Satellite-derived Precipitation Products Research Articles

Temporal and spatial satellite data augmentation for deep learning-based rainfall nowcasting

Abstract The significance of improving rainfall prediction methods has escalated due to climate change-induced flash floods and severe flooding. In this study, rainfall nowcasting has been studied utilizing NASA Giovanni satellite-derived precipitation products and the convolutional long short-term memory (ConvLSTM) approach. The goal of the study is to assess the impact of data augmentation on flood nowcasting. Due to data requirements of deep learning-based prediction methods, data augmentation is performed using eight different interpolation techniques. Spatial, temporal, and spatio-temporal interpolated rainfall data are used to conduct a comparative analysis of the results obtained through nowcasting rainfall. This research examines two catastrophic floods that transpired in Türkiye Marmara Region in 2009 and the Central Black Sea Region in 2021, which are selected as the focal case studies. The Marmara and Black Sea regions are prone to frequent flooding, which, due to the dense population, has devastating consequences. Furthermore, these regions exhibit distinct topographical characteristics and precipitation patterns, and the frontal systems that impact them are also dissimilar. The nowcast results for the two regions exhibit a significant difference. Although data augmentation significantly reduced the error values by 59% for one region, it did not yield the same effectiveness for the other region.

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

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

Evaluation of Satellite-Derived Precipitation Products for Streamflow Simulation of a Mountainous Himalayan Watershed: A Study of Myagdi Khola in Kali Gandaki Basin, Nepal

This study assesses four Satellite-derived Precipitation Products (SPPs) that are corrected and validated against gauge data such as Soil Moisture to Rain—Advanced SCATterometer V1.5 (SM2RAIN-ASCAT), Multi-Source Weighted-Ensemble Precipitation V2.8 (MSWEP), Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM Final run V6 (GPM IMERGF), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS). We evaluate the performance of these SPPs in Nepal’s Myagdi Khola watershed, located in the Kali Gandaki River basin, for the period 2009–2019. The SPPs are evaluated by validating the gridded precipitation products using the hydrological model, Soil and Water Assessment Tool (SWAT). The results of this study show that the SM2RAIN-ASCAT and GPM IMERGF performed better than MSWEP and CHIRPS in accurately simulating daily and monthly streamflow. GPM IMERGF and SM2RAIN-ASCAT are found to be the better-performing models, with higher NSE values (0.63 and 0.61, respectively) compared with CHIRPS and MSWEP (0.45 and 0.41, respectively) after calibrating the model with monthly data. Moreover, SM2RAIN-ASCAT demonstrated the best performance in simulating daily and monthly streamflow, with NSE values of 0.57 and 0.63, respectively, after validation. This study’s findings support the use of satellite-derived precipitation datasets as inputs for hydrological models to address the hydrological complexities of mountainous watersheds.

How accurate are infrared-only and rain gauge-adjusted multi-satellite precipitation products in the southwest monsoon precipitation estimation across India?

A dense network of rain gauges and considerably large variability of the southwest monsoon precipitation across the country make India a suitable test-bed to evaluate any satellite-based precipitation product. In this paper, three real-time infrared-only precipitation products derived from the INSAT-3D satellite namely, INSAT Multispectral Rainfall (IMR), Corrected IMR (IMC) and Hydro-Estimator (HEM) and three rain gauge-adjusted Global Precipitation Measurement (GPM)-based multi-satellite precipitation products namely, Integrated Multi-satellitE Retrievals for GPM (IMERG), Global Satellite Mapping of Precipitation (GSMaP) and an Indian merged satellite-gauge product (INMSG) have been evaluated over India at a daily timescale for the southwest monsoon seasons of 2020 and 2021. An evaluation against rain gauge-based gridded reference dataset shows noticeable reduction of bias in IMC product over IMR, primarily over the orographic regions. However, INSAT-3D infrared-only precipitation retrieval algorithms have limitations in shallow and convective precipitation estimation. Among rain gauge-adjusted multi-satellite products, INMSG is shown to be the best product in the monsoon precipitation estimation over India due to use of rather larger number of rain gauges than IMERG and GSMaP products. All satellite-derived precipitation products, i.e. infrared-only and gauge-adjusted multi-satellite products underestimate heavy monsoon precipitation by 50-70%. The bias decomposition analysis indicates that a simple statistical bias correction would considerably improve the performance of the INSAT-3D precipitation products over the central India, but the same might not work over the west coast due to rather larger contributions of both positive and negative hit bias components. Although rain gauge-adjusted multi-satellite precipitation products show very small or negligible total biases in the monsoon precipitation estimation, positive and negative hit bias components are considerable over the west coast and central India. Furthermore, rain gauge-adjusted multi-satellite precipitation products underestimate very heavy to extremely heavy precipitation with larger magnitudes than the INSAT-3D derived precipitation products over the central India. Among the rain gauge-adjusted multi-satellite precipitation products, INMSG has smaller bias and error than IMERG and GSMaP products for very heavy to extremely heavy monsoon precipitation over the west coast and central India. Preliminary results of this study would be useful for end users in choosing a better precipitation product for real-time and research applications as well as for algorithm developers in further improving these products.

Comprehensive evaluation of satellite-derived precipitation products considering spatial distribution difference of daily precipitation over eastern China

Study regionEight main basins at eastern China. Study focusThis study proposes a comprehensive framework using Shannon’s entropy-weighting method for assessing satellite precipitation data from spatial and temporal perspectives. First, three new spatial evaluation metrics, the false monitoring score (FMS), spatial coverage approximation (SCA), and precipitation spatial difference index (PSDI), were introduced based on spatial distribution differences of daily precipitation. Then, the error and performance of the five satellite precipitation datasets over eastern China were analyzed. New hydrological insights for the regionThe spatial metrics, SCA, PSDI, and fraction skill score (FSS), provide an effective quantitative evaluation of the daily spatial distribution of precipitation. CMADS and MSWEP appear to be superior at reproducing the spatial distribution of precipitation. Except for CHIRPS, all other four datasets have a tendency to underestimate precipitation measurements in the study area. According to the evaluation, the latest version of MSWEP may be a suitable alternative for use in water and climate research due to its relatively high accuracy in eastern China. The spatial error analysis method utilized in this study can be transferred to evaluating other gridded precipitation products. Meanwhile, the spatial error indices are also acceptable as optimization targets for future satellite precipitation data production.

Evaluation of Performance of Three Satellite-Derived Precipitation Products in Capturing Extreme Precipitation Events over Beijing, China

Extreme precipitation events have a more serious impact on densely populated cities and therefore reliable estimation of extreme precipitation is very important. Satellite-derived precipitation products provide precipitation datasets with high spatiotemporal resolution. For improved applicability to estimating urban extreme precipitation, the performance of such products must be evaluated regionally. This study evaluated three satellite-derived precipitation products, the Integrated Multi-satellite Retrievals for GPM (IMERG_V06), Multi-Source Weighted-Ensemble Precipitation (MSWEP V2), and China Meteorological Forcing Dataset (CMFD), in capturing extreme precipitation using observations acquired at 36 rainfall stations during 2001–2016 in Beijing, China. Results showed that MSWEP had the highest accuracy regarding daily precipitation data, with the highest correlation coefficient and the lowest absolute deviation between MSWEP and the rainfall station observations. CMFD demonstrated the best ability for correct detection of daily precipitation events, while MSWEP maintained the lowest rate of detecting non-rainy days as rainy days. MSWEP performed better in estimating precipitation amount and the number of precipitation days when daily precipitation was <50 mm; CMFD performed better when daily precipitation was >50 mm. All three products underestimated extreme precipitation. The Structural Similarity Index, which is a map comparison technique, was used to compare the similarities between the three products and rainfall station observations of two extreme rainstorms: “7.21” in 2012 and “7.20” in 2016. MSWEP and CMFD showed higher levels of similarity in terms of spatial–temporal structure. Overall, despite systematic underestimation, MSWEP performed better than IMERG and CMFD in estimating extreme precipitation in Beijing.

Coupling of satellite-derived precipitation products with Bartlett-Lewis model to estimate intensity-frequency-duration curves for remote areas

Intensity-frequency-duration (IFD) curves (also known as intensity-duration-frequency (IDF) curves in some regions) are widely used to take into consideration the probability of occurrence of extreme precipitation for the design of water related infrastructure. Derivation of IFD curves is based on statistical analysis of observed extreme precipitation events and thus the reliability of the curves mostly depends on the record length of observations. At sub-hourly timescales however, ground-based precipitation observations are often absent or very limited in terms of spatial coverage and record length, particularly in remote areas around the world. Given the availability of satellite-derived precipitation products (SPPs) with finer spatiotemporal resolution and near-global coverage, there is great potential to use these products in the planning and design of infrastructure. This paper proposes a novel framework, SIFD (Satellite-derived IFD curves) to combine long-term daily gauge data and sub-hourly SPP to estimate IFD curves for time intervals from 0.5 to 24 h. A recent variant of the Random Parameter Bartlett-Lewis Rectangular Pulse model (RPBLRPX) was used to disaggregate daily gauge data to derive IFD curves. In the absence of sub-daily rain gauge data, the statistics required for the RPBLRPX model were obtained using the Integrated Multi-satellitE Retrievals for GPM (IMERG; 30-min, 0.1° resolution) product derived from the Global Precipitation Measurement (GPM) mission. Performance of the SIFD framework was tested against gauge data from seven Australian sites across a wide range of climatologies. The disaggregation scheme coupled with the RPBLRPX model was capable of reproducing the wet-period mean intensity, wet-period standard deviation, and wet-period fraction with reasonable accuracy across the sub-daily timescales. The coefficient of determination (r2) values were 0.39 (mean intensity), 0.38 (standard deviation), and 0.87 (wet-period fraction) at 0.5 h timescale. Furthermore, despite some differences in methodology and record length, the IFD estimates computed using the SIFD framework were fairly close to those of the Bureau of Meteorology (BoM). The skill metrics for Sydney were the best with a bias ratio (BR) of 1.01, root mean square error (RMSE) of 13% of the mean of the BoM estimates, and a Nash-Sutcliffe coefficient of efficiency (NSE) of 0.97. The worst case was found at Hobart (BR = 1.43, RMSE = 66%, and NSE = 0.26). Overall, the results indicate that the proposed SIFD framework could provide a viable alternative to estimate unbiased IFD curves at the sub-hourly timescale in the absence of adequate sub-hourly ground-based observations.

How well do multi-satellite products capture the space-time dynamics of precipitation? Part I: five products assessed via a wavenumber-frequency decomposition

AbstractAs more global satellite-derived precipitation products become available, it is imperative to evaluate them more carefully for providing guidance as to how well precipitation space-time features are captured for use in hydrologic modeling, climate studies and other applications. Here we propose a space-time Fourier spectral analysis and define a suite of metrics which evaluate the spatial organization of storm systems, the propagation speed and direction of precipitation features, and the space-time scales at which a satellite product reproduces the variability of a reference “ground-truth” product (“effective resolution”). We demonstrate how the methodology relates to our physical intuition using the case study of a storm system with rich space-time structure. We then evaluate five high-resolution multi-satellite products (CMORPH, GSMaP, IMERG-early, IMERG-final and PERSIANN-CCS) over a period of two years over the southeastern US. All five satellite products show generally consistent space-time power spectral density when compared to a reference ground gauge-radar dataset (GV-MRMS), revealing agreement in terms of average morphology and dynamics of precipitation systems. However, a deficit of spectral power at wavelengths shorter than 200 km and periods shorter than 4 h reveals that all satellite products are excessively “smooth”. The products also show low levels of spectral coherence with the gauge-radar reference at these fine scales, revealing discrepancies in capturing the location and timing of precipitation features. From the space-time spectral coherence, the IMERG-final product shows superior ability in resolving the space-time dynamics of precipitation down to 200 km and 4 h scales compared to the other products.

Spatio-temporal variability of monsoon precipitation and their effect on precipitation triggered landslides in relation to relief in Himalayas

Orography of Himalaya governs the regional weather system and monsoon of Indian sub-continent. The intense persistent precipitation in the form of rainfall during monsoon season causes landslides which are the most frequent naturally occurring hazards in the Himalaya. This study attempts to investigate the spatio-temporal variability of precipitation and their effect on precipitation triggered landslides during monsoon season (June–September) in relation to relief in Himalayan region by utilizing satellite-derived precipitation products (GPM) over the span of 2000–2018 in addition to NASA Landslide Viewer, Global Landslide Catalog and Global Risk Data Platform data for landslides and ASTER DEM for elevation. The results exhibit that the Eastern Himalayas received the highest amount of precipitation of 2385 mm with intensity of 19.5 mm/day, followed by the Central Himalayas with 1860 mm and intensity of 17.5 mm/day and the least in the Western Himalayas with 1400 mm and intensity of 15 mm/day, respectively. The monsoon precipitation in the lower elevations (below 2600 m) are mostly responsible for causing a vast majority of the precipitation induced landslide events with a maximum of 68.66% in the Central Himalayas followed by the Western Himalayas at 62.23% and the least in the Eastern Himalayas at 41.16%. The overall accumulated precipitation and intensity during monsoon season and landslide distribution were strongly correlated with relief pattern. This comprehensive study signifies how relief regulated the occurrences of landslides in monsoon season and recommends vegetation cover and least interference of human-induced land use to alleviate the landslides.

Comparison of Satellite Derived Rainfall Estimations: CMORPH, IMERG and GSMaP with Observed Precipitation

Being largely dependent on agriculture, the overall development and water resource management of Bangladesh are greatly influenced by the accurate estimation of precipitation. Different satellite derived precipitation products, covering a large area, are very useful for rainfall estimation. In this study, three different satellite precipitation datasets namely Climate Prediction Center MORPHING (CMORPH), the global Satellite Mapping of Precipitation (GSMaP), and the Integrated Multi-satellitE Retrievals for GPM (IMERG) daily data are spatially analyzed and compared with the observed rainfall data from 20th May 2020 to 21st May 2020. It is observed that the satellite products matched well with the observed data set but the amount varied. Also, the spatial distribution of CMORPH and GSMaP with the observed precipitation is represented for 02 May 2019.

Drought Detection over Papua New Guinea Using Satellite-Derived Products

This study evaluates the World Meteorological Organization’s (WMO) Space-based Weather and Climate Extremes Monitoring (SWCEM) Demonstration Project precipitation products over Papua New Guinea (PNG). The products evaluated were based on remotely-sensed precipitation, vegetation health, soil moisture, and outgoing longwave radiation (OLR) data. The satellite precipitation estimates of the Climate Prediction Center/National Oceanic and Atmospheric Administration’s (CPC/NOAA) morphing technique (CMORPH) and Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) were assessed on a monthly timescale over an 18-year period from 2001 to 2018. Station data along with the ERA5 reanalysis were used as the reference datasets for assessment purposes. In addition, a case study was performed to investigate how well the SWCEM precipitation products characterised drought in PNG associated with the 2015–2016 El Niño. Overall statistics from the validation study suggest that although there remains significant variability between satellite and ERA5 rainfall data in remote areas, this difference is much less at locations where rain gauges exist. The case study illustrated that the Vegetation Health Index (VHI), OLR anomaly and the Standardized Precipitation Index (SPI) were able to reliably capture the spatial and temporal aspects of the severe 2015–2016 El Niño-induced drought in PNG. Of the three, VHI appeared to be the most effective, in part due to its reduced incidence of false alarms. This study is novel as modern-day satellite-derived products have not been evaluated over PNG before. A focus on their value in monitoring drought can bring great value in mitigating the impact of future droughts. It is concluded that these satellite-derived precipitation products could be recommended for operational use for drought detection and monitoring in PNG, and that even a modest increase in ground-based observations will increase the accuracy of satellite-derived observations remotely.

Assessment and comparison of five satellite precipitation products in Australia

Quality satellite-derived precipitation products (SPPs) are needed for water resources inventories and management, particularly in poorly gauged regions around the world. The latest version of five SPPs were assessed against SILO (Scientific Information for Land Owners) gauge-based gridded precipitation dataset in Australia over a 5-year period from October 2014 to September 2019. The evaluation was carried out using a 0.50° grid at daily, seasonal, and annual temporal scales. The assessed SPPs were the Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG), TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Centre (CPC) MORPHing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and PERSIANN-CDR (Climate Data Record). PERSIANN does not include any ground-based observations for bias-correction, while the other four products are bias-corrected against gauge-based data. Bias ratio and correlation coefficient for the five SPPs showed that the overall performance of IMERG and TMPA was better than that of CMORPH, PERSIANN, and PERSIANN-CDR for Australia. Seasonal analysis showed that IMERG had the better skill in winter. Overall, IMERG appeared to be the best SPP for Australia. However, TMPA also performed reasonably well, considering the climatological calibration implemented recently in the precipitation processing algorithm. The Structural Similarity Index (SSI), a map comparison technique using a moving window-based approach, was used to compare similarities between a pair of gridded precipitation maps in terms of local mean, variance, and covariance. All the SPPs showed discrepancies in the spatial structure of the mean annual precipitation, predominantly over some high precipitation areas in Australia. These local scale differences were not detectable in conventional cell by cell comparison or simply by visual inspection. Therefore, SSI could be an effective method to evaluate satellite precipitation estimation algorithm.

Accuracy analysis of IMERG and CMORPH precipitation data over North China

Large-scale agricultural production in North China makes the study of precipitation in this area vital. The performance of the Integrated Merged Multisatellite Retrievals for the Global Precipitation Measurement (IMERG) and the Climate Prediction Center morphing technique (CMORPH) precipitation products for 2015 was evaluated against daily precipitation data from 404 rain gauges in North China. Relative errors, correlation coefficients, Pearson’s chi-squared test values, and root mean square errors, as well as the probability of detection (POD), false alarm ratio, and critical success index, were used to analyze the accuracy of both IMERG and CMORPH precipitation products on daily, monthly, and seasonal timescales. The probability density function (PDF) was also considered. Overall, both products overestimated ground precipitation, especially in summer. Positive correlation coefficients between satellite-derived and rain-gauge monthly precipitation data were higher over plains and coastal areas, compared with plateau regions. The PODs of both IMERG and CMORPH data were highest in summer. The PODs of IMERG data were much higher than for CMORPH data in autumn. The PODs over coastal regions, plains, and plateaus at lower latitudes also were considerably better than over inland and plateau areas at higher latitudes. The precipitation products performed best over coastal areas, plains, and areas with high rainfall. Both CMORPH and IMERG products were prone to identifying non-rainy days as rainy days. They also overestimated light (0.1-9.9 mm d-1) and moderate (10-24.9 mm d-1) precipitation events, although the IMERG product was more sensitive to precipitation. Accordingly, we find that both of these satellite-derived precipitation products require further modification to enable them to substitute for gauge precipitation data in North China.

Evaluating extreme precipitation estimations based on the GPM IMERG products over the Yangtze River Basin, China

Accurate estimation of extreme precipitation is important for hydrological prediction and flood risk management. Recent research suggests that satellite-derived precipitation products can provide an alternative to gauged data, making it essential to evaluate the accuracy of these products. This study aimed to quantitatively evaluate the application of the Global Precipitation Measurement (GPM) mission’s Integrated Multi-satellite Retrievals of GPM data (GPM IMERG) over the Yangtze River Basin. Both extreme precipitation events exceeding the 90th percentile and annual total precipitation have been compared and examined with gauge observations for 2014–2017. We evaluated the performance of the GPM IMERG product during an extreme precipitation event in July 2014. In general, the GPM-derived estimates agreed well with the gauge data at monthly time scales (Pearson’s correlation coefficient of 0.8637), most likely because of monthly adjustment to gauges within the GPM dataset. The agreement between GPM-derived and gauge estimates was less pronounced at daily time scales. The IMERG product performed best in upstream areas of the Yangtze River Basin over monthly time scales, giving a probability of detection of 0.7739 and a Heidke’s Skill Score of 0.5116. This indicates that satellite precipitation data performed well in high-altitude regions. The GPM data produced a good estimation of extreme precipitation events with short–medium recurrence intervals but underestimated all return periods. During extreme precipitation events, the GPM product detected the precipitation process over the whole basin, yielding Pearson’s correlation coefficients of 0.9137–0.9979. This study shows that the GPM-based estimates are useful for extreme precipitation event simulation over the Yangtze River Basin and provide a new resource for flood forecasting in this region.

Assessment of Satellite-Derived Precipitation Products for the Beijing Region

Performance of four satellite precipitation products, namely, the China Meteorological Forcing Dataset (CMFD), Climate Prediction Center morphing technique (CMORPH), as well as 3B42 calibrated and 3B42-RT dataset, which are derived from the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA), were evaluated from daily to annual temporal scales over Beijing, using observations from 36 ground meteorological stations. Five statistical properties and three categorical metrics were used to test the results. The assessment showed that all four satellite precipitation products captured the temporal variability of precipitation. Although four satellite precipitation products captured the trend of more precipitation in the northeastern regions, all four products showed different distribution from the observations for 2001–2015 over Beijing. All precipitation products tended to overestimate moderate precipitation events and underestimate heavy precipitation events over Beijing, except for 3B42RT, which tended to overestimate most precipitation events. By comparison, the CMFD performed better than the CMORPH, 3B42 calibrated, and 3B42-RT datasets, having the higher correlation coefficient and low root mean squared difference, and mean absolute difference at all temporal scales. The average correlation coefficient of the CMFD, CMORPH, 3B42 calibrated, and 3B42-RT products for all 36 stations were 0.70, 0.60, 0.59, and 0.54 for daily precipitation and 0.78, 0.32, 0.74, and 0.44 for monthly precipitation. Overall, the CMFD was the most reliable for the Beijing region.

A new downscaling-integration framework for high-resolution monthly precipitation estimates: Combining rain gauge observations, satellite-derived precipitation data and geographical ancillary data

Deriving high quality precipitation estimates at high spatial resolution is of prime importance for many hydrological, meteorological, and environmental investigations. Rain gauge observations and satellite-derived precipitation data are two main sources of precipitation estimates. Gauge observations are accurate and reliable, but are heavily point-based and sparse in areas of rugged or complex terrains. Satellite-derived precipitation products can cover large areas, but they are generally characterized by inherent bias. To optimize the use of both datasets, we propose in this paper, a downscaling-integration framework to generate high quality monthly precipitation datasets at 1 km spatial resolution by merging rain gauge observations and TRMM 3B43 products. Firstly, an area-to-point kriging (ATPK) approach is used to downscale the original TRMM product to 1 km, so as to ensure a fair comparison with rain gauge data. Then, the downscaled TRMM precipitation datasets are integrated with the gauge observations using geographically weighted regression kriging (GWRK). The geographical factors (i.e. longitude, latitude and elevation) are also used as auxiliary variables in the GWRK model. Applying this approach to an experiment conducted at the middle and lower reaches of the Yangtze River in China from 2001 to 2014 shows that: (1) the downscaled monthly TRMM precipitation data by ATPK are more accurate than the original TRMM estimates; (2) the GWRK model employing the downscaled TRMM precipitation data and geographical factors provides better monthly precipitation estimates than the conventional ordinary kriging (OK) interpolation and the commonly used merging methods (i.e. geographical difference analysis, GDA and kriging with external drift, KED); (3) the GWRK method reduces the influence of the inaccuracy (bias) of satellite-derived precipitation data on the precipitation estimates compared to GDA. The approach presented in this study has provided an efficient alternative for solving the scale mismatch problem between point-based gauge data and low resolution satellite data, and producing improved precipitation data at high spatial resolution.

Statistical comparison of satellite-retrieved precipitation products with rain gauge observations over Bangladesh

ABSTRACTIn this investigation, six satellite-derived precipitation products namely Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Climate Prediction Centre (CPC) Morphing Technique (CMORPH), Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) final run both non gauge-calibrated (IMERG) and gauge-calibrated (IMERG-GC), and Global Satellite Mapping of Precipitation (GSMaP) for GPM both non gauge-calibrated (GSMaP) and gauge-calibrated (GSMaP-GC) are evaluated over Bangladesh, using ground-based rain gauge observations as reference over a 3 years period from January 2014 to December 2016. Nine widely used categorical and volumetric statistical matrices such as bias, probability of detection, volumetric hit index, false alarm ratio, volumetric false alarm ratio, critical success index, volumetric critical success index, miss index, and volumetric miss index are employed to exploit the performance of the precipitation products in detecting extremes above different quantile thresholds (i.e. 50%, 75%, and 90% quantiles) for various temporal window (i.e. 3 h, 6 h, 12 h, and 24 h). The bias values show that none of the satellite rainfall data sets are ideal for detecting extreme rainfall accumulations. In fact, all products lose their detection skills consistently as the extreme precipitation thresholds (50%, 75%, and 90% quantiles) increase. The results indicate that PERSIANN shows the worst performance over the study region. Overall, GSMaP-GC performs better than the other precipitation products. However, the FAR values of GSMaP are also higher over monsoon and post-monsoon months. The categorical and volumetric scores reveal that the detection skill increases remarkably for all rainfall data sets throughout the year with the increase of extreme quantile thresholds. At higher temporal accumulations, the detection capability of the products also improves considerably, and this improvement is more significant during monsoon period. The performance is relatively poor for all precipitation data sets over the cold months. In general, all six satellite precipitation products are doing well in detecting the occurrence of rainfall but are not so good in estimating the amount of rainfall.

Evaluation of High-Resolution Gridded Precipitation Data in Arid and Semiarid Regions: Heihe River Basin, Northwest China

Abstract The reliability of three satellite-derived precipitation products, Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and the Climate Prediction Center morphing technique (CMORPH) satellite-only (CMORPH-RAW) and gauge-corrected versions (CMORPH-CRT), and three gauge-based precipitation datasets, Asian Precipitation–Highly Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE), National Climate Center of China Meteorological Administration (CN05.1), and Institute of Tibetan Plateau Research, Chinese Academy of Sciences (ITPCAS), is evaluated via comparisons with rain gauge observations from stations over the Heihe River basin (HRB) for the period from 1998 to 2012. The results show that the observed climatology, interannual variability, the detection of precipitation events, and probability density functions (PDFs) are reasonably well represented by the high-resolution precipitation products (HRPPs), with APHRODITE presenting the best performance, CN05.1 and ITPCAS exhibiting similar performances, and CMORPH-CRT showing a poor performance. The bias-correction algorithms applied in CMORPH-CRT improve the accuracy of CMORPH-RAW slightly but fail to improve the rainfall detection skill. TRMM consistently outperforms CMORPH-CRT at various scales, whereas CMORPH-CRT is comparable to TRMM in summer. The spatial correlations, normalized root-mean-square error (NRMSE), and probability of detection (POD) show that all datasets perform better in summer than in winter. Except for CMORPH-RAW, the HRPPs could adequately reproduce the unimodal characteristics of annual cycle, although they overestimate the magnitude of the warm season precipitation. The HRPPs could capture the overall spatial distribution and decadal trend of extreme precipitation indices. However, the satellite-derived products overestimate the wet day precipitation and underestimate the consecutive dry days, although the TRMM generates relatively better results.

Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile

Precipitation is a key parameter for the study of climate change and variability and the detection and monitoring of natural disaster such as drought. Precipitation datasets that accurately capture the amount and spatial variability of rainfall is critical for drought monitoring and a wide range of other climate applications. This is challenging in many parts of the world, which often have a limited number of weather stations and/or historical data records. Satellite-derived precipitation products offer a viable alternative with several remotely sensed precipitation datasets now available with long historical data records (+30years), which include the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) datasets. This study presents a comparative analysis of three historical satellite-based precipitation datasets that include Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B43 version 7 (1998–2015), PERSIANN-CDR (1983–2015) and CHIRPS 2.0 (1981–2015) over Chile to assess their performance across the country and for the case of the two long-term products the applicability for agricultural drought were evaluated when used in the calculation of commonly used drought indicator as the Standardized Precipitation Index (SPI). In this analysis, 278 weather stations of in situ rainfall measurements across Chile were initially compared to the satellite data. The study area (Chile) was divided into five latitudinal zones: North, North-Central, Central, South-Central and South to determine if there were a regional difference among these satellite products, and nine statistics were used to evaluate their performance to estimate the amount and spatial distribution of historical rainfall across Chile. Hierarchical cluster analysis, k-means and singular value decomposition were used to analyze these datasets to better understand their similarities and differences in characterizing rainfall patterns across Chile. Monthly analysis showed that all satellite products highly overestimated rainfall in the arid North zone. However, there were no major difference between all three products from North to South-Central zones. Though, in the South zone, PERSIANN-CDR shows the lowest fit with high underestimation, while CHIRPS 2.0 and TMPA 3B43 v7 had better agreement with in situ measurements. The accuracy of satellite products were highly dependent on the amount of monthly rainfall with the best results found during winter seasons and in zones (Central to South) with higher amounts of precipitation. PERSIANN-CDR and CHIRPS 2.0 were used to derive SPI at time-scale of 1, 3 and 6 months, both satellite products presented similar results when it was compared in situ against satellite SPI's. Because of its higher spatial resolution that allows better characterizing of spatial variation in precipitation pattern, the CHIRPS 2.0 was used to mapping the SPI-3 over Chile. The results of this study show that in order to use the CHIRPS 2.0 and PERSIANN-CDR datasets in Chile to monitor spatial patterns in the rainfall and drought intensity conditions, these products should be calibrated to adjust for the overestimation/underestimation of rainfall geographically specially in the North zone and seasonally during the summer and spring months in the other zones.

Evaluation of Monthly Satellite-Derived Precipitation Products over East Africa

Abstract East Africa experienced in the 2001–11 time period some of the worst drought events to date, culminating in the high-impact drought of 2010/11. Long-term monitoring of precipitation is thus essential, and satellite-based precipitation products can help in coping with the relatively sparse rain gauge ground networks of this area of the world. However, the complex topography and the marked geographic variability of precipitation in the region make precipitation retrieval from satellites problematic and product validation and intercomparison necessary. Six state-of-the-art monthly satellite precipitation products over East Africa during the 2001–09 time frame are evaluated. Eight areas (clusters) are identified by investigating the precipitation seasonality through the Global Precipitation Climatology Centre (GPCC) climatological gauge data. Seasonality was fully reproduced by satellite data in each of the GPCC-identified clusters. Not surprisingly, complex terrain (mountain regions in particular) represents a challenge for satellite precipitation estimates, as demonstrated by the standard deviations of the six-product ensemble. A further confirmation comes from the comparison between satellite estimates and rain gauge measurements as a function of terrain elevation. The 3B42 product performs best, although the satellite–gauge comparative analysis was not completely independent since a few of the products include a rain gauge bias correction.