellite Rainfall Estimates (SREs) can provide rainfall information at finer spatial and temporal resolutions, however their performance varies with respect to gauged precipitation data in different climatic regions. A limited number of studies investigated the performance of SREs for spatio-temporal (regional) drought analysis, which is a key component for developing tools for regional drought planning and management. In this study, the performance of two recent SREs (data length > 30 years), which includes Artificial Neural Networks Climate Data Record (PERSIANN-CDR) and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) are selected for spatio-temporal drought assessment over different climatic regions located in Iran. Firstly, the accuracy of SREs was evaluated for deriving standardized precipitation index (SPI) at different time scales (1, 3, 6, 9 and 12 months) for four climatic regions during the period of 1983–2012. Secondly, the performance of SREs was evaluated for regional drought assessment based on the concept of the Severity-Areal-Frequency (SAF) curves. It was observed that the performance of SREs can be different with respect to gauge data in terms of quantifying drought characteristics (e.g., severity, duration, and frequency), identification of major historical droughts, and a significant difference can be observed based on the SAF analysis. For example, the number of drought events based on shorter time scales (SPI-1 and 3) found to be greater for SREs in comparison to gauge information for all climatic regions. While investigating the major historical droughts, discrepancies can be observed between these two types of data sets. For example, gauge data suggests wetness (i.e., SPI-3 > 0.5) near southern Iran, whereas, SREs show droughts (SPI < -1.0) in the same spatial domain. The performance of SREs with respect to gauge data varies largely in terms of quantifying the frequency component embedded in the SAF curves for selected climatic regions located in Iran. Our research findings can be useful for drought assessment in ungagged basins, as well as to develop regional drought management plans to improve water security by integrating multivariate nature of drought events.

Evaluation and Bias Correction of CHIRP Rainfall Estimate for Rainfall-Runoff Simulation over Lake Ziway Watershed, Ethiopia

In Lake Ziway watershed in Ethiopia, the contribution of river inflow to the water level has not been quantified due to scarce data for rainfall-runoff modeling. However, satellite rainfall estimates may serve as an alternative data source for model inputs. In this study, we evaluated the performance and the bias correction of Climate Hazards Group InfraRed Precipitation (CHIRP) satellite estimate for rainfall-runoff simulation at Meki and Katar catchments using the Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological model. A non-linear power bias correction method was applied to correct CHIRP bias using rain gauge data as a reference. Results show that CHIRP has biases at various spatial and temporal scales over the study area. The CHIRP bias with percentage relative bias (PBIAS) ranging from −16 to 20% translated into streamflow simulation through the HBV model. However, bias-corrected CHIRP rainfall estimate effectively reduced the bias and resulted in improved streamflow simulations. Results indicated that the use of different rainfall inputs impacts both the calibrated parameters and its performance in simulating daily streamflow of the two catchments. The calibrated model parameter values obtained using gauge and bias-corrected CHIRP rainfall inputs were comparable for both catchments. We obtained a change of up to 63% on the parameters controlling the water balance when uncorrected CHIRP satellite rainfall served as model inputs. The results of this study indicate that the potential of bias-corrected CHIRP rainfall estimate for water balance studies.

Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River basin

Abstract. Satellite rainfall estimates (SREs) are prone to bias as they are indirect derivatives of the visible, infrared, and/or microwave cloud properties, and hence SREs need correction. We evaluate the influence of elevation and distance from large-scale open water bodies on bias for Climate Prediction Center-MORPHing (CMORPH) rainfall estimates in the Zambezi basin. The effectiveness of five linear/non-linear and time–space-variant/-invariant bias-correction schemes was evaluated for daily rainfall estimates and climatic seasonality. The schemes used are spatio-temporal bias (STB), elevation zone bias (EZ), power transform (PT), distribution transformation (DT), and quantile mapping based on an empirical distribution (QME). We used daily time series (1998–2013) from 60 gauge stations and CMORPH SREs for the Zambezi basin. To evaluate the effectiveness of the bias-correction schemes spatial and temporal cross-validation was applied based on eight stations and on the 1998–1999 CMORPH time series, respectively. For correction, STB and EZ schemes proved to be more effective in removing bias. STB improved the correlation coefficient and Nash–Sutcliffe efficiency by 50 % and 53 %, respectively, and reduced the root mean squared difference and relative bias by 25 % and 33 %, respectively. Paired t tests showed that there is no significant difference (p &lt; 0.05) in the daily means of CMORPH against gauge rainfall after bias correction. ANOVA post hoc tests revealed that the STB and EZ bias-correction schemes are preferable. Bias is highest for very light rainfall (&lt; 2.5 mm d−1), for which most effective bias reduction is shown, in particular for the wet season. Similar findings are shown through quantile–quantile (q–q) plots. The spatial cross-validation approach revealed that most bias-correction schemes removed bias by &gt; 28 %. The temporal cross-validation approach showed effectiveness of the bias-correction schemes. Taylor diagrams show that station elevation has an influence on CMORPH performance. Effects of distance &gt; 10 km from large-scale open water bodies are minimal, whereas effects at shorter distances are indicated but are not conclusive for a lack of rain gauges. Findings of this study show the importance of applying bias correction to SREs.